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Assessing variability in the production of pasture using GIS and remote sensing techniques Smith, Steven Murray 1988

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ASSESSING VARIABILITY IN THE PRODUCTION OF PASTURE USING GIS AND REMOTE SENSING TECHNIQUES By STEVEN MURRAY SMITH B.A., The University of Otago, 1976 M.Ap.Sc, Lincoln College, The University of Canterbury, 1979 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY i n THE FACULTY OF GRADUATE STUDIES (Department of S o i l Science) He accept t h i s thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA November 1988 (3) Steven Murray Smith, 1988 In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department The University of British Columbia Vancouver, Canada DE-6 (2/88) i i ABSTRACT I n f o r m a t i o n r e l a t i n g t o t h e s p a t i a l c h a r a c t e r i s t i c s o f b i o p h y s i c a l r e s o u r c e s has been d i f f i c u l t t o i n c o r p o r a t e i n t o l a n d management. I n t h i s s t u d y s t a t i s t i c a l a n a l y s i s was used t o d e m o n s t r a t e t h a t f o r a g e y i e l d and q u a l i t y were i n f l u e n c e d by t h e w a t e r b a l a n c e and s o i l p h y s i c a l p r o p e r t i e s . T r a d i t i o n a l e m p i r i c a l m o d e l l i n g t e c h n i q u e s were o f l i m i t e d u t i l i t y as p r e d i c t o r s o f y i e l d and q u a l i t y . However, m u l t i v a r i a t e s t a t i s t i c a l t e c h n i q u e s p r o v i d e p r e d i c t o r v a r i a b l e s f o r i n d i v i d u a l f o r a g e c u t s b u t n o t f o r a c o mplete g r o w i n g season. GIS p r o v i d e d s e v e r a l d i s t i n c t advantages o v e r t r a d i t i o n a l s t a t i s t i c a l t e c h n i q u e s . F i r s t , i t p r o v i d e d t e c h n i q u e s t o i n t e r p o l a t e p o i n t d a t a (such as f o r a g e y i e l d and q u a l i t y v a r i a b l e s ) , and p r o v i d e s p a t i a l d i s t r i b u t i o n s f o r a wide number o f b i o p h y s i c a l p r o p e r t i e s . S e c o n d l y , o v e r l a y i n g f o r a g e v a r i a b l e s s uch as y i e l d w i t h a d i g i t a l e l e v a t i o n model i n a c a t e g o r i c manner p r o v i d e d o u t p u t d i s p l a y i n g t h e s p a t i a l r e l a t i o n s h i p s between t h e v a r i a b l e s . R e l a t i o n s h i p s d e r i v e d from o v e r l a y s u s i n g e l e v a t i o n and w a t e r r e t e n t i o n p r o p e r t i e s p r o v i d e d good s p a t i a l p r e d i c t i o n s f o r s e v e r a l f o r a g e v a r i a b l e s . T h i r d l y , d i g i t i z e d e o l o u r - I R a e r i a l p hotographs were i n c o r p o r a t e d i n t o t h e GIS where t h e p i x e l i n f o r m a t i o n was combined as map o v e r l a y s v i a a r e g r e s s i o n e q u a t i o n . The r e s u l t i n g o u t p u t p r o v i d e d v e r y i i i accurate s p a t i a l predictions for forage y i e l d and qua l i t y parameters. F i n a l l y , economic data was generated i n a s p a t i a l context and the r e s u l t i n g display was used to assess the e f f e c t s of i r r i g a t i o n and management on forage y i e l d and q u a l i t y . The r e s u l t s suggest that the GIS techniques combined with remote sensing and economic data o f f e r e x c i t i n g p o s s i b i l i t i e s to model and present s p a t i a l data. i v TABLE OP CONTENTS CHAPTER PAGE ABSTRACT i i LIST OF SYMBOLS v i i LIST OF TABLES v i i i LIST OF FIGURES X ACKNOWLEDGEMENTS x i i i 1. INTRODUCTION 1 1.1 O b j e c t i v e s 2 1.2 Background 5 1.2.1 S p a t i a l and. temporal v a r i a t i o n o f s o i l p r o p e r t i e s and crop y i e l d 5 1.2.2 G e o s t a t i s t i c s 6 1.2.3 M o d e l l i n g Techniques 9 1.2.4 Remote Sensing 11 1.2.5 Geographic Information Systems 12 2. STUDY SITE 15 2.1 S i t e s e l e c t i o n 15 2.2 L o c a t i o n and environmental s e t t i n g 15 2.3 S i t e management 18 3. MATERIALS AND METHODS 20 3.1 P r e l i m i n a r y f i e l d work 20 3.2 Sampling and sample p r e p a r a t i o n 23 3.2.1 S o i l sampling 24 3.2.2 V e g e t a t i o n sampling 24 3.2.3 M o n i t o r i n g program 24 3.2.4 C a l c u l a t i o n o f water balance 26 3.3 A n a l y t i c a l procedures 27 3.3.1 S o i l a n a l y s i s 27 3.3.2 F o l i a r a n a l y s i s 28 3.4 P r e s e n t a t i o n o f r e s u l t s 28 V CHAPTER PAGE 4. UNIVARIATE AND MULTIVARIATE STATISTICS 3 0 4.1 S t a t i s t i c a l methods 30 4.2 P r e s e n t a t i o n o f r e s u l t s 30 4.3 S p a t i a l and t e m p o r a l v a r i a b i l i t y 32 4.4 G e o s t a t i s t i c s 47 4.5 S p a t i a l r e l a t i o n s h i p s and p r e d i c t i v e models 60 4.5.1 L i n e a r r e l a t i o n s h i p s 60 4.5.2 M u l t i v a r i a t e t e c h n i q u e s 66 4.5.2.1 D i s c r i m i n a n t a n a l y s i s and p r i n c i p a l component a n a l y s i s 67 4.5.2.2 A s s e s s i n g t h e u t i l i t y o f PCA and DA t o improve l i n e a r r e l a t i o n s h i p s 74 4.5.2.3 C l u s t e r a n a l y s i s 78 5. REMOTE SENSING 87 5.1 Image a n a l y s i s t e c h n i q u e s 87 5.2 Image a n a l y s i s r e s u l t s 87 5.3 S p e c t r a l r e f l e c t a n c e t e c h n i q u e s 96 5.4 S p e c t r a l r e f l e c t a n c e r e s u l t s 96 6. GEOGRAPHIC INFORMATION SYSTEM AND MODEL VALIDATION 104 6.1 Ge o g r a p h i c i n f o r m a t i o n system t e c h n i q u e s 104 6.2 S p a t i a l s t a t i s t i c s 106 6.3 C a r t o g r a p h i c m o d e l l i n g 110 6.4 GIS a p p l i c a t i o n s 113 6.5 Model v a l i d a t i o n 121 6.6 Economic i m p l i c a t i o n s 125 7. CONCLUSIONS 137 7.1 U n i v a r i a t e and m u l t i v a r i a t e s t a t i s t i c s 137 7.2 Remote s e n s i n g 138 7.3 G e o g r a p h i c i n f o r m a t i o n system 138 7.4 Recommendations 140 v i PAGE REFERENCES 143 APPENDICES 153 1 Summary o f p e d o l o g i c and taxonomic f e a t u r e s f o r s o i l s e r i e s w i t h i n t h e s t u d y a r e a . 153 2 F i e l d c a l i b r a t i o n o f n e u t r o n probe. 154 3 B i o p h y s i c a l v a r i a b l e s from 114 s t u d y s i t e s . 158 4 C e n t r a l tendency s t a t i s t i c s f o r s e l e c t e d b i o p h y s i c a l v a r i a b l e s . 246 5 Summary o f n o t a b l e c o r r e l a t i o n c o e f f i c i e n t s between y i e l d , key f o l i a r e lements and energy components w i t h b i o p h y s i c a l v a r i a b l e s . 261 v i i LIST OF SYMBOLS V a r i o u s a b b r e v i a t i o n s and symbols have been used t h r o u g h o u t t h e t e x t i n an e f f o r t t o keep d e s c r i p t i o n s t o a minimum. S o i l c h e m i c a l p r o p e r t i e s were sampled a t 2 depths 0 t o 25 and 25 t o 50-cm, w h i l e s o i l p h y s i c a l p r o p e r t i e s were sampled a t 0 t o 25, 25 t o 50, 50 t o 75, and 75 t o 100-cm d e p t h i n c r e m e n t s . Components o f t h e w a t e r b a l a n c e a r e d e r i v e d from s o i l m o i s t u r e measurements a t s e v e r a l depths c e n t r e d around 15, 30, 45, 60, and 90-cm. A b b r e v i a t i o n s ( e . g . , EVAP, f o r e v a p o t r a n s p i r a t i o n ) a r e i m m e d i a t e l y f o l l o w e d by a d i g i t t o r e p r e s e n t t h e c u t number f o r t h e y e a r o f i n t e r e s t , and a second d i g i t w h i c h r e p r e s e n t s t h e d e p t h o f i n t e r e s t f o r w h i c h t h e v a r i a b l e was d e t e r m i n e d . Examples a r e p r o v i d e d below. S o i l c h e m i c a l and p h y s i c a l p r o p e r t i e s PH-1 MG-2 o r Mg-2 TEB BS CEC PSC 1.5-MPA-4 AWSCERD AWSCTOT ERD86 ELEV PHYS S o i l pH (CaCl 2) , 0 t o 25-cm. Exchangeable Mg (m.e. % ) , 25 t o 50-cm. T o t a l exchangeable b a s e s . Base s a t u r a t i o n . C a t i o n exchange c a p a c i t y . P a r t i c l e s i z e c l a s s . 1.5-MPa w a t e r r e t e n t i o n (mm), 75 t o 100-cm A v a i l a b l e w a t e r s t o r a g e c a p a c i t y (mm), w i t h i n t h e e f f e c t i v e r o o t i n g d e p t h . AWSC (mm), f o r t o t a l p r o f i l e (0 t o 100-cm). E f f e c t i v e r o o t i n g d e p t h (m), o v e r t h e 1986 grow i n g season ( d e t e r m i n e d from n e u t r o n probe m o n i t o r i n g ) . e l e v a t i o n (cm) above l o w e s t p o i n t i n s t u d y a r e a . p h y s i o g r a p h i c p o s i t i o n ; 1 = r i d g e , 2 = s i d e s l o p e , 3 = d e p r e s s i o n . Water b a l a n c e EVAP4-1 C u m u l a t i v e e v a p o t r a n s p i r a t i o n (mm/day) up t o c u t 4 from 0 t o 225-mm. EP3-2 Change i n s o i l w a t e r s t o r a g e (mm), from t h e s t a r t o f m o n i t o r i n g up t o c u t 3 w i t h i n ERD. DEF-5 C u m u l a t i v e s o i l w a t e r d e f i c i t (mm), up t o c u t 5 w i t h i n ERD. ST2-2 S o i l w a t e r s t o r a g e (mm), p r i o r t o c u t 2 w i t h i n ERD. AUG29-1 S o i l w a t e r c o n t e n t ( t h e t a , %v/v) on August 29, from 0 t o 23-cm ( c e n t r e d a t 15-cm). AUG29-2 S o i l w a t e r c o n t e n t (% v / v ) , on August 29, from 23 t o 38-cm ( c e n t r e d a t 30-cm). AUG29-3 S o i l w a t e r c o n t e n t (% v / v ) , on August 29, from 0 t o 98cm. v i i i LIST OF TABLES PAGE 1. Average monthly t e m p e r a t u r e and p r e c i p i t a t i o n from A b b o t s f o r d A i r p o r t , p r e c i p i t a t i o n and d e p t h t o w a t e r t a b l e f o r t h e s t u d y p e r i o d . 18 2. A p p r o x i m a t e d a t e s and amounts o f i r r i g a t i o n w a t e r a p p l i e d o v e r t h e s t u d y s i t e i n 1986 and 1987. 19 3. A c o m p a r i s o n o f CV's f o r major f e r t i l i z e r e l e ments and key f o l i a r v a r i a b l e s . 36 4. P r o b a b i l i t y l e v e l s ( S t u d e n t ' s t ) t h a t means f o r y i e l d , f o l i a r e lements and energy components between h a r v e s t s , on d r y l a n d s i t e s , a r e s i g n i f i c a n t l y d i f f e r e n t . 40 5. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between t r e a t m e n t s . 45 6. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between y e a r s . 46 7. Par a m e t e r s from i s o t r o p i c s e m i - v a r i o g r a m s f o r y i e l d and key v a r i a b l e s . 51 8. Parameter v a l u e s f o r some i s o t r o p i c s e m i - v a r i o g r a m s f o r s o i l s and r e l a t e d d a t a . 53 9. L i n e a r and m u l t i p l e r e g r e s s i o n models f o r p r e d i c t i n g d r y m a t t e r and f o l i a r n i t r o g e n from b i o p h y s i c a l d a t a b a s e . 66 10. M u l t i v a r i a t e c l a s s i f i c a t i o n used f o r m u l t i p l e d i s c r i m i n a n t a n a l y s i s . 68 11. R e s u l t s o f m u l t i p l e d i s c r i m i n a n t a n a l y s i s on y i e l d and n u t r i t i o n a l q u a l i t y c l a s s i f i c a t i o n f o r 1986. 69 12. R e s u l t s o f m u l t i p l e d i s c r i m i n a n t a n a l y s i s on y i e l d and n u t r i t i o n a l q u a l i t y c l a s s i f i c a t i o n f o r 1987. 70 13. V a r i a b l e s c o m p r i s i n g t h e f i r s t f o u r o r f i v e p r i n c i p a l components f o r t h e 1986 and 1987 b i o p h y s i c a l d a t a base. 73 14. F o l i a r v a r i a b l e s (weighted) and t h e v a r i a n c e f o r wh i c h t h e y a c c o u n t when used i n m u l t i p l e s t e p w i s e r e g r e s s i o n t o p r e d i c t key v a r i a b l e s . 76 15. Mean v a l u e s f o r y i e l d and key v a r i a b l e s from c l u s t e r c l a s s e s f o r combined t r e a t m e n t s o v e r two y e a r s ( c l u s t e r i n g c a r r i e d o u t u s i n g w a t e r b a l a n c e components). 81 16. Mean v a l u e s and ranges o f y i e l d and key v a r i a b l e s f o r c l u s t e r c l a s s e s from c u t 3 1987 ( d r y l a n d t r e a t m e n t ) . 83 17. Mean and range f o r y i e l d and key v a r i a b l e s w i t h i n c l a s s e s i d e n t i f i e d by c l u s t e r a n a l y s i s u s i n g IR p i x e l v a l u e s from t h e August 1987 image. 90 18. L i n e a r and m u l t i p l e r e g r e s s i o n models f o r p r e d i c t i n g y i e l d and key v a r i a b l e s from remote s e n s i n g d a t a . 95 ix PAGE 19. Correlation c o e f f i c i e n t s (a) and regression equations (b) from spectral reflectance measurements with selected s o i l physical and chemical properties from 0 to 25-cm. 100 20. S p a t i a l coincidence s t a t i s t i c s between dryland s i t e s i n 1986 and 1987 showing s i t e s which remain constantly low, high or show increases or decreases i n production between consecutive cuts. 113 21. S p a t i a l coincidence s t a t i s t i c s showing the re l a t i o n s h i p between high production areas (dryland s i t e s , 1987), and available water storage capacity within the e f f e c t i v e rooting depth. 114 22. Percentage of v a l i d a t i o n s i t e s c o r r e c t l y predicted for key variables from GIS overlay using elevation and 1.5-MPa water retention. 121 23. Typical input format for Ration Optimizer model. 128 24. Comparison of preferred cut from i r r i g a t e d and dryland forage treatments i n 1986 and 1987. 129 25. Comparison of i r r i g a t e d versus dryland forage as selected by Ration Optimizer program. 131 26. Comparison of production costs for 3 production classes. 134 X LIST OF FIGURES PAGE 1. Overview o f s t u d y i l l u s t r a t i n g t h e manner i n w h i c h t h e d a t a base was a n a l y z e d . 4 2. I d e a l i z e d s e m i - v a r i o g r a m . 8 3. L o c a l i t y map. 17 4. S a m p l i n g d e s i g n f o r s t u d y a r e a . 21 5. D i g i t a l e l e v a t i o n model f o r s t u d y a r e a . 22 6. S a m p l i n g d e s i g n a t i n d i v i d u a l s i t e s . 23 7. Number o f g r o w i n g days between f i v e c o n s e c u t i v e c u t s i n 1986 and 1987. 34 8. S e a s o n a l v a r i a t i o n i n t h e t a v a l u e s (0 t o 15-cm) f o r two p h y s i o g r a p h i c a l l y c o n t r a s t i n g s i t e s . 38 9. F o l i a r energy components (mean and range) from m a t e r i a l grown a t d r y l a n d and i r r i g a t e d s i t e s i n 1986 and 1987 ( w e i g h t e d ) . 41 10. The e f f e c t s o f t i m e and t r e a t m e n t on y i e l d , f o l i a r n i t r o g e n and d i g e s t i b l e energy. 42 11. V a r i a t i o n i n f o l i a r element l e v e l s (mean and range) o v e r t h e 1987 g r o w i n g season ( d r y l a n d o n l y ) . 43 12. T y p i c a l s e m i - v a r i o g r a m s f o r y i e l d from c u t 1 and c u t 5. 50 13. T h i s p l o t i l l u s t r a t e s ( p o s s i b l e ) p e r i o d i c b e h a v i o u r w h i c h has been d e s c r i b e d as t h e " h o l e - e f f e c t " . 54 14. S e m i - v a r i o g r a m s f o r s e l e c t e d v a r i a b l e s . 56 15. I n t e r p o l a t e d ( k r i g e d ) v a l u e s f o r (a) s o i l pH, (b) w a t e r r e t e n t i o n (1.5-MPa) and, f o l i a r n i t r o g e n ( c u t 1, 1986). 58 16. C o r r e l o g r a m s f o r y i e l d and f o l i a r n i t r o g e n ( c u t 4 and 5) 1986. 61 17. C o r r e l o g r a m s f o r y i e l d and f o l i a r n i t r o g e n ( c u t 4 and 5) 1987. 62 18. S c a t t e r p l o t o f e v a p o t r a n s p i r a t i o n v e r s u s y i e l d f o r c u t 5 1987. 64 19. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l a s s e s ( c l u s t e r i n g c a r r i e d out u s i n g w a t e r b a l a n c e components, p=0.10). 80 20. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l a s s e s ( c l u s t e r i n g c a r r i e d o u t u s i n g v a r i a b l e s i d e n t i f i e d w i t h d i s c r i m i n a n t a n a l y s i s , p=0.05). 85 21. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l u s t e r c l a s s e s ( C l u s t e r i n g c a r r i e d o u t u s i n g v a r i a b l e s i d e n t i f i e d w i t h p r i n c i p a l components a n a l y s i s , p=0.05). 86 x i PAGE 22. D i g i t a l c o l o u r IR image f o r s t u d y a r e a i n August 1987 p r i o r t o c u t 4. 88 23. Frequency d i s t r i b u t i o n o f IR p i x e l v a l u e s showing t h e c l a s s l i m i t s as d e t e r m i n e d by c l u s t e r a n a l y s i s . 89 24. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g key v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l u s t e r c l a s s e s ( C l u s t e r i n g c a r r i e d o u t u s i n g IR and IR w i t h g r e e n and r e d dye l a y e r s , p=0.05). 90 25. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g a c c e s s o r y b i o p h y s i c a l v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l a s s e s ( C l u s t e r i n g c a r r i e d o u t u s i n g IR and IR w i t h g r e e n and r e d dye l a y e r s , p=0.05). 92 26. S u p e r v i s e d c l a s s i f i c a t i o n o f t h e August image (a) o r i g i n a l c l a s s i f i c a t i o n and, (b) f i l t e r e d (7x7) image. 9 3 27. Dendrogram from c l u s t e r a n a l y s i s u s i n g 670,1050, 2200-nm and RIR3 and RIR6 r e f l e c t a n c e r a t i o s showing t h e c l e a r d i f f e r e n t i a t i o n o f i r r i g a t e d v e r s u s d r y l a n d s i t e s when two c l u s t e r groups a r e chosen. 103 28. S p a t i a l l y c h a r a c t e r i z i n g d a t a v a r i a t i o n ( i n s e t (a) shows p o i n t d a t a f o r c u t 2 and i n s e t (b) shows t h e same d a t a a f t e r k r i g i n g ) . 107 29. A s s e s s i n g c o i n c i d e n c e among mapped d a t a . 109 30. A s i m p l e c a r t o g r a p h i c model. I l l 31. P l a n i m e t r i c map showing a r e a s w i t h g r e a t e r t h a n 75% r e d u c t i o n i n y i e l d between c u t 2 and 3. 112 32. V a l i d a t i o n o f y i e l d as p r e d i c t e d from m u l t i p l e r e g r e s s i o n w i t h 5 x 5m p i x e l v a l u e s u s i n g GIS. 116 33. P l a n i m e t r i c map showing a GIS o v e r l a y o f two e l e v a t i o n c l a s s e s w i t h t h r e e y i e l d c l a s s e s ( t h e i r r i g a t e d t r e a t m e n t i s shown on t h e r i g h t ) . 117 34. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between GIS c l a s s e s (GIS c l a s s e s d e r i v e d from o v e r l a y s o f e l e v a t i o n and 1.5-MPa w a t e r r e t e n t i o n , p=0.10). 120 35. V a l i d a t i o n o f y i e l d , f o l i a r N and P and d i g e s t i b l e energy as p r e d i c t e d v i a r e g r e s s i o n w i t h s p e c t r a l r e f l e c t a n c e d a t a . 122 36. V a l i d a t i o n o f f o l i a r N as p r e d i c t e d v i a r e g r e s s i o n w i t h f o l i a r K and Mg. 123 37. P l a n i m e t r i c map o f f o l i a r N d i s t r i b u t i o n d e r i v e d from GIS ( d r y l a n d o n l y ) . 124 38. C o s t s u r f a c e s f o r (a) d r y l a n d and (b) i r r i g a t e d t r e a t m e n t s f o r c u t 1 (upper) and c u t 5, 1987. 136 x i i 39. N e u t r o n probe c a l i b r a t i o n 40. N e u t r o n probe c a l i b r a t i o n PAGE e q u a t i o n s f o r 1986. 156 e q u a t i o n s f o r 1987. 157 x i i i ACKNOWLEDGEMENTS I t i s my g r e a t p l e a s u r e t o thank t h e p e o p l e who have i n v a r i o u s ways c o n t r i b u t e d t o t h i s t h e s i s . T h i s s t u d y was made p o s s i b l e by a New Z e a l a n d N a t i o n a l R e s e a r c h A d v i s o r y C o u n c i l F e l l o w s h i p . Mr George Boeve was k i n d enough t o p r o v i d e t h e l a n d on w h i c h t o con d u c t t h e s t u d y . My s u p e r v i s o r , Dr Hans S c h r e i e r , p r o v e d a s t i m u l a t i n g s o u r c e o f i d e a s and i n s p i r a t i o n t h r o u g h o u t t h e s t u d y , and h i s good humour was always a p p r e c i a t e d on f i e l d t r i p s . O t h e r committee members, Drs D. Moon, L. L a v k u l i c h , and B. H o l l a l s o p r o v i d e d a d v i c e and a s s i s t a n c e when r e q u i r e d . S e v e r a l o t h e r s t u d e n t s a s s i s t e d w i t h f i e l d and l a b o r a t o r y work. W i t h o u t t h e i r a s s i s t a n c e much would have been l e f t undone. P r e s e n t and former s t a f f o f t h e Department o f S o i l S c i e n c e a l s o a s s i s t e d w i t h t e c h n i c a l a d v i c e : P a t t i C a r b i s , B e r n i e Von S p i n d l e r and E v e l i n e W o l t e r s o n . R a o u l W i a r t g u i d e d me t h r o u g h t h e image a n a l y s i s and Sandra Brown a s s i s t e d w i t h s t a t i s t i c a l a n a l y s i s . G l e n n S m i t h o f E a s t C h i l l i w a c k A g r i c u l t u r a l C o o p e r a t i v e p r o v i d e d a c c e s s t o t h e R a t i o n O p t i m i z e r Model. F i n a l l y , I would l i k e t o thank my w i f e Sandy and son N i c h o l a s f o r t h e i r s u p p o r t and u n d e r s t a n d i n g . I p r o m i s e t o l e a v e t h e computer a l o n e f o r a w h i l e ! 1 CHAPTER 1 INTRODUCTION Information r e l a t i n g to the s p a t i a l v a r i a b i l i t y of biophysical resources has been d i f f i c u l t to incorporate into land management and planning. S o i l v a r i a b i l i t y i s an important factor and i t has received considerable attention over the l a s t two decades i n land management and planning. S o i l s c i e n t i s t s and agronomists commonly use models to explain the factors involved i n determining processes r e l a t i n g to s o i l p r oductivity and plant growth. Unfortunately, many of these models are developed i n homogeneous, i s o t r o p i c laboratory conditions, while f i e l d conditions are anisotropic and non-homogeneous. Thus, the p r e d i c t i v e c a p a b i l i t y of many models i s l o s t i n the spectrum of s p a t i a l v a r i a b i l i t y because they are l i m i t e d i n t h e i r a b i l i t y to accommodate r e a l i t y . Our i n a b i l i t y to describe adequately and to communicate the s p a t i a l v a r i a b i l i t y of key biophysical properties remains a major obstacle to interpretations of f i e l d research and subsequent application to management. The aim of t h i s study was to assess the u t i l i t y of a geographic information system (GIS) 2 and remote s e n s i n g techniques i n c o n j u n c t i o n w i t h v a r i o u s s t a t i s t i c a l methods t o develop and assess forage p r o d u c t i o n models. The u t i l i t y o f d e r i v e d models was asse s s e d s p a t i a l l y u s i n g the GIS, and the model's u t i l i t y t o p r o v i d e t o o l s f o r a g r i c u l t u r a l f i e l d management assessed. 1.1 Objectives 1. M o n i t o r s o i l s p a t i a l and "temporal v a r i a b i l i t y and crop (forage) performance u s i n g a d i g i t a l data base. 2. Examine and model the r e l a t i o n s h i p s between s o i l p r o p e r t i e s and f o r a g e p r o d u c t i o n s t a t i s t i c a l l y w i t h the use of a GIS and remote s e n s i n g t e c h n i q u e s . 3. T e s t the d e r i v e d model(s) i n two s t e p s : ( i ) i d e n t i f i c a t i o n o f key v a r i a b l e s o r combinations of v a r i a b l e s i n f l u e n c i n g y i e l d s ; ( i i ) v a l i d a t i o n o f d e r i v e d model(s) on the b a s i s o f s t a t i s t i c a l and GIS a n a l y s i s . 4. Assess the u t i l i t y o f d i g i t a l data bases and GIS f o r a g r i c u l t u r a l f i e l d management. The approach i n t h i s study was t o make use o f l o c a l l y v a l i d , simple approximations t o f u n c t i o n a l r e l a t i o n s h i p s e s t a b l i s h e d a t sampled s i t e s u s i n g the techniques o u t l i n e d above. A p r e l i m i n a r y step t o g a i n some understanding i n the dependence s t r u c t u r e of the data s e t i s c o r r e l a t i o n a n a l y s i s . The d e r i v e d m a t r i x can g i v e i n f o r m a t i o n on d i f f e r e n c e s i n v a r i a n c e o f the v a r i a b l e s and on c o r r e l a t i o n s , but t h i s i s 3 l i m i t e d to measures of l i n e a r dependence between single v a r i a b l e s . Relationships derived using t h i s approach are generally empirical i n nature, and re l a t i o n s h i p s may be coincidental or r e a l , but mechanisms remain hidden. The use of multivariate techniques such as p r i n c i p a l component analysis and discriminant analysis can be used to reduce an abundance of measurements of many variables to a few p r i n c i p a l components or factors. These new variables can then be tested v i a l i n e a r or stepwise regression analysis. These techniques assume there i s no s p a t i a l dependence i n the data. In most instances however, soil-agronomic data i s s p a t i a l l y dependent; a r e l a t i o n s h i p which weakens with increasing distance. G e o s t a t i s t i c s provides mechanisms to determine t h i s covariance function (e.g., semi-variance), and use the data for pr e d i c t i n g values at unvisited s i t e s (kriging). F i n a l l y , using a l l of these techniques to help i d e n t i f y primary a t t r i b u t e data, the GIS can be used to store, manipulate and display t h i s information i n a dynamic and f l e x i b l e manner. Coupled with appropriate models, these data can provide a basis for p r e d i c t i v e and managerial c a p a b i l i t y . An overview of the study i s provided i n F i g . 1. SPATIAL VARIABILITY DIGITAL DATA BASE VARIABILITY IN T IME AND BETWEEN TREATMENTS CONVENTIONAL ASSESSMENT QEOSTATISTICS SPATIAL RELATIONSHIPS AND PREDICTIVE MODELS REMOTE SENSINO ECONOMIC IMPLICATIONS S IGNIF ICANCE TESTS IMPLICATIONS - SPATIAL DEPENDENCE - SAMPLING - EXPT. DESIGN LINEAR CATEGORIC CORRELATION REGRESSION MULTIPLE REGRESSION PCA DA BEST PREDICTOR VARIABLES - SINGLE AND MULTIPLE CLUSTER ASSESSMENT OF KEY AND ASSOCIATED VARIABLES BEST DISCRIMINATING VARIABLES BY -CUT, YEAR TREATMENT SPATIAL D I S T R I B U T I O N OF GROUPS S IGNIF ICANCE TESTS FOR KEY VARIABLES ASSOCIATED VARIABLES G I S ASSESS U T I L I T Y TO DEVELOP AND.TEST SPATIAL MODELS 5 1.2 Background 1.2.1 S p a t i a l and temporal v a r i a t i o n of s o i l properties and crop y i e l d S o i l v a r i a b i l i t y i s t h e p r o d u c t o f s o i l - f o r m i n g f a c t o r s o p e r a t i n g and i n t e r a c t i n g o v e r a continuum o f s p a t i a l and t e m p o r a l s c a l e s . L i t e r a t u r e on t h e s u b j e c t o f s o i l v a r i a b i l i t y i s v o l u m i n o u s and e x c e l l e n t summaries a r e p r o v i d e d by B e c k e t t and Webster (1971), N i e l s e n e t a l . (1973), Campbell (1979), W i l d i n g and Drees (1983), Burrough (1983) and Webster and Burgess (1983). H i s t o r i c a l l y , s o i l v a r i a b i l i t y was p u r s u e d p r i m a r i l y f o r p e d o l o g i c and s o i l s u r v e y r e a s o n s , such as d e f i n i t i o n o f c l a s s d i f f e r e n t i a e ( A r k l e y 1976), d e t e r m i n i n g t h e c o m p o s i t i o n o f mapping u n i t s (Adams and W i l d e 1976a,b, C r o s s o n and P r o t z 1974), s a m p l i n g d e s i g n and m o d e l l i n g o f p e d o g e n i c p r o c e s s e s ( N o r r i s 1971). Many o f t h e s e s t u d i e s used a c l a s s i c a l approach t o s t a t i s t i c a l a n a l y s i s w h i c h assumes t h a t t h e s a m p l i n g u n i t mean i s t h e e x p e c t e d v a l u e f o r t h e e n t i r e u n i t , w i t h an e s t i m a t i o n e r r o r e x p r e s s e d by t h e w i t h i n - u n i t v a r i a n c e (Trangmar e t a l . 1985). E x p e r i m e n t a l d a t a s e t s t r a d i t i o n a l l y r e l i e d on a n a l y t i c a l t e c h n i q u e s w h i c h a r e d i r e c t i o n a l l y p r e d i c t i v e s uch as r e g r e s s i o n and a n a l y s i s o f v a r i a n c e . I n r e c e n t y e a r s however, t h e r e has been a s h i f t i n emphasis from t r a d i t i o n a l s o i l s u r v e y i n t e r p r e t a t i o n s and s t u d i e s p r i m a r i l y c o n c e r n e d w i t h e f f e c t s on a s i n g l e v a r i a t e , t oward more modern e n v i r o n m e n t a l q u e s t i o n s a t v a r y i n g l e v e l s o f d e t a i l . Recent s t u d i e s now encompass an 6 integrated resource management approach to areas such as catchments or natural land management units. Increasingly, more attention i s being given to describing and modelling such areas as complete systems rather than examining i n d i v i d u a l components i n i s o l a t i o n . These considerations suggest that multivariate experimentation and analysis i s both necessary and highly desirable. Many multivariate procedures, such as discriminant analysis, have been developed and applied (Webster and Burrough 1974). Other techniques which are useful i n establishing r e l a t i o n s h i p s between sets of variables include c l u s t e r analysis (Schreier and Lavkulich 1979), and p r i n c i p a l component analysis (Oliver and Webster 1987a), although none of these have been widely used i n soil-agronomic studies. 1.2.2 G e o s t a t i s t i c s Many natural properties vary continuously i n space, but the pattern and scale of t h e i r v a r i a t i o n i s not always apparent. Thus, to represent t h e i r v a r i a t i o n , i n d i v i d u a l values at unsampled locations must be estimated from information recorded at sampled s i t e s . Recent developments i n s t a t i s t i c a l theory enable s p a t i a l relationships between samples to be quantified and used fo r subsequent int e r p o l a t i o n of values at unsampled locations. These developments are based on the theory of regionalized variables formalized by Matheron (1965), and derived from Krige's empirical ideas that the s p a t i a l estimation of gold 7 c o n t e n t c o u l d be improved by t a k i n g t h e degree o f a u t o c o r r e l a t i o n , between a d j a c e n t samples i n t o c o n s i d e r a t i o n . E x c e l l e n t summaries on t h e development and a p p l i c a t i o n o f t h i s t h e o r y t o s o i l s c i e n c e and agronomy a r e g i v e n by V i e i r a e t a l . (1983), Webster (1985), Trangmar e t a l . (1985) and O l i v e r (1987) , and o n l y a b r i e f o u t l i n e o f t h e t h e o r y w i l l be g i v e n h e r e . The s e m i - v a r i o g r a m i s t h e c e n t r a l t o o l o f r e g i o n a l i z e d v a r i a b l e t h e o r y . I t examines t h e d i f f e r e n c e between p o s i t i o n s o f two v a l u e s where t h e d i f f e r e n c e i s dependent on d i s t a n c e o f s e p a r a t i o n ( l a g ) and t h e i r o r i e n t a t i o n ( x , y ) . Data s t r u c t u r e from t h e s e m i - v a r i o g r a m p r o v i d e s t h e b a s i s f o r o p t i m a l e s t i m a t i o n by k r i g i n g . The c h a r a c t e r i s t i c s o f an i d e a l i z e d s e m i - v a r i o g r a m a r e shown i n F i g . 2, where t h e sample v a r i a b i l i t y i s i n c r e a s i n g as d i s t a n c e i n c r e a s e s between samples. The range o f s p a t i a l dependence ( A l f o r s p h e r i c a l model and A2 f o r l i n e a r model) i s d e t e r m i n e d by t h e v a l u e a t w h i c h t h e s e m i - v a r i a n c e r e a c h e s a more o r l e s s c o n s t a n t v a l u e , t h e s i l l ( C ) . The nugget v a r i a n c e (Co) , r e p r e s e n t s u n e x p l a i n e d v a r i a n c e o f t h e p r o p e r t y w h i c h cannot be d e t e c t e d a t t h e s c a l e o f s a m p l i n g . The p a r a m e t e r s o f t h e s e m i - v a r i o g r a m model p r o v i d e t h e i n f o r m a t i o n f o r k r i g i n g , w h i c h i s a method o f o p t i m a l l o c a l e s t i m a t i o n . K r i g e d e s t i m a t e s a t any unsampled l o c a t i o n a r e t h e most p r e c i s e p o s s i b l e from t h e a v a i l a b l e d a t a . The use o f i r r e g u l a r l y s c a t t e r e d d a t a however, l e a d s t o some l o s s o f p r e c i s i o n (McBratney e t a l . 1982). K r i g e d 8 estimates are u s u a l l y presented as i s a r i t h m i c maps or t h r e e dimensional p l o t s t o i l l u s t r a t e the v a r i a t i o n . The methods of g e o s t a t i s t i c s are now being a p p l i e d i n c r e a s i n g l y t o the a n a l y s i s of v a r i a b i l i t y i n f i e l d experiments and f o r improving sampling designs (Greminger e t a l . 1985, N i e l s e n and Bouma 1985, O l i v e r and Webster 1987b, Tabor e t a l . 1984). Semi-variance Nugget Variance (Co) F i g u r e 2 . Total Distance. I d e a l i z e d semi-variogram. 9 1.2.3 M o d e l l i n g Techniques I n o r d e r t o u n d e r s t a n d more f u l l y t h e r e l a t i o n s h i p s between t h e p r o p e r t i e s o f a complex system a m o d e l l i n g approach i s o f t e n u n d e r t a k e n . Models u s u a l l y f a l l i n t o two b r o a d c a t e g o r i e s ; e m p i r i c a l ( o r c o r r e l a t i v e ) and m e c h a n i s t i c ( o r e x p l a n a t o r y ) models. The former d e s c r i b e r e l a t i o n s h i p s between v a r i a b l e s w i t h o u t r e f e r r i n g t o any u n d e r l y i n g s t r u c t u r e t h a t may e x i s t between t h e v a r i a b l e s . M e c h a n i s t i c models, on t h e o t h e r hand, a t t e m p t e x p l i c i t l y t o r e p r e s e n t cause and e f f e c t between v a r i a b l e s . Computer s i m u l a t i o n models a r e now b e i n g w i d e l y used t o c h a r a c t e r i z e a g r o - e c o l o g i c a l systems i n q u a n t i t a t i v e terms as a f u n c t i o n o f management ( e . g . , W h i s l e r e t a l . 1986, Swaney e t a l . 1986) and e n v i r o n m e n t a l v a r i a b l e s such as weather (e . g . , F r a n c i s c o 1988, Kornher and T o r s s e l l 1983), s o i l f e r t i l i t y (Brown e t a l . 1986) and c r o p w a t e r use ( e . g . , Tanner and S i n c l a i r 1983, Hanks and Rasmussen 1982). To e x t r a p o l a t e r e s u l t s on a s p a t i a l b a s i s u s i n g t h e s e m e c h a n i s t i c models w i t h c o n f i d e n c e , i m p l i e s a r e q u i r e m e n t f o r l a r g e amounts o f b a s i c d a t a t o d e v e l o p and r u n t h e models (Brinkman and S t e i n 1987). De W i t and Van K e u l e n (1987) s u g g e s t t h a t d e t e r m i n i n g t h e a c t u a l a g r i c u l t u r a l p o s s i b i l i t i e s f o r a r e g i o n r e q u i r e s f a r t o o many d a t a u s i n g such models. They and o t h e r s s u g g e s t many e n v i r o n m e n t a l i n v e s t i g a t i o n s now t e n d t o a p p l y b a s i c d a t a t o t h e use o f s e m i - q u a n t i t a t i v e i n t e r p r e t a t i o n s . These p r o v i d e a compromise between t h e degree o f d e t a i l t h a t i s r e t a i n e d i n t h e i models and t h e minimum number o f d i f f e r e n t d a t a t h a t a r e needed f o r t h e i r u se. T h i s emphasis i s a f u n c t i o n o f s c a l e and d a t a a v a i l a b i l i t y , and t h e s e m i - q u a n t i t a t i v e a pproach i s o f t e n a p p r o p r i a t e f o r c e r t a i n e v a l u a t i o n s ( N i x 1987) . These s i m p l i f i e d models can be d e v e l o p e d t o a d e q u a t e l y a d d r e s s c o n c e r n s as t o t h e n e c e s s a r y l e v e l o f g e n e r a l i z a t i o n and o f t h e a s s o c i a t e d b a s i c d a t a and c o s t s (Bouma e t a l . 1986). I t i s t h i s a p p r o a c h , combined w i t h GIS, wh i c h has been adopted h e r e f o r t h e r e s e a r c h r e p o r t e d i n t h i s t h e s i s . On t h e i n d i v i d u a l farm s c a l e , f o r example, t h e l i k e l y f u t u r e p r o s p e c t o f more c o m p e t i t i v e p r i c e s f o r a g r i c u l t u r a l p r o d u c t s makes i t l i k e l y t h a t farm e x p e n d i t u r e s a s s o c i a t e d w i t h t r a d i t i o n a l f a r m i n g p r a c t i c e s may need t o be r e v i e w e d c r i t i c a l l y . W i t h r e c e n t advances i n t e c h n o l o g y ( G o e r i n g 1985, Hummel 1985), s i g n i f i c a n t changes i n p r o d u c t i o n c o s t s , and t h e p o s s i b i l i t y o f e n f o r c e d e n v i r o n m e n t a l l a w s , c o n s i d e r a b l e a t t e n t i o n i s now b e i n g g i v e n t o t h e p o s s i b i l i t y o f u s i n g v a r i a b l e management t e c h n i q u e s w i t h i n a s i n g l e f i e l d ( W i l k i n s 1986, L u e l l e n 1985). Some o f t h e v a r i a b l e s r e q u i r e d t o d e v e l o p a management system o r model t o c a r r y o u t such a t a s k may be r e p r e s e n t e d i n a g e o g r a p h i c d a t a base ( e . g . , s o i l maps), o r t h e y may be i n f e r r e d from i n f o r m a t i o n on s o i l - l a n d s c a p e r e l a t i o n s h i p s . The s u c c e s s o f e x t r a p o l a t i o n t h e n depends on t h e a c c u r a c y o f t h e i n f e r e n c e s and t h e q u a l i t y and d e t a i l o f t h e a d d i t i o n a l i n f o r m a t i o n used. Most a g r o - e c o l o g i c a l m o d e l l i n g s t r a t e g i e s r e l y on t h e t r a n s f e r o f i n f o r m a t i o n by a n a l o g y ( N i x 1968, 1987). G e n e r a l l y a " r e p r e s e n t a t i v e " s i t e i s s e l e c t e d f o r e x p e r i m e n t a t i o n and r e s u l t s a r e e x t r a p o l a t e d t o o t h e r s i t e s t h a t a r e c l a s s i f i e d as h a v i n g s i m i l a r p r o p e r t i e s . T h i s p r o c e s s assumes t h a t a l l o c c u r r e n c e s o f a d e f i n e d c l a s s w i l l r espond s i m i l a r l y ; an a s s u m p t i o n w h i c h i s n o t always met. The s u c c e s s o f t h i s a pproach w i l l depend, t o a l a r g e degree, on t h e n a t u r e o f e s t a b l i s h e d r e l a t i o n s h i p s between b i o p h y s i c a l v a r i a b l e s and c r o p r e s p o n s e . 1.2 .4 Remote S e n s i n g I n t h e 1960's and 70*s, r e s e a r c h and development i n remote s e n s i n g t e c h n o l o g y produced new methods and s u g g e s t e d f u t u r e p o s s i b i l i t i e s f o r t h e c o l l e c t i o n and a n a l y s i s o f s u r f a c e i n f o r m a t i o n (Bauer 1975). There a r e two approaches t o t h e a n a l y s i s o f r e m o t e l y sensed d a t a ; p h o t o g r a p h i c and d i g i t a l l y o r i e n t e d . Remote s e n s i n g u s i n g p h o t o g r a p h i c t e c h n i q u e s s u f f e r s a s l i g h t r e d u c t i o n i n a c c u r a c y w h i c h can be a t t r i b u t e d t o t h e e x t r a s t e p s needed t o p r o c e s s and d i g i t i z e t h e f i l m . D i g i t a l image a n a l y s i s has r e c e i v e d a g r e a t d e a l o f a t t e n t i o n i n r e c e n t y e a r s , p a r t i c u l a r l y s p e c t r a l a n a l y s i s i n t h e 350 t o 1400-nm w a v e l e n g t h i n t e r v a l (Wiegand 1984). The method, r e f i n e d by T u c k e r (1977,1981) and o t h e r s , i s c l o s e t o b e i n g r o u t i n e l y used t o e s t i m a t e one o r s e v e r a l v a r i a b l e s i n c r o p p r o d u c t i o n . Because c r o p c a n o p i e s i n t e g r a t e g r o w i n g c o n d i t i o n s and respond t o management, s p e c t r a l o b s e r v a t i o n s p r o v i d e a d i r e c t l o o k a t c r o p c a n o p i e s . A l s o , t h e s p a t i a l l y i n t e g r a t e d s p e c t r a l o b s e r v a t i o n s may more a c c u r a t e l y r e p r e s e n t average f i e l d 12 c o n d i t i o n s t h a n do t r a d i t i o n a l p o i n t measurements i f f i e l d l e v e l i n f e r e n c e i s d e s i r e d . The use o f v i s i b l e r e d (600 t o 700-nm) and n e a r - i n f r a r e d (750 t o 1100-nm) s p e c t r a l d a t a has had t h e most a p p l i c a t i o n s w i t h a v a r i e t y o f v e g e t a t i o n t y p e s (Tucker e t a l . 1981). These d a t a have been used t o e s t i m a t e p a s t u r e and f o r a g e biomass (Tucker e t a l . 1975, W a l l e r e t a l . 1981, D e e r i n g e t a l . 1975, R i c h a r d s o n e t a l . 1983), m o n i t o r c r o p c o n d i t i o n and e s t i m a t e s e v e r i t y o f d r o u g h t s t r e s s (Thompson and Wehmanen 1979). These methods have a l s o been c i t e d by numerous a u t h o r s as a means t o o b t a i n r a p i d e s t i m a t e s o f canopy v a r i a b l e s a t c o n s i d e r a b l y l e s s expense t h a n t r a d i t i o n a l t e c h n i q u e s . A r e v i e w o f t h e s e t e c h n i q u e s i s g i v e n i n Tucker (1979, 1980). N e a r - i n f r a r e d w a v e l e n g t h s between 1400 and 2400-nm have been used s u c c e s s f u l l y t o p r e d i c t t h e n u t r i t i v e v a l u e o f f o r a g e s p a r t i c u l a r l y under l a b o r a t o r y c o n d i t i o n s (Shenk e t a l . 1979, 1981, B e n g t s s o n and L a r s s o n 1984). Barnes (1980), summarizes much o f t h e work t o d a t e , and pro p o s e s a r e a s f o r f u r t h e r work. 1.2.5 G e o g r a p h i c I n f o r m a t i o n Systems " O v e r l a y mapping" i s a term t h a t has come t o be used o v e r t h e p a s t decade o r so t o r e f e r t o a p a r t i c u l a r way o f o r g a n i z i n g and m a n i p u l a t i n g s p a t i a l i n f o r m a t i o n . McHarg (1969) was one o f t h e best-known exponents o f t h i s t e c h n i q u e . By t h e l a t e 1970's d r a m a t i c advances had been made i n t h e development and a p p l i c a t i o n o f c o m p u t e r - a s s i s t e d c a r t o g r a p h y p a r t i c u l a r l y i n N o r t h A m e r i c a ( T e i c h o l z and B e r r y 1983) , and T o m l i n s o n (1984) c o n s e r v a t i v e l y s u g g e s t s t h e r e may be as many as 4000 GIS systems i n N o r t h A m e r i c a by 1990. The main purpose o f a GIS i s t o p r o c e s s s p a t i a l i n f o r m a t i o n . A GIS can be d e f i n e d as an i n t e r n a l l y r e f e r e n c e d , automated, s p a t i a l i n f o r m a t i o n system d e s i g n e d f o r d a t a management, mapping and a n a l y s i s . The t e c h n o l o g y now p r o v i d e s a means f o r q u a n t i t a t i v e m o d e l l i n g o f s p a t i a l r e l a t i o n s h i p s ( B e r r y 1987a), and systems p r o v i d e managers w i t h an a n a l y t i c " t o o l b o x " e n a b l i n g them t o a d d r e s s complex i s s u e s i n new ways (Dangermond 1986). A GIS can r e t r i e v e a t t r i b u t e s a t a l o c a t i o n ; r e c l a s s i f y map c a t e g o r i e s ; o v e r l a y maps; measure s i m p l e o r w e i g h t e d d i s t a n c e o r c o n n e c t i v i t y ; c a l c u l a t e t e r r a i n i n f o r m a t i o n from e l e v a t i o n d a t a ; and, c h a r a c t e r i z e c a r t o g r a p h i c neighbourhoods. By o r g a n i z i n g t h e s e o p e r a t i o n s i n a l o g i c a l manner, a g e n e r a l i z e d c a r t o g r a p h i c m o d e l l i n g approach can be d e v e l o p e d . T h i s approach has p r o v e n t o be p a r t i c u l a r l y e f f e c t i v e i n p r e s e n t i n g s p a t i a l a n a l y s i s t e c h n i q u e s ( B e r r y 1985). As t h e management o f l a n d has always r e q u i r e d s p a t i a l i n f o r m a t i o n as i t s c o r n e r s t o n e p r o c e s s , N i x (1987) s u g g e s t s more a t t e n t i o n be g i v e n t o t h e i d e n t i f i c a t i o n and measurement o f p r i m a r y a t t r i b u t e d a t a and i t s s t o r a g e w i t h i n a s p a t i a l l y r e f e r e n c e d GIS. T h i s approach t o d a t a s t o r a g e and m a n i p u l a t i o n has s e v e r a l advantages. F i r s t , i t i s open-ended, f l e x i b l e , and e n a b l e s a dynamic approach t o problem s o l v i n g ( e . g . , "what i f ? " a n a l y s i s ) . S e c o n d l y , t h e d a t a base i s s p a t i a l l y r e f e r e n c e d , and 14 t h i r d l y , i t can p r o v i d e t h e b a s i s f o r r e s o u r c e management a t a number o f s c a l e s . L i n k i n g t h e GIS d a t a base w i t h e x i s t i n g i n v e n t o r y d a t a bases ( e . g . , s o i l maps) may e n a b l e r e l i a b l e p r e d i c t i o n s t o be made o v e r w i d e r a r e a s and p r o v i d e a n o v e l way t o p r e s e n t s p a t i a l d a t a . I t can be c o n s i d e r e d as an i n t e g r a t i n g t o o l t o a n a l y z e v a r i a b l e i n t e r a c t i o n s i n a dynamic manner. Bouma e t a l . (1986) s u g g e s t t h a t t h e use o f GIS and a p p r o p r i a t e t r a n s f e r f u n c t i o n s w i l l e n a b l e t h e use o f e x p r e s s i o n s f o r a r e a s o f l a n d t h a t a r e r e p r e s e n t e d on s o i l maps. C o n v e r s e l y , v a l u e s w h i c h a r e d e r i v e d from s e v e r a l o t h e r d a t a ( e . g . , s o i l t a x a ) can be a s s i g n e d by s o i l map u n i t t h r o u g h an assignment t a b l e o r t h e GIS d a t a base. 15 CHAPTER TWO STUDY SITE 2.1 S i t e s e l e c t i o n S i t e s e l e c t i o n was made on t h e b a s i s o f s e v e r a l c r i t e r i a . F i r s t , t h e s i t e had t o be r e p r e s e n t a t i v e o f a s u i t e o f s o i l s common t o an a r e a w h i c h was u t i l i z e d f o r f o r a g e p r o d u c t i o n . Second, t h e s i t e s h o u l d e x h i b i t pronounced s p a t i a l v a r i a b i l i t y o f s o i l s and topography so as t o maximize t h e range o f c r o p r e s p o n s e . T h i r d , t h e s i t e had t o remain i n f o r a g e p r o d u c t i o n f o r t h e d u r a t i o n o f t h e s t u d y w i t h o u t c o n f l i c t i n g w i t h normal farm management o p e r a t i o n s . F i n a l l y , t h e s i t e had t o be w i t h i n r e a s o n a b l e t r a v e l l i n g d i s t a n c e from UBC t o f a c i l i t a t e t h e v e r y i n t e n s i v e m o n i t o r i n g program. 2.2 Location and environmental s e t t i n g A f t e r a p r e l i m i n a r y r e c o n n a i s s a n c e , a f i v e h e c t a r e s t u d y a r e a was chosen i n M a t s q u i P r a i r i e , Lower F r a s e r V a l l e y , B r i t i s h C o l umbia. Forage f o r hay and s i l a g e i s a major c r o p i n t h i s a r e a and i n v o l v e s a 4 t o 6 y e a r c y c l e i n a s s o c i a t i o n w i t h c o r n (Zea mays) w h i c h i s a l s o used f o r s i l a g e . The sward was composed o f o r c h a r d g r a s s ( D a c t y l i s g l o m e r a t a L.) and c l o v e r ( T r i f o l i u m r e p e n s L . ) . 16 The s i t e l i e s a p p r o x i m a t e l y 10-km n o r t h e a s t o f A b b o t s f o r d , c l o s e t o t h e F r a s e r R i v e r ( F i g . 3 ) . The c l i m a t e o f t h e a r e a can g e n e r a l l y be d e s c r i b e d as h a v i n g warm, r a i n y w i n t e r s and r e l a t i v e l y c o o l , d r y summers (Hare and Thomas 1979). W i n t e r p r o d u c e s some o f t h e c l o u d i e s t and r a i n i e s t w eather i n Canada, w h i l e summers have f r e q u e n t l o n g p e r i o d s o f sunny weather when t e m p e r a t u r e s a r e warm and r a i n f a l l i s low. S o i l m o i s t u r e d e f i c i t s a p p r o a c h i n g 100-mm f r e q u e n t l y d e v e l o p o v e r t h e gr o w i n g season and i r r i g a t i o n i s r e q u i r e d on many s o i l s t o m a i n t a i n h i g h l e v e l s o f p r o d u c t i o n . T a b l e 1 shows t h e average monthly t e m p e r a t u r e and p r e c i p i t a t i o n a t A b b o t s f o r d a i r p o r t and c o n d i t i o n s a t t h e s i t e o v e r t h e s t u d y p e r i o d . The M a t s q u i P r a i r i e i s an a l l u v i a l f l o o d p l a i n c o n s i s t i n g o f g e n t l y u n d u l a t i n g f l u v i a l d e p o s i t s from t h e F r a s e r R i v e r w h i c h l i e m o s t l y below 10 meters i n e l e v a t i o n and i n c l u d e b o t h l a t e r a l and v e r t i c a l a c c r e t i o n d e p o s i t s . The s o i l s o f t h e a r e a a r e d e s c r i b e d i n d e t a i l by L u t t m e r d i n g (1981a,b). Four d i f f e r e n t s o i l s e r i e s o c c u r w i t h i n t h e s t u d y a r e a and t h e s o i l p a t t e r n i s p a r t i a l l y r e l a t e d t o t h e topography ( L u t t m e r d i n g 1981a). M a t s q u i s o i l s o c c u r i n t h e h i g h e s t t o p o g r a p h i c p o s i t i o n s and have a sandy p a r t i c l e s i z e c l a s s t h r o u g h o u t t h e p r o f i l e . Monroe s o i l s o c c u r i n s i m i l a r p o s i t i o n s b u t have c o a r s e loamy t e x t u r e . Lower s i t e s and d e p r e s s i o n s a r e o c c u p i e d by F a i r f i e l d and Page s o i l s r e s p e c t i v e l y b o t h w i t h f i n e loamy p a r t i c l e s i z e c l a s s . A summary o f s a l i e n t p e d o l o g i c and taxonomic f e a t u r e s f o r t h e f o u r s o i l s i s g i v e n i n Appendix 1. 17 F i g u r e 3. L o c a t i o n o f s tudy a r e a . 18 T a b l e 1. Average monthly t e m p e r a t u r e and. p r e c i p i t a t i o n from A b b o t s f o r d a i r p o r t , p r e c i p i t a t i o n and d e p t h t o w a t e r t a b l e f o r s t u d y p e r i o d . A c t u a l Depth t o Average Average P r e c i p i t a t i o n w a t e r t a b l e Temperature P r e c i p i t a t i o n (°C) (mm) 1986 (mm) 1987 (mm) 1986 (cm) 1987 (cm) J a n u a r y 1.3 207 220 169 — 28 F e b r u a r y 4.2 164 197 88 - 55 March 5.6 145 185 170 - -A p r i l 8.6 104 141 128 - 94 May 12.2 73 144 126 - 100+ June 14.9 60 51 26 64 100+ J u l y 16.9 38 72 51 70 100+ August 16.7 49 1 14 100+ 100+ September 14.4 86 93 25 100+ 100+ O c t o b e r 10.1 170 102 20 96 100+ November 5.7 191 244 111 54 -December 3.1 215 157 213 — — Year 9.5 1502 1609 1141 2.3 S i t e Management As much as p o s s i b l e t h e s t u d y a r e a was managed i n t h e same way as t h e re m a i n d e r o f t h e f i e l d . I n t h e s p r i n g o f each y e a r a n i m a l manure was a p p l i e d t o t h e s t u d y a r e a a t a r a t e o f a p p r o x i m a t e l y 40,000 l i t r e s / h a . I n a d d i t i o n , 290-kg/ha o f 21-7-21-3 (N-P-K-S) was a p p l i e d e a r l y i n t h e g r o w i n g season p r i o r t o t h e f i r s t c u t . Subsequent t o t h e second and f o l l o w i n g c u t s 170-kg/ha 46-0-0 was a p p l i e d t o t h e i r r i g a t e d s i t e and 224-kg/ ha 31-10-0-7 was a p p l i e d t o t h e d r y l a n d s i t e . 19 I r r i g a t i o n was a p p l i e d i n b o t h y e a r s a f t e r t h e t h i r d c u t , a l t h o u g h v a r y i n g amounts were a p p l i e d a t each a p p l i c a t i o n ( T a b l e 2) . T a b l e 2. T i m i n g o f f o r a g e c u t s , a e r i a l p hotography m i s s i o n s and a p p r o x i m a t e d a t e s and amounts o f i r r i g a t i o n w a t e r a p p l i e d o v e r s t u d y s i t e i n 1986 and 1987. Forage A e r i a l I r r i g a t i o n Date Cut No. Photo (mm) 1986 May 14 1 J u l y 5 2 August 5 3 August 13 70 August 29 4 September 12 60 O c t o b e r 7 5 1987 A p r i l 29 1 June 12 1 June 16 2 J u l y 6 90 J u l y 14 3 August 1 80 August 11 75 August 19 2 August 21 4 August 26 70 September 29 5 20 CHAPTER 3 MATERIALS AND METHODS The study was conducted from early spring 1986 over two complete growing seasons to October 1987. 3.1 P r e l i m i n a r y f i e l d work The f i v e hectare study area was s t r a t i f i e d using a 20 meter g r i d . Grid c e l l s i z e was determined on the basis of the frequency of the repeating ridge-hollow topography. One hundred and fourteen permanent sampling s i t e s were randomly located within each g r i d c e l l i n March 1986 and these formed the focus for a l l subsequent sampling and monitoring (Fig. 4). Two management scenarios were imposed; i r r i g a t e d versus dryland forage production. Two t h i r d s of the s i t e s were located i n the dryland area as i r r i g a t i o n i s not yet a prevalent form of management i n forage production i n t h i s area. This s i t u a t i o n i s changing r a p i d l y however. A twenty meter buffer zone was imposed between the two management areas so as to avoid accidental watering of dryland s i t e s . 21 380 m IRRIGATED DRYLAND < U0 m — — F i g u r e 4 . Sampling d e s i g n f o r study area. A d i g i t a l e l e v a t i o n model was a l s o generated, u s i n g a S p e c t r a - P h y s i c s umbrella type l a s e r , t o enable p r e c i s e t o p o g r a p h i c c o n t r o l ( F i g . 5). Approximately 2 3 00 data p o i n t s were generated u s i n g a f i v e meter square g r i d d e s i g n . F i g u r e 5. D i g i t a l e l e v a t i o n model f o r study area. 23 3.2 Sampling and sample p r e p a r a t i o n The l a y o u t of the sampling and m o n i t o r i n g d e s i g n a t each s i t e i s shown i n F i g . 6. Biomass sampling o.25m2 Neutron probe access tube Soil sampling F i g u r e 6. Sampling d e s i g n at i n d i v i d u a l s i t e s . 24 3.2.1 S o i l sampling S o i l samples were c o l l e c t e d from two l o c a t i o n s a d j a c e n t t o each s i t e and b u l k e d f o r c h e m i c a l a n a l y s i s i n March 1986 and a g a i n i n November o f t h e same y e a r . Samples were t a k e n a t two d e p t h s ; 0 t o 25 and 25 t o 50-cm. S o i l samples were a l s o c o l l e c t e d o v e r 25-cm i n c r e m e n t s t o one meter a t each s i t e d u r i n g t h e p r o c e s s o f i n s t a l l i n g a n e u t r o n probe a c c e s s t u b e . These samples were r e t a i n e d t o d e t e r m i n e w a t e r r e t e n t i o n c h a r a c t e r i s t i c s and t e x t u r e . A l l s o i l samples were r e t u r n e d t o t h e l a b o r a t o r y , a i r - d r i e d and c r u s h e d w i t h a hand r o l l e r . Subsamples were used f o r c h e m i c a l and p h y s i c a l a n a l y s i s . 3.2.2 Vegetation sampling V e g e t a t i o n samples were c o l l e c t e d f i v e t i m e s i n each y e a r u s u a l l y 1 t o 2 days p r i o r t o h a r v e s t by t h e f a r m e r . The v e g e t a t i o n i n a 0.25-m2 q u a d r a t a d j a c e n t t o t h e n e u t r o n probe a c c e s s t u b e was hand c l i p p e d 5-cm above ground l e v e l . Samples were t r a n s p o r t e d t o t h e l a b o r a t o r y and d r i e d f o r 4 t o 5 days i n a p o t h o l e d r i e r a t 60°C b e f o r e w e i g h i n g . A f t e r w e i g h i n g t o d e t e r m i n e d r y m a t t e r , samples were mixed, ground t o p a s s a 1-mm s c r e e n u s i n g a W i l e y M i l l and s t o r e d f o r subsequent a n a l y s i s . 3.2.3 Monitoring program A l u m i n i u m n e u t r o n probe a c c e s s t u b e s were i n s t a l l e d a t each s i t e and measurement o f s o i l w a t e r c o n t e n t was c a r r i e d out a p p r o x i m a t e l y f o r t n i g h t l y a t 15, 30, 45, 60, and 90-cm d e p t h s . The n e u t r o n probe (Campbell P a c i f i c N u c l e a r Model 503) was c a l i b r a t e d o v e r t h e f u l l range o f s o i l w a t e r c o n t e n t s e x p e r i e n c e d by g r a v i m e t r i c s a m p l i n g a d j a c e n t t o t h e a c c e s s t u b e s (Greacen, 1981). Each sample was t a k e n from a 15-cm d e p t h range c o r r e s p o n d i n g t o t h e v e r t i c a l d e p t h o f t h e probe zone o f i n f l u e n c e . C a l i b r a t i o n e q u a t i o n s and t h e i r d e r i v a t i o n s a r e g i v e n i n Appendix 2. G r a v i m e t r i c w a t e r c o n t e n t s were c o n v e r t e d t o a v o l u m e t r i c b a s i s f o r t h e whole s o i l u s i n g b u l k d e n s i t i e s measured a t each d e p t h . To a v o i d l o s s o f measurement a c c u r a c y a t s h a l l o w d e p t h s where t h e sphere o f i n f l u e n c e o f t h e probe may e x t e n d above t h e s o i l s u r f a c e , s e p a r a t e c a l i b r a t i o n s were made f o r t h e 15-cm probe d e p t h . Two r a i n g a u g e s were a l s o i n s t a l l e d on each p l o t p r i o r t o p e r i o d s o f i r r i g a t i o n so as t o m o n i t o r a d d i t i o n a l w a t e r r e c e i v e d from t h i s s o u r c e . A d i p w e l l was a l s o i n s e r t e d i n one o f t h e deeper d e p r e s s i o n s t o m o n i t o r w a t e r t a b l e l e v e l s t h r o u g h o u t t h e g r o w i n g s e a s o n . Two a e r i a l photography m i s s i o n s u s i n g c o l o u r - i n f r a r e d (IR) (23 x 23 cm photogrammetric format) f i l m were f l o w n i n 1987, t h e f i r s t i n June and t h e second i n August. A c o l o u r e d marker f l a g was p l a c e d a d j a c e n t t o each o f t h e s a m p l i n g / m o n i t o r i n g s i t e s p r i o r t o t h e m i s s i o n s so each c o u l d be r e a d i l y l o c a t e d on t h e d i g i t i z e d image. 3.2 . 4 C a l c u l a t i o n o f w a t e r b a l a n c e The b a s i c components o f t h e s o i l w a t e r b a l a n c e a r e ; P = E + D + SS where P i s p r e c i p i t a t i o n , E i s e v a p o t r a n s p i r a t i o n , D i s d r a i n a g e and SS i s t h e change i n s o i l m o i s t u r e between two s u c c e s s i v e measurements. T h i s e q u a t i o n may be r e a r r a n g e d t o s o l v e f o r any i n d i v i d u a l component o r c o m b i n a t i o n o f components. As i s common i n many s i m p l e w a t e r b a l a n c e models, p r e c i p i t a t i o n and change i n s o i l w a t e r s t o r a g e i n t h e r o o t zone a r e e s t i m a t e d on t h e ass u m p t i o n t h a t r u n o f f and d r a i n a g e from t h e p r o f i l e a r e z e r o o v e r t h e p e r i o d o f e x a m i n a t i o n . I n t h i s s t u d y t h e f o l l o w i n g p r o c e d u r e was used t o q u a n t i f y components o f i n t e r e s t . E - P (<SS) = (St- - S t f ) / t where St,- and S t f a r e i n i t i a l and f i n a l w a t e r s t o r a g e v a l u e s r e s p e c t i v e l y . I n p e r i o d s o f z e r o r a i n o r i r r i g a t i o n change i n s o i l w a t e r s t o r a g e i s a t t r i b u t e d t o w a t e r use by t h e p l a n t . E = ( S t f - S t f + P) / t Once t h e upper and l o w e r l i m i t s o f t h e w a t e r r e s e r v o i r were d e t e r m i n e d f o r each s i t e t h e p r o f i l e w a t e r c o n t e n t was e a s i l y e x p r e s s e d as a s o i l m o i s t u r e d e f i c i t . S o i l w a t e r s t o r a g e was c a l c u l a t e d from w a t e r c o n t e n t s e x p r e s s e d o v e r t h e a p p r o p r i a t e d e p t h o f i n t e r e s t . 27 3.3 A n a l y t i c a l procedures 3.3.1 S o i l analysis The c h e m i c a l methods o f a n a l y s i s used a r e d e s c r i b e d i n McKeague (1978) u n l e s s o t h e r w i s e s t a t e d . C a t i o n exchange c a p a c i t y (CEC) and exchangeable c a t i o n s were measured w i t h t h e ammonium a c e t a t e (pH 7.0) method. W h i l e an e x t r a c t a n t b u f f e r e d a t pH 7.0 w i l l n o t i n d i c a t e t h e e f f e c t i v e n a t u r a l CEC o f a c i d i c s o i l s , t h i s method i s t h e most w i d e l y used i n an a g r i c u l t u r a l s e t t i n g and p e r m i t s comparison w i t h f i n d i n g s from o t h e r s t u d i e s . T o t a l c a r b o n was d e t e r m i n e d u s i n g a Leco I n d u c t i o n Furnace (Model 521) w i t h a c a r b o n a n a l y z e r (Model 572) . F o r t h e s e non-c a l c a r e o u s s o i l s , t h e r e s u l t s w i l l n o t be i n f l a t e d by c a r b o n a t e s . T o t a l n i t r o g e n was d e t e r m i n e d by t h e K j e l d a h l method u s i n g a b l o c k d i g e s t e r , w i t h c o l o u r i m e t r i c a n a l y s i s o f ammonium u s i n g a T e c h n i c o n A u t o a n a l y z e r . A v a i l a b l e phosphorus was d e t e r m i n e d u s i n g Bray P - l e x t r a c t i o n (0.03 N NH4F and 0.025 N HC1), a m o d i f i e d a s c o r b i c a c i d c o l o u r development t e c h n i q u e , and c o l o u r d e t e r m i n a t i o n w i t h a s p e c t r o p h o t o m e t e r (Murphy and R i l e y 1962). T o t a l s u l p h u r was d e t e r m i n e d w i t h a F i s h e r S u l p h u r A n a l y z e r (Model 47) , w h i c h u t i l i z e s h i g h - t e m p e r a t u r e i g n i t i o n i n oxygen, f o l l o w e d by t i t r a t i o n o f S0 2. Water r e t e n t i o n c h a r a c t e r i s t i c s were measured on d i s t u r b e d samples (<2-mm) f o l l o w i n g McKeague (1978) a t 0.03 and 1.5-MPa. A l t h o u g h t h i s method i s n o t recommended a t t e n s i o n s around 0.01-MPa many o f t h e 0.03-MPa d e t e r m i n a t i o n s w i l l n o t be u n r e a l i s t i c as s u b s u r f a c e h o r i z o n s a t most s i t e s were sandy and e x h i b i t e d 28 s i n g l e g r a i n s t r u c t u r e . Hand t e x t u r i n g o f t h e same samples was used t o a s s i g n s o i l s t o t h e f a m i l y p a r t i c l e s i z e c l a s s e s o f S o i l Taxonomy ( S o i l S urvey S t a f f 1975). 3.3.2 F o l i a r analysis F o l i a r a n a l y s i s f o l l o w e d t h e b l o c k d i g e s t i o n p r o c e d u r e g i v e n by P a r k i n s o n and A l l e n (1975). T o t a l n i t r o g e n (N) and phosphorus (P) were d e t e r m i n e d by c o l o u r i m e t r i c a n a l y s i s u s i n g a T e c h n i c o n A u t o a n a l y z e r . C a l c i u m (Ca), magnesium (Mg), p o t a s s i u m (K) , z i n c ( Z n ) , i r o n (Fe)/ a l u m i n i u m ( A l ) , and manganese (Mn) were d e t e r m i n e d u s i n g a P e r k i n - E l m e r Atomic A b s o r p t i o n S p e c t r o p h o t o m e t e r . Crude p r o t e i n (CP) was e s t i m a t e d from t o t a l N u s i n g t h e f o l l o w i n g r e l a t i o n s h i p (Raymond, 1969): CP = 6.25 x T o t a l N The method used i n t h e d e t e r m i n a t i o n o f a c i d d e t e r g e n t f i b r e (ADF) i s d e s c r i b e d i n Waldern (1971). Of t h e many c h e m i c a l methods based on t h e s o l u b i l i t y o f p a r t o f t h e d r y m a t t e r o f f o r a g e s i n c e r t a i n s o l u t i o n s , ADF i s u s u a l l y most h i g h l y c o r r e l a t e d w i t h d i g e s t i b i l i t y o f energy. A c c o r d i n g l y , d i g e s t i b l e energy (DE) was d e r i v e d u s i n g t h e f o l l o w i n g r e l a t i o n s h i p a f t e r McQueen and M a r t i n (1981) ; DE (Mj/kg DM) =(-0.0495xADF+4.297)x4.814 3 . 4 Presentation of r e s u l t s Because s e v e r a l d i f f e r e n t t e c h n i q u e s were used t o examine t h e d a t a base t h e r e s u l t s from each a r e p r e s e n t e d s e p a r a t e l y . 29 Chapter 4 presents the r e s u l t s from univariate and multivariate s t a t i s t i c a l analysis and Chapter 5 provides r e s u l t s from the remote sensing analysis. The u t i l i t y of i n t e r a c t i v e computer graphics and GIS for s p a t i a l analysis i s presented i n Chapter 6. 30 CHAPTER FOUR UNIVARIATE AND MULTIVARIATE STATISTICS 4.1 S t a t i s t i c a l methods S t a t i s t i c a l analyses were ca r r i e d out using the SPSS/PC+ programs (Norusis 1986a,b), while semi-variograms were computed using a procedure given by Robertson (1987). I n i t i a l l y , d e s c r i p t i v e s t a t i s t i c s including frequency d i s t r i b u t i o n , c o r r e l a t i o n and regression was used to i d e n t i f y s i g n i f i c a n t r e l a t i o n s h i p s i n the data base. Follow-up procedures included multiple stepwise regression, discriminant analysis, p r i n c i p a l component analysis and c l u s t e r analysis based on the average distance c a l c u l a t i o n method described by Ward (1963), using the UBC-CGroup program (Patterson and Whittaker, 1978). The s i g n i f i c a n c e of c l u s t e r groupings was determined with a Mann-Whitney U-test (Siegel, 1956). 4 . 2 Presentation of r e s u l t s Biophysical data from 114 sample s i t e s c o l l e c t e d over the two year study period are tabulated i n Appendix 3. For ease of comparison and int e r p r e t a t i o n these data are presented by treatment i . e . , i r r i g a t e d versus dryland. The f u l l biophysical data set i s presented, but for brevity, only a l i m i t e d amount of d i s c u s s i o n w i l l be p r e s e n t e d on t h e s p a t i a l and t e m p o r a l v a r i a b i l i t y o f t h e b i o p h y s i c a l d a t a base as t h e r e s u l t s r e v e a l s i m i l a r p a t t e r n s t o many o t h e r p u b l i s h e d works f o r s i m i l a r s o i l -l a n d s c a p e s (see W i l d i n g and Drees 1983, B e c k e t t and Webster 1971, Adams and W i l d e 1976a,b, and N i e l s e n and Bouma 1985). O t h e r d e r i v e d v a r i a b l e s such as w a t e r b a l a n c e components a r e n o t p r e s e n t e d b u t , can be c a l c u l a t e d from t h e d a t a p r e s e n t e d o r , can be o b t a i n e d from t h e a u t h o r on r e q u e s t . A summary o f d e s c r i p t i v e s t a t i s t i c s f o r s e l e c t e d b i o p h y s i c a l v a r i a b l e s i s g i v e n i n Appendix 4. These r e s u l t s a r e based on r e l a t i o n s h i p s d e r i v e d from a s u b s e t o f t h e d a t a base used t o d e v e l o p t h e models wh i c h f o l l o w . T h i r t y randomly s e l e c t e d s i t e s ( 2 6 % ) , two t h i r d s o f whi c h were from t h e d r y l a n d t r e a t m e n t , were removed f o r subsequent model v a l i d a t i o n . A l l f o l i a r element and energy components were w e i g h t e d by m u l t i p l y i n g by a p p r o p r i a t e d r y m a t t e r q u a n t i t i e s p r i o r t o s t a t i s t i c a l a n a l y s i s . T h i s w e i g h t i n g more a c c u r a t e l y r e f l e c t s t h e q u a n t i t y o f each element a t i n d i v i d u a l s i t e s . T h i s was c o n s i d e r e d i m p o r t a n t when c o n s i d e r i n g n u t r i e n t q u a n t i t i e s b e i n g consumed by an a n i m a l . S p e c t r a l r e f l e c t a n c e d a t a , however, was examined u s i n g b o t h w e i g h t e d and unweighted d a t a f o r combined t r e a t m e n t s . The t e c h n i q u e p r o v i d e s a " d i r e c t - l o o k " a t t h e canopy and so can p r o v i d e u s e f u l i n f o r m a t i o n on y i e l d and f o r a g e c o m p o s i t i o n . The a b i l i t y o f t h e t e c h n i q u e t o d i f f e r e n t i a t e between t h e two t r e a t m e n t s was a l s o examined. S e v e r a l s t a t i s t i c a l t e c h n i q u e s have been employed t o examine t h e d a t a , a l l o f w h i c h have been summarized by B e c k e t t and Webster (1971), Webster (1977), W i l d i n g and Drees (1983). R u s s e l l and D a l e (1987), p r o v i d e a u s e f u l assessment o f t h e u t i l i t y o f many o f t h e s e methods. 4 . 3 S p a t i a l and temporal v a r i a b i l i t y Of t h e summary s t a t i s t i c s p r e s e n t e d i n Appendix 4, c o e f f i c i e n t o f v a r i a t i o n (CV) i s r e g a r d e d as an a p p r o p r i a t e measure t o compare v a r i a b i l i t y between p r o p e r t i e s because i t i s d i m e n s i o n l e s s . CV i s not an a p p r o p r i a t e i n d e x when i t c o v a r y s w i t h t h e p r o p e r t y o f i n t e r e s t o r v a l u e s used l i e c l o s e t o l a b o r a t o r y e r r o r s . CV's show c o n s i d e r a b l e d i f f e r e n c e s between c h e m i c a l p r o p e r t i e s , b e i n g a minimum f o r pH and a maximum f o r exchang e a b l e K. V a r i a b i l i t y i s g e n e r a l l y g r e a t e s t i n t h e l o w e r d e p t h i n c r e m e n t , (25 t o 50-cm) whi c h may be a r e f l e c t i o n o f t h e f a c t t h a t s i t e s were sampled by 25-cm de p t h i n c r e m e n t s and not on a h o r i z o n b a s i s . S o i l K has t h e h i g h e s t CV f o r any s o i l c h e m i c a l p r o p e r t y f o l l o w e d c l o s e l y by N and P. A l t h o u g h t h e r e i s a c o n s i d e r a b l e range i n t h e degree o f v a r i a t i o n o f d i f f e r e n t c h e m i c a l p r o p e r t i e s , t h e d a t a show a tendency f o r CV's t o d e c r e a s e s l i g h t l y i n 1987 and f o r i r r i g a t e d s i t e s t o have l o w e r CV's. T h i s i s p a r t i c u l a r l y n o t i c e a b l e f o r exchangeable K. Ot h e r p r o p e r t i e s such as pH show l i t t l e v a r i a t i o n between y e a r o r t r e a t m e n t . I r r i g a t e d s i t e s have s l i g h t l y l o w e r CV's a t a l l d e p t h s f o r most p r o p e r t i e s s t u d i e d . M a j o r f e r t i l i z e r elements i n t h e s o i l t e n d t o i n c r e a s e from s p r i n g t o f a l l i n 1986, t h e most n o t a b l e i n c r e a s e b e i n g i n a v a i l a b l e P. T o t a l N showed s l i g h t i n c r e a s e s i n s u r f a c e l a y e r s and t h e r e v e r s e i n s u b s u r f a c e l a y e r s . T h i s p a t t e r n p r o b a b l y r e f l e c t s t h e r a p i d u p t a k e o f f e r t i l i z e r N by p l a n t s t h r o u g h o u t t h e g r o w i n g season and t h e subsequent s t r o n g l e a c h i n g o f any r e m a i n i n g N down t h e p r o f i l e d u r i n g t h e f a l l . Of t h e major f e r t i l i z e r e l e m e n t s , t o t a l N was s i g n i f i c a n t l y d i f f e r e n t between t h e two t r e a t m e n t s p r i o r t o commencement o f t h e s t u d y . D r y l a n d and i r r i g a t e d s i t e s had a mean N c o n t e n t o f 0.19% and 0.14% r e s p e c t i v e l y . By t h e f a l l o f 1986 a v a i l a b l e P was s i g n i f i c a n t l y h i g h e r on i r r i g a t e d s i t e s . T h i s v a r i a t i o n i n s o i l c h e m i s t r y a p p a r e n t l y had l i t t l e e f f e c t on 1986 p r o d u c t i o n , as h i g h e r y i e l d s a l t e r n a t e between d r y l a n d and i r r i g a t e d t r e a t m e n t s t h r o u g h o u t t h e g r o w i n g season (see T a b l e 5 and Appendix 4 ) . Mean y i e l d s f o r b o t h t r e a t m e n t s d e c l i n e Over t h e g r o w i n g season due t o a c o m b i n a t i o n o f management and growing c o n d i t i o n s . E a r l y i n t h e season, poor t r a f f i c a b i l i t y and/or d r y i n g c o n d i t i o n s , c o u p l e d w i t h h i g h growth r a t e s means t h e c r o p has been g r o w i n g f o r a l o n g e r p e r i o d b e f o r e i t i s c u t , compared t o l a t e r i n t h e season. T h i s p a t t e r n i s shown i n F i g . 7. A f t e r t h e t h i r d c u t r e g r o w t h r a t e s b e g i n t o s l o w , e s p e c i a l l y on d r y l a n d s i t e s . 34 F i g u r e 7. Number of growing days between f i v e c o n s e c u t i v e cuts i n 1986 and 1987. Y i e l d CV's t e n d t o i n c r e a s e from t h e f i r s t t o t h e l a s t c u t , t h i s t r e n d b e i n g more pronounced i n 1987 when a marked s o i l m o i s t u r e d e f i c i t l e d t o s e v e r e y i e l d d e p r e s s i o n s on s a n d i e r , d r y l a n d s i t e s . The e f f e c t s o f i r r i g a t i o n on y i e l d CV i s a l s o c l e a r l y shown i n 1987. D r y l a n d CV's i n c r e a s e from 17 t o 42%, from c u t 1 t o 5. CV's f o r t h e two t r e a t m e n t s a r e v e r y s i m i l a r u n t i l i r r i g a t i o n commences a f t e r t h e t h i r d c u t , when i r r i g a t e d s i t e s show r e l a t i v e l y s t a b l e CV's. Most f o l i a r elements e x h i b i t n e a r normal f r e q u e n c y d i s t r i b u t i o n s e x c e p t f o r Fe and A l w h i c h showed s t r o n g l y skewed d i s t r i b u t i o n s . Lowest CV's f o r f o l i a r e l ements were a s s o c i a t e d w i t h N and P f o r b o t h y e a r s and b o t h t r e a t m e n t s . CV's f o r N t e n d e d t o d e c r e a s e s l i g h t l y o v e r t h e g r o w i n g season w h i l e f o r P t h e r e v e r s e t r e n d was e v i d e n t . O t h e r major f o l i a r e l ements such as Mg and K a r e r e l a t i v e l y c o n s t a n t o v e r b o t h y e a r s and between t r e a t m e n t s . T a b l e 3 l i s t s CV's f o r t h e 3 major f e r t i l i z e r e l ements and key f o l i a r v a r i a b l e s (key f o l i a r v a r i a b l e s i n c l u d e t h o s e f o l i a r e l e m e n t s and energy components c o n s i d e r e d i m p o r t a n t i n l i v e s t o c k n u t r i t i o n ; Ca, N, P, DE [ J . S h e l f o r d , 1987, p e r s o n a l communication]) * I t i s e v i d e n t from t h i s t a b l e t h a t t h e s e a s o n a l v a r i a b i l i t y i n f o r a g e CV's i s l o w e r t h a n t h e s p a t i a l v a r i a b i l i t y f o r t h e major f e r t i l i z e r e l e m e n t s , p a r t i c u l a r l y f o r p o t a s s i u m . T h i s s u g g e s t s t h a t e s t i m a t i n g mean v a l u e s f o r c r o p v a r i a b l e s d i r e c t l y from t h e p l a n t w i l l r e q u i r e s m a l l e r sample s i z e s t h a n u s i n g an i n d i r e c t method o f e s t i m a t i o n , such as a 36 r e g r e s s i o n e q u a t i o n . The s p a t i a l and t e m p o r a l v a r i a t i o n i n s o i l c h e m i c a l e l e m e n t s a l s o seems t o b e a r l i t t l e r e l a t i o n t o t h e wide f l u c t u a t i o n s i n y i e l d and f o r a g e q u a l i t y . T a b l e 3. A comparison o f CV's f o r major f e r t i l i z e r e lements and key f o l i a r v a r i a b l e s . F e r t i l i z e r element N K S p r i n g 1986 32 40 80 F a l l 1986 15 35 75 CV % 1987 Key v a r i a b l e DM N P DE 1 17 9 8 2 2 17 8 6 6 3 16 8 6 4 4 30 6 9 3 5 42 7 10 4 1 21 14 6 3 2 17 11 5 4 3 13 8 5 4 4 18 8 6 2 5 20 6 9 4 Most s o i l p h y s i c a l p r o p e r t i e s d i s p l a y e d s l i g h t l y n e g a t i v e l y skewed d i s t r i b u t i o n s i n d i c a t i n g a tendency towards l o g n o r m a l d i s t r i b u t i o n ( N i e l s e n e t a l . 1973, Lee e t a l . 1985). H i l l e l (1980) q u o t e s CV's f o r s e v e r a l commonly measured s o i l p h y s i c a l p r o p e r t i e s and shows w a t e r r e t e n t i o n a t v a r y i n g t e n s i o n s t o be m o d e r a t e l y v a r i a b l e , 10 t o 100%. Bascomb and J a r v i s (1976) r e p o r t 0.03-MPa CV's a t t h e low end o f t h i s range between 10 and 30%. These v a l u e s agree c l o s e l y w i t h d a t a f o r t h i s s t u d y w i t h b o t h 0.03 and 1.5-MPa CV's l y i n g between 7 and 43%. CV's f o r w a t e r r e t e n t i o n i n c r e a s e s t e a d i l y w i t h d e p t h . A v a i l a b l e w a t e r s t o r a g e c a p a c i t y (AWSC) f o l l o w s a s i m i l a r t r e n d t o w a t e r r e t e n t i o n w i t h most d i s t r i b u t i o n s b e i n g n e g a t i v e l y skewed and CV's l y i n g i n t h e 11 t o 36% range. Marked changes i n s o i l w a t e r c o n t e n t ( t h e t a ) a t d r y l a n d s i t e s o c c u r r e d o v e r t h e gr o w i n g season, w i t h t h e most pronounced f l u c t u a t i o n s o c c u r r i n g on sandy r i d g e s o c c u p i e d by M a t s q u i and Monroe s o i l s . T h i s v a r i a t i o n , f o r two s o i l s from t h e d r y l a n d a r e a , i s shown i n F i g . 8. I r r i g a t i o n i n 1987, a d r i e r t h a n average y e a r , produced u n i f o r m l y m o i s t s o i l p r o f i l e s t h r o u g h o u t t h e l a t t e r p a r t o f t h e gr o w i n g season. P i x e l b r i g h t n e s s CV's a r e a l s o low w i t h i n f r a r e d s e n s i t i v e dye l a y e r v a l u e s b e i n g c o n s i s t e n t l y low. The June image has h i g h e r CV's t h a n August, w h i c h i s somewhat s u r p r i s i n g f o r t h e d r y l a n d t r e a t m e n t , g i v e n t h a t t h e range o f c r o p v i g o u r i n t h e l a t t e r image i s markedly g r e a t e r t h a n t h e former. 38 F i g u r e 8. Seasonal v a r i a t i o n i n t h e t a v a l u e s (0 t o 15-cm) f o r two p h y s i o g r a p h i c a l l y c o n t r a s t i n g s i t e s . Spectral reflectance values i n the red portion of the spectrum (630-nm, 670-nm), have the highest CV's, p a r t i c u l a r l y on i r r i g a t e d s i t e s . This area of the spectrum i s s e n s i t i v e to s o i l background reflectance, so the presence of any bare s o i l within the f i e l d of view could account f o r t h i s increased v a r i a b i l i t y . Near-infrared (NIR) CV's (725 to 2200-nm) on the other hand are consistently higher on dryland s i t e s , a r e f l e c t i o n of the greater v a r i a t i o n i n canopy conditions found here. Significance t e s t s (Student's t and Wilcoxon) were conducted to determine i f y i e l d s , f o l i a r elements and energy components were s i g n i f i c a n t l y d i f f e r e n t between cuts. Results for selected variables between consecutive cuts are presented i n Table 4. V i r t u a l l y a l l variables are s i g n i f i c a n t l y d i f f e r e n t between consecutive cuts f o r both dryland and i r r i g a t e d treatments. Forage at the time of harvest i s the cumulative r e s u l t of plant growth and p r e v a i l i n g environmental conditions. The l a t t e r determines the plant composition which i n turn controls the l i m i t s of n u t r i t i v e value (Van Soest et a l . 1978). Generally speaking, factors that retard plant development w i l l promote the maintenance of forage qu a l i t y over longer periods. Snaydon (1972), demonstrated that water stress induced a crop of lower y i e l d but higher d i g e s t i b i l i t y . ADF i s one compositional predictor of forage quali t y , e s p e c i a l l y d i g e s t i b i l i t y , which r e f l e c t s preharvest his t o r y . ADF values 40 d e c l i n e o v e r t h e g r o w i n g season w i t h DE showing c o r r e s p o n d i n g i n c r e a s e s ( F i g . 9 ) . T a b l e 4. P r o b a b i l i t y l e v e l s ( S t u d e n t ' s t ) t h a t means f o r y i e l d , f o l i a r e lements and energy components between h a r v e s t s , on d r y l a n d s i t e s , a r e s i g n i f i c a n t l y d i f f e r e n t , p=0.05. 1986 1987 h a r v e s t h a r v e s t p r o p e r t y 1--2 2-3 3--4 4--5 1--2 2--3 3--4 4--5 DM 0. , 00 0. ,00 0. ,00 0. .00 0. .11 0. ,00 0. ,00 0. .00 Ca 0. ,00 0. ,00 0. ,00 0. ,01 0. ,00 0. ,00 0. .43 0. .00 Mg 0. ,19 0. , 00 0. ,00 0. , 00 0. , 00 0. , 00 0. , 00 0. ,00 K 0. , 00 0. ,00 0. ,00 0. ,00 0. .18 0. , 00 0. ,00 0. .00 N 0. ,00 0. .00 0. ,00 0. ,00 0. .01 0. ,00 0. .00 0. ,00 P 0. . 00 0. .00 0. , 00 0. ,00 0. .01 0. .00 0. . 00 0. , 00 Zn 0. ,00 0. ,00 0. .00 0. , 00 0. .00 0. , 00 0. .00 0. , 00 Mn 0. ,00 0. .00 0. .00 0. ,00 0. .00 0. ,00 0. . 00 0. , 00 ADF 0. ,00 0. ,00 0, ,00 0. ,00 0. ,00 0. ,00 0. , 00 0. ,00 DE 0. .00 0. ,00 0. .00 0. .00 0. .12 0. .00 0. .00 0. .00 V a r i a t i o n i n mean y i e l d and s e l e c t e d v a r i a b l e s f o r t h e two t r e a t m e n t s o v e r 1986 and 1987 i s shown i n F i g u r e 10. F o l i a r e l e m e n t s from t h e d r y l a n d t r e a t m e n t i n 1987 a r e shown i n F i g u r e 11 and t h e t r e n d s shown i n b o t h f i g u r e s f o l l o w p a t t e r n s r e p o r t e d by o t h e r s ( e . g . , Metson and Saunders 1978, Thompson and Warren 1979, N e i l s e n and Cunningham 1964). 41 DE (MJ/kg DM) DE (MJ/kg DM) 1 2 3 4 Cut No. 1 2 3 4 Cut No. CP (%) CP (%) 2 3 4 5 Cut No. 1 2 3 4 Cut No. 50 40 30 20 10 ADF (%) ADF (%) 1986 Dryland - i 1 1 1 1 1 2 3 4 5 Cut No. 2 3 4 Cut No. F i g u r e 9. F o l i a r energy components (mean and range) from m a t e r i a l grown a t d r y l a n d and i r r i g a t e d s i t e s i n 1986 and 1987 ( w e i g h t e d ) . 42 . Dry Matter (T/ha) Dry Matter (T/ha) 1088 6-5 : 4-3-1987 — ° - Dryland \A~^B 2-- B ~ Dryland \ Irrigated 1-n • Irrigated 1 1 1 1 ' 1 2 3 4 5 Cut No. (J 1 2 3 4 5 Cut No. Foliar N (*) . Foliar N (56) 1086 1987 4-3 . ^ 2 — ° — Dryland 1 ~B - Dryland Irrigated . .1— I. • • ••- , i ..... i . i 0 - A r _ Irrigated 1 —1 r — 1 1 1 2 3 4 5. Cut No. 1 2 3 4 Cut No. 20 18 DE (Mj/kg DM) 14i 12 1986 — 3 — Dryland Irrigated 20 18 ie 14 12 DE (MJ/kg DM) 1987 - ° - Dryland Irrigated 1 2 3 4 5 Cut No. 1 2 3 4 5 Cut No. F i g u r e 10. The e f f e c t s of time and treatment on y i e l d , f o l i a r n i t r o g e n and d i g e s t i b l e energy. 43 Magnesium (%) Calcium (56) 1 2 3 4 5 Cut No. ~ i 1 1 1 — 1 2 3 4 5 Cut NO. Nitrogen (%) 1 2 3 4 Cut No. Potassium (%) 1 2 3 4 Cut No, 250 Manganese (ppm) Phosphorus (%) 1 2 3 4 5 Cut No. - I 1 1 1 1-1 2 3 4 5 Cut No, F i g u r e 11. V a r i a t i o n i n f o l i a r element l e v e l s (mean and range) over the 1987 growing season (dryland o n l y ) . A Mann-Whitney U - t e s t was conducted t o examine p o t e n t i a l d i f f e r e n c e s i n b i o p h y s i c a l p r o p e r t i e s and r e m o t e l y sensed d a t a between t h e two t r e a t m e n t s . The r e s u l t s a r e p r e s e n t e d i n T a b l e 5 and show some i n t e r e s t i n g p a t t e r n s . F o r example, d r y l a n d s i t e s p r o d u c e d h i g h e r t o t a l biomass i n 1986, a t r e n d w h i c h i s n o t i c e a b l y r e v e r s e d i n 1987 ( F i g . 10) . F o l i a r e l ements and energy components f l u c t u a t e e a r l y i n t h e g r o w i n g s e a s o n , and show a t e n d e n c y t o become h i g h e r on i r r i g a t e d s i t e s f o r t h e f o u r t h and f i f t h c u t s i n 1986 and t h e r e v e r s e i n 1987. I n 1987, d e s p i t e h i g h e r a v a i l a b l e P l e v e l s i n t h e f a l l o f 1986 on i r r i g a t e d s i t e s , t h e r e were no s i g n i f i c a n t d i f f e r e n c e s i n y i e l d f o r e i t h e r o f t h e f i r s t two c u t s . A p p l i c a t i o n o f f e r t i l i z e r s and a n i m a l manures a t t h e s t a r t o f t h e 1987 g r o w i n g season p r o b a b l y negated t h i s d i f f e r e n c e i n P s t a t u s . F o l i a r N i s h i g h e r on d r y l a n d s i t e s f o r t h e f i r s t c u t . T h i s may be a r e f l e c t i o n o f more N b e i n g a v a i l a b l e t o p l a n t s i n s u b s u r f a c e h o r i z o n s e a r l y i n t h e g r o w i n g season due t o l o w e r l e a c h i n g r a t e s o v e r t h e l a t t e r p a r t o f t h e 1986 g r o w i n g season. L a t e r i n t h e s e a s o n , i r r i g a t e d s i t e s have h i g h e r N c o n t e n t s w h i c h p r o b a b l y r e f l e c t t h e h i g h e r f e r t i l i z e r N r a t e s b e i n g a p p l i e d t h e r e . The i r r i g a t e d t r e a t m e n t has h i g h e r mean w a t e r r e t e n t i o n c a p a b i l i t i e s f o r t h e 3 depths l i s t e d i n T a b l e 5. AWSC a t 75 t o 100-cm i s a l s o h i g h e r on t h e i r r i g a t e d t r e a t m e n t , w h i l e t h e r e v e r s e i s t r u e f o r AWSCERD. 45 T a b l e 5. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t s between t r e a t m e n t s f o r s e l e c t e d v a r i a b l e s ( v a r i a b l e s l i s t e d a r e s i g n i f i c a n t l y d i f f e r e n t between d r y l a n d and i r r i g a t e d t r e a t m e n t s a t p=0.05). Depth/Date P r o p e r t y S o i l c h e m i s t r y s p r i n g 1986 0-25cm f a l l 1986 0-25cm S o i l p h y s i c s 0-25cm 25-50cm 50-75cm 75-100cm ERD pH, t o t a l N, exch . Mg, K A v a i l . P, exch. Ca, Mg 1.5-MPa 1.5-MPa PSC 0.03-MPa, AWSC AWSC Forage y i e l d and q u a l i t y 1986 May 14 J u l y 5 August 5 August 29 Oct o b e r 7 1987 A p r i l 29 June 16 J u l y 14 August 21 September 29 DM, f o l i a r K, N, P, ADF, DE DM, f o l i a r Ca, K, N, P DM, f o l i a r K, N, P, ADF, DE DM, f o l i a r Ca, K, N, P, ADF, DE DM, f o l i a r Ca, K, N, ADF, DE, DMTOT f o l i a r N f o l i a r Ca DM, f o l i a r K, N, P, ADF, DE DM, f o l i a r Ca, K, N, P, ADF, DE DM, f o l i a r Ca, K, N, P, ADF, DE, DMTOT Thet a S p e c t r a l r e f l e c t a n c e 1986 1987 1987 August 21 Mayl4-2, J u l 5 - 1 , 2 , Augl9-1,2,3, Aug29-1,2,3, Sep9-1,2,3, Sep20-1,2,3, O c t 7 - l , 2 , 3 J u l l 4 - 1 , 2 , 3 , Aug5-1,2,3, Aug21-1,2,3, S e p l 7 - 1 , 2 , 3 , O c t 2 - l , 2 , 3 630-nm, 670-nm, 870-nm, 900-nm, 1050-nm, 1200-nm, 2200-nm, 725nm, RIR1,RIR2, RIR3, RIR4, RIR5, RIR6 P i x e l b r i g h t n e s s v a l u e s 1987 August 21 Green, Red, I n f r a r e d T h i s change p r o b a b l y r e f l e c t s t h e f a c t t h a t ERD's were g e n e r a l l y deeper on d r y l a n d s i t e s as a r e s u l t o f p l a n t s e x p l o i t i n g w a t e r from l o w e r i n t h e p r o f i l e d u r i n g p e r i o d s o f s o i l m o i s t u r e s t r e s s . F a m i l y p a r t i c l e s i z e c l a s s was s l i g h t l y c o a r s e r on t h e d r y l a n d t r e a t m e n t o v e r t h e 50 t o 75-cm range b u t no o t h e r . S p e c t r a l r e f l e c t a n c e v a l u e s were s i g n i f i c a n t l y d i f f e r e n t between t r e a t m e n t s f o r a l l w a v e l e n g t h s e x c e p t 550 and 1600-nm. The 1600-nm w a v e l e n g t h i s a s t r o n g m o i s t u r e a b s o r p t i o n band, w h i l e t h e 550-nm p o r t i o n o f t h e spectrum i s n o t s t r o n g l y r e l a t e d t o any o f t h e key v a r i a b l e s b e i n g measured. A Mann-Whitney s i g n i f i c a n c e t e s t was a l s o p e r f o r m e d t o a s s e s s whether y i e l d and key v a r i a b l e s were s i g n i f i c a n t l y d i f f e r e n t between t h e two y e a r s ( T a b l e 6 ) . T a b l e 6. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t s i n d i c a t i n g v a r i a b l e s w h i c h were s i g n i f i c a n t l y d i f f e r e n t between 1986 and 1987 (p=0.05). D r y l a n d . I r r i g a t e d . Cut No. p r o p e r t y Cut No. p r o p e r t y 1 DM N P DE 1 DM N P DE 2 DM Ca P DE 2 DM Ca N P DE 3 DM Ca P 3 Ca N 4 DM Ca N P DE 4 DM Ca N P DE 5 DM Ca N P DE 5 DM Ca N P DE The r e s u l t s show t h a t most v a r i a b l e s are s i g n i f i c a n t l y d i f f e r e n t between the two y e a r s , which i s not s u r p r i s i n g c o n s i d e r i n g the d i f f e r e n t growing c o n d i t i o n s and c u t dates. Cut 3 f o r both treatments had the most i n common between the two y e a r s . 4 . 4 G e o s t a t i s t i c s In many f i e l d s t u d i e s p o p u l a t i o n e s t i m a t e s are o f t e n based on random s m a l l - a r e a samplings. So long as assumptions r e g a r d i n g n o r m a l i t y and sample independence are met, p a r a m e t r i c s t a t i s t i c s f o r sampling designs such as these p r o v i d e optimal e s t i m a t e s of v a r i a n c e about unbiased means. Of t e n however, assumptions about sample independence cannot be met i n f i e l d s t u d i e s because o f a u t o c o r r e l a t i o n : samples c o l l e c t e d c l o s e t o one another are o f t e n more s i m i l a r t o one another than are samples c o l l e c t e d f u r t h e r away, whether i n space and time. Methods of g e o s t a t i s t i c s which are not dependent upon the assumptions o f s p a t i a l independence are b e i n g a p p l i e d i n c r e a s i n g l y t o the a n a l y s i s of s p a t i a l v a r i a b i l i t y . A n a l y s i s of the s p a t i a l s t r u c t u r e of the data u s i n g semi-variograms lea d s t o the use o f k r i g i n g f o r p r e d i c t i o n purposes. One of the primary reasons t o compute a semi-variogram i s t o e s t a b l i s h v a l u e s f o r the l a g or range (A), the s i l l (C) and the nugget (Co) which are used f o r a c c u r a t e k r i g i n g ( f i g u r e 2 ) . The range i s an important parameter not o n l y f o r k r i g i n g but a l s o i n d e v e l o p i n g sampling d e s i g n s . Measurements separated by 48 d i s t a n c e s c l o s e r t h a n A a r e c o r r e l a t e d , whereas t h o s e measurements s e p a r a t e d by d i s t a n c e s g r e a t e r t h a n A a r e n o t c o r r e l a t e d . When t h e range, i s s m a l l e r t h a n t h e c l o s e s t s a m p l i n g d i s t a n c e , a pure nugget e f f e c t i s o b s e r v e d , and t h e p r o p e r t y o f i n t e r e s t has a c o m p l e t e l y random s p a t i a l d i s t r i b u t i o n w i t h r e s p e c t t o t h e s a m p l i n g space. I f more samples a r e t a k e n a t c l o s e r s p a c i n g , t h e s e m i - v a r i o g r a m may r e v e a l some s t r u c t u r e . The p r e s e n c e o f a p u r e nugget e f f e c t i s t h e o n l y s i t u a t i o n t h a t t h e o r e t i c a l l y a l l o w s f o r t h e use o f c l a s s i c a l s t a t i s t i c a l methods ( V i e i r a e t a l . 1983). Because t h e program t o be used f o r i n t e r p o l a t i o n assumed a l i n e a r s e m i - v a r i o g r a m i t was i m p o r t a n t t o c o n f i r m f o r each v a r i a b l e o f i n t e r e s t t h a t t h e l i n e a r model p r o v i d e d a good f i t t o t h e d a t a . Semi-variograms were computed u s i n g t h e p r o c e d u r e g i v e n by R o b e r t s o n (1987) and t h e models were f i t t e d t o t h e s e m i - v a r i o g r a m by w e i g h t e d l e a s t - s q u a r e s a n a l y s i s (SAS I n s t i t u t e 1985). I s o t r o p i c o r u n i - d i r e c t i o n a l s e m i - v a r i o g r a m s were computed i n t h e d i r e c t i o n o f maximum s o i l - p h y s i o g r a p h i c change. The e q u a t i o n s most commonly used t o e s t i m a t e p a r a m e t e r s o f i s o t r o p i c s e m i - v a r i o g r a m s a r e t h e l i n e a r and s p h e r i c a l models (Burgess and Webster 1980). The G a u s s i a n and e x p o n e n t i a l forms a r e r a r e l y used because t h e i r i n f i n i t e r anges i m p l y v e r y c o n t i n u o u s p r o c e s s e s w h i c h r a r e l y o c c u r i n f i e l d s o i l s . When f i t t e d t o t h e same e x p e r i m e n t a l s e m i - v a r i o g r a m , t h e s p h e r i c a l model g e n e r a l l y g i v e s l o n g e r ranges and s m a l l e r nugget v a r i a n c e s t h a n t h e l i n e a r form (Trangmar e t a l . 1985). T h i s p a t t e r n was a l s o e v i d e n t i n t h i s s t u d y . Both t h e nugget v a r i a n c e and t h e range a r e c r i t i c a l p a r a m e t e r s f o r k r i g i n g . S e m i - v a r i o g r a m s i n d i c a t e t h a t many p r o p e r t i e s range from e s s e n t i a l l y s p a t i a l l y independent where s e m i - v a r i a n c e i s r e l a t i v e l y c o n s t a n t , t o s t r o n g l y dependent where s e m i - v a r i a n c e i n c r e a s e s s i g n i f i c a n t l y w i t h i n c r e a s i n g d i s t a n c e . T h i s e f f e c t seems t o be s e a s o n a l , w i t h many s e m i - v a r i o g r a m s f o r y i e l d and key v a r i a b l e s e x h i b i t i n g l a r g e nugget v a r i a n c e e a r l y i n t h e g r o w i n g s e a s o n , and s t r o n g e r s p a t i a l dependence l a t e r i n t h e g r o w i n g season ( F i g . 1 2 ) . I s o t r o p i c s e m i - v a r i o g r a m s f o r y i e l d and key v a r i a b l e s a r e shown i n T a b l e 7. L i n e a r and s p h e r i c a l models have been f i t t e d t o t h e s e m i - v a r i o g r a m s , w i t h l i n e a r models o c c u r r i n g most f r e q u e n t l y . They a r e u s u a l l y o f t h e form shown i n F i g . 2, w i t h t h e nugget v a r i a n c e i n d i c a t i n g t h e v a r i a t i o n t h a t remains u n r e s o l v e d . A l m o s t 50% o f t h e v a r i a b l e s have nugget v a r i a n c e o f 50% o r g r e a t e r w h i c h i n d i c a t e s t h a t even i f model f i t i s good ( i . e . r 2 >0.80), h a l f t h e v a r i a b i l i t y cannot be a c c o u n t e d f o r . Ranges f o r f o l i a r e lements v a r y from 20 t o o v e r 1000-m b u t ranges o f 30 t o 60-m o c c u r w i t h some f r e q u e n c y . Trangmar e t a l . (1985) r e p o r t t h a t s p h e r i c a l models d e s c r i b e d r i c e y i e l d s w e l l , w h i l e f o r y i e l d d a t a i n t h i s s t u d y l i n e a r and s p h e r i c a l models were o f s i m i l a r u t i l i t y . No c o n s i s t e n t range c o u l d be e s t a b l i s h e d under t h e p r e s e n t s t u d y c o n d i t i o n s f o r y i e l d o r key v a r i a b l e s , i l l u s t r a t i n g t h e dynamic n a t u r e o f f o r a g e p r o d u c t i o n w i t h i n a s i n g l e g r o w i n g season. 50 0.5 0.4 0,3-Seml-varlance A A DM1 1987 0,2^ ' ' ' ' r-0 20 40 60 80 100 120 Distance (m) 0,5 Semi-variance 0,4-0,3-0,2-0,1 0 A A DM5 1987 T 1 1 1 1 0 20 40 60 80 100 120 Distance (m) F i g u r e 12. T y p i c a l semi-variograms f o r y i e l d from cut 1 and cu t 5.' 51 T a b l e 7. Parameters from i s o t r o p i c s e m i - v a r i o g r a m s f o r y i e l d and key v a r i a b l e s . V a r i a b l e Model — 1986 -1 Range (m) (% Nugget o f s i l l ) Model - 1987 Range (m) Nugget (% o f s i l l ) DM1 L 417 39 S >1000 100 2 S 19 100 L 29 100 3 L 69 - 70 L 80 40 4 S 80 20 L >1000 2 5 L 72 33 L 179 — C a l L 29 9 L 136 80 2 L 73 50 L 20 2 3 L 60 60 L 24 23 4 S 35 10 L 114 7 5 S 78 50 L 69 10 NI L 26 13 L 178 50 2 - - - L 29 60 3 L 29 42 - - -4 L >1000 - L >1000 -5 S >1000 — L 56 66 P l L 36 41 L 39 69 2 L >1000 - L 20 100 3 L 39 33 L 59 55 4 L 45 33 L >1000 -5 E 80 0 L >1000 — DEI S 19 10 L 29 100 2 - - - L 29 42 3 S 23 0 L 52 50 4 S 137 30 L 81 0 5 L 69 40 L 71 8 L; L i n e a r , S; S p h e r i c a l . 52 F o l i a r Ca and DE p r o v i d e t h e most c o n s i s t e n t r e s u l t s w i t h i n each g r o w i n g season, w h i l e f o l i a r N i s t h e l e a s t c o n s i s t e n t . I n g e n e r a l , f o r most f o l i a r v a r i a b l e s c o l l e c t e d g r e a t e r t h a n 60 meters a p a r t c l a s s i c a l s t a t i s t i c a l a n a l y s i s r e q u i r i n g i n d e p e n d e n t o r random samples can p r o b a b l y be a p p l i e d . F o r i n t e r s a m p l e d i s t a n c e s l e s s t h a n 60 m e t e r s , t h e a s s u m p t i o n o f independence r e q u i r e s v e r i f i c a t i o n . I f r e s u l t s a r e t o be t r a n s f e r r e d t o o t h e r f i e l d s w i t h unknown s p a t i a l s t r u c t u r e sample a r e a s s h o u l d be a t l e a s t 60 meters a p a r t f o r e s t i m a t i n g mean v a l u e s . F o r y i e l d i n c u t 1, 4 and 5 samples s h o u l d be c o l l e c t e d as f a r a p a r t as p o s s i b l e . T a b l e 8 shows parameter v a l u e s o f some i s o t r o p i c s e m i-v a r i o g r a m s f o r a s e l e c t i o n o f b i o p h y s i c a l p r o p e r t i e s . The d a t a show t h a t c o n s i d e r a b l e v a r i a t i o n e x i s t s between v a r i a b l e s , and between d e p t h s o r s a m p l i n g p e r i o d s f o r t h e same v a r i a b l e . Once a g a i n 50% o f t h e v a r i a b l e s shown have nugget v a r i a n c e g r e a t e r t h a n 50% o f t h e s i l l , i n d i c a t i n g t h a t h a l f t h e v a r i a t i o n remains u n a c c o u nted f o r . S e m i - v a r i o g r a m ranges depend on t h e s c a l e o f o b s e r v a t i o n and t h e s p a t i a l i n t e r a c t i o n o f p r o c e s s e s a f f e c t i n g each p r o p e r t y a t t h e s a m p l i n g s c a l e used. The shape o f t h e e x p e r i m e n t a l semi-v a r i o g r a m may h e l p i d e n t i f y t r e n d e f f e c t s o r t h e i n f l u e n c e o f some o t h e r phenomena w h i c h may e x p l a i n t h e v a r i a t i o n . 53 T a b l e 8. Parameter v a l u e s f o r some i s o t r o p i c s e m i - v a r i o g r a m s f o r s o i l s and r e l a t e d d a t a ( d r y l a n d and i r r i g a t e d t r e a t m e n t s combined). Depth/ Range Nugget Model f i t P r o p e r t y Date m (% o f s i l l ) Model r 2 pH 0-25cm >1000 - L 0.87 P 25-50cm 39 79 L 0.06 N 0-25cm 50 60 L 0.38 N 25-50cm 82 60 L 0.61 Mg sp'g 0-25cm 69 46 L 0.75 Mg f a l l 0-25cm 49 11 L 0.72 K 0-25cm 39 32 L 0.44 AWSC 0-25cm 56 35 L 0.74 AWSC 25-50cm 126 56 L 0.16 AWSC 50-75cm 39 64 L 0.29 AWSC 75-100cm 49 63 L 0.15 AWSC ERD 24 0 S 0.84 0.03-MPa 0-25cm 84 50 L 0.60 1.5-MPa 0-25cm 52 33 L 0.82 1.5-MPa 25-50cm 128 91 L 0.38 PSC 75-100cm 336 25 L 0.45 The t a 15cm 108 2 L 0.83 L o c a l t r e n d s a r e v e r y d i f f i c u l t t o i d e n t i f y due t o t h e pr e s e n c e o f s h o r t range v a r i a t i o n i n most s o i l s and n o i s y d a t a o v e r s h o r t ranges (Webster and Burgess 1980). P e r i o d i c phenomena such as p a r e n t m a t e r i a l d e p o s i t i o n and r e p e a t i n g l a n d f o r m s a r e o f t e n - q u o t e d s o u r c e s o f s o i l v a r i a t i o n ( B u t l e r 1959). T h i s v a r i a t i o n i s e x p r e s s e d i n t h e s e m i - v a r i o g r a m as a " h o l e - e f f e c t . " P o s s i b l e p e r i o d i c b e h a v i o u r can be seen i n F i g . 13 f o r p a r t i c l e s i z e c l a s s o v e r t h e 25 t o 50-cm d e p t h . T h i s p a t t e r n i s p r o b a b l y due t o t h e s t r a t i g r a p h i c n a t u r e o f t h e sediments d e p o s i t e d by the nearby F r a s e r R i v e r , and may re p r e s e n t the degree o f subsurface sandiness a s s o c i a t e d w i t h the occurrence o f r i d g e s . S i m i l a r behaviour f o r f l u v i a l d e p o s i t s was r e p o r t e d by Trangmar (1984). Semi-variance 2 , Lag .,(m) F i g u r e 13. T h i s p l o t i l l u s t r a t e s ( p o s s i b l e ) p e r i o d i c behaviour which has been d e s c r i b e d as the " h o l e - e f f e c t " (see t e x t ) . The s e m i - v a r i o g r a m parameters r e p o r t e d i n T a b l e s 7 and 8 a r e common f o r many s o i l p r o p e r t i e s (Trangmar e t a l . 1985). Some examples, i l l u s t r a t i n g f i t t e d models a r e g i v e n i n F i g . 14. F i g u r e 15 shows t h e s e same d a t a as 3 - d i m e n s i o n a l (3-D) p l o t s a f t e r k r i g i n g . The s e m i - v a r i o g r a m f o r 1.5-MPa w a t e r r e t e n t i o n shows t h e r e i s no s p a t i a l a u t o c o r r e l a t i o n a t t h i s s c a l e o f s a m p l i n g , and t h e s e m i - v a r i o g r a m i s pure nugget (91% i n t h i s c a s e ) . T h i s e f f e c t a r i s e s from v e r y l a r g e p o i n t - t o - p o i n t v a r i a t i o n a t s h o r t d i s t a n c e s o f s e p a r a t i o n . The l i n e a r model p r o v i d e s a poor f i t t o t h e d a t a (r 2=0.38), s u g g e s t i n g poor e s t i m a t e s w i t h k r i g i n g . The model f o r f o l i a r N ( c u t 1 ) , i s r e f e r r e d t o as a l i n e a r model w i t h s i l l (Webster 1977), and s u g g e s t s t h a t p o i n t s <26-m a p a r t a r e v e r y s t r o n g l y a u t o c o r r e l a t e d . That t h e nugget v a r i a n c e i s o n l y 13% o f t h e p o p u l a t i o n v a r i a n c e s u g g e s t s t h a t s p a t i a l a u t o c o r r e l a t i o n a t t h e 1 t o 26-m s c a l e a c c o u n t s f o r most o f t h e v a r i a t i o n i n f o l i a r N c o n t e n t a c r o s s t h e f i e l d . The s e m i - v a r i o g r a m f o r s o i l pH shows l i n e a r i n c r e a s e f o r i n t e r s a m p l e d i s t a n c e s up t o a t l e a s t 120 meters w i t h no a p p a r e n t s i l l , p o s s i b l y i n d i c a t i n g t h e p r e s e n c e o f t r e n d e f f e c t s (Webster and B urgess 1980) . G i v e n t h e l i m i t a t i o n s o f t h e p r e s e n t s a m p l i n g scheme, t h i s s e m i - v a r i o g r a m i n d i c a t e s t h a t samples f o r pH i n t h i s f i e l d s h o u l d be as f a r a p a r t from each o t h e r as p o s s i b l e . T h i s w i l l a l l o w samples t h a t a r e s p a t i a l l y dependent t o form an u n b i a s e d e s t i m a t e o f t h e f i e l d a verage s i n c e samples w i l l n o t r e p r e s e n t one s e c t i o n o f t h e f i e l d more t h a n t h e o t h e r . Semi-variance 1.5-MPa 25-50cm r2 = 0.39 0 20 40 60 80 100 120 Lag (m) 0.1 0.08 0.06 0.04 0.02 Seml-varlance 0 -a ft a—A FOL. N r2 = 0.69 0 20 40 60 80 100 120 Lag (m) 0.2 Semi-variance 0.15H 0.11 0.05 0 pH 0-25cm r2 = 0.87 — i 1 1 1 1 — 0 20 40 60 80 100 120 Lag (m) F i g u r e 14. Semi-variograms f o r s e l e c t e d v a r i a b l e s . 57 The r e s u l t s of the analysis showed that most variables would be suit a b l e for input to the kr i g i n g algorithm given i t s requirement f o r a l i n e a r semi-variogram. Where model f i t i s poor however, ( i . e . , r 2 <0.80), then the values for the range and nugget variance are l i k e l y to be of les s u t i l i t y i n any subsequent sampling design or inte r p o l a t i o n procedures. 5 8 F i g u r e 1 5 . I n t e r p o l a t e d ( k r i g e d ) v a l u e s f o r ( a ) s o i l p H , ( b ) w a t e r r e t e n t i o n ( 1 . 5 - M P a ) , a n d ( c ) f o l i a r n i t r o g e n ( c u t 1 , 1 9 8 6 ) . 59 The s u c c e s s o f t h e f i t t e d model i n i n t e r p o l a t i o n was e v a l u a t e d by u s i n g a 11 j a c k - k n i f i n g " t e c h n i q u e where t h e v a l u e a t a measured s i t e i s e s t i m a t e d u s i n g t h e s u r r o u n d i n g known s i t e s and t h e e s t i m a t e compared t o t h e known v a l u e . Models can be c o n s i d e r e d a p p r o p r i a t e when t h e average e r r o r i s c l o s e t o z e r o . V a l i d a t i o n r e s u l t s f o r t h e t h r e e models p r e s e n t e d above d i s p l a y a wide range i n t h e i r p r e d i c t i v e a c c u r a c y . F o l i a r N was p r e d i c t e d w i t h l e a s t a c c u r a c y w i t h o n l y 3% o f t h e v a l i d a t i o n s e t b e i n g p r e d i c t e d a c c u r a t e l y and e r r o r s r a n g i n g from 5 t o 90%, w i t h most s i t e s b e i n g o v e r - e s t i m a t e d by an average 39%. Water r e t e n t i o n a t 1.5-MPa c o r r e c t l y e s t i m a t e d 13% o f t h e v a l i d a t i o n s e t . E r r o r s ranged from o v e r - e s t i m a t e s o f 116% t o under-e s t i m a t e s o f 20%. F o r t h i s s o i l p r o p e r t y s i t e s were o v e r -e s t i m a t e d 47% o f t h e t i m e and u n d e r - e s t i m a t e d 40% o f t h e t i m e . S o i l pH had t h e b e s t p r e d i c t i v e a c c u r a c y , w h i c h i s n o t t o o s u r p r i s i n g g i v e n t h e l i n e a r model p r o v i d e d t h e b e s t f i t t o t h e s e m i - v a r i o g r a m . Twenty s i x p e r c e n t o f t h e v a l i d a t i o n s i t e s were a c c u r a t e l y p r e d i c t e d by k r i g i n g , w i t h e r r o r s l e s s t h a n 10% (0.5 pH u n i t ) i n a l l c a s e s and a v e r a g i n g 3% (0.15 pH u n i t ) . T h i r t y seven p e r c e n t o f s i t e s were o v e r - e s t i m a t e d and a s i m i l a r number u n d e r - e s t i m a t e d . 60 4.5 S p a t i a l relationships and p r e d i c t i v e models 4.5.1 Linear relationships Correlation analysis was used to examine relationships between y i e l d , f o l i a r elements, energy components and biophysical v a r i a b l e s . Figures 16 and 17 i l l u s t r a t e f o r y i e l d and f o l i a r N, from cuts 4 and 5, how these rel a t i o n s h i p s were described. The figures i l l u s t r a t e how variables which may be correlated with the variable of i n t e r e s t may be autocorrelated, allowing r e l a t i o n s h i p s to be c l a r i f i e d and redundant data to be removed for subsequent regression analysis. Similar c o r r e l a t i o n c o e f f i c i e n t s were derived using the v a l i d a t i o n data set. I t i s apparent that properties associated with s o i l water storage and u t i l i z a t i o n occur repeatedly i n t h i s analysis, i n d i c a t i n g the importance of these factors i n l a t e season forage production. Similar findings have been reported by numerous other workers (e.g., Wallis et a l . 1983, P a r f i t t et a l . 1985a,b). The influence of these properties seems to be greatest i n the surface layer, which i s understandable given the high concentration of plant roots i n t h i s zone. During the early part of the growing season relationships are weaker and l e s s c l e a r , which confirms that a wide range of properties or processes are contributing to forage production. Weaker rela t i o n s h i p s e x i s t between y i e l d and remote sensing techniques but i t confirms that q u a n t i f i c a t i o n of these variables i s possible by remote means (e.g., Tucker 1977, Bedard and Lapointe 1987, Patel et a l . 1985). 61 0.62 S-1 0.61 N-1 AWSC-1  Q 9 0 / o. 0.03MPa-1 0.66 AWSCTOT ST5-1 0.68 AWSC-1 0.62 v0.42 PSC-1 0.39 FN 4 o.7g\ '0A2 0.03MPa-1 N-1 Mg-1 K-1 .0.55 0.49 0.49 FN 5 0.85 '0.49 0.41 AWSC-1 0.45 OCT7-1 0 . 9 0 \ ^0.87 0.03MPa-1 F i g u r e 16. C o r r e l o g r a m s 4 and 5) 1986. f o r y i e l d and f o l i a r n i t r o g e n (cut 62 0.67 PSC-1 0.79 0.03MPa-1 C 0.86 1.5MPa-1 EP3-1 -0.70 EVAP3-1 0.82 JUL14-1 ST2-1 AWSC-4 0.03MPa-4 0.72 PSC-3 0.70 CEC-1 /0.44 FN 5 PSC-1 0.59 ST4-2 §- EP3-2 0,59 -0.89 EVAP3-2 -0.89 * SEP17-3 F i g u r e 17. Correlograms f o r y i e l d and f o l i a r n i t r o g e n (cut 4 and 5) 1987. Appendix 5 p r o v i d e s t h e most n o t a b l e c o r r e l a t i o n s between y i e l d and key v a r i a b l e s and a s s o c i a t e d b i o p h y s i c a l p r o p e r t i e s . The b e s t r e l a t i o n s h i p s can be found when d r y l a n d and i r r i g a t e d t r e a t m e n t s a r e combined t o produce a g r e a t e r s p r e a d i n t h e d a t a . I t i s e v i d e n t from F i g . 18 however, t h a t two r e a s o n a b l y d i s t i n c t sample p o p u l a t i o n s e x i s t , each o f w h i c h s h o u l d be t r e a t e d s e p a r a t e l y . C o r r e l a t i o n s f o r d r y l a n d and i r r i g a t e d t r e a t m e n t s a l o n e t e n d t o be l o w e r t h a n f o r t h e combined d a t a s e t and, i n g e n e r a l , most r e l a t i o n s h i p s a c c o u n t f o r a maximum o f 30 t o 50% o f t h e t o t a l v a r i a n c e i n t h e p r o p e r t y o f i n t e r e s t . V e r y few, r e l a t i o n s h i p s w i t h r>0.50 o c c u r e a r l y i n t h e g r o w i n g season and i t seems t h a t u n t i l some v a r i a b l e ( s ) become l i m i t i n g , s i g n i f i c a n t c o r r e l a t i o n s w i t h measured b i o p h y s i c a l v a r i a b l e s a r e n o t e v i d e n t . I t would appear t h a t e a r l y i n t h e g r o w i n g season weak r e l a t i o n s h i p s e x i s t between s o i l c h e m i c a l and p h y s i c a l p r o p e r t i e s and key v a r i a b l e s . As t h e season p r o g r e s s e s and m o i s t u r e s t r e s s becomes a p p a r e n t , t h e r e l a t i o n s h i p s s t r e n g t h e n and s h i f t t o components o f t h e w a t e r b a l a n c e , and s o i l p h y s i c a l p r o p e r t i e s a s s o c i a t e d w i t h w a t e r r e t e n t i o n and s t o r a g e . 64 5 DM (T/ha) 4 3-2 0 Cut 5 1987 . A Dryland • • • • rn • e g g n § • A A • B (A. A • • • • A A A A : A A rrigated • • • • • • 0 0,5 1 1,5 2 2 . 5 - 3 Evapotranspiration (mm/day) F i g u r e 18. S c a t t e r p l o t of e v a p o t r a n s p i r a t i o n v e r s u s y i e l d f o r cut 5 1987. 65 In an empirical approach, the development of pr e d i c t i v e models uses l i n e a r and stepwise multiple regression procedures based on the best c o e f f i c i e n t s of determination. The pr e d i c t i v e a b i l i t y of t h i s technique proved poor for both dryland and i r r i g a t e d treatments. No relationships which could account for more than 50% of the variance i n the key variable of i n t e r e s t were obtained from any of the measured biophysical properties. This occurred despite a l l the input variables to the regression analysis being s i g n i f i c a n t l y correlated (p=0.05) with the appropriate key variable. Table 9 shows rela t i o n s h i p s for dry matter (DM) and f o l i a r N on dryland s i t e s i n 1986 and 1987. In 1986 the strongest relationships with cut 4 were obtained from theta values, while for cut 5 s o i l N and Mg provided better r e l a t i o n s h i p s . In 1987 the l a s t two cuts show the strongest re l a t i o n s h i p s with components of the water balance, i n p a r t i c u l a r , change i n s o i l water storage (E-P). In general, for the l a t t e r part of the growing season only marginal improvements i n p r e d i c t i v e a b i l i t y of models was achieved when using variables from the ent i r e biophysical data set as against l i n e a r or multiple stepwise regressions from water balance data alone. 66 T a b l e 9. L i n e a r and m u l t i p l e r e g r e s s i o n models f o r p r e d i c t i n g d r y m a t t e r and f o l i a r n i t r o g e n from b i o p h y s i c a l d a t a b a s e . Y e a r E q u a t i o n r 2 s.e. p r e d i c t i o n 1986 DM4 = -0.09 + 0.02(May28-1) 0.22 0.23 FN4 = -0.51 + 0.1(May28-l) 0.23 0.86 DM5 = 0.52 + 2.6(N-1) + 0.44(Mg-l) 0.34 0.41 FN 5 = 1.8 + 1.2(Mg-l) + l l . l ( N - l ) 0.35 1. 39 1987 DM4 = 1.8 - 0.8(EP3-1) 0.30 0.28 FN4 = 6.9 - 3.2(EP3-1) 0.36 1.04 DM5 = 0.02 + 1.7 x 10" 3 (ST4-2) 0.38 0.22 FN 5 = 4.9 - 0.9(EP3-2) 0.34 0.97 4.5.2 M u l t i v a r i a t e techniques The purpose and c h a r a c t e r i s t i c s o f many i n n o v a t i v e s t a t i s t i c a l t e c h n i q u e s , w h i c h were d e v e l o p e d i n mathematics, e n g i n e e r i n g and m i n i n g , and a r e now b e i n g i n c r e a s i n g l y a p p l i e d i n a g r i c u l t u r e , a r e o u t l i n e d by R u s s e l l and D a l e (1987). M u l t i v a r i a t e methods have been used i n t h e s t u d y o f s o i l s f o r a l m o s t 20 y e a r s , b u t emphasis has been a l m o s t e x c l u s i v e l y on s o i l c l a s s i f i c a t i o n ( N o r r i s 1970, M u i r e t a l . 1970, A r k l e y 1976), and s o i l s u r v e y (Webster and Burrough 1972, O l i v e r and Webster 1987a). Because o f t h e c o m p l e x i t y o f s o i l - p l a n t r e l a t i o n s h i p s , and t h e i n a b i l i t y o f s i m p l e l i n e a r r e l a t i o n s h i p s t o p r o v i d e adequate p r e d i c t i v e a b i l i t y , m u l t i v a r i a t e methods p r o v i d e a l t e r n a t i v e t e c h n i q u e s w h i c h can s i m p l i f y and d e s c r i b e d a t a c o m p l e x i t y i n an o b j e c t i v e manner. These s i m p l i f i e d r e l a t i o n s h i p s can t h e n be examined u s i n g more t r a d i t i o n a l s t a t i s t i c a l t e c h n i q u e s . S e v e r a l m u l t i v a r i a t e methods a r e d e s c r i b e d i n c l u d i n g d i s c r i m i n a n t a n a l y s i s , c l u s t e r a n a l y s i s , and p r i n c i p a l component a n a l y s i s . 4.5.2.1 Discriminant analysis and p r i n c i p a l component analysis I t i s n o t t h e aim o f d i s c r i m i n a n t a n a l y s i s (DA) t o c r e a t e a c l a s s i f i c a t i o n system; r a t h e r i t c a l c u l a t e s t h e v a r i a n c e -c o v a r i a n c e m a t r i c e s w i t h i n p r e - d e f i n e d c l a s s e s . The aim i s t o compare t h e c l a s s e s and t o g e n e r a t e h y p o t h e s e s as t o why t h e y may d i f f e r . M u l t i p l e d i s c r i m i n a n t a n a l y s i s i s used t o r e d e s c r i b e t h e space c o n t a i n i n g t h e c l a s s e s . The f i r s t d i s c r i m i n a n t f u n c t i o n i n d i c a t e s w h i c h v a r i a b l e s b e s t d i s c r i m i n a t e t h e groups i n r e l a t i o n t o t h e i r w i t h i n - g r o u p v a r i a t i o n . The second f u n c t i o n i s o r t h o g o n a l t o t h e f i r s t , and a c c o u n t s f o r a s m a l l e r amount o f t h e v a r i a n c e ( N o r r i s 1970). I f t h e r e a r e n v a r i a b l e s and k c l a s s e s , t h e maximum number o f such f u n c t i o n s i s t h e l e s s e r o f n and ( k - 1 ) . I n i t i a l l y a c l a s s i f i c a t i o n was d e r i v e d from a s i m p l e p r o b a b i l i t y a n a l y s i s o f key v a r i a b l e s b e i n g above c r i t i c a l 68 l e v e l s f o r a n i m a l n u t r i t i o n . The r e s u l t s o f t h i s a n a l y s i s were i n c o n c l u s i v e and as a r e s u l t a second c l a s s i f i c a t i o n , w h i c h combined y i e l d c l a s s e s 2 w i t h t h e f i r s t , was d e v e l o p e d . T h i s c l a s s i f i c a t i o n i s o u t l i n e d i n T a b l e 10. The r e s u l t s o f t h e d i s c r i m i n a n t a n a l y s i s a r e p r e s e n t e d i n T a b l e 11 and 12 f o r b o t h t r e a t m e n t s i n 1986 and 1987 r e s p e c t i v e l y . The t a b l e s have been s i m p l i f i e d t o g i v e an i n d i c a t i o n o f w h i c h t y p e o f v a r i a b l e b e s t d i s t i n g u i s h e s t h e c l a s s e s . T a b l e 10. M u l t i v a r i a t e c l a s s i f i c a t i o n used f o r m u l t i p l e d i s c r i m i n a n t a n a l y s i s . Y i e l d and N u t r i t i o n a l Q u a l i t y C l a s s i f i c a t i o n C l a s s D e s c r i p t i o n 1 H i g h p r o d u c t i o n and g r e a t e r t h a n 50% o f key v a r i a b l e s (Ca, N, P, DE) above c r i t i c a l l e v e l s 3 . 2 Medium p r o d u c t i o n and g r e a t e r t h a n 50% o f key v a r i a b l e s above c r i t i c a l l e v e l s . 3 Low p r o d u c t i o n and g r e a t e r t h a n 50% o f key v a r i a b l e s above c r i t i c a l l e v e l s . 4 H i g h p r o d u c t i o n and l e s s t h a n 50% o f key v a r i a b l e s above c r i t i c a l l e v e l s . 5 Medium p r o d u c t i o n and l e s s t h a n 50% o f key v a r i a b l e s above c r i t i c a l l e v e l s . 6 Low p r o d u c t i o n and l e s s t h a n 50% o f key v a r i a b l e s above c r i t i c a l l e v e l s . 2 H i g h = > 1 SD above mean p r o d u c t i o n , Medium = w i t h i n +/-1 SD o f mean p r o d u c t i o n , Low = > 1 SD below mean p r o d u c t i o n f o r r e s p e c t i v e c u t s . 3 C r i t i c a l l e v e l s f o r key elements were s e t as f o l l o w s : Ca; 0.55%, CP; 15%, P; 0.43% and, DE; 13.5 Mj/kg DM, G. S m i t h , 1988, p e r s o n a l communication, ADAS 1975). 69 T a b l e 11. R e s u l t s o f m u l t i p l e d i s c r i m i n a n t a n a l y s i s on y i e l d and n u t r i t i o n a l q u a l i t y c l a s s i f i c a t i o n f o r 1986. DRYLAND 1986 Cut No. C l a s s e s 1 1-6 2 2-6 3 1-5 4 1-6 1-5 -3,5,6 F u n c t i o n 1 W-ST4 CHEM <4>5 6 ( 5 8 % ) 6 1 FZn FMn (2) (68%) 1 FK AWSC-4 (2) (64%) 1 AWSCERD (1) (54%) 1 FOL CHEM (11) (69%) 2 FOL CHEM (10) (92%) 2 PSC W-ST W-ST (4) (95%) 2 FOL W-RET (5) (79%) 2 AWSC-2 CHEM (5) (82%) 2 ALL (27) (100%) IRRIGATED Cut No C l a s s e s 1986 1 4-6 2 2,4-6 3 1-6 4 1-5 5 1-3 F u n c t i o n 1 FOL W-RET W-ST CHEM (18) (91%) 1 W-ST W-RET (V) (91%) 1 CHEM W-RET (8) (93%) 1 ELEV PHYS W-RET (7) (61%) 1 ELEV W-RET FOL (8) (99%) 2 ALL (20) (100%) 2 PSC CHEM (7) (99%) 2 K PSC-2 Na (3) (94%) 2 FOL CHEM (6) (89%) 2 ALL (30) (100%) B i o p h y s i c a l v a r i a b l e s have been d i v i d e d i n t o s e v e r a l groups as f o l l o w s : FOL = f o l i a r e l e m e n t s , CHEM = s o i l c h e m i c a l p r o p e r t i e s from 0 t o 25-cm, W-ST = wa t e r s t o r a g e c h a r a c t e r i s t i c s , W-RET = wa t e r r e t e n t i o n c h a r a c t e r i s t i c s , PHYS = p h y s i o g r a p h i c p o s i t i o n , ELEV = e l e v a t i o n , ALL = v a r i a b l e s drawn from a l l g r o u p s . I n a d d i t i o n where l e s s t h a n 5 v a r i a b l e s make up a d i s c r i m i n a n t f u n c t i o n , t h e y a r e l i s t e d i n d i v i d u a l l y , e.g., AWSCERD. 5 Number o f v a r i a b l e s w h i c h a r e i n c l u d e d i n t h e f u n c t i o n . 6 P e r c e n t a g e o f t o t a l v a r i a n c e a c c o u n t e d f o r by d i s c r i m i n a n t f u n c t i o n . 70 T a b l e 12. R e s u l t s o f m u l t i p l e d i s c r i m i n a n t a n a l y s i s on y i e l d and n u t r i t i o n a l q u a l i t y c l a s s i f i c a t i o n f o r 1987 (see t a b l e 11 f o r e x p l a n a t i o n o f a b b r e v i a t i o n s ) . DRYLAND 1987 Cut No. C l a s s e s 1 1-3 2 1-6 3 1-3 4 1-3 5 1-3 F u n c t i o n 1 FOL PHYS CHEM (7) (86%) 1 FOL W-ST (6) (59%) 1 FOL (5) (77%) 1 FOL CHEM (13) (79%) 1 FOL W-RET CHEM (15) (91%) 2 ALL (28) (100%) 2 ALL (18) (83%) 2 ALL (30) (100%) 2 ALL (23) (100%) 2 ALL (21) (100% IRRIGATED Cut No. C l a s s e s 1987 1 4-6 2 2,4-6 3 1-6 4 1-5 5 1-3 F u n c t i o n 1 FOL (3) (88%) 1 W-ST ELEV (7) (98%) 1 W-RET W-ST CHEM (10) (40%) 1 Na (1) (96%) 1 ALL (16) (93%) 2 CHEM W-ST (9) (97%) 2 ALL (17) (99%) 2 FOL PSC CHEM (6) 2 ELEV PHYS (7) (99%) 2 ALL (20) (100: (72%) 71 Some trends are evident i n the 1986 data. For dryland s i t e s , f o l i a r elements provide good separation between classes i n aftermath cuts, p a r t i c u l a r l y Mn and Zn which occur i n cuts 2, 3 and 5. At i r r i g a t e d s i t e s i n 1986 the water retentive c h a r a c t e r i s t i c s of the s o i l assume greater importance as discriminating variables throughout the growing season. F o l i a r Mn and Zn occur i n cuts 1, 4 and 5 with Fe and A l commonly occurring i n the second function. Elevation and physiographic p o s i t i o n also appear as discriminating variables i n the f i r s t discriminant function for cuts 4 and 5. This indicates that these variables or properties associated with them such as p a r t i c l e s i z e c l a s s or drainage have some influence i n separating classes l a t e r i n the season. For dryland s i t e s i n 1987 f o l i a r elements are again important variables i n discriminating classes f o r a l l cuts. Variables with highest loadings include K, Mg, Mn and Zn with AWSCERD also loading highly for the f i r s t 4 cuts. I r r i g a t e d s i t e s show a less repeatable or predictable pattern with variables from a l l biophysical data groups contributing at various cuts. In 1986 water retention/storage c h a r a c t e r i s t i c s are commonly selected, a pattern which i s repeated for only 2 of 5 cuts i n 1987. F o l i a r Zn, Mn and Mg are s t i l l extracted but i n general they load less highly than other variables such as water retention and water storage. Elevation and physiographic p o s i t i o n are again extracted, but t h i s time for cuts 2 and 4. 0 P r i n c i p a l components analysis (PCA),is regarded by some as a standard technique for revealing multivariate structure (Oliver and Webster 1987a). The analysis transforms an o r i g i n a l set of correlated variables into a new set of mutually uncorrelated p r i n c i p a l components (PC's), i n such a way that a few p r i n c i p a l components represent a large proportion of the v a r i a t i o n present i n the o r i g i n a l data (Cuanalo and Webster 1970). A precise physical meaning cannot be given to the components although a rough picture may be gained by projecting the f i r s t two p r i n c i p a l components (which account for the greatest amount of t o t a l variance), and in t e r p r e t i n g how the data i s d i s t r i b u t e d i n these two axes. The data were f i r s t standardized by r e - s c a l i n g each of the o r i g i n a l v ariables to zero mean and unit variance, so that the analysis was e f f e c t i v e l y performed on the c o r r e l a t i o n matrix. Table 13 gives the variables for the f i r s t 4 to 5 PC's from the 1986 and 1987 data sets. Most of the interpretable information i s i n the f i r s t two PC's, which account for 36 and 4 6% of the t o t a l variance f o r 1986 and 1987 respectively. The variables were projected i n the plane of the f i r s t two p r i n c i p a l axes but did not produce d i s t i n c t l y clustered groups nor were they r e a d i l y interpretable. In 1986 the f i r s t PC represents exchangeable cations, water storage and cumulative evapotranspiration p r i o r to cut 2. The second PC represents y i e l d and key variables from cut 4, the t h i r d cut 1 and, the fourth cut 5 for the same variables. 73 Table 13. Variables comprising the f i r s t 4 or 5 p r i n c i p a l components for the 1986 and 1987 biophysical data base (figures i n brackets indicate the percentage of t o t a l variance explained by each component). 1986 Component 1 Component 2 Component 3 Component 4 Component 5 CEC-1 DM-4 FP-1 FCa-5 FP-2 EVAP2-2 FCa-4 . DM-1 DM-5 DM-2 S-l FP-4 DMTOT DEF-5 FCa-2 AWSC-3 EVAP4-1 FCa-1 (8.3) (6.9) AWSC-1 (15.5) (10.8) EVAP2-1 Mg-2 1.5-MPa-4 (20.7) 1987 Component 1 Component 2 Component 3 Component 4 FP-4 CEC-1 DM-1 DM-4 N-1 FP-1 EVAP5-1 CEC-2 FN-1 FP-5 TEE—1 (11. DM-5 TEB-2 FCa-5 N-2 FCa-4 AWSC-3 DMTOT EVAP3-2 DEF-5 AWSC-4 EP5-2 (18.7) (27.3) The 1987 analysis revealed that y i e l d and key variables for cut 4 and 5 and water balance variables for the f i f t h cut were i d e n t i f i e d i n the f i r s t p r i n c i p a l component. The second p r i n c i p a l component consists of exchange c h a r a c t e r i s t i c s of the s o i l combined with i t s water storage c h a r a c t e r i s t i c s , while the t h i r d and fourth components consist of y i e l d and key variables for cut 1 and 2 respectively. Comparing those biophysical variables i d e n t i f i e d with PCA to those from DA shows that there are indeed some variables common to both. In 1986 CEC, AWSC, S - l and 1.5-MPa data are common to both, and i n 1987 AWSC i s common to the two methods. I t would appear that s o i l physical properties associated with water retention and storage are useful variables i n explaining the v a r i a b i l i t y i n forage y i e l d and qu a l i t y v a r i a b l e s . 4.5.2.2 Assessing the u t i l i t y of PCA and DA to improve l i n e a r r e l a t i o n s h i p s In an attempt to assess the u t i l i t y of discriminant analysis to provide predictor variables for the y i e l d - n u t r i t i o n a l q u a l i t y c l a s s i f i c a t i o n , a multiple stepwise regression was run for each cut and treatment. The f o l i a r variables which best discriminated classes within the y i e l d and n u t r i t i o n a l q u a l i t y c l a s s i f i c a t i o n were used i n the analysis and the re s u l t s are given i n Table 14. For each cut the independent variable l i s t included f o l i a r K, Mg, Zn and Mn. Y i e l d p r e d i c t i o n was not included as i t was not appropriate to regress t h i s variable against weighted f o l i a r variables. Unweighted regressions of y i e l d versus f o l i a r variables could not account for more than 44% of t o t a l variance. Table 14 shows the percent variance explained by each f o l i a r element and, that the input variables from DA do indeed provide good p r e d i c t i v e a b i l i t y for weighted key f o l i a r v a r i a b l e s . Some equations account fo r up to 97% of t o t a l variance. Other i n t e r e s t i n g patterns which emerge i n Table 14 are the reoccurrence of Mg and K as predictors on dryland s i t e s i n 1986 and 1987. The pattern i s less pronounced on i r r i g a t e d treatments where Zn i s more prevalent, p a r t i c u l a r l y i n 1987. The best p r e d i c t i v e relationships are generally achieved with DE and the worst with Ca. No i n d i v i d u a l treatment seems to have consistently better p r e d i c t i v e a b i l i t y than the other. The r o l e of K, Zn, Mg, and Mn i n d i s t i n g u i s h i n g q u a l i t y differences i n forages may well warrant further i n v e s t i g a t i o n . The f i n a l step i n t h i s analysis involved the p r e d i c t i o n of y i e l d and key variables from a combination of unweighted independent variables derived from remote sensing (pixel values) and DA (AWSC's, PSC's, PHYS, ELEV, 0.03 and 1.5-MPa values). Sixty eight percent of the variance i n y i e l d could be accounted for using GRN, RED and IR p i x e l values while for other key variables only 20 to 30% could be accounted for using any of the above input v a r i a b l e s . 76 Table 14. F o l i a r variables (weighted) and the percent variance for which they account when used i n multiple stepwise regression to predict Key f o l i a r variables. 1986 DRYLAND 1986 IRRIGATED Cut Key Var % Var % Var % Cut Key Var % Var % Var % 1 Ca Mg 45 Mn 55 N Mg 80 K 82 P K 83 Zn 88 DE K 76 Zn 78 2 Ca Mg 61 Mn 75 N Zn 62 Mg 67 K 69 P Mg 41 Mn 48 Zn 51 DE Zn 44 Mn 56 K 61 3 Ca Mg 54 Mn 65 N Mg 68 K 84 P Mg 58 K 65 Mn 67 DE Mg 68 K 82 Mn 85 4 Ca Mg 67 Mn 73 K 82 N Mg 85 K 90 P Mg 81 K 85 DE Mg 90 K 94 5 Ca Mg 60 Zn 75 Mn 77 N K 89 Mg 94 Mn 95 P Zn 48 DE K 88 Mg 96 Zn 97 1987 DRYLAND Cut Key Var % Var % Var % 1 Ca Mg 84 N Zn 86 Mg 90 P K 91 Zn 93 DE K 82 2 Ca Mg 61 Zn 67 Mn 72 N K 90 K 90 P Zn 84 DE Zn 82 K 86 Mn 88 3 Ca Zn 63 N Mg 83 K 89 Zn 91 P Mn 70 Zn 75 DE Zn 78 Mn 86 K 90 4 Ca Mg 63 N Zn 85 Mg 89 K 93 P Mg 52 K 64 DE K 87 K 87 5 Ca Mg 46 Mn 64 N Mg 88 K 95 Mn 96 P Mg 80 K 88 DE Mg 88 K 96 1987 IRRIGATED Cut Key Var % Var % Var % Var % 1 Ca Mn 62 N K 51 P K 43 DE K 59 2 Ca Mg 51 N Zn 54 P Zn 64 DE Zn 65 3 Ca Mg 60 N Mg 58 P Mg 63 DE Mg 55 4 Ca Mg 74 N Mg 91 P Mg 84 DE Mg 88 5 Ca Mg 80 N Zn 90 P Mg 89 DE Zn 88 K 64 Mg 58 Zn 50 Mg 68 Mn 71 Mn 75 K 65 K 76 Mg 78 K 76 Mg 77 Mn 83 K 84 Zn 85 K 88 K 84 Mn 86 Mn 77 K 79 K 96 K 88 K 94 Mn 84 Zn 85 Mg 93 K 96 K 94 Mg 90 K 95 1 Ca Mg 20 N Mn 46 P Zn 75 DE Zn 81 2 Ca Mg 58 N K 53 P Zn 76 DE Zn 51 3 Ca Mg 25 N Mg 70 P Zn 61 DE Zn 53 4 Ca Mg 13 N Zn 77 P Zn 71 DE Zn 78 5 Ca Mg 24 N Zn 83 P Zn 63 DE Zn 84 Mn 44 K 52 K 52 Mn 79 K 82 K 86 Mg 89 Mn 65 Mg 64 Mg 79 K 84 Mn 54 K 60 K 85 Mn 66 Mn 62 K 70 Mn 35 K 84 K 86 K 86 Regression analysis of y i e l d and key variables using the p r i n c i p a l components could not account for more than 50% of the variance i n the property of in t e r e s t . Some c l a s s i f i c a t i o n of the samples was sought as a basis for eventual s p a t i a l analysis, so the p r i n c i p a l components were s i m p l i f i e d on the basis of c o r r e l a t i o n analysis ( i . e . , highly correlated variables were removed), and c l u s t e r analysis was performed using the remaining varia b l e s . Because the aim was to assess the u t i l i t y of the method to provide indicator variables for y i e l d - q u a l i t y the l a t t e r were removed p r i o r to c l u s t e r i n g . The 1986 data set was clustered using CEC-1, AWSC-3, 1.5-MPa-4, EVAP2-1, EVAP4-1 and DEF-5, while i n 1987 CEC-1, TEB-1, N-1, AWSC-4, EVAP3-2 and EVAP5-1 were chosen. Examination of the r e s u l t i n g dendrograms f o r both years suggested three or four d i s t i n c t classes existed. The r e s u l t s of s i g n i f i c a n c e tests between the classes demonstrated that i n 1986 the c l u s t e r i n g variables provided good separation between i r r i g a t e d and some dryland s i t e s . Within the dryland treatment s i g n i f i c a n t differences were detected for y i e l d and some key variables f o r cuts 1, 2 and 5, but no others. Differences i n key variables between i r r i g a t e d and dryland treatments were s i g n i f i c a n t i n a l l cuts except the f i r s t and l a s t , although y i e l d differences were detected i n the l a s t cut. In 1987 cl a s s membership was less d i s t i n c t with one i r r i g a t e d s i t e being combined with 3 dryland s i t e s to form a separate group. The discriminating a b i l i t y of the c l u s t e r i n g 78 variables was poor for the f i r s t three cuts with few s i g n i f i c a n t differences between classes. For cut 4 and 5 s i g n i f i c a n c e tests show a l l key variables are s i g n i f i c a n t l y d i f f e r e n t , an in d i c a t i o n that the c l u s t e r i n g variables are useful discriminators at t h i s stage i n the growing season. The r e s u l t s of PCA show that the technique, as i t was applied here, provided some useful i n d i c a t o r variables for forage y i e l d and qua l i t y for cuts over the l a t t e r part of each year. 4.5.2.3 C l u s t e r A n a l y s i s Cluster analysis i s a technique for measuring the degree of s i m i l a r i t y among variables using a multi-variable database. The average distance c l u s t e r analysis method (Ward 1963) was used, and the degree of s i m i l a r i t y among variables was determined using various combinations of variables from the biophysical data base. The c l u s t e r i n g program (Patterson and Whittaker 1978), shows how c l o s e l y the input variables are related to one another. A Mann-Whitney U si g n i f i c a n c e t e s t was conducted to determine i f classes were s i g n i f i c a n t l y d i f f e r e n t i n terms of accessory biophysical variables i n addition to the variables which were used i n the c l u s t e r procedure. A large number of c l u s t e r i n g procedures were applied to the data set. Generally, combinations of variables from a l l components of the biophysical data base i d e n t i f i e d from c o r r e l a t i o n analysis were used. Only those analyses which were useful i n delineating s i g n i f i c a n t l y d i f f e r e n t groups or were useful as predictors are presented here. I n i t i a l l y , two c l u s t e r i n g procedures were c a r r i e d out for combined treatments using variables from the ent i r e physico-chemical and water balance data base. Once s i g n i f i c a n t classes had been i d e n t i f i e d using these biophysical variables the classes were assigned to y i e l d and key v a r i a b l e data for each cut from both years. The analysis demonstrated that the water balance data provided the best pre d i c t i v e a b i l i t y for y i e l d and key variables, p a r t i c u l a r l y over the l a t t e r stages of the growing season. The r e s u l t s , presented i n F i g . 19 and Table 15, also show that the technique can r e a d i l y discriminate between i r r i g a t e d (class 4) and dryland (class 1, 2, 3) forage. Those s i t e s occurring on the highest and d r i e s t positions (class 3) were not always c l e a r l y discriminated from t h e i r neighbours (class l and 2). They were c l e a r l y distinguishable at the end of the 1986 growing season, but not at a l l for cut 5 i n 1987. The fact that 1987 was a d r i e r than average year may have meant that most s i t e s were under s i g n i f i c a n t moisture stress over the l a t t e r stages of the growing season, and t h i s may have reduced the differences between them. I t i s cl e a r however, that water balance variables can provide good d i f f e r e n t i a t i o n between the two treatments over the entire growing season, and to a lesser extent they can d i s t i n g u i s h higher, droughtier s i t e s within the dryland treatment. 80 2 Q 2 o co 2 Q 2 Q to a O u . to O u . CM « O lO lO z LU IL Q z Q_ u. l i - a co ra CO z 0. UJ u . u . a CM CM CM z 0. LU L  L  a r- r~ z LL a O L  a o o LL IO 0. •+ if z Q-LU LI- IL Q D CO co z LU L  Q CM CM CM Z LU 2 L  a Q r-Z LU L  Q CM a O LL z LU IL u . Q CM CM CM z a. LU U-L a z CO lf> lO 2 o O U-IO lO IO IO IO lO io z Q. LU 2 z a. UJ u. LU Q Q U- LL Q o L IO l O z 0- LU L  IL Q z 0. LU LU L  Q CM CM z LU U- Q 2 z UJ a L  L  Q CO z U-CM CM CM z 0- LU LL U- a z a. O u. C\J IS O LL o u-CO 03 CD CO LU a O IO IO lO z a. LU u_ LL Q •* •* z a. LU u_ LL a CO co CO z LU U- LL a z Q-U-IO lO IO z Q_ LU u. LL a •* T)-z a. LU IL u_ Q co co CO z Q_ LU u . U- Q CM CO Figure 19. Results of Mann-Whitney U sign i f i c a n c e t e s t i n d i c a t i n g variables that were s i g n i f i c a n t l y d i f f e r e n t between classes (clustering c a r r i e d out using water balance components, p=0.10). 81 Table 15. Mean values for yield, and key variables from c l u s t e r classes f o r combined treatments over two years (clustering c a r r i e d out using water balance components). 1986 Dry Matter (t/ha) Cut 1 2 3 4 5 Class 6.2 4.0 2.8 1.1 1.7 Class 5.8 4. 2. 1, 1, Class 4.9 3.4 2.5 0.8 0.9 Class 5.2 4.7 2.4 1.4 1.1 Ca (%) Cut Class 1 Class 2 Class 3 Class 1 0.18 0.20 0.23 0.20 2 0.40 0.42 0.37 0.45 3 0.40 0.41 0.39 0.42 4 0.53 0.52 0.60 0.55 5 0.50 0.47 0.57 0.53 N (%) Cut Class 1 Class 2 Class 3 Class 1 2.29 2.39 1.99 2.33 2 2.24 2.37 1.96 2.39 3 2.98 3.15 2.81 3.10 4 3.67 3.71 3.43 4.09 5 3.38 3.48 4.08 4.12 P (%) Cut Class 1 Class 2 Class 3 Class 1 0. 35 0.36 0.35 0.36 2 0.36 0.35 0. 34 0.35 3 0.32 0.31 0.34 0.33 4 0.31 0. 32 0.31 0.30 5 0.28 0.29 0.35 0.39 DE (Mj/kg DM) Cut Class 1 Class 2 Class 3 Class 1 12.3 12.5 12.5 12.4 2 12.4 12.3 12.5 12.4 3 13.5 13.5 13.5 13.2 4 13.7 13.7 13.3 13.8 5 13.5 13.4 14.6 14.5 82 Table 15 cont. 1987 Dry Matter (t/ha) Cut Class 1 Class 2 Class 3 Class 1 3.6 3.3 3.5 3.5 2 3.6 3.4 4.0 3.7 3 2.6 2.7 2.2 2.1 4 1.3 1.4 1.1 2.7 5 0.8 0.7 0.6 1.7 Ca (%) Cut Class 1 Class 2 Class 3 Class 1 0.32 0.40 0.45 0.35 2 0.57 0. 63 0.81 0.68 3 0.59 0.62 0.84 0.79 4 0.91 0.93 1.08 1.01 5 0.73 0.78 1.21 0.91 N (%) Cut Class 1 Class 2 Class 3 Class 1 3.25 3.36 2.79 2.93 2 3.35 3.42 2.99 3.02 3 3.51 3.57 3.71 3.66 4 3.76 3.73 3.84 3.69 5 3.96 4.05 3.89 4.09 P (%) Cut Class 1 Class 2 Class 3 Class 1 0.30 0.30 0.30 0.32 2 0.32 0.32 0.34 0.32 3 0.32 0.32 0.33 0.33 4 0.30 0.31 0.29 0.34 5 0.29 0.29 0.27 0.33 DE (Mj/kg DM) Cut Class 1 Class 2 Class 3 Class 1 13.8 13.9 13.9 13.7 2 12.9 12.7 13.0 12.6 3 14.0 13.9 14.3 13.9 4 13.9 13.9 14.1 13.4 5 14.9 15.0 15.6 14.0 83 Cluster using y i e l d and key variables on an i n d i v i d u a l cut basis produced s i g n i f i c a n t l y d i f f e r e n t classes both within and between treatments for most variables. An example of the range of key variables within c l u s t e r classes i s shown i n Table 20. When the same classes are applied to associated biophysical variables, s i g n i f i c a n c e tests show few of them to be s i g n i f i c a n t l y d i f f e r e n t . This also seems to indicate that variables other than those being monitored i n the study are contributing to the differences seen i n key variab l e s . Clustering was also c a r r i e d out using variables i d e n t i f i e d as having some re l a t i o n s h i p with key variables from previous data examination (e.g., discriminant a n a l y s i s ) . Table 16. Mean values and ranges for key variables f o r c l u s t e r classes from cut 3 1987, dryland treatment. Class DM Ca N P DE T/ha % % % Mj/kg DM 1 2.4 1.16 8.18 0.76 33.4 (2 .2-2.5) (0. 69-1.98) (6.89-9.53) (0. 70-0.83) (29.2-37.6) 2 2.7 1.72 9.81 0.89 38.9 (2 .4-2.9) (1. 01-2.57) (7.43-11.20) (0. 82-0.98) (35.1-42.9) 3 3.2 2.23 11.71 1. 04 44.7 (2 .8-3.8) (1. 35-2.88) (10.99-12.78) (0. 96-1.09) (39.7-50.9) 4 2.0 1.27 7.16 0.66 29.1 (1 .7-2.2) (0. 92-2.18) (5.99-8.06) (0. 55-0.72) (25.1-32.0) 84 Clustering with variables i d e n t i f i e d from DA demonstrated that d i s t i n c t classes could be delineated within treatments which were s i g n i f i c a n t l y d i f f e r e n t for some key variables. Class d i f f e r e n t i a t i o n seemed to be strongest over the l a t t e r stages of the growing season as i s shown i n F i g . 20. Clustering on those biophysical variables i d e n t i f i e d with PCA produced r e s u l t s very s i m i l a r to those found with DA (Fig. 21) . In t h i s instance, data from both treatments were combined, and i t i s apparent that i n most cases the variables used could r e a d i l y d i f f e r e n t i a t e dryland (class 1 and 2) from i r r i g a t e d (class 3) treatments. Separating s i g n i f i c a n t classes within the dryland treatment was more d i f f i c u l t and the best r e s u l t s i n 1986 were found early and l a t e i n the growing season. For 1987 good separation between classes was found i n the l a s t 2 cuts. I t i s apparent that the relationships between crop production and biophysical variables are complex and the l i k e l i h o o d of universal models being applicable over a complete growing season are remote. GIS may provide a useful a l t e r n a t i v e . 85 1987 1 2 3 1 D M 3 D M 5 Ca1 C a 3 C a 4 N 3 N 4 N 5 P 3 0 E 3 D E 5 D M 4 D M 5 C a 4 C a 5 N 4 N 5 P 4 P 5 D E 4 D E 5 2 D M 3 D M 4 D M 5 C a 2 C a 3 C a 4 C a 5 N 3 N 4 N 5 P 3 P 4 P 5 D E 3 D E 4 D E 5 3 x F i g u r e 20. R e s u l t s of Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y between c l u s t e r c l a s s e s ( C l u s t e r i n g c a r r i e d out u s i n g v a r i a b l e s i d e n t i f i e d w i t h d i s c r i m i n a n t a n a l y s i s , p=0.05). 8 6 1986 1 2 3 1 \ D M 1 D M 2 D M 5 N1 N 5 P1 p 6 D E 1 0E6 D M 1 D M 2 D M 3 D M 4 D M 5 C a 1 C a 2 C a 4 C a 5 N1 N 2 N 3 N 4 N 6 P1 P 2 P 3 P 4 P 5 D E 1 D E 2 D E 3 D E 4 D E 6 2 D M 4 D M 5 C a 4 C a 6 N 4 N 5 P 2 P 4 P 6 D E 4 D E 5 N\ D M 2 D M 3 D M 4 D M 6 C a 2 C a 4 N 2 N 4 P 2 P 3 P 4 D E 2 D E 3 D E 4 3 D M 3 D M 4 D M 5 C a 2 C a 4 C a 5 N1 N 3 N 4 N 5 P 3 P 4 P 6 D E 3 D E 4 D E 5 D M 4 D M 5 C a 4 C a 5 N 4 N 6 P 2 P 4 P 6 D E 4 D E 6 F i g u r e 21. R e s u l t s of Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l u s t e r c l a s s e s ( C l u s t e r i n g c a r r i e d out u s i n g v a r i a b l e s i d e n t i f i e d w i t h p r i n c i p a l components a n a l y s i s , p=0.05). 87 CHAPTER FIVE REMOTE SENSING 5.1 Image analysis techniques C o l o u r IR t r a n s p a r e n c y p o s i t i v e s from t h e a e r i a l photography m i s s i o n s were p l a c e d i n an O p t r o n i c s C-4500 f i l m s c a n n e r and scanned t h r e e t i m e s w i t h b l u e g r e e n o r r e d f i l t e r s t o d e t e r m i n e t h e p i x e l b r i g h t n e s s v a l u e s o f t h e t h r e e c o l o u r s e n s i t i v e dye l a y e r s . S c a n n i n g was c a r r i e d o u t a t 50-um r e s o l u t i o n . The d i g i t a l d a t a was t h e n v i s u a l l y d i s p l a y e d u s i n g a M e r i d i a n PC-image a n a l y s i s system and a l l s a m p l i n g s i t e s l o c a t e d on t h e image v i a t h e f i e l d markers mentioned p r e v i o u s l y (see s e c t i o n 3.2.3). The p i x e l r e s o l u t i o n c o v e r e d a 0.2 x 0.2 meter ground a r e a . The p i x e l v a l u e s c o r r e s p o n d i n g t o a 2 x 2 meter t e s t a r e a l o c a t e d a d j a c e n t t o t h e n e u t r o n probe a c c e s s t u b e and used f o r v e g e t a t i o n s a m p l i n g were l o c a t e d and t h e mean v a l u e s f o r t h e b l u e , g r e e n and r e d s e n s i t i v e dye l a y e r s were e x t r a c t e d . These d a t a were t h e n s c r u t i n i s e d u s i n g c l u s t e r a n a l y s i s t o d e t e r m i n e an optimum c l a s s i f i c a t i o n scheme f o r t h e image. 5.2 Image analysis r e s u l t s The d i g i t i z e d a e r i a l photographs were d i s p l a y e d and a n a l y z e d u s i n g a M e r i d i a n - P C Image A n a l y s i s System. Each s a m p l i n g s i t e 88 was located and mean p i x e l values from three colour s e n s i t i v e dye layers i n a 4m2 area, including that used for biomass harvests, was recorded. Preliminary analysis of the two images showed that there was very l i t t l e p i x e l d i f f e r e n t i a t i o n i n any dye layer for the June image. Accordingly, the following discussion w i l l concentrate on the August image (Fig. 22). A U G U S T 1 9 S 7 Figure 22. D i g i t a l colour IR image for study area i n August 1987 p r i o r to cut 4 (the horizontal l i n e separates the dryland [upper] from the i r r i g a t e d treatment). 89 The p i x e l b r i g h t n e s s v a l u e s were examined i n s e v e r a l ways. F i r s t , frequency d i s t r i b u t i o n s f o r the 84 samples showed the p i x e l v a l u e s to be polymodal. In an attempt t o d i f f e r e n t i a t e meaningful c l a s s e s a c l u s t e r a n a l y s i s was c a r r i e d out u s i n g IR p i x e l v a l u e s . T h i s area of the spectrum was chosen because c o r r e l a t i o n a n a l y s i s had shown t h a t i t produced the s t r o n g e s t r e l a t i o n s h i p s with y i e l d and key v a r i a b l e s . From the r e s u l t i n g dendrogram, 3 c l a s s e s were chosen, d e s i g n a t e d 1, 2, and 3 i n F i g . 23. A l l 3 c l a s s e s r e v e a l e d unique c o n d i t i o n s (Table 17) and the r e s u l t s of s i g n i f i c a n c e t e s t s (Fig.24) showed t h a t y i e l d , f o l i a r elements and p i x e l b r i g h t n e s s v a l u e s were s i g n i f i c a n t l y d i f f e r e n t between c l a s s e s . 10 Frequency •8-6 4 2 0 August CLASS 2 CLASS 1 CLASS 3 150 160 170 180 190 IR Pixel Brightness Values F i g u r e 23. Frequency d i s t r i b u t i o n of IR p i x e l v a l u e s showing the c l a s s l i m i t s , as determined by c l u s t e r a n a l y s i s . 90 Tabl e 17. Mean and range f o r y i e l d and key elements w i t h i n c l a s s e s i d e n t i f i e d by c l u s t e r a n a l y s i s o f IR p i x e l v a l u e s . August 1987 DM Ca N P DE C l a s s T/ha % % % Mj/kg DM 1 1.7 1.60 6.58 0.56 24.3 (0 .6-3.9) (0.57-3 .27) (2.6-14.0) CO. 19-1. 32) (9.4-51.5) 2 1.2 1. 08 4 . 57 0.37 5.86 (0 .4-1.9) (0.42-2 .21) (1.6-7.3) (0. 11-0. 63) (5.8-26.7) 3 2.6 2 ; 90 9.73 0. 89 35.3 (2 .1-3.1) (1.60-5 .03) (6.9-12.1) (0. 65-1. 08) (29.9-42.2) YIELD - KEY VARIABLES C L A S S 1 2 3 \ DM FP DM FP 1 FCa DE FCa DE FN FN GRN \ DM FP 2 RED FCa DE IR \ FN GRN' GRN \ 3 RED RED IR IR PIXEL VALUES F i g u r e 24. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g key v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l u s t e r c l a s s e s ( C l u s t e r i n g c a r r i e d out u s i n g IR and IR with green and r e d dye l a y e r s , p=0.05). The a b i l i t y of these groups to d i f f e r e n t i a t e between other biophysical properties (Fig. 25) i s not so consistent. In an e f f o r t to ascertain the u t i l i t y of the imagery to d i f f e r e n t i a t e s o i l properties, several further c l u s t e r groupings were made using various dye layer combinations. The r e s u l t s of s i g n i f i c a n c e t e s t s for the best dye layer combination are shown i n the lower portion of F i g . 25. The r e s u l t s indicate that c l u s t e r i n g on a l l 3 dye layers produces the best d i f f e r e n t i a t i o n between classes for the greatest number of properties. As green i s the dye layer l e a s t responsive to canopy variables i t may be responsible for i l l u s t r a t i n g the differences i n underlying s o i l properties. I t also indicates that the vegetation, at t h i s stage of the growing season, i s providing an i n d i r e c t i n d i c a t i o n of the f e r t i l i t y and water retentive c h a r a c t e r i s t i c s of the underlying s o i l . The data from the key variable t r a i n i n g set were then used to c l a s s i f y the e n t i r e image. This was accomplished by using the mean p i x e l value for the infrared layer as input to a supervised c l a s s i f i c a t i o n . This provided a s p a t i a l c l a s s i f i c a t i o n of the image which c l e a r l y i l l u s t r a t e d the d i s t r i b u t i o n of the classes. Figure 26 (a) and (b) show the o r i g i n a l c l a s s i f i c a t i o n and the e f f e c t of f i l t e r i n g the images respectively. The f i l t e r i n g process assigns the most common value found within a moving window of s p e c i f i e d dimensions. 92 I R CLASS 1 2 3 •1 \ P 0.03MPa K ASWC1 PSC2 pH AWSC1 N AWSC 2 Mg ASWCERD 2 pH Mg2 K K2 Na Na2 pH2 1.5MPa4 \ \ \. \ \ pH PSC2 P 1.5MPa2 Ca K 3 pH CEC Ca AWSC1 Na AWSC2 TEB AWSC4 pH CEC Ca AWSC2 K PSC2 BS 1.5MPa1 TEB1.5MPa2 G R N R E D I R F i g u r e 25. R e s u l t s of Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g a c c e s s o r y b i o p h y s i c a l v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between c l u s t e r c l a s s e s ( C l u s t e r i n g c a r r i e d out u s i n g IR and IR with green and red dye l a y e r s , p=0.05). 93 (a) (b) Super" v i s e d C 1 a s s i f i c a t i on o f August I n a g e — C l i n 1 Class 2 Class 3 F i g u r e 26. Su p e r v i s e d c l a s s i f i c a t i o n o f August 1987 image showing (a) o r i g i n a l c l a s s i f i c a t i o n and, (b) f i l t e r e d (7x7) image. 94 The a b i l i t y of t h i s technique to be able to display the lo c a t i o n of d i s t i n c t forage production-quality classes i n a rapid manner makes t h i s technique a valuable management t o o l at c e r t a i n times of the year. More importantly, t h i s type of information can be d i r e c t l y entered to a GIS to form an extra dimension to any cartographic modelling undertaken. The u t i l i t y of d i g i t a l imagery as a p r e d i c t i v e t o o l for y i e l d and qu a l i t y prediction was also assessed. The r a t i o of red to in f r a r e d p i x e l values produced c o e f f i c i e n t s of determination ranging from -0.50 to -0.55 f o r key variables on i r r i g a t e d s i t e s and -0.68 for f o l i a r P on dryland s i t e s . Green and red s e n s i t i v e dye layer p i x e l values showed s i g n i f i c a n t , but weaker, rela t i o n s h i p s with y i e l d and a l l key f o l i a r elements except Ca. Multiple stepwise regression could not explain a large proportion of the variance when predicting key variables (Table 18) . Infrared and red-infrared r a t i o s were consistently used as regression variables but equations do not account for 50% of the variance for eithe r treatment. S l i g h t l y stronger relationships were found on dryland s i t e s . Combining data from the two treatments markedly improved pre d i c t i v e a b i l i t y . 95 Table 18. Linear and multiple regression models for predicting y i e l d and key variables from remote sensing data. Pi x e l brightness values r 2 S.E. Dryland DM4 = -0.6 + 0.03(IR) -6(RIR) 0. 36 0 .27 FN4 = 3.8 + 0.14(IR) - 23(RIR) 0. 37 1 .03 DE4 = -10 + 0.5(IR) - 86(RIR) 0. 37 3 .86 Ir r i g a t e d DM4 = 7.4 - 7(RIR) 0. 28 0 .44 FN4 = 24 - 20(RIR) 0. 22 1 .45 DE4 = 90 - 81(RIR) 0. 25 5 .41 Combined Treatments DM4 = -8.3 + 0.1(GRN) - 0.2(RED) + 0.07(IR) 0. 68 0 .45 FP4 = 1.2 + 8.4 X 10"3 (GRN) - 1.3 (RIR) -6.6 x 10"3 (IR) 0. 32 0 . 02 DE4 = -113 + 1.4 (GRN) - 2 (RED) + 0.9 (IR) 0. 69 5 .78 Spectral reflectance DM4 = -1.4 + 33.6(RIR5) - 1.2(670-nm) + 0. 61 0 .62 0.08(1050-nm) FCa4 = -1.8 + 39.2(RIR5) - 1.4(670-nm) + 0. 63 0 .69 0.09(1050-nm) FN4 = 1.04 - 1.5(670-nm) + 0.17(1050-nm) 0. 55 2 .43 FP4 = -0.5 + 12.1(RIR5) - 0.45(670-nm) + 0. 67 0 .21 0.03(1050-nm) FP4 = 0.36 - 1.06(RIR5)7 0. 48 0. .03 DE4 = -16.3 + 433(RIR5) - 15.6(670-nm) + 1.04(1050-nm) 0. 60 8 .31 Unweighted 96 5.3 Spectral reflectance techniques Immediately p r i o r to the fourth forage cut i n August 1987 spectral reflectance readings were obtained f o r 30 randomly selected s i t e s using a Barringer HHRR Mark II radiometer. Readings were taken using a series of preset f i l t e r s i n the radiometer over the following wavelengths: 550, 630, 870, 900, 1050, 1200, 1600, 2200, and 725-nm respectively. The 725-nm f i l t e r has a f i l t e r width of +/~ 2-nm and a l l other f i l t e r s have +/- 10-nm wavelength width. The radiometer was mounted on a tr i p o d approximately 1.5 meters above the vegetation surface, and sampling was ca r r i e d out between 1030 and 1430 hours under calm, c l e a r sky conditions. The instrument measures the r e l a t i v e radiance l e v e l s of the selected target within a few seconds f o r each wavelength. A l l measurements were recorded manually i n the f i e l d . C a l i b r a t i o n measurements were ca r r i e d out at each s i t e to ensure the same weather and l i g h t conditions as the t e s t measurements using a barium sulphate reference (Robinson and Biehl, 1979). In addition to i n d i v i d u a l wavelengths, the following red/IR reflectance r a t i o s were also used to examine the data; 63 0/870, 630/1050, 630/725, 670/870, 670/1050, and 670/725-nm, referred to subsequently as RIR1 to RIR6 respectively. 5 . 4 Spectral reflectance r e s u l t s Spectral reflectance measurements produced varying c o e f f i c i e n t s of determination with key variables depending on whether the data was weighted. Weighted variables produced higher c o e f f i c i e n t s for a l l key variables over a wider range of wavelengths which i s understandable given the close r e l a t i o n s h i p between green biomass and r e f l e c t i o n i n t h i s area of the spectrum. The only exception was P which showed better c o r r e l a t i o n s with unweighted data. The red and NIR portions of the spectrum and t h e i r r a t i o s seemed equally useful, producing r values between -0.54 and 0.62 with key variab l e s . Multiple regression of weighted key variables with spectral reflectance readings from combined treatments, demonstrated that two d i s t i n c t wavelengths and t h e i r r a t i o have consistently good p r e d i c t i v e a b i l i t y ; red; 670-nm, NIR; 1050-nm and, 670/1050-nm, (Table 18) . This confirms s i m i l a r r e l a t i o n s h i p s developed by others for biomass production (Bedard and Lapointe 1987, Hardisky et a l . 1984, Waller et a l . 1981), although the rela t i o n s h i p s reported here are closer to those given by king et a l . (1986). They regressed green herbage mass with 660/730-nm reflectance and found r 2 values of 0.67 for August cuts and 0.42 for June cuts. Richardson et a l . (1983) , had much greater success i n determining f o l i a r N content, reporting r 2 values of 0.84 from l i n e a r regression of A l i c i a grass N content with NIR (760 to 900-nm) reflectance. Strong relationships have also been reported using the same wavelengths under laboratory conditions (Norris et a l . 1976). Multiple l i n e a r regression of NIR with N produced r 2 values of 0.98. The removal of "background noise" associated with f i e l d conditions, and the 98 uniform nature of chopped laboratory t e s t samples, probably accounts for a large part of the improvement. The u t i l i t y of simple l i n e a r models to predict forage y i e l d and q u a l i t y i s unclear. Many workers have found relationships with l e a f area index (LAI) to be non-linear (Asrar et a l . 1984), or non-linear above c r i t i c a l LAI's (king et a l . 1986), while others report l i n e a r relationships (Pearson et a l . 1976). Most agree however that the degree of non-linearity i s associated with f o l i a g e angle, LAI and the proportion of bare ground v i s i b l e . The amount of senescent material i n the canopy may also have some e f f e c t (Asrar et a l . 1984). king et a l . (1986) also suggest that on grazed ryegrass pastures 660/730-nm reflectance measurements could not be used above LAI 3-4, which corresponded to about 2t DM ha"1. Spectral data were also used to explore possible r e l a t i o n s h i p s with other biophysical variables which may be observed d i r e c t l y i n areas not vegetated or, may be expressed i n d i r e c t l y v i a canopy spectral reflectance. Stoner et a l . (1980) used laboratory spectral data to investigate the r e l a t i o n s h i p s between s o i l reflectance and s o i l properties. They observed a high pr e d i c t i v e r e l a t i o n s h i p between spectral values and s o i l properties i n the 520 to 1750-nm wavelength range. Crouse et a l . (1983) report that Landsat Thematic Mapper near and middle infrared bands (760-900-nm, 1550-1750-nm and 1040-1250-nm), produced the best relationships with s o i l physical and chemical properties. Reported regression equations had r 2 values ranging from 0.47 for organic carbon to 0.05 for exchangeable sodium. Table 19 shows a s e l e c t i o n of some of the more notable correlations and regressions with biophysical variables for the two treatments. Fewer rela t i o n s h i p s can be seen on the i r r i g a t e d treatment where canopy closure i s more complete and fewer wavelengths are useful i n e s t a b l i s h i n g relationships with the biophysical v a r i a b l e s . On dryland s i t e s , where canopy conditions vary from almost complete closure to very l i t t l e green biomass (on sandy ridge c r e s t s ) , the 630/870-nm r a t i o provided the best c o r r e l a t i o n s . These r e s u l t s are s i m i l a r to those reported by Schreier et a l . (1988), who note good relationships between bare s o i l s and r e f l e c t i o n i n the 510 to 870-nm range. They also report that the best relationships were obtained when s o i l s were dry, which i s also consistent with t h i s study. Topsoil moisture contents at the time measurements were made, were on average, 30% lower on dryland s i t e s and i n extreme cases, up to 90% lower. 100 Table 19. Correlation c o e f f i c i e n t s (a) and, regression equations (b) from spectral reflectance measurements with selected s o i l physical and chemical properties from 0 to 25-cm. (a) Wavelength nm property 630 670 2200 RIR1 RIR2 RIR3 RIR4 RIR5 RIR6 Dryland pH -0. ,64 -0. .55 -0. .64 -0. .68 -0/ 65 CEC -0. ,64 -0. ,51 -0. .64 -0. .77 -0. .76 -0. 69 -0. ,67 -0. , 56 N -0. ,62 -0. ,58 -0. .71 -0. ,70 -0. .59 -0. 69 -0. ,67 -0. ,54 AWSC -0. ,68 -0. .58 -0. .74 -0. .84 -0. .80 -0. 71 -0. .75 -0. ,64 PSC -0. ,74 -0. .73 -0. .74 -0. .77 -0. .71 -0. 80 -0. .72 -0. ,63 -0. .66 0.03-MPa -0. ,70 -0. .63 -0. ,76 -0. .86 -0. .79 -0. 76 -0. .79 -0. ,67 -0. .56 1.5-MPa -0. ,67 -0. .64 -0. .73 -0. .82 -0. .73 -0. 77 -0. .77 -0. ,65 -0, .61 Wavelength nm property 630 1200 1600 RIR2 RIR3 RIR4 Ir r i g a t e d PH PSC 0.03-MPa 1.5-MPa -0.78 -0.59 0.69 0.55 -0.69 -0.74 -0.75 -0.59 -0.55 -0.63 (b) Dryland r 2 S.E. N-1 = 0.37 - 5.1 x 10"3 (2200-nm) - 0.47(RIR3) 0.64 0.04 CEC-1 = 25.6 - 65(RIR1) 0.54 4.54 AWSC-1 = 91 - 0.43(870-nm) - 20.7(RIR1) 0.79 5.91 0.03-MPa-l = 64 - 0.39(1200-nm) - 132(RIR1) 0.87 3.29 1.5-MPa-l = 28 - 0.19(1200-nm) - 60(RIR1) 0.82 1.77 Spectral methods of biomass estimation are se n s i t i v e to the amount of green leaf biomass present. The 500 to 700-nm wavelength region corresponds to the i n vivo red region of chlorophyll absorption and i s inversely related to the 101 chlorophyll density. The 750 to 1100-nm region produces reflectances proportional to the green l e a f density (Tucker 1980). Ratio combinations of these two wavelength regions thus contains information related to the chlorophyll-green leaf i n t e r a c t i o n . The use of these data to estimate forage biomass has been reported widely (Colwell 1974, Maxwell 1976, Pearson et a l . 1976). Red and infrared spectral data are highly s e n s i t i v e to projected green l e a f area index and t h e i r u t i l i t y i n assessing biomass i s t i e d to the r e l a t i o n s h i p of the green l e a f area index to the biomass for the cover type i n question. Thus spectral data are not always related to crop biomass at a given point i n time (Tucker 1980). To determine i f spectral reflectance data could d i s t i n g u i s h various y i e l d - q u a l i t y differences, c l u s t e r analysis was applied to the data. I n i t i a l c l u s t e r i n g had shown that 3 s i t e s were d i s t i n c t l y d i f f e r e n t from a l l others and were consistently j o i n i n g very l a t e i n the sequence as o u t l i e r s . Examination of t h e i r spectral c h a r a c t e r i s t i c s revealed they were s i g n i f i c a n t l y d i f f e r e n t at 630, 670 and 220-nm, (probably due to a larger proportion of dead material and bare ground present), and they were removed from subsequent analysis. Haggar and Isaac (1985) demonstrated that a s i m i l a r technique could also be used to d i f f e r e n t i a t e between green vegetation and bare ground. Several wavelength combinations were t r i e d and the f i n a l combination used for c l u s t e r i n g was 670, 1050, 2200-nm and RIR3 and RIR6 102 r a t i o s . This combination of wavelengths could r e a d i l y detect the difference between dryland (sites 3 to 76) and i r r i g a t e d ( s i t e s 82 to 112) treatments but, subdivisions within treatments produced few s i g n i f i c a n t differences, for biophysical properties (Fig. 27). Although the spectral method can s u f f e r from environmental v a r i a b i l i t y i n small-scale applications, i t has shown for cut 4 i n 1987, i t i s a useful predictor of y i e l d and some key variables f o r standing forage. Clustering spectral wavelengths enables d i f f e r e n t i a t i o n of i r r i g a t e d and dryland s i t e s and s i t e s with l i t t l e green biomass were also delineated using t h i s technique. While there are obvious problems associated with deriving c a l i b r a t i o n s over a whole growing season, for s p e c i f i c cuts the method may y i e l d more precise information about c e r t a i n vegetation c h a r a c t e r i s t i c s than conventional f i e l d sampling methods (Clevers and Horton 1986). \ 103 . Site _ 3 _ 9 - 10 _ 56 69 _ 1.2 _ U __22 _ 3 6 _ 6 6 _ 7 4 _ 7 6 _ 82 _ 83 85 _ 8 6 _ 88 _ 91 _ 96 _ 1 0 2 _ 1 0 5 _ 1 1 2 Figure 27. Dendrogram from c l u s t e r analysis using 670, 1050, 2200-nm and RIR3 and RIR6 reflectance r a t i o s showing the c l e a r d i f f e r e n t i a t i o n of i r r i g a t e d versus dryland s i t e s when two c l u s t e r groups are chosen. 104 CHAPTER SIX GEOGRAPHIC INFORMATION SYSTEM AND MODEL VALIDATION 6.1 Geographic Information System Techniques The GIS for data storage, manipulation and display was pMAP (SIS 1986), a raster-based 8 system for micro-computer use (see also section 1.3.5). Because pMAP i s designed around a "map algebra', the program enables a user to interrogate the data base using language very s i m i l a r to that used for normal communication. For example SLICE ELEVATION INTO 15 FOR CONTOURS converts a d i g i t a l elevation model on the overlay "ELEVATION' into a contour map ("CONTOURS1), with 15 evenly spaced contour i n t e r v a l s , or RENUMBER SOIL.MAP FOR CROP.SUIT recodes the overlay 'SOIL.MAP1 into a new overlay 'CROP.SUIT', based on reassigning classes on the basis of s u i t a b i l i t y for the crop i n question. By l i n k i n g the various commands into a sequence i t i s possible to create an unlimited number of maps to tackle a n a l y t i c a l problems i n s p a t i a l information processing. These Uses a g r i d c e l l data structure for input and processing. 105 command sequences are referred to as "cartographic models' (SIS 1986). By developing c l e a r cartographic models i t i s possible to i d e n t i f y the primary variables needed to solve a p a r t i c u l a r problem. Exploring relationships within a complex data base i s a more d i f f i c u l t issue which sometimes requires the s i m p l i f i c a t i o n of input parameters using multivariate s t a t i s t i c a l techniques. The use of s t a t i s t i c a l techniques can often h i g h l i g h t s i g n i f i c a n t but non-linear r e l a t i o n s h i p s which can be handled using categoric techniques i n the GIS. The l a t t e r are generally simpler and often r e t a i n p r e d i c t i v e c a p a b i l i t y . Because in d i v i d u a l s tend to c o l l e c t or describe features i n d i f f e r e n t ways, i t i s sometimes prudent to s c r u t i n i z e data p r i o r to GIS analysis. Brinkman and Stein (1987) suggest that the use of c o r r e l a t i o n and covariance can be used i n order to gain some insight i n the dependence structure of the data, and that these procedures usually demonstrate there i s considerable redundancy i n the storage of some raw data. Multivariate techniques such as PCA and DA perform s i m i l a r functions and generally r e s u l t i n s i g n i f i c a n t reductions i n data s i z e . Burrough (1986) outlines some useful guidelines as to when data should be c l a s s i f i e d before GIS analysis begins. A s i m i l a r approach to that outlined above was adopted i n t h i s study i n order to reduce the large number of po t e n t i a l map overlays. 106 6.2 S p a t i a l S t a t i s t i c s S p a t i a l s t a t i s t i c s seek to characterize the geographic d i s t r i b u t i o n , or pattern of mapped data. They describe the s p a t i a l v a r i a t i o n i n the data, rather than assuming a t y p i c a l value to be uniformly d i s t r i b u t e d i n space. Some simple examples of how s p a t i a l s t a t i s t i c a l analysis can be conducted using GIS are shown i n Figs. 28 and 29. Figure 28 shows f i e l d data from two consecutive forage cuts on i r r i g a t e d s i t e s i n 1987. The tabular data i d e n t i f y the lo c a t i o n for each sampling s i t e and the dry matter c o l l e c t e d . T r a d i t i o n a l s t a t i s t i c a l analysis involves f i t t i n g a numerical d i s t r i b u t i o n to the data to determine the t y p i c a l response. The s t a t i s t i c s at the bottom of the table show the average y i e l d as 3.7 t/ha i n cut 2 and 2.1 t/ha i n cut 3. These parameters describe the central tendency of the data i n numerical data space and are assumed uniformly d i s t r i b u t e d i n geographic space. 7 1 0 7 S i t e Cut 2 Cut 3 F i g u r e 28. S p a t i a l l y c h a r a c t e r i z i n g data v a r i a t i o n ( i n s e t (a) shows p o i n t data f o r c u t 2 and i n s e t (b) shows the same data a f t e r k r i g i n g ) . 108 Inset (a) shows cut 2 data i n a manner which incorporates l o c a t i o n a l information. Analogous to t r a d i t i o n a l s t a t i s t i c s , a density function i s f i t t e d to the data. Thus, the data i s characterized i n geographic space rather than numerical space. A continuous surface can be f i t t e d through the data points, i n t h i s case v i a k r i g i n g (inset (b)). The d i s t r i b u t i o n shown i n inset b shows considerable deviation from the average of 3.7 t / ha implied by t r a d i t i o n a l s t a t i s t i c a l analysis. Figure 29 depicts a s p a t i a l analysis between the two data sets. The two d i s t r i b u t i o n s are compared (that i s , one i s subtracted from the other) to generate a coincidence surface showing the percentage change i n y i e l d from cut 2 to cut 3. With t h i s output area with marked decreases i n y i e l d can be i s o l a t e d and the data base queried as to possible causes. 109 F i g u r e 29. A s s e s s i n g c o i n c i d e n c e among mapped data. 110 6.3 Cartographic Modelling The sequential processing of GIS overlays enables users to perform a wide range of map analyses (Berry 1987b, Berry and Reed 1987). The l o g i c a l sequencing of map processing involves; - r e t r i e v a l of one or more maps from the data base; - processing of those data as required by the user; - creation of a new map containing the r e s u l t s ; and - storage of the new map for subsequent manipulation. This c y c l i c a l processing provides an extremely f l e x i b l e structure s i m i l a r to "evaluating nested parentheticals" i n t r a d i t i o n a l algebra (Berry 1987a,c). A simple example using these techniques i s outlined i n Fig . 30. The flowchart i n the upper l e f t portion of the figure shows the maps as boxes and processing operations are indicated as l i n e s . The model uses cut 2 and 3 data from the dryland treatment i n 1987 to create a map of the percent change i n y i e l d (inset (a)). The contour map was derived from a d i g i t a l elevation model to show the s p a t i a l d i s t r i b u t i o n of higher, generally coarser textured s i t e s versus lower, f i n e r textured s i t e s . A tabular summary of the coincidence between these two maps i s shown i n inset (b). The s p a t i a l analysis indicates that the largest decreases i n y i e l d are associated with the two higher elevation classes. The lowest decreases or s l i g h t increases i n y i e l d are associated with the lower two elevation classes. The same information i s presented as a planimetric map i n Fig . 31 i s o l a t i n g those areas with greater than 75% reduction i n y i e l d between the two cuts. I N T E R P O L A T E 1 1 1 (b) C o i n c i d e n c e t a b l e f o r Map 1 = % CHANGE w i t h Map 2 = CONTOUR Map •1 # L a b e l Map 2 # L a b e l # . % o f c e l l s c e l l s c r o s s t o t a l 1 135 - 1 2 0 t o -75% 1 309 0 t o 65 cm 16 1.2 1 135 II 2 679 66 t o 131 cm 55 4 . 2 1 135 II 3 321 132 t o 19 6 cm 64 4 . 9 2 606 - 7 4 t o -30% 1 309 0 t o 65 cm 105 8 . 0 2 606 it 2 679 66 t o 131 cm 312 23 . 8 2 606 II 3 321 132 t o 19 6 cm 189 14 . 4 3 568 - 2 9 t o +13% 1 3 09 0 t o 65 cm 188 14 . 4 3 568 II 2 679 66 t o 131 cm 312 23 . 8 3 568 II 3 321 132 t o 19 6 cm 68 5 .2 F i g u r e 3 0 . A s i m p l e c a r t o g r a p h i c m o d e l . 1 J S c a l e : 5.0 meters Per C e l l F i g u r e 31. P l a n i m e t r i c map showing areas with g r e a t e r than 75% r e d u c t i o n i n y i e l d between cut 2 and 3. 113 6 . 4 GIS Applications The preceding examples demonstrate that GIS enables a broad range of s p a t i a l map analysis to be c a r r i e d out. In order to gain some understanding of the way i n which production varies within and between cuts, each cut was subdivided into production categories 9 and coincidence s t a t i s t i c s determined. The aim was to t r y and i d e n t i f y s i t e s which consistently produced at high or low l e v e l s . Table 20 shows these coincidence s t a t i s t i c s for the two years. Table 20. Sp a t i a l coincidence s t a t i s t i c s between dryland s i t e s i n 1986 and 1987 showing s i t e s which remain constantly low, high or show increases or decreases i n production between consecutive cuts. 1986 Cut 1987 Cut 1-2 % 2-3 % 3-4 % 4-5 % 1-2 % 2-3 % 3-4 % 4-5 % remain low 1 4 2 5 9 2 2 6 remain high 1 5 2 8 4 0 2 5 increase 25 29 26 10 12 37 12 14 decrease 28 19 25 26 36 18 26 22 The production range was divided into three equal i n t e r v a l s to represent low, medium and high production categories. 114 The data c l e a r l y i l l u s t r a t e the dynamic nature of production fo r the study area over the two years. At no time was more than 10% of the area consistently producing at high or low production. Approximately 40-50% of the area consistently yielded within the medium production category on a consistent basis, while the balance showed either increases or decreases i n production between consecutive cuts. The e f f e c t s of in d i v i d u a l biophysical variables can also be assessed using s i m i l a r techniques, and Table 21 shows coincidence s t a t i s t i c s for high production areas i n r e l a t i o n to AWSCERD for 1987 dryland s i t e s . The table shows that the greatest proportion of high y i e l d i n g s i t e s also have higher AWSC le v e l s within the e f f e c t i v e rooting depth. This type of f l e x i b i l i t y demonstrates the ease with which s p a t i a l r e l a t i o n s h i p s can be explored between any number of var i a b l e s . Table 21. Spa t i a l coincidence s t a t i s t i c s showing the rel a t i o n s h i p between high production areas (dryland s i t e s , 1987) and a v a i l a b l e water storage capacity within the e f f e c t i v e rooting depth. Y i e l d AWSCERD mm % of Cross Cut 4 HIGH 73-130 3.4 HIGH 131-187 12.3 HIGH 188-243 9.3 Cut 5 HIGH 73-130 4.7 HIGH 131-187 18.5 HIGH 188-243 16.4 115 The u t i l i t y of d i g i t a l imagery was also assessed using the GIS by deriving y i e l d predictions for each g r i d c e l l on the d i g i t a l elevation model v i a the regression equation developed for the combined treatments at cut 4 1987. This entailed averaging 625 of the o r i g i n a l 0.2 x 0.2m p i x e l s to produce 5 x 5m p i x e l values for each dye layer and then combining the separate overlays i n the GIS using the regression equation. Figure 32 shows the accuracy of the predictions using the v a l i d a t i o n data set. A t o t a l of 57% of s i t e s were c o r r e c t l y predicted and a l l others were within 0.1 t/ha of the measured value. To assess the relationships between elevation and y i e l d derived from the imagery, these two maps were categorised and then o v e r l a i d . The r e s u l t i n g display i s presented i n Fig . 3 3 and shows the d i s t r i b u t i o n of y i e l d classes i n r e l a t i o n to low elevation (0 to 101cm) and high elevation (102 to 2 04cm) s i t e s . The dominance of map value 12 and 25 (high yield) over the r i g h t t h i r d of the map c l e a r l y demarcates the i r r i g a t e d treatment from the dryland, and some appreciation can.be gained of the r e l a t i v e contribution from each elevation c l a s s . Within the dryland treatment the narrow horizontal s t r i a t i o n s associated with map value 16 correspond to the higher, droughtier s i t e s on ridges with corresponding low y i e l d s for t h i s time of the growing season. Map value 8 corresponds with hollows and the areas immediately adjacent to them, while map value 22 corresponds to sideslope areas mostly associated with ridges. 116 Predic ted o 1 2 Actual 4 F i g u r e 32. V a l i d a t i o n of y i e l d as p r e d i c t e d from m u l t i p l e r e g r e s s i o n w i t h 5 x 5m p i x e l v a l u e s u s i n g GIS. 117 E L E V - Y I E L D Scale: 5.0 meters Per C e l l Symbol Value Label tf C e l l s % Map +++ 3 LOW ELV X LOW YIELD 112 5.19 = 8 LOW ELV X MEDIUM YIELD 592 27.46 C C D 12 LOW ELV X HIGH YIELD 420 19.48 16 HIGH ELV X LOW YIELD 108 5.01 W V 22 HIGH ELV X MEDIUM YIELD 585 27.13 eeo 25 HIGH ELV X HIGH YIELD 339 15.72 Total No. of C e l l s = 2156 F i g u r e 33. P l a n i m e t r i c map showing a GIS o v e r l a y of two e l e v a t i o n c l a s s e s with t h r e e y i e l d , c l a s s e s (the i r r i g a t e d , treatment i s shown on the r i g h t ) . 118 The a b i l i t y of GIS to produce s p a t i a l l y r e l i a b l e r e s u l t s i n a manner which i s r e a d i l y updatable as new scenarios are explored or as models become more refined serves to highlight the u t i l i t y of such systems i n land management and modelling. As an o v e r a l l integrative t o o l the u t i l i t y of the GIS was assessed using a combination of variables derived from a va r i e t y of sources. As demonstrated above p i x e l values from d i g i t a l imagery can be evaluated s p a t i a l l y using a regression equation to p r edict y i e l d as well as other forage v a r i a b l e s . Forage qu a l i t y was evaluated using the DEM and 1.5-MPa water retention i n t o p s o i l s . The aim was to t e s t whether these "permanent" c h a r a c t e r i s t i c s , which are r e l a t i v e l y easy to measure or can be derived from e x i s t i n g published sources, could be used i n a p r e d i c t i v e manner. Both elevation and water retention have been shown to be related to y i e l d and q u a l i t y of forage v i a l i n e a r and multivariate techniques. The procedure used involved overlaying maps of elevation (3 cl a s s e s ) , with 1.5-MPa values (3 cla s s e s ) , to produce a categoric c l a s s i f i c a t i o n with a possible 9 classes. The r e s u l t i n g overlay produced 8 categories, with only 6 of the 8 classes occurring at sampling s i t e s . Class differences between y i e l d and key variables were assessed using s i g n i f i c a n c e tests and r e s u l t s are presented for cut 4 and 5 i n F i g . 34. Dige s t i b l e energy i s s i g n i f i c a n t l y d i f f e r e n t between most classes while the pattern for other key variables and y i e l d i s le s s c l e a r . The c l a s s i f i c a t i o n i s of l i m i t e d u t i l i t y for f o l i a r 119 N which appears unrelated to water retention or elevation. Figure 34 does show that the c l a s s i f i c a t i o n i s able to delineate between low elevation s i t e s with high 1.5-MPa values, and high elevation s i t e s with low 1.5-MPa values (class 3 and 7 r e s p e c t i v e l y ) . The r e s u l t s indicate that not a l l classes from the GIS overlay are s i g n i f i c a n t l y d i f f e r e n t . For example, class 4 and 6 were poorly discriminated and few variables were d i f f e r e n t between classes 3 and 5, 4 and 7, 5 and 6, and 6 and 7. In these cases the p r e d i c t i v e a b i l i t y of the GIS i s l i m i t e d and classes may need to be combined to make d e f i n i t e statements about key v a r i a b l e differences. In addition the small s i z e of the v a l i d a t i o n set (30) compared to the number of GIS classes used (6) meant that some classes had less than 3 members, thus n u l l i f y i n g the v a l i d i t y of the s i g n i f i c a n c e t e s t . Y i e l d and f o l i a r data from the o r i g i n a l sampling s i t e s were c l a s s i f i e d using the GIS categories and the class l i m i t s for these s i t e s determined. The u t i l i t y of the GIS overlay was then assessed by comparing the GIS c l a s s i f i c a t i o n for the v a l i d a t i o n data set with the class l i m i t s derived from the o r i g i n a l sampling s i t e s . The r e s u l t s are presented i n Table 22 and show that of the four variables which occur i n F i g . 34 the p r e d i c t i v e accuracy of the GIS overlay i s quite high, p a r t i c u l a r l y i n cut 5. This improvement i s not unexpected given that elevation and 1.5-MPa water retention are more c r i t i c a l to plant growth and q u a l i t y at t h i s time. 120 F i g u r e 34. R e s u l t s o f Mann-Whitney U s i g n i f i c a n c e t e s t i n d i c a t i n g v a r i a b l e s t h a t were s i g n i f i c a n t l y d i f f e r e n t between G I S c l a s s e s ( G I S c l a s s e s d e r i v e d from o v e r l a y s o f e l e v a t i o n and 1.5-MPa wa t e r r e t e n t i o n (0-25cm), p=0.10). 121 Table 22. Percentage of v a l i d a t i o n s i t e s c o r r e c t l y predicted for key variables from GIS overlay using elevation and 1.5-MPa water retention. Cut 4 DM Ca P DE Cut 5 DM Ca P DE % % % % % % % % 60 65 90 50 75 85 80 90 6 . 5 Model V a l i d a t i o n V a l i d a t i o n of l i n e a r models i s shown i n Figs. 3 6 and 37. The best regression models for the predi c t i o n of dry matter, f o l i a r N and P and d i g e s t i b l e energy were obtained using remote sensing techniques and f o l i a r elements. As with the d i g i t a l imagery data p r e d i c t i v e s t a t i s t i c a l models can also be s p a t i a l l y evaluated. For example, s p a t i a l p r e d i c t i o n of f o l i a r N for cut 4 1987 can be derived by evaluating the regression model ( r 2 = 0.97) that considers f o l i a r Mg and f o l i a r K (Fig. 38) . The map has been smoothed for presentation but s p a t i a l v a l i d a t i o n of the o r i g i n a l point data shows that i t i s only 25% accurate and that many s i t e s are overestimated by as much as 60%. This contrasts sharply with the t r a d i t i o n a l method of v a l i d a t i o n as shown i n F i g . 37. 122 Predicted DM (T/ha) Cut4 1887 A A A AA AA A A A A A 2 Actual 14 Predicted 12H 10 8 8 4 2 0 Foliar N (*) Cut4 1987 A A A A A A A A A A A A A A A ^ A A A A A A • A 0 2 4 6 8 10 12 14 Actual 1.4 1.2 0.8 0.6 0.4 0.2 Predicted Foliar P (*) OuU 1887 A A A A A A A A A A A .A A A A A A — I 1 1 1 1 1 0.2 0.4 0.6 0.8 1 1.2 1.4 Actual 60 Predicted 40 30 20 H 10 DE (M]/kg DM) Cut4 1887 A . A A AA A A A A 10 — I 1— 20 30 Actual — i — 40 60 F i g u r e 3 5 . V a l i d a t i o n of y i e l d , f o l i a r N and P and d i g e s t i b l e energy as p r e d i c t e d v i a r e g r e s s i o n with s p e c t r a l r e f l e c t a n c e data. 123 Predicted 5 -1 H Foliar N [%) + + • • • • + • + + + + + + • + • + • + • • • • Predicted + From GIS i i ) 1 2 i 3 i 4 i 5 r 6 7 Actual F i g u r e 36. V a l i d a t i o n of f o l i a r N as p r e d i c t e d v i a r e g r e s s i o n with f o l i a r K and Mg. FN487 Scale: 5.0 meters Per C e l l Symbol Va 1 ue Label # C e l l s % Map 3 1.6 - 2. 8 PERCENT N 19 1 .45 8 2.9 - 4. 1 PERCENT N 244 18 .64 10 4.2 - 5. 4 PERCENT N 798 60 .96 14 5.5 - 6. 7 PERCENT N 236' 18 .03 -a-a-a 16 6.8 - 7. 9 PERCENT N 12 0 .92 Total No. of C e l l s = 1309 • F i g u r e 37. P l a n i m e t r i c map of f o l i a r N d i s t r i b u t i o n d e r i v e d from GIS ( d r y l a n d o n l y ) . 125 6.6 Economic Implications Most farm managers are well aware of production v a r i a b i l i t y within i n d i v i d u a l f i e l d s . Fewer are aware of the p o t e n t i a l changes i n forage q u a l i t y which may accompany the former. Higher y i e l d i n g s i t e s are not always synonymous with high q u a l i t y . Changing farm management techniques (e.g., Wilkens 1986, Putt 1987) , now make i t more fe a s i b l e to explore the p o s s i b i l i t i e s of handling each forage cut and forage derived from d i f f e r e n t management techniques ( i . e . i r r i g a t e d versus dryland) separately. The recent a r r i v a l of Ag-bags 1 0, for example, has meant that i n d i v i d u a l forage cuts can be stored and fed separately depending on the qu a l i t y of the feed and the n u t r i t i o n a l requirements of the target animal. The preceding discussion has shown that there are s i g n i f i c a n t differences i n y i e l d and qu a l i t y of forage cuts within a p a r t i c u l a r year and between treatments and that the seasonal v a r i a t i o n i s generally higher than the s p a t i a l v a r i a t i o n . The next l o g i c a l step i s to determine how these differences may a f f e c t the n u t r i t i o n a l requirements of a l a c t a t i n g dairy cow, and how a farm manager might attempt to optimize forage production and costs over the growing season. The evaluation was ca r r i e d out using "Ration Optimizer", a l i n e a r , l e a s t cost (feed cost per cow per day), r a t i o n balancing program developed by the East Chilliwack A g r i c u l t u r a l "Tube-shaped" p l a s t i c bags used for e n s i l i n g forages, each holding approximately 150 tonnes. 126 Cooperative. Nutrient requirements are s p e c i f i e d according to the dairy cow's body weight, milk production and milk f a t percentage. Up to 10 d i f f e r e n t feeds can be selected from a l i b r a r y of 50 feeds (concentrates, forages and minerals), and the l e a s t cost optimum combination of feeds i s calculated. Optimum rations are lea s t costed based on 5 major nutrient requirements including crude protein (CP), t o t a l d i g e s t i b l e nutrients (TDN), ADF, and f o l i a r Ca and P. Provisions are made for maximum l i m i t s f or any or a l l feeds a v a i l a b l e (G. Smith, 1988, personal communication). The model enabled comparisons to be made between i r r i g a t e d and dryland forage, and various s t r a t i f i c a t i o n s of in d i v i d u a l cuts based on; (a) a lea s t cost analysis and, (b) scenarios where costs were standardized and forage was selected on qual i t y alone. Production costs for i n d i v i d u a l forage cuts are approximate only and are presented for comparative purposes. Costs i n the model included annual fixed costs such as land rent a l , f e r t i l i z e r s and harvesting and variable costs such as mowing, raking and i r r i g a t i o n . Costs per cut were held constant for the two years. For the purposes of ratio n optimization the model used a mature l a c t a t i n g cow weighing 635-kg, producing 27-kg of milk per day with 3.8% f a t . Nine inputs to the model were used and the program was forced to select 10-kg of "18% TEX" (a dairy feed concentrate), 2.5-kg of grass hay, and 12-kg of corn s i l a g e 127 per day as part of the animal's basic r a t i o n . These 3 components were regarded as t y p i c a l of the basic d i e t for the above dairy cow. The model was also free to sel e c t as much calcium supplement as was needed. The remaining 5 inputs were various combinations of forage cuts. The model was run to determine which cut i n a p a r t i c u l a r year or which treatment within a given year was optimal, given the above constraints. A f t e r the f i r s t i t e r a t i o n , the cut selected was removed and the model was forced to sel e c t from the remaining cuts. This procedure was repeated u n t i l a l l 5 cuts or treatments had been selected or rejected by the model. The same process was repeated with standardized costs ( i . e . the cost of production f o r each cut was set to the same value) for the 5 forage/treatment inputs, so the model selected forage on qua l i t y alone. An example of i n i t i a l model inputs i s shown i n Table 23. Here the model i s optimizing a r a t i o n based on 5 dryland forage cuts made i n 1987, with cut 3 having been selected a f t e r the f i r s t i t e r a t i o n ( i . e . the cut with the lowest cost per tonne). The preference with which the model selected between dryland and i r r i g a t e d forages i n 1986 and 1987 are shown i n Table 24a and b respectively. Figures i n brackets show the cut selected where costs have been standardized for a l l cuts and model s e l e c t i o n was on a q u a l i t y basis alone. I t i s i n t e r e s t i n g to note that only i n 1986 on dryland plots was cut 1 the cheapest to produce despite t h i s always being the highest y i e l d i n g cut. Yields were very high for t h i s p a r t i c u l a r 128 cut but the forage qua l i t y was low due to the l a t e cut date and lodging of the crop. Subsequently the cheapest source of forage was grown i n cuts 2 or 3 ir r e s p e c t i v e of year or treatment. Table 23. Typical input format for Ration Optimizer model. AMT MIN11 MAX12 DRY CP TDN Ca P ADF PRICE/ FED(kg) (kg) (kg) MATTER TONNE _ $ — (%fed) (— as % of dry matter — ) 18%TEX 10. 0 10. 0 10. 0 88. 0 20. 5 83. 0 1 .10 0. 70 10. 0 185. 00 HAY 2. 5 2. 5 2. 5 88. 0 12. 0 55. 0 0 .45 0. 30 40. 0 100. 00 CORN- 12. 0 12. 0 12. 0 25. 0 8. 7 65. 0 0 .26 0. 22 30. 0 30. 00 SLG LIME 0. 0 0. 0 99. 0 100. 0 0. 0 0. 0 38 . 00 0. 02 0. 0 80. 00 CUT 1 0. 0 0. 0 99. 0 30. 0 20. 4 67. 8 0 .35 0. 30 28. 4 36. 24 2 0. 0 0. 0 99. 0 30. 0 21. 0 62. 3 0 .59 0. 32 32 . 6 32. 79 3 15. 2 0. 0 99. 0 30. 0 22. 0 68. 6 0 . 61 0. 32 27. 9 32. 35 4 0. 0 0. 0 99. 0 30. 0 23. 4 68. 3 0 .92 0. 30 28. 1 41. 77 5 0. 0 0. 0 99. 0 30. 0 24. 8 74. 2 0 .77 0. 29 23 . 5 56. 60 TOTALS 39. 7 18. 6 3. 3 13. 6 0. 142 0. 08 3. 9 $ 2. 95 (wt) RATIOS (Ca/P 1.59)(%dry matter 18.0 73.2 0.77 0.48 21.2) DAILY 1.40 20.0 3.0 13.6 0.65 0.46 21.0 REQUIREMENTS min max wt (. .min wt. .) (.-... min % . . . .) 1 1 Minimum amount the model must s e l e c t when optimizing r a t i o n . 12 Maximum amount model can select when optimizing ration. 129 Table 24. Comparison of preferred cut from i r r i g a t e d and dryland forage treatments i n 1986 and 1987. A. DRYLAND Cut 3 1 2 5 4 (4) (5) (3) (2) (1) 15 1986 $/ Amt fed tonne13cow/day (kg) 1 4 27.66 26.52 27.11 33.13 40.63 2.90 2.93 2.94 2.99 3.10 15.9 17.6 17.6 15.9 15.7 Cut 1987 $/ $/ Amt fed tonne cow/day (kg) 3 (3/5) 2 (4/5) 32.35 32.79 1 (1/5) 36.24 4 (5/2) 41.77 5 1 6 (2) 56.60 2.95 3.01 3.02 3.10 3.59 15.2 16.8 15.4 15.3 B. IRRIGATED 1986 $/ $/ Amt fed Cut tonne cow/day (kg) 2 (4/5) 26.86 2.93 17.5 3 (4) 29.98 2.95 16.3 1 (3) 28.65 2.96 17.5 4 (1/2) 37.34 3.04 15.6 5 1 6 (2) 41.29 3.29 $/ 1987 $/ Amt fed Cut tonne cow/day (kg) 4 (5) 30.86 2.95 16.0 2 (3) 29.62 2.97 17.1 3 (1) 33.51 2.97 15.3 1 (4) 33.30 2.98 15.6 5 (2) 35.82 3.00 15.2 1 3 Cost of production per tonne of forage Amount of forage (kg) fed to animal as part of d a i l y r a t i o n . 1 5 Value i n brackets shows cut chosen when model selects on q u a l i t y alone. Where 2 cuts are chosen that which contributes the greatest quantity to the t o t a l feed i s shown f i r s t and, t h i s cut i s removed for subsequent i t e r a t i o n s . 1 6 No f e a s i b l e solution; model re j e c t s cut f o r not meeting minimum ADF requirements. 130 For dryland s i t e s cut 3 produced the lowest cost/cow/day for both years, while on i r r i g a t e d s i t e s the pattern i s les s clear. Cuts 4 or 5, and cut 5 on the i r r i g a t e d treatment i n 1986, were v always selected l a s t , and i n some cases cut 5 was rejected for f a i l i n g to meet minimum requirements (usually ADF). Production costs of forage are generally lowest i n the f i r s t 3 cuts, with correspondingly lower qua l i t y i r r e s p e c t i v e of treatment. There i s no r e a l "treatment e f f e c t " however, u n t i l a f t e r the t h i r d cut when i r r i g a t i o n commences. The cost of forage grown r i s e s over the growing season i n two ways; (i) y i e l d s are lower (on dryland s i t e s i n pa r t i c u l a r ) , i n l a t e r cuts or, ( i i ) production costs increase on i r r i g a t e d s i t e s as t h i s management practice commences mid season. For t h i s reason cuts 1 to 3 are generally chosen f i r s t with cut 4 or 5 being selected l a s t . When costs are standardized for each cut the trend i s reversed, with l a t e r cuts or combinations of cuts being selected f i r s t . From a management perspective i t i s i n t e r e s t i n g to note how the program views the differences i n forage as a r e s u l t of i r r i g a t i o n (Table 25) . In 1987 cut 1 and 2 are selected from the i r r i g a t e d treatment, as are cut 4 and 5. Early i n the season production i s s l i g h t l y higher on i r r i g a t e d s i t e s for cut 1, thus reducing the cost per tonne of forage. I t i s unclear whether t h i s increased production i s a carry-over e f f e c t from management i n the 1986 growing season or some other f a c t o r ( s ) . By cut 4 and 5 the cost per tonne i s lower on i r r i g a t e d s i t e s as 131 production has markedly declined at many dryland s i t e s while fix e d costs remain constant. In both years the cheapest forage was always selected on the f i r s t i t e r a t i o n , a trend which* was often, but not always, reversed when s e l e c t i o n was based on qual i t y alone. In 1986, 4 of 5 cuts selected on qu a l i t y alone were i r r i g a t e d , a trend which i s completely reversed i n 1987. The se l e c t i o n for cut 3, remains constant i n both years, with dryland s i t e s being preferred under a l l scenarios. Table 25. Comparison of i r r i g a t e d versus dryland forage as selected by Ration Optimizer program. 1986 Treatment $/ $/ Amt fed Cut chosen tonne cow/day (kg) 1 dry ( i r r . ) 1 7 26. 52 (28. 65) 1 8 2.93 17.6 2 i r r . (dry) 26. 86 (27. 11) 2.93 17 .5 3 dry ( i r r . ) 27. 66 (29. 98) 2.90 15.9 4 i r r . ( i r r . ) 37. 34 3.04 15.6 5 dry ( i r r . ) 33. 13 (41. 29) 2.99 15.9 1987 Treatment $/ $/ Amt fed Cut chosen tonne cow/day (kg) 1 i r r . (dry) 33. 30 (36. 24) 2.98 15.6 2 i r r . (dry) 29. 62 (32. 79) 2.97 17.1 3 dry ( i r r . ) 32. 35 (33. 51) 2.95 15.2 4 i r r . (dry) 30. 86 (41. 77) 2.95 16.0 5 i r r . (dry) 35. 82 (56. 60) 3.00 15.2 7 Treatment chosen by model when based on qu a l i t y alone. Cost of production when model selects on qu a l i t y alone. 132 A comparison between various production categories 1 9 for both years i s presented i n Table 26. In 1986, the cheapest cost/cow/day r a t i o n was derived from the same sequence of cuts, 3, 2, 1, 5 and 4 i r r e s p e c t i v e of production category. Feeding animals from high production s i t e s cost from $2.85 to 2.97 per day, while for the same cuts from low production s i t e s , costs ranged from $2.99 to $3.17. For a herd of 100 animals t h i s could represent a difference of $5000 to over $7000, assuming the same ra t i o n i s fed for 3 65 days. Raising low production s i t e s into the medium production category, " a more r e a l i s t i c goal, may also produce savings up to $4000 over a year. Similar patterns are evident i n 1987 but the sequence of cuts selected shows no consistent pattern except f o r cut 3 and cut 5 being consistently chosen f i r s t and l a s t respectively. One simple method to s t r a t i f y a f i e l d , or any landscape element, i s by i t s physiographic p o s i t i o n . Using the DEM the GIS can c l a s s i f y each s i t e as being a ridge (convex), sideslope (sloping), or hollow (concave). Analysis of data from these 3 locations revealed that i n a l l cases forages grown on ridges resulted i n the highest feeding costs, over a l l cuts, for both years. The cheapest forage was grown i n hollows i n 1986 and on sideslopes for a l l but the l a s t cut i n 1987. For cuts early i n the season these r e s u l t s are surprising. I t was suspected that 1 9 High production = s i t e s > 1 SD above mean Medium = s i t e s within +/- 1 SD of mean Low = s i t e s > 1 SD below mean. the sandy, better drained ridges would have higher production rates during the wet spring period, an assumption which i s not supported by these findings. In 1986 the model detected no difference i n the qua l i t y of forage over the 5 cuts, between the 3 physiographic positions ( i . e . there were no s i g n i f i c a n t l i m i t i n g factors although some qual i t y differences e x i s t ) . The pattern was le s s c l e a r i n 1987 with only cut 3 and 5 having s i m i l a r q u a l i t y c h a r a c t e r i s t i c s for the 3 posi t i o n s . 134 Table 26. Comparison of production costs for 3 y i e l d classes ( a l l s i t e s ) . High Production 1986 1987 $/ $/ Amt fed $/ $/ Amt fed Cut tonne cow/day (kg) Cut tonne cow/day (kg) 3 24.37 2.85 16.0 3 28.58 2.90 15.3 2 23.45 2.88 17.6 4 29.40 2.93 15.9 1 23.56 2.88 17.7 2 27.90 2.93 16.8 5 28.70 2.91 15.6 1 30.71 2.94 15.6 4 32.45 2.97 15.7 5 33.26 2.97 15.3 Medium Production 1986 $/ $/ Amt fed Cut tonne cow/day (kg) 3 29.14 2 27.20 1 27.31 5 35.14 4 38.14 2.93 2.94 2 .94 3.01 3.05 16, 17, 17, 15, 15, Cut X987 $/ $/ Amt fed tonne cow/day (kg) 3 33.30 2 32.87 1 36.08 4 39.30 5 2 0 46.80 2.97 3.02 3.02 3.06 3 .40 15.2 16.9 15.5 15.3 Low Production 1986 1987 $/ $/ Amt fed $/ $/ Amt fed Cut tonne cow/day (kg) Cut tonne cow/day (kg) 3 33.15 2.99 . 16.1 3 40.05 3.06 15.1 2 31.15 3.01 17.5 1 42.55 3.11 15. 3 1 31.84 3.02 17.5 2 38.80 3.12 17.0 5 45.65 3.16 15.3 4 46.80 3.18 15.3 4 45.65 3 .17 15.6 5 2 0 73.80 3.94 — Figure 39 shows the s p a t i a l cost ($/tonne DM) d i s t r i b u t i o n for cut 1 and 5 from both treatments i n 1987 predicted from the No f e a s i b l e solution; model rejects cut for not meeting minimum ADF requirements. 135 y i e l d regression equation from the p i x e l values. I t i s evident that costs are s i m i l a r i n cut 1 as no treatment e f f e c t i s imposed u n t i l a f t e r cut 3. The d i s t r i b u t i o n f or cut 5 i s markedly d i f f e r e n t with dryland s i t e s i l l u s t r a t i n g the ef f e c t s of moisture stress on y i e l d and the consequent dramatic r i s e i n the cost per tonne of dry matter produced. These high cost s i t e s correspond c l o s e l y to ridges or areas with low AWSC. The ef f e c t s of i r r i g a t i o n are d i s t i n c t , with average costs per tonne of dry matter only showing s l i g h t increases over the growing season despite input costs r i s i n g with the commencement of i r r i g a t i o n . 136 F i g u r e 38. C o s t s u r f a c e s from d r y l a n d (a) and i r r i g a t e d (b) t r e a t m e n t s f o r c u t 1 (upper) and 5, 1987. 137 CHAPTER SEVEN CONCLUSIONS The following conclusions can be reached from t h i s study: 7.1 Univariate and multivariate s t a t i s t i c s Temporal v a r i a b i l i t y i n forage y i e l d and q u a l i t y under i r r i g a t e d conditions are lower than the s p a t i a l v a r i a b i l i t y of major f e r t i l i z e r elements. This suggests that sampling the canopy d i r e c t l y to estimate crop parameters w i l l require smaller sample numbers than an i n d i r e c t method such as a regression equation. Semi-variograms for i n d i v i d u a l biophysical variables vary throughout the growing season which suggests that designing an optimum sampling scheme for forage variables would be d i f f i c u l t . From t h i s study i t appears benefits may accrue from concentrating on biophysical properties which are r e l a t i v e l y constant over time and are r e a d i l y characterized s p a t i a l l y . Such an analysis i s l i k e l y to improve our understanding of the ro l e of s p a t i a l e f f e c t s i n s o i l - c r o p r e l a t i o n s . Categoric techniques such as discriminant analysis i d e n t i f i e d several f o l i a r variables (k, Zn, Mn, Mg) which could account f o r s i g n i f i c a n t portions of t o t a l variance i n a 138 multivariate c l a s s i f i c a t i o n of forage y i e l d and q u a l i t y . These same variables when used i n multiple stepwise regression produced p r e d i c t i v e equations which could account fo r up to 97% of t o t a l variance for key f o l i a r variables of i n t e r e s t . 7.2 Remote sensing The best p r e d i c t i v e equations for y i e l d and q u a l i t y were obtained using multiple stepwise regression procedures with d i g i t a l imagery and spectral reflectance values i n the red and NIR portions of the spectrum. Derived equations could account for up to 70% of the v a r i a b i l i t y . D i g i t a l image analysis combined with c l u s t e r i n g techniques was able to separate s i g n i f i c a n t l y d i f f e r e n t classes i n terms of y i e l d and key variables within the study area. 7.3 Geographic information system GIS analysis showed that i t can serve useful functions at the i n d i v i d u a l f i e l d scale. In addition to the benefits of e f f i c i e n t data management, i t enables the rapid generation and v a l i d a t i o n of s p a t i a l data, such as f e r t i l i t y status. When combined with t r a d i t i o n a l s t a t i s t i c a l methods, GIS adds a s p a t i a l dimension to the v a l i d a t i o n of regression models. In t h i s study the u t i l i t y of d i g i t a l imagery when downloaded to the GIS was extremely high, providing good p r e d i c t i v e a b i l i t y of forage y i e l d v i a s p a t i a l prediction using a regression equation. Using variables i d e n t i f i e d from s t a t i s t i c a l analysis, 139 the cartographic modelling c a p a b i l i t i e s of GIS enabled the derivation of a categoric c l a s s i f i c a t i o n system for y i e l d and key variables over the l a t t e r part of the growing season. The analysis of forage y i e l d and quality revealed that the cost per tonne and q u a l i t y of harvested forage varies widely over the growing season, and between treatments. The a b i l i t y of a farm manager to be able to i d e n t i f y low production areas using the GIS and increase production l e v e l s within them represents po t e n t i a l savings of thousands of d o l l a r s per year. The differences i n forage qu a l i t y associated with i r r i g a t i o n are not c l e a r . In a d r i e r than average year dryland forage i s generally of higher quality, while y i e l d s are correspondingly lower. The "dynamic simulation" ( s p a t i a l what i f analysis) c a p a b i l i t i e s of GIS endows the user with the f l e x i b i l i t y to change input parameters as new findings suggest further p o s s i b i l i t i e s . Although a very intensive data set was used i n t h i s study, the s t a t i s t i c a l and GIS analyses have shown that, i n i n t e n s i v e l y managed situations a few key biophysical variables may be d i c t a t i n g production patterns. For p r e d i c t i v e or GIS modelling purposes i t appears that greater u t i l i t y may be derived from examining the s p a t i a l nature of r e l a t i v e l y permanent biophysical properties. These are usually r e a d i l y measurable or can be derived from e x i s t i n g data bases and i n combination with remotely sensed data have shown themselves to be useful p r e d i c t i v e and modelling t o o l s . 140 Although many of the t r a d i t i o n a l s t a t i s t i c a l techniques employed i n t h i s study did not provide highly accurate p r e d i c t i v e models, t h i s i s not t o t a l l y unexpected. Given the complex s p a t i a l and temporal v a r i a t i o n present within the study area, the multiple cropping within a single growing season, and the diverse nature of the data base, i t i s useful to be able to demonstrate the po t e n t i a l of several techniques. I t i s evident that f o r s i m i l a r studies, which r e l y on simple, usually r e a d i l y obtainable data, remote sensing and GIS techniques can provide useful t o o l s to monitor and model forage production on a farm scale. Because remotely sensed data i s captured s p a t i a l l y , i t does not s u f f e r from the requirement to interpolate to unknown areas, although t h i s can be achieved using appropriate models. Hence the s p a t i a l q u a l i t y of the data i s generally higher. U t i l i z i n g other variables such as rad i a t i o n or actual evapotranspiration i n a s p a t i a l context using GIS may enable the generation of better p r e d i c t i v e models for areas as small as in d i v i d u a l farms to physio-climatic regions. Although GIS has usually been regarded as primarily a t o o l for storage and display of s p a t i a l data, i t s u t i l i t y to s p a t i a l l y model and predict are increasing rapidly. 7 . 4 Recommendations 1. Conduct further g e o s t a t i s t i c a l analysis of s o i l physical properties i d e n t i f i e d as being useful predictors for crop y i e l d and qu a l i t y . This may help determine more c l e a r l y the 141 nature of s p a t i a l relationships between the crop and the property of i n t e r e s t . 2. Conduct remote sensing missions over a f u l l growing season to determine i f p r e d i c t i v e equations can be developed for the whole growing season. 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PAGE Orthic Gleysol depressions s i l t y clay loam over fin e sand at > 50 cm poor 154 APPENDIX 2 F i e l d c a l i b r a t i o n of neutron probe The response of the neutron meter i s not s o l e l y dependent on the parameter being measured (theta), but depends to a s i g n i f i c a n t degree on other properties of the medium. For the neutron meter a c a l i b r a t i o n equation of the following form i s required; theta (9) = bn + a where theta (cm3 cm"3 ) i s the volumetric content of free water (water released on drying at 105°C) , n i s the r a t i o of the count rate i n the s o i l to the count rate i n some standard, b i s the c a l i b r a t i o n c o e f f i c i e n t and a i s the intercept constant (Greacen 1981). Count rate depends la r g e l y on theta, but i t i s also affected by other s o i l properties, namely the dry bulk density of the s o i l and various other chemical components of the s o i l and s o i l s olution. Separate c a l i b r a t i o n s were ca r r i e d out for each year that measurements were made. This was done to account for any d r i f t i n the accuracy of the neutron probe between years. In 1986 f i v e p a i r s of access tubes were i n s t a l l e d over a range of s o i l s and topographic positions. The tubes were de s t r u c t i v e l y sampled i n p a i r s over as wide a range of theta as was possible. The wet end of the range was sampled i n A p r i l - May usually a f t e r s i g n i f i c a n t r a i n f a l l events and the dry end was sampled i n 155 September. Four volumetric samples were obtained immediately adjacent to the tube over the depths at which theta was rout i n e l y measured. The four samples were analyzed separately for theta and bulk density then averaged to e s t a b l i s h the required regression c a l i b r a t i o n n = b8 + a. The same procedure was followed i n 1987, with four pairs of access tubes. Two c a l i b r a t i o n equations were derived, one for the 15 cm depth and the other for a l l other depths. The equations used are shown i n figure 39 and figure 40. 156 (a) 0,6 Soli Water Content {% v/v) 0,4 0,3 0,21 0,1 0 o - 15cm Y = -0 ,18 + 0 . 3 2 X A r 2 = 0 .93 A A/ A /A /A 1986 0 0,5 1 1.5 Ratio 2,5 F i g u r e 39. F i e l d c a l i b r a t i o n s i n 1986 f o r Campbell P a c i f i c Neutron Probe f o r (a) the s u r f a c e l a y e r a t 15-cm and, (b) a l l sub s u r f a c e depths. 157 (a) 0,4 0.3 0,2 0.1 Soil Water Content {% v/v) 0 0 - 15cm A Y - -0.12 + 0.25X r2 = 0.93 yL A/ 1987 0 0.5 1 1.5 2 2.5 Ratio „ Soli Water Content [% v/v) 0.5T 5 -30 - 90cm Y = -0,03 + 0.18X 0,4- r2 - 0.89 0.3-0,2-0.1-n 1987 (JH 1 1 1 1 1 0 0.5 1 1,5 2 2,5 • Ratio F i g u r e 40. F i e l d c a l i b r a t i o n s i n 1987 f o r Campbell P a c i f i c Neutron Probe f o r (a) the s u r f a c e l a y e r at 15-cm depth and, (b) a l l s u b s u r f a c e depths. 158 APPENDIX 3 Biophysical variables from 114 study s i t e s . S o i l chemical properties for 76 dryland s i t e s , 1986. Org. Tot.Bray Exch. bases — Tot. Si t e Depth pH C N P meq/lOOg BS S No. cm CaCl 2 % % ppm Ca Mg K CEC % % 1 0-25 5.2 2.4 0 .24 64 11 .2 1.8 0.99 22 .7 62 0 . 031 25-50 5.4 1.5 0 .16 23 9 .7 1.6 1.09 20 .5 61 0 .019 2 0-25 5.2 2.5 0 .26 58 11 • 5 2.1 0.67 24 . 6 58 0 .030 25-50 5.1 1.5 0 . 14 16 9 .6 1.9 0.54 20 .6 59 0 .017 3 0-25 5.3 2.0 0 .20 48 11 .9 1.7 0.54 21 .4 66 0 .025 25-50 5.4 1.0 0 .09 20 6 .2 1.0 0.26 12 .9 58 0 .010 4 0-25 5.2 1.6 0 .17 30 10 .5 1.7 0.70 19 .4 67 0 .028 25-50 5.5 0.8 0 .08 9 8 .4 1.7 0.54 17 .8 60 0 .009 5 0-25 5.3 2.3 0 .23 29 10 . 1 1.6 0.77 24 .9 50 0 .028 25-50 5.3 1.3 0 . 15 11 9 .5 2.0 0.42 19 .8 61 0 . 015 6 0-25 5.6 2.1 0 .23 32 12 .5 1.7 0.64 24 . 1 62 0 .032 25-50 5.5 1.5 0 .16 12 12 . 1 2.1 0.64 22 .8 65 0 .018 7 0-25 5.2 2.6 0 .25 53 9 .7 2.1 2.49 25 .7 56 0 . 030 25-50 5.1 1.0 0 .11 16 •7 .4 1.7 1. 60 21 .6 50 0 .012 8 0-25 5.1 2.4 0 . 25 35 9 . 4 1.8 0.99 26 .7 46 0 . 032 25-50 5.3 .1.7 0 . 18 17 8 .9 1.6 0.83 21 .7 53 0 . 020 9 0-25 5.2 2.7 0 .27 83 8 .5 2.0 2.33 26 .5 49 0 .032 25-50 5.4 1.9 0 .22 45 7 .6 1.9 2.17 25 . 0 47 0 . 026 10 0-25 5.3 2.1 0 .24 50 9 .2 1.9 2.23 27 . 3 49 0 . 028 25-50 5.2 1.2 0 . 14 19 9 . 0 2.1 1.13 24 . 1 51 0 . 014 11 0-25 5.4 2.2 0 .24 68 9 . 1 1.6 2.04 30 . 1 42 0 . 032 25-50 5.2 1.8 0 .17 28 8 . 1 1.7 1.50 27 .2 42 0 .019 12 0-25 5.4 2.0 0 .22 35 12 . 1 2.0 0.93 26 .8 56 0 .028 25-50 5.4 1.3 0 .15 14 10 .4 2 .1 0.67 20 . 1 66 0 . 017 13 0-25 5.2 1.8 0 .21 32 11 .2 2 . 0 0.77 22 . 1 64 0 . 026 25-50 5.2 1.0 0 . 12 14 10 .4 2.0 0.58 24 . 0 54 0 .013 14 0-25 5.2 2.6 0 .28 73 8 .7 1.9 1.89 27 . 6 46 0 .036 25-50 5.3 1.5 0 .19 27 9 .6 1.9 1.63 19 .7 67 0 .020 15 0-25 5.0 2.1 0 .23 44 9 .4 1.8 0.80 22 .9 53 0 . 028 25-50 5.0 2.1 0 . 16 21 8 .9 1.9 0.96 19 . 1 62 0 .019 16 0-25 5.1 1.1 0 .23 54 10 .1 1.5 0.64 22 .2 55 0 .027 25-50 5.0 2.2 0 ;13 9 10 .2 1.7 0.64 18 . 3 69 0 .014 17 0-25 4.8 1.6 0 .21 36 9 .2 1.7 0.77 17 . 3 68 0 . 026 25-50 5.1 1.9 0 .17 26 8 .6 1.6 0.64 2 0 .5 53 0 .019 18 0-25 4.8 1.4 0 .21 28 8 .7 1.7 0.86 17 .3 66 0 . 025 25-50 5.0 2.3 0 . 15 11 8 .5 1.6 0.70 14 .9 73 0 .017 19 0-25 4.9 1.3 0 .21 24 9 .0 1.7 0.70 22 .6 51 0 .025 25-50 4.9 2.5 0 . 14 13 8 .4 1.8 0.64 18 . 1 60 0 . 016 20 0-25 5.4 1.5 0 .26 32 12 .4 1.7 0. 67 22 .8 65 0 .031 25-50 5.1 2.2 0 . 17 10 7 .6 1.4 0.48 21 .4 45 0 .019 21 0-25 4.9 0.9 0 .24 35 11 .9 1.9 0.77 21 .4 68 0 .028 25-50 5.0 0.9 0 . 10 14 8 . 1 1.5 0.51 14 .9 68 0 .012 159 Org. Tot.Bray Exch. bases — Tot. S i t e Depth pH C N P - — meq/lOOg BS S No. cm C a C l 2 % % ppm Ca Mg K CEC % % 22 0-25 5. 1 2. 4 0. 26 49 10. 6 1. 6 0. 70 23 . 9 54 0. 030 25-50 5. 0 1. 8 0. 16 52 8. 2 1. 3 0. 35 17. 2 58 0. 021 23 0-25 5. 2 2. 4 0. 25 44 21. 1 1. 3 0. 60 24. 0 96 0. 031 25-50 5. 3 1. 5 0. 15 14 14. 1 1. 4 0. 59 20. 3 80 0. 018 24 0-25 5. 1 1. 8 0. 19 37 21. 6 1. 4 0. 60 20. 3 100 0. 023 25-50 4. 9 1. 2 0. 18 21 18. 1 0. 4 0. 20 17. 6 100 0. 014 25 0-25 5. 0 2. 4 0. 24 64 10. 7 1. 7 0. 77 22. 4 59 0. 031 25-50 5. 0 1. 7 0. 18 28 10. 3 1. 9 0. 70 20. 4 64 0. 019 26 0-25 5. 1 2. 0 0. 22 74 10. 8 1. 8 0. 90 21. 1 64. 0. 027 25-50 5. 0 2. 2 d. 22 30 10. 8 1. 9 0. 83 22 . 4 61 0. 024 27 0-25 5. 1 2. 6 0. 26 37 11. 1 1. 6 0. 67 23. 3 58 0. 031 25-50 4. 8 2. 0 0. 22 19 9. 1 1. 5 0. 58 20. 8 54 0. 024 28 0-25 5. 7 2. 4 0. 25 34 14. 3 1. 7 0. 83 23. 1 73 0. 031 25-50 5. 1 2. 2 0. 24 36 10. 6 1. 9 0. 70 23 . 3 57 0. 026 29 0-25 5. 2 2. 5 0. 24 44 11. 6 1. 4 0. 64 21. 8 63 0. 029 25-50 5. 0 1. 5 0. 15 11 8. 9 1. 5 0. 42 19. 8 55 0. 016 30 0-25 5. 3 2. 8 0. 27 48 13 . 0 1. 5 1. 31 24. 6 65 0. 033 25-50 5. 1 1. 9 0. 19 22 9. 7 1. 7 0. 77 21. 1 58 0. 020 31 0-25 4. 5 1. 3 0. 10 53 3. 4 0. 7 0. 80 9. 9 50 0. 016 25-50 4. 5 0. 5 0. 05 28 2. 7 0. 6 0. 26 7. 5 48 0. 016 32 0-25 4. 9 1. 8 0. 20 39 9. 3 1. 5 0. 38 19. 5 58 0. 023 25-50 4. 9 0. 4 0. 06 15 3 . 0 0. 6 0. 13 9 . 5 40 0. 008 33 0-25 5. 0 2. 2 0. 25 41 5. 5 0. 8 0. 38 24 . 0 28 0. 028 25-50 5. 0 1. 6 0. 18 15 6. 0 1. 1 0. 48 21. 0 36 0. 028 34 0-25 4. 6 0. 7 0. 07 64 1. 5 0. 3 0. 19 8. 8 23 0. 010 25-50 4. 9 0. 4 0. 02 17 2. 2 0. 4 0. 19 6. 1 46 0. 006 35 0-25 4. 8 1. 4 0. 14 80 1. 3 0. 2 0. 13 14. 6 12 0. 017 25-50 4. 9 1. 3 0. 13 23 5. 6 1. 1 0. 35 15. 2 47 0. 014 36 0-25 5. 0 0. 6 0. 05 23 2 . 7 0. 5 0. 19 8 . 2 42 0. 008 25-50 5. 0 0. 3 0. 02 11 1. 8 0. 4 0. 10 6. 9 34 0. 005 37 0-25 5. 3 1. 9 0. 21 33 6. 0 0. 9 0. 19 21. 5 33 0. 022 25-50 5. 1 1. 5 0. 14 22 6. 1 1. 3 0. 19 22 . 6 34 0. 016 38 0-25 4. 8 2. 5 0. 27 53 7 . 6 1. 5 0. 70 21. 7 46 0. 029 25-50 4. 8 2. 1 0. 21 24 3. 3 0. 7 0. 32 22 . 8 , 19 0. 023 39 0-25 5. 0 2. 2 0. 22 49 4. 9 0. 9 0. 38 20. 4 31 0. 025 25-50 5. 3 1. 4 0. 15 15 7. 7 1. 5 0. 32 19. 6 49 0. 016 40 0-25 4. 9 2. 3 0. 22 44 5. 5 1. 1 0. 51 22. 1 32 0. 027 25-50 5. 0 1. 7 0. 18 14 7. 7 1. 6 0. 64 35. 7 28 0. 020 41 0-25 4. 8 2. 1 0. 23 44 3. 9 0. 9 0. 48 22 . 0 24 0. 026 25-50 5. 0 1. 3 0. 14 17 5. 7 1. 4 0. 45 19. 0 40 0. 015 42 0-25 4. 8 2. 5 0. 25 41 4. 6 1. 1 0. 42 23. 3 27 0. 030 25-50 4. 9 1. 9 0. 00 44 7. 4 1. 9 0. 67 21. 3 47 0. 024 43 0-25 5. 4 2. 5 0. 21 46 8. 3 1. 2 0. 45 22. 7 44 0. 027 25-50 4. 9 1. 7 0. 18 35 7. 7 1. 7 0. 51 17. 9 56 0. 020 44 0-25 4. 9 2. 1 0. 22 58 8. 6 1. 9 0. 54 22 . 9 49 0. 027 25-50 5. 1 1. 2 0. 19 13 7. 3 1. 5 0. 35 17. 8 52 0. 016 160 Org. Tot.Bray Exch. bases — Tot. Si t e Depth pH C N P meq/lOOg BS S No. cm CaCl 2 % % ppm Ca Mg K CEC % % 45 0-25 5 .4 1 .8 0 . 12 31 5 .7 1.4 0 .19 18 . 1 41 0 .022 25-50 5 .2 1 .1 0 .27 12 6 .9 1.2 0 .32 18 . 1 47 0 .015 46 0-25 5 .5 2 .6 0 .18 47 6 .7 1.0 0 .29 26 .5 31 0 .033 25-50 5 .0 1 .7 0 .20 20 9 . 1 2.0 0 .51 20 .4 57 0 .019 47 0-25 5 .0 1 .9 0 . 11 41 6 .3 1.3 0 .45 21 . 6 38 0 .025 25-50 5 .2 0 .9 0 .19 14 5 .8 1.4 0 .38 17 .2 45 0 .012 48 0-25 5 .2 1 .8 0 . 12 37 5 .6 0.8 0 .32 20 .0 34 0 .023 25-50 4 .9 1 .0 0 .23 12 5 .5 1.1 0 .42 17 .8 40 0 .012 49 0-25 4 .8 2 .1 0 .14 47 7 .1 1.6 0 .58 24 .1 39 0 .028 25-50 5 .0 1 .3 0 .22 17 8 .9 1.9 0 .77 17 .8 66 0 .014 50 0-25 4 .7 1 .9 0 . 12 50 8 .9 1.9 0 .42 20 .5 55 0 .025 25-50 5 .2 1 .1 0 .26 14 9 .2 1.8 0 .64 16 .9 70 0 .013 51 0-25 5 .4 2 .4 0 . 14 43 13 .1 1.9 0 .64 24 .8 64 0 . 030 25-50 5 . 1 1 .3 0 .20 8 8 .7 1.7 1 .28 20 .4 58 0 . 015 52 0-25 5 .2 1 .8 0 . 13 36 13 .6 2.6 0 .29 21 .4 78 0 .023 25-50 5 . 1 0 .9 0 .22 12 5 . 1 1.1 0 .45 18 . 4 37 0 .012 53 0-25 5 .0 2 .0 0 . 12 31 8 .6 1.4 0 .32 21 . 0 50 0 . 026 25-50 5 .0 1 .0 0 .25 5 9 .3 1.9 0 .29 18 .9 61 0 .012 54 0-25 5 .4 2 .3 0 .22 16 13 .4 1.7 0 .51 24 .4 64 0 .030 25-50 5 . 1 2 .0 0 .28 20 11 . 1 2.0 0 . 64 24 . 3 57 0 .025 55 0-25 5 .0 2 .7 0 .24 69 11 .6 2.0 0 .93 26 • 2 56 0 .033 25-50 5 .0 2 .5 0 .31 21 10 .8 2.1 0 .70 23 .7 58 0 .026 56 0-25 4 .8 3 .0 0 . 19 73 10 .9 1.9 0 .64 25 .5 53 0 .037 25-50 5 . 1 1 . 9 0 .20 16 10 .2 1.8 0 .45 21 . 0 60 0 .022 57 0-25 5 . 1 2 . 1 0 . 12 29 11 . 3 1.5 0 .35 21 .5 62 0 . 025 25-50 5 .2 1 .7 0 .25 8 10 .3 1.5 0 .26 17 . 6 69 0 .013 58 0-25 4 .9 2 .5 0 . 10 24 12 . 1 1.8 0 .42 23 .8 61 0 .029 25-50 5 .4 1 .0 0 .23 5 11 .2 1.7 0 . 16 17 .7 74 0 .013 59 0-25 5 .7 2 .1 0 .13 22 12 .8 1.7 0 .32 24 . 3 61 0 .025 25-50 5 .7 1 .2 0 .24 5 10 .5 1.6 0 . 19 18 . 6 67 0 .014 60 0-25 5 .2 2 .2 0 . 13 13 11 .5 1.6 0 .22 24 .9 54 0 .026 25-50 5 .3 1 .0 0 .25 15 10 .3 1.4 0 . 10 17 .9 66 0 .013 61 0-25 5 .2 2 .5 0 .11 26 12 .8 1.7 0 .29 23 .7 63 0 .028 25-50 5 .5 1 .1 0 .20 4 11 .0 1.6 0 . 13 18 . 1 71 0 .012 62 0-25 5 .5 1 .9 0 . 11 15 12 .5 1.5 0 .16 20 .0 72 0 .023 25-50 5 .6 1 . 4 0 . 19 7 11 .7 1.8 0 . 16 18 .8 73 0 .015 63 0-25 5 .9 1 .8 0 .19 11 13 . 6 1.5 0 .22 19 .4 80 0 .021 25-50 5 .5 1 .8 0 .18 9 13 .2 1.8 0 . 13 22 .9 66 0 .020 64 0-25 5 .5 1 .7 0 .22 9 12 .3 1.6 0 . 19 18 .7 76 0 .019 25-50 5 .5 2 .2 0 . 18 11 14 .0 1.9 0 .19 23 .9 68 0 .024 65 0-25 5 .6 1 .8 0 . 15 18 10 .5 1.5 0 .42 18 . 1 69 0 .020 25-50 5 .6 1 .7 0 .18 16 8 .5 1.2 0 . 16 19 .4 51 0 .018 66 0-25 5 .8 1 .6 0 . 07 23 11 .5 1.7 0 .35 19 . 1 72 0 .018 25-50 6 . 0 0 .6 0 .07 6 10 .2 2.5 0 .13 15 .2 85 0 . 007 67 0-25 5 . 1 2 .7 0 . 13 26 11 .0 1.3 0 .51 24 . 1 54 0 .033 25-50 5 .6 1 .2 0 .27 2 9 .8 1.4 0 .16 18 .9 61 0 .006 Org. Tot.Bray Exch. bases — Tot. Si t e Depth pH C N P meq/lOOg BS S No cm CaCl 2 % % ppm Ca Mg K CEC % % 68 0-25 5.3 2 . 5 0 .17 56 12. 1 1.5 0. 58 24.6 58 0 .031 25-50 5.4 1. 7 0 .18 12 10. 2 1.6 0. 32 19.3 63 0 .013 69 0-25 5.4 1. 7 0 .10 13 9. 6 1.3 0. 35 16. 6 68 0 .020 25-50 5.4 0. 9 0 .27 7 8. 3 1.7 0. 13 16.1 64 0 .019 70 0-25 5.0 2. 6 0 . 19 31 9. 5 1.4 0. 32 24.4 46 0 .030 25-50 5.2 1. 8 0 .20 24 8. 3 1.3 0. 13 20.9 47 0 .010 71 0-25 5.0 1. 9 0 .09 37 8. 6 1.3 0. 26 19.2 53 0 .021 25-50 5.0 0. 8 0 .23 6 7. 2 1.3 0. 13 13 .9 63 0 .021 72 0-25 4.8 2. 3 0 .12 36 8. 2 1.2 0. 20 21.9 44 0 .027 25-50 5.0 1. 7 0 .22 8 7. 5 1.2 0. 08 18. 0 49 0 .009 73 0-25 4.8 2 . 2 0 .07 24 7. 7 1.0 0. 30 17.8 51 0 .027 25-50 5.2 0. 7 0 .20 6 6. 6 0.9 0. 04 12.8 60 0 . 014 74 0-25 4.9 2. 0 0 . 07 31 6. 1 0.8 0. 14 19.3 37 0 .024 25-50 5.0 0. 7 0 .24 8 6. 0 0.9 0. 09 12.8 59 0 .008 75 0-25 5.0 2. 1 0 . 12 22 9. 0 1.2 0. 18 22.8 46 0 .027 25-50 5.1 1. 1 0 .28 8 7. 5 1.2 0. 10 15.3 58 0 .008 76 0-25 5.1 2. 4 0 • 15 23 9. 7 1.4 0. 19 22.4 51 0 .029 25-50 5.0 1. 5 0 .23 10 7. 2 1.3 0. 09 20.9 41 0 . 012 S o i l chemical properties for 38 i r r i g a t e d s i t e s , 1986. Org. Tot. Bray Exch. bases Tot. SiteDepth pH C N P meq/lOOg BS S No. cm • i CaCl 2 % % ppm Ca Mg K CEC % % 80 0-25 5. 2 2. 2 0. 19 23 10. 5 1. 7 0 .80 24. 2 54 0 .028 25-50 5. 3 1. 8 0. 21 13 9. 9 1. 7 0 .62 20. 2 61 0 .016 81 0-25 5. 4 2. 0 0. 23 51 11. 4 1. 6 0 .56 23. 4 58 0 .026 25-50 5. 4 1. 3 0. 13 12 9. 9 2. 0 0 .52 19. 9 63 0 . 021 82 0-25 5. 5 2. 2 0. 26 35 12. 0 1. 4 0 .51 24. 9 56 0 .027 25-50 5. 5 1. 3 0. 23 15 10. 9 1. 5 0 .46 21. 2 61 0 .013 83 0-25 5. 5 2. 7 0. 26 48 12. 5 1. 5 0 .69 27. 9 53 0 .034 25-50 5. 3 2 . 3 0. 18 28 11. 1 1. 5 0 . 66 24 . 8 54 0 . 015 84 0-25 5. 5 2 . 6 0. 18 26 12 . 5 1. 7 0 .52 27. 1 55 0 .032 25-50 5. 4 1. 9 0. 07 11 10. 2 1. 8 0 .46 23 . 0 54 0 .027 85 0-25 5. 3 1. 8 0. 27 52 9. 9 1. 1 0 .36 19. 6 58 0 . 021 25-50 5. 2 0. 8 0. 22 17 7. 7 1. 1 0 . 15 13. 9 65 0 . 020 86 0-25 5. 1 2. 6 0. 09 47 11. 6 1. 4 0 .61 25. 5 54 0 .034 25-50 5. 2 2. 1 0. 12 28 11. 1 1. 5 0 .58 23. 1 57 0 .009 87 0-25 5. 7 1. 8 0. 03 49 12. 6 1. 3 0 .41 20. 5 70 0 . 024 25-50 5. 6 0. 7 0. 19 7 9. 9 1. 3 0 .24 16. 6 69 0 .026 88 0-25 5. 3 1. 3 0. 12 47 7. 9 1. 0 0 .28 14. 8 63 0 .017 25-50 5. 2 0. 3 0. 27 11 3. 9 0. 6 0 .21 13. 2 36 0 .009 Org. Tot. Bray Exch. bases Tot. SiteDepth pH C N P meq/lOOg BS S No. cm CaCl 2 % % ppm Ca Mg K CEC % % 89 0-25 5. 1 1. 8 0. 21 27 8. 6 1.8 0. 56 20. 0 55 0. 024 25-50 5. 3 1. 1 0. 27 8 8. 6 2.2 0. 46 18. 8 60 0. 005 90 0-25 5. 2 2. 5 0. 11 68 12. 2 1.4 0. 54 24. 7 58 0. 033 25-50 5. 4 2. 2 0. 23 18 13. 1 1.4 0. 17 23. 9 62 0. 011 91 0-25 5. 2 2. 6 0. 16 38 12. 9 1.5 0. 34 25. 9 57 0. 030 25-50 5. 2 1. 1 0. 22 8 10. 1 1.2 0. 14 16. 9 68 0. O i l 92 0-25 5. 2 2. 3 0. 10 39 10. 4 1.6 0. 56 22. 2 57 0. 028 25-50 5. 1 1. 5 0. 26 23 8. 7 1.8 0. 56 19. 8 56 0. 031 93 0-25 5. 2 2. 1 0. 16 68 10. 4 1.3 0. 29 22. 3 54 0. 026 25-50 5. 3 0. 9 0. 23 8 8. 1 1.3 0. 19 15. 6 62 0. 016 94 0-25 5. 0 2. 4 0. 09 64 10. 2 1.5 0. 34 23. 3 52 0. 032 25-50 5. 1 1. 5 0. 23 12 9. 1 1.5 0. 20 19. 3 56 0. 017 95 0-25 5. 0 2. 2 0. 20 38 10. 2 1.3 0. 22 21. 7 54 0. 027 25-50 5. 1 0. 9 0. 23 9 8. 9 1.2 0. 11 15. 9 65 0. 031 96 0-25 5. 1 2. 2 0. 10 63 9. 4 1.2 0. 44 20. 2 55 0. 026 25-50 5. 1 1. 8 0. 23 26 10. 0 1.2 0. 20 22. 5 51 0. 010 97 0-25 5. 1 2. 3 0. 10 45 8. 7 1.1 0. 39 20. 5 50 0. 025 25-50 5. 1 1. 1 0. 23 25 7. 5 1.3 0. 13 15. 0 60 0. 021 98 0-25 5. 1 2. 4 0. 13 30 10. 1 1.2 0. 32 22. 1 53 0. 027 25-50 5. 1 0. 8 0. 26 8 7. 9 1.2 0. 17 17. 1 55 0. 010 99 0-25 5. 1 2. 3 0. 00 35 8. 6 1.1 0. 20 22. 6 44 0. 027 25-50 5. 0 1. 3 0. 28 12 6. 9 1.2 0. 11 17. 0 49 0. 010 100 0-25 5. 3 2. 4 0. 18 38 10. 4 1.2 0. 25 22. 7 53 0. 030 25-50 5. 0 2. 1 0. 20 25 8. 6 1.3 0. 16 23. 0 44 0. 014 101 0-25 5. 1 2. 7 0. 10 66 10. 6 1.2 0. 13 24. 9 48 0. 034 25-50 5. 0 1. 6 0. 24 13 8. 6 1.3 0. 06 22 . 9 44 0. 025 102 0-25 5. 1 2. 0 0. 10 55 9. 4 1.5 0. 17 21. 2 53 0. 025 25-50 5. 2 0. 9 0. 22 19 8. 1 1.2 0. 13 16. 4 58 0. 018 103 0-25 5. 0 2 . 3 0. 14 57 9. 5 1.3 0. 19 22. 5 49 0. 030 25-50 5. 2 0. 9 0. 25 9 6. 5 1.1 0. 08 15. 7 49 0. 010 104 0-25 5. 3 2. 1 0. 14 27 9. 9 1.2 0. 20 21. 8 52 0. 025 25-50 4. 9 1. 3 0. 20 16 7. 6 1.5 0. 15 19. 3 48 0. 010 105 0-25 5. 3 2. 4 0. 10 44 10. 0 1.4 0. 26 25. 2 46 0. 029 25-50 5. 2 1. 3 0. 19 12 8. 5 1.5 0. 08 18. 1 56 0. 015 106 0-25 5. 0 1. 9 0. 10 28 9. 0 1.4 0. 25 20. 4 52 0. 023 25-50 5. 3 0. 9 0. 21 6 8. 4 1.4 0. 19 17. 9 56 0. 015 107 0-25 5. 3 2. 0 0. 13 32 10. 2 1.3 0. 40 21. 3 56 0. 025 25-50 5. 3 1. 6 0. 14 9 8. 6 1.8 0. 22 17. 8 60 0. O i l 108 0-25 5. 1 2. 0 0. 10 32 8. 9 1.4 0. 53 17. 3 63 0. 025 25-50 5. 3 1. 3 0. 18 17 7. 6 1.7 0. 47 19. 3 51 0. 019 109 0-25 5. 2 1. 6 0. 15 30 8. 2 1.3 0. 58 16. 8 60 0. 021 25-50 5. 3 1. 1 0. 25 15 7. 0 1.3 0. 47 14 . 3 62 0. 015 110 0-25 5. 4 1. 8 0. 21 27 10. 0 1.3 0. 51 20. 4 58 0. 023 25-50 5. 4 1. 5 0. 25 18 9. 9 1.3 0. 49 19. 2 61 0. 013 111 0-25 5. 5 2. 5 0. 15 34 12. 6 1.2 0. 53 25. 7 56 0. 032 25-50 5. 7 2. 0 0. 25 21 12. 4 1.2 0. 51 23. 2 61 0. 017 Org. Tot. Bray Exch. bases Tot. SiteDepth pH C N P meq/lOOg — - BS S No. cm i CaCl 2 % % ppm Ca Mg K CEC % % 112 0-25 5. 7 2. 5 0. 21 60 12. 4 1.4 0 .59 23 .2 062 0 .032 25-50 5. 5 1. 4 0. 25 14 10. 1 1.4 0 .47 24 .8 48 0 .025 113 0-25 5. 3 2. 3 0. 13 83 11. 9 1.4 0 .82 20 . 0 71 0 .030 25-50 5. 2 2. 0 0. 22 39 10. 4 1.3 0 .67 20 .0 62 0 .017 114 0-25 5. 4 2. 4 0. 10 62 11. 4 1.2 0 .66 24 .9 53 0 .031 25-50 5. 5 1. 1 0. 25 8 10. 0 1.3 0 .42 24 .9 47 0 . 024 115 0-25 5. 6 2. 0 0. 16 38 12. 5 1.3 0 .54 23 .9 60 0 . 026 25-50 5. 6 0. 9 0. 21 13 10. 5 1.5 0 .29 23 .9 52 0 .013 116 0-25 5. 8 2. 3 0. 14 53 14. 6 1.1 0 .42 23 .9 68 0 .029 25-50 5. 9 1. 5 0. 16 19 11. 6 1.3 0 .39 23 .9 56 0 .010 117 0-25 6. 0 2. 0 0. 21 43 13. 9 1.3 0 .43 22 .7 69 0 .026 25-50 5. 6 1. 3 0. 14 16 11. 4 1.6 0 .30 18 .8 71 0 .018 S o i l chemical properties for 76 dryland s i t e s , 1987. Tot. Bray — Exch. bases — Si t e Depth pH N P — meq/lOOg — BS No. cm i CaCl 2 % ppm Ca Mg K CEC % 1 0-25 5.2 0.25 190 10 .4 1.8 0 .98 22 .7 58 25-50 5.3 0.12 50 9 .2 1.7 0 .67 20 .5 57 2 0-25 5.1 0.22 95 9 .7 1.8 0 .73 24 . 6 50 25-50 5.0 0.17 48 8 .3 1.8 0 .46 20 .6 52 3 0-25 5.2 0.21 225 10 .7 1.6 0 . 65 21 .4 61 25-50 5.2 0.07 102 6 .4 1.1 0 .39 12 .9 62 4 0-25 5.1 0.18 105 9 .5 1.8 0 .70 19 .4 62 25-50 5.3 0.09 30 8 .4 2.1 0 .62 17 .8 63 5 0-25 5.3 0.23 95 10 .2 1.7 0 .50 24 .9 51 25-50 5.2 0.22 18 8 .5 2.1 0 .24 19 .8 56 6 0-25 5.3 0.21 100 11 . 2 1.8 0 .54 24 .1 57 25-50 5.3 0.10 25 10 . 3 2.1 0 .40 22 .8 57 7 0-25 5.3 0.26 180 8 . 6 2 . 0 1 .76 25 . 7 48 25-50 5.2 0.13 48 7 .5 1.8 1 .24 21 . 6 50 8 0-25 5.1 0.24 150 9 .3 1.7 0 .70 26 .7 44 25-50 5.1 0.12 25 7 .8 1.7 0 .61 21 .7 47 9 0-25 4.9 0.27 233 7 .9 1.8 1 .41 26 .5 42 25-50 5.0 0.21 98 7 .6 1.7 1 .57 25 .0 44 10 0-25 5.0 0.24 133 8 .3 1.8 1 .41 27 .3 43 25-50 5.0 0.10 43 7 .8 2.0 0 .85 24 .1 45 11 0-25 5.2 0.25 123 9 .0 1.6 1 .45 30 .1 40 25-50 5.0 0.11 40 7 .2 1.8 1 .06 27 .2 37 Tot. Bray — Exch. bases — S i t e Depth pH N P — meq/lOOg — BS No. cm i C a C l 2 % ppm Ca Mg K CEC % •12 0-25 5 .2 0 .23 105 10 .3 1 .8 0 .49 26 .8 48 25-50 5 .0 0 .16 40 9 .1 1 .9 0 .46 20 .1 58 13 0-25 5 .1 0 .20 98 10 .3 1 .7 0 .52 22 .1 57 25-50 5 .0 0 . 11 35 8 .6 1 .9 0 .43 24 .0 46 14 0-25 5 .0 0 .28 193 8 .2 1 .9 1 .36 27 .6 42 25-50 5 .0 0 .20 105 8 . 1 1 .8 1 .32 19 .7 59 15 0-25 4 .9 0 .21 115 7 .8 1 .7 0 .95 22 .9 46 25-50 5 .1 0 .11 40 7 .8 1 .8 0 .70 19 . 1 54 16 0-25 4 .9 0 .20 100 9 .0 1 .5 0 .49 22 .2 50 25-50 5 .0 0 . 11 23 8 .9 1 .5 0 .42 18 .3 60 17 0-25 4 .9 0 .21 148 8 . 6 1 .7 0 .65 17 .3 64 25-50 4 .9 0 .12 53 7 .7 1 .8 0 .42 20 .5 49 18 0-25 4 .7 0 .24 145 7 .1 1 .7 0 .89 17 .3 56 25-50 4 .8 0 .15 50 6 .9 1 .5 0 .71 14 .9 63 19 0-25 4 .7 0 .24 193 7 . 6 1 .6 0 .62 22 .6 44 25-50 4 .7 0 .11 33 6 .6 1 .6 0 .48 18 .1 49 20 0-25 4 .9 0 .24 130 6 .6 1 .6 0 .51 22 .8 39 25-50 4 .8 0 .19 50 8 . 1 1 . 6 0 .50 21 .4 48 21 0-25 4 .9 0 .20 90 9 .0 1 .6 0 .52 21 .4 53 25-50 4 .9 0 .07 40 6 .3 1 .2 0 .39 14 .9 54 22 0-25 5 . 0 0 .22 155 9 .2 1 .4 0 . 33 23 .9 46 25-50 4 .9 0 .04 38 4 . 9 1 .4 0 .06 17 .2 38 23 0-25 5 .0 0 .22 120 8 . 6 1 .6 0 .59 24 .0 45 25-50 5 .1 0 . 17 45 8 .4 1 .6 0 .52 20 . 3 52 24 0-25 4 .7 0 .21 133 7 .9 1 .6 0 .51 20 .3 50 25-50 4 .9 0 . 12 83 7 . 3 1 .7 0 .46 17 .6 55 25 0-25 5 .1 0 .24 115 9 .2 1 .5 0 .54 22 .4 51 25-50 5 . 0 0 . 16 43 8 .9 1 .7 0 .49 20 .4 55 26 0-25 5 .0 0 .21 143 8 .5 1 .6 0 .73 21 .1 52 25-50 4 .9 0 .21 78 10 .9 2 .0 0 .65 22 .4 61 27 0-25 5 .1 0 .25 145 8 .9 1 .3 0 .36 23 .3 46 25-50 5 .0 0 .22 53 6 .6 1 . 1 0 .39 20 .8 39 28 0-25 5 .6 0 .25 102 11 .2 1 .3 0 .46 23 . 1 57 25-50 5 .0 0 .24 115 8 .4 1 .6 0 .42 23 .3 45 29 0-25 5 .0 0 .22 150 7 .0 1 .0 0 .20 21 .8 38 25-50 4 .8 0 . 16 50 6 .5 1 .2 0 .26 19 .8 41 30 0-25 5 . 3 0 .26 118 11 . 0 1 . 3 0 .51 24 .6 53 25-50 5 .1 0 .19 38 8 .7 1 .5 0 .52 21 . 1 51 31 0-25 4 .5 0 .06 165 2 .1 0 .3 0 .06 9 .9 28 25-50 4 .6 0 .02 55 2 .2 0 .5 0 . 16 7 .5 40 32 0-25 4 .7 0 .20 123 4 .0 0 .8 0 . 15 19 .5 26 25-50 4 .7 0 .05 35 7 .4 1 .4 0 .25 9 .5 97 33 0-25 4 .8 0 .24 113 9 .4 1 .5 0 .49 24 . 0 48 25-50 4 .8 0 .20 48 8 .8 1 .7 0 .51 21 .0 53 34 0-25 4 .6 0 .07 200 3 .2 0 .6 0 .26 8 .8 47 25-50 4 .7 0 .02 63 3 .0 0 .6 0 .29 6 . 1 65 Tot. Bray — E x c h . bases — Si t e Depth pH N P — meq/lOOg — BS No. cm CaCl 2 % ppm Ca Mg K CEC % 35 0-25 4. 6 0. 13 145 4. 6 1. 0 0. 45 14. 6 42 25-50 4. 7 0. 13 43 6. 7 1. 5 0. 32 15. 2 57 36 0-25 4. 4 0. 08 135 3. 5 0. 7 0. 12 8. 2 54 25-50 4. 6 0. 02 43 2 . 8 0. 6 0. 12 6. 9 52 37 0-25 5. 1 0. 21 57 10. 3 1. 5 0. 13 21. 5 56 25-50 5. 0 0. 12 18 8. 5 2. 0 0. 19 22. 6 48 38 0-25 4. 7 0. 25 95 7. 2 1. 5 0. 31 21. 7 42 25-50 4. 8 0. 12 30 8. 4 2. 0 0. 46 22. 8 48 39 0-25 5. 0 0. 23 75 9. 5 1. 7 0. 45 20. 4 58 25-50 5. 0 0. 12 45 8. 6 1. 8 0. 36 19. 6 56 40 0-25 4. 7 0. 24 130 8. 0 1. 7 0. 71 22. 1 48 25-50 4. 8 0. 19 55 8. 2 1. 8 0. 58 35. 7 30 41 0-25 4. 8 0. 20 83 7. 8 2. 0 0. 70 22. 0 49 25-50 4. 8 0. 16 63 7. 4 1. 9 0. 48 19. 0 53 42 0-25 4. 8 0. 23 90 9. 1 2. 2 0. 69 23 . 3 52 25-50 4. 8 0. 16 78 7. 3 2. 0 0. 53 21. 3 47 43 0-25 5. 1 0. 21 173 10. 3 1. 6 0. 47 22 . 7 55 25-50 4 . 9 0. 14 70 8. 1 1. 9 0. 35 17. 9 59 44 0-25 4. 8 0. 23 110 8. 2 1. 8 0. 36 22. 9 46 25-50 4. 9 0. 13 33 7. 5 1. 7 0. 35 17. 8 55 45 0-25 4. 9 0. 18 113 8. 2 1. 9 0. 26 18. 1 58 25-50 4. 9 0. 12 20 7. 7 2. 2 0. 19 18. 1 56 46 0-25 5. 0 0. 26 118 10. 3 1. 9 0. 36 26. 5 48 25-50 4. 9 0. 21 45 8. 4 1. 9 0. 42 20. 4 54 47 0-25 4. 8 0. 16 73 7. 8 1. 8 0. 49 21. 6 47 25-50 5. 0 0. 08 18 7. 4 • 1. 9 0. 33 17. 2 57 48 0-25 4. 9 0. 14 60 7. 7 1. 4 o. 32 20. 0 48 25-50 5. 0 0. 08 15 7. 7 1. 8 0. 19 17. 8 56 49 0-25 4. 6 0. 21 118 6. 9 1. 7 0. 22 24. 1 37 25-50 4. 8 0. 11 23 7. 3 1. 7 0. 25 17. 8 52 50 0-25 4. 6 0. 23 105 7. 1 1. 7 0. 33 20. 5 46 25-50 4. 8 0. 07 18 7. 4 1. 6 0. 20 16. 9 56 51 0-25 5. 0 0. 25 138 10. 7 1. 7 0. 45 24. 8 53 25-50 4. 9 0. 16 63 8. 1 1. 7 0. 41 20. 4 50 52 0-25 4. 8 0. 18 118 7. 6 1. 7 0. 54 21. 4 46 25-50 4. 9 0. 07 18 7. 6 1. 8 0. 30 18. 4 53 53 0-25 4 . 8 0. 21 65 8. 9 1. 5 0. 26 21. 0 51 25-50 4. 8 0. 14 25 8. 2 1. 8 0. 10 18. 9 54 54 0-25 4. 8 0. 27 80 9. 3 1. 6 0. 80 24. 4 48 25-50 4. 8 0. 18 25 8. 8 1. 7 0. 41 24. 3 45 55 0-25 4. 7 0. 26 123 9. 3 1. 8 0. 34 26. 2 44 25-50 4. 8 0. 20 45 8. 9 1. 8 0. 36 23. 7 47 56 0-25 4. 8 0. 20 38 9. 7 1. 4 0. 11 25. 5 45 25-50 4. 8 0. 13 25 8. 4 1. 6 0. 15 21. 0 49 57 0-25 4. 6 0. 29 83 9. 1 1. 8 0. 19 21. 5 52 25-50 5. 0 0. 10 10 8. 8 1. 3 0. 04 17. 6 59 Tot. Bray — Exch. bases — S i t e Depth pH N P — meq/lOOg — BS No. cm CaCl 2 % ppm Ca Mg K CEC % 58 0-25 4 .8 0. 23 50 10. 3 1. 7 0. 17 23. 8 52 25-50 5 .0 0. 12 15 9. 3 1. 5 0. 10 17. 7 63 59 0-25 5 .0 0. 21 57 9. 6 1. 5 0. 10 24. 3 47 25-50 5 . 1 0. 13 18 8. 7 1. 6 0. 07 18. 6 56 60 0-25 4 .8 0. 22 60 8. 7 1. 5 0. 26 24. 9 42 25-50 4 .8 0. 14 18 8. 8 1. 5 0. 14 17. 9 59 61 0-25 4 .9 0. 25 70 10. 6 1. 5 0. 38 23. 7 53 25-50 5 .1 0. 07 53 8. 6 1. 4 0. 14 18. 1 57 62 0-25 5 .1 0. 22 120 11. 2 1. 4 0. 17 20. 0 64 25-50 5 .2 0. 08 18 9. 8 1. 8 0. 15 18. 8 63 63 0-25 5 .2 0. 20 45 10. 6 1. 4 0. 17 19. 4 63 25-50 5 .3 0. 22 10 12. 6 1. 6 0. 08 22. 9 63 64 0-25 5 .0 0. 16 75 9. 1 1. 5 0. 17 18. 7 59 25-50 5 .1 0. 25 30 13. 9 1. 7 0. 19 23. 9 67 65 0-25 4 .8 0. 27 83 7. 9 1. 4 0. 31 18. 1 53 25-50 4 .9 0. 15 110 6. 9 1. 1 0. 17 19. 4 43 66 0-25 5 .2 0. 22 57 9. 7 2 . 0 0. 28 19 . 1 64 25-50 5 .5 0. 17 20 10. 1 1. 6 0. 21 15. 2 79 67 0-25 4 .7 0. 18 90 9. 2 1. 3 0. 15 24. 1 45 25-50 4 .9 0. 07 20 7. 2 1. 2 0. 10 18. 9 46 68 0-25 4 .9 0. 26 55 10. 9 1. 5 0. 52 24. 6 53 25-50 4 .8 0. 14 23 8. 1 1. 4 0. 20 19. 3 51 69 0-25 4 .9 0. 18 40 8. 1 1. 2 0. 22 16. 6 58 25-50 4 .8 0. 12 25 6. 7 1. 4 0. 23 16. 1 53 70 0-25 4 .7 0. 25 115 8. 4 1. 4 0. 27 24. 4 42 25-50 4 .8 0. 14 35 8. 1 1. 4 0. 22 20. 9 47 71 0-25 4 .6 0. 19 53 7. 3 1. 1 0. 15 19. 2 45 25-50 4 .6 0. 06 13 5. 5 1. 0 0. 08 13 . 9 49 72 0-25 4 .7 0. 23 90 7. 3 1. 2 0. 33 21. 9 41 25-50 4 .7 0. 14 60 7. 0 1. 2 0. 16 18. 0 48 73 0-25 4 . 6 0. 20 135 6. 5 1. 1 0. 21 17. 8 45 25-50 4 .8 0. 06 63 5. 8 1. 0 0. 14 12. 8 55 74 0-25 4 .7 0. 20 127 7. 3 1. 1 0. 14 19. 3 45 25-50 4 .8 0. 06 50 5. 5 1. 0 0. 05 12. 8 52 75 0-25 4 .8 0. 21 102 8. 5 1. 2 0. 17 22. 8 44 25-50 4 .8 0. 10 38 6. 7 1. 3 0. 10 15. 3 54 76 0-25 4 .7 0. 25 85 8. 2 1. 3 0. 17 22. 4 44 25-50 4 .6 0. 14 25 6. 1 1. 2 0. 10 20. 9 36 167 S o i l chemical properties for 38 i r r i g a t e d s i t e s , 1987. Tot. Bray — Exch. bases — S i t e Depth pH N P — meq/lOOg — BS No. cm C a C l 2 % ppm Ca Mg K CEC % ' 80 0-25 4.9 0. 20 68 9. 1 1.7 0. 41 24. 2 47 25-50 4.9 0. 16 63 8. 9 1.6 0. 35 20. 2 55 81 0-25 5.0 0. 19 100 9. 4 1.6 0. 60 23 . 4 50 25-50 5.0 0. 12 38 9. 0 1.7 0. 48 19. 9 57 82 0-25 5.2 0. 22 95 10. 9 1.5 0. 45 24. 9 52 25-50 5.2 0. 15 38 9. 6 1.4 0. 38 21. 2 54 83 0-25 5.1 0. 26 195 11. 0 1.6 0. 77 27. 9 48 25-50 5.0 0. 21 95 9. 8 1.4 0. 65 24. 8 48 84 0-25 5.2 0. 26 88 10. 4 1.7 0. 52 27. 1 47 25-50 4.9 0. 13 30 7. 6 1.7 0. 47 23. 0 43 85 0-25 4.9 0. 17 113 8. 5 1.3 0. 37 19. 6 52 25-50 4.9 0. 03 55 4. 5 0.8 0. 18 13 . 9 40 86 0-25 4.9 0. 27 150 10. 0 0.5 0. 64 25. 5 44 25-50 4.9 0. 17 65 9. 7 0.4 0. 47 23. 1 46 87 0-25 5.1 0. 17 148 10. 5 0.4 0. 40 20. 5 56 25-50 5.0 0. 07 45 8. 3 0.5 0. 15 16. 6 55 88 0-25 4.8 0. 11 160 3. 4 1.2 0. 21 14. 8 34 25-50 4.7 0. 03 65 8. 2 0.7 0. 12 13 . 2 68 89 0-25 4.8 0. 17 65 8. 1 0.9 0. 39 20. 0 47 25-50 4.9 0. 09 33 8. 1 1.3 0. 28 18. 8 52 90 0-25 4.7 0. 26 127 10. 2 0.3 0. 34 24 . 7 44 25-50 4.8 0. 22 83 11. 2 0.4 0. 20 23. 9 50 91 0-25 4.8 0. 26 55 12. 5 0.5 0. 27 25. 9 52 25-50 4.9 0. 11 88 9. 1 0.3 0. 15 16. 9 57 92 0-25 4.8 0. 24 148 10. 1 0.7 0. 43 22. 2 51 25-50 4.8 0. 14 57 8. 5 0.8 0. 29 19. 8 50 93 0-25 4.8 0. 22 110 9. 6 0.4 0. 21 22. 3 46 25-50 4.8 0. 10 35 7. 9 0.4 0. 13 15. 6 55 94 0-25 4.7 0. 24 200 9. 1 0.6 0. 44 23. 3 44 25-50 4.9 0. 12 38 8. 0 0.6 0. 22 19. 3 46 95 0-25 4.7 0. 23 138 9. 6 0.3 0. 22 21. 7 47 25-50 4.8 0. 07 23 8. 2 0.4 0. 13 15. 9 55 96 0-25 4.7 0. 21 230 8. 5 0.3 0. 30 20. 2 46 25-50 4.7 0. 19 78 8. 9 0.3 0. 18 22 . 5 42 97 0-25 4.8 0. 23 183 14. 2 1.0 0. 32 20. 5 77 25-50 4.7 0. 10 48 7. 0 0.3 0. 11 15. 0 51 98 0-25 4.7 0. 23 248 8. 9 0.4 0. 21 22. 1 44 25-50 4.7 0. 11 45 7. 6 0.4 0. 10 17. 1 48 99 0-25 4.8 0. 25 153 9. 0 0.2 0. 25 22. 6 42 25-50 4.8 0. 09 25 6. 4 0.1 0. 08 17. 0 39 100 0-25 4.8 0. 24 150 9. 7 0.3 0. 17 22. 7 45 25-50 4.7 0. 18 100 7. 8 0.3 0. 10 23. 0 36 Tot. Bray — Exch. bases — S i t e Depth pH N P — meq/lOOg — BS No. cm C a C l 2 % ppm Ca Mg K CEC % 101 0-25 4. 8 0. 24 180 10. 0 0.3 0. 15 24. 9 42 25-50 4. 8 0. 19 50 8. 5 0.4 0. 12 22. 9 40 102 0-25 4. 7 0. 21 115 9. 3 1.3 0. 23 21. 2 51 25-50 4. 8 0. 06 65 8. 5 1.2 0. 12 16. 4 61 103 0-25 4. 7 0. 22 205 9. 0 1.4 0. 31 22. 5 48 25-50 4. 8 0. 12 40 7. 3 1.4 0. 13 15. 7 57 104 0-25 5. 2 0. 17 90 8. 6 1.3 0. 38 21. 8 47 25-50 5. 2 0. 09 30 7. 5 1.6 0. 15 19. 3 49 105 0-25 5. 1 0. 23 143 9. 9 1.6 0. 26 25. 2 47 25-50 5. 1 0. 11 40 7. 9 1.5 0. 15 18. 1 53 106 0-25 4. 9 0. 18 158 8. 6 1.4 0. 23 20. 4 51 25-50 4. 9 0. 12 25 8. 6 1.4 0. 14 17. 9 58 107 0-25 4. 9 0. 21 188 11. 9 1.8 0. 32 21. 3 66 25-50 5. 0 0. 11 28 8. 7 1.8 0. 19 17. 8 61 108 0-25 5. 0 0. 19 120 9. 0 1.5 0. 43 17. 3 64 25-50 4. 9 0. 15 70 8. 3 1.8 0. 42 19. 3 55 109 0-25 4. 9 0. 13 210 6. 9 1.3 0. 42 16. 8 52 25-50 5. 0 0. 06 48 5. 0 1.2 0. 24 14. 3 46 110 0-25 5. 0 0. 18 203 9. 4 1.4 0. 41 20. 4 56 25-50 5. 2 0. 12 57 9. 0 1.2 0. 33 19. 2 56 111 0-25 5. 3 0. 23 405 12. 9 1.3 0. 43 25. 7 58 25-50 5. 4 0. 16 60 11. 1 1.3 0. 42 23. 2 56 112 0-25 5. 4 0. 23 185 12. 2 1.2 0. 65 23. 2 61 25-50 5. 1 0. 11 53 8. 2 1.2 0. 45 24. 8 40 113 0-25 5. 0 0. 22 213 10. 6 1.3 0. 53 20. 0 63 25-50 4. 9 0. 21 125 10. 1 1.4 0. 58 20. 0 61 114 0-25 5. 1 0. 25 242 11. 4 1.3 0. 62 24. 9 54 25-50 5. 2 0. 13 40 9. 4 1.3 0. 27 24. 9 44 115 0-25 5. 2 0. 21 110 10. 8 1.4 0. 40 23 . 9 53 25-50 5. 2 0. 11 28 9. 7 1.7 0. 30 23. 9 50 116 0-25 5. 4 0. 26 203 13. 2 1.2 0. 40 23. 9 63 25-50 5. 4 0. 10 23 10. 1 1.6 0. 22 23 . 9 50 117 0-25 5. 4 0. 20 88 12. 4 1.2 0. 30 22. 7 62 25-50 5. 4 0. 13 53 10. 5 1.2 0. 31 18. 8 64 Dry Matter and f o l i a r elements for 76 dryland sites,1986 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm 1 May 14 6.7 0. 17 0. 12 3 .33 2 .09 0. 34 16 55 0. 00 60 J u l 0 5 3.0 0. 39 0. 17 3 .53 1 .90 0. 36 12 167 0. 01 107 AugOS 1.7 0. 37 0. 18 3 .98 3 . 08 0. 36 16 150 0. 01 83 Aug 2 9 1.1 0. 60 0. 22 3 .33 3 .41 0. 32 22 320 0. 04 94 0ct07 1.7 0. 41 0. 26 3 .25 3 .52 0. 28 23 2230 0. 25 114 2 Mayl4 5.3 0. 17 0. 13 3 .38 2 .43 0. 38 21 185 0. 02 78 J u l 05 3.3 0. 38 0. 16 3 .50 2 .00 0. 33 12 167 0. 01 92 Aug 05 1.9 0. 53 0. 21 3 .73 2 .88 0. 34 15 120 0. 01 91 Aug 2 9 1.2 0. 61 0. 25 3 .45 3 .70 0. 37 23 210 0. 02 110 Oct07 2.7 0. 54 0. 21 3 .10 2 .98 0. 26 17 210 0. 02 108 3 May 14 4.8 0. 36 0. 12 3 .03 2 .50 0. 38 24 75 0. 00 50 J u l 05 4.8 0. 96 0. 20 3 .25 2 .40 0. 32 19 117 0. 01 40 Aug 05 3.2 0. 97 0. 23 3 .70 3 .19 0. 34 19 100 0. 01 45 Aug29 1.4 0. 99 0. 32 3 .38 3 .94 0. 34 30 320 0. 03 72 Oct07 2.3 0. 95 0. 27 2 .83 3 .50 0. 21 20 200 0. 02 82 4 May 14 7.5 0. 19 0. 14 3 .43 2 .23 0. 38 20 505 0. 05 95 J u l 0 5 3.9 0. 34 0. 17 3 . 60 2 . 08 0. 38 13 107 0. 00 128 Aug 05 2.2 0. 36 0. 19 3 .43 2 .95 0. 34 15 90 0. 00 96 Aug29 1.5 0. 49 0. 23 3 .88 3 .62 0. 35 23 220 0. 02 123 Oct07 2.4 0. 46 0. 22 3 . 15 3 .26 0. 28 18 210 0. 02 114 5 Mayl4 5.8 0. 19 0. 13 3 .20 2 . 27 0. 35 15 75 0. 00 75 JU105 4.1 0. 72 0. 22 3 .80 2 .33 0. 38 17 217 0. 02 99 Aug 05 2.8 0. 80 0. 31 3 .98 3 .42 0. 31 17 150 0. 01 79 Aug 2 9 1.0 0. 59 0. 26 3 .48 3 .74 0. 32 23 140 0. 01 86 Oct07 2.5 0. 58 0. 28 2 . 63 3 .21 0. 20 22 910 0. 11 106 6 May 14 7.1 0. 17 0. 12 3 .00 2 .20 0. 36 17 55 0. 00 72 J u l 05 4.7 0. 49 0. 19 3 .60 2 .15 0. 39 13 77 0. 01 99 Aug 05 2.9 0. 53 0. 20 3 .60 3 .05 0. 35 18 90 0. 00 70 Aug 2 9 1.4 0. 60 0. 24 3 .35 3 .74 0. 35 25 140 0. 01 80 0ct07 2.5 0. 69 0. 25 3 .13 3 .33 0. 24 19 250 0. 03 83 7 Mayl4 3.9 0. 34 0. 13 3 . 05 2 .72 0. 40 23 145 0. 01 73 J u l 05 4.8 0. 50 0. 20 3 .73 2 .13 0. 34 22 107 0. 01 88 Aug 05 1.9 0. 43 0. 17 4 .35 3 .20 0. 32 24 110 0. 00 83 Aug 2 9 1.2 0. 54 0. 23 4 .25 3 .68 0. 32 27 180 0. 01 101 Oct 07 2 . 2 0. 48 0. 21 3 . 33 3 .88 0. 24 19 230 0. 03 92 8 Mayl4 8.3 0. 17 0. 12 3 . 10 2 .13 0. 34 17 185 0. 02 47 JU105 3.9 0. 38 0. 17 3 .65 1 .95 0. 33 13 117 0. 01 88 Aug 05 3.1 0. 29 0. 17 3 .90 2 .72 0. 31 16 90 0. 00 76 Aug29 1.7 0. 50 0. 23 4 .03 3 .63 0. 32 25 390 0. 05 104 Oct07 2.7 0. 35 0. 22 3 .28 3 .63 0. 23 18 760 0. 09 92 9 Mayl4 5.9 0. 49 0. 18 4 .23 2 .87 0. 42 25 175 0. 02 56 J u l 0 5 5.4 0. 31 0. 15 4 . 08 2 .03 0. 36 16 77 0. 00 111 Aug 05 4.1 0. 26 0. 17 4 . 05 2 .90 0. 27 17 80 0. 00 99 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm Aug29 0. 9 0. 47 0. 26 3 .33 3 .35 0. 31 33 3100 0. 34 145 0ct07 1. 7 0. 37 0. 23 3 .25 3 .78 0. 25 23 1450 0. 17 125 10 May 14 4. 2 0. 42 0. 17 4 .33 2 .89 0. 42 22 85 0. 01 59 J u l 05 3. 8 0. 76 0. 23 3 .93 2 .90 0. 40 28 87 0. 01 58 Aug 05 3. 0 0. 50 0. 21 4 .40 2 .77 0. 34 21 90 0. 01 80 Aug29 1. 2 0. 61 0. 22 4 .08 4 .03 0. 34 26 190 0. 01 107 Oct07 2. 1 0. 60 0. 22 3 .63 3 .76 0. 24 24 190 0. 02 82 11 May 14 4. 2 0. 74 0. 25 3 .98 3 .04 0. 44 26 355 0. 04 54 J u l 05 3. 9 0. 77 0. 23 4 .28 2 .62 0. 37 23 87 0. 01 67 Aug 05 1. 8 0. 71 0. 21 4 .05 3 .21 0. 39 26 170 0. 01 59 Aug29 1. 1 0. 83 0. 24 3 .95 3 .63 0. 31 27 250 0. 02 70 Oct07 2. 6 0. 82 0. 22 3 .25 3 .72 0. 17 22 370 0. 05 63 12 May 14 6. 6 0. 22 0. 12 3 .40 2 .30 0. 36 15 55 0. 00 45 J u l 05 3. 8 0. 53 0. 19 3 .75 2 .30 0. 37 16 317 0. 03 92 Aug05 2. 4 0. 44 0. 19 3 .78 3 . 11 0. 35 16 90 0. 00 72 Aug29 1. 2 0. 69 0. 25 3 .43 3 .95 0. 34 27 250 0. 02 103 Oct07 2. 6 0. 54 0. 22 2 .90 3 .07 0. 15 22 270 0. 03 100 13 May 14 6. 7 0. 22 0. 13 3 .30 2 .33 0. 39 17 45 0. 00 67 J u l 05 3 . 8 0. 40 0. 18 3 .45 2 . 18 0. 39 13 67 0. 01 118 Aug 05 3. 2 0. 40 0. 19 3 .60 2 .81 0. 43 18 90 0. 00 101 Aug 2 9 0. 9 0. 65 0. 26 3 .48 3 .64 0. 36 25 230 0. 02 106 Oct07 1. 1 0. 52 0. 24 2 .70 3 .39 0. 27 21 380 0. 05 115 14 Mayl4 4. 9 0. 81 0. 24 3 .90 3 . 18 0. 42 26 115 0. 01 30 J u l 0 5 4. 4 0. 82 0. 27 4 .48 3 .01 0. 36 26 117 0. 01 52 Aug 05 3. 0 0. 34 0. 18 4 .38 3 .31 0. 31 23 140 0. 01 72 Aug29 0. 6 0. 74 0. 26 3 . 63 4 . 06 0. 37 33 240 0. 02 96 0ct07 1. 4 0. 69 0. 22 3 .15 4 .06 0. 24 22 150 0. 02 79 15 May 14 4. 5 0. 27 0. 15 3 .58 2 .37 0. 38 19 105 0. 01 55 J u l 0 5 3. 3 0. 84 0. 25 4 .48 2 .91 0. 36 21 187 0. 02 97 Aug 05 2. 8 0. 76 0. 23 4 .23 3 .31 0. 35 22 380 0. 04 74 Aug29 1. 0 0. 76 0. 28 3 .53 3 .90 0. 31 27 650 0. 08 98 0ct07 2. 3 0. 53 0. 25 3 . 13 3 .42 0. 18 23 980 0. 11 105 16 Mayl4 6. 4 0. 18 0. 11 3 .03 1 .57 0. 35 16 165 0. 02 50 J u l 05 3. 5 0. 60 0. 22 3 .85 3 .06 0. 37 18 237 0. 02 115 Aug 05 2. 1 0. 55 0. 21 3 .55 3 .24 0. 33 18 120 0. 01 87 Aug 2 9 1. 2 0. 78 0. 27 3 .55 3 .36 0. 31 23 370 0. 04 108 Oct 07 1. 1 0. 60 0. 24 2 .80 3 .88 0. 22 22 680 0. 08 102 17 Mayl4 5. 2 0. 14 0. 11 3 . 15 2 .19 0. 35 15 45 0. 00 50 J u l 05 4. 2 0. 50 0. 20 3 .53 2 .47 0. 34 15 97 0. 01 102 Aug 05 3. 8 0. 63 0. 22 3 .73 3 .45 0. 34 17 110 0. 00 81 Aug29 1. 1 0. 61 0. 24 3 .75 3 .62 0. 32 25 240 0. 02 127 Oct 07 1. 9 0. 71 0. 26 2 .90 3 .50 0. 19 20 270 0. 03 90 18 Mayl4 7. 5 0. 14 0. 11 2 .85 2 .10 0. 32 15 75 0. 01 56 J u l 0 5 5. 1 0. 38 0. 21 3 .68 2 .47 0. 35 18 97 0. 01 130 Aug 05 3. 2 0. 47 0. 22 3 .83 3 .48 0. 39 20 80 0. 00 104 Aug29 1. 6 0. 40 0. 27 3 .55 3 .32 0. 31 27 920 0. 10 135 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm 0ct07 1. 8 0. 60 0. 25 2 .88 19 May 14 6. 9 0. 15 0. 12 3 .25 J u l 05 3. 6 0. 37 0. 16 3 . 13 Aug 05 2. 6 0. 40 0. 20 3 .68 Aug29 1. 1 0. 62 0. 26 3 .53 Oct 07 2. 2 0. 51 0. 26 3 .15 20 May 14 5. 7 0. 12 0. 08 2 .03 J u l 0 5 4. 1 0. 36 0. 15 3 .50 Aug 05 2. 8 0. 38 0. 18 3 .80 Aug29 1. 3 0. 60 0. 23 3 .68 Oct07 2. 0 0. 36 0. 26 3 .20 21 May 14 5. 3 0. 14 0. 10 2 .63 J u l 05 4. 8 0. 44 0. 20 3 .53 Aug 05 2. 9 0. 56 0. 23 3 .68 Aug 2 9 1. 4 0. 63 0. 26 3 .40 Oct07 1. 9 0. 61 0. 26 2 .95 22 May 14 6. 0 0. 16 0. 09 2 .80 J u l 0 5 3. 7 0. 50 0. 18 3 .53 Aug 05 3. 4 0. 44 0. 18 3 .53 Aug 2 9 1. 6 0. 68 0. 25 3 .50 Oct 07 1. 2 0. 81 0. 27 2 .48 23 Mayl4 8. 7 0. 16 0. 13 3 . 60 JU105 4. 0 0. 27 0. 16 3 .43 Aug 05 2. 8 0. 31 0. 17 3 .68 Aug29 1. 5 0. 36 0. 22 3 .70 Oct07 1. 7 0. 42 0. 23 3 .33 24 May 14 7. 9 0. 18 0. 12 3 .40 J u l 0 5 3. 3 0. 36 0. 19 4 .23 Aug 05 2. 9 0. 40 0. 19 4 .20 Aug29 1. 1 0. 56 0. 26 3 .20 Oct07 1. 4 0. 60 0. 26 2 .60 25 Mayl4 6. 8 0. 18 0. 13 3 .45 J u l 05 3 . 4 0. 41 0. 18 3 .45 Aug 05 2. 7 0. 39 0. 19 3 .78 Aug 2 9 1. 0 0. 64 0. 26 3 .85 Oct07 1. 3 0. 59 0. 26 3 .00 26 May 14 7. 6 0. 21 0. 16 3 .38 J u l 0 5 3. 5 0. 53 0. 20 3 .95 Aug 05 2. 1 0. 51 0. 23 4 . 05 Aug29 0. 9 0. 66 0. 26 3 .45 Oct07 1. 4 0. 48 0. 26 2 .88 27 May 14 7. 7 0. 25 0. 16 3 .55 J u l 05 3. 3 0. 33 0. 19 4 .03 Aug 05 2. 4 0. 32 0. 19 3 .88 Aug29 0. 8 0. 46 0. 25 3 .53 Oct07 1. 6 0. 30 0. 24 2 .98 28 May 14 7. 1 0. 20 0. 14 3 .43 3 .26 0 . 19 23 220 0 . 03 114 2 .09 0 .37 20 75 0 .00 80 2 .32 0 .35 18 187 0 .01 132 2 .55 0 .36 19 180 0 .01 121 4 .01 0 .33 27 330 0 .03 79 3 .38 0 .22 25 510 0 . 06 124 1 .99 0 .34 15 45 0 .00 53 2 .02 0 .33 14 57 0 .00 91 2 .59 0 .32 16 80 0 .00 78 3 .61 0 .31 23 250 0 .02 83 3 .42 0 .23 26 1980 0 .23 109 2 .19 0 .38 16 35 0 .00 77 2 .40 0 .36 17 57 0 . 00 131 3 .03 0 .33 19 220 0 .02 86 3 .72 0 .30 24 180 0 .01 120 3 .35 0 .24 22 420 0 .05 116 1 .77 0 .32 12 35 0 .00 32 2 .37 0 .36 16 127 0 .00 78 2 .56 0 .35 15 80 0 . 00 56 3 .54 0 .33 24 190 0 . 02 85 3 .72 0 .24 21 480 0 . 06 109 2 . 19 0 .37 19 305 0 .00 56 2 .25 0 . 36 14 157 0 .00 90 2 .76 0 .31 16 100 0 .00 89 3 .32 0 .28 20 600 0 . 06 99 3 .18 0 .34 19 360 0 . 04 114 2 .14 0 .34 15 55 0 . 00 53 2 . 63 0 .37 20 167 0 .01 120 3 .24 0 .33 17 60 0 .00 69 3 .56 0 .32 21 760 0 . 08 97 3 .40 0 .23 21 350 0 .04 112 2 .45 0 .37 17 45 0 .00 72 2 .46 0 .40 19 197 0 .02 106 3 .58 0 .29 18 100 0 .00 84 3 .64 0 . 29 25 270 0 . 02 99 3 .54 0 .22 21 280 0 .04 92 2 .52 0 .37 20 505 0 .05 80 2 .43 0 .35 18 117 0 .01 119 3 .42 0 .31 20 200 0 . 02 115 3 .54 0 . 28 24 280 0 . 03 121 3 .47 0 . 18 23 1620 d . 18 116 2 .92 0 .37 17 225 0 .02 73 2 .54 0 .36 20 327 0 .03 129 3 .06 0 .28 20 160 0 .01 106 3 .50 0 .27 23 880 0 .10 128 3 .39 0 .20 25 1800 0 .22 129 2 .52 0 .38 16 245 0 . 02 51 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm J u l 05 5. 0 0. 36 0. 16 3 .68 2 .51 0. 33 13 97 0. 00 78 Aug 05 2. 8 0. 43 0. 20 4 .05 3 .60 0. 34 20 70 0. 00 75 Aug 2 9 1. 6 0. 70 0. 25 3 .68 3 .81 0. 25 23 240 0. 02 105 Oct07 2. 3 0. 77 0. 23 2 .88 3 .82 0. 18 25 220 0. 02 92 29 Mayl4 4. 9 0. 18. 0. 13 3 .23 2 .45 0. 38 16 105 0. 01 61 J u l 05 4. 6 0. 31 0. 16 3 .23 2 .06 0. 33 12 107 0. 00 122 Aug05 3. 0 0. 38 0. 19 2 .90 2 .58 0. 29 13 80 0. 00 105 Aug 2 9 1. 2 0. 54 0. 25 2 .95 3 .84 0. 34 22 180 o . 01 126 Oct07 1. 6 0. 56 0. 27 2 .75 3 .58 0. 27 24 190 0. 02 136 30 Mayl4 8. 0 0. 19 0. 11 3 .05 2 .14 0. 34 15 55 0. 00 53 J u l 0 5 4. 2 0. 24 0. 11 2 .73 2 . 11 0. 32 15 87 0. 00 65 Aug 05 3. 5 0. 35 0. 19 4 .15 3 .35 0. 30 13 80 0. 00 65 Aug29 0. 9 0. 52 0. 23 3 .70 3 .59 0. 27 23 190 0. 01 74 Oct07 2. 3 0. 46 0. 21 3 .45 3 . 68 0. 25 20 230 0. 03 82 31 May 14 5. 1 0. 23 0. 14 3 .45 1 .96 0. 36 18 55 0. 00 91 J u l 0 5 3 . 0 0. 40 0. 21 3 .03 1 .84 0. 33 18 57 0. 01 155 Aug 05 2. 8 0. 37 0. 20 3 .30 2 .89 0. 34 19 80 0. 00 115 Aug29 0. 4 0. 60 0. 27 3 .18 3 .35 0. 28 24 300 0. 03 115 Oct07 0. 6 0. 53 0. 29 2 . 65 4 .40 0. 35 29 1380 0. 13 153 32 May 14 5. 3 0. 23 0. 14 3 .40 2 .22 0. 37 17 95 0. 01 76 J u l 0 5 3. 7 0. 71 0. 21 3 .33 2 .33 0. 36 12 107 0. 01 124 Aug 05 3. 5 0. 58 0. 19 3 .13 2 .62 0. 27 14 90 0. 00 86 Aug 2 9 0. 8 0. 83 0. 29 3 .15 3 .56 0. 29 24 200 0. 02 100 Oct07 1. 1 0. 80 0. 27 2 .28 3 .73 0. 23 23 400 0. 05 126 33 May 14 7. 9 0. 20 0. 14 3 .68 2 .49 0. 37 17 75 0. 00 64 J u l 0 5 3. 5 0. 36 0. 17 3 .88 2 .29 0. 33 16 226 0. 02 98 Aug 05 3 . 2 0. 35 0. 20 4 .75 3 .57 0. 28 17 240 0. 01 89 Aug29 1. 1 0. 64 0. 26 3 .50 3 .68 0. 27 24 270 0. 03 110 Oct07 1. 1 0. 58 0. 22 3 . 03 3 .41 0. 22 21 220 0. 02 98 34 May 14 6. 0 0. 18 0. 12 3 . 03 1 .67 0. 33 14 75 0. 01 82 J u l 0 5 2. 6 0. 38 0. 20 2 .83 1 .86 0. 35 17 167 0. 01 155 Aug05 2. 8 0. 33 0. 18 3 .45 2 .78 0. 32 18 110 0. 01 125 Aug29 0. 5 0. 47 0. 23 3 .00 3 . 12 0. 29 21 190 0. 02 136 Oct07 0. 7 0. 40 0. 24 3 . 38 4 . 14 0. 37 23 250 0. 03 151 35 Mayl4 5. 4 0. 16 0. 12 3 . 15 2 .26 0. 37 16 45 0. 00 65 J u l 0 5 5. 0 0. 41 0. 21 3 . 08 2 .47 0. 37 21 187 0. 01 127 Aug 05 3. 3 0. 43 0. 20 3 .70 2 .87 0. 36 19 110 0. 01 116 Aug 2 9 1. 3 0. 58 0. 26 3 .55 2 .98 0. 36 24 230 0. 02 150 Oct07 1. 2 0. 61 0. 28 2 .85 3 . 62 0. 27 27 420 0. 05 142 36 Mayl4 4. 7 0. 31 0. 15 3 . 28 2 .21 0. 38 16 65 0. 01 75 J u l 05 3. 5 0. 36 0. 18 2 .58 2 .00 0. 34 18 57 0. 00 120 Aug 05 2. 4 0. 47 0. 21 3 .00 2 .92 0. 38 17 90 0. 00 91 Aug 2 9 0. 8 0. 78 0. 34 2 . 13 3 .65 0. 34 22 170 0. 01 120 0ct07 1. 6 0. 71 0. 34 2 .28 3 .55 0. 25 26 1540 0. 16 137 37 May 14 4. 8 0. 21 0. 13 3 .35 2 .13 0. 36 16 55 0. 00 67 J u l 0 5 3 . 6 0. 83 0. 24 3 .65 2 .58 0. 37 18 107 0. 00 110 Aug 05 3. 1 0. 66 0. 21 3 .45 2 .83 0. 37 16 110 0. 01 80 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm Aug 2 9 1. 4 0. 77 0. 29 3 . 03 3 .86 0. 33 22 170 0. 01 87 Oct07 1. 3 0. 69 0. 31 2 .70 3 .17 0. 18 23 1080 0. 14 102 38 May 14 6. 5 0. 14 0. 11 3 .03 1 .96 0. 35 19 35 0. 00 77 J u l 05 4. 0 0. 30 0. 16 3 .38 1 .95 0. 34 14 97 0. 00 115 Aug 05 2. 5 0'. 34 0. 17 3 .68 2 .59 0. 35 14 60 0. 00 109 Aug 2 9 1. 0 0. 45 0. 23 3 .48 3 .37 0. 32 23 390 0. 04 140 Oct 07 1. 5 0. 33 0. 28 2 .78 3 .30 0. 20 30 2900 0. 31 160 39 Mayl4 7. 3 0. 18 0. 12 2 .95 2 .05 0. 35 15 85 0. 00 62 J u l 05 4. 2 0. 48 0. 20 3 .33 2 .39 0. 34 18 67 0. 00 112 Aug 05 3. 0 0. 42 0. 19 3 .60 3 .25 0. 30 18 80 0. 00 99 Aug 2 9 1. 4 0. 53 0. 25 3 .58 3 .83 0. 32 24 130 0. 01 112 Oct07 2. 2 0. 52 0. 25 3 .08 3 .50 0. 20 23 130 0. 01 116 40 May 14 8. 1 0. 18 0. 15 4 . 15 2 .73 0. 37 15 165 0. 01 67 J u l 05 5. 0 0. 37 0. 19 4 .43 2 .73 0. 34 16 87 0. 00 114 Aug 05 3. 7 0. 30 0. 20 4 .33 3 .53 0. 28 20 670 0. 07 105 Aug 2 9 1. 3 0. 48 0. 25 3 .80 3 .20 0. 32 24 220 0. 02 130 Oct07 2. 1 0. 46 0. 25 3 .43 3 .81 0. 23 21 360 0. 04 140 41 May 14 6. 8 0. 14 0. 12 2 .90 2 .31 0. 38 17 65 0. 00 82 Jul05 3. 2 0. 29 0. 18 3 . 58 2 .21 0. 35 17 287 0 . 02 151 Aug 05 2 . 6 0. 28 0. 19 3 .60 3 . 33 0. 29 17 100 0. 00 119 Aug 2 9 0. 8 0. 39 0. 24 3 .73 3 .75 0. 32 21 220 0. 02 156 Oct07 1. 0 0. 18 0. 27 3 .13 3 . 19 0. 22 33 2550 0. 30 147 42 May 14 5. 4 0. 17 0. 14 3 . 30 2 . 66 0. 36 18 65 0. 00 58 J u l 05 4. 0 0. 29 0. 17 3 .33 2 . 10 0. 38 16 127 0. 00 117 Aug 05 3 . 1 0. 32 0. 18 4 .48 3 .36 0. 31 16 100 0. 01 106 Aug 2 9 1. 1 0. 70 0. 26 3 .30 3 .99 0. 31 21 290 0. 02 81 Oct07 2. 0 0. 28 0. 23 3 .80 3 .91 0. 33 26 850 0. 10 122 43 May 14 6. 7 0. 20 0. 12 2 .88 2 .43 0. 37 17 45 0. 00 62 Jul05 4. 0 0. 46 0. 16 2 .78 2 .23 0. 35 15 187 0. 01 93 Aug 05 3. 3 0. 57 0. 21 3 .38 3 . 12 0. 31 23 120 0. 01 80 Aug29 1. 1 0. 36 0. 22 3 .70 3 .67 0. 36 21 230 0. 02 121 Oct07 1. 5 0. 55 0. 23 3 .28 3 .97 0. 28 21 150 0. 01 93 44 Mayl4 5. 5 0. 14 0. 13 3 .48 2 .32 0. 38 17 65 0. 00 73 Jul05 4. 7 0. 30 0. 17 3 .63 2 .01 0. 34 16 127 0. 01 137 Aug 05 3. 5 0. 25 0. 17 3 .73 2 .81 0. 30 15 120 0. 01 107 Aug 2 9 1. 0 0. 35 0. 23 3 .60 3 .93 0. 35 23 190 0. 01 130 0ct07 2. 0 0. 33 0. 26 3 .30 3 .53 0. 34 29 810 0. 10 135 45 May 14 4. 8 0. 18 0. 12 2 .90 1 .98 0. 34 17 55 0. 00 53 Jul05 4. 0 0. 30 0. 14 2 .55 1 .96 0. 36 14 117 0. 00 123 Aug 05 3. 3 0. 52 0. 20 3 .80 2 .98 0. 36 19 80 0. 00 102 Aug 2 9 1. 3 0. 60 0. 27 3 .18 3 .71 0. 33 24 230 0. 01 115 Oct07 1. 5 0. 36 0. 26 3 .18 3 .04 0. 39 26 1880 0. 21 126 46 May 14 6. 5 0. 21 0. 14 3 .53 2 .42 0. 37 17 65 0. 01 62 J u l 05 4. 3 0. 36 0. 17 3 .40 2 .05 0. 37 14 127 0. 01 111 Aug 05 2. 7 0. 33 0. 17 3 .75 2 .82 0. 31 16 120 0. 00 87 Aug 2 9 1. 1 0. 33 0. 23 3 .53 4 . 00 0. 29 22 930 0. 10 100 Oct07 1. 3 0. 38 0. 22 3 .55 3 .98 0. 38 24 190 0. 02 112 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm 47 Mayl4 5. 8 0. 20 0. 13 2 .95 2 .16 0. 34 18 45 0. 00 70 JU105 4. 1 0. 52 0. 21 3 .35 2 .33 0. 37 17 77 0. 01 126 Aug05 3. 7 0. 44 0. 20 3 .48 2 .87 0. 34 17 70 0. 00 104 Aug29 1. 4 0. 46 0. 24 3 .25 3 .15 0. 28 19 140 0. 01 101 Oct07 1. 9 0. 53 0. 26 3 .13 3 .48 0. 35 26 150 0. 02 122 48 May 14 5. 3 0. 17 0. 13 3 .30 2 .26 0. 35 18 35 0. 01 91 Jul05 4. 7 0. 33 0. 16 3 .25 2 .18 0. 36 18 187 0. 01 159 Aug 05 3. 0 0. 28 0. 16 3 .75 2 .53 0. 35 15 80 0. 00 121 Aug 2 9 1. 1 0. 39 0. 23 3 .53 3 .86 0. 33 19 160 0. 01 163 Oct07 1. 0 0. 43 0. 25 3 . 10 3 .05 0. 35 27 780 0. 10 153 49 Mayl4 5. 0 0. 18 0. 14 3 .35 2 .72 0. 37 16 45 0. 00 79 JU105 3. 8 0. 34 0. 14 2 .45 2 . 19 0. 36- 14 97 0. 01 136 Aug 05 3. 2 0. 36 0. 19 3 .43 2 .91 0. 30 14 80 0. 00 107 Aug29 1. 4 0. 28 0. 25 3 .30 4 .04 0. 34 21 150 0. 01 132 Oct07 1. 6 0. 49 0. 26 3 . 15 3 .24 0. 36 25 310 0. 04 159 50 May 14 7. 0 0. 12 0. 12 2 .90 2 .18 0. 34 16 65 0. 00 70 J u l 05 3 . 2 0. 27 0. 14 2 .78 2 .28 0. 40 14 67 0. 00 142 Aug 05 2. 8 0. 37 0. 19 3 .50 2 .75 0. 33 17 110 0. 00 119 Aug 2 9 1. 0 0. 49 0. 27 2 .83 3 .70 0. 30 25 160 0. 01 131 Oct07 1. 2 0. 50 0. 26 3 .00 3 .51 0. 38 27 240 0. 03 146 51 May 14 6. 3 0. 18 0. 13 2 . 60 2 . 33 0. 33 16 435 0. 05 61 Jul05 4. 4 0. 42 0. 19 3 .05 2 . 27 0. 37 18 117 0. 00 93 Aug 05 3 . 5 0. 35 0. 19 2 . 65 2 . 55 0. 30 15 80 0. 00 92 Aug 2 9 0. 8 0. 51 0. 26 2 . 33 3 .57 0. 35 22 170 0. 01 105 Oct07 1. 7 0. 59 0. 27 2 .83 3 .45 0. 40 26 330 0. 04 128 52 May 14 5. 0 0. 16 0. 10 2 .48 2 .22 0. 35 15 45 0. 00 61 J u l 05 3. 3 0. 53 0. 19 3 . 15 1 .99 0. 36 15 117 0. 00 108 Aug 05 3. 1 0. 38 0. 19 3 .45 2 .80 0. 37 18 110 0. 01 106 Aug29 0. 9 0. 57 0. 24 3 . 15 3 .73 0. 30 23 160 0. 01 95 Oct07 1. 2 0. 58 0. 25 2 .88 3 .27 0. 38 24 370 0. 04 121 53 Mayl4 5. 6 0. 18 0. 13 3 .08 2 .40 0. 35 18 45 0. 00 52 J u l 05 3 . 9 0. 33 0. 18 3 .40 2 .37 0. 38 17 97 0. 00 128 Aug 05 3. 9 0. 34 0. 18 3 . 35 2 .83 0. 29 16 130 0. 01 106 Aug 2 9 1. 0 0. 41 0. 23 2 .95 3 .29 0. 30 22 130 0. 01 105 Oct 07 1. 4 0. 33 0. 25 2 .73 2 .99 0. 35 27 1540 0. 17 149 54 May 14 6. 1 0. 19 0. 14 3 .38 2 . 60 0. 35 18 75 0. 01 61 Jul05 5. 3 0. 35 0. 19 3 .50 2 .57 0. 37 19 207 0. 01 140 Aug 05 3. 6 0. 29 0. 19 3 .90 3 .24 0. 28 17 560 0. 06 96 Aug 2 9 1. 2 0. 40 0. 22 3 .50 3 .78 0. 36 22 160 0. 01 101 Oct 0.7 2. 2 0. 29 0. 24 3 .28 3 .28 0. 35 28 1280 0. 15 129 55 May 14 7. 8 0. 18 0. 15 3 .10 2 .80 0. 35 19 45 0. 00 71 Jul05 5. 5 0. 28 0. 16 3 .40 2 .29 0. 34 14 77 0. 00 96 Aug05 3. 9 0. 26 0. 17 3 .38 2 .80 0. 30 15 80 0. 00 105 Aug29 1. 3 0. 38 0. 22 3 .53 4 .00 0. 29 18 200 0. 01 130 Oct07 1. 4 0. 32 0. 25 3 .25 3 .57 0. 38 29 1230 0. 14 150 56 May 14 5. 9 0. 16 0. 12 2 .83 2 .27 0. 34 17 215 0. 02 69 J u l 05 5. 0 0. 37 0. 18 3 .58 2 . 10 0. 35 15 77 0. 00 138 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm Aug 05 2. 6 0. 34 0. 17 3. 73 2 .90 0. 33 15 70 0. 00 116 Aug29 0. 9 0. 52 0. 25 3. 28 3 .71 0. 28 26 200 0. 01 130 Oct 07 1. 6 0. 49 0. 26 2. 93 3 .44 0. 38 30 380 0. 04 150 57 May 14 5. 2 0. 20 0. 13 2. 55 2 .63 0. 36 17 55 0. 00 88 J u l 05 4. 4 0. 48 0. 21 2. 43 2 .17 0. 40 14 107 0. 00 137 Aug 05 2. 6 0. 50 0. 25 1. 88 3 .69 0. 31 14 60 0. 00 102 Aug29 0. 8 0. 66 0. 32 1. 68 3 .95 0. 32 24 220 0. 01 98 Oct07 1. 4 0. 54 0. 28 1. 75 3 .52 0. 39 30 120 0. 01 143 58 May 14 5. 9 0. 18 0. 16 3. 23 2 .63 0. 36 19 465 0. 05 72 J u l 05 3. 9 0. 33 0. 18 3. 30 2 .22 0. 34 17 237 0. 02 140 Aug 05 2. 4 0. 29 0. 17 3. 55 3 .07 0. 34 19 100 0. 00 112 Aug29 0. 9 0. 49 0. 25 2. 73 3 .63 0. 28 23 230 0. 01 115 Oct 07 1. 7 0. 41 0. 24 2. 88 3 .08 0. 37 29 100 0. 01 154 59 Mayl4 4. 1 0. 22 0. 15 3. 08 2 .60 0. 37 15 215 0. 02 45 J u l 0 5 3. 4 0. 68 0. 22 3. 35 2 .58 0. 39 16 197 0. 01 92 Aug05 2. 3 0. 77 0. 23 2. 65 2 .92 0. 35 17 120 0. 01 68 Aug 2 9 1. 3 0. 77 0. 32 2. 28 3 .72 0. 31 23 210 0. 01 71 Oct07 1. 3 0. 78 0. 34 2 . 13 3 .76 0. 36 30 1130 0. 15 115 60 May 14 4. 8 0. 18 0. 13 2. 48 2 .23 0. 34 16 45 0. 00 106 J u l 05 5. 1 0. 53 0. 21 2. 90 2 .13 0. 37 15 197 0. 02 139 Aug 05 2. 0 0. 43 0. 25 1. 58 2 .82 0. 34 17 250 0. 03 127 Aug29 0. 7 0. 46 0. 27 1. 83 3 .59 0. 31 24 300 0. 03 116 Oct07 1. 3 0. 37 0. 31 1. 58 2 .99 0. 34 35 1910 0. 23 192 61 Mayl4 5. 7 0. 22 0. 15 3 . 00 2 .60 0. 35 18 55 0. 00 71 J u l 0 5 3. 6 0. 44 0. 25 2. 08 2 . 18 0. 39 15 77 0. 00 178 Aug 05 2. 8 0. 57 0. 22 1. 78 3 .04 0. 31 17 80 0. 00 79 Aug29 0. 9 0. 68 0. 27 1. 70 3 .73 0. 36 25 150 0. 01 88 Oct07 1. 2 0. 82 0. 29 1. 78 3 .58 0. 35 28 360 0. 04 112 62 Mayl4 6. 0 0. 19 0. 14 2. 73 2 .15 0. 34 19 165 0. 02 64 J u l 05 3. 9 0. 50 0. 19 2 . 53 2 . 03 0. 3 6 13 127 0. 01 107 Aug 05 3. 4 0. 32 0. 18 2. 05 2 .70 0. 36 16 60 0. 00 87 Aug29 0. 8 0. 54 0. 28 1. 88 3 .42 0. 32 26 140 0. 01 112 Oct 07 0. 9 0. 52 0. 30 2. 03 3 .48 0. 40 34 380 0. 05 161 63 May 14 6. 3 0. 25 0. 16 3. 08 2 .60 0. 36 19 55 0. 01 61 J u l 0 5 4. 1 0. 55 0. 23 2. 03 2 .18 0. 60 14 77 0. 01 116 Aug 05 4. 3 0. 31 0. 17 1. 85 2 .59 0. 32 14 60 0. 00 83 Aug 2 9 1. 3 0. 51 0. 27 1. 38 3 .72 0. 35 23 140 0. 01 76 Oct 07 1. 8 0. 55 0. 32 1. 73 3 .50 0. 36 30 580 0. 07 126 64 May 14 5. 7 0. 21 0. 16 3 . 33 2 . 65 0. 37 19 435 0. 04 90 J u l 0 5 5. 5 0. 31 0. 17 2. 85 2 .63 0. 37 17 97 0. 01 122 Aug 05 1. 5 0. 38 0. 20 2. 90 3 .70 0. 31 20 80 0. 00 107 Aug29 0. 8 0. 48 0. 25 2. 15 3 .86 0. 34 23 170 0. 01 119 Oct07 1. 2 0. 46 0. 25 2. 40 3 .50 0. 35 24 140 0. 01 129 65 May 14 6. 2 0. 15 0. 12 2. 75 2 .70 0. 37 16 55 0. 00 68 J u l 05 4. 7 0. 26 0. 15 3. 33 2 .43 0. 33 13 77 0. 00 97 Aug 05 2. 6 0. 36 0. 20 3 . 35 3 .45 0. 29 16 70 0. 00 94 Aug29 1. 1 0. 44 0. 27 2. 70 3 .99 0. 35 26 140 0. 01 117 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm Oct07 1. 5 0. 45 0. 28 2. 78 3 .28 0. 33 29 500 0. 06 160 66. May 14 3. 8 0. 22 0. 13 3. 08 2 .13 0. 35 16 55 0. 01 54 J u l 0 5 3. 8 0. 28 0. 13 1. 63 2 .20 0. 36 20 77 0. 00 117 Aug 05 2. 6 0. 41 0. 26 2. 20 3 .64 0. 34 21 60 0. 00 78 Aug 2 9 0. 9 0. 53 0. 31 1. 88 4 .13 0. 32 25 130 0. 01 88 Oct07 1. 3 0. 55 0. 30 2. 08 3 .48 0. 40 28 350 0. 04 112 67 May 14 3. 8 0. 15 0. 12 2. 90 2 .20 0. 35 20 95 0. 01 76 J u l 05 4. 1 0. 27 0. 11 1. 78 1 .95 0. 33 17 107 0. 00 166 Aug 05 3. 5 0. 38 0. 22 2. 98 3 .14 0. 30 20 70 0. 00 111 Aug 2 9 0. 9 0. 45 0. 25 2. 50 3 .66 0. 30 21 140 0. 00 94 Oct07 1. 2 0. 44 0. 30 2. 68 3 .62 0. 35 31 960 0. 11 127 68 May 14 6. 7 0. 17 0. 12 2. 93 2 .40 0. 36 17 45 0. 00 45 J u l 05 4. 4 0. 21 0. 10 1. 95 2 .19 0. 34 13 117 0. 00 95 Aug 05 2. 9 0. 31 0. 20 4. 08 2 .88 0. 33 18 120 0. 01 85 Aug 2 9 0. 8 0. 38 0. 22 3. 15 3 .81 0. 31 17 210 0. 02 84 Oct07 1. 2 0. 42 0. 25 3. 70 3 .79 0. 36 28 300 0. 04 112 69 Mayl4 5. 3 0. 18 0. 11 2. 33 1 .98 0. 31 14 55 0. 00 67 J u l 0 5 4. 0 0. 46 0. 19 2. 50 2 .24 0. 33 15 127 0. 01 127 Aug 05 3. 3 0. 39 0. 24 1. 88 3 .00 0. 33 18 80 0. 00 94 Aug 2 9 1. 0 0. 44 0. 26 1. 90 3 .52 0. 28 21 200 0. 02 112 Oct07 1. 2 0. 48 0. 28 2. 35 3 .34 0. 39 27 330 0. 04 155 70 Mayl4 6. 8 0. 18 0. 13 3. 10 2 .58 0. 36 15 55 0. 00 56 J u l 0 5 5. 5 0. 23 0. 11 1. 98 2 .20 0. 33 14 157 0. 01 100 Aug 05 1. 9 0. 34 0. 23 2. 48 2 .84 0. 33 20 70 0. 00 90 Aug 2 9 0. 8 0. 43 0. 23 1. 98 3 .32 0. 31 20 130 0. 01 89 Oct07 1. 7 0. 45 0. 25 2. 58 3 . 12 0. 33 26 170 0. 02 115 71 May 14 5. 9 0. 23 0. 14 3. 10 2 . 10 0. 36 16 55 0. 00 104 J u l 05 3. 6 0. 33 0. 17 2. 43 1 .80 0. 33 12 177 0. 01 14 2 Aug 05 2. 4 0. 30 0. 21 2 . 20 2 .29 0. 34 16 50 0. 00 119 Aug29 1. 3 0. 38 0. 26 2. 13 3 .68 0. 34 26 230 0. 02 130 Oct07 1. 5 0. 47 0. 30 2. 28 2 .93 0. 39 31 290 0. 04 175 72 May 14 6. 0 0. 16 0. 12 2. 83 2 . 18 0. 37 18 45 0. 00 83 J u l 05 4. 5 0. 36 0. 16 2. 35 2 .03 0. 35 13 127 0. 01 142 Aug 05 2. 3 0. 34 0. 22 2. 68 2 .80 0. 34 21 60 0. 00 103 Aug 2 9 1. 0 0. 55 0. 28 2. 08 3 .77 0. 33 25 200 0. 02 116 0ct07 1. 2 0. 65 0. 30 2. 13 3 .22 0. 39 26 380 0. 05 138 73 Mayl4 5. 1 0. 20 0. 15 3. 10 2 .28 0. 36 17 45 0. 00 100 J u l 0 5 3. 6 o . 37 0. 22 2. 10 2 .60 0. 36 17 117 0. 01 140 Aug 05 2. 3 0. 41 0. 25 1. 78 3 .08 0. 29 21 60 0. 00 128 Aug29 0. 7 0. 47 0. 31 1. 75 3 .92 0. 35 27 180 0. 01 150 0ct07 1. 0 0. 40 0. 29 2 . 25 3 .49 0. 43 29 450 0. 05 168 74 May 14 4. 7 0. 23 0. 14 3 . 28 2 .38 0. 3 6 19 35 0. 00 78 J u l 05 3. 9 0. 58 0. 22 2. 73 2 .85 0. 38 18 127 0. 01 135 Aug 05 2. 4 0. 46 0. 22 1. 83 2 .77 0. 31 19 70 0. 00 93 Aug 2 9 1. 1 0. 57 0. 29 1. 68 4 . 04 0. 35 26 170 0. 01 107 Oct07 1. 7 0. 55 0. 31 2. 15 3 .27 0. 35 29 670 0. 08 141 75 Mayl4 6. 2 0. 19 0. 14 3. 03 2 .38 0. 37 19 45 0. 00 68 177 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No. Date T/ha % % % % % ppm ppm % ppm JU105 4.3 0.40 0.17 2.30 2.20 0.34 14 347 0.03 132 Aug05 2.8 0.49 0.25 1.78 3.13 0.31 16 50 0.00 90 Aug29 1.1 0.61 0.29 1.55 4.62 0.34 25 150 0.01 94 Oct07 0.5 0.68 0.32 2.05 3.78 0.39 32 530 0.06 130 76 Mayl4 6.5 0.23 0.15 3.18 2.47 0.36 17 55 0.00 74 JU105 4.5 0.42 0.20 2.75 2.41 0.36 17 107 0.01 140 Aug05 2.1 0.40 0.26 1.88 3.58 0.30 21 60 0.00 97 Aug29 0.8 0.50 0.30 1.78 3.67 0.31 25 170 0.02 118 Oct07 1.4 0.31 0.36 1.83 3.06 0.33 38 5000 0.47 192 Dry Matter and f o l i a r elements for 38 i r r i g a t e d sites,1986 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm i ppm % ppm 80 Mayl4 4.8 0. 19 0. 12 3 .35 2 .42 0. 36 17 45 0. 00 68 J u l 0 5 3.1 0. 38 0. 16 3 .28 1 .82 0. 34 15 437 0. 05 135 Aug 05 2.4 0. 28 0. 16 3 . 68 2 .94 0. 34 19 90 0. 00 94 Aug 2 9 1.2 0. 39 0. 20 3 .73 3 .87 0. 30 21 240 0. 01 108 Oct07 1.0 0. 46 0. 24 3 .45 3 .99 0. 39 23 1300 0. 09 109 81 May 14 4.7 0. 15 0. 11 2 .88 2 .41 0. 36 17 295 0. 02 58 J u l 0 5 3.6 0. 41 0. 16 3 .15 2 . 16 0. 33 13 97 0. 00 94 Aug 05 2.7 0. 59 0. 24 3 .20 3 .48 0. 30 20 90 0. 00 90 Aug29 1.7 0. 60 0. 26 3 . 18 4 .13 0. 30 25 250 0. 00 97 Oct07 1.1 0. 58 0. 25 3 .43 4 .30 0. 40 22 520 0. 03 93 82 May 14 4.4 0. 18 0. 12 3 . 43 2 .32 0. 37 16 35 0. 00 48 J u l 05 4.5 0. 43 0. 17 3 . 58 2 .37 0. 33 16 107 0. 00 103 Aug 05 1.7 0. 39 0. 18 4 . 03 3 .54 0. 30 18 90 0. 00 75 Aug29 .1.8 0. 46 0. 22 3 .75 3 .73 0. 29 20 28 0 0. 01 87 Oct07 1.1 0. 52 0. 21 3 . 60 4 . 16 0. 38 21 480 0. 02 67 83 May 14 3.9 0. 18 0. 11 3 .30 2 .36 0. 38 17 35 0. 00 50 J u l 05 5.0 0. 34 0. 18 3 .63 2 .63 0. 35 18 397 0. 03 102 Aug 05 2.3 0. 34 0. 18 3 .83 3 .06 0. 30 20 150 0. 01 74 Aug29 1.2 0. 46 0. 23 3 .33 3 .79 0. 29 23 280 0. 01 92 0ct07 1.3 0. 20 0. 23 3 .20 3 .30 0. 40 25 3350 0. 36 98 84 May 14 5.1 0. 20 0. 11 3 .23 2 .19 0. 38 15 35 0. 00 64 J u l 05 4.6 0. 46 0. 19 2 .95 2 .31 0. 32 15 77 0. 00 87 Aug 05 3 . 0 0. 3 6 0. 21 3 .38 2 .75 0. 32 17 70 0. 00 77 Aug 2 9 1.6 0. 49 0. 28 3 .40 4 .28 0. 32 24 210 0. 01 99 Oct07 1.5 0. 50 0. 27 3 .68 4 .35 0. 39 20 490 0. 03 81 85 May 14 3.9 0. 18 0. 10 2 . 68 2 . 19 0. 36 15 35 0. 00 72 JU105 4.3 0. 55 0. 20 3 .00 2 .72 0. 38 17 127 0. 00 113 Aug 05 3.1 0. 45 0. 23 3 .35 3 .51 0. 34 20 70 0. 00 98 Aug29 1.3 0. 61 0. 25 3 .53 4 .04 0. 34 23 290 0. 00 91 Oct 07 1.3 0. 63 0. 25 3 . 18 4 .47 0. 45 21 620 0. 04 72 178 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm 86 May 14 4.8 0. 20 0. 12 3 .48 2 .55 0. 36 17 45 0. 00 66 J u l 05 5.3 0. 36 0. 15 3 .05 2 .49 0. 33 16 147 0. 00 106 Aug 05 2.2 0. 46 0. 21 3 .75 3 .69 0. 31 20 80 0. 00 102 Aug29 1.1 0. 54 0. 23 3 .48 4 .29 0. 26 25 310 0. 01 120 Oct07 1.5 0. 43 0. 24 3 .78 4 .41 0. 47 24 1350 0. 11 113 87 May 14 4.5 0. 25 0. 13 3 .33 2 .43 0. 37 18 45 0. 00 65 J u l 0 5 5.6 0. 51 0. 19 2 .78 2 .42 0. 37 18 577 0. 05 118 Aug05 2.3 0. 52 0. 22 2 .65 3 .48 0. 32 18 90 0. 00 76 Aug29 1.7 0. 63 0. 28 2 .93 4 .29 0. 36 24 310 0. 01 85 Oct 07 1.3 0. 61 0. 27 2 .78 4 .18 0. 45 23 1560 0. 11 91 88 May 14 4.0 0. 22 0. 14 3 .35 2 .15 0. 36 18 45 0. 00 57 J u l 05 4.8 0. 36 0. 20 3 .20 2 .14 0. 36 19 1127 0. 12 128 Aug05 2.0 0. 40 0. 19 3 .28 2 .64 0. 34 16 70 0. 00 97 Aug 2 9 1.6 0. 58 0. 25 3 .50 3 .62 0. 33 22 250 0. 01 108 Oct07 0.8 0. 68 0. 25 3 . 10 4 .26 0. 43 23 570 0. 04 93 89 May 14 4 . 2 0. 21 0. 15 3 . 58 2 .59 0. 39 17 45 0. 00 75 J u l 05 5.0 0. 33 0. 16 3 . 10 2 .18 0. 34 16 187 0. 01 124 Aug 05 2.7 0. 35 0. 21 3 .55 3 .05 0. 31 17 80 0. 00 108 Aug 2 9 1.2 0. 41 0. 25 3 .65 4 . 18 0. 31 22 700 0. 06 104 Oct07 1.1 0. 40 0. 25 3 .45 3 .99 0. 37 20 1500 0. 08 105 90 May 14 3.7 0. 20 0. 15 3 .48 2 .78 0. 41 20 45 0. 00 72 J u l 05 5.6 0. 33 0. 19 3 .35 3 .07 0. 39 19 167 0. 00 107 Aug 05 2.8 0. 36 0. 24 2 .80 3 .39 0. 27 16 70 0. 00 93 Aug 2 9 1.3 0. 54 0. 29 2 . 65 4 .30 0. 26 25 330 0. 01 115 Oct07 1.7 0. 43 0. 30 2 .85 4 . 11 0. 41 25 1440 0. 10 , 118 91 Mayl4 4.8 0. 25 0. 15 3 .28 2 . 60 0. 37 18 55 0. 00 74 J u l 05 5.0 0. 55 0. 2 6 2 .80 2 .71 0. 39 22 387 0. 03 145 Aug 05 2.7 0. 42 0. 27 2 .08 3 .59 0. 37 21 50 0. 00 91 Aug29 1.0 0. 57 0. 29 2 .40 4 .47 0. 30 26 230 0. 01 91 0ct07 1.2 0. 67 0. 30 2 .20 4 .47 0. 43 26 360 0. 02 89 92 May 14 4.8 0. 19 0. 14 3 .38 2 .48 0. 35 16 45 0. 00 75 J u l 05 8.9 0. 35 0. 21 3 .48 2 .86 0. 40 17 167 0. 01 129 Aug 05 2.4 0. 38 0. 20 2 .98 2 .97 0. 28 16 60 0. 00 85 Aug29 1.4 0. 59 0. 27 3 .03 4 .30 0. 29 24 660 0. 05 110 Oct07 1.0 0. 61 0. 28 3 . 08 4 .47 0. 3 8 22 600 0. 05 89 93 Mayl4 4.6 0. 21 0. 14 3 .23 2 .23 0. 36 17 35 0. 00 58 J u l 0 5 4.4 0. 36 0. 18 2 .95 2 .29 0. 37 15 167 0. 01 119 Aug 05 2 . 3 0. 34 0. 20 2 .53 3 .14 0. 36 20 60 0. 00 89 Aug29 1.6 0. 43 0. 28 2 .70 4 . 03 0. 31 23 220 0. 01 90 Oct07 1.4 0. 30 0. 29 2 .20 3 .10 0. 33 24 2900 0. 27 132 94 May 14 3.7 0. 26 0. 15 3 .38 2 .63 0. 38 19 35 0. 00 67 J u l 0 5 5.4 0. 41 0. 17 3 .00 2 .22 0. 36 15 117 0. 00 106 Aug 05 2.4 0. 45 0. 22 2 .03 2 .73 0. 36 20 80 0. 00 108 Aug 2 9 1.3 0. 64 0. 31 2 .48 4 .46 o . 31 26 260 0. 00 105 Oct07 1.0 0. 56 0. 29 2 .53 4 .22 0. 40 25 750 0. 05 103 95 May 14 4.3 0. 26 0. 15 3 .00 2 .58 0. 36 16 45 0. 00 71 J u l 05 4.6 0. 47 0. 20 1 .73 2 .26 0. 38 16 97 0. 00 136 179 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm i ppm % ppm Aug 05 2.1 0. 41 0. 25 1. 63 2 .99 0. 39 19 80 0. 00 94 Aug 2 9 1.5 0. 58 0. 32 1. 53 4 .23 0. 31 24 150 0. 00 101 0ct07 1.0 0. 56 0. 29 1. 90 4 . 11 0. 43 25 300 0. 02 100 96 May 14 4.4 0. 21 0. 13 2. 88 2 .48 0. 35 16 35 0. 00 54 J u l 0 5 5.5 0. 39 0. 17 2 . 73 2 .14 0. 35 13 217 0. 01 99 Aug 05 2.1 0. 46 0. 24 2. 80 2 .90 0. 38 19 100 0. 00 105 Aug29 1.3 0. 48 0. 26 3. 03 4 .05 0. 28 21 220 0. 01 88 Oct 07 0.9 0. 34 0. 33 2. 73 4 .35 0. 40 28 3250 0. 33 103 97 May 14 4.9 0. 16 0. 12 3. 03 2 .13 0. 34 15 45 0. 00 57 J u l 05 3.8 0. 34 0. 15 2. 63 2 .17 0. 31 13 97 0. 00 104 Aug 05 2.7 0. 33 0. 19 3. 30 2 .74 0. 33 16 60 0. 00 101 Aug29 1.3 0. 42 0. 23 2. 98 3 .34 0. 34 22 460 0. 02 122 0ct07 0.8 0. 37 0. 23 3. 23 3 .51 0. 44 21 520 0. 03 112 98 Mayl4 6.3 0. 21 0. 14 3 . 08 2 .38 0. 34 15 35 0. 00 77 J u l 05 3.8 0. 45 0. 20 2 . 70 2 .33 0. 33 13 107 0. 01 132 Aug 05 2.4 0. 40 0. 25 2. 58 3 .45 0. 31 22 70 0. 00 92 Aug29 1.3 0. 58 0. 30 2. 48 4 .35 0. 29 26 390 0. 03 106 Oct07 1.2 0. 51 0. 32 2. 18 4 .23 0. 42 20 600 0. 05 94 99 May 14 4.4 0. 19 0. 12 2. 80 2 .35 0. 36 17 35 0. 00 75 J u l 0 5 4.6 0. 48 0. 21 2. 30 2 .23 0. 34 17 187 0. 02 127 Aug 05 2.5 0. 43 0. 25 1. 90 3 .24 0. 33 21 60 0. 00 95 Aug 2 9 1.2 0. 61 0. 30 2. 00 4 .10 0. 29 23 340 0. 01 87 Oct07 1.0 0. 67 0. 30 2. 33 4 .25 0. 40 22 630 0. 03 82 100 May 14 6.9 0. 18 0. 13 2. 63 2 .68 0. 37 18 125 0. 01 77 J u l 05 4.3 0. 36 0. 22 2. 43 2 .61 0. 35 19 437 0. 04 115 Aug 05 2.6 0. 46 0. 26 1. 90 3 .29 0. 31 19 60 0. 00 96 Aug29 1.4 0. 50 0. 32 1. 73 4 .03 0. 28 23 460 0. 01 114 Oct07 1.2 0. 48 0. 31 2. 03 3 .96 0. 36 24 770 0. 05 99 101 May 14 6.4 0. 18 0. 12 2. 70 2 .11 0. 34 16 55 0. 00 69 J u l 05 4.7 0. 48 0. 19 3. 05 2 .34 0. 35 13 107 0. 01 140 Aug 05 3.3 0. 46 0. 21 2. 35 3 .12 0. 34 19 60 0. 00 97 Aug 2 9 1.6 0. 61 0. 26 2. 93 4 . 67 0. 30 26 390 0. 00 106 Oct07 1.7 0. 48 0. 30 2. 55 4 .09 0. 35 27 2600 0. 21 123 102 Mayl4 4.6 0. 19 0. 12 2. 85 2 .06 0. 33 14 35 0. 00 50 J u l 0 5 3.9 0. 54 0. 20 2. 75 2 .36 0. 36 16 207 0. 01 127 Aug 05 1.5 0. 64 0. 25 2. 68 3 .55 0. 37 21 110 0. 01 90 Aug29 1.0 0. 74 0. 29 2. 35 4 .76 0. 34 26 260 0. 01 93 Oct07 1.1 0. 82 0. 29 2 . 13 4 .22 0. 32 24 590 0. 04 78 103 May 14 6.8 0. 21 0. 14 3 . 13 2 . 37 0. 37 17 45 0. 00 67 J u l 05 6.1 0. 33 0. 18 2 . 43 2 . 09 0. 38 15 107 0. 00 112 Aug 05 2.7 0. 35 0. 25 2. 05 3 .18 0. 35 18 50 0. 00 91 Aug 2 9 1.4 0. 48 0. 30 1. 98 4 .81 0. 33 28 250 0. 01 103 Oct07 1.1 0. 48 0. 31 1. 90 4 .46 0. 42 25 740 0. 05 94 104 May 14 5.6 0. 24 0. 15 3. 18 2 .41 0. 37 17 45 0. 00 77 J u l 0 5 5.2 0. 49 0. 22 2. 28 2 .43 0. 37 19 157 0. 01 126 Aug 05 2.3 0. 42 0. 25 1. 58 2 .98 0. 34 17 50 0. 00 86 Aug 2 9 1.3 0. 54 0. 31 1. 98 4 .54 0. 33 24 330 0. 00 101 180 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm Oct 07 0.9 0. 48 0. 32 1 .80 4 .04 0. 35 24 1950 0. 16 101 105 Mayl4 5.6 0. 26 0. 13 3 .33 2 .32 0. 36 16 45 0. 00 75 J u l 0 5 5.7 0. 32 0. 17 3 .20 2 .27 0. 36 15 97 0. 00 116 Aug 05 2.4 0. 40 0. 19 3 .13 2 .57 0. 36 16 70 0. 00 95 Aug 2 9 2.2 0. 51 0. 27 3 .08 4 .09 0. 27 20 • 330 0. 00 93 Oct07 1.3 0. 53 0. 25 2 .98 3 .95 0. 41 20 900 0. 03 83 106 May 14 5.1 0. 19 0. 13 3 .38 2 .24 0. 37 18 45 0. 00 98 J u l 0 5 5.0 0. 60 0. 22 2 .98 2 .68 0. 37 17 267 0. 03 166 Aug 05 2.1 0. 53 0. 22 2 .63 2 .90 0. 37 22 70 0. 00 102 Aug29 1.6 0. 88 0. 31 2 . 15 4 .36 0. 32 22 560 0. 02 105 Oct07 0.8 0. 75 0. 27 2 .05 4 .05 0. 36 21 880 0. 05 85 107 May 14 6.2 0. 18 0. 12 3 .23 2 .07 0. 36 16 35 0. 00 70 J u l 05 6.3 0. 26 0. 18 2 .30 2 .28 0. 36 19 1407 0. 15 142 Aug 05 2.2 0. 36 0. 24 3 .28 3 .48 0. 33 22 60 0. 00 93 Aug 2 9 1.7 0. 48 0. 28 3 .40 4 . 11 0. 33 23 410 0. 01 98 Oct07 1.4 0. 32 0. 26 3 .03 3 .67 0. 40 20 1900 0. 14 99 108 May 14 5.5 0. 22 0. 14 3 .73 2 .62 0. 38 16 55 0. 00 67 J u l 0 5 5.5 0. 42 0. 17 3 .50 2 .50 0. 37 17 l l 7 0. 01 110 Aug 05 2.1 0. 44 0. 20 3 . 65 3 .20 0. 31 20 70 0. 00 76 Aug29 1.7 0. 51 0. 26 3 .65 4 .17 0. 29 22 450 0. 01 94 0ct07 1.2 0. 34 0. 24 3 .53 3 .82 0. 36 21 2150 0. 14 84 109 May 14 5.7 0. 24 0. 14 3 .55 2 .47 0. 38 18 45 0. 00 85 J u l 05 8.0 0. 38 0. 21 3 . 15 2 .86 0. 34 21 117 0. 00 120 Aug 05 3.1 0. 41 0. 24 4 .23 3 .48 0. 30 21 50 0. 00 94 Aug 2 9 1.4 0. 44 0. 25 3 .75 4 .14 0. 29 23 290 0. 01 99 Oct 07 1.3 0. 39 0. 25 3 . 18 3 .96 0. 37 22 1270 0. 09 106 110 May 14 7.0 0. 19 0. 12 3 .23 2 .38 0. 35 18 55 0. 00 68 J u l 05 4.6 0. 30 0. 16 3 .40 2 .30 0. 31 14 277 0. 02 125 Aug 05 1.9 0. 31 0. 16 3 .73 2 .70 0. 29 17 80 0. 00 74 Aug 2 9 1.5 0. 39 0. 23 3 .85 4 . 17 0. 25 21 270 0. 01 94 Oct 07 1.2 0. 40 0. 23 3 .50 4 .44 0. 36 21 890 0. 05 92 111 Mayl4 6.3 0. 21 0. 12 3 .18 2 . 12 0. 36 15 35 0. 00 55 J u l 05 4.9 0. 42 0. 17 3 .18 2 .38 0. 33 13 127 0. 00 95 Aug 05 2.3 0. 36 0. 17 3 .45 2 .58 0. 38 17 60 0. 00 81 Aug 2 9 1.5 0. 47 0. 23 3 .90 4 .11 0. 32 19 310 0. 00 70 Oct07 1.2 0. 38 0. 23 3 .33 3 .71 0. 39 20 1740 0. 12 72 112 May 14 5.6 0. 19 0. 10 2 .78 1 .90 0. 32 12 35 0. 00 46 J u l 05 5.3 0. 64 0. 22 4 .10 2 .74 0. 38 19 217 0. 01 105 Aug 05 2.5 0. 48 0. 21 4 .13 3 .67 0. 34 23 80 0. 00 75 Aug 2 9 1.7 0. 63 0. 27 3 . 68 4 . 00 0. 30 23 1400 0. 13 95 Oct07 1.2 0. 74 0. 25 3 .18 4 .63 0. 37 22 830 0. 06 65 113 Mayl4 7.2 0. 25 0. 14 3 .58 2 .43 0. 38 18 45 0. 00 59 J u l 05 4.2 0. 45 0. 19 3 .65 2 .48 0. 32 19 487 0. 05 98 Aug 05 2.4 0. 41 0. 18 3 .98 3 .37 0. 30 19 70 0. 00 71 Aug29 1.8 0. 51 0. 22 3 .53 3 .54 0. 25 18 450 0. 00 90 Oct07 1.6 0. 36 0. 26 3 . 15 3 .86 0. 37 25 2850 0. 25 97 181 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm 114 May 14 7.5 0. 25 Or 14 3 .33 2 .45 0. 36 18 95 0. 01 56 J u l 05 4.2 0. 45 0. 17 3 .20 2 .39 0. 34 16 167 0. 01 97 Aug 05 1.9 0. 50 0. 18 3 .45 2 .62 0. 33 21 70 0. 00 81 Aug29 1.2 0. 65 0. 25 3 .25 3 .80 0. 31 22 410 0. 01 88 0ct07 0.9 0. 58 0. 24 3 .00 4 .06 0. 37 22 1010 0. 08 75 115 May 14 6.2 0. 16 0. 09 2 .63 1 .98 0. 34 14 65 0. 00 45 J u l 0 5 5.3 0. 47 0. 18 3 .20 2 .33 0. 37 18 117 0. 01 104 Aug 05 2.7 0. 47 0. 21 3 .18 3 .57 0. 30 23 70 0. 00 75 Aug29 1.4 0. 43 0. 22 3 .55 3 .78 0. 32 21 280 0. 00 75 Oct07 1.0 0. 52 0. 24 3 .23 4 .08 0. 40 22 700 0. 04 77 116 May 14 6.6 0. 23 0. 12 3 . 38 2 .31 0. 36 16 45 0. 00 42 J u l 0 5 3.9 0. 71 0. 21 3 .53 2 .56 0. 35 17 357 0. 04 82 Aug 05 2.3 0. 44 0. 17 3 .18 2 .90 0. 31 18 70 0. 00 65 Aug 2 9 1.7 0. 65 0. 23 3 .15 3 .55 0. 30 20 450 0. 01 78 Oct07 0.9 0. 62 0. 25 3 .05 4 . 14 0. 39 24 1590 0. 14 82 117 May 14 5.5 0. 20 0. 11 2 .88 1 .74 0. 34 15 35 0. 00 64 J u l 0 5 3.3 0. 41 0. 16 2 .93 2 .01 0. 36 13 127 0. 01 117 Aug 05 1.8 0. 37 0. 17 3 .05 2 .64 0. 43 19 80 0. 00 91 Aug29 1.1 0. 64 0. 24 3 .08 3 . 38 0. 44 18 580 0. 01 108 Oct07 0.9 0. 64 0. 23 2 .83 3 .69 0. 46 20 890 0. 03 85 Dry Matter and f o l i a r elements for 7 6 dryland s i t e s , 1987 i t e Cut DM Ca Mg K N P Zn Fe A l Mn No Date T/ha % % % % % ppm i ppm % ppm 1 Apr29 4.2 0. 37 0.15 3 .63 3 .80 0. 31 13 129 n.d. 66 J u n l 6 3.9 0. 46 0.18 3 .73 3 .71 0. 33 22 118 n.d. 81 J u l 14 2.4 0. 53 0.23 4 .38 3 .99 0. 32 25 150 n.d. 86 Aug21 1.0 0. 95 0.29 3 .72 4 . 16 0. 31 26 150 0.02 86 Sep29 0.5 0. 48 0. 29 3 .13 3 .76 0. 29 37 2460 0.19 141 2 Apr 2 9 3.5 0. 34 0.15 4 .53 3 .58 0. 32 19 89 n.d. 97 J u n l 6 3.1 0. 61 0.20 3 .58 3 .61 0. 34 19 98 n.d. 84 J u l 14 2.2 0. 76 0.25 3 .75 3 .78 0. 34 22 240 n.d. 76 Aug21 1.5 0. 55 0.28 3 .80 3 .59 0. 33 30 2170 0.24 104 Sep29 0.7 0. 64 0.24 3 .75 3 .73 0. 34 27 130 0.01 81 3 Apr29 5.4 0. 54 0.15 3 . 08 3 .61 0. 29 12 89 n.d. 64 J u n l 6 3.1 1. 59 0.28 3 .90 3 .73 0. 31 25 188 n.d. 58 J u l l 4 2.0 1. 08 0.26 3 .60 3 .96 0. 32 25 110 n.d. 71 Aug21 0.5 1. 18 0.32 3 . 62 4 .06 0. 29 29 210 0.02 92 Sep29 0.7 0. 93 0.30 3 .03 4 .25 0. 29 31 520 0.05 105 4 Apr 2 9 4.3 0. 43 0.16 3 .73 3 .84 0. 28 16 119 n.d. 96 J u n l 6 4.1 0. 56 0.21 3 . 68 3 .81 0. 30 21 168 n.d. 100 J u l l 4 2.7 0. 57 0.23 4 . 13 4 .04 0. 31 23 160 n.d. 96 Aug21 1.0 0. 98 0.28 4 .80 4 .28 0. 33 24 240 0.02 88 182 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm Sep29 0 .9 0. 94 0. 30 3 .53 4 .44 0. 32 30 340 0.04 102 5 Apr 2 9 4 .7 0. 33 0. 14 3 . 13 3 .49 0. 27 11 149 n.d. 68 J u n l 6 3 .4 0. 97 0. 24 2 .85 3 .62 0. 28 19 128 n.d. 74 J u l 14 2 .7 0. 92 0. 28 3 .05 3 .78 0. 31 21 100 n.d. 73 Aug 21 1 .3 1. 12 0. 30 2 .90 3 .77 0. 30 24 310 0.04 72 Sep29 0 .2 0. 75 0. 28 2 .70 3 .84 0. 30 24 120 0.01 79 6 Apr 2 9 4 .6 0. 42 0. 14 3 .40 2 .99 0. 29 13 79 n.d. 72 J u n l 6 3 .4 0. 87 0. 23 3 .15 3 . 14 0. 29 19 98 n.d. 82 J u l l 4 3 .4 0. 75 0. 24 3 .45 3 .43 0. 32 23 100 n.d. 86 Aug21 1 .3 0. 92 0. 27 4 .07 4 .12 0. 33 25 190 0.01 71 Sep29 0 .7 0. 94 0. 28 2 .95 3 .81 0. 30 21 140 0.01 74 7 Apr 2 9 4 .1 0. 43 0. 15 3 .63 3 .11 0. 29 17 89 n.d. 76 J u n l 6 5 .3 0. 36 0. 14 2 .85 3 .32 0. 31 25 78 n.d. 82 J u l l 4 2 .8 0. 53 0. 21 3 .95 3 .55 0. 32 26 180 n.d. 88 Aug21 1 .2 0. 78 0. 21 3 . 62 3 .76 0. 31 26 110 0. 01 74 Sep29 1 .2 0. 39 0. 18 4 .05 3 .62 0. 31 21 110 0.01 73 8 Apr 2 9 3 . 4 0. 23 0. 13 3 .43 3 .64 0. 30 14 79 n.d. 69 J u n l 6 3 .7 0. 46 0. 16 3 .40 3 .81 0. 32 16 68 n.d. 80 J u l 14 2 .2 0. 66 0. 23 3 .73 3 .74 0. 33 22 160 n.d. 76 Aug21 1 .3 0. 46 0. 28 3 .85 3 .76 0. 30 28 2570 0.26 104 Apr 2 9 1 .0 0. 62 0. 25 3 .53 3 .99 0. 32 25 260 0.03 92 9 Apr 2 9 3 .4 0. 31 0. 14 3 . 68 3 .81 0. 33 20 99 n.d. 80 J u n l 6 2 .9 0. 44 0. 17 3 .70 3 .94 0. 31 21 78 n.d. 87 J u l l 4 2 .5 0. 57 0. 24 4 .63 4 .04 0. 35 26 100 n.d. 86 Aug21 0 .8 0. 79 0. 25 3 .47 3 .92 0. 33 27 150 0. 01 84 Sep29 0 .9 0. 64 0. 23 3 . 15 3 .87 0. 27 27 110 0.01 96 10 Apr 2 9 3 .7 0. 49 0. 18 4 . 00 3 .57 0. 31 19 149 n.d. 77 J u n l 6 3 .9 0. 53 0. 20 3 .90 3 .61 0. 30 25 218 n.d. 93 J u l l 4 2 . 1 0. 56 0. 23 4 .40 3 .57 0. 34 25 180 n.d. 82 Aug21 0 .7 0. 81 0. 22 3 .55 3 .69 0. 32 26 240 0. 02 71 Sep29 0 .9 0. 58 0. 21 2 .85 3 .65 0. 29 28 210 0. 02 73 11 Apr29 3 .0 0. 84 0. 21 4 . 65 3 .61 0. 33 20 199 n.d. 69 J u n l 6 3 .7 0. 61 0. 19 3 .83 3 .72 0. 31 24 78 n.d. 79 J u l l 4 2 .3 0. 74 0. 24 3 .73 3 .93 0. 31 27 140 n.d. 88 Aug21 0 .9 0. 89 0. 23 3 .52 3 .69 0. 30 23 170 0.01 76 Sep29 0 .8 0. 84 0. 22 2 .88 3 .91 0. 27 27 150 0.01 76 12 Apr 2 9 4 .2 0. 32 0. 14 4 .38 3 .44 0. 34 11 69 n.d. 60 J u n l 6 3 .9 0. 46 0. 20 4 .05 3 .51 0. 34 19 88 n.d. 80 J u l l 4 2 .4 0. 61 0. 23 3 .93 3 .77 0. 34 25 250 n.d. 86 Aug21 1 .2 0. 75 0. 25 3 .40 3 .69 0. 32 21 190 0.02 87 Sep29 0 .8 0. 72 0. 20 2 .55 3 .79 0. 28 24 120 0.01 94 13 Apr 2 9 3 .7 0. 35 0. 17 3 .97 3 .61 0. 33 14 119 n.d. 68 J u n l 6 3 .3 0. 69 0. 21 3 .60 3 . 68 0. 35 18 88 n.d. 72 J u l 14 2 .6 0. 58 0. 25 3 .88 3 .77 0. 34 25 380 n.d. 91 Aug21 0 .5 1. 05 0. 30 2 .85 3 .94 0. 29 22 410 0.04 99 Sep29 0 .1 0. 58 0. 25 3 .40 3 .60 0. 22 26 200 0.01 107 14 Apr 2 9 2 .8 0. 51 0. 17 3 .55 3 .22 0. 30 22 119 n.d. 83 183 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm Ju n l 6 3 .7 0. 45 0. 19 3 .53 3 .41 0. 33 23 128 n. d. 93 J u l 14 2 .5 0. 44 0. 20 3 .80 3 .58 0. 32 29 100 n.d. 83 Aug21 1 .1 0. 90 0. 23 3 .47 3 .65 0. 29 26 170 0.02 77 Sep29 1 .0 0. 46 0. 29 3 .23 3 .23 0. 25 33 1150 0.11 111 15 Apr 2 9 2 .8 0. 53 0. 18 3 . 38 3 .41 0. 31 14 159 n.d. 56 J u n l 6 3 .5 0. 69 0. 19 3 .83 3 .51 0. 34 20 68 n.d. 55 JU114 2 .8 0. 76 0. 25 3 .75 3 .60 0. 33 25 250 n.d. 77 Aug21 1 .5 1. 04 0. 27 3 .47 3 .68 0. 29 25 230 0.01 86 Sep29 0 .6 0. 74 0. 25 3 .62 3 .91 0. 28 24 200 0.02 91 16 Apr 2 9 2 .6 0. 48 0. 15 3 .40 3 .59 0. 31 13 99 n.d. 75 J u n l 6 3 .5 0. 71 0. 21 3 .65 3 .77 0. 31 17 78 n.d. 79 J u l l 4 2 .8 0. 94 0. 26 3 .38 3 .91 0. 32 23 170 n.d. 78 Aug21 0 .6 1. 17 0. 31 2 .97 4 .05 0. 28 24 170 0.01 93 Apr29 0 .3 0. 82 0. 34 1 .93 3 .32 0. 22 25 1170 0.11 114 17 Apr 2 9 3 .0 0. 71 0. 21 3 .43 3 .22 0. 30 12 149 n.d. 69 J u n l 6 4 .2 1. 21 0. 29 3 . 63 3 .44 0. 31 20 138 n.d. 72 J u l l 4 3 .4 0. 85 0. 25 3 .50 3 .59 0. 31 19 150 n.d. 69 Aug21 1 .7 0. 83 0. 25 3 .77 3 .51 0. 28 22 130 0. 00 76 Sep29 0 .4 0. 79 0. 24 3 . 00 3 .79 0. 27 25 160 0.01 85 18 Apr29 5 .5 0. 20 0. 14 3 .03 3 .64 0. 31 12 89 n.d. 75 Ju n l 6 2 .7 0. 97 0. 27 3 .53 3 .71 0. 34 23 118 n.d. 92 J u l l 4 2 .8 0. 90 0. 28 3 .40 3 .75 0. 33 24 170 n.d. 73 Aug21 1 .7 1. 13 0. 29 2 .84 3 .49 0. 28 26 290 0.03 84 Sep29 0 .5 0. 82 0. 27 2 .38 3 .82 0. 26 26 140 0.01 84 19 Apr29 2 .8 0. 38 0. 17 3 . 65 3 .58 0. 33 15 199 n.d. 103 Ju n l 6 3 .5 0. 50 0. 21 3 .68 3 .51 0. 32 21 98 n.d. 101 J u l 14 2 .9 0. 35 0. 26 3 .68 3 .59 o. 31 28 1700 n.d. 125 Aug21 1 .2 0. 92 0. 28 3 .60 3 .86 0. 29 27 140 0.01 98 Sep29 0 .6 0. 71 0. 27 3 .20 4 .04 0. 30 27 150 0.01 101 20 Apr 2 9 3 .8 0. 33 0. 15 3 .78 3 .64 0. 29 11 89 n.d. 52 J u n l 6 3 .4 0. 60 0. 18 3 . 65 3 .76 0. 31 17 88 n.d. 64 J u l l 4 2 .7 0. 72 0. 23 3 .70 3 .71 0. 32 21 120 n.d. 70 Aug21 1 .3 0. 79 0. 25 4 .00 3 .67 0. 28 24 170 0. 01 83 Sep29 1 .2 0. 56 0. 24 2 .68 3 . 66 0. 26 26 350 0.04 94 21 Apr29 3 .6 0. 56 0. 17 3 .20 3 .77 0. 28 13 149 n.d. 64 J u n l 6 3 .9 0. 75 0. 22 3 .90 3 .44 0. 32 19 108 n.d. 79 J u l l 4 3 .2 0. 67 0. 24 3 .48 3 .77 0. 34 22 170 n.d. 95 Aug21 1 .4 1. 09 0. 30 3 .15 3 .98 0. 30 27 140 0.01 89 Sep29 0 .7 0. 86 0. 30 2 .88 3 .94 0. 26 30 130 0. 01 105 22 Apr29 3 .9 0. 56 0. 17 3 . 15 3 .12 0. 28 12 139 n.d. 64 J u n l 6 3 .3 0. 79 0. 22 3 .35 3 .61 0. 31 18 108 n.d. 68 J u l 14 3 . 1 0. 81 0. 27 2 .97 3 .95 0. 34 23 120 n.d. 87 Aug21 1 .4 0. 98 0. 29 2 .67 3 . 60 0. 26 25 150 0. 01 109 Sep29 0 . 1 0. 92 0. 29 2 .28 4 .01 0. 28 25 200 0. 02 123 23 Apr 2 9 3 .1 0. 38 0. 15 3 .43 3 . 14 0. 31 10 109 n.d. 83 J u n l 6 3 .9 0. 49 0. 21 3 .85 3 .01 0. 34 21 88 n.d. 93 J u l 14 2 .4 0. 42 0. 27 3 .43 3 .27 0. 34 26 1640 n.d. 105 184 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm Aug21 1. 6 0. 49 0. 26 3 .72 3 .38 0. 29 25 1690 0.15 125 Sep29 0. 6 0. 54 0. 20 2 .58 3 .90 0. 29 27 130 0.01 88 24 Apr 2 9 3. 0 0. 50 0. 16 3 .33 3 .58 0. 32 9 89 n.d. 70 Ju n l 6 3. 1 0. 77 0. 25 3 .95 3 .67 0. 34 19 118 n.d. 82 J u l 14 2. 8 0. 57 0. 24 3 .82 3 .82 0. 34 19 80 n.d. 83 Aug21 1. 3 1. 27 0. 30 3 .35 3 .60 0. 28 24 440 0.05 79 Apr 2 9 0. 2 1. 08 0. 32 2 .35 4 .18 0. 26 26 170 0.01 109 25 Apr29 3. 3 0. 47 0. 16 3 .63 2 .99 0. 31 10 149 n.d. 73 J u n l 6 3. 5 0. 74 0. 23 3 . 63 2 .89 0. 33 20 138 n.d. 89 J u l 14 2. 5 0. 79 0. 25 3 .40 3 .01 0. 31 21 180 n.d. 80 Aug21 1. 3 0. 87 0. 27 3 .42 3 .63 0. 29 23 180 0.02 89 Sep29 0. 7 0. 73 0. 28 3 .38 4 . 05 0. 28 26 210 0.02 105 26 Apr 2 9 3. 2 0. 46 0. 15 3 .30 3 .27 0. 30 12 109 n.d. 82 J u n l 6 4. 1 1. 31 0. 27 4 .45 3 .45 0. 33 25 198 n.d. 76 J u l l 4 3. 0 0. 91 0. 27 4 . 18 3 .75 0. 33 24 100 n.d. 85 Aug21 1. 5 1. 30 0. 31 3 .79 3 .90 0. 28 23 230 0.02 95 Sep29 0. 8 0. 90 0. 30 3 . 15 4 .55 0. 29 24 120 0.01 94 27 Apr 2 9 3. 0 0. 21 0. 12 3 .45 2 .69 0. 29 7 129 n.d. 83 Ju n l 6 3. 7 0. 44 0. 19 3 .53 2 .89 0. 31 20 138 n.d. 106 J u l l 4 2 . 5 0. 40 0. 22 3 .50 2 .95 0. 32 19 170 n.d. 119 Aug21 1. 2 0. 83 0. 24 3 .47 3 .49 0. 27 24 100 0.01 114 Sep29 0. 9 0. 40 0. 23 3 .63 3 .71 0. 28 24 100 0.01 123 28 Apr 2 9 3. 4 0. 51 0. 17 4 .33 3 .30 0. 29 10 179 n.d. 67 J u n l 6 4 . 6 1. 44 0. 28 4 .85 3 .31 0. 32 28 188 n.d. 81 J u l l 4 3. 1 1. 19 0. 26 3 .83 3 .50 0. 34 25 300 n.d. 79 Aug21 1. 8 1. 09 0. 25 3 .85 3 .74 0. 28 23 210 0.01 85 Sep29 1. 2 0. 86 0. 23 4 . 00 3 .99 0. 27 22 110 0. 01 84 29 Apr 2 9 2. 8 0. 24 0. 13 2 .98 3 .37 0. 34 22 149 n.d. 91 Ju n l 6 4. 5 0. 51 0. 25 2 .88 3 .41 0. 33 21 118 n.d. 108 J u l 14 2. 1 0. 50 0. 25 3 .08 3 .31 0. 32 20 100 n.d. 106 Aug21 1. 2 0. 82 0. 31 2 .27 3 .45 0. 28 26 1750 0.17 131 Sep29 0. 7 0. 78 0. 30 2 . 10 4 .11 0. 27 23 140 0. 01 109 30 Apr29 4. 2 0. 30 0. 13 3 . 18 2 .91 0. 36 23 119 n.d. 64 J u n l 6 5. 1 0. 46 0. 19 3 .58 2 .84 0. 34 22 428 n.d. 78 J u l l 4 2. 9 0. 44 0. 21 3 . 18 2 .76 0. 31 20 380 n.d. 85 Aug21 1. 4 0. 99 0. 26 3 .77 4 .01 0. 30 27 190 0.02 67 Sep29 1. 2 0. 90 0. 27 3 . 67 4 . 11 0. 32 23 110 0.01 64 31 Apr 2 9 3. 6 0. 37 0. 16 2 .60 3 . 14 0. 31 25 69 n.d. 138 Ju n l 6 4. 0 0. 67 0. 29 2 . 35 3 .26 0. 35 25 108 n.d. 109 J u l l 4 2. 0 0. 73 0. 32 2 .25 3 .40 0. 33 34 120 n.d. 122 Aug21 0. 4 1. 02 0. 32 1 .52 3 .79 0. 28 26 220 0.01 112 Sep29 0. 2 1. 40 0. 41 1 .40 4 .04 0. 25 24 110 0.01 130 32 Apr 2 9 3. 9 0. 62 0. 19 3 .35 3 .09 0. 34 21 159 n.d. 89 J u n l 6 4. 2 1. 32 0. 28 3 .53 3 . 18 0. 35 25 218 n.d. 97 J u l l 4 3. 6 0. 85 0. 28 3 . 15 3 .46 0. 34 26 140 n.d. 108 Aug21 0. 7 1. 36 0. 33 2 .55 3 .98 0. 29 26 150 0. 01 105 Apr29 0. 2 1. 26 0. 33 1 .73 3 .98 0. 25 23 140 0.01 111 185 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm 33 Apr 2 9 2 .7 0. 33 0. 15 3 .67 2. .91 0. 29 22 109 n.d. 85 J u n l 6 3 .2 0. 50 0. 21 4 .03 2 .99 0. 31 20 68 n.d. 94 J u l l 4 3 .8 0. 42 0. 21 3 .63 3 .34 0. 28 20 100 n.d. 108 Aug21 1 .4 1. 01 0. 27 3 .80 3 .76 0. 28 27 210 0.02 102 Sep29 0 .9 0. 65 0. 20 3 .58 4 .10 0. 29 24 120 0.01 83 34 Apr 2 9 3 .6 0. 45 0. 17 3 . 65 2 .94 0. 31 29 99 n.d. 118 J u n l 6 4 .0 o . 61 0. 27 3 .08 3 .09 0. 34 24 498 n.d. 112 J u l l 4 1 .8 0. 55 0. 27 3 .05 3 .37 0. 31 24 160 n.d. 129 Aug21 0 .7 1. 14 0. 31 2 .47 3 .57 0. 24 21 140 0.01 103 Sep29 0 .4 1. 18 0. 33 2 .53 3 .47 0. 25 24 110 0.01 97 35 Apr 2 9 3 .3 0. 55 0. 22 4 .23 3 .29 0. 27 28 189 n.d. 104 J u n l 6 4 . 1 0. 69 0. 24 3 .90 3 .44 0. 30 24 128 n.d. 109 J u l l 4 2 .7 0. 77 0. 28 3 .73 3 .76 0. 33 26 290 n.d. 104 Aug21 1 .4 1. 24 0. 32 2 .60 3 .44 0. 26 30 1340 0.12 114 Sep29 0 .3 1. 06 0. 32 2 .73 4 .31 0. 25 26 130 0.01 139 36 Apr 2 9 3 .7 0. 52 0. 22 4 .00 2 .99 0. 29 24 109 n.d. 94 J u n l 6 4 .2 0. 73 0. 25 2 .98 3 .27 0. 33 21 178 n.d. 104 J u l l 4 3 .0 0. 80 0. 30 2 .65 3 .69 0. 34 23 320 n.d. 100 Aug21 0 .7 1. 07 0. 33 1 .90 3 .87 0. 28 28 270 0. 02 116 Sep29 0 .2 1. 10 0. 38 1 .50 3 .81 0. 25 25 200 0.00 124 37 Apr 2 9 2 .9 0. 50 0. 16 2 .88 2 .79 0. 31 21 139 n.d. 70 J u n l 6 3 .2 0. 75 0. 25 2 .88 2 .88 0. 33 21 118 n.d. 81 J u l l 4 2 .8 0. 91 0. 29 2 .63 3 .14 0. 35 23 100 n.d. 74 Aug21 1 .4 1. 38 0. 37 2 .25 3 .84 0. 32 27 210 0.02 84 Sep29 0 .7 1. 20 0. 41 1 .98 3 . 88 0. 31 25 120 0.01 84 38 Apr 2 9 3 .7 0. 25 0. 15 3 . 18 3 .27 0. 34 24 109 n.d. 104 J u n l 6 4 .6 0. 71 0. 25 3 .92 3 .41 0. 36 24 168 n.d. 108 J u l l 4 3 .0 0. 78 0. 27 3 . 63 3 .65 0. 34 22 160 n.d. 101 Aug21 1 .0 1. 26 0. 26 3 .40 3 .96 0. 30 26 290 0.03 89 Sep29 0 .9 0. 72 0. 25 2 . 98 3 .66 0. 25 23 100 0.01 97 39 Apr 2 9 3 .5 0. 50 0. 18 3 .48 3 .31 0. 33 25 119 n.d. 98 J u n l 6 2 .7 0. 74 0. 22 3 .30 3 .51 0. 33 21 78 n.d. 96 J u l l 4 2 .8 0. 61 0. 24 3 .50 3 .68 0. 31 21 220 n.d. 110 Aug21 1 .5 1. 16 0. 29 3 .27 3 .87 0. 29 28 310 0.02 107 Sep29 0 .6 0. 91 0. 29 2 .70 3 .97 0. 27 26 100 0. 01 113 40 Apr 2 9 3 .6 0. 22 0. 13 3 .20 2 .94 0. 33 20 99 n.d. 105 J u n l 6 3 .2 0. 53 0. 21 3 .80 3 .09 0. 32 21 58 n.d. 118 J u l l 4 2 .9 0. 42 0. 21 3 .53 3 .31 0. 32 19 70 n.d. 118 Aug21 1 .5 0. 88 0. 26 3 .30 3 .77 0. 27 25 130 0. 01 102 Apr 2 9 1 .0 0. 79 0. 27 3 . 13 3 .97 0. 26 28 150 0. 01 105 41 Apr 2 9 2 .9 0. 14 0. 13 3 . 18 3 . 19 0. 31 21 169 n.d. 105 J u n l 6 3 .4 0. 31 0. 20 3 .88 3 .27 0. 33 18 48 n.d. 123 J u l l 4 2 .5 0. 34 0. 22 3 .78 3 .46 0. 30 21 60 n.d. 115 Aug21 1 .4 0. 66 0. 25 3 . 30 3 .55 0. 32 22 100 0.01 108 Sep29 0 .8 0. 55 0. 26 3 .20 3 .97 0. 27 25 80 0.01 131 42 Apr 2 9 3 .2 0. 38 0. 16 3 .23 2 .94 0. 29 22 149 n.d. 92 J u n l 6 2 .7 0. 60 0. 23 3 .95 3 .09 0. 31 18 108 n.d. 93 186 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm J u l l 4 2. 5 0. 30 0. 21 4 .03 3 .12 0. 30 18 360 n.d. 130 Aug21 1. 4 0. 73 0. 24 3 .92 3 .67 0. 29 23 160 0.01 115 Sep29 1. 0 0. 65 0. 26 3 .50 3 .91 0. 28 24 100 0.01 132 43 Apr 2 9 3. 2 0 1.6 0. 19 2 .93 3 .34 0. 27 24 119 n.d. 96 J u n l 6 2. 9 0. 78 0. 24 2 .70 3 .41 0. 31 19 68 n.d. 78 J u l l 4 2. 9 0. 61 0. 25 2 .95 3 .66 0. 31 21 110 n.d. 81 Aug21 1. 6 1. 11 0. 31 2 .22 3 .82 0. 29 22 100 0.00 77 Sep29 1. 0 0. 84 0. 30 2 .08 4 .09 0. 30 24 160 0.01 93 44 Apr 2 9 3-. 5 0. 21 0. 15 3 .45 2 .81 0. 34 21 169 n.d. 108 J u n l 6 3. 0 0. 30 0. 19 3 .38 2 .94 0. 36 18 68 n.d. 133 J u l l 4 2. 4 0. 31 0. 20 3 . 60 2 .90 0. 33 20 100 n.d. 115 Aug21 0. 6 0. 71 0. 25 3 .27 3 .58 0. 32 22 320 0.01 106 Sep29 0. 7 0. 45 0. 25 2 .98 3 .81 0. 28 27 100 0.01 155 45 Apr 2 9 3. 5 0. 33 0. 15 3 .18 3 . 14 0. 31 23 189 n.d. 96 Ju n l 6 3. 0 0. 63 0. 21 3 .05 3 .01 0. 34 19 88 n.d. 95 J u l l 4 2. 3 0. 59 0. 24 2 .93 3 .41 0. 36 21 70 n.d. 88 Aug 21 1. 5 0. 93 0. 29 2 .37 3 .73 0. 34 23 160 0.01 106 Sep29 0. 9 0. 76 0. 27 2 .58 3 .97 0. 32 20 90 0.01 101 46 Apr 2 9 3. 1 0. 34 0. 15 2 .90 3 .18 0. 30 22 109 n.d. 80 J u n l 6 3. 4 0. 51 0. 23 2 .90 3 .27 0. 34 21 548 n.d. 87 J u l 14 3. 2 0. 48 0. 25 3 .38 3 . 65 0. 33 21 70 n.d. 102 Aug21 1. 1 0. 83 0. 30 3 .35 3 .94 0. 30 24 140 0.01 111 Sep29 0. 7 0. 64 0. 29 3 .00 4 .09 0. 30 24 80 0.01 129 47 Apr 2 9 3. 6 0. 40 0. 15 3 .50 3 .24 0. 31 24 99 n.d. 69 J u n l 6 3. 8 0. 36 0. 22 3 .53 3 .41 0. 33 16 48 n.d. 105 J u l l 4 3. 3 0. 43 0. 25 3 .78 3 .58 0. 33 19 190 n.d. 119 Aug21 1. 5 0. 87 0. 30 3 .55 3 .75 0. 31 26 230 0.01 107 Sep29 1. 0 0. 66 0. 28 2 .50 4 .09 0. 29 24 140 0.01 109 48 Apr 2 9 3. 0 0. 34 0. 14 3 .05 3 .27 0. 29 20 109 n.d. 99 Ju n l 6 3. 5 0. 46 0. 22 3 .65 3 .37 0. 31 20 68 n.d. 127 J u l l 4 3. 3 0. 41 0. 22 3 .65 3 .52 0. 29 20 90 n.d. 130 Aug21 1. 8 0. 64 0. 26 3 .49 3 .47 0. 28 25 90 0.00 122 Apr 2 9 0. 8 0. 55 0. 27 3 .03 3 .94 o. 27 23 90 0.01 124 49 Apr 2 9 2. 6 0. 28 0. 14 2 .60 3 . 19 0. 29 19 159 n.d. 92 Ju n l 6 3. 0 0. 50 0. 21 2 .85 3 .31 0. 33 17 278 n.d. 102 J u l l 4 2 . 6 0. 60 0. 27 3 . 10 3 .54 0. 32 20 80 n.d. 106 Aug21 1. 8 0. 66 0. 31 2 .65 3 .50 0. 30 27 100 0.01 136 Sep29 0. 9 0. 67 0. 32 2 .40 3 .94 0. 29 28 110 0.01 157 50 Apr 2 9 3. 2 0. 26 0. 15 3 .13 2 .96 0. 36 20 129 n.d. 98 J u n l 6 3. 2 0. 58 0. 21 3 .03 2 .94 0. 38 18 128 n.d. 126 J u l l 4 2. 6 0. 40 0. 22 3 . 10 2 .88 0. 35 19 110 n.d. 121 Aug21 1. 6 0. 89 0. 31 2 .40 3 .65 0. 33 27 140 0.00 125 Sep29 0. 7 0. 81 0. 33 2 . 05 4 .25 0. 35 26 90 0.01 131 51 Apr 2 9 3. 5 0. 48 0. 17 2 .70 3 .17 0. 34 20 129 n.d. 70 J u n l 6 3. 1 0. 66 0. 24 2 .30 3 .18 0. 35 23 78 n.d. 84 J u l l 4 2. 7 0. 86 0. 31 1 .93 3 .30 0. 34 18 90 n.d. 74 Aug21 1. 9 1. 01 0. 32 2 . 10 3 .80 0. 33 24 120 0.01 82 187 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % 3 % % % ppm ppm % ppm Sep29 1 .2 0. 94 0. 34 1. 75 3 .91 0. 32 20 70 0. 01 66 52 Apr 2 9 4 .3 0. 23 0. 13 2. 73 3 .16 0. 33 20 89 n. d. 85 J u n l 6 2 .8 0. 59 0. 21 2. 68 3 .27 0. 36 24 78 n. d. 115 J u l 14 3 .1 0. 63 0. 26 2. 83 3 .39 0. 33 20 70 n. d. 90 Aug21 1 .1 1. 11 0. 31 2. 45 3 .83 0. 31 24 360 0. 03 108 Sep29 0 .5 1. 07 0. 33 2. 05 4 .10 0. 31 19 70 0. 00 79 53 Apr 2 9 4 .1 0. 17 0. 14 3. 60 3 .34 0. 31 21 209 n. d. 85 J u n l 6 3 .1 0. 30 0. 19 3. 33 3 .51 0. 34 24 58 n. d. 107 J u l l 4 2 .7 0. 42 0. 24 3. 43 3 .46 0. 33 23 100 n. d. 114 Aug21 1 . 1 0. 78 0. 29 2. 65 3 .68 0. 30 29 170 0. 01 121 Sep29 0 .9 0. 60 0. 28 2. 28 3 .88 0. 29 27 100 0. 01 112 54 Apr 2 9 4 .0 0. 22 0. 15 3. 65 3 . 12 0. 28 22 249 n. d. 84 Ju n l 6 4 .4 0. 36 0. 17 3. 53 3 .38 0. 31 24 148 n. d. 88 J u l 14 2 .2 0. 43 0. 22 4 . 18 3 .46 0. 33 23 150 n. d. 106 Aug21 1 .1 0. 66 0. 24 3. 40 3 .55 0. 31 23 140 0. 01 113 Sep29 0 .6 0. 52 0. 25 3. 08 3 .85 0. 29 26 110 0. 01 126 55 Apr 2 9 4 .2 0. 17 0. 12 2. 93 3 .24 0. 27 20 69 n. d. 89 J u n l 6 3 .5 0. 40 0. 22 3 . 33 3 . 19 0. 31 26 138 n. d. 121 J u l 14 2 .9 0. 36 0. 23 3 . 15 3 .24 0. 26 20 110 n. d. 129 Aug21 1 .3 0. 56 0. 26 3 . 10 3 .27 0. 25 25 550 0. 06 134 Sep29 0 .6 0. 51 0. 25 3. 23 3 .95 0. 27 26 70 0. 01 139 56 Apr 2 9 3 . 1 0. 33 0. 15 3 . 03 3 .92 0. 31 22 129 n. d. 106 J u n l 6 5 .5 0. 52 0. 22 2. 85 3 .91 0. 33 25 108 n. d. 112 J u l l 4 2 .8 0. 90 0. 31 2. 95 4 .09 0. 36 28 210 n. d. 108 Aug21 2 . 1 1. 48 0. 36 2. 42 3 .91 0. 33 33 630 0. 07 126 Apr 2 9 1 .0 1. 08 0. 37 2. 08 4 .39 0. 32 32 210 0. 02 125 57 Apr 2 9 3 .2 0. 33 0. 14 2 . 30 3 .07 0. 31 22 109 n. d. 97 J u n l 6 4 .2 0. 79 0. 26 1. 73 3 .31 0. 34 27 198 n. d. 119 J u l l 4 2 .1 0. 78 0. 27 1. 58 3 .52 0. 36 22 120 n. d. 89 Aug21 1 . 1 1. 21 0. 32 1. 45 3 .76 0. 33 29 440 0. Q5 103 Sep29 0 .3 1. 01 0. 31 1. 25 4 .49 0. 31 26 130 0. 01 112 58 Apr 2 9 4 .0 0. 19 0. 17 3. 28 3 .13 0. 31 25 499 n. d. 115 J u n l 6 3 .6 0. 47 0. 21 2. 68 3 .27 0. 33 25 108 n. d. 113 J u l l 4 2 .5 0. 59 0. 28 2. 38 3 .52 0. 31 26 140 n. d. 132 Aug21 1 .3 0. 84 0. 28 2. 10 3 .66 0. 28 23 120 0. 01 117 Sep29 1 .2 0. 70 0. 28 1. 85 4 .11 0. 29 25 100 0. 01 111 59 Apr 2 9 2 .9 0. 28 0. 14 2 . 50 3 .31 0. 26 17 99 n. d. 75 J u n l 6 4 .6 0. 62 0. 24 1. 87 3 .41 0. 31 24 158 n. d. 105 J u l l 4 2 .5 0. 53 0. 27 2 . 18 3 .39 0. 34 25 110 n. d. 120 Aug21 0 .5 0. 93 0. 34 1. 65 3 .78 0. 30 24 190 0. 02 94 Sep29 0 .3 0. 72 0. 33 1. 43 4 .02 0. 28 25 100 0. 01 107 60 Apr 2 9 3 .8 0. 20 0. 15 3. 10 3 .25 0. 24 21 119 n. d. 96 J u n l 6 5 . 1 0. 38 0. 22 2. 30 3 .26 0. 28 22 88 n. d. 104 J u l 14 2 .5 0. 38 0. 24 2. 30 3 .30 0. 29 23 280 n. d. 131 Aug21 1 .2 0. 69 0. 28 2. 10 3 .68 0. 28 27 120 0. 01 110 Sep29 0 .5 0. 61 0. 30 1. 63 3 .93 0. 28 27 120 0. 01 138 61 Apr 2 9 3 .4 0. 34 0. 15 3 . 35 2 .79 0. 28 22 129 n. d. 84 188 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm J u n l 6 4 .4 1. 15 0. 24 3. 35 2 .88 0. 31 29 188 n.d. 102 J u l l 4 2 .0 1. 09 0. 26 3. 13 3 .64 0. 33 26 120 n.d. 107 Aug21 1 .4 1. 31 0. 28 2. 70 4 .12 0. 33 27 130 0.01 80 Sep29 1 . 1 1. 39 0. 30 2. 20 4 .28 0. 31 25 110 0.01 100 62 Apr29 3 .3 0. 38 0. 19 4. 40 3 .34 0. 34 28 129 n.d. 98 J u n l 6 3 .7 0. 39 0. 21 2. 28 3 .41 0. 34 21 48 n.d. 107 J u l 14 2 .3 0. 63 0. 28 1. 95 3 .52 0. 34 23 80 n.d. 103 Aug21 0 .9 0. 96 0. 35 1. 60 3 .53 0. 30 28 100 0.00 103 Sep29 0 .1 0. 80 0. 35 1. 53 4 .09 0. 26 26 90 0.01 121 63 Apr 2 9 3 .7 0. 53 0. 17 2. 75 3 .06 0. 35 22 99 n.d. 84 J u n l 6 4 .4 0. 68 0. 22 2. 10 3 . 19 0. 36 27 98 n.d. 100 J u l l 4 2 .2 0. 50 0. 26 2. 23 3 .42 0. 32 25 80 n.d. 104 Aug21 1 .7 1. 07 0. 35 1. 97 3 .59 0. 27 28 120 0.00 105 Sep29 1 .1 1. 00 0. 36 1. 53 3 .78 0. 28 28 130 0.01 113 64 Apr 2 9 4 .2 0. 19 0. 12 2. 73 3 .21 0. 29 19 99 n.d. 83 J u n l 6 3 .4 0. 36 0. 19 2. 35 3 .41 0. 31 23 58 n.d. 110 J u l l 4 2 .8 0. 41 0. 22 2. 53 3 .49 0. 29 23 130 n.d. 122 Aug21 1 .1 0. 82 0. 28 1. 97 3 .60 0. 27 26 220 0.02 108 Apr 2 9 0 .5 0. 63 0. 31 1. 73 3 .78 0. 26 24 90 0.01 111 65 Apr29 2 .9 0. 19 0. 13 3. 65 2 .91 0. 27 18 139 n.d. 81 J u n l 6 3 .2 0. 28 0. 17 3. 33 2 .87 0. 29 25 58 n.d. 104 J u l l 4 2 . 3 0. 30 0. 20 3. 63 3 . 09 0. 30 28 100 n.d. 121 Aug21 1 .5 0. 60 0. 25 3. 05 3 .28 0. 30 22 110 0.01 114 Sep29 0 .7 0. 76 0. 33 1. 65 4 .16 0. 33 25 110 0.01 94 66 Apr 2 9 3 .4 0. 38 0. 16 2. 93 3 .84 0. 33 22 279 n.d. 83 J u n l 6 3 .2 0. 57 0. 21 2. 43 3 .81 0. 33 24 68 n.d. 86 J u l l 4 2 .8 0. 61 0. 27 2. 63 4 .03 0. 35 26 100 n.d. 90 Aug21 1 .9 1. 07 0. 35 1. 67 3 .60 0. 33 26 130 0.01 77 Sep29 0 .9 0. 43 0. 24 2. 95 4 .01 0. 30 26 100 0.01 123 67 Apr 2 9 2 .5 0. 28 0. 16 2. 78 3 .36 0. 32 20 539 n.d. 76 J u n l 6 3 . 1 0. 48 0. 21 2. 50 3 .44 0. 33 26 58 n.d. 102 J u l l 4 3 .1 0. 76 0. 28 2. 30 3 .59 0. 32 27 110 n.d. 114 Aug21 1 .3 1. 07 0. 31 2. 05 4 .07 0. 33 26 200 0.01 87 Sep29 1 . 1 1. 01 0. 31 1. 48 4 . 17 0. 31 24 70 0.01 92 68 Apr 2 9 3 .4 0. 22 0. 13 3 . 08 2 .99 0. 34 15 129 n.d. 63 J u n l 6 3 .5 0. 38 0. 18 3 . 42 3 .19 0. 35 25 68 n.d. 103 J u l l 4 2 .7 0. 38 0. 22 3 . 65 3 .38 0. 32 24 110 n.d. 114 Aug21 1 . 1 0. 64 0. 25 2 . 87 3 .48 0. 28 23 180 0.01 102 Sep29 0 .6 0. 51 0. 25 2. 63 3 .94 0. 28 23 150 0.02 110 69 Apr 2 9 3 .3 0. 19 0. 13 2. 93 3 .44 0. 33 17 129 n.d. 85 J u n l 6 3 .8 0. 30 0. 17 2. 55 3 .56 0. 31 24 68 n.d. 114 J u l l 4 2 .5 0. 37 0. 25 2. 63 3 .59 0. 30 26 140 n.d. 129 Aug21 1 . 1 0. 64 0. 31 1. 87 3 .85 0. 30 27 160 0.01 108 Sep29 0 .9 0. 47 0. 27 1. 50 3 .98 0. 31 26 90 0.01 133 70 Apr 2 9 4 .4 0. 36 0. 16 3. 10 3 .36 0. 35 20 119 n.d. 76 J u n l 6 3 .9 0. 64 0. 24 3 . 00 3 .61 0. 34 29 78 n.d. 89 J u l l 4 2 .5 0. 97 0. 31 2. 78 3 .63 0. 33 27 110 n.d. 106 189 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm Aug21 1. 7 1. 29 0. 36 2. 17 4 .20 0. 31 27 120 0.00 77 Sep29 1. 1 1. 14 0. 36 1. 80 4 .89 0. 32 26 130 0.01 86 71 Apr 2 9 3. 6 0. 22 0. 14 2. 68 2 .91 0. 30 20 109 n.d. 91 J u n l 6 3. 3 0. 47 0. 25 2. 15 2 .88 0. 31 25 58 n.d. 125 J u l l 4 2. 1 0. 51 0. 29 1. 83 2 .97 0. 32 24 60 n.d. 128 Aug21 1. 1 0. 72 0. 33 1. 35 3 .70 0. 32 28 150 0.01 112 Sep29 0. 7 0. 88 0. 39 1. 55 4 .29 0. 34 26 110 0.01 105 72 Apr 2 9 3. 2 0 1.2 0. 13 2. 73 3 .40 0. 29 16 99 n.d. 99 J u n l 6 3. 2 0. 75 0. 29 3. 00 3 .41 0. 31 30 178 n.d. 149 J u l 14 2. 7 0. 71 0. 30 2. 45 3 .53 0. 32 29 90 n.d. 122 Aug21 2. 0 0. 86 0. 36 1. 82 3 .73 0. 31 29 120 0.01 120 Apr 2 9 1. 1 0. 94 0. 35 1. 58 4 .42 0. 32 26 100 0.01 117 73 Apr 2 9 3. 0 0. 37 0. 17 2. 78 3 .47 0. 32 20 119 n.d. 89 J u n l 6 3 . 6 0. 38 0. 23 2. 03 3 .53 0. 31 26 58 n.d. 133 J u l l 4 1. 9 0. 54 0. 31 2. 03 3 .66 0. 31 23 220 n.d. 135 Aug21 1. 0 0. 71 0. 32 1. 45 3 .48 0. 42 22 210 0.01 109 Sep29 0. 5 0. 52 0. 30 1. 53 3 .85 0. 28 27 80 0.01 135 74 Apr29 3. 3 0 i.2 0. 14 2. 35 3 .53 0. 33 18 259 n.d. 78 J u n l 6 2. 7 0. 57 0. 24 1. 93 3 .71 0. 33 26 98 n.d. 119 J u l l 4 2. 0 0. 71 0. 29 2. 18 3 .84 0. 34 27 100 n.d. 114 Aug21 1. 4 0. 99 0. 36 1. 82 4 .05 0. 35 27 210 0.01 105 Sep29 0. 7 0. 77 0. 36 1. 18 4 . 03 0. 30 28 110 0.01 131 75 Apr29 4. 1 0. 22 0. 14 3. 05 3 .04 0. 39 18 219 n.d. 78 J u n l 6 3. 5 0. 60 0. 23 2. 00 3 . 11 0. 37 26 78 n.d. 123 J u l l 4 2. 3 0. 66 0. 26 2. 13 3 .03 0. 31 24 50 n.d. 108 Aug21 1. 6 1. 04 0. 33 1. 75 3 .74 0. 30 28 140 0.01 96 Sep29 1. 1 0. 96 0. 32 1. 48 3 .65 0. 39 21 70 0.01 90 76 Apr 2 9 2. 7 0. 37 0. 16 3. 63 3 .73 0. 31 24 129 n.d. 80 J u n l 6 3. 5 0. 50 0. 25 2. 73 3 .83 0. 34 25 78 n.d. 99 J u l l 4 1. 9 0. 94 0. 30 3. 23 4 .03 0. 33 30 90 n.d. 113 Aug21 1. 2 1. 11 0. 33 2. 20 3 .93 0. 32 29 240 0. 01 106 Sep29 1. 1 1. 02 0. 33 1. 83 4 .04 0. 35 25 130 0.01 101 Dry Matter and f o l i a r elements for 38 i r r i g a t e d s i t e s , 1987 S i t e Cut DM Ca Mg K N P Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm 80 Apr 2 9 2.7 0.38 0.13 3 .40 3 .41 0. 35 18 100 n.d. 62 J u n l 6 3.9 0.81 0.21 3 .18 3 .40 0. 33 29 135 n.d. 100 J u l l 4 2.7 0.86 0.25 3 .75 3 .81 0. 32 25 380 n.d. 100 Aug21 2.5 0.81 0.27 4 .57 3 .83 0. 33 24 680 0.01 109 Sep29 2.2 0.71 0.26 3 .23 3 .94 0. 29 23 310 0.03 99 81 Apr29 2.6 0.47 0.15 3 .45 3 .73 0. 33 19 . 90 n.d. 53 J u n l 6 4.4 0.71 0.21 4 .05 3 .61 0. 31 25 285 n.d. 85 190 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm J u l 14 2 .0 0. 98 0. 26 3 .65 4 .03 0. 33 26 490 n.d. 80 Aug21 2 .3 0. 97 0. 28 3 .79 3 .96 0. 32 23 830 0.01 99 Sep29 1 .6 0. 95 0. 27 3 .52 3 .82 0. 29 20 190 0.02 74 82 Apr 2 9 4 .4 0. 31 0. 12 3 .65 2 .41 0. 32 20 80 n.d. 55 J u n l 6 6 .3 0. 59 0. 17 3 .05 2 .53 0. 32 30 105 n.d. 72 J u l l 4 1 .9 0. 61 0. 19 3 .35 3 . 15 0. 33 26 460 n.d. 71 Aug21 2 .4 1. 05 0. 25 3 .92 3 .64 0. 32 25 1280 0. 04 89 Sep29 2 .3 0. 68 0. 24 4 .50 3 .82 0. 31 20 130 0.01 55 83 Apr 2 9 2 .3 0. 36 0. 13 4 .40 3 . 14 0. 31 19 110 n.d. 52 J u n l 6 3 .5 0. 50 0. 18 3 .63 3 .04 0. 32 25 195 n.d. 78 J u l l 4 1 .9 0. 30 0. 24 4 .15 3 .62 0. 33 27 2170 n.d. 107 Aug21 2 .3 0. 53 0. 30 3 .99 3 .45 0. 32 32 4210 0.30 137 Sep29 1 .0 0. 45 0. 27 4 .38 4 .01 0. 28 21 410 0.05 86 84 Apr 2 9 3 .4 0. 62 0. 15 2 .90 2 .81 0. 34 20 150 n.d. 70 J u n l 6 3 .0 0. 65 0. 21 3 .48 2 .91 0. 33 26 85 n.d. 78 J u l l 4 2 .4 0. 83 0. 27 3 .20 3 .64 0. 33 27 300 n.d. 90 Aug21 2 .2 1. 76 0. 37 2 .94 4 .01 0. 33 29 930 0.03 101 Sep29 2 .3 1. 37 0. 32 2 .58 3 .69 0. 29 25 170 0.02 74 85 Apr 2 9 3 .5 0. 56 0. 16 3 . 60 3 .31 0. 35 21 120 n.d. 65 J u n l 6 3 . 1 0. 91 0. 23 3 . 68 3 .41 0. 31 29 105 n.d. 73 J u l l 4 2 .4 1. 08 0. 27 2 .98 3 .92 0. 35 26 730 n.d. 96 Aug21 3 .0 1. 26 0. 31 3 .67 3 .73 0. 36 26 1580 0.05 106 Sep29 1 .7 0. 98 0. 29 5 .25 4 .20 0. 35 22 280 0. 03 75 86 Apr 2 9 3 .5 0. 53 0. 15 3 .45 2 .53 0. 35 19 100 n.d. 54 J u n l 6 3 .1 1. 12 0. 21 3 .88 2 .77 0. 34 31 95 n.d. 99 J u l 14 1 .9 1. 07 0. 23 3 . 05 3 .98 0. 36 26 400 n.d. 91 Aug21 2 .7 1. 18 0. 27 4 .37 4 .35 0. 37 29 870 0.05 102 Sep29 1 .9 0. 99 0. 27 4 . 13 4 .19 0. 32 23 120 0.01 78 87 Apr 2 9 3 . 3 0. 48 0. 14 3 .38 2 .01 0. 31 17 70 n.d. 56 Ju n l 6 3 .5 0. 86 0. 20 2 .82 2 .41 0. 33 25 75 n.d. 82 J u l l 4 2 . 1 0. 99 0. 28 2 .78 3 .76 0. 37 24 290 n.d. 79 Aug21 3 . 1 1. 65 0. 34 2 .47 3 .97 0. 34 27 910 0. 04 94 Sep29 1 .6 1. 17 0. 28 2 .38 4 .22 0. 33 24 280 0.03 79 88 Apr 2 9 3 .4 0. 47 0. 18 3 . 03 2 . 11 0. 30 22 120 n.d. 83 J u n l 6 4 .0 1. 26 0. 28 2 .60 2 .33 0. 35 28 165 n.d. 99 J u l l 4 2 . 1 1. 29 0. 29 2 . 68 4 .38 0. 36 26 550 n.d. 84 Aug21 2 .8 1. 12 0. 29 2 .42 4 .12 0. 37 22 640 0.01 81 Sep29 1 .8 1. 20 0. 30 2 .20 4 .27 0. 34 19 110 0.01 63 89 Apr29 3 .9 0. 32 0. 14 3 . 10 2 .33 0. 33 19 110 n.d. 70 J u n l 6 3 .7 0. 63 0. 19 3 . 13 2 .26 0. 32 22 95 n.d. 93 J u l 14 2 . 1 0. 66 0. 26 2 .95 3 .85 0. 32 25 400 n.d. 106 Aug21 2 .9 1. 02 0. 29 3 .04 3 .38 0. 32 21 1110 0. 03 101 Sep29 1 .8 0. 98 0. 29 3 .28 3 . 67 0. 32 21 290 0.04 71 90 Apr29 3 .5 0. 23 0. 13 2 .90 3 . 13 0. 34 19 50 n.d. 81 J u n l 6 3 .8 0. 42 0. 22 2 .23 3 .24 0. 33 28 155 n.d. 109 J u l 14 2 .4 0. 41 0. 32 2 . 30 3 .81 0. 32 28 860 n.d. 108 Aug21 2 .3 0. 73 0. 36 2 .52 3 .81 0. 36 26 1220 0.05 114 191 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm : ppm % ppm Sep29 1 .6 0. 65 0. 39 1. 63 4 .01 0. 30 24 160 0. 01 85 91 Apr 2 9 3 .4 0. 32 0. 14 2. 50 3 .31 0. 34 18 90 n. d. 71 J u n l 6 3 .3 0. 72 0. 24 1. 85 3 .06 0. 36 24 85 n. d. 92 J u l 14 1 .7 0. 73 0. 31 1. 73 3 .78 0. 36 30 320 n. d. 94 Aug21 2 .0 1. 36 0. 36 1. 52 4 .49 0. 39 28 800 0. 05 95 Sep29 1 .3 1. 09 0. 30 1. 13 4 .38 0. 36 22 110 0. 01 67 92 Apr 2 9 5 .0 0. 21 0. 14 2. 93 2 .79 0. 34 14 50 n. d. 77 J u n l 6 2 .9 0. 66 0. 22 3. 90 2 .94 0. 33 22 115 n. d. 105 J u l l 4 2 .4 0. 96 0. 28 2. 63 3 .43 0. 32 26 560 n. d. 76 Aug21 2 .4 0. 95 0. 32 2. 94 3 .96 0. 36 28 1280 0. 08 100 Sep29 2 . 1 0. 42 0. 36 2. 15 3 .74 0. 32 31 3340 0. 30 155 93 Apr 2 9 3 .3 0. 22 0. 14 2. 70 2 .41 0. 36 18 60 n. d. 65 J u n l 6 3 .7 0. 43 0. 21 2. 40 2 .52 0. 33 20 45 n. d. 107 J u l l 4 1 .8 0. 58 0. 28 2. 28 4 .02 0. 36 27 320 •n. d. 96 Aug21 2 .6 0. 69 0. 34 1. 64 3 .68 0. 30 27 370 0. 01 104 Sep29 1 .4 0. 86 0. 36 1. 73 4 . 11 0. 35 25 140 0. 01 88 94 Apr 2 9 3 .7 0. 22 0. 13 3. 30 3 .01 0. 31 16 80 n. d. 61 J u n l 6 3 .0 0. 80 0. 26 3 . 68 3 .27 0. 33 26 95 n. d. 108 J u l 14 2 .2 0. 56 0. 25 3 . 13 3 .68 0. 33 29 420 n. d. 117 Aug21 2 .9 0. 99 0. 33 2. 47 4 .01 0. 35 27 530 0. 01 92 Sep29 1 .6 0. 91 0. 29 2. 20 4 .10 0. 39 25 230 0. 02 83 95 Apr 2 9 4 .5 0. 24 0. 15 3. 10 2 .94 0. 32 22 100 n. d. 65 Ju n l 6 3 .7 0. 56 0. 24 2. 93 3 . 17 0. 34 21 85 n. d. 86 J u l 14 2 .6 0. 69 0. 26 3. 03 3 .58 0. 33 27 300 n. d. 100 Aug21 2 .9 0. 88 0. 36 1. 49 3 .72 0. 36 29 490 0. 00 101 Sep29 1 .9 0. 82 0. 29 1. 53 3 .94 0. 35 24 140 0. 01 73 96 Apr 2 9 4 .9 0. 19 0. 13 3. 13 2 .41 0. 33 17 140 n. d. 58 J u n l 6 3 .5 0. 34 0. 17 4 . 03 2 .56 0. 31 21 65 n. d. 81 J u l l 4 2 .4 0. 32 0. 20 2. 88 3 . 08 0. 32 25 180 n. d. 94 Aug21 3 .7 0. 53 0. 23 4. 27 3 .50 0. 32 22 200 0. 01 104 Sep29 1 .8 0. 56 0. 29 2. 60 3 .84 0. 28 22 100 0. 01 93 97 Apr 2 9 3 .6 0. 24 0. 14 3. 15 2 .61 0. 32 21 120 n. d. 74 J u n l 6 4 . 1 0. 34 0. 19 4. 08 2 .56 0. 31 24 95 n. d. 111 J u l l 4 2 .9 0. 43 0. 27 1. 92 3 . 52 0. 31 24 470 n. d. 99 Aug21 3 .9 0. 64 0. 31 2. 29 3 .59 0. 34 28 530 0. 01 106 Sep29 1 .4 0. 80 0. 32 1. 80 4 .30 0. 31 24 130 0. 01 80 98 Apr 2 9 5 .2 0. 44 0. 17 3 . 33 2 .81 0. 32 21 230 n. d. 71 Ju n l 6 4 .3 0. 83 0. 23 2. 65 2 .98 0. 33 24 165 n. d. 99 J u l l 4 2 .8 1. 04 0. 30 2 . 58 3 .76 0. 33 31 420 n. d. 93 Aug21 3 .9 0. 78 0. 32 2. 04 3 .43 0. 32 25 310 0. 01 102 Sep29 1 .5 1. 15 0. 32 1. 70 4 . 17 0. 37 21 120 0. 01 68 99 Apr 2 9 5 .6 0. 26 0. 15 3. 08 3 .05 0. 31 18 90 n. d. 79 J u n l 6 4 .3 0. 69 0. 24 3. 60 3 .00 0. 34 26 75 n. d. 100 J u l l 4 2 .3 1. 33 0. 31 2. 63 3 .73 0. 34 30 170 n. d. 87 Aug21 3 .3 0. 99 0. 33 1. 67 3 .40 0. 34 27 530 0. 02 92 Sep29 1 .6 0. 94 0. 31 1. 90 4 .19 0. 37 20 100 0. 01 72 192 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm 100 Apr 2 9 4 .0 0. 24 0. 14 3 .65 2 .61 0. 30 17 100 n.d. 71 J u n l 6 3 .5 0. 33 0. 19 3 .65 2 .70 0. 32 21 55 n.d. 86 J u l l 4 2 .6 0. 43 0. 25 2 .48 3 .23 0. 32 28 600 n.d. 110 Aug21 3 .6 0. 68 0. 30 2 .67 3 .43 0. 35 24 610 0.03 102 Sep29 1 .8 0. 66 0. 33 2 .38 4 .13 0. 32 27 120 0.01 118 101 Apr 2 9 2 .9 0. 47 0. 15 3 .27 3 .41 0. 29 16 160 n.d. 61 J u n l 6 3 .7 1. 01 0. 26 3 .25 3 .10 0. 33 24 125 n.d. 111 J u l 14 2 .3 1. 20 0. 30 2 .83 3 .90 0. 36 30 360 n.d. 95 Aug21 3 .6 0. 83 0. 34 2 .19 3 .43 0. 32 26 610 0.01 126 Sep29 2 .2 0. 98 0. 31 3 .38 4 .18 0. 34 25 410 0.05 106 102 Apr 2 9 3 .8 0. 28 0. 15 2 .75 3 .18 0. 34 16 60 n.d. 80 J u n l 6 3 .7 0. 76 0. 27 2 .60 3 .33 0. 33 24 55 n.d. , 82 J u l 14 2 .4 1. 03 0. 32 1 .85 3 .68 0. 34 29 2070 n.d. 99 Aug21 2 .9 1. 06 0. 35 1 .42 3 .58 0. 34 24 460 0.01 106 Sep29 1 .7 1. 16 0. 36 1 .23 4 .05 0. 31 23 140 0.02 84 103 Apr 2 9 3 .4 0. 21 0. 13 2 .88 2 .44 0. 37 18 230 n.d. 54 J u n l 6 3 .7 0. 51 0. 22 3 .60 2 .78 0. 33 23 55 n.d. 93 J u l l 4 2 .0 0. 44 0. 29 2 .85 3 .40 0. 32 34 350 n.d. 108 Aug21 2 .8 0. 73 0. 32 2 . 52 3 .73 0. 33 23 390 0.01 100 Sep29 1 .9 0. 72 0. 33 2 .30 3 .98 0. 30 26 290 0. 03 106 104 Apr29 4 .2 0. 26 0. 15 3 .25 2 .22 0. 37 18 100 n.d. 70 J u n l 6 3 .6 0. 51 0. 21 3 .30 2 .55 0. 30 19 215 n.d. 93 J u l 14 2 .0 0. 63 0. 25 3 .48 3 .65 0. 33 28 150 n.d. 87 Aug21 3 .0 0. 96 0. 31 2 .42 3 .45 0. 33 24 760 0.01 93 Sep29 1 .9 0. 94 0. 31 2 .53 4 .14 0. 33 22 140 0.01 78 105 Apr 2 9 2 .2 0. 37 0. 18 3 .88 3 .14 0. 34 19 100 n.d. 65 J u n l 6 3 .1 0. 79 0. 23 3 . 05 3 .06 0. 33 22 75 n.d. 72 J u l l 4 2 . 1 0. 74 0. 27 2 .53 3 .50 0. 32 26 370 n.d. 81 Aug21 2 .9 0. 90 0. 29 2 .44 3 .33 0. 31 24 780 0.01 74 Sep29 1 .9 0. 85 0. 31 2 . 13 4 .53 0. 41 22 360 0.05 70 106 Apr 2 9 3 .5 0. 66 0. 19 1 .90 3 .24 0. 36 19 140 n.d. 71 J u n l 6 3 .1 1. 21 0. 32 1 .98 3 .43 0. 32 25 115 n.d. 113 J u l l 4 2 . 1 0. 91 0. 33 1 . 68 3 .62 0. 32 28 720 n.d. 111 Aug21 2 . 1 1. 27 0. 35 1 .22 3 .82 0. 35 23 1020 0.01 87 Sep29 1 .3 0. 62 0. 35 2 . 10 4 .81 0. 37 23 180 0.01 81 107 Apr 2 9 3 .8 0. 24 0. 13 3 .33 2 .81 0. 37 17 60 n.d. 79 J u n l 6 3 .1 0. 78 0. 25 2 .30 3 .09 0. 35 24 125 n.d. 79 J u l l 4 2 .2 0. 65 0. 28 2 .90 3 .75 0. 36 27 460 n.d. 100 Aug21 2 .5 0. 89 0. 39 2 .14 3 .91 0. 33 27 1370 0.05 110 Sep29 1 .9 0. 81 0. 24 2 .90 3 .88 0. 29 23 310 0.04 89 108 Apr29 3 .4 0. 46 0. 16 3 .50 2 .62 0. 31 20 150 n.d. 61 J u n l 6 3 .8 0. 62 0. 20 3 .48 2 .84 0. 31 25 65 n.d. 86 J u l 14 2 .0 1. 14 0. 28 3 .43 4 .18 0. 35 28 670 n.d. 79 Aug21 2 .8 1. 13 0. 29 3 .84 3 .84 0. 33 26 1680 0.03 103 Sep29 1 .7 0. 67 0. 31 4 .90 4 .25 0. 34 24 210 0.02 71 109 Apr29 3 .8 0. 16 0. 11 3 .58 3 . 11 0. 32 16 210 n.d. 63 J u n l 6 3 .8 0. 38 0. 19 4 .38 3 .45 0. 34 24 135 n.d. 104 193 S i t e Cut DM Ca Mg K N p Zn Fe A l Mn No Date T/ha % % % % % ppm ppm % ppm J u l l 4 1 .9 0. 30 0. 19 3 .40 3 .03 0. 33 20 370 n.d. 105 Aug21 2 .5 0. 65 0. 24 3 .89 3 .53 0. 36 23 430 0.01 98 Sep29 2 .7 0. 44 0. 25 4 .85 3 .94 0. 35 25 160 0.01 122 110 Apr 2 9 2 .8 0. 18 0. 10 3 .33 2 .99 0. 33 16 70 n.d. 54 Ju n l 6 3 .8 0. 37 0. 17 3 .53 2 .88 0. 28 18 135 n.d. 76 J u l l 4 2 . 1 0. 40 0. 19 3 .70 3 .06 0. 32 26 220 n.d. 81 Aug21 3 .7 0. 52 0. 22 3 .92 3 .44 0. 35 22 580 0.02 93 Sep29 2 .6 1. 10 0. 29 4 .95 3 .60 0. 28 21 140 0.01 95 111 Apr 2 9 3 .0 0. 48 0. 14 3 .43 2 .94 0. 31 17 110 n.d. 47 J u n l 6 4 .0 0. 52 0. 18 3 .83 3 .07 0. 30 18 105 n.d. 56 J u l 14 1 .9 0. 63 0. 22 3 .73 3 .59 0. 30 27 430 n.d. 77 Aug21 2 .2 1. 07 0. 27 4 .24 3 . 19 0. 30 24 1410 0.05 93 Sep29 1 .8 1. 24 0. 28 4 .88 4 .27 0. 28 21 200 0.01 73 112 Apr 2 9 3 .8 0. 37 0. 15 3 .85 3 .04 0. 33 17 130 n.d. 55 J u n l 6 4 .7 0. 86 0. 23 4 . 60 3 .21 0. 33 24 175 n.d. 63 J u l 14 2 .0 1. 38 0. 29 3 .33 4 . 09 0. 36 30 790 n.d. 71 Aug21 2 .4 1. 24 0. 30 4 .04 3 .81 0. 45 25 980 0. 03 85 Sep29 1 .8 0. 49 0. 25 4 .93 4 .39 0. 33 24 230 0. 02 61 113 Apr 2 9 4 .5 0. 21 0. 11 3 . 58 3 .15 0. 32 17 80 n.d. 60 J u n l 6 3 .9 0. 38 0. 17 3 .78 2 .95 0. 31 22 85 n.d. 79 J u l l 4 2 . 1 0. 49 0. 22 4 .03 3 .65 0. 32 26 330 n.d. 74 Aug21 2 .7 0. 56 0. 23 4 .42 3 .46 0. 34 21 510 0.01 64 Sep29 1 .8 0. 98 0. 29 4 .40 3 .71 0. 28 20 110 0.01 74 114 Apr 2 9 3 .5 0. 37 0. 13 3 .48 2 .77 0. 34 17 100 n.d. 51 J u n l 6 3 .1 0. 66 0. 20 3 .88 2 .81 0. 30 23 55 n.d. 62 J u l 14 2 .0 0. 83 0. 26 3 .53 3 .96 0. 33 28 360 n.d. 77 Aug21 2 .9 0. 85 0. 26 3 .67 3 .55 0. 34 24 1010 0.03 84 Sep29 2 .0 0. 65 0. 26 3 .20 4 .02 0. 33 22 270 0. 03 74 115 Apr29 3 .2 0. 24 0. 12 3 . 18 2 .84 0. 36 19 80 n.d. 56 J u n l 6 3 .2 0. 59 0. 21 3 .83 3 .03 0. 32 28 55 n.d. 75 J u l l 4 2 .0 0. 67 0. 24 3 .35 3 .50 0. 30 25 520 n.d. 81 Aug21 3 .1 0. 79 0. 26 3 .57 3 . 64 0. 32 24 530 0.01 86 Sep29 1 .9 0. 85 0. 26 3 .65 3 .98 0. 40 18 120 0.01 66 116 Apr 2 9 3 .0 0. 25 0. 11 3 .05 2 .51 0. 31 16 140 n.d. 40 J u n l 6 3 .5 0. 88 0. 21 3 .60 2 .95 0. 35 23 75 n.d. 66 J u l l 4 2 .2 0. 86 0. 24 3 .30 3 .85 0. 31 25 520 n.d. 69 Aug21 2 .4 1. 01 0. 26 3 .77 3 .73 0. 31 24 700 0. 02 83 Sep29 1 .4 1. 05 0. 29 2 . 60 4 .07 0. 33 21 290 0.03 65 117 Apr 2 9 3 . 3 0. 36 0. 16 3 . 03 2 .74 0. 36 18 180 n.d. 97 Ju n l 6 3 .9 0. 87 0. 24. 2 .90 3 . 02 0. 34 21 65 n.d. 64 J u l l 4 2 . 1 0. 97 0. 27 3 .65 3 . 62 0. 32 22 540 n.d. 72 Aug21 2 .3 1. 51 0. 33 2 .52 3 .89 0. 36 23 1170 0.02 81 Sep29 1 .1 1. 94 0. 41 2 .25 4 . 19 0. 37 17 160 0.01 44 194 Forage q u a l i t y components for 76 dryland s i t e s , 1986 Si t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP 1 Date % Mj % % No Date % Mj % % Mayl4 36. 6 12. 0 57. 3 13. 1 10 May 14 30. 4 13. 4 65. 3 18 . 1 J u l 05 35. 1 12. 3 59. 2 11. 9 J u l 05 34. 6 12. 4 59. 9 18 . 1 Aug 05 26. 4 14. 4 70. 6 19. 3 Aug 05 29. 2 13. 7 66. 9 17 . 3 Aug29 29. 9 13. 6 66. 0 21. 3 Aug29 27. 8 14. 1 68. 7 25. 2 Oct07 29. 5 13. 7 66. 6 22. 0 0ct07 27. 4 14. 2 69. 3 23. 5 2 Mayl4 35. 4 12. 3 58. 9 15. 2 11 May 14 24. 8 14. 8 72. 7 19. 0 J u l 05 34. 8 12. 4 59. 6 12. 5 Jul05 33. 6 12. 7 61. 1 16. 4 Aug 05 27. 1 14. 2 69. 7 18. 0 Aug 05 26. 0 14. 5 71. 1 20. 1 Aug29 29. 6 13. 6 66. 5 23. 1 Aug2 9 28. 3 13. 9 68. 1 22 . 7 Oct07 28. 9 13. 8 67. 3 18. 6 Oct07 26. 2 14. 4 70. 8 23. 3 3 Mayl4 32. 4 13. 0 62. 8 15. 6 12 Mayl4 35. 8 12. 1 58. 3 14 . 4 Jul05 33. 9 12. 6 60. 9 15. 0 Jul05 35. 8 12. 2 58 . 4 14 . 4 Aug 05 27. 5 14. 1 69. 1 19. 9 Aug 05 29. 2 13 . 7 66. 9 19 . 4 Aug 2 9 25. 7 14. 6 71. 4 24. 6 Aug 2 9 28. 8 13. 8 67. 4 24. 7 Oct07 25. 6 14. 6 71. 6 21. 9 0ct07 28. 9 13. 8 67. 3 19. 2 4 May 14 34. 7 12. 4 59. 8 13. 9 13 May 14 33 . 2 12 . 8 61. 7 14 . 6 J u l 05 35. 0 12. 3 59. 3 13 . 0 J u l 05 39. 1 11. 4 54. 0 13 . 6 Aug 05 28. 3 14. 0 68. 2 18. 4 Aug 05 31. 8 13. 1 63 . 5 17. 6 Aug 2 9 30. 0 13. 5 65. 9 22. 6 Aug 2 9 28. 9 13 . 8 67. 4 22 . 8 Oct 07 29. 4 13 . 7 66. 6 20. 4 Oct07 27. 7 14. 1 68. 9 21. 2 5 May 14 33. 4 12. 7 61. 5 14. 2 14 Mayl4 29. 5 13. 7 66. 6 19. 9 Jul05 34. 7 12. 4 59. 8 14. 6 Jul05 36. 0 12. 1 58. 0 18. 8 Aug 05 27. 1 14. 2 69. 7 21. 4 Aug 05 28. 5 13. 9 67. 9 20. 7 Aug 2 9 29. 1 13. 7 67 . 0 23. 4 Aug 2 9 26. 0 14.. 5 71. 1 25. 4 Oct07 28. 1 14. 0 68. 3 20. 1 Oct07 24. 8 14. 8 72. 6 25. 4 6 May 14 34. 8 12. 4 59. 6 13. 8 15 May 14 33 . 4 12. 7 61. 5 14 . 8 J u l 05 36. 2 12 . 1 57. 8 13. 4 Jul05 34. 5 12. 5 60. 0 18 . 2 Aug 05 28. 6 13. 9 67. 8 19. 1 Aug 05 29. 9 13 . 6 66. 1 20. 7 Aug29 30. 5 13. 4 65. 3 23. 4 Aug29 27. 5 14. 1 69. 1 24. 4 Oct07 26. 8 14. 3 70. 0 20. 8 Oct07 28. 3 13. 9 68. 1 21. 4 7 Mayl4 31. 0 13. 3 64. 6 17. 0 16 Mayl4 35. 3 12 . 3 59. 0 9 . 8 J u l 05 35. 4 12. 2 58. 8 13. 3 Jul05 33. 9 12. 6 60. 8 19. 1 Aug 05 28. 4 13. 9 68. 0 20. 0 Aug 05 30. 1 13. 5 65. 8 20. 3 Aug 2 9 30. 6 13. 4 65. 0 23. 0 Aug 2 9 31. 1 13. 3 64. 4 21. 0 Oct07 26. 4 14 . 4 70. 6 24. 3 Oct07 24. 7 14 . 8 72. 8 24 . 3 8 May 14 33. 8 12. 6 60. 9 13. 3 17 May 14 31. 1 13 . 3 64 . 5 13 . 7 J u l 05 35. 6 12. 2 58. 6 12 . 2 Jul05 33. 9 12. 6 60. 8 15. 4 Aug 05 29. 1 13. 8 67. 1 17. 0 Aug 05 29. 7 13. 6 66. 2 21. 6 Aug29 31. 3 13. 2 64. 2 22. 7 Aug 2 9 30. 5 13. 4 65. 3 22 . 6 0ct07 27. 4 14. 1 69. 2 22. 7 0ct07 27. 3 14. 2 69. 5 21. 9 9 Mayl4 28. 7 13. 8 67. 6 17. 9 18 Mayl4 40. 0 11. 1 52. 8 13 . 1 Jul05 37. 3 11. 8 56. 4 12. 7 Jul05 35. 0 12. 3 59. 4 15. 4 Aug 05 30. 1 13. 5 65. 8 18. 1 Aug 05 28. 1 14. 0 68. 4 21. 8 195 i t e No Cut Date ADF % DE Mj TDN % CP % Aug29 32. 7 12. 9 62. 4 20. 9 Oct07 26. 6 14. 3 70. 3 23. 6 19 May 14 41. 3 10. 8 51. 2 13. 1 J u l 05 35. 2 12. 3 59. 1 14. 5 Aug 05 32. 1 13. 0 63. 2 15. 9 Aug29 27. 3 14. 2 69. 4 25. 1 0ct07 29. 9 13. 6 66. 0 21. 1 20 Mayl4 38. 0 11. 6 55. 4 12. 4 J u l 05 34. 9 12. 4 59. 5 12. 6 Aug 05 30. 2 13. 5 65. 6 16. 2 Aug29 32. 6 12. 9 62. 5 22. 6 Oct 07 29. 8 13. 6 66. 1 21. 4 21 Mayl4 39. 2 11. 4 54. 0 13. 7 J u l 0 5 35. 7 12. 2 58. 5 15. 0 Aug 05 29. 4 13. 7 66. 7 18. 9 Aug 2 9 29. 4 13. 7 66. 7 23. 3 Oct07 28. 9 13. 8 67. 3 20. 9 22 May 14 40. 6 11. 0 52. 1 11. 1 J u l 0 5 35. 2 12. 3 59. 1 14. 8 Aug 05 29. 9 13. 6 66. 0 16. 0 Aug 2 9 28. 2 14. 0 68. 2 22. 1 Oct07 25. 3 14. 7 72. 0 23. 3 23 May 14 32. 9 12. 8 62. 1 13. 7 J u l 05 35. 1 12. 3 59. 2 14. 1 Aug 05 30. 1 13. 5 65. 8 17. 3 Aug 2 9 32. 0 13. 1 63. 3 20. 8 Oct07 30. 7 13. 4 65. 0 19. 9 24 May 14 39. 7 11. 2 53. 2 13. 4 J u l 05 35. 7 12. 2 58. 5 16. 4 Aug 05 30. 6 13. 4 65. 1 20. 3 Aug 2 9 29. 0 13. 8 67. 2 22. 3 Oct07 28. 0 14. 0 68. 5 21. 3 25 May 14 39. 2 11. 3 53. 9 15. 3 J u l 05 35. 7 12. 2 58. 5 15. 4 Aug 05 30. 5 13. 4 65. 2 22. 4 Aug29 28. 7 13. 8 67. 5 22. 8 Oct 07 26. 6 14. 4 70. 4 22. 1 26 Mayl4 35. 9 12. 1 58. 2 15. 8 J u l 05 35. 7 12. 2 58. 4 15. 2 Aug 05 30. 1 13. 5 65. 7 21. 4 Aug29 27. 6 14. 1 69. 0 22. 1 Oct07 34. 9 12. 4 59. 6 21. 7 27 Mayl4 35. 9 12. 1 58. 3 18. 3 J u l 05 37. 1 11. 9 56. 7 15. 9 Aug 05 30. 9 13. 3 64. 7 19. 1 Aug29 32. 1 13. 0 63. 1 21. 9 0ct07 30. 7 13. 4 65. 0 21. 2 S i t e Cut ADF DE TDN CP No Date % Mj % % Aug 2 9 32. 3 13. 0 62. 9 20. 8 0ct07 28. 9 13. 8 67. 4 20. 4 29 May 14 38. 8 11. 4 54. 4 15. 3 J u l 05 34. 8 12. 4 59. 6 12. 9 Aug05 32. 8 12. 9 62. 2 16. 1 Aug 2 9 29. 5 13. 6 66. 5 24. 0 Oct07 32. 0 13. 0 63. 2 22 . 4 30 May 14 41. 6 10. 8 50. 7 13. 4 J u l 0 5 35. 9 12. 1 58. 3 13 . 2 Aug 05 29. 1 13. 7 67. 0 20. 9 Aug29 29. 5 13 . 7 66. 6 22. 4 0ct07 29. 5 13. 7 66. 6 23 . 0 31 May 14 32. 2 13. 0 63. 0 12 . 3 Jul 0 5 33 . 4 12. 7 61. 5 11. 5 Aug 05 29. 6 13. 6 66. 4 18. 1 Aug29 31. 2 13. 3 64. 4 20. 9 Oct07 23 . 5 15. 1 74. 4 27. 5 32 May 14 36. 1 12. 1 58. 0 13. 9 J u l 05 33. 9 12. 6 60. 8 14'. 6 Aug 05 33 . 2 12. 8 61. 7 16. 4 Aug 2 9 26. 9 14. 3 70. 0 22 . 3 Oct07 26. 6 14. 3 70. 3 23. 3 33 Mayl4 27. 3 14. 2 69. 4 15. 6 Jul 0 5 34. 5 12. 5 60. 0 14. 3 Aug 05 28. 4 13. 9 68. 0 22 . 3 Aug 2 9 30. 4 13. 4 65. 4 23 . 0 Oct07 30. 5 13 . 4 65. 2 21. 3 34 May 14 34. 9 12. 4 59. 5 10. 4 Jul 0 5 32. 8 12. 9 62 . 2 11. 6 Aug 05 31. 0 13. 3 64. 7 17. 4 Aug 2 9 36. 4 12 . 0 57 . 6 19 . 5 Oct07 25. 0 14. 7 72 . 4 25. 9 35 May 14 39. 0 11. 4 54. 1 14. 1 J u l 0 5 34. 2 12. 5 60. 4 15. 4 Aug 05 31. 0 13. 3 64. 6 17. 9 Aug29 31. 9 13. 1 63 . 4 18. 6 Oct07 29. 4 13. 7 66. 7 22 . 6 36 May 14 36. 3 12 . 0 57. 7 13 . 8 J u l 05 35. 1 12. 3 59. 2 12. 5 Aug 05 27 . 6 14. 1 69. 1 18. 3 Aug 2 9 28. 4 13. 9 68. 0 22. 8 Oct07 29. 6 13. 6 66. 4 22. 2 37 Mayl4 38. 0 11. 6 55. 4 13 . 3 J u l 05 33 . 6 12. 7 61. 2 16. 1 Aug 05 29. 6 13. 6 66. 4 17. 7 Aug29 24. 6 14. 8 72. 9 24 . 1 Oct07 29. 6 13. 6 66. 4 19. 8 196 S i t e No Cut Date ADF % DE Mj TDN % CP % S i t e Cut No Date ADF % DE Mj TDN % CP % 28 May 14 38. 0 11. 6 55.4 15. 8 38 Mayl4 40. 0 11. 2 52.9 12 . 3 J u l 05 34. 4 12. 5 60.1 15. 7 J u l 05 34. 0 12. 6 60.7 12 . 2 Aug 05 31. 4 13. 2 64.0 22. 5 Aug 05 32. 9 12. 8 62.1 16. 2 Aug29 28. 2 14. 0 68.2 23. 8 Aug29 31. 2 13. 3 64.4 21. 1 Oct07 26. 9 14. 3 69.9 23. 9 0ct07 31. 7 13 . 1 63.7 20. 6 39 May 14 39. 2 11. 3 53.9 12. 8 49 May 14 33. 0 12. 8 62.0 17. 0 J u l 0 5 32. 8 12. 9 62.3 14. 9 J u l 0 5 34. 9 12. 4 59.5 13 . 7 Aug 05 31. 3 13. 2 64.2 20. 3 Aug 05 31. 9 13. 1 63.4 18. 2 Aug 2 9 29. 9 13. 6 66.1 23. 9 Aug29 29. 3 13. 7 66.9 25. 3 0ct07 28. 8 13. 8 67.4 21. 9 Oct 07 32. 3 13. 0 62.9 20. 3 40 May 14 31. 0 13. 3 64.6 17. 1 50 May 14 35. 0 12. 3 59.4 13 . 6 J u l 05 33. 4 12. 7 61.5 17. 1 J u l 05 34. 5 12. 5' 60.1 14 . 2 Aug 05 32. 0 13. 1 63.3 22. 1 Aug 05 31. 9 13. 1 63.4 17. 2 Aug 2 9 29. 7 13. 6 66.2 20. 0 Aug29 27. 8 14 . 1 68.8 23 . 1 Oct07 29. 2 13. 7 66.9 23. 8 Oct07 27. 9 14. 0 68.7 21. 9 41 May 14 35. 2 12. 3 59.1 14. 4 51 May 14 34. 5 12. 5 60. 0 14 . 6 J u l 0 5 34. 6 12. 4 59.9 13. 8 J u l 05 33. 0 12. 8 62.0 14. 2 Aug 05 28. 4 13. 9 68.0 20. 8 Aug 05 31. 6 13. 2 63.8 15. 9 Aug 2 9 30. 4 13 . 4 65.3 23. 4 Aug29 27. 7 14. 1 68.9 22 . 3 Oct07 33 . 3 12. 7 61.6 19. 9 Oct07 30. 3 13. 5 65.5 21. 6 42 May 14 34. 5 12. 5 60.1 16. 6 52 Mayl4 31. 6 13. 2 63.8 13. 9 J u l 0 5 35. 1 12. 3 59.2 13. 1 Jul 0 5 33 . 5 12. 7 61.3 12. 4 Aug 05 30. 6 13. 4 65.1 21. 0 Aug 05 32. 9 12. 8 62.1 17. 5 Aug29 27. 9 14. 0 68.6 24. 9 Aug29 28. 2 14. 0 68.3 23 . 3 Oct07 30. 1 13. 5 65.8 24. 4 Oct07 25. 0 14. 7 72.4 20. 4 43 May 14 33. 1 12. 8 61.8 15. 2 53 May 14 34. 6 12 . 4 59.9 15. 0 J u l 05 35. 3 12. 3 58.9 13. 9 J u l 0 5 33 . 9 12. 6 60.9 14 . 8 Aug 05 31. 7 13. 1 63.6 19. 5 Aug 05 32. 6 12. 9 62.5 17 . 7 Aug 2 9 31. 3 13. 2 64.2 22. 9 Aug29 31. 3 13. 2 64.3 20. 6 Oct 07 26. 5 14. 4 70.5 24. 8 Oct07 30. 9 13 . 3 64.7 18. 7 44 May 14 37. 3 11. 8 56.4 14. 5 54 May 14 32 . 0 13 . 1 63 . 3 16. 3 J u l 05 34. 7 12. 4 59.8 12. 6 J u l 05 33 . 1 12. 8 61.9 16. 1 Aug 05 30. 8 13. 3 64.8 17. 6 Aug 05 29. 8 13. 6 66.1 20. 3 Aug 2 9 30. 6 13. 4 65.1 24. 6 Aug29 30. 4 13 . 4 65.4 23 . 6 Oct07 30. 8 13. 3 64.8 22. 1 Oct07 31. 0 13. 3 64.5 20. 5 45 May 14 35. 6 12. 2 58.6 12. 4 55 Mayl4 33 . 9 12. 6 60.8 17. 5 J u l 0 5 36. 5 12. 0 57.4 12. 3 Jul 0 5 38. 0 11. 6 55.5 14. 3 Aug05 29. 2 13. 7 66.9 18. 6 Aug 05 31. 4 13 . 2 64.1 17 . 5 Aug29 29. 0 13. 8 67.2 23 . 2 Aug29 30. 9 13 . 3 64.7 25. 0 Oct07 31. 0 13. 3 64.6 19. 0 0ct07 30. 3 13 . 5 65.5 22 . 3 46 May 14 35. 2 12. 3 59.1 15. 1 56 May 14 34. 4 12. 5 60.2 14 . 2 J u l 05 36. 7 11. 9 57.2 12. 8 J u l 0 5 37. 9 11. 7 55.6 13 . 1 Aug 05 32 . 7 12. 9 62.4 17. 6 Aug 05 31. 6 13. 1 63.8 18. 1 Aug 2 9 31. 2 13. 3 64.3 25. 0 Aug29 28. 5 13. 9 67.8 23. 2 Oct07 29. 3 13. 7 66.8 24. 9 Oct07 30. 3 13. 5 65.5 21. 5 47 May 14 33. 5 12. 7 61.4 13. 5 57 Mayl4 32. 3 13. 0 62.9 16. 4 J u l 05 34. 4 12. 5 60.2 14. 6 Jul 0 5 36. 6 12. 0 57.3 13. 6 197 i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % Aug 05 30. 8 13. 3 64. 8 17. 9 Aug 05 30. 8 13. 4 64. 9 23 . 1 Aug29 29. 4 13. 7 66. 6 19. 7 Aug29 26. 8 14. 3 70. 0 24. 7 0ct07 30. 2 13. 5 65. 6 21. 8 Oct07 31. 6 13. 2 63 . 8 22. 0 48 Mayl4 33. 1 12. 8 61. 8 14. 1 58 May 14 36. 7 11. 9 57. 2 16. 4 J u l 05 35. 4 12. 3 58. 9 13. 6 J u l 05 36. 0 12. 1 58. 0 13 . 9 Aug05 32. 7 12. 9 62. 4 15. 8 Aug05 31. 8 13. 1 63 . 6 19. 2 Aug 2 9 32. 4 13. 0 62. 8 24. 1 Aug 2 9 30. 2 13. 5 65. 6 22 . 7 Oct07 35. 0 12. 3 59. 3 19. 1 Oct07 33 . 5 12. 7 61. 4 19. 3 59 Mayl4 32. 9 12. 8 62. 1 16. 3 68 May 14 33 . 1 12. 8 61. 9 15. 0 J u l 05 33. 8 12. 6 61. 0 16. 1 J u l 05 28. 8 13. 8 67. 4 13. 7 Aug 05 28. 3 13. 9 68. 1 18. 3 Aug 05 31. 7 13. 1 63. 7 18. 0 Aug29 25. 0 14. 7 72. 3 23 . 3 Aug 2 9 31. 3 13. 2 64. 2 23 . 8 Oct07 27. 7 14. 1 68. 9 23. 5 Oct07 30. 4 13. 4 65. 4 23. 7 60 May 14 33. 2 12. 8 61. 8 13. 9 69 Mayl4 34. 8 12. 4 59. 6 12. 4 J u l 05 38. 2 11. 6 55. 3 13. 3 J u l 0 5 33. 5 12. 7 61. 3 14. 0 Aug05 29. 7 13. 6 66. 3 17. 6 Aug 05 30. 1 13. 5 65. 7 18. 8 Aug29 29. 0 13. 8 67. 2 22. 4 Aug29 29. 9 13. 6 66. 0 22 . 0 Oct07 35. 0 12. 4 59. 4 18. 7 Oct07 33. 6 12. 7 61. 2 20. 9 61 May 14 35. 1 12. 3 59. 2 16. 3 70 May 14 32. 4 13 . 0 62. 7 16. 1 J u l 05 36. 0 12. 1 58. 1 13. 6 Jul 0 5 35. 5 12. 2 58. 7 13. 8 Aug 05 29. 8 13. 6 66. 2 19. 0 Aug 05 32. 0 13. 1 63 . 3 17. 8 Aug29 24. 9 14. 7 72. 5 23 . 3 Aug 2 9 29. 7 13. 6 66. 3 20. 8 Oct07 31. 5 13. 2 64. 0 22. 4 Oct07 33 . 5 12. 7 61. 4 19. 5 62 May 14 35. 1 12. 3 59. 2 13 . 4 71 May 14 34. 2 12. 5 60. 5 13 . 1 J u l 05 34. 3 12. 5 60. 3 12. 7 J u l 05 34. 8 12. 4 59. 6 11. 3 Aug 05 30. 1 13. 5 65. 8 16. 9 Aug 05 32. 6 12. 9 62. 6 14. 3 Aug 2 9 23. 2 15. 2 74. 8 21. 4 Aug2 9 30. 6 13. 4 65. 2 23. 0 0ct07 32. 0 13. 1 63. 2 21. 8 0ct07 33. 2 12. 8 61. 8 18. 3 63 May 14 33 . 1 12. 8 61. 9 16. 3 72 Mayl4 33. 3 12. 8 61. 6 13. 6 J u l 0 5 36. 4 12. 0 57. 5 13. 6 J u l 0 5 36. 2 12. 1 57. 8 12. 7 Aug 05 31. 4 13. 2 64. 1 16. 2 Aug05 31. 9 13. 1 63. 4 17. 5 Aug 2 9 28. 5 13. 9 67. 8 23. 3 Aug29 26. 3 14. 4 70. 7 23 . 6 0ct07 32. 9 12. 8 62. 1 21. 9 Oct07 30. 9 13. 3 64. 7 20. 1 64 May 14 35. 1 12. 3 59. 3 16. 6 73 May 14 34. 9 12. 4 59. 5 14 . 2 J u l 0 5 35. 2 12. 3 59. 1 16. 4 J u l 0 5 31. 1 13. 3 64. 4 16. 3 Aug 05 29. 7 13. 6 66. 3 23. 1 Aug 05 30. 7 13. 4 65. 0 19. 3 Aug 2 9 29. 6 13. 6 66. 4 24. 1 Aug 2 9 28. 7 13. 9 67. 6 24. 5 Oct07 32. 3 13 . 0 62. 9 21. 9 Oct07 30. 9 13. 3 64. 7 21. 8 65 May 14 36. 1 12. 1 57. 9 16. 9 74 May 14 31. 6 13. 1 63 . 7 14. 9 J u l 05 35. 4 12. 2 58. 8 15. 2 Jul 0 5 34. 7 12. 4 59. 8 17. 8 Aug 05 30. 6 13. 4 65. 1 21. 6 Aug 05 30. 9 13. 3 64. 7 17. 3 Aug29 29. 9 13. 6 66. 0 24. 9 Aug29 27. 2 14. 2 69. 6 25. 3 Oct07 33 . 5 12 . 7 61. 4 20. 5 Oct07 30. 5 13. 4 65. 2 20. 4 66 Mayl4 35. 3 12. 3 59. 0 13. 3 75 Mayl4 35. 4 12. 2 58. 8 14. 9 JU105 35. 5 12. 2 58. 7 13. 8 Jul 0 5 34. 6 12. 4 59. 9 13. 8 Aug 05 30. 5 13. 4 65. 3 22. 8 Aug 05 30. 6 13. 4 65. 2 19. 6 Aug29 18. 0 16. 4 81. 5 25. 8 Aug 2 9 26. 2 14. 4 70. 8 28. 9 198 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % Oct 07 31. 8 13.1 63.5 21.8 Oct07 30. 3 13.5 65.5 23 . 6 67 May 14 34. 9 12.4 59.5 13.8 76 May 14 33 . 4 12.7 61.4 15. 4 J u l 05 36. 8 11.9 57.0 12.2 J u l 05 35. 1 12.3 59.2 15. 1 Aug 05 31. 4 13.2 64.1 19.6 Aug 05 29. 8 13.6 66.1 22 . 4 Aug29 27. 4 14.2 69.3 22.9 Aug 2 9 30. 6 13.4 65.1 22. 9 Oct07 32. 7 12.9 62.4 22.6 0ct07 31. 8 13.1 63.6 19. 1 Forage Quality components for 38 i r r i g a t e d s i t e s , 1986 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % 80 May 14 33. 9 12.6 60.9 15.1 89 May 14 34. 0 12.6 60.6 16. 2 J u l 0 5 34. 3 12.5 60.4 11.4 J u l 0 5 33 . 7 12.7 61.1 13. 6 Aug 05 29. 0 13.8 67.2 18.4 Aug 05 31. 8 13.1 63.6 19. 1 Aug 2 9 27. 6 14.1 69.0 24.2 Aug29 28. 5 13.9 67.8 26. 1 Oct07 23 . 6 15.1 74.2 24.9 Oct07 26. 9 14.3 69.9 24 . 9 81 May 14 36. 8 11.9 57.0 15.1 90 Mayl4 33. 3 12.7 61.5 17. 4 J u l 0 5 32 . 8 12 .9 62.2 13 . 5 J u l 0 5 33 . 8 12 . 6 61.0 19 . 2 Aug05 29. 7 13 . 6 66. 3 21.8 Aug 05 31. 6 13.2 63.8 21. 2 Aug29 29. 4 13.7 66.7 25.8 Aug29 27. 6 14.1 69.1 26. 9 Oct07 26. 1 14.5 71.0 26.9 Oct07 27. 1 14.2 69.6 25. 7 82 May 14 39. 3 11.3 53.8 14.5 91 Mayl4 34. 1 12.6 60.5 16. 3 J u l 0 5 34. 1 12.6 60.5 14.8 J u l 0 5 34. 6 12.4 59.9 16. 9 Aug 05 31. 0 13.3 64.6 22.1 Aug 05 31. 0 13.3 64.6 22. 4 Aug 2 9 29. 6 13 . 6 66.4 23.3 Aug 2 9 28. 6 13.9 67.7 27. 9 Oct07 23 . 2 15.2 74.7 26.0 0ct07 25. 5 14.6 71.7 27. 9 83 May 14 37. 0 11.9 56.8 14.8 92 Mayl4 37. 2 11.8 56.5 15. 5 J u l 0 5 34. 9 12.4 59.5 16.4 J u l 0 5 33. 2 12.8 61.8 17. 9 Aug 05 33. 2 12.8 61.7 19.1 Aug 05 35. 6 12.2 58.6 18. 6 Aug 2 9 29. 8 13 . 6 66.1 23.7 Aug 2 9 27 . 8 14.1 68 . 7 26. 9 Oct07 31. 6 13.2 63.8 20.6 Oct07 24. 1 14.9 73.5 27. 9 84 May 14 37. 3 11.8 56. 3 13.7 93 May 14 34 . 8 12.4 59.7 13. 9 J u l 0 5 34. 4 12.5 60.2 14.4 J u l 05 35. 6 12.2 58.6 14. 3 Aug 05 33. 3 12.8 61.6 17.2 Aug 05 31. 4 13.2 64.1 19. 6 Aug 2 9 29. 1 13.8 67.1 26.8 Aug 2 9 29. 3 13.7 66.8 25. 2 Oct07 24. 3 14.9 73.3 27.2 Oct07 28. 8 13.8 67.5 19. 4 85 May 14 34. 5 12.5 60.1 13.7 94 May 14 35. 1 12.3 59.3 16. 4 J u l 0 5 34. 5 12.5 60.1 17.0 J u l 0 5 35. 4 12.3 58.9 13. 9 Aug 05 29. 2 13.7 66.9 21.9 Aug 05 32. 7 12.9 62.3 17. 1 Aug 2 9 27. 6 14.1 69.0 25.3 Aug29 31. 3 13.2 64.2 27. 9 Oct07 24. 2 14.9 73.4 27.9 Oct07 22. 3 15.4 75.9 26. 4 86 May 14 34. 9 12.4 59.5 15.9 95 Mayl4 33. 3 12.7 61.6 16. 1 JU105 33. 8 12.6 61.0 15.6 J u l 05 35. 3 12.3 58.9 14. 1 Aug 05 30. 0 13.5 65.8 23.1 Aug 05 34. 6 12.4 59.9 18. 7 199 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % Aug29 28. 2 14. 0 68.3 26. 8 Aug29 29. 2 13. 7 67.0 26. 4 Oct07 25. 5 14. 6 71.7 27. 6 Oct07 25. 4 14. 6 71.8 25. 7 87 Mayl4 33. 5 12. 7 61.3 15. 2 96 May 14 33 . 3 12. 7 61.6 15. 5 J u l 05 34. 2 12. 5 60.4 15. 1 J u l 05 34. 8 12. 4 59.6 13. 4 Aug 05 29. 9 13. 6 66.0 21. 8 Aug 05 31. 8 13. 1 63.5 18. 1 Aug29 29. 0 13. 8 67.2 26. 8 Aug 2 9 30. 0 13. 5 65.9 25. 3 0ct07 24. 4 14. 9 73.2 26. 1 0ct07 27. 7 14. 1 68.9 27. 2 88 Mayl4 33. 8 12. 6 60.9 13. 4 97 May 14 34. 0 12. 6 60.6 13. 3 J u l 05 36. 7 11. 9 57.1 13. 4 J u l 0 5 35. 5 12. 2 58.7 13 . 6 Aug 05 32. 7 12. 9 62.4 16. 5 Aug 05 34. 0 12. 6 60.7 17. 1 Aug 2 9 27. 3 14. 2 69.3 22. 6 Aug29 31. 6 13. 2 63 .8 20. 9 Oct07 23. 5 15. 1 74.4 26. 6 Oct07 28. 3 14. 0 68.2 21. 9 98 May 14 32. 3 13. 0 62.9 14. 9 108 Mayl4 31. 5 13. 2 64.0 16. 4 J u l 05 34. 8 12. 4 59. 6 14. 6 J u l 0 5 35. 3 12. 3 59.0 15. 6 Aug 05 32. 2 13. 0 63.0 21. 6 Aug 05 31. 5 13. 2 64.0 20. 0 Aug 2 9 25. 8 14. 5 71.4 27. 2 Aug29 28. 3 13. 9 68.1 26. 1 Oct07 24. 6 14. 8 72.9 26. 4 Oct07 28. 4 13. 9 67.9 23 . 9 99 May 14 35. 4 12. 2 58.8 14. 7 109 May 14 33. 5 12. 7 61.3 15. 4 J u l 05 35. 4 12. 3 58.9 13. 9 J u l 0 5 34. 3 12. 5 60. 3 17. 9 Aug 05 31. 4 13. 2 64.1 20. 3 Aug 05 31. 4 13 . 2 64.1 21. 8 Aug 2 9 36. 3 12. 0 57.7 25. 6 Aug2 9 30. 4 13 . 4 65.3 25. 9 Oct07 24. 1 14. 9 73.5 26. 6 Oct07 26. 2 14. 4 70.9 24. 8 100 Mayl4 35. 0 12. 3 59.4 16. 8 110 May 14 32 . 7 12. 9 62.4 14. 9 J u l 05 37. 5 11. 7 56.1 16. 3 J u l 05 35. 5 12. 2 58.8 14. 4 Aug 05 31. 6 13 . 1 63.7 20. 6 Aug 05 32. 0 13 . 1 63.3 16. 9 Aug 2 9 29. 0 13. 8 67.2 25. 2 Aug 2 9 29. 3 13. 7 66.8 26. 1 Oct 07 26. 7 14. 3 70.2 24. 8 Oct07 25. 6 14. 6 71.7 27. 8 101 Mayl4 34. 8 12. 4 59.6 13. 2 111 May 14 34. 8 12. 4 59.7 13. 3 J u l 05 35. 1 12. 3 59.2 14. 6 Jul 0 5 34. 9 12. 4 59.5 14. 9 Aug 05 32. 7 12. 9 62.4 19. 5 Aug 05 33 . 4 12. 7 61.5 16. 1 Aug 2 9 27 . 4 14. 2 69.3 29 . 2 Aug 2 9 29. 3 13 . 7 66.8 25. 7 Oct07 25. 3 14. 7 72.0 25. 6 Oct07 29. 7 13. 6 66.3 23 . 2 102 Mayl4 36. 8 11. 9 57.0 12. 9 112 May 14 35. 2 12. 3 59.2 11. 9 J u l 0 5 34. 9 12. 4 59.5 14. 8 J u l 0 5 35. 4 12. 3 58.9 17. 1 Aug 05 32. 3 13. 0 63.0 22. 2 Aug 05 31. 6 13. 2 63.8 22 . 9 Aug 2 9 20. 5 15. 8 78.2 29. 8 Aug 2 9 28. 6 13. 9 67.6 25. 0 0ct07 24. •1 15. 0 73.6 26. 4 Oct07 31. 1 13. 3 64.5 28. 9 103 May 14 35. 0 12. 3 59.4 14. 8 113 May 14 32. 6 12. 9 62.5 15. 2 J u l 0 5 36. 4 12. 0 57.6 13. 1 J u l 0 5 34. 3 12. 5 60.3 15. 5 Aug 05 32. 5 12. 9 62.6 19. 9 Aug 05 30. 4 13. 4 65.4 21. 1 Aug 2 9 29. 1 13 . 8 67.1 30. 1 Aug 2 9 27. 0 14. 3 69.8 22 . 1 Oct07 24. 5 14. 9 73.1 27. 9 Oct07 27. 8 14. 1 68.7 24 . 1 104 May 14 33. 8 12. 6 60.9 15. 1 114 May 14 33 . 7 12. 7 61.1 15. 3 J u l 05 35. 9 12. 1 58.2 15. 2 J u l 0 5 34 . 1 12. 6 60.6 14 . 9 Aug 05 32. 6 12. 9 62.4 18. 6 Aug 05 28. 7 13 . 8 67.6 16. 4 Aug 2 9 28. 2 14. 0 68.2 28. 4 Aug 2 9 27. 7 14. 1 68.9 23. 8 Oct07 26. 8 14. 3 70.1 25. 3 Oct07 26. 2 14. 4 70.8 25. 4 200 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % 105 May 14 33.3 12.7 61.5 14. 5 115 May 14 34.3 12. 5 60. 3 12. 4 J u l 05 36.5 12.0 57.4 14. 2 J u l 05 35.0 12. 3 59. 4 14. 6 Aug 05 33.7 12.7 61.0 16. 1 Aug 05 28.1 14. 0 68. 4 22. 3 Aug 2 9 34.6 12.4 59.8 25. 6 Aug29 28.8 13 . 8 67 . 4 23 . 6 Oct07 26.0 14.5 71.1 24. 7 Oct07 25.2 14. 7 72. 1 25. 5 106 May 14 36.4 12.0 57.5 14. 0 116 May 14 33.6 12. 7 61. 2 14. 4 J u l 05 34.7 12.4 59.8 16. 8 J u l 0 5 33.7 12. 7 61. 0 16. 0 Aug 05 30.9 13.3 64.8 18. 1 Aug 05 3 0.2 13. 5 65. 6 18. 1 Aug29 27.4 14.2 69.3 27. 3 Aug29 28.4 13. 9 67. 9 22. 2 Oct 07 24.1 15.0 73.6 25. 3 Oct07 24.7 14. 8 72 . 8 25. 9 107 May 14 33.3 12.7 61.5 12. 9 117 May 14 37.3 11. 8 56. 4 10. 9 J u l 05 36.3 12.0 57.7 14. 2 J u l 05 34.1 12. 6 60. 6 12 . 6 Aug 05 33.1 12.8 61.8 21. 8 Aug05 30.4 13. 4 65. 3 16. 5 Aug29 28.2 14.0 68.3 25. 7 Aug29 28. 0 14. 0 68. 6 21. 1 Oct07 27.4 14.2 69.3 22 . 9 Oct07 26.3 14. 4 70. 7 23. 1 Forage Quality components : for 76 dryland s i t e s , 1987 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % 1 Apr 2 9 29.3 13 .7 66.8 23 . 8 10 Apr 2 9 26.7 14. 3 70. 2 22 . 3 J u n l 6 33.8 12.6 60.9 23. 2 Ju n l 6 35.3 12. 3 58. 9 22 . 6 J u l l 4 29.0 13.8 67.1 25. 0 J u l l 4 23.4 15. 1 74. 5 22. 3 Aug21 26.9 14.3 69.9 26. 0 Aug21 30.5 13. 4 65. 3 23. 1 Sep29 28.5 13.9 67.9 23. 5 Sep29 24.5 14. 8 73. 0 22. 8 2 Apr29 28.7 13.8 67.6 22. 4 11 Apr 2 9 27.1 14. 2 69. 6 22 . 6 J u n l 6 31.9 13.1 63.4 22. 6 J u n l 6 31.1 13. 3 64. 5 23. 3 J u l 14 27.9 14.0 68.6 23. 7 J u l l 4 26.0 14. 5 71. 0 24 . 6 Aug21 29.5 13.6 66.5 22. 4 Aug21 29.6 13. 6 66. 3 23. 1 Sep29 24.3 14.9 73.3 23. 3 Sep29 24.1 14. 9 73 . 6 24. 5 3 Apr29 30.2 13.5 65.7 22. 6 12 Apr29 30.8 13. 3 64. 8 21. 5 J u n l 6 32.0 13.1 63.3 23 . 3 Ju n l 6 31.5 13. 2 64. 0 21. 9 JU114 26.5 14.4 70.4 24. 7 J u l l 4 28.8 13. 8 67. 5 23. 6 Aug21 25.0 14.7 72.4 25. 4 Aug21 29.9 13. 6 66. 1 23. 1 Sep29 21.3 15.6 77.2 26. 6 Sep2 9 24.8 14 . 8 72. 6 23 . 7 4 Apr 2 9 28.8 13.8 67 . 5 24 . 0 13 Apr 2 9 31.4 13 . 2 64. 1 22 . 6 J u n l 6 29.0 13.8 67.2 23. 8 Ju n l 6 30.5 13. 4 65. 2 23 . 0 J u l 14 27.8 14.1 68.8 25. 2 J u l l 4 28.1 14. 0 68. 4 23. 5 Aug21 26.9 14.3 70.0 26. 7 Aug21 27.5 14. 1 69. 1 24. 6 Sep29 22.8 15.3 75.3 27. 7 Sep29 23.1 15. 2 74. 9 22. 5 5 Apr 2 9 30.9 13.3 64.7 21. 8 14 Apr 2 9 28.0 14. 0 68. 5 20. 1 J u n l 6 28.3 13.9 68.0 22. 6 Ju n l 6 36.5 12. 0 57. 4 21. 3 J u l l 4 25.5 14.6 71.8 23. 6 J u l l 4 27.1 14. 2 69. 6 22. 4 Aug21 27.4 14.2 69.3 23. 6 Aug21 30.3 13. 5 65. 5 22. 8 201 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % Sep29 22. 9 15. 2 75.2 24.0 Sep29 26. 6 14. 3 70.3 20. 2 6 Apr 2 9 29. 6 13. 6 66.4 18.7 15 Apr 2 9 26. 8 14. 3 70.0 21. 3 J u n l 6 31. 2 13. 3 64.4 19.6 Junl6 31. 6 13. 1 63.8 21. 9 J u l 14 29. 6 13. 6 66.4 21.4 JU114 27. 5 14. 1 69.1 22. 5 Aug21 28. 3 13. 9 68.1 25.7 Aug21 28. 7 13. 9 67.6 23 . 0 Sep29 21. 4 15. 6 77.1 23.8 Sep29 23 . 1 15. 2 74.9 24. 5 7 Apr 2 9 29. 4 13. 7 66.7 19.4 16 Apr 2 9 28. 3 14. 0 68.1 22 . 4 J u n l 6 33. 1 12. 8 61.8 20.7 Ju n l 6 29. 9 13. 6 66.0 23. 6 JU114 29. 6 13. 6 66.5 22.2 J u l 14 27. 7 14. 1 68.9 24. 4 Aug21 30. 4 13. 4 65.4 23.5 Aug21 25. 1 14. 7 72.3 25. 3 Sep29 26. 8 14. 3 70.1 22.6 Sep29 28. 9 13. 8 67.3 20. 8 8 Apr 2 9 29. 3 13. 7 66.8 22 .8 17 Apr 2 9 30. 4 13. 4 65.3 20. 1 J u n l 6 32. 6 12. 9 62.5 23.8 Ju n l 6 30. 7 13. 4 64.9 21. 5 J u l l 4 26. 3 14. 4 70.7 23.3 J u l l 4 28. 4 13. 9 67.9 25. 6 Aug21 29. 8 13. 6 66.2 23.5 Aug21 29. 8 13 . 6 66.2 21. 9 Sep29 24. 3 14. 9 73.4 24.9 Sep29 23. 1 15. 2 74.9 23. 7 9 Apr 2 9 26. 3 14. 4 70.7 23.8 18 Apr 2 9 30. 5 13. 4 65.2 22 . 8 J u n l 6 30. 3 13. 5 65.5 24.6 Junl6 32. 8 12. 9 62.3 23 . 2 J u l l 4 22. 6 15. 3 75.6 25.2 J u l l 4 29. 7 13. 6 66.3 23. 4 Aug21 29. 7 13. 6 66.2 24.5 Aug 21 28. 7 13. 9 67.6 21. 8 Sep29 23 . 9 15. 0 73 . 8 24.2 Sep29 21. 6 15. 5 76. 9 23 . 9 19 Apr 2 9 29. 1 13. 8 67.1 22.4 28 Apr 2 9 26. 6 14. 4 70.4 20. 6 J u n l 6 44. 5 10. 1 47.0 21.9 Ju n l 6 36. 8 11. 9 57.1 20. 7 J u l l 4 28 . 7 13. 9 67.6 22 . 4 J u l 14 29. 5 13. 7 66.6 21. 9 Aug21 27 . 5 14. 1 69.1 24.1 Aug21 30. 0 13. 5 65.9 23 . 4 Sep29 22 . 1 15. 4 76.1 25.2 Sep29 22. 0 15. 5 76. 3 24. 9 20 Apr 2 9 29. 0 13. 8 67.1 22.8 29 Apr 2 9 28. 3 13 . 9 68.1 21. 1 J u n l 6 32. 7 12. 9 62.3 23.5 Ju n l 6 32. 1 13. 0 63.2 21. 3 J u l l 4 25. 5 14. 6 71.7 23.2 J u l l 4 29. 9 13. 6 66.0 20. 7 Aug21 29. 6 13. 6 66.5 22.9 Aug21 29. 3 13. 7 66.8 21. 6 Sep29 26. 6 14. 4 70.4 22.9 Sep29 24. 3 14. 9 73.3 25. 7 21 Apr29 29. 9 13. 6 66.0 23.6 30 Apr 2 9 27. 0 14. 2 69.7 18. 2 J u n l 6 32 . 8 12. 9 62.2 21.5 Ju n l 6 32 . 1 13 . 0 63.1 17. 8 J u l l 4 28. 0 14. 0 68.5 23.6 J u l l 4 29. 4 13. 7 66.7 17. 2 Aug21 27. 0 14. 3 69.8 24.9 Aug 21 27. 4 14 . 2 69.3 25. 1 Sep29 22. 2 15. 4 76.0 24.6 Sep29 22. 4 15. 3 75.8 25. 7 22 Apr 2 9 29. 7 13. 6 66. 3 19.5 31 Apr 2 9 28. 0 14. 0 68.5 19. 6 J u n l 6 32 . 9 12 . 9 62.1 22 . 6 Ju n l 6 33 . 3 12 . 7 61. 6 20. 4 J u l l 4 26. 0 14 . 5 71.1 24.7 J u l 14 28. 0 14. 0 68.6 21. 2 Aug21 27. 2 14. 2 69.6 22.5 Aug21 28. 2 14. 0 68.3 23. 7 Sep29 22. 8 15. 3 75.3 25.0 Sep29 18. 7 16. 2 80.6 25. 3 23 Apr 2 9 27. 7 14. 1 68.8 19.6 32 Apr 2 9 29. 0 13. 8 67.2 19. 3 J u n l 6 46. 4 9. 6 44.5 18.8 Ju n l 6 35. 2 12. 3 59.1 19. 9 J u l l 4 30. 0 13. 5 65.9 20.4 J u l l 4 28. 5 13. 9 67.8 21. 6 Aug21 30. 9 13. 3 64.7 21.1 Aug21 25. 8 14. 5 71.4 24. 9 Sep29 23. 0 15. 2 75.0 24.4 Sep29 20. 6 15. 8 78.1 24. 9 24 Apr 2 9 26. 7 14. 3 70.2 22.4 33 Apr 2 9 26. 9 14. 3 69.9 18. 2 202 i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % > J u n l 6 30. 8 13. 3 64.8 22.9 Ju n l 6 35. 0 12 . 3 59.3 18. 1 J u l l 4 28. 9 13. 8 67.3 23.9 J u l l 4 30. 9 13. 3 64.7 20. 9 Aug21 27. 0 14. 2 69.8 22.5 Aug21 27. 0 14. 2 69.8 23. 5 Sep29 19. 8 16. 0 79.2 26.1 Sep29 21. 8 15. 5 76. 6 25. 6 25 Apr 2 9 29. 0 13 . 8 67.2 18.7 34 Apr 2 9 27. 9 14. 0 68.6 18. 4 J u n l 6 32. 9 12. 8 62.1 18.1 Ju n l 6 33. 9 12. 6 60.8 19. 3 J u l l 4 37. 7 11. 7 55.9 18.8 JU114 27. 5 14. 1 69.1 21. 0 Aug21 29. 7 13. 6 66.3 22.7 Aug21 26. 0 14. 5 71.2 22. 3 Sep29 22. 3 15. 4 75.9 25.3 Sep29 19. 3 16. 1 79.9 21. 7 26 Apr 2 9 27. 3 14. 2 69.4 20.4 35 Apr 2 9 27. 4 14. 2 69.3 20. 6 Ju n l 6 32. 6 12. 9 62.4 21.6 Junl6 31. 9 13. 1 63.5 21. 5 J u l 14 26. 0 14. 5 71.1 23.5 J u l 14 27. 3 14. 2 69.4 23. 5 Aug21 25. 1 14. 7 72.2 24.4 Aug21 30. 6 13. 4 65.1 21. 5 Sep29 18. 0 16. 4 81.6 28.5 Sep29 19. 7 16. 0 79.2 27. 0 27 Apr 2 9 29. 2 13. 7 66.9 16.8 36 Apr 2 9 29. 2 13. 7 66.9 18. 7 J u n l 6 31. 1 13. 3 64.4 18.1 Ju n l 6 28. 4 13 . 9 68.0 20. 4 JU114 . 29. 4 13. 7 66.7 18.4 JU114 27. 6 14. 1 69.0 23 . 1 Aug21 30. 5 13. 4 65.3 21.8 Aug21 28. 6 13. 9 67.7 24. 2 Sep29 25. 5 14. 6 71.8 23.2 Sep29 22. 4 15. 3 75.7 23. 8 37 Apr 2 9 25. 6 14. 6 71.7 17.4 47 Apr 2 9 27. 0 14. 3 69.8 20. 3 J u n l 6 32. 8 12. 9 62.2 18.0 Junl6 32 . 9 12. 8 62.1 21. 3 J u l 14 26. 9 14. 3 69.9 19.6 J u l l 4 29. 0 13. 8 67.2 22. 4 Aug21 25. 3 14. 7 72.1 24.0 Aug 21 27. 8 14. 1 68.8 23. 4 Sep29 20. 8 15. 7 77.8 24.2 Sep29 22 . 6 15. 3 75.5 25. 6 38 Apr 2 9 29. 3 13. 7 66.8 20.4 48 Apr 2 9 29. 7 13. 6 66.2 20. 4 J u n l 6 34. 1 12. 6 60.5 21.3 Ju n l 6 33. 6 12. 7 61.2 21. 1 J u l l 4 27. 5 14. 1 69.1 22 . 8 J u l l 4 28. 7 13. 9 67.6 22. 0 Aug21 26. 3 14. 4 70.8 24.8 Aug21 28. 8 13. 8 67.5 21. 7 Sep29 21. 9 15. 5 76.5 22.9 Sep29 23. 5 15. 1 74.4 24. 6 39 Apr 2 9 28. 1 14. 0 68.3 20.7 49 Apr 2 9 27. 6 14 . 1 69.0 19. 9 J u n l 6 33. 1 12. 8 61.8 21.9 Junl6 33 . 8 12. 6 61.0 20. 7 J u l l 4 27. 3 14. 2 69.4 23.0 J u l l 4 27. 6 14. 1 69.0 22. 1 Aug21 27. 0 14. 3 69.8 24.2 Aug21 30. 2 13. 5 65. 6 21. 9 Sep29 22. 3 15. 4 76.0 24.8 Sep29 24. 6 14. 8 72.9 24 . 6 40 Apr29 29. 2 13. 7 67.0 18.4 50 Apr 2 9 26. 8 14. 3 70.0 18. 5 J u n l 6 33. 0 12. 8 61.9 19.3 Ju n l 6 32. 7 12. 9 62.3 18. 4 J u l l 4 23 . 8 15. 0 74.0 20.7 J u l l 4 29. 6 13. 6 66.4 18. 0 Aug21 27. 5 14. 1 69.1 23 . 6 Aug 21 29. 3 13 . 7 66.9 22 . 8 Sep29 21. 5 15. 6 77 . 0 24.8 Sep29 21. 4 15. 6 77.1 26. 6 41 Apr 2 9 28. 9 13. 8 67.3 19.9 51 Apr 2 9 27. 4 14. 2 69.3 19. 8 J u n l 6 33. 0 12. 8 62.0 20.4 Ju n l 6 32. 2 13 . 0 63.0 19. 9 J u l l 4 26. 7 14. 3 70.2 21.6 J u l l 4 27. 7 14. 1 68.9 20. 6 Aug21 29. 7 13. 6 66.3 22.2 Aug21 27. 7 14. 1 68.9 23. 8 Sep29 23. 7 15. 0 74.1 24.8 Sep29 19. 9 15. 9 79.0 24. 4 42 Apr29 28. 6 13 . 9 67.7 18.4 52 Apr 2 9 28. 9 13. 8 67.3 19. 8 J u n l 6 33. 6 12. 7 61.1 19.3 Ju n l 6 33. 4 12. 7 61.4 20. 4 J u l 14 28. 9 13. 8 67.4 19.5 J u l 14 28. 6 13. 9 67.7 21. 2 203 i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % Aug21 29. 9 13. 6 66.0 22. 9 Aug21 27. 7 14. 1 68.9 23. 9 Sep29 24. 9 14. 7 72.5 24. 4 Sep29 20. 3 15. 9 78.6 25. 6 43 Apr 2 9 27. 1 14. 2 69.7 20. 9 53 Apr 2 9 29. 7 13. 6 66.3 20. 9 J u n l 6 31. 6 13. 2 63.8 21. 3 Ju n l 6 46. 7 9. 6 44.2 21. 9 J u l 14 27. 8 14. 1 68.8 22. 9 J u l l 4 28. 7 13. 8 67.5 21. 6 Aug21 26. 1 14. 5 71.0 23. 9 Aug21 29. 9 13. 6 66.0 23. 0 Sep29 21. 1 15. 7 77.4 25. 6 Sep29 23. 7 15. 0 74.1 24. 3 44 Apr 2 9 28. 9 13. 8 67.3 17. 6 54 Apr 2 9 27. 2 14. 2 69.5 19. 5 J u n l 6 34. 1 12. 6 60.5 18. 4 Ju n l 6 31. 9 13. 1 63.4 21. 1 J u l 14 29. 7 13. 6 66.3 18. 1 J u l l 4 28. 5 13. 9 67.8 21. 6 Aug21 30. 4 13. 4 65.3 22. 4 Aug21 18. 3 16. 3 81.1 22. 2 Sep29 29. 5 13. 6 66.5 23. 8 Sep29 27. 0 14. 3 69.9 24. 1 45 Apr 2 9 28. 8 13. 8 67.4 19. 6 55 Apr 2 9 29. 2 13. 7 67.0 20. 3 J u n l 6 33. 6 12. 7 61.2 18. 8 Junl6 31. 3 13. 2 64.1 19. 9 J u l l 4 26. 6 14. 3 70.3 21. 3 J u l l 4 30. 2 13. 5 65.6 20. 3 Aug21 28. 7 13. 8 67.6 23 . 3 Aug21 31. 9 13. 1 63.4 20. 5 Sep29 23. 9 15. 0 73.9 24. 8 Sep29 25. 9 14. 5 71.2 24. 7 46 Apr 2 9 27. 7 14. 1 68.9 19. 9 56 Apr 2 9 26. 9 14. 3 70. 0 24. 5 J u n l 6 34. 0 12. 6 60.7 20. 4 Junl6 32 . 2 13. 0 63.0 24. 4 J u l l 4 28. 9 13. 8 67.4 22. 8 J u l 14 27. 6 14. 1 69 .0 25. 6 Aug21 28. 6 13. 9 67.8 24. 6 Aug21 26. 7 14. 3 70.1 24. 5 Sep29 25. 1 14. 7 72.3 25. 6 Sep29 22. 3 15. 4 75.9 27. 4 57 Apr 2 9 27. 6 14. 1 69.0 19. 2 67 Apr 2 9 26. 0 14. 5 71.1 21. 0 J u n l 6 34. 8 12. 4 59.7 20. 7 Ju n l 6 34. 6 12. 4 59.9 21. 5 J u l 14 25. 2 14. 7 72.2 22. 0 J u l l 4 29. 7 13. 6 66.2 22. 5 Aug21 28. 3 13. 9 68.1 23. 5 Aug21 26. 8 14. 3 70.1 25. 4 Sep29 20. 8 15. 7 77.9 28. 1 Sep29 23. 4 15. 1 74.4 26. 1 58 Apr29 29. 8 13. 6 66.2 19. 6 68 Apr 2 9 29. 6 13. 6 66.4 18. 7 J u n l 6 31. 9 13. 1 63.5 20. 4 Junl6 34. 3 12. 5 60.4 19. 9 J u l l 4 29. 0 13. 8 67.2 22. 0 J u l 14 29. 3 13 . 7 66.8 21. 1 Aug21 28. 7 13. 8 67.5 22. 9 Aug21 29. 2 13. 7 66. 9 21. 7 Sep29 27. 4 14. 2 69.3 25. 7 Sep29 26. 0 14. 5 71.1 24. 6 59 Apr 2 9 30. 0 13 . 5 65.9 20. 7 69 Apr 2 9 28. 6 13. 9 67.7 21. 5 J u n l 6 31. 6 13. 2 63.8 21. 3 Ju n l 6 34 . 9 12. 4 59.6 22. 3 J u l l 4 27. 8 14. 1 68.8 21. 2 J u l l 4 22 . 5 15. 3 75.6 22. 5 Aug21 29. 9 13. 6 66.0 23. 6 Aug21 29. 5 13. 7 66.6 24. 1 Sep29 26. 4 14. 4 70.6 25. 1 Sep29 28. 5 13. 9 67.8 24. 9 60 Apr 2 9 29. 1 13. 7 67 . 0 20. 3 70 Apr 2 9 29. 9 13. 6 66.1 21. 0 J u n l 6 30. 4 13. 4 65.4 20. 4 Ju n l 6 31. 3 13 . 2 64.2 22. 6 J u l l 4 29. 3 13 . 7 66.8 20. 6 J u l l 4 25. 6 14. 6 71.7 22. 7 Aug21 29. 0 13. 8 67.2 23. 0 Aug21 24. 4 14. 9 73.2 26. 3 Sep29 27. 2 14. 2 69.6 24. 6 Sep29 18. 8 16. 2 80.5 30. 6 61 Apr 2 9 27. 2 14. 2 69.6 17. 4 71 Apr 2 9 28. 1 14. 0 68.3 18. 2 J u n l 6 30. 5 13. 4 65. 3 18. 0 Junl6 33. 4 12. 7 61.5 18. 0 J u l l 4 27. 0 14. 2 69.7 22. 7 J u l 14 28. 4 13. 9 68.0 18. 6 Aug21 26. 5 14. 4 70.5 25. 8 Aug 21 29. 4 13. 7 66.7 23. 1 Sep29 22. 1 15. 4 76.2 26. 7 Sep29 23. 5 15. 1 74.4 26. 8 204 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % • % No Date % Mj , % % 62 Apr 2 9 27. 9 14.0 68.7 20.9 72 Apr 2 9 27. 3 14.2 69.4 21. 2 J u n l 6 35. 0 12.3 59.3 21.3 Junl6 34. 9 12.4 59.5 21. 3 J u l l 4 29. 1 13.7 67.0 22.0 J u l 14 28. 7 13.8 67.5 22. 1 Aug21 26. 7 14.3 70.2 22.1 Aug21 29. 4 13.7 66.6 23. 3 Sep29 26. 6 14.4 70.4 25.6 Sep29 23. 5 15.1 74.4 27. 6 63 Apr 2 9 27. 5 14.1 69.2 19.1 73 Apr 2 9 27. 9 14.0 68.6 21. 7 J u n l 6 31. 6 13.2 63.8 19.9 Ju n l 6 32. 7 12.9 62.4 22. 1 J u l l 4 29. 7 13.6 66.2 21.4 J u l l 4 27. 7 14.1 68.9 22. 9 Aug21 30. 2 13.5 65.6 22.5 Aug21 28. 6 13.9 67.7 21. 8 Sep29 25. 9 14.5 71.2 23.6 Sep29 27. 8 14.1 68.7 24. 1 64 Apr 2 9 30. 8 13.3 64.9 20.1 74 Apr 2 9 30. 7 13.4 65.0 22. 1 J u n l 6 31. 7 13.1 63 . 6 21.3 Ju n l 6 32 . 3 13.0 62.9 23 . 2 J u l l 4 29. 9 13.6 66.0 21.8 J u l l 4 23. 3 15.1 74.6 24. 0 Aug21 30. 1 13.5 65.8 22.5 Aug21 27. 1 14.2 69.7 25. 3 Sep29 26. 5 14.4 70.5 23.6 Sep29 24. 7 14.8 72.7 25. 2 65 Apr 2 9 28. 1 14.0 68.3 18.2 75 Apr 2 9 31. 2 13.3 64.4 19. 0 J u n l 6 32. 7 12.9 62.4 17.9 Ju n l 6 33. 4 12.7 61.4 19. 4 J u l l 4 29. 7 13.6 66. 3 19.3 J u l l 4 27. 3 14.2 69.4 18. 9 Aug21 30. 8 13.3 64.8 20.5 Aug 21 28 . 2 14.0 68.3 23. 4 Sep29 23. 8 15.0 74.0 26.0 Sep29 23. 6 15.1 74.3 22. 8 66 Apr 2 9 27. 6 14.1 69.0 24.0 76 Apr 2 9 29. 0 13.8 67.1 23. 3 J u n l 6 33. 5 12.7 61.4 23.8 Ju n l 6 33 . 7 12.7 61.1 23. 9 J u l l 4 27. 0 14.3 69.8 25.2 J u l 14 24. 4 14.9 73.2 25. 2 Aug21 28. 7 13.8 67.5 22.5 Aug21 26. 2 14.4 70.8 24. 6 Sep29 27. 8 14.1 68.8 25.0 Sep29 23. 7 15.0 74.1 25. 3 Forage Quality components for 38 i r r i g a t e d s i t e s , 1987 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % MJ % % No Date % MJ % % 80 Apr 2 9 26. 6 14 .3 70. 3 21. 3 89 Apr 2 9 29. 2 13 .7 66. 9 14. 6 J u n l 6 27. 9 14 .0 68. 6 21. 3 Junl6 28. 8 13 .8 67. 4 14. 1 JU114 28. 5 13 .9 67. 8 23. 8 J u l l 4 29. 0 13 .8 67. 2 24. 1 Aug21 29. 0 13 .8 67. 2 23. 9 Aug21 32. 0 13 .1 63 . 3 21. 1 Sep29 27. 4 14 .2 69. 3 24. 6 Sep29 28. 8 13 .8 67. 5 22. 9 81 Apr 2 9 28. 2 14 .0 68. 2 23. 3 90 Apr 2 9 30. 8 13 .4 64. 9 19. 6 J u n l 6 36. 5 12 .0 57. 4 22 . 6 Ju n l 6 30. 4 13 . 4 65. 4 20. 3 J u l l 4 29. 2 13 .7 66. 9 25. 2 J u l l 4 31. 5 13 .2 63. 9 23. 8 Aug21 29. 4 13 .7 66. 7 24. 8 Aug21 30. 8 13 .3 64. 8 23. 8 Sep29 26. 7 14 .3 70. 2 23. 9 Sep29 29. 3 13 .7 66. 8 25. 1 82 Apr 2 9 28. 7 13 .8 67. 5 15. 1 91 Apr 2 9 30. 8 13 .4 64. 9 20. 7 J u n l 6 32. 4 13 .0 62. 7 15. 8 Junl6 32. 7 12 .9 62. 3 19. 1 J u l l 4 31. 6 13 .2 63. 8 19. 7 J u l l 4 28. 9 13 .8 67. 3 23. 6 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % Aug21 28. 8 13 .8 67. 5 22. 8 Sep29 29. 1 13 .7 67. 0 23. 9 83 Apr29 28. 3 13 .9 68. 1 19. 6 J u n l 6 31. 4 13 .2 64. 1 19. 0 JU114 32. 1 13 .0 63. 1 22. 6 Aug21 33. 1 12 .8 61. 8 21. 6 Sep29 28. 6 13 .9 67. 7 25. 1 84 Apr 2 9 29. 6 13 .6 66. 4 17. 6 Ju n l 6 33. 2 12 .8 61. 7 18. 2 J u l 14 29. 7 13 .6 66. 2 22. 7 Aug21 28. 9 13 .8 67. 3 25. 1 Sep29 29. 0 13 .8 67. 2 23. 1 85 Apr 2 9 26. 8 14 .3 70. 0 20. 7 J u n l 6 32. 7 12 .9 62. 4 21. 3 J u l l 4 26. 0 14 .5 71. 2 24. 5 Aug21 30. 7 13 .4 65. 0 23. 3 Sep29 30. 7 13 .4 65. 0 26. 3 86 Apr 2 9 27. 9 14 .0 68. 6 15. 8 J u n l 6 33. 4 12 .7 61. 4 17. 3 J u l l 4 25. 9 14 .5 71. 2 24. 9 Aug21 28. 7 13 .9 67. 6 27. 2 Sep29 27. 8 14 . 1 68. 8 26. 2 87 Apr 2 9 36. 2 12 . 1 57. 8 12. 6 J u n l 6 31. 5 13 .2 64. 0 15. 1 J u l l 4 26. 6 14 . 3 70. 3 23. 5 Aug21 28. 9 13 .8 67. 3 24. 8 Sep29 28. 4 13 .9 68. 0 26. 4 88 Apr29 28. 4 13 .9 67. 9 13. 2 J u n l 6 32. 9 12 .8 62. 1 14. 6 J u l l 4 23. 5 15 . 1 74. 3 27. 4 Aug21 27. 6 14 . 1 69. 0 25. 8 Sep29 23. 6 15 .1 74. 3 26. 7 98 Apr 2 9 31. 0 13 .3 64. 6 17. 6 J u n l 6 32. 0 13 .1 63. 3 18. 6 J u l 14 29. 0 13 .8 67. 1 23. 5 Aug21 31. 2 13 .3 64. 4 21. 4 Sep29 27. 8 14 .1 68. 7 26. 1 99 Apr 2 9 29. 8 13 .6 66. 2 19. 1 J u n l 6 33. 3 12 .7 61. 6 18. 8 J u l l 4 28. 6 13 .9 67. 7 23. 3 Aug21 30. 9 13 .3 64. 7 21. 3 Sep29 27. 2 14 .2 69. 5 26. 2 100 Apr 2 9 30. 8 13 .3 64. 8 16. 3 Ju n l 6 32. 7 12 .9 62. 4 16. 9 J u l l 4 30. 7 13 .4 65. 0 20. 2 Aug21 30. 9 13 .3 64. 7 21. 4 Sep29 30. 3 13 .5 65. 5 25. 8 Aug21 28. 6 13 .9 67. 7 28. 1 Sep29 23. 8 15 .0 74. 0 27. 4 92 Apr 2 9 29. 3 13 .7 66. 7 17. 4 Ju n l 6 31. 5 13 .2 64. 0 18. 4 J u l l 4 29. 0 13 .8 67. 2 21. 4 Aug21 30. 5 13 .4 65. 3 24. 8 Sep29 34. 7 12 .4 59. 8 23. 4 93 Apr 2 9 29. 5 13 .7 66. 5 15. 1 Ju n l 6 32. 6 12 .9 62. 5 15. 8 J u l l 4 28. 6 13 .9 67. 7 25. 1 Aug21 30. 6 13 .4 65. 1 23 . 0 Sep29 28. 5 13 .9 67. 8 25. 7 94 Apr 2 9 29. 9 13 .6 66. 0 18. 8 Junl6 48. 4 9 .2 42. 0 20. 4 J u l 14 29. 0 13 .8 67. 1 23. 0 Aug21 30. 3 13 .5 65. 5 25. 1 Sep29 25. 1 14 .7 72 . 3 25. 6 95 Apr 2 9 29. 7 13 .6 66. 3 18. 4 Ju n l 6 33. 2 12 .8 61. 7 19. 8 J u l l 4 30. 4 13 .4 65. 4 22 . 4 Aug21 31. 9 13 . 1 63. 4 23 . 3 Sep29 28. 8 13 .8 67. 4 24. 6 96 Apr 2 9 30. 3 13 .5 65. 5 15. 1 Junl6 33. 0 12 .8 62. 0 16. 0 J u l l 4 30. 7 13 .4 65. 0 19. 3 Aug21 32. 0 13 .1 63. 3 21. 9 Sep29 29. 9 13 .5 66. 0 24. 0 97 Apr 2 9 31. 3 13 .2 64. 2 1.6. 3 Ju n l 6 33. 3 12 .7 61. 6 16. 0 J u l l 4 31. 3 13 .2 64. 2 22. 0 Aug21 31. 3 13 .2 64. 2 22 . 4 Sep29 27. 9 14 .0 68. 7 26. 9 108 Apr 2 9 25. 5 14 .6 71. 8 16. 4 Junl6 59. 7 6 .5 27. 2 17. 8 J u l l 4 24. 0 15 .0 73 . 6 26. 1 Aug21 31. 4 13 .2 64. 0 24. 0 Sep29 26. 2 14 .4 70. 8 26. 6 109 Apr 2 9 31. 0 13 . 3 64 . 6 19. 4 Ju n l 6 33. 1 12 .8 61. 8 21. 6 J u l l 4 28. 6 13 .9 67. 7 18. 9 Aug 21 29. 7 13 .6 66. 3 22. 1 Sep29 30. 0 13 .5 65. 8 24. 6 110 Apr 2 9 27. 3 14 .2 69. 4 18. 7 Junl6 33. 7 12 .7 61. 1 18. 0 J u l 14 28. 2 14 . 0 68. 3 19. 1 Aug21 31. 9 13 . 1 63. 5 21. 5 Sep29 32. 2 13 .0 63. 0 22. 5 206 S i t e Cut ADF DE TDN CP S i t e Cut ADF DE TDN CP No Date % Mj % % No Date % Mj % % 101 Apr 2 9 27. 4 14.2 69.3 21.3 111 Apr 2 9 27. 0 14.3 69.8 18.4 J u n l 6 29. 2 13.7 66.9 19.4 Ju n l 6 33. 9 12.6 60.9 19.2 J u l l 4 25. 6 14.6 71.6 24.4 J u l 14 28. 1 14.0 68.3 22.4 Aug21 32. 5 13.0 62.7 21.4 Aug21 28. 8 13.8 67.5 19.9 Sep29 29. 3 13.7 66.8 26.1 Sep29 25. 8 14.5 71.4 26.7 102 Apr 2 9 31. 2 13.2 64.3 19.9 112 Apr 2 9 29. 0 13.8 67.2 19.0 J u n l 6 30. 6 13.4 65.1 20.8 Ju n l 6 29. 3 13.7 66.8 20.1 J u l l 4 31. 6 13.2 63.8 23.0 J u l l 4 25. 7 14.6 71.5 25.6 Aug21 31. 3 13.2 64.1 22.4 Aug21 31. 6 13.1 63.8 23.8 Sep29 28. 2 14.0 68.2 25.3 Sep29 27. 4 14; 2 69.3 27.4 103 Apr 2 9 31. 1 13.3 64.4 15.3 113 Apr 2 9 30. 3 13.5 65.5 19.7 Ju n l 6 30. 8 13.3 64.9 17.4 Ju n l 6 30. 4 13.4 65.4 18.4 J u l l 4 25. 8 14.5 71.4 21.3 J u l l 4 27. 5 14.1 69.1 22 .8 Aug21 30. 2 13.5 65.7 23.3 Aug21 30. 4 13.4 65.4 21.6 Sep29 29. 7 13.6 66.3 24.9 Sep29 28. 7 13.9 67.6 23.2 104 Apr 2 9 30. 6 13.4 65.1 13.9 114 Apr 2 9 28. 6 13.9 67.7 17.3 Ju n l 6 32. 2 13.0 63.1 15.9 Ju n l 6 33. 4 12.7 61.5 17.6 J u l l 4 31. 2 13.3 64.3 22.8 J u l l 4 25. 7 14.6 71.5 24.8 Aug21 31. 4 13.2 64.0 21.6 Aug21 30. 6 13.4 65.1 22.2 Sep29 29. 0 13.8 67.2 25.9 Sep29 28. 6 13.9 67-7 25.1 105 Apr29 28. 0 14.0 68-. 5 19.6 115 Apr 2 9 30. 3 13.5 65.5 17.8 J u n l 6 32 . 0 13.1 63 . 3 19.1 Ju n l 6 33 . 0 12.8 61.9 18.9 J u l 14 31. 3 13.2 64.2 21.9 J u l l 4 29. 5 13.7 66. 6 21.9 Aug21 30. 2 13.5 65. 6 20.8 Aug21 31. 8 13.1 63.5 22.8 Sep29 28. 1 14.0 68.3 28.3 Sep29 28. 5 13.9 67.9 24.9 106 Apr 2 9 27. 6 14.1 69.0 20.3 116 Apr29 28. 4 13.9 68.0 15.7 J u n l 6 31. 7 13.1 63.7 21.4 Ju n l 6 32. 6 12.9 62.5 18.4 J u l 14 23. 9 15.0 73.9 22.6 J u l 14 26. 3 14.4 70.7 24 .1 Aug21 27. 5 14.1 69.2 23.9 Aug21 30. 2 13.5 65.6 23.3 Sep29 23. 9 15.0 73.8 30.1 Sep29 23. 0 15.2 75. 0 25.4 107 Apr 2 9 31. 1 13.3 64.4 17.6 117 Apr 2 9 29. 2 13.7 66.9 17.1 J u n l 6 30. 4 13.5 65.4 19.3 Junl6 29. 0 13.8 67.2 18.9 J u l 14 26. 3 14.4 70.7 23.4 J u l 14 27. 1 14.2 69.6 22.6 Aug21 30. 7 13.4 65.0 24.4 Aug21 28. 9 13.8 67.4 24.3 Sep29 27. 6 14.1 69.0 24.3 Sep29 30. 0 13.6 66.1 26.2 S o i l physical properties for 76 dryland s i t e s (numbers a t head of columns correspond t o sampling depths; i . e . 1 = 0 t o 25-cm, e t c ) . MPa % v/v AWSC mm PSC h i h2 h i h2 h i h2 h l 1 h 2 2 S i t e 1 2 3 4 Tot ERD 1 2 3 4 1- -2 - -3 - -4 -1 63 60 60 48 230 230 4 4 4 3 42 17 40 16 33 14 27 8 2 60 65 58 58 240 240 4 4 4 5 41 17 39 13 33 10 32 9 3 60 38 28 18 142 143 4 2 1 1 38 14 21 6 16 5 12 5 4 60 58 50 48 215 215 4 5 4 3 39 15 36 13 29 9 26 7 5 63 63 60 50 235 235 4 4 4 4 41 16 40 15 37 13 29 9 6 63 57 75 58 252 189 4 4 4 4 41 16 37 14 42 12 35 12 7 65 60 60 65 250 125 4 5 5 4 45 19 41 17 40 16 40 14 8 58 58 63 63 240 240 4 4 4 4 42 19 41 18 40 15 37 12 9 65 68 60 62 255 191 4 4 5 4 45 19 45 18 40 16 39 14 10 65 63 63 65 255 128 4 5 5 5 45 19 43 18 40 15 38 12 11 65 60 58 63 245 184 4 5 5 5 45 19 42 18 38 15 38 13 12 65 63 57 28 212 213 3 4 3 1 44 18 42 17 37 14 17 6 13 58 55 35 15 162 163 4 3 1 1 40 17 37 15 20 6 10 4 14 63 60 58 58 237 178 4 5 4 4 43 18 41 17 36 13 34 11 15 60 63 58 30 210 210 4 4 3 2 41 17 40 15 35 12 18 6 16 58 55 50 13 175 131 4 4 2 1 41 18 37 15 27 7 8 3 17 58 55 45 28 185 185 4 4 4 2 40 17 35 13 27 9 17 6 18 60 55 55 55 225 225 4 4 4 4 42 18 40 18 38 16 32 10 19 55 48 35 47 185 139 4 5 4 3 39 17 36 17 23 9 29 10 20 55 58 48 47 207 208 4 3 5 4 39 17 40 17 35 16 29 10 21 58 30 38 35 160 160 4 3 3 3 39 16 20 8 22 7 21 7 22 58 58 15 18 147 111 3 2 1 1 40 17 34 11 9 3 10 3 23 53 60 53 50 215 215 4 4 4 4 42 21 38 14 30 9 29 9 24 55 53 50 13 170 128 4 3 1 1 38 16 33 12 27 7 7 2 25 58 53 48 8 165 165 4 4 4 1 40 17 38 17 30 11 5 2 26 58 60 58 53 227 114 4 4 5 5 39 16 41 17 40 17 37 16 27 55 58 55 58 225 169 4 4 4 4 40 18 40 17 38 16 36 13 28 63 63 55 58 237 119 4 5 5 4 42 17 41 16 39 17 34 11 29 60 58 47 28 192 193 4 4 4 1 40 16 39 16 29 10 15 4 30 60 58 58 52 227 228 4 4 5 3 41 17 4 0 17 40 17 29 8 31 33 62 60 23 177 177 1 1 1 1 21 8 29 4 28 4 14 5 32 50 38 20 23 130 130 4 2 1 1 37 17 25 10 13 5 14 5 33 55 58 53 45 210 210 4 4 4 4 40 18 40 17 39 18 32 14 34 30 15 20 30 95 95 1 1 1 1 19 7 10 4 12 4 17 5 35 38 48 38 33 155 155 2 3 1 1 25 10 36 17 23 8 19 6 36 33 23 28 18 100 100 1 1 1 1 22 9 14 5 17 6 12 5 37 53 50 45 33 180 180 4 5 3 2 40 19 38 18 30 12 20 7 38 58 53 50 45 205 205 4 4 4 4 41 18 40 19 37 17 29 11 39 58 55 53 53 217 218 4 5 4 4 42 19 41 19 39 18 33 12 40 63 58 53 50 222 111 4 4 4 4 43 18 41 18 40 19 36 16 41 58 60 50 63 230 58 4 5 5 5 42 19 40 16 39 19 38 13 42 60 60 53 53 225 113 4 4 4 4 43 19 41 17 41 20 40 19 43 55 53 40 45 192 96 5 5 5 5 40 18 38 17 36 20 37 19 44 50 53 45 50 197 148 4 4 4 4 40 20 39 18 36 18 37 17 MPa % v/v •AWSC mm PSC h i h2 h i h2 h i h2 h l 1 h 2 2 S i t e 1 2 3 4 Tot ERD 1 2 3 4 1- "2 i _ _"5 1- -4-45 50 48 48 43 187 141 4 4 4 4 38 18 36 17 35 16 30 13 46 55 53 45 43 195 146 4 4 5 5 39 17 38 17 35 17 33 16 47 50 50 43 40 182 183 4 5 4 4 37 17 34 14 28 11 26 10 48 50 47 43 33 172 173 4 4 4 4 37 17 33 14 28 11 23 10 49 53 50 52 45 200 150 4 4 4 4 40 19 36 16 35 14 29 11 50 55 55 48 38 195 195 4 4 4 3 40 18 39 17 30 11 24 9 51 53 53 53 50 207 208 4 4 5 4 41 20 40 19 38 17 33 13 52 55 53 55 53 215 215 4 4 4 4 41 19 40 19 39 17 38 17 53 58 58 50 25 190 190 4 4 4 1 41 18 39 16 34 14 16 6 54 53 55 58 53 217 109 4 4 5 4 40 19 40. 18 40 17 38 17 55 43 48 48 48 185 93 4 4 4 4 39 22 39 20 37 18 37 18 56 63 60 48 33 202 203 4 3 2 1 42 17 40 16 27 8 19 6 57 58 58 57 63 235 235 4 4 5 4 40 17 38 15 37 14 37 12 58 63 55 57 60 235 235 4 4 5 4 42 17 37 15 37 14 34 10 59 55 60 55 58 228 228 5 5 5 5 39 17 38 14 36 14 36 13 60 50 60 62 58 230 115 4 4 5 4 37 17 40 16 39 14 35 12 61 45 70 78 63 255 255 4 4 4 3 35 17 40 12 40 9 35 10 62 60 55 75 78 267 201 4 3 1 1 40 16 37 15 36 6 35 4 63 60 55 45 50 210 210 4 4 5 4 40 16 39 17 35 17 34 14 64 60 43 40 20 162 81 4 4 5 5 40 16 35 18 33 17 22 14 65 50 60 55 50 215 108 4 2 4 4 37 17 38 14 38 16 34 14 66 70 52 45 8 175 175 3 3 3 1 44 16 35 14 29 11 6 3 67 55 53 55 57 220 165 4 4 5 4 40 18 38 17 36 14 37 14 68 55 55 53 55 217 163 4 4 5 4 42 20 40 18 38 17 37 15 69 58 57 57 62 235 117 4 4 4 4 40 17 37 14 37 14 39 14 70 48 58 63 58 225 225 4 4 4 3 42 23 41 18 38 13 35 12 71 53 48 50 53 202 203 4 4 4 4 36 15 30 11 31 11 31 10 72 53 52 50 47 202 203 4 4 3 3 38 17 35 14 31 11 29 10 73 53 50 48 33 182 183 4 4 4 3 37 16 30 10 27 8 19 6 74 53 48 45 40 185 185 4 4 4 3 37 16 30 11 27 9 22 6 75 48 50 50 53 200 200 4 4 4 4 38 19 34 14 32 12 34 13 76 53 55 50 50 207 208 4 4 4 4 41 20 38 16 31 11 31 11 S o i l . physical . properties for 38 i r r i g a t e d s i t e s - MPa % ; v/v --AWSC mm PSC h i h2 h i h2 h i h2 hl'h2' S i t e 1 2 3 4 Tot ERD 1 2 3 4 -1- 2- •3- •4-80 50 48 55 58 210 158 4 4 4 4 41 21 38 19 37 15 36 13 81 53 55 55 55 218 109 4 4 5 5 41 20 40 18 39 17 38 16 82 53 50 58 58 218 109 4 4 5 4 43 22 41 21 41 18 39 16 83 60 63 55 58 235 176 4 4 4 4 43 19 43 18 41 19 38 15 84 60 55 53 53 220 220 4 5 4 4 43 19 41 19 33 12 31 10 • MPa % v/v AWSC mm PSC h i h2 h i h2 h i h2 h l 1 h 2 2 S i t e 1 2 3 4 Tot ERD 1 2 3 4 1- -2 -3 -4 85 58 53 28 35 173 129 4 3 1 1 38 15 32 11 15 4 19 5 86 48 48 43 42 180 135 5 5 4 4 40 21 39 20 32 15 29 12 87 50 48 45 40 183 91 4 4 4 3 39 19 32 13 29 11 25 9 88 38 23 28 43 130 98 2 1 1 2 27 12 15 6 17 6 25 8 89 58 55 50 38 200 200 4 4 4 2 41 18 41 19 32 12 22 7 90 58 55 65 60 238 178 5 5 5 5 42 19 43 21 42 16 42 18 91 55 60 60 55 230 173 5 5 5 5 42 20 41 17 42 18 40 18 92 53 58 55 60 225 113 4 5 5 4 41 20 40 17 40 18 40 16 93 55 55 55 53 218 109 5 5 5 4 41 19 40 18 39 17 37 16 94 53 58 60 62 233 174 4 4 5 4 41 20 42 19 41 17 39 14 95 63 60 55 53 230 230 4 4 4 3 43 18 38 14 34 12 31 10 96 60 60 58 60 238 119 4 4 4 4 41 17 42 18 40 17 38 14 97 63 57 57 62 240 180 5 5 4 4 42 17 37 14 37 14 39 14 98 60 50 50 50 210 158 4 5 5 4 41 17 39 19 33 13 30 10 99 58 50 50 53 210 158 4 4 4 3 40 17 32 12 31 11 31 10 100 55 63 58 58 233 116 4 4 5 4 42 20 42 17 39 16 36 13 101 68 63 58 63 250 125 4 4 5 4 44 17 42 17 40 17 37 12 102 65 65 58 48 235 235 4 4 3 2 40 14 43 17 33 10 27 8 103 63 63 58 62 245 123 4 4 5 4 42 17 41 16 39 16 39 14 104 53 50 55 58 215 108 5 5 5 5 39 18 38 18 38 16 38 15 105 50 58 50 55 213 106 4 4 5 5 40 20 39 16 38 18 39 17 106 55 55 55 45 210 105 4 5 5 3 39 17 40 18 39 17 30 12 107 55 53 55 48 210 158 4 5 5 4 41 19 41 20 41 19 34 15 108 58 58 53 53 220 165 5 5 5 5 40 17 39 16 40 19 39 18 109 47 43 53 53 195 98 3 3 5 3 33 14 26 9 37 16 33 12 110 50 30 48 45 173 86 3 4 5 3 37 17 30 18 34 15 27 9 111 53 50 48 55 205 103 4 4 5 4 39 18 38 .18 36 17 34 12 112 55 48 50 50 203 203 4 4 3 1 42 20 34 15 29 9 26 6 113 55 60 60 53 228 171 5 5 5 5 41 19 44 20 43 19 41 20 114 55 50 55 58 218 109 4 5 4 3 42 20 40 20 3 6 14 34 11 115 50 50 60 58 218 163 5 5 5 3 41 21 39 19 39 15 33 10 116 45 48 53 53 198 99 4 5 5 5 41 23 39 20 38 17 36 15 117 48 53 53 53 205 205 4 5 5 4 40 21 39 18 34 13 33 12 -0.03 MPa s u c t i o n . 2 -1.5 MPa s u c t i o n . 210 Theta values f o r 7 6 dryland s i t e s , 1986 APR23 MAYO5 % v/v - % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 44 . 1 44 . 0 42 .8 42 .6 39 .7 42 .6 45 . 1 44 .5 41 .8 42 .7 40 . 1 42 . 9 2 44 .1 43 .6 42 .6 42 .1 42 .4 43 .0 44 .5 43 .8 41 .2 41 .6 42 . 0 42 .6 3 41 .0 35 .0 33 . 1 28 .9 19 .3 31 .4 40 .5 33 .7 33 .2 30 . 1 18 .7 31 .2 4 43 .2 42 .6 41 .3 40 .0 37 .9 41 .0 44 .3 42 .4 41 .6 40 .0 38 .3 41 . 3 5 43 .3 45 .0 43 .9 42 .5 41 .0 43 .2 43 .5 45 .3 43 .9 42 .3 40 .6 43 . 1 6 43 .1 43 .2 41 .6 41 .4 41 .7 42 .2 44 .0 42 .7 41 .4 41 .2 42 .0 42 .3 7 45 . 1 44 .7 43 .5 44 .4 44 .8 44 .5 45 .5 45 .2 43 .0 43 .8 44 .7 44 .4 8 42 .9 44 .3 43 .3 42 .6 43 .8 43 .4 44 .5 43 .7 41 .4 42 .1 43 . 1 43 . 0 9 42 .9 45 .1 46 .3 43 .9 45 .5 44 .8 43 . 1 45 . 1 46 .0 44 .0 45 .4 44 .7 10 43 .2 44 .2 43 .3 43 .7 45 .9 44 .1 43 . 1 43 .9 42 .9 43 .5 45 . 3 43 .7 11 43 .8 44 .2 43 .1 43 .7 44 . 0 43 .8 43 .5 44 . 1 42 .6 43 .0 44 . 6 43 .5 12 44 .4 45 . 0 42 .9 41 .4 24 .2 39 . 6 44 .2 44 . 0 42 . 3 40 .5 23 .5 38 . 9 13 41 .8 42 .4 37 .2 26 .4 20 .4 33 .7 41 .9 41 .3 36 .4 25 .2 20 . 0 33 .0 14 43 .4 45 .5 41 .7 42 .6 44 .8 43 .6 44 .2 44 .2 42 .4 42 .3 44 .3 43 .5 15 44 .4 43 .5 41 .7 39 .4 28 .3 39 .5 44 .6 42 .8 40 .6 39 .6 29 .4 39 .4 16 43 .5 43 .2 41 .2 28 .4 22 .2 35 .7 43 .8 41 . 4 40 .8 27 .9 23 .4 35 .5 17 40 .4 40 .8 39 .3 39 . 1 37 .6 39 .4 40 .9 41 . 3 39 .5 38 .9 37 .7 39 .7 18 44 . 1 43 .7 42 .7 42 . 0 41 .0 42 .7 44 .7 43 .9 42 .9 41 .9 40 .7 42 .8 19 43 . 5 43 .2 41 . 3 40 .9 43 .2 42 .4 43 .4 43 . 2 41 . 3 39 .8 42 .2 41 .9 20 44 .8 44 .9 42 .9 41 .1 42 . 0 43 .1 44 .9 44 .5 43 . 0 42 .0 42 . 1 43 .3 21 42 .6 36 .9 32 .9 38 .0 36 .7 37 .4 43 .5 36 .3 32 .7 36 .8 36 .8 37 .2 22 39 .5 37 .7 21 .7 14 . 1 32 . 1 29 .0 39 .7 37 .0 21 .8 14 . 1 33 .4 29 .2 23 43 .0 42 . 1 40 .8 39 .3 39 .8 41 .0 44 .6 42 .6 40 .0 39 .2 39 .8 41 .2 24 40 .7 39 .3 36 .6 28 .9 12 .4 31 .6 41 .8 38 .6 36 .3 27 .7 12 .5 31 .4 25 42 .6 44 .0 40 .2 40 .0 24 .0 38 .2 42 .4 43 .8 40 .0 40 .6 23 .4 38 .0 26 43 . 1 44 .6 43 .8 43 .9 43 .6 43 .8 43 .2 44 .5 44 .2 42 .9 41 .5 43 .3 27 43 . 1 43 .7 43 .8 41 .3 42 .8 42 .9 42 .0 44 .5 44 .0 40 .4 42 . 7 42 .7 28 42 .2 44 .8 43 .3 43 .2 41 .9 43 . 1 43 . 3 44 .3 43 .0 43 .0 41 .6 43 . 0 29 42 .0 42 .6 41 .5 39 .6 38 .9 40 .9 42 .0 43 . 1 41 .6 38 .4 39 .3 40 . 9 30 44 .3 43 .6 43 .2 41 .7 42 . 0 42 .9 43 .7 44 .5 43 .8 42 . 1 42 .0 43 .2 31 23 .0 19 .0 23 .3 32 . 1 35 .3 26 .5 23 .0 18 .7 23 .7 32 .2 37 . 1 27 . 0 32 40 .6 38 .1 30 .5 32 .5 36 .0 35 .5 40 .9 37 . 4 31 . 1 33 .4 36 . 9 35 .9 33 43 .5 44 .5 42 .9 42 . 3 40 .3 42 .7 44 .8 44 .7 42 . 9 42 .0 40 .8 43 . 1 34 22 .0 17 .7 22 .9 31 .8 34 .7 25 .8 22 .8 17 . 3 22 .8 32 .0 35 .8 26 . 1 35 39 .3 43 .2 41 .3 33 .2 34 .6 38 .3 39 . 3 43 .2 40 .7 32 .8 35 .2 38 .2 36 26 . 1 29 .9 34 .4 28 .2 18 . 1 27 .3 27 . 0 30 . 0 35 .9 29 .2 19 . 5 28 . 3 37 43 .4 4.3 .8 42 .7 39 . 6 33 . 8 40 .7 44 . 1 42 .8 42 . 0 39 .8 32 . 9 40 . 3 38 44 .1 44 .0 42 .3 42 . 1 39 .2 42 .3 44 .5 44 . 1 42 .2 41 .3 39 . 1 42 .2 39 42 .2 44 .2 43 .8 41 .6 40 .4 42 .5 42 .6 44 .3 42 .7 41 .2 39 .8 42 . 1 40 42 . 1 44 .9 44 .0 42 .9 43 .5 43 .5 42 . 1 45 . 1 43 .2 43 .4 43 .0 43 .4 41 42 .7 42 .4 42 .4 42 .3 44 .0 42 .8 43 .2 42 .9 42 .0 42 .2 42 .8 42 .6 42 42 .7 43 .8 42 .2 42 .2 43 . 5 42 .9 42 . 5 43 .1 42 .4 42 .7 42 .9 42 .7 43 42 .8 43 .1 43 .7 42 .7 43 .6 43 .2 42 .8 42 . 9 43 .7 42 .5 44 .2 43 .2 44 43 . 1 41 .9 42 .6 42 .7 44 .2 42 .9 42 .7 42 . 0 42 .5 42 .7 44 .8 42 .9 45 41 .6 43 .0 41 .7 41 .6 43 .0 42 .2 41 .5 42 .4 41 .9 40 .6 42 .2 41 .7 2 1 1 APR23 MAY05 % v/v • % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 46 43 . 1 44 .9 41 .7 43 . 1 43 .2 43 .2 43 .8 44 .9 41 .8 42 .7 43 .2 43 .3 47 42 .7 42 .1 41 .3 41 .9 41 .4 41 .8 42 .3 41 .6 40 .5 41 .2 41 .0 41 .3 48 41 .6 43 .1 41 .6 42 . 1 40 .5 41 .8 40 .9 42 .7 41 .9 41 .7 39 .9 41 .4 49 42 .6 43 .1 41 .5 40 .9 41 .4 41 .9 43 .9 43 .0 41 .7 40 .9 41 .4 42 .2 50 44 .2 44 .3 42 .5 40 .2 40 .4 42 .3 43 .1 44 .4 42 . 1 40 . 1 39 .8 41 .9 51 43 .2 45 .1 43 .1 42 .6 43 .5 43 .5 43 .0 44 .3 42 .3 42 .0 43 .0 42 .9 52 42 .3 43 .3 41 .7 42 .7 43 .7 42 .8 42 .6 43 . 1 42 .5 42 .4 44 .2 43 .0 53 41 .7 41 .3 40 .9 40 .2 31 .6 39 .1 42 .5 41 .6 40 .6 40 .3 32 . 1 39 .4 54 42 .6 44 . 1 42 .7 42 .9 43 .7 43 .2 42 .5 44 .4 43 . 1 43 .2 44 .3 43 .5 55 43 .2 44 .9 44 .0 41 .8 43 .5 43 .5 43 .3 44 .3 43 .0 41 .7 43 .4 43 .2 56 44 .7 44 . 3 41 .1 33 .9 40 .8 41 .0 44 .7 43 .3 40 .6 33 .7 41 .0 40 .7 57 42 .2 42 .0 42 .4 41 .7 43 .5 42 .4 42 .5 42 .2 42 .3 42 .0 42 .7 42 .3 58 43 .5 41 .9 43 . 0 42 .4 43 .9 43 .0 42 .8 42 .9 41 .5 42 .3 44 . 1 42 .7 59 41 .8 42 .5 41 . 6 43 . 1 43 .8 42 .6 42 .7 42 .7 42 .4 42 .7 43 .4 42 .8 60 43 .0 42 .5 42 .2 42 .9 44 .0 42 .9 43 .6 42 .8 41 .5 42 .8 43 .6 42 .9 61 42 .3 41 .8 41 .7 39 .6 39 .8 41 .0 42 .6 41 .5 41 .4 40 .0 38 .7 40 .8 62 39 .8 41 .6 35 .4 32 .6 21 .9 34 .3 38 .5 41 .6 34 .8 32 .4 22 .4 33 .9 63 41 .2 46 .7 43 .9 43 .2 44 .2 43 .8 42 .2 46 .5 43 .3 42 .8 43 . 1 43 .6 64 39 .5 43 .9 45 .8 43 .2 42 .7 43 .0 40 .3 43 .8 44 .9 42 .7 43 .6 43 . 1 65 41 .4 37 . 1 41 .5 44 .2 42 . 1 41 .3 41 .9 36 .9 41 .7 44 .3 42 .6 41 .5 66 39 .6 41 .6 42 .4 36 .7 18 .0 35 .6 39 .7 41 .6 41 .5 37 .5 16 .9 35 .4 67 43 .8 42 .9 41 .7 42 .6 44 .3 43 . 0 43 .5 43 .5 42 .6 42 .1 44 . 3 43 .2 68 45 .0 44 . 1 41 .9 41 .5 44 . 0 43 .3 44 .7 44 .5 41 .6 41 . 6 43 .8 43 .2 69 41 .4 41 .9 42 .6 44 .6 43 .7 42 .8 41 .9 41 .9 43 .2 43 .5 43 .1 42 .7 70 43 .0 43 .6 42 .2 43 .1 44 .2 43 .2 44 .3 44 .2 41 . 6 42 .8 43 . 6 43 . 3 71 39 .8 39 .8 41 .2 41 .9 43 . 1 41 . 1 40 .0 39 . 2 41 .2 42 .0 43 . 3 41 . 1 72 41 .4 41 . 1 41 .2 41 .6 42 .8 41 .6 42 .0 41 .2 41 .1 41 .2 42 .5 41 .6 73 40 .1 40 .6 39 .3 38 .4 43 .2 40 .3 40 .1 39 .4 39 .5 37 .9 42 .6 39 .9 74 40 .4 38 .9 36 .6 30 .4 27 .7 34 .8 40 .1 39 .5 36 .8 31 . 1 28 .4 35 .2 75 43 .3 40 .5 40 .1 42 . 1 44 .3 42 . 1 43 .1 40 .5 39 .2 41 . 3 44 .2 41 .7 76 44 .7 42 .8 41 .1 40 .0 43 .3 42 .4 44 .2 42 .6 40 .3 41 . 3 43 . 1 42 .3 MAY14 MAY28 % v/v •— % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 43.9 44.8 43.1 43.3 40.2 43.1 45.9 46.4 43.6 44.4 40.5 44.2 2 45.0 44.1 41.1 42.0 42.7 43.0 44.8 46.6 42.6 42.3 40.2 43.3 3 40.1 34.5 33.6 29.9 18.9 31.4 41.1 34.9 33.1 29.3 18.9 31.5 4 43.3 43.5 41.6 41.0 39.0 41.7 44.6 44.7 42.3 41.1 39.5 42.4 5 44.9 45.6 44.3 43.1 41.0 43.8 44.8 47.0 44.7 44.4 41.0 44.4 6 45.0 43.3 41.7 41.7 42.7 42.9 46.4 43.8 42.5 42.4 43.6 43.7 7 46.2 45.1 43.4 44.6 45.6 45.0 48.5 46.7 45.2 44.8 46.4 46.3 8 44.4 44.5 42.6 42.4 44.0 43.6 45.9 46.1 43.4 43.5 47.0 45.2 9 43.2 44.7 46.4 45.2 45.2 44.9 45.5 46.1 46.1 45.2 46.2 45.8 MAY14 MAY28 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 10 43 .9 45 .2 43 .9 44 .1 45 .4 44 .5 45 .9 45 .4 44 .2 44 .9 47 .6 45 .6 11 44 .5 44 .8 43 .2 43 .8 44 .9 44 .2 46 .5 46 .7 44 .1 44 .6 46 .0 45 .6 12 45 .0 43 .7 42 .7 40 .6 24 .9 39 .4 42 .5 45 . 3 43 .9 42 . 1 23 .2 39 .4 13 43 .1 41 .1 37 .2 26 .9 20 .9 33 .9 44 .2 42 .9 38 .7 25 .8 20 .3 34 .4 14 43 .4 45 .3 42 .4 42 .6 43 .7 43 .5 46 .7 46 .9 43 .4 44 .0 45 .2 45 .2 15 45 .2 43 .8 41 .1 40 .1 30 .5 40 .2 46 .1 44 .9 42 .9 41 .0 30 .3 41 .0 16 43 .7 42 .1 41 .7 29 .4 23 .4 36 . 1 44 .7 43 .7 41 .9 28 . 1 23 .8 36 .4 17 40 .7 41 .4 39 .3 39 .6 37 .6 39 .7 43 .1 42 . 6 40 .8 40 .3 39 . 1 41 .2 18 45 .3 43 .2 43 .5 42 .9 41 .6 43 .3 46 .2 45 . 3 44 .6 44 .0 41 .8 44 .4 19 43 .9 44 .0 41 .4 40 .6 43 .2 42 . 6 45 .4 45 .3 42 .6 41 .5 42 .9 43 .5 20 44 .8 45 .6 43 .6 42 .3 42 .3 43 .7 45 .9 45 .9 44 .8 43 .5 42 .9 44 .6 21 42 .4 36 .6 33 .0 37 .2 36 .9 37 .2 44 .1 37 . 6 33 .3 38 .7 38 . 1 38 .4 22 39 .7 38 .0 23 .0 15 .7 34 .4 30 .2 42 .6 38 .8 23 .2 14 .2 33 .9 30 .5 23 44 .8 42 .4 41 .1 39 .8 39 .5 41 .5 45 .3 43 .4 42 .1 40 .4 40 .5 42 .4 24 41 .5 39 .5 36 .8 29 .3 13 .3 32 . 1 43 .0 41 .7 36 .9 28 .9 12 .1 32 .5 25 42 .8 43 .6 41 .0 40 .5 24 .4 38 .5 45 .2 45 . 1 46 .1 41 .9 24 .1 40 .5 26 44 .1 45 .4 44 .2 44 .0 44 .0 44 .3 44 .4 46 . 3 45 .8 44 .0 44 .2 44 .9 27 42 .4 44 .7 44 .5 41 .4 43 .2 43 .2 44 .5 45 .8 45 .0 43 .0 44 .4 44 .5 28 43 .8 45 .2 43 . 1 43 .4 42 .9 43 .7 45 .1 46 . 6 44 .7 45 . 1 43 .7 45 .0 29 43 .2 43 .2 42 .0 39 .1 39 .5 41 .4 45 .1 44 .9 43 .0 40 .5 40 .8 42 .8 30 43 .9 44 .7 43 .1 42 . 6 42 .5 43 .4 45 .0 45 . 8 45 .2 43 . 6 42 .3 44 .4 31 23 .8 20 .4 25 . 6 33 . 6 36 .9 28 . 1 22 .8 19 .5 24 . 9 32 . 6 38 . 0 27 . 6 32 40 .7 38 .4 32 .1 33 .7 37 .1 36 .4 42 .8 39 • 6 31 .5 34 .5 38 . 6 37 .4 33 44 .7 44 .8 43 .2 42 • 3 40 .8 43 .2 46 .1 46 . 6 4 3 . 0 43 .2 41 . 6 44 . 1 34 24 .6 19 . 1 24 .3 33 .2 35 .5 27 .3 24 .3 18 . 1 24 . 1 33 .0 38 . 1 27 .5 35 39 .7 43 .7 41 .0 32 .2 35 .6 38 .5 41 . 0 44 .0 42 .4 33 . 6 36 .4 39 .5 36 27 .5 31 .4 36 .4 29 . 1 18 .5 28 .6 26 .9 30 .8 36 .5 30 . 1 21 .2 29 . 1 37 43 .9 43 .6 43 .0 40 .1 33 .5 40 .8 45 . 1 44 .0 43 .5 40 .9 32 .9 41 . 3 38 44 . 3 44 . 1 42 .8 42 .0 39 .4 42 .5 45 .8 46 .2 43 .8 42 .8 40 . 3 43 .8 39 43 .2 44 .6 43 .3 42 .1 40 .5 42 .8 44 .5 45 .7 44 .7 43 .2 40 .9 43 .8 40 42 .8 45 .3 43 .7 43 .5 43 .3 43 .7 44 .5 46 .2 44 .8 43 .9 44 .4 44 .8 41 42 .9 42 .9 42 .8 42 . 6 44 . 1 43 . 1 42 .3 44 . 1 42 .3 41 .0 41 .9 42 . 3 42 42 .7 44 . 1 42 .5 43 . 3 44 .4 43 . 4 45 .4 45 . 3 44 .2 43 .5 45 .7 44 . 8 43 44 . 1 43 . 1 44 .0 44 . 1 44 .0 43 .9 45 .6 44 . 3 44 . 3 43 .9 44 .9 44 .6 44 42 .5 41 .9 42 .3 43 .0 44 .7 42 .9 44 .4 44 . 1 43 .2 43 .7 45 .5 44 .2 45 43 .2 43 .0 41 .9 41 .9 43 .1 42 .6 44 .1 43 .7 43 .2 42 .3 43 .7 43 .4 46 44 . 0 45 .0 42 .5 43 .5 44 .1 43 .8 45 . 6 45 .9 43 . 3 44 . 6 44 .7 44 .8 47 42 .3 42 .3 41 .1 41 . 1 41 .0 41 .6 44 .5 44 .0 42 . 3 42 .7 41 . 3 43 . 0 48 43 .9 43 .6 42 .1 41 .2 41 .7 42 .5 42 .6 44 . 1 42 .2 42 .8 42 .4 42 .8 49 43 .3 43 .5 41 .7 41 .5 42 .5 42 .5 45 .5 44 .2 43 .4 42 .4 42 .4 43 .6 50 45 .3 44 .7 43 .0 40 .8 40 .4 42 .8 45 .1 45 .5 43 .9 41 .1 40 .2 43 .2 51 44 .7 45 .2 43 .0 43 .9 44 .4 44 .2 45 .1 45 .8 43 .5 44 .0 44 .4 44 .6 52 42 .6 43 .4 42 .5 43 .0 44 .5 43 .2 44 . 6 45 . 1 43 .4 43 .9 45 .2 44 .4 53 43 .3 42 .0 41 .8 40 .8 32 .3 40 .0 43 . 1 43 .5 42 .5 41 .4 32 .7 40 .7 54 44 .9 44 .8 43 .0 43 .0 44 .7 44 .1 44 .5 45 .5 44 .0 44 .5 44 .5 44 .6 55 44 .3 44 .9 44 .6 42 .8 44 .7 44 .3 45 .5 47 .0 45 .0 43 .4 45 . 1 45 .2 MAY14 MAY28 , % v/v % v/v • — -S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 56 45 .9 45 .0 41 .7 34 .3 41 .5 41 .7 46 .3 45 .4 43 .1 35 .4 41 .7 42 .4 57 43 .5 42 .4 42 .3 43 .2 43 .9 43 .1 43 .0 44 .2 43 .7 43 .7 44 .6 43 .8 58 44 .4 43 .3 43 .1 43 .6 43 .8 43 .6 46 .2 43 .3 44 .5 43 .3 44 .4 44 .3 59 42 .8 43 .2 42 .9 42 . 6 44 . 1 43 . 1 44 .5 43 .4 42 .6 43 .9 45 .4 44 . 0 60 44 .5 44 .3 42 .3 44 .5 43 .7 43 .9 45 .3 44 .2 42 .7 44 .8 43 .9 44 .2 61 44 .6 42 .3 41 .6 40 .9 39 .2 41 .7 44 .0 42 . 6 42 .3 40 .4 40 .0 41 .9 62 39 .5 42 .7 36 .5 33 .6 26 .5 35 .8 40 .7 42 .9 35 .4 33 .0 21 .7 34 .7 63 42 .8 46 .6 44 .7 44 .9 44 .1 44 . 6 43 .6 48 . 1 45 . 1 44 .4 45 .2 45 .3 64 41 .4 44 .6 46 .0 43 .9 43 .8 43 .9 42 .0 45 .1 46 .7 44 .4 43 .6 44 .4 65 42 .5 38 .4 41 .8 44 .9 42 .7 42 . 1 43 .5 38 .5 42 .9 44 .9 43 .4 42 .6 66 41 .1 42 .5 42 .4 36 .8 18 .7 36 .3 42 .2 42 .8 43 .4 38 .1 17 . 1 36 .7 67 45 .1 44 .3 43 .1 42 .4 44 .7 43 .9 45 .1 44 . 9 43 .4 42 .8 45 . 6 44 .4 68 45 .5 44 .7 42 .1 42 .3 44 .3 43 .8 44 .1 47 .0 42 .8 43 .5 45 .4 44 . 6 69 40 .9 44 .1 45 .0 44 .0 43 .8 43 .6 43 .6 43 .2 44 .4 45 .0 44 .3 44 . 1 70 44 .9 43 .8 42 .6 43 .0 43 .3 43 .5 45 .5 45 .3 43 . 1 43 .3 44 .5 44 .3 71 42 .3 40 .6 41 .6 42 .9 44 .3 42 .3 42 . 0 41 .0 42 . 1 42 .6 44 .8 42 .5 72 43 . 1 41 .7 41 .7 42 .5 43 .6 42 .5 43 . 6 42 . 1 42 . 3 43 .2 44 .4 43 .1 73 42 .2 40 .2 41 .4 40 .3 43 .5 41 .5 41 .4 40 .4 38 .5 37 . 1 39 .0 39 . 3 74 41 .2 40 .4 37 .6 34 .5 32 .9 37 .3 41 .7 39 .8 37 . 1 32 .9 30 .9 36 .5 75 44 .2 41 .2 40 .8 42 .2 44 .3 42 .6 44 . 6 42 .1 40 .7 42 .5 44 .6 42 .9 76 45 .3 43 .4 41 .8 40 .8 43 .8 43 .0 46 . 1 45 .3 41 .4 41 .8 45 .3 44 .0 JUNO4 JUN23 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 40 .2 44 .4 43 .7 43 .3 38 .9 42 .1 39 .7 41 .8 40 .2 41 .7 42 .7 41 .2 2 41 . 3 43 .8 42 .7 42 .5 42 .4 42 .5 42 .6 42 . 6 39 . 6 38 .5 40 .7 40 .8 3 38 .3 29 .2 27 .7 24 . 6 18 . 0 27 . 6 38 .4 25 . 0 19 .7 16 .9 15 . 3 23 . 1 4 40 .4 43 .4 41 .7 41 . 0 38 .7 41 .0 40 .3 40 .8 37 .7 34 .8 34 .8 37 .7 5 41 .8 45 .1 43 .4 43 .7 41 .4 43 . 1 41 .8 43 .4 40 . 1 39 .7 37 .3 40 .5 6 41 .2 42 .5 42 .7 43 .0 43 .7 42 .6 41 .8 41 .0 39 .4 38 .9 40 .6 40 .3 7 43 .5 44 .6 44 .8 46 .2 47 .0 45 .2 43 .4 43 .8 41 .9 42 .9 44 .3 43 .2 8 42 .4 43 .7 43 .9 43 .9 47 .9 44 .4 42 .6 42 .6 40 .5 39 .5 45 .7 42 .2 9 38 .4 43 .4 45 .0 45 .7 48 .0 44 .1 38 .8 40 .5 40 .8 44 .4 46 .4 42 .2 10 41 .9 43 .7 44 .2 45 .1 47 .5 44 .5 42 .2 43 .1 41 .9 42 .9 45 .3 43 . 1 11 41 .3 45 .8 44 .1 45 .3 46 .6 44 .6 42 .9 43 .5 41 .6 42 .0 45 .3 43 .1 12 39 . 8 43 .4 41 .5 40 .8 21 .2 37 .3 41 .4 41 .0 38 .4 37 .7 13 .6 34 .4 13 39 .0 40 .3 36 .3 22 .0 18 .0 31 .1 39 .9 39 .1 30 .2 11 .5 12 .2 26 .6 14 41 .2 45 .3 43 .2 43 .7 45 .2 43 .7 41 .7 42 .5 40 .3 40 .9 42 .9 41 .7 15 41 .2 42 .5 40 .6 39 .5 27 .4 38 .2 41 .6 40 .2 40 . 1 37 .6 25 .1 36 .9 16 38 .7 40 .3 40 . 1 23 .4 21 .6 32 .8 40 .9 37 .4 35 .7 15 .0 13 .4 28 .5 17 37 .2 40 .7 38 .9 39 .2 37 . 4 38 .7 39 . 1 39 .5 35 .2 33 .4 28 .5 35 .1 18 42 .3 43 .5 44 .5 43 .3 41 .7 43 .1 42 .8 42 . 6 40 .4 39 .5 39 .5 41 .0 19 40 .1 41 .8 41 .7 41 .6 43 .8 41 .8 41 . 1 41 .1 38 .3 35 .7 41 . 1 39 .4 JUNO4 JUN23 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 20 39 .9 43 .5 43 .5 42 .4 43 .4 42 .5 40 .1 40 .1 39 .7 39 .9 39 .5 39 .9 21 36 .6 35 .2 29 .2 37 .2 38 .4 35 .3 39 .6 30 .1 21 .4 31 . 1 36 .5 31 .7 22 38 .5 35 .8 16 .8 10 .6 28 .3 26 .0 38 .0 31 .3 11 .8 6. 14 16 .5 20 .7 23 41 .3 42 .4 41 .4 39 .3 40 .9 41 .1 41 .7 41 .6 37 .9 32 .0 37 . 3 38 . 1 24 38 .8 38 . 1 35 . 0 26 .5 10 .9 29 .9 38 .2 36 . 0 31 .1 19 .0 6 .4 26 .1 25 38 .3 42 .4 40 .4 40 .6 22 .4 36 .8 39 . 3 40 .7 37 .0 37 .3 18 .2 34 .5 26 40 .9 45 . 1 45 .5 44 .9 44 .4 44 .2 42 .2 43 .5 42 .9 42 .2 42 . 0 42 . 6 27 38 .5 43 .4 43 .9 42 .2 45 .4 42 .7 39 .5 40 .1 39 .8 39 .2 42 . 1 40 .1 28 40 .9 45 .3 43 .5 45 .5 43 .9 43 .8 41 .8 43 .1 41 .0 40 .9 41 .7 41 .7 29 40 .1 42 .9 42 .0 40 .0 40 .5 41 .1 40 .9 42 .0 39 .6 35 .9 28 .4 37 .4 30 41 .8 44 .8 44 . 1 43 .2 44 . 1 43 .6 42 .4 43 .2 41 .9 4 0 .1 40 .6 41 .7 31 16 .2 15 .0 18 .3 26 .7 36 .3 22 .5 17 .5 12 .3 11 .9 18 .4 30 .5 18 .1 32 38 .3 36 .4 27 .2 30 .8 36 .7 33 .9 39 .7 36 .1 23 .5 21 .2 32 .4 30 .6 33 41 .7 46 .0 44 .2 44 .2 41 .3 43 .5 42 .0 41 .7 40 .3 40 . 3 38 .5 40 .6 34 16 .3 14 .4 19 .0 29 .6 36 . 6 23 .2 18 .2 11 .2 13 .4 21 .9 31 .4 19 .2 35 34 .8 42 .7 41 .4 30 .8 34 .8 36 .9 35 .3 41 .2 38 .4 24 .4 29 . 1 33 .7 36 19 .4 22 . 1 30 .5 29 . 3 18 .9 24 . 1 21 . 1 17 . 1 20 .9 23 .8 14 .4 19 . 5 37 37 .5 42 .2 42 .9 40 .5 29 .9 38 .6 40 .6 40 .9 40 . 1 36 .8 24 .8 36 .6 38 41 .4 44 .3 42 .9 42 .8 40 .5 42 .4 41 .5 41 .4 39 .5 39 .1 37 .8 39 .8 39 39 .7 44 .0 44 .2 43 . 5 39 .7 42 .2 40 .0 42 .9 41 .4 40 .1 38 . 6 40 .6 40 40 .2 46 .1 44 .6 44 .8 45 .0 44 .2 40 .5 42 .4 41 .2 41 .7 43 .4 41 .8 41 40 .3 43 .0 44 .5 44 .0 44 .0 43 .2 40 .9 42 .4 41 .6 41 .9 44 .3 42 .2 42 39 .2 43 .4 44 .5 46 .3 46 .5 44 .0 39 .5 40 .8 40 .0 41 .5 44 .4 41 .2 43 40 .3 43 .5 44 .1 45 .3 46 .5 43 .9 41 .0 40 .3 40 .5 41 .7 44 .4 41 .6 44 40 .4 42 .4 42>9 44 .2 46 .4 43 .2 39 .6 40 .0 40 . 1 41 .2 44 .6 41 . 1 45 38 .1 42 .2 42 .0 42 .3 43 .9 41 .7 4 0 .7 39 .9 39 .0 38 .4 40 .7 39 .7 46 40 .0 45 .5 43 .2 45 .2 45 .5 43 .9 41 .6 42 .9 39 .2 40 .6 42 .5 41 .3 47 40 . 7 40 .7 40 . 2 41 .5 41 . 6 40 .9 41 . 1 40 . 0 36 . 6 37 .5 39 .4 38 .9 48 37 .4 41 .8 42 . 1 42 .5 41 .7 41 .1 38 . 6 40 .7 39 .2 39 .1 40 .2 39 . 6 49 41 .6 43 . 6 41 .7 41 .4 43 . 1 42 . 3 42 . 0 41 .8 39 .2 37 .9 40 .2 40 .2 50 40 . 0 44 .1 42 .8 40 .2 39 . 6 41 .3 40 .6 43 .2 40 . 3 36 .4 36 .0 39 .3 51 39 .7 44 .3 42 .5 43 .8 44 .6 43 .0 41 .6 42 .2 39 .3 40 .7 41 .7 41 . 1 52 38 .5 43 .1 42 .5 45 .1 44 .8 42 .8 39 .6 41 . 3 39 .5 41 .0 42 .1 40 .7 53 38 .3 41 .4 41 .5 41 .0 31 .0 38 .7 39 .4 41 .2 39 .9 38 .9 24 .6 36 .8 54 39 .6 44 .7 43 .9 43 .3 45 .3 43 .4 40 .8 42 .7 41 .1 40 .4 42 .9 41 .6 55 41 .0 45 .8 45 .0 43 .3 45 .6 44 .1 40 .4 43 .2 41 .5 40 .2 42 .5 41 .6 56 42 .0 44 . 1 41 .4 33 .3 41 .8 40 .5 42 .6 42 .8 39 . 6 28 .9 35 .3 37 .8 57 38 .6 41 .6 42 .6 43 .1 44 .2 42 .0 40 .4 40 .7 40 .7 40 .1 42 .2 40 .8 58 42 .0 42 .6 43 .2 43 .3 44 . 1 43 .0 42 .'4 41 .5 40 .5 41 .1 41 .8 41 .5 59 39 .2 42 . 1 42 . 1 43 .8 45 .0 42 .4 40 .2 40 .5 40 . 1 41 .3 41 .3 40 .7 60 40 .9 42 .1 42 .5 44 .4 44 .8 42 .9 42 .4 40 .5 38 .6 40 .7 43 .2 41 .1 61 40 . 1 40 .4 41 .0 40 .5 38 .8 40 .2 41 .8 39 .8 38 .8 37 .2 32 .7 38 . 1 62 35 .7 40 .4 32 .5 29 .3 20 .0 31 i 6 37 .8 40 . 0 27 .5 21 .6 13 .9 28 .2 63 38 .7 45 .7 44 .2 44 .6 45 .3 43 .7 40 .0 44 .2 42 . 0 41 .5 42 .2 42 .0 64 38 .4 44 .4 46 .5 44 .3 45 .3 43 .8 38 .6 43 .5 43 .9 42 .4 42 .7 42 .2 65 40 .4 35 .5 41 .1 45 .6 43 .8 41 .3 40 .1 35 .5 39 .2 42 .9 42 .5 40 . 0 215 JUNO4 JUN23 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 66 36. 6 40. 6 42. 4 36. 8 14. 0 34. 1 38. 0 40. 3 40. 0 33. 1 9. 7 32. 2 67 39. 9 42. 8 43. 0 42. 9 46. 3 43. 0 40. 5 40. 9 40. 2 41. 5 43. 6 41. 4 68 41. 8 45. 2 43. 3 43. 8 46. 1 44. 0 42. 3 44. 4 41. 0 40. 5 43. 6 42. 3 69 39. 3 40. 5 44. 0 45. 0 45. 5 42. 9 39 . 5 39. 8 41. 4 42. 8 42. 8 41. 3 70 41. 0 44. 4 42. 0 43. 3 46. 0 43. 3 42. 1 42. 4 40. 1 39. 5 42. 5 41. 3 71 38. 3 38. 1 41. 2 42. 9 44. 2 40. 9 38. 3 36. 6 38. 3 39. 5 42. 0 38. 9 72 39. 0 40. 7 40. 9 43. 1 44 . 5 41. 6 39. 5 39. 5 39. 0 39. 9 40. 9 39. 8 73 38. 7 38. 2 38. 9 37. 3 42. 4 39. 1 38. 7 38. 5 36. 7 31. 7 35. 2 36. 2 74 38. 5 37. 7 34. 3 30. 3 27. 3 33. 6 38. 5 37. 0 31. 6 24. 8 20. 3 30. 4 75 40. 4 39. 7 39. 5 43. 0 45. 1 41. 5 40. 6 38. 5 36. 2 39. 4 43. 0 39. 6 76 42. 6 43. 0 41. 3 41. 4 44. 9 42. 6 43. 5 42. 0 38. 2 38. 4 41. 1 40. 6 JUL05 JUL2 3 — % v / v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 41 .9 43 .2 41 . 0 41 .9 34 . 5 40 .5 38 .6 43 .2 41 .2 42 .5 36 .0 40 .3 2 42 .8 41 .6 39 .2 38 .6 43 . 1 41 .0 40 .2 42 .3 40 .9 40 .8 44 .7 41 .8 3 38 .7 24 .4 18 .8 16 .7 15 .6 22 .8 36 .9 26 .6 21 .8 18 .4 15 .0 23 .8 4 40 .4 41 .4 39 .0 35 .3 34 .6 38 .1 38 .2 41 .9 39 .7 37 .4 34 .8 38 .4 5 41 .8 43 .8 41 . 0 38 .0 37 .9 40 .5 39 .8 43 . 3 41 .7 42 .4 38 .2 41 . 1 6 43 . 1 41 . 3 39 .9 39 .8 40 .8 41 . 0 39 . 3 41 .7 41 . 1 41 . 3 41 .9 41 . 0 7 44 .2 43 .7 41 .9 42 .3 45 . 1 43 .4 40 . 3 42 . 7 42 .5 43 .5 45 . 7 43 . 0 8 43 .6 43 .4 41 .1 39 . 3 44 .7 42 .4 40 .9 42 .9 41 . 6 41 .4 45 . 0 42 .4 9 37 .9 40 .0 40 .1 43 . 3 47 .2 41 .7 36 .2 41 .0 41 .2 44 .4 46 .6 41 .9 10 42 .5 43 . 0 41 .6 42 .9 45 .9 43 .2 40 .0 42 .9 43 .2 43 .7 46 .7 43 .3 11 43 .1 43 . 6 41 .3 41 .4 45 .3 42 .9 39 .7 43 .0 42 .2 44 .0 46 .5 43 .1 12 42 .2 41 .7 39 .7 37 .0 13 .1 34 .7 39 .8 41 .4 40 . 3 39 .7 14 .1 35 .0 13 40 .2 39 .7 28 .6 9 .8 12 .8 26 .2 38 . 1 38 .3 31 . 0 12 .9 12 .1 26 . 5 14 42 . 3 43 .2 40 .0 40 .6 43 .5 41 .9 39 .3 43 .0 41 .5 42 .1 43 .8 41 .9 15 43 .2 42 .3 39 .8 38 .5 26 .5 38 .1 40 . 6 41 .2 40 .0 38 .2 25 .4 37 .1 16 42 .2 36 .8 34 .9 14 .4 13 .2 28 .3 37 .8 38 . 1 38 .2 16 .9 12 .0 28 .6 17 40 .8 40 .8 35 .5 34 .2 27 .5 35 .8 37 .5 39 . 6 37 .2 37 .2 29 .2 36 .1 18 43 .4 42 .4 40 .6 40 .7 39 .6 41 .3 40 .8 41 .9 41 .9 41 .5 39 .8 41 .2 19 43 .2 42 .4 38 .4 35 .2 42 . 1 40 .3 39 .7 40 .8 40 .0 39 .3 42 .2 40 .4 20 41 .8 40 .9 40 . 0 39 .8 40 . 1 40 . 5 39 .7 41 .9 41 .7 40 . 6 39 . 8 40 . 7 21 40 . 6 29 .7 20 .5 30 .7 37 .2 31 .7 37 . 3 32 .4 26 .7 32 . 5 35 . 4 32 . 9 22 37 .7 31 .7 11 .7 5 . 1 15 .2 20 .3 37 . 6 35 .5 15 . 1 5 .8 14 .4 21 .7 23 43 . 1 41 .9 39 .0 31 .7 37 .2 38 .6 39 .1 40 .8 39 .3 35 .8 37 .6 38 .5 24 39 .4 35 .8 31 .3 17 .7 6 .6 26 .2 36 . 3 35 .8 34 .2 20 .7 5. 61 26 .5 25 41 .4 42 .6 37 .2 37 . 1 18 .0 35 .3 37 .4 41 . 1 39 .2 40 . 0 20 . 3 35 .6 26 42 .9 44 .4 42 .7 41 .4 42 .2 42 .7 39 .2 43 .9 43 .2 43 .7 43 . 1 42 .6 27 41 .3 40 .9 40 .9 38 .7 42 .9 40 .9 38 . 1 41 .6 42 .2 40 .2 42 .8 41 .0 28 42 .3 43 .8 40 .7 41 .4 42 .5 42 . 1 38 .8 43 .6 41 .9 43 .5 42 .4 42 .0 29 41 .8 43 .5 41 .3 35 .4 25 .4 37 .5 38 .0 41 .0 40 . 6 36 .1 25 .4 36 .2 216 JUL05 JUL23 % v/v % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 30 43 .1 43 .2 42 .0 41 .1 40 .4 42 .0 39 .8 42 .9 43 .3 40 .9 42 . 3 41 .8 31 19 .7 12 .6 11 .3 16 .9 29 .6 18 .0 15 .4 13 .0 13 .9 19 .7 28 .9 18 .2 32 40 .1 36 .3 22 .6 19 .7 30 .4 29 .8 37 . 1 35 .2 22 .9 21 .7 31 .7 29 .7 33 42 .9 42 .7 40 .6 40 .2 39 .4 41 .2 40 .6 42 .6 42 .5 42 .2 39 .5 41 .5 34 18 .0 9. 77 11 .7 20 .6 29 .3 17 .9 15 .3 11 . 6 13 .4 20 .3 28 .2 17 .8 35 36 .5 41 .0 38 .5 23 .2 28 .6 33 .6 33 .0 40 .8 38 .8 25 .0 27 .7 33 .0 36 21 .0 17 . 0 20 .2 22 .9 13 .5 18 .9 18 .6 17 .6 22 .2 23 .0 14 .1 19 .1 37 42 . 1 41 .7 41 .2 37 .0 25 . 1 37 .4 38 . 1 40 .6 41 .9 39 .2 27 .3 37 .4 38 43 .7 42 .4 40 .2 38 .9 37 .5 40 .5 40 .5 42 .3 41 .3 40 .5 38 .2 40 .6 39 41 .6 42 .8 41 .2 40 . 1 38 .6 40 .9 38 .4 42 .7 42 .0 41 .3 37 .5 40 .4 40 40 .3 43 .4 41 .8 42 .2 43 .0 42 .2 37 .9 43 .6 42 .2 43 .3 43 .1 42 .0 41 42 .6 43 .7 41 .6 41 .4 44 .5 42 .7 38 .6 41 .1 41 .3 42 .8 46 .5 42 .0 42 40 .2 41 .0 40 .5 41 .9 44 .4 41 .6 37 .7 41 .5 42 .0 42 .6 44 .4 41 .7 43 42 .0 40 .9 41 .6 43 .1 44 .2 42 .4 39 .7 41 .2 42 .4 43 .8 45 .0 42 .4 44 40 .7 39 .3 40 . 1 41 .5 44 .4 41 .2 39 .3 40 .8 41 .4 42 . 3 43 .9 41 .6 45 42 .4 42 .0 40 .2 38 .8 40 .9 40 .9 38 .4 40 .9 41 .2 40 .4 42 . 3 40 .6 46 42 .7 43 .3 38 .5 40 .3 42 .6 41 .5 39 .1 44 .0 41 .2 42 .7 42 .7 41 .9 47 41 .5 40 . 1 36 . 8 37 .8 40 . 3 39 .3 37 .9 40 .2 39 .2 39 .4 40 . 0 39 . 3 48 39 . 7 41 .4 39 . 5 40 . 0 40 .9 40 . 3 37 .3 40 .4 40 . 2 40 . 1 40 .4 39 .7 49 43 . 1 42 .6 39 .4 38 . 3 39 .8 40 . 6 40 .3 41 . 8 39 .9 39 .0 41 .2 40 . 5 50 43 .2 44 .4 41 .5 36 . 1 35 .0 40 .0 40 . 3 42 . 9 40 .8 38 . 4 36 .4 39 .8 51 41 .9 42 .7 39 .8 40 .9 41 . 0 41 . 3 39 .3 42 .8 41 .2 42 .2 42 .0 41 .5 52 41 .6 43 .3 40 .5 40 .8 42 .0 41 .7 38 .0 41 .4 40 .9 42 .6 43 .8 41 .3 53 41 .5 41 .0 40 .1 38 .7 22 .9 36 .8 37 .8 40 .3 40 .3 39 .1 27 .4 37 .0 54 41 .6 42 .7 40 .5 40 .6 43 .2 41 .7 38 .3 42 .2 41 .9 42 .5 44 .9 42 .0 55 43 . 1 44 .4 41 .9 40 .1 42 .8 42 .5 38 .6 44 .0 42 .3 42 .3 43 .8 42 .2 56 43 .8 43 .2 39 .8 27 .3 34 .4 37 .7 39 .9 42 .2 38 .0 28 .4 35 . 3 36 .8 57 41 .9 40 .5 40 .1 41 .2 42 .0 41 . 1 37 .7 40 .3 41 .2 41 .8 42 .2 40 .7 58 44 .0 41 .9 41 .2 40 .9 42 .6 42 .1 41 .2 41 .0 41 .4 41 .7 43 . 0 41 .7 59 42 .3 41 .2 40 .4 41 .2 42 . 6 41 .5 39 .2 41 .3 40 .6 42 . 1 42 . 6 41 .2 60 43 .9 42 .2 39 .8 41 . 3 42 .7 42 .0 40 .0 40 .9 41 .2 42 . 6 42 . 6 41 .5 61 42 .7 40 .7 39 .9 38 .8 32 .5 38 .9 39 .4 39 .7 39 .7 39 .5 33 . 6 38 .4 62 38 . 5 40 .6 27 .5 20 . 5 12 .0 27 .8 35 .2 39 .7 31 .5 24 . 1 13 . 3 28 .8 63 41 .7 44 .9 42 .0 42 .2 41 .5 42 .5 38 .4 44 .6 43 .9 43 .5 43 .4 42 .8 64 39 .7 44 .3 45 .0 41 .9 43 .2 42 .8 37 .0 42 .5 45 .1 43 .5 47 .3 43 .1 65 41 .9 36 .2 39 .3 40 .8 42 .7 40 .2 39 . 1 34 .7 40 .0 44 .0 42 .8 40 .1 66 39 .7 41 .0 32 .6 32 .6 8 .9 30 .9 36 .4 40 .1 40 .6 34 .7 11 .7 32 .7 67 42 . 0 42 . 0 40 .7 41 . 1 44 .2 42 . 0 39 .5 41 . 0 41 .5 42 .2 44 .2 41 .7 68 43 .5 44 .6 40 .8 40 .7 43 .8 42 .7 39 .6 42 .9 41 .0 42 .0 44 .7 42 .1 69 40 .2 40 .6 42 .4 43 .4 43 .2 42 .0 38 .0 40 . 1 42 .6 44 .2 42 .8 41 .5 70 42 .2 42 .9 40 .7 40 .6 43 .1 41 .9 40 .0 42 .4 40 . 6 41 .9 43 .9 41 .8 71 39 . 3 38 .0 38 .4 40 . 1 42 .2 39 .6 37 .2 38 .0 39 .5 40 .0 43 . 0 39 .5 72 41 .8 41 .4 39 .5 40 .5 40 .6 40 .8 38 .9 39 .6 40 .5 41 .3 41 .8 40 .4 73 40 .5 38 .7 37 .3 30 .8 33 .2 36 . 1 37 .5 36 .8 38 .0 31 .7 34 .4 35 .7 74 38 .8 37 .5 32 .9 23 .9 18 .9 30 .4 37 . 1 36 . 2 33 . 0 25 .1 19 . 6 30 .2 75 41 .2 39 .1 36 .4 39 .6 43 .8 40 .0 39 .7 38 . 0 37 .8 40 .8 43 .9 40 .0 76 43 .9 41 .9 38 .7 37 .9 40 .8 40 .7 41 .0 41 .8 39 .3 39 .4 42 .1 40 .7 AUG05 AUG19 % v / v % V / V S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 30 .0 38 .1 38 .1 40 .6 32 .6 35 .9 25 .3 36 .4 37 .7 39 . 0 27 .6 33 .2 2 34 .7 37 .2 36 .6 36 .7 40 .5 37 .1 30 .5 35 .8 34 .4 34 .4 39 .3 34 .9 3 30 .3 18 .1 15 .0 14 . 1 13 .0 18 .1 23 .0 13 .9 9 .2 9 .6 10 . 1 13 .2 4 32 .1 37 .8 37 .0 32 .9 32 .7 34 .5 29 .0 36 .0 34 . 1 29 .2 30 .9 31 .8 5 34 .2 39 .3 38 .7 38 .6 36 .4 37 .4 30 .2 36 .3 36 .5 37 .3 34 .4 34 .9 6 32 .2 37 .4 38 .2 38 .4 40 .1 37 .3 29 .3 35 .5 36 . 1 37 .7 40 .2 35 .7 7 30 .6 38 .1 39 .6 41 .8 44 .8 39 .0 25 .8 36 .5 39 .2 40 .5 44 .6 37 .3 8 34 .4 39 .0 38 .7 39 .2 43 .4 39 .0 29 .5 36 .6 36 .2 37 .4 41 .5 36 .2 9 24 .3 35 .7 37 .0 39 .5 44 .5 36 .2 20 .3 33 .9 36 .8 38 .0 42 .9 34 .4 10 33 .4 39 .3 39 .6 41 .3 45 .5 39 .8 28 .7 39 . 1 38 .4 39 .7 44 .0 38 . 0 11 30 .2 39 .4 39 .4 39 .5 45 .6 38 .8 23 .9 36 . 3 37 .0 37 .5 45 .8 36 .1 12 33 .3 38 .3 35 .4 35 .4 12 .3 30 .9 29 .9 35 .3 32 .7 29 .4 10 . 0 27 .4 13 31 .5 30 .0 22 .4 7 .8 11 . 1 20 . 6 26 . 1 25 .3 17 . 1 4 .5 9 .8 16 .6 14 27 .0 38 .4 38 .5 40 .0 43 .6 37 .5 22 .7 37 .6 36 .8 38 .4 43 .3 35 .8 15 33 .6 36 .4 34 .8 36 .7 21 .8 32 .7 28 .9 34 .5 32 .9 33 .7 18 .3 29 .7 16 29 .7 29 .9 30 .3 12 .8 11 .4 22 .8 24 .8 25 .5 22 .7 10 .6 10 .4 18 .8 17 30 .6 33 .0 30 . 1 31 .1 25 .8 30 .1 27 .3 28 .8 25 .6 27 .9 21 .3 26 .2 18 32 .3 38 .7 38 .4 39 .2 38 .2 37 .4 27 .7 36 .6 38 .0 36 .8 36 .6 35 .1 19 33 .6 37 .4 35 . 6 33 .8 41 .9 36 .5 28 .7 35 .8 31 .6 28 .8 41 .2 33 .2 20 31 .0 35 .3 38 .3 38 .5 38 .8 36 .4 26 .6 32 .2 37 .4 36 .7 36 . 1 33 .8 21 29 .5 21 .3 17 .5 28 .9 33 .9 26 .2 24 .2 17 .5 13 .4 25 .0 32 . 1 22 .4 22 29 .3 20 .7 7 .6 4 .5 13 . 7 15 .2 19 . 2 13 . 6 4 .6 2 .9 13 . 1 10 .7 23 32 .7 35 .7 35 .8 28 .3 35 .9 33 .7 28 .2 31 .2 30 .7 24 .5 34 .5 29 .8 24 28 .5 27 . 3 26 .5 15 .4 5 .4 20 . 6 23 .6 23 . 1 18 . 8 10 .3 4 .7 16 .1 25 29 .4 37 .8 33 .5 34 .7 17 .8 30 . 6 25 .7 35 .4 27 .8 30 . 8 16 .3 27 . 2 26 34 .2 39 . 1 39 .1 40 .6 41 .4 38 .9 28 .4 34 .8 36 .0 39 .2 41 .2 35 .9 27 28 .9 35 .2 38 . 6 37 .6 41 . 6 36 .4 24 .5 32 .7 36 .6 34 .5 39 .6 33 .6 28 33 .7 38 .4 36 .9 40 .9 41 . 6 38 .3 27 .3 33 .3 34 .0 40 . 1 40 .4 35 .0 29 30 .2 37 .9 37 .8 32 .6 19 .7 31 . 6 26 .8 36 . 3 35 .8 26 .6 15 .4 28 . 2 30 33 . 1 38 . 1 40 .4 40 .1 39 .7 38 .3 27 . 1 34 .4 39 . 1 39 .2 34 .7 34 .9 31 8 .9 5 .9 7 .9 14 .5 27 .3 12 .9 5 .0 2 .5 3 .4 10 .0 25 .6 9 .3 32 30 . 1 26 .5 16 .4 15 .7 29 .2 23 .6 21 .7 20 .9 11 .4 12 .0 26 .9 18 .6 33 31 .2 36 .3 37 .5 39 .6 39 .0 36 .7 25 .0 31 . 0 34 .8 38 .8 37 .0 33 . 3 34 7 .0 4 .8 9 . 1 18 .1 27 .4 13 . 3 4 .0 2 .7 6 . 1 14 .8 25 .3 10 . 6 35 25 .0 3 6 .3 35 .5 20 .6 26 .5 28 .8 17 .8 31 .3 29 .4 16 .9 25 .5 24 .2 36 13 .5 9 .0 13 .8 20 .2 12 .0 13 .7 8 .5 5 .5 7 .8 13 .7 10 .4 9 .2 37 31 .8 37 .1 38 .9 33 .8 22 .6 32 .8 28 .3 34 .3 35 .7 29 . 1 20 .0 29 .5 38 31 . 5 35 .0 36 . 6 37 .8 36 .2 35 .4 25 .7 32 .3 35 .1 36 .8 36 .4 33 . 2 39 30 .6 38 . 1 39 . 1 39 .0 37 .4 36 .8 24 .6 35 .6 38 .0 37 .4 35 . 0 34 . 1 40 30 .9 37 .3 38 .9 38 .6 35 .5 36 .2 20 .8 31 .2 35 .6 38 .9 39 .7 33 . 3 41 30 .7 37 .9 38 .4 40 .9 45 .0 38 .6 25 .2 36 .5 37 .8 39 .8 44 .3 36 .7 42 30 .8 34 .4 37 . 1 40 .3 43 .8 37 .3 21 .5 28 .9 33 .9 39 .0 42 .5 33 .2 43 32 .3 34 .7 39 .9 41 .5 43 .4 38 .4 26 .4 30 .7 36 .8 40 .6 42 .9 35 .5 44 32 .1 33 .5 37 .8 40 .7 43 .6 37 . 6 25 .4 29 . 1 35 .6 39 .6 43 .2 34 .6 45 32 .0 35 .7 38 .0 38 .3 39 .8 36 .8 28 .0 33 .6 36 .0 35 .8 38 .7 34 .4 46 31 .6 39 .4 35 .4 40 .1 42 .9 37 .9 28 .5 35 .0 31 . 1 39 .3 41 .2 35 .0 AUG05 AUG19 -• % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 47 35 .2 35 .9 33 .8 36 .8 38 .0 35 .9 30 .2 31 .8 30 .9 34 .6 36 .6 32 .8 48 31 . 1 36 .7 37 .0 37 .8 39 .2 36 .3 24 .9 34 .0 35 .0 35 .2 35 .5 32 .9 49 35 .0 37 .3 36 .5 36 .8 39 .0 36 .9 29 .9 34 .0 35 .0 34 .9 38 .0 34 .4 50 34 .5 39 .2 37 .3 32 .4 33 .8 35 .4 29 .2 35 .7 34 .8 27 . 1 31 .0 31 .6 51 30 .7 37 .7 36 .2 39 .4 40 .4 36 .9 27 . 0 36 .2 34 .5 38 .4 38 .5 34 .9 52 23 .4 37 .6 36 .6 39 .6 41 .5 35 .7 24 .7 34 .1 35 .0 36 .4 38 .5 33 .7 53 30 .9 35 .5 36 .9 37 .0 20 .4 32 .1 26 .1 29 .7 33 .0 32 . 3 15 . 6 27 .3 54 30 .5 37 .3 38 .5 39 .2 42 .7 37 .7 24 . 1 33 .2 36 .7 38 .0 42 .8 35 .0 55 31 .0 39 .2 38 .8 39 .2 42 .7 38 .2 24 .9 34 .2 35 .5 37 .6 40 .6 34 .6 56 35 .3 38 .0 35 .9 23 .9 31 .8 33 .0 29 .8 33 .9 31 .9 19 .2 29 .0 28 .8 57 31 .7 35 .4 37 .5 39 .6 41 .4 37 .1 25 .7 32 .2 34 .4 37 .2 40 .4 34 .0 58 34 .6 37 .0 37 .6 39 .7 42 .3 38 .2 30 .3 33 .8 35 .9 37 .4 40 .7 35 .6 59 34 .9 36 .6 37 .7 41 .0 42 .0 38 .4 30 .3 33 .7 35 .2 39 .0 40 .5 35 .7 60 32 .8 36 .8 36 .7 40 .9 42 .3 37 .9 27 .0 32 . 1 33 .5 39 .8 41 .5 34 .8 61 33 . 1 33 .8 36 .2 36 .5 30 .2 33 .9 28 .5 30 . 1 32 .5 33 .5 26 .9 30 .3 62 28 . 1 35 .8 22 .8 17 .9 11 .4 23 .2 22 .1 30 .6 18 .0 14 .2 10 .5 19 . 1 63 32 .4 40 .0 40 .1 41 .5 42 .2 39 .2 26 .9 36 .5 37 .2 39 .6 41 . 0 36 .2 64 30 .5 38 .4 41 .9 41 .1 42 .9 39 .0 24 .6 34 .9 38 .8 40 . 1 41 .3 35 .9 65 32 .8 26 .6 33 .9 41 .7 42 .4 35 .5 25 .1 20 .4 31 .3 42 .0 41 .2 32 .0 66 29 .0 33 .4 39 .9 31 .7 8 .7 28 .5 22 . 1 29 .9 36 .4 25 .5 6 .2 24 . 0 67 32 . 4 37 .4 39 .3 39 . 0 43 .2 38 .2 28 . 6 35 . 0 36 . 6 37 . 0 41 .8 35 .8 68 33 .2 39 .8 38 .7 39 .9 43 .3 39 .0 26 .9 38 .1 36 .8 37 .4 43 . 0 36 .4 69 31 .8 31 .9 38 .9 42 .2 42 .6 37 .5 25 .0 28 .2 37 .5 42 .7 42 .8 35 .3 70 34 .9 38 .9 37 .5 37 .9 42 .3 38 .3 29 .8 36 .1 33 .9 34 .6 40 .7 35 .0 71 33 .1 30 . 0 35 .7 38 .9 41 . 2 35 .8 27 .9 26 . 5 33 .7 37 .7 39 .1 33 .0 72 32 .1 33 .4 34 .9 38 .8 40 .4 35 .9 25 .9 29 .2 31 .4 37 .8 37 .9 32 .4 73 31 .8 29 .0 34 .4 27 .2 29 .5 30 .4 25 .6 26 .7 31 .4 22 .7 25 . 6 26 .4 74 33 .2 29 .6 26 .8 20 .9 17 .9 25 .7 26 .0 24 .2 22 . 1 17 .8 15 .4 21 . 1 75 34 .5 32 .7 32 .3 38 .4 43 .7 36 .3 29 .7 30 .2 29 .4 36 .7 42 .5 33 .7 76 35 .5 38 .4 34 .2 35 .5 39 .7 36 . 6 32 .4 34 .9 31 .9 33 .3 37 .8 34 . 1 AUG29 SEP09 % v / v — . . % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 22.0 33.9 34.8 37.1 24.2 30.4 20.7 31.3 33.3 35.5 21.5 28.4 2 26.2 32.9 32.7 32.1 37.3 32.2 25.2 31.8 31.0 30.3 36.3 30.9 3 18.1 11.2 6.8 6.3 7.5 9.9 16.1 10.7 6.0 5.3 5.3 8.7 4 24.8 32.2 29.5 25.7 28.1 28.1 22.4 29.8 26.6 22.8 25.9 25.5 5 26.6 33.6 33.9 35.6 31.7 32.3 25.0 32.5 32.5 33.4 28.3 30.3 6 24.8 32.8 33.7 35.1 38.0 32.9 23.5 31.2 31.6 33.4 36.6 31.2 7 21.2 33.5 37.5 40.0 43.5 35.1 20.0 32.9 37.7 40.4 42.9 34.8 8 25.7 33.6 34.4 34.9 39.4 33.6 24.5 32.1 32.3 33.9 38.1 32.2 9 16.4 30.2 33.9 35.0 41.5 31.4 16.0 28.7 33.1 34.2 39.3 30.3 10 24.8 36.8 37.2 37.4 43.4 35.9 24.2 35.7 36.4 37.5 43.3 35.4 219 AUG29 SEP09 % v/v % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 11 20 .4 33 .2 35 .6 37 .0 44 .4 34 . 1 19 .4 31 .9 35 .5 36 .8 43 .9 33 .5 12 25 .4 31 .0 28 .2 25 .2 7 .9 23 .6 21 .9 27 .9 25 . 6 21 .8 6 .5 20 .7 13 17 .9 20 .4 13 .3 4 .1 8 .3 12 .8 15 .2 17 .6 12 • 3 3 .3 6 .7 11 . 0 14 19 .5 34 .5 35 .0 34 .9 42 .0 33 .2 17 .3 33 .5 33 .7 34 .2 41 . 6 32 .1 15 25 .7 29 .7 29 .7 30 .0 16 .7 26 .4 23 .7 28 .7 26 .8 27 .0 14 .7 24 .2 16 18 .7 21 .0 18 .3 8 .2 9 .9 15 .2 15 .2 19 .7 16 .1 7 .3 8 .4 13 .3 17 22 .6 25 .0 20 .7 22 .5 18 .0 21 .8 19 .2 22 .2 17 .8 18 .4 14 .9 18 .5 18 24 .5 34 .2 35 .2 33 .6 32 .6 32 .0 22 .7 32 .9 33 .5 31 .0 30 .3 30 . 1 19 24 .4 32 .8 27 .5 26 .3 41 . 1 30 .4 23 .4 31 .5 26 . 1 23 .7 40 .1 29 .0 20 21 .6 29 .4 34 .9 34 .2 33 .6 30 .7 20 .4 28 .3 33 .6 33 . 1 31 .4 29 .4 21 18 .8 15 .4 10 .1 20 .3 28 .9 18 .7 17 .5 14 .2 8 .9 16 .3 24 .6 16 .3 22 11 .8 9 .9 3 .0 1 .5 11 .7 7 .6 9 .6 8 .7 2 .3 1 . 0 10 .4 6 .4 23 22 .3 25 .1 25 .6 20 .7 32 .6 25 .3 19 .3 23 .0 22 .2 18 .7 30 .7 22 .8 24 17 .7 17 .7 13 .0 6 .5 3 .7 11 .8 14 .7 15 .7 11 .0 5 .1 3 .3 10 .0 25 21 .8 31 .3 23 .9 27 .7 14 .6 23 .9 19 .7 28 .2 21 .9 24 .5 12 .6 21 .4 26 22 .9 30 .2 32 .2 37 .9 41 .0 32 .8 20 .8 27 .8 31 . 1 36 .8 40 .1 31 .3 27 36 .2 31 .3 34 .4 31 .7 38 .6 34 .4 18 .9 27 .4 33 .8 30 .8 38 .0 29 .8 28 22 .1 30 .3 31 . 5 39 .1 38 .0 32 . 2 21 .0 28 .0 30 .9 38 .0 36 .3 30 .8 29 23 .0 32 .9 32 .3 21 .0 12 .9 24 .4 20 .2 30 .2 29 .8 18 .4 10 .9 21 .9 30 23 .3 31 .9 37 .7 38 .2 30 .4 32 .3 21 .4 30 . 0 36 .2 36 .8 27 .3 30 .3 31 3 .2 1 .6 2 . 1 6 .8 23 .8 7 . 5 3 .6 1 . 1 1 .5 5 . 6 22 . 4 6 .8 32 14 .4 15 .7 7 .6 9 . 1 23 .9 14 .2 12 .2 14 .0 6 . 6 6 .8 21 .7 12 .2 33 19 .4 27 .1 32 .3 36 .8 34 .7 30 . 1 17 .7 25 .2 31 .3 35 .8 33 .9 28 .8 34 2 .9 1 .7 4 .8 11 .3 22 .8 8 .7 3 . 6 1 .4 3 .9 9 .7 21 . 2 8 .0 35 11 .4 24 .9 24 .4 12 .7 22 .2 19 . 1 10 .1 21 . 8 20 .5 10 .9 19 .5 16 .6 36 5 .8 3 .6 3 .9 6 .5 7 .8 5 .5 5 .5 3 .0 3 . 0 4 .8 6 . 0 4 .5 37 23 .5 31 .3 31 .5 24 .2 16 .5 25 .4 22 . 1 29 .5 29 .4 20 .9 14 .4 23 .3 38 19 .5 28 .5 32 .3 35 .3 32 .9 29 .7 17 .6 26 .9 31 .2 34 .0 30 .2 28 .0 39 19 .4 31 .5 35 .1 35 .4 31 .9 30 .7 18 . 1 30 .3 33 .4 33 .4 29 . 6 29 .0 40 16 .8 28 .0 33 .4 38 .9 37 .0 30 .8 14 .9 26 .7 31 .4 37 .0 34 .6 28 .9 41 21 .9 34 .5 36 .8 38 .7 43 .5 35 .1 21 .2 34 .6 36 .4 37 .9 41 .4 34 . 3 42 17 .6 25 . 1 31 . 1 38 .2 41 .7 30 .7 15 .9 24 .0 30 .5 37 .4 41 .8 29 .9 43 20 .8 27 .2 35 .2 38 .2 41 .9 32 .7 19 .6 25 . 1 34 .9 38 .3 41 .9 32 .0 44 19 . 1 23 .9 33 .3 36 .6 42 .6 31 . 1 18 .3 23 . 1 31 .8 36 .5 42 . 0 30 .3 45 23 .8 30 .7 33 .3 32 . 8 38 .2 31 .7 23 .4 30 . 0 32 .4 32 .8 37 . 6 31 .2 46 22 .5 31 .0 28 .4 37 .1 39 .6 31 .7 20 .3 28 . 1 26 .8 35 .7 38 .5 29 .9 47 24 .9 27 .3 27 .2 32 .7 32 .7 29 .0 22 .2 25 .9 25 .3 29 .7 29 .4 26 .5 48 21 .8 31 . 0 31 .9 32 .9 31 .5 29 .8 23 . 0 28 .9 30 . 2 30 .4 28 .2 28 . 2 49 26 .6 30 .9 32 .6 32 .1 36 .2 31 .7 24 .2 29 . 6 31 .6 30 . 6 35 .1 30 .2 50 24 .9 31 .8 29 .5 23 .3 28 .2 27 .5 21 .3 28 .4 26 . 1 20 .4 25 .7 24 .4 51 22 .4 33 . 1 32 .2 35 .9 37 .5 32 .2 21 .7 31 .7 31 .7 34 .5 35 .1 31 .0 52 18 .6 30 .4 31 .5 33 .8 35 . 6 30 . 0 18 .8 28 .5 29 .5 30 . 1 32 .8 27 .9 53 19 .7 26 . 1 28 . 5 28 .6 13 . 5 23 .3 17 .7 22 .7 24 .3 23 .7 12 .2 20 . 1 54 19 .7 29 .2 34 .3 36 .4 41 .8 32 .3 17 .7 27 .0 32 .0 35 .7 41 . 1 30 .7 55 19 .4 30 . 1 32 .9 36 .1 39 .9 31 .7 19 . 1 28 .3 31 .4 35 .2 40 .0 30 .8 56 24 .8 29 .7 26 .6 16 .0 26 .9 24 .8 22 .4 27 .0 24 .2 14 .0 25 .4 22 .6 2 2 0 AUG29 SEP09 • % v / v — — % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 57 20 .4 28 .2 32 .7 34 .6 37 . 1 30 .6 18 .7 26 .1 30 .9 32 .7 34 .3 28 . 6 58 26 .6 31 .0 34 .0 35 .2 38 .2 33 • 0 26 . 1 30 .3 33 . 1 33 .8 36 .9 32 .0 59 25 .4 30 .4 33 .2 35 .8 38 .9 32 .7 24 .2 28 .9 31 .2 34 .1 36 .4 31 .0 60 22 . 1 29 .0 30 .5 38 .2 40 .7 32 .1 21 .4 28 .3 29 .6 37 .6 39 .7 31 .3 61 24 .6 27 .2 28 .7 29 .4 23 .3 26 .7 22 .6 25 .8 26 .6 26 .2 20 .7 24 .4 62 15 .6 25 .0 13 .4 9 . 1 8 .8 14 .4 13 .1 21 .2 11 .5 6 .1 7 .4 11 .9 63 21 .5 32 .5 34 .4 37 .2 38 .1 32 .7 19 .3 31 .1 31 .7 35 • 0 35 .8 30 .6 64 19 .6 31 .4 36 .2 37 .8 40 .0 33 .0 18 .7 29 .2 34 .7 37 .3 38 .7 31 .7 65 17 .5 15 .5 28 .1 40 .8 40 .8 28 .5 15 .2 12 .9 26 .6 40 .4 38 .6 26 .7 66 18 .4 25 .5 32 .4 20 .1 4 .6 20 .2 16 .2 22 .8 27 .8 16 .7 4 .0 17 .5 67 23 .7 32 .7 34 .4 34 .0 40 .9 33 . 1 22 .2 31 .6 33 .4 32 .5 40 .3 32 .0 68 23 .6 34 .7 34 .5 34 .9 41 .3 33 .8 21 .6 33 .0 33 .0 33 .5 40 .5 32 .3 69 20 .1 25 .4 34 .7 40 .7 42 .3 32 .6 19 .0 23 .7 32 .8 39 .9 41 .7 31 .4 70 24 .7 33 .3 31 .6 31 .0 37 .9 31 .7 22 .8 31 .0 28 . 6 29 .2 36 .3 29 .6 71 22 .6 22 .9 30 .2 35 .0 36 .8 29 .5 22 .5 21 .2 27 .2 31 .9 34 .2 27 .4 72 20 .4 25 .1 27 .7 34 .6 35 .2 28 .6 19 .8 23 .6 25 .2 32 .3 33 .0 26 .8 73 18 .9 22 .7 27 .3 19 .4 23 .2 22 .3 17 .9 19 .7 23 .4 16 .6 22 .0 19 .9 74 19 .1 18 .7 17 .0 13 .9 13 .6 16 .5 16 . 3 15 .6 13 .9 11 .1 11 . 0 13 . 6 75 24 .5 26 .9 26 .3 34 .0 40 .3 30 .4 22 .7 25 .0 24 .3 32 .4 38 .7 28 .6 76 27 .2 31 .8 28 .6 30 . 6 35 .9 30 .8 25 .2 30 . 0 27 .3 28 .9 33 . 8 29 . 0 SEP20 OCT07 — • -% v / v ~ — % v/v-Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 21 .9 32 .3 32 .9 34 .7 21 .3 28 .6 38 .3 40 .3 38 . 3 38 .1 22 .3 35 .5 2 25 .6 32 .3 31 .3 30 . 0 35 .8 31 .0 39 .8 39 .8 37 .3 34 .1 34 .6 37 . 1 3 17 .7 10 .3 6 .0 5 .4 4 .9 8 .9 35 .9 20 . 0 11 .4 6 .6 4 .7 15 .7 4 22 .7 29 .8 27 .1 22 .2 24 .7 25 .3 38 .2 40 .4 36 .5 26 .1 24 . 6 33 .2 5 25 .9 33 .3 32 .9 31 .7 25 .1 29 .8 38 .8 41 .6 38 .9 34 .3 24 .7 35 .7 6 25 . 0 31 .4 31 .7 32 . 5 34 .3 31 .0 40 . 0 39 .2 36 . 5 34 . 0 33 . 1 36 . 5 7 23 .4 33 .6 39 .3 40 . 1 43 .0 35 .9 39 .9 41 .2 40 .8 41 .6 42 .4 41 .2 8 25 .8 33 .0 33 .6 34 .2 37 .9 32 .9 40 .6 41 .3 39 .0 37 .9 38 .4 39 .4 9 17 .7 28 .8 33 .4 34 .3 38 .1 30 .5 36 .2 38 .4 38 .4 38 .0 39 . 3 38 .1 10 26 .8 37 .4 37 .0 37 . 4 44 .0 36 .5 41 . 3 42 .4 40 .9 41 .5 43 .5 41 .9 11 21 .7 32 .6 35 .0 36 .8 45 .0 34 .2 39 . 3 41 . 3 39 .5 40 .1 42 .5 40 .6 12 22 .3 27 .1 25 .3 21 .5 6 .2 20 .5 38 .5 39 .6 34 .2 25 .4 6 .4 28 .8 13 15 . 1 17 .5 12 .0 3 .6 6 .3 10 .9 35 .2 33 • 0 22 . 1 5 .6 6 . 1 20 .4 14 19 .7 33 .6 33 .7 33 .8 41 .6 32 .5 39 .6 41 .3 39 .0 35 .9 40 . 1 39 .2 15 25 .3 28 .7 26 .3 25 .8 14 .0 24 .0 39 .1 38 .9 36 .6 32 . 1 14 .3 32 .2 16 14 .6 19 .2 16 .2 7 .6 8 . 1 13 . 1 34 .6 29 .9 22 . 0 8 . 1 7 .8 20 .5 17 19 .9 22 .7 17 .6 16 .5 12 .0 17 .7 36 . 3 34 .3 23 .3 18 .6 13 .5 25 .2 18 24 . 1 33 .3 33 .8 30 .5 28 .6 30 .1 38 .3 40 .0 39 .3 36 .9 30 .9 37 . 1 19 24 .4 31 .9 26 .1 25 .8 39 .7 29 .6 38 .3 39 .2 36 .2 32 .2 40 .0 37 .2 20 22 .3 28 .6 34 .4 32 .0 31 .0 29 .6 39 .1 39 .9 39 .3 37 .3 32 .0 37 .5 221 SEP20 OCT07 -% v/v % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 3 0cm 45cm 60cm 90cm Avg. 21 18 .2 13 .7 8 .6 15 .5 21 .7 15 .5 38 .5 27 .7 14 .8 18 .8 20 .4 24 .0 22 10 . 1 9 .4 2 .4 0 .5 9 .5 6 .4 33 .2 25 .3 7 .1 1 .3 9 .4 15 .2 23 20 .0 22 .9 21 .8 17 .2 28 .5 22 .1 38 .5 37 . 0 31 .7 20 .6 27 .8 31 . 1 24 14 .1 15 .4 11 .1 5 .1 2 .8 9 .7 32 .0 28 .2 19 .6 7 .9 2 .9 18 .1 25 20 .5 28 .9 21 .3 22 .6 12 .1 21 . 1 36 .8 38 .4 30 . 1 26 .1 12 .5 28 .8 26 20 .8 27 .9 30 .9 37 .0 39 .5 31 .2 36 .5 37 .9 36 .5 39 .0 38 .7 37 .7 27 20 .4 28 .2 33 .9 30 .6 37 .4 30 .1 37 .6 39 .6 40 .1 36 .0 37 .7 38 .2 28 21 .7 28 .7 30 .9 38 .3 35 .5 31 .0 39 .0 40 .6 37 .8 38 .6 35 • 0 38 .2 29 22 .2 29 .8 28 .7 16 .8 10 .8 21 .7 37 . 0 38 .7 36 . 6 23 .8 35 . 3 34 .3 30 23 .3 30 .4 37 .3 36 • 0 25 .5 30 .5 39 .9 40 .9 39 . 1 35 .9 24 .4 36 .0 31 4 .8 2 .7 2 .1 6 .3 21 .8 7 .5 14 .9 11 .4 11 . 1 14 .2 23 .2 14 .9 32 13 .4 15 .4 6 .2 6 .5 20 .1 12 .3 36 .7 31 .8 15 .7 9 .8 19 .2 22 .6 33 19 .3 25 .9 30 .9 36 .4 33 .5 29 .2 37 .9 39 .2 38 .5 38 .3 33 .2 37 .4 34 5 .5 1 .8 3 .7 9 .7 21 . 1 8 .3 17 .3 12 .3 12 .9 16 .6 21 . 6 16 .1 35 10 .8 22 .1 20 .7 11 .0 19 .9 16 .9 30 .9 38 .4 34 .0 18 .3 20 .5 28 .4 36 6 .4 2 .7 3 .3 4 .7 5 .6 4 .6 18 .0 13 .4 11 .8 7 .6 5 . 6 11 .3 37 22 .2 29 .3 28 .5 19 .7 13 .1 22 .6 38 .7 39 . 1 37 . 3 24 .2 13 .7 30 .6 38 20 .0 27 .6 31 .0 33 .5 27 .4 27 .9 36 .4 38 .2 37 .5 3 6 .6 29 .9 35 .7 39 18 .8 30 . 6 33 .6 33 .0 27 .4 28 .7 35 .2 39 .4 39 .0 37 .2 28 .2 35 .8 40 18 .0 27 .2 31 .8 38 .1 33 . 6 29 .7 34 .4 39 .7 38 .9 40 .1 35 .9 37 .8 41 21 .3 34 .8 37 .2 38 .5 40 .8 34 .5 37 .5 40 .2 40 .2 40 .2 42 . 1 40 .0 42 18 .3 24 .6 31 .8 37 .5 41 .7 30 .8 34 .6 36 .6 37 .5 38 .9 41 .7 37 .9 43 21 .9 25 .7 35 . 1 39 .2 41 .8 32 .7 38 . 6 38 .5 38 . 6 39 .3 41 .4 39 . 3 44 18 .1 23 .3 32 .5 37 .8 42 .4 30 .8 37 .8 36 .3 38 .0 39 .6 41 .8 38 .7 45 25 .6 30 .6 32 .5 32 .5 38 .5 31 .9 38 .9 40 .2 39 .1 36 .6 38 . 6 38 .7 46 21 .4 28 .0 26 .9 35 .2 37 .6 29 .8 37 .7 40 .4 34 .4 37 .0 37 .5 37 .4 47 24 . 1 25 .9 25 .1 29 .8 27 .1 26 .4 37 .9 37 .5 32 .9 31 .0 26 .4 33 .1 48 24 .6 29 .9 28 .8 31 .2 27 .0 28 . 3 36 .1 38 .4 36 .2 34 .1 28 . 6 34 .7 49 25 .1 30 .0 31 . 6 30 . 0 33 .3 30 . 1 38 .8 39 .2 37 . 0 33 .8 33 .9 36 . 6 50 21 .8 27 .7 26 .1 19 .7 23 . 1 23 .7 35 .0 37 .9 34 .2 23 .0 22 .3 30 .4 51 22 .8 32 .0 31 .6 33 .8 34 .3 30 .9 37 • 5 40 .2 36 .5 35 .2 34 . 1 36 .7 52 19 .9 28 .3 28 .2 29 .3 31 .4 27 .4 35 .5 37 .8 34 .4 31 .2 29 .9 33 .8 53 19 .0 22 .8 22 .8 22 .6 11 . 1 19 .7 35 .7 35 .8 32 .0 25 .2 11 .1 28 .0 54 20 .4 28 . 0 33 .5 36 .2 40 .9 31 .8 38 .6 40 .3 38 .2 37 .7 40 .8 39 . 1 55 20 .7 29 .7 31 .3 35 .6 40 .6 31 .6 37 .1 40 .5 38 .5 37 .6 40 .1 38 .7 56 22 .8 27 . 3 23 .8 13 . 6 25 .2 22 .5 37 .8 38 . 7 34 .8 18 . 6 24 . 3 30 .9 57 20 .4 27 .4 31 .0 31 . 6 32 .4 28 .6 36 .5 37 .5 37 .3 33 .5 32 . 0 35 .4 58 26 .8 31 .1 34 .1 33 .5 35 .6 32 .2 40 .4 39 .4 34 .6 35 .9 35 .7 37 .2 59 24 .8 28 .6 30 .8 33 .3 35 .7 30 .7 38 .7 39 .0 37 .5 35 .8 35 .0 37 .2 60 23 .7 30 .1 30 .3 37 .4 39 .4 32 .2 38 .5 38 .9 37 .3 40 .0 39 .2 38 .8 61 24 .7 25 .8 26 .3 24 .3 19 .7 24 .2 37 .5 36 .9 34 .7 28 .1 19 .6 31 .4 62 13 .3 20 .9 10 .7 5 .8 6 .7 11 .5 33 .6 36 .0 17 .4 6 .7 6 .4 20 .0 63 21 .2 30 .8 32 .5 35 .4 36 .2 31 .2 37 .2 41 .5 39 • 0 36 .6 34 .1 37 .7 64 21 . 1 31 .4 35 .7 37 .8 38 .7 33 .0 35 .7 41 .2 43 . 1 41 .5 41 .0 40 .5 65 15 .6 13 .8 19 .9 39 . 6 38 .5 25 .5 34 .2 29 . 1 34 . 1 39 .7 38 .1 35 .0 66 16 .2 22 .4 26 .1 15 .5 3 .6 16 .8 34 .6 36 .0 32 .8 17 .9 3 .5 25 .0 222 SEP20 OCT07 % v/v • % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 67 24. 9 32. 7 33 .7 32. 4 40. 3 32. 8 38. 3 40. 9 38. 6 34. 7 39. 5 38. 4 68 23. 8 33. 5 33 .8 33. 4 40. 6 33. 0 39. 8 42. 4 37. 3 35. 3 39. 0 38. 8 69 21. 8 24. 9 33 .8 40. 3 41. 2 32. 4 37. 7 37. 9 39. 2 40. 4 39. 0 38. 8 70 24. 4 31. 4 29 .3 28. 6 33. 4 29. 4 38. 7 41. 3 37. 0 32. 8 34. 4 36. 8 71 22. 6 22. 0 27 .2 30. 1 32. 5 26. 9 35. 6 32. 6 32. 7 32. 0 31. 7 32. 9 72 20. 1 25. 1 26 .3 32. 7 31. 3 27. 1 37. 2 36. 3 34. 2 35. 7 31. 2 34. 9 73 18. 2 20. 0 22 .1 15. 1 20. 5 19. 2 33. 3 30. 2 28. 8 18. 9 20. 4 26. 3 74 16. 0 16. 0 13 .0 10. 1 9. 3 12. 9 34. 0 29. 8 22. 9 13. 7 8. 9 21. 9 75 24. 2 25. 7 24 .5 31. 9 38. 5 29. 0 38. 6 36. 2 30. 2 32. 4 36. 2 34. 7 76 26. 6 30. 4 27 .8 28. 2 33. 7 29. 3 40. 9 39. 0 33. 2 30. 9 32. 9 35. 4 Theta values f o r 38 i r r i g a t e d s i t e s , 1986 APR23 MAY05 % v / v — % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 42 .4 44 .0 42 .5 42 .5 44 . 0 43 . 1 43 .4 43 . 2 42 .5 42 . 5 43 . 3 43 .0 81 42 .5 43 .3 42 .6 43 .4 43 .2 43 .0 43 .3 43 .0 41 .7 42 .8 43 .3 42 .8 82 43 .8 44 .7 42 .6 42 .7 43 .8 43 .5 44 .1 44 .7 43 .0 42 .2 43 . 6 43 .5 83 44 .4 45 .4 44 .2 43 .4 44 .6 44 .4 44 .8 45 .8 43 .8 43 .4 44 . 5 44 .5 84 42 .5 42 .1 41 .4 39 .7 39 .9 41 . 1 42 .5 42 .2 42 .1 39 . 0 39 .9 41 . 1 85 40 . 1 39 .9 32 . 6 23 .5 38 .0 34 .8 40 .8 39 .6 32 . 0 23 .5 39 . 5 35 .1 86 44 .0 44 .0 41 .6 41 .0 41 .7 42 .4 44 .2 44 .4 41 . 0 40 .1 42 . 1 42 .3 87 40 .8 37 .9 38 .5 39 .0 35 .9 38 .4 41 .0 38 . 1 37 .8 39 .2 35 .8 38 .4 88 30 .4 31 . 0 36 .1 35 .5 40 .0 34 .6 31 . 6 35 .5 35 .4 35 . 1 40 .5 35 .6 89 41 .1 43 .3 42 .3 40 .6 31 . 8 39 .8 40 .9 43 .6 41 .8 40 .9 31 .5 39 .7 90 42 .5 44 .2 44 .4 42 .5 45 .0 43 .7 43 .4 45 .0 43 .5 42 .6 44 .7 43 .8 91 43 .9 42 .9 43 .8 42 .8 41 .5 43 .0 45 .2 42 .0 43 .9 43 . 1 41 .9 43 .2 92 43 . 1 43 .1 43 .0 43 .7 43 .8 43 .3 43 .4 42 .4 43 .5 43 .0 43 .9 43 .3 93 42 .7 42 .2 41 .8 42 .8 43 .6 42 . 6 42 .0 42 .5 42 . 0 42 .5 43 .3 42 .5 94 44 .0 43 .0 42 .3 42 .3 42 .6 42 .8 43 .3 43 .2 42 .0 42 .8 42 .8 42 .8 95 41 .6 41 .1 41 .4 40 .7 41 .4 41 . 3 42 .7 41 .9 41 .0 41 .3 40 . 3 41 .5 96 42 . 1 43 .4 43 .4 43 .8 43 .2 43 .2 41 .7 43 . 6 42 .7 43 .1 43 .3 42 .9 97 41 . 6 40 .7 39 .4 41 . 6 44 . 5 41 . 6 42 . 1 40 . 6 38 . 9 42 . 1 44 .4 41 .7 98 40 .6 41 .0 40 .6 39 .2 42 .9 40 .9 39 .8 40 .7 40 .4 38 .9 43 .3 40 .6 99 42 .7 40 .6 40 .6 40 .5 41 .9 41 .3 42 .3 40 .7 40 .9 40 .9 41 .5 41 . 3 100 43 .5 44 .2 42 .4 42 . 3 43 .2 43 . 1 44 .2 44 . 1 42 .2 41 .7 42 .8 43 .0 101 43 .2 44 . 0 43 .8 42 .9 43 .0 43 .4 43 .6 43 .8 43 .6 42 .6 43 . 0 43 . 3 102 42 .5 42 .3 40 .5 38 .8 39 .8 40 .8 43 .3 42 .2 40 . 1 38 .8 39 .9 40 .8 103 41 .6 43 .3 43 .1 43 .2 43 .4 42 .9 41 .9 43 .2 43 . 1 42 .6 43 .9 42 .9 104 41 .6 42 .0 41 .5 42 .9 43 .5 42 .3 42 .2 41 . 1 41 .8 42 .5 42 .9 42 .1 105 43 .6 43 .2 43 .0 42 .0 44 .4 43 .2 43 .2 42 .7 42 .8 41 .4 44 .7 42 .9 106 41 .8 43 .0 42 .5 43 . 1 37 .8 41 .6 41 .5 42 .5 42 .6 42 .6 37 . 1 41 .3 107 43 .4 43 .4 42 .9 42 .0 42 .7 42 .9 43 .5 43 .5 43 . 3 41 .3 42 . 3 42 .8 223 APR23 MAYO5 % v/v % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 108 41. 7 42. 6 42 .2 41. 7 43. 3 42. 3 43 .0 43. 1 42. 2 42. 3 42 .4 42. 6 109 37. 5 38. 4 41 .4 43. 6 42. 6 40. 7 37 .9 38. 4 41. 7 43. 0 43 .2 40. 8 110 40. 9 42. 3 41 .7 41. 2 41. 1 41. 4 41 .9 41. 7 41. 7 40. 3 40 .9 41. 3 111 41. 4 44. 1 43 .3 43. 0 43. 1 43. 0 42 .9 43. 7 43. 0 42. 5 42 .9 43. 0 112 42. 5 40. 6 38 .5 39. 5 36. 0 39. 4 42 .1 41. 1 39. 2 39. 4 36 .3 39. 6 113 43. 2 45. 4 44 .8 43. 7 43. 3 44. 1 43 .7 45. 8 45. 2 43. 0 42 .7 44. 1 114 43. 6 43. 0 42 .7 42. 4 43. 3 43. 0 43 .8 43. 5 42. 3 42. 1 43 .0 42. 9 115 43. 6 43. 0 42 .7 42. 1 42. 1 42. 7 43 .2 43. 2 42. 2 41. 2 42 . 1 42. 4 116 43. 4 44. 1 42 .3 42. 8 43. 5 43. 2 43 .5 44. 8 42 . 2 43. 1 43 .0 43 . 3 117 42. 7 43. 3 42 .2 41. 9 42. 6 42. 5 43 . 0 42. 3 41. 1 41. 9 42 .9 42. 3 MAY14 MAY28 : % v/V % V / V S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 43 .9 44 . 1 42 .3 42 . 6 43 .9 43 .4 45 . 1 45 . 2 44 . 0 43 .8 44 . 7 44 .5 81 42 .8 43 . 4 42 .1 43 .3 43 .5 43 .0 44 .4 44 .5 43 . 6 44 .7 44 . 6 44 .4 82 43 .9 43 .2 42 .5 42 .9 42 .7 43 .0 45 .9 46 .0 45 .2 43 .4 44 .7 45 .0 83 44 .7 45 .9 44 .2 43 .5 44 .6 44 .6 46 . 4 47 .3 45 . 6 44 .2 45 . 1 45 .7 84 43 .7 42 .7 41 .4 39 .7 40 .2 41 . 5 44 .8 44 . 1 42 . 8 40 .5 40 . 5 42 . 6 85 41 .5 40 . 1 32 .5 24 . 1 39 .9 35 .6 42 . 1 41 .4 32 .6 24 .2 40 .2 36 . 1 86 44 . 1 44 .2 41 .6 40 .5 42 .9 42 .7 45 .3 45 .4 43 .0 42 .4 43 .3 43 .9 87 42 .7 43 .2 40 .9 39 .7 41 .5 41 .6 43 . 3 40 .0 39 .8 39 .9 35 .5 39 .7 88 32 .3 32 .7 36 .3 37 .3 40 .6 35 .8 31 .6 31 .9 37 . 1 36 .2 40 .9 35 .5 89 42 .5 44 .0 42 .6 40 .8 33 .3 40 .6 43 .0 44 .9 43 .2 42 .0 33 .4 41 .3 90 43 . 1 45 .0 44 .6 42 .8 45 .5 44 .2 45 .5 46 .6 46 . 1 43 .7 46 .4 45 .7 91 45 .5 42 .2 43 .7 43 .1 41 .9 43 .3 45 .5 44 . 0 45 .2 44 .7 43 . 1 44 .5 92 44 .3 43 .3 43 .5 43 .4 43 .9 43 .7 44 .4 44 .7 44 .5 44 .2 45 .6 44 .7 93 43 .5 41 .8 41 .9 42 . 3 44 .0 42 .7 44 .3 44 . 1 43 .3 44 . 1 44 .7 44 . 1 94 44 .4 44 .8 43 .7 43 .5 44 .8 44 .2 45 .6 45 . 0 44 .1 43 .7 45 .0 44 .7 95 43 .1 42 . 1 41 .8 40 . 6 41 . 0 41 .7 44 .3 43 . 6 42 . 3 42 . 0 40 .8 42 . 6 96 42 .1 43 .7 43 .8 43 .6 43 .8 43 .4 43 .7 44 .4 45 . 0 44 .5 45 .2 44 .6 97 43 .0 41 .2 39 .5 42 .0 45 .0 42 . 1 44 .2 42 .4 39 .9 43 .3 46 .6 43 .3 98 41 .1 42 .0 40 .8 40 .2 42 .5 41 .3 42 .8 42 .5 42 . 1 40 .6 44 .8 42 . 6 99 43 .2 39 . 9 40 .3 41 .2 41 .8 41 . 3 43 .4 42 .4 41 . 9 41 .9 43 .2 42 . 6 100 44 .5 44 .9 43 .2 42 .5 43 .0 43 .6 45 .8 45 .9 44 . 3 43 .4 44 .2 44 .7 101 41 .8 43 .0 43 .9 43 .0 43 .5 43 .1 46 . 1 45 .5 44 .9 44 .0 45 .1 45 .1 102 42 .7 41 .4 41 .6 40 .2 40 .4 41 .3 44 .3 42 .9 41 .5 40 .0 40 .6 41 .9 103 42 .0 43 .5 43 .7 42 .8 44 .4 43 .3 43 .7 44 .9 45 .0 43 .8 45 .4 44 .5 104 44 . 1 42 . 1 42 .2 42 .5 44 .0 43 .0 43 . 6 42 .5 42 .7 44 .6 44 .5 43 . 6 105 44 .7 43 .6 43 .6 42 .7 44 . 3 43 .8 45 .0 44 .5 45 .2 43 .1 45 . 6 44 .7 106 42 .2 43 .4 42 .2 43 . 1 38 . 6. 41 .9 43 .7 44 .8 43 .9 44 .2 38 .9 43 . 1 107 44 .2 42 .8 43 .9 42 .8 42 .6 43 .3 45 .9 44 .2 45 .0 43 . 3 43 .6 44 .4 108 42 .9 43 . 1 42 .0 42 .3 43 .0 42 .7 44 . 3 44 . 1 44 . 3 43 .7 44 .4 44 .2 109 38 .9 38 .0 41 .5 43 .6 42 .7 40 .9 40 .4 39 . 1 42 .9 44 .0 44 .3 42 .2 2 2 4 MAY14 MAY28 % v/v % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 110 42.7 42.5 41.4 41.0 41.8 41.9 43.5 43.9 43.3 41.7 42.8 43.1 111 42.8 43.7 43.4 42.9 43.8 43.3 43.6 44.9 44.4 44.2 44.2 44.3 112 43.3 40.8 39.8 39.9 38.0 40.4 43.9 42.9 40.8 41.2 36.4 41.0 113 44.7 46.1 45.0 43.2 43.8 44.6 45.4 47.1 46.5 44.5 45.0 45.7 114 44.6 41.5 41.6 42.5 43.3 42.7 46.4 46.0 43.9 42.9 44.1 44.7 115 44.3 43.1 42.9 41.6 42.0 42.8 46.4 45.1 44.1 43.1 42.0 44.2 116 43.9 44.5 43.2 42.9 44.3 43.8 44.2 46.1 44.7 43.6 45.2 44.8 117 43.3 43.2 42.0 42.3 42.9 42.7 45.2 43.7 43.5 43.0 43.8 43.8 JUNO4 JUN23 v/v % v/v-Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 40 .3 43 .2 43 .9 43 .8 45 .3 43 .3 39 .7 40 .1 39 .6 39 .8 41 .3 40 .1 81 41 .7 43 .3 42 .7 44 .7 45 .3 43 .5 40 .0 41 .7 39 .4 40 .8 42 .3 40 .8 82 39 . 1 43 .5 43 .2 43 .6 44 .5 42 .8 38 .5 40 .5 39 .7 40 .0 42 .6 40 .3 83 42 .5 46 .8 44 .8 44 .7 45 .6 44 .9 41 .6 44 .6 41 .2 41 .4 43 .8 42 .5 84 39 .2 41 .7 42 .2 38 .9 40 .4 40 .5 40 .1 40 .5 39 .0 35 .7 35 .8 38 .2 85 37 .2 39 .1 28 .4 19 . 0 37 .1 32 . 2 38 .5 37 .9 24 .7 13 .7 24 .0 27 .8 86 40 .3 42 .7 42 .3 41 .2 43 .1 41 .9 40 .0 40 . 9 38 .3 38 .4 40 .9 39 .7 87 38 .6 36 .8 38 .3 39 . 0 33 .9 37 .3 38 .9 34 .8 36 .2 36 . 1 30 .4 35 .3 88 24 .7 24 .4 30 .6 32 . 6 42 .0 30 .8 25 .7 19 .8 22 . 2 26 .0 39 . 3 26 .6 89 39 . 5 42 .8 42 . 3 41 . 1 30 .5 39 . 3 39 .8 41 . 6 40 .2 37 .2 28 . 2 37 .4 90 40 .5 44 .8 45 .1 43 . 1 46 .9 44 . 1 39 .8 42 .9 41 . 6 39 .4 44 .3 41 . 6 91 42 .0 41 .6 44 .3 44 .4 43 .3 43 . 1 40 .4 38 . 1 39 . 3 40 .5 40 . 3 39 .7 92 41 .4 42 .0 42 .9 44 . 1 45 .6 43 .2 40 .6 40 .5 40 . 1 41 .3 41 .8 40 .9 93 39 .3 42 .3 41 .5 43 .4 44 .8 42 .3 40 .0 40 .7 39 . 1 40 .8 42 . 1 40 .5 94 40 .5 41 .5 43 .3 43 .7 44 .1 42 .6 39 .8 40 .5 38 .9 40 .6 41 .3 40 .2 95 39 .2 40 .5 39 .7 40 .2 39 .9 39 .9 39 .7 39 .0 36 .4 35 .7 35 .1 37 .2 96 39 .3 42 .2 42 .9 44 .2 45 .4 42 .8 39 .7 41 .0 41 .7 41 . 3 42 .6 41 .3 97 38 .6 39 .8 38 .3 41 .9 45 .2 40 .7 40 .0 38 .9 35 . 4 40 .5 42 .7 39 .5 98 36 .0 39 .8 40 . 6 39 .2 43 .5 39 .8 36 .2 37 .4 36 .4 33 .4 40 .2 36 .7 99 42 . 0 40 . 1 39 .6 41 .3 42 .7 41 .1 41 . 1 37 .6 37 .4 36 .5 39 . 1 38 .4 100 39 .8 42 .4 42 .6 42 .0 43 .8 42 .1 41 .5 41 .5 40 .5 39 .5 40 .6 40 .7 101 40 . 0 43 .2 43 .8 42 .9 44 .9 42 .9 41 . 0 41 .9 41 . 0 40 . 2 42 . 2 41 . 2 102 38 .7 39 .9 39 .4 38 .5 39 . 1 39 .1 40 .0 39 .4 36 .1 33 .9 31 .4 36 .2 103 40 .2 43 .0 43 .3 45 .5 45 .4 43 .5 38 .4 39 .5 39 .9 39 .7 42 . 1 39 .9 104 40 .7 41 .2 41 .4 43 .7 44 .6 42 .3 40 . 1 39 .2 38 .7 40 .0 40 .7 39 .8 105 40 .8 43 .4 43 .6 42 .7 45 .3 43 .2 41 . 1 41 .9 41 .1 38 .9 43 .3 41 .2 106 39 .0 42 .6 43 .0 44 .2 37 .2 41 .2 39 .0 40 .8 39 . 1 40 .1 33 .6 38 .5 107 40 .6 43 .6 44 .7 43 .9 43 .5 43 .3 40 .5 41 .2 40 .8 40 .8 41 .1 40 .9 108 40 .9 42 .8 42 .8 42 .8 44 .6 42 .8 39 .4 40 .5 39 . 0 40 .2 42 .2 40 .3 109 35 .2 37 .2 40 .7 45 .0 44 .2 40 .5 33 .8 31 .5 37 . 6 41 .9 41 .7 37 .3 110 39 .3 42 .0 41 .7 40 .5 42 .3 41 .2 39 . 1 40 .3 39 .1 38 .2 40 .6 39 .5 111 39 .2 42 .4 43 .6 44 .4 44 .5 42 .8 39 . 1 39 .9 39 .8 40 .5 40 .9 40 .0 JUNO4 JUN23 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 112 39.2 39.8 37.9 39.7 33.3 38.0 113 39.6 45.1 45.4 44.1 45.3 43.9 114 40.1 43.6 43.4 43.0 44.3 42.9 115 39.9 43.0 42.6 42.5 41.8 42.0 116 39.7 44.8 43.1 43.4 45.1 43.2 117 40.4 43.3 41.6 42.8 42.4 42.1 40.3 38.2 33.9 35.6 25.3 34.7 38.7 42.1 41.6 39.6 41.5 40.7 41.2 41.1 39.0 39.2 40.6 40.2 41.7 40.6 39.8 39.2 40.4 40.3 40.0 41.2 39.3 41.1 41.7 40.7 41.1 40.7 39.8 38.7 39.7 40.0 JUL05 JUL23 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 41 .1 42 .3 40 .5 40 .2 42 .2 41 .3 39 .0 42 .6 42 .1 42 .0 43 . 0 41 .7 81 41 .1 41 .6 40 .3 41 .5 43 .4 41 .6 38 .7 42 . 1 41 .2 42 .7 43 .5 41 .6 82 40 .7 42 . 1 40 .9 40 .1 40 .6 40 .9 39 .0 42 .4 42 .0 42 .0 43 .7 41 .8 83 42 .5 44 .5 42 .3 41 . 1 43 .5 42 .8 40 .8 45 .5 43 . 0 43 .2 44 .8 43 .4 84 40 .2 40 .4 38 .8 35 .1 34 .8 37 . 8 38 .1 40 . 8 40 .0 37 .4 36 . 3 38 .5 85 39 .2 37 .5 24 .5 12 .7 22 .3 27 . 2 37 .1 37 .9 26 . 6 14 .2 24 .4 28 . 1 86 40 .7 42 .0 38 .3 37 .5 41 .2 39 .9 38 .2 40 .9 40 .0 39 .8 41 .5 40 . 1 87 38 .7 35 .2 35 .8 35 .6 30 .3 35 . 1 37 .3 35 .5 37 .5 37 .2 31 .6 35 .8 88 27 .0 20 .0 21 .3 23 .9 38 .4 26 . 1 24 .5 21 .6 24 .8 25 .2 40 .3 27 . 3 89 39 .4 41 .8 39 . 5 37 .3 28 . 3 37 .3 37 . 6 41 .4 41 .4 40 .4 27 . 6 37 .7 90 40 .6 42 .2 41 .9 38 .7 43 .31 41 .4 37 .3 42 .8 43 .9 42 .4 44 .9 42 .2 91 42 .4 39 .0 40 .1 40 .7 40 .1 40 .5 41 .6 40 .7 42 . 2 42 .4 40 .9 41 .6 92 41 .5 41 . 3 40 .5 40 .9 42 .1 41 .3 39 .2 41 .2 41 .8 42 .5 43 .5 41 . 6 93 40 .9 41 .3 39 .3 40 . 1 41 .3 40 .6 38 . 1 41 . 1 40 .6 42 . 1 43 .3 41 .0 94 40 .6 40 . 0 39 .3 40 .2 41 .8 40 .4 40 . 3 40 .7 41 .4 42 .4 42 . 3 41 .4 95 40 .5 39 .6 36 .6 36 .2 34 . 6 37 .5 38 .2 39 .4 39 .2 37 .9 35 .5 38 . 0 96 40 .8 42 .3 41 .9 41 .0 41 .8 41 .6 37 .4 41 .4 41 .9 43 .7 43 .8 41 .6 97 40 .7 39 .5 36 .6 39 .4 43 .3 39 .9 39 .4 39 .7 37 .8 41 . 1 44 .0 40 .4 98 37 .2 38 .5 36 .8 32 .9 39 .3 36 .9 36 .2 37 .8 39 . 1 36 .9 40 .5 38 . 1 99 41 .2 37 .8 36 . 3 37 .5 38 .5 38 .3 41 .2 38 .9 39 .0 40 .4 40 . 1 39 .9 100 42 .0 42 .6 40 .8 40 .2 40 .9 41 .3 39 .6 42 .2 41 .5 41 .8 42 .3 41 .5 101 42 .5 42 .9 42 . 1 41 . 1 42 .5 42 .2 38 .7 42 .4 42 .1 42 .2 43 .6 41 .8 102 40 .9 40 .6 36 .6 34 .1 29 .6 36 .4 38 .6 39 .2 38 .6 34 .7 31 .3 36 .5 103 40 .0 40 .3 40 .2 40 .1 43 . 0 40 .7 38 .7 40 .9 42 . 6 42 . 0 43 .8 41 .6 104 40 .8 4 0 .6 39 .3 39 .4 40 . 6 40 .1 40 .0 39 . 0 39 .2 41 .9 42 .0 40 .4 105 41 .6 41 . 1 40 .6 38 .7 43 .0 41 .0 39 .7 42 .2 41 .6 41 . 1 44 .0 41 .7 106 39 .5 41 . 1 39 .3 40 .4 33 .4 38 .7 37 .5 41 .3 40 .8 43 .2 35 .0 39 .6 107 41 .0 40 . 6 40 . 3 39 .3 41 .4 40 .5 39 . 6 41 .8 41 .6 41 .5 40 .9 41 .1 108 41 .2 40 .4 37 .3 40 .4 42 .0 40 .3 38 .4 41 .4 40 .8 41 .8 42 .5 41 .0 109 33 .7 30 .7 36 .6 41 .9 41 .8 36 .9 31 .8 34 .8 40 .6 42 .9 41 .2 38 .3 110 39 .5 41 . 1 39 .8 38 .4 41 .0 40 .0 36 .8 39 . 3 40 .4 40 .5 41 .9 39 .8 111 40 . 0 40 .4 39 .6 40 .4 41 .6 40 .4 38 .7 41 .0 41 .7 41 .8 41 .8 41 .0 112 41 .0 39 .0 35 .0 36 .4 24 .6 35 .2 38 .0 38 .8 36 .6 38 .7 24 .5 35 .3 113 40 .8 42 .7 41 .5 39 .3 40 .8 41 .0 38 .9 43 .7 44 .4 42 .9 42 .1 42 .4 JUL05 JUL23 % v/v • % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 114 42.0 42.2 40.7 40.3 41.3 41.3 115 42.5 40.6 40.6 39.7 40.6 40.8 116 41.6 41.9 40.6 40.5 42.2 41.4 117 41.9 41.1 39.8 38.4 39.1 40.1 39.4 41.7 41.8 41.5 42.5 41.4 40.2 40.4 41.7 40.9 41.2 40.9 38.5 41.8 42.2 41.7 43.8 41.6 39.2 41.6 41.1 40.7 40.6 40.6 AUG05 AUG19 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 35 .0 38 .4 39 .1 40 .1 42 .1 39 .0 39 .4 41 .5 41 .7 41 . 1 43 .0 41 .3 81 33 .6 38 .5 28 .9 40 .6 42 .4 36 .8 38 .0 41 .5 40 .1 42 . 1 42 .0 40 .7 82 31 .9 38 .0 38 .4 39 .3 40 .4 37 .6 38 .3 43 . 4 42 .7 42 .3 44 .2 42 .2 83 33 .1 39 .9 38 .6 40 .5 44 . 5 39 .3 39 .2 43 .7 40 .7 40 . 6 43 .5 41 . 5 84 33 .4 37 . 1 37 .4 34 .2 34 .2 35 .3 37 .3 40 .3 39 .6 35 .2 32 .2 36 .9 85 32 .3 30 .8 18 .8 10 .5 20 .9 22 .6 37 .0 38 .5 27 .7 15 .0 23 .2 28 . 3 86 28 .7 36 .5 37 .0 36 .9 39 .9 35 .8 33 .8 38 .9 37 .3 37 .3 38 .6 37 .2 87 32 .6 29 . 0 34 . 3 34 .6 27 .8 31 .7 37 .4 36 .1 37 .6 37 .2 31 .8 36 . 0 88 15 .7 12 .9 16 .5 21 .7 38 .2 21 .0 22 .6 17 .1 18 . 1 21 .0 37 .4 23 .2 89 32 .4 38 .5 37 .8 34 .4 25 .2 33 .7 36 .6 42 .3 41 .6 40 .3 27 .4 37 .7 90 29 .7 38 .4 39 .9 38 . 1 43 .4 37 .9 38 .3 41 .7 41 .6 4 l .4 32 .2 39 . 0 91 35 .6 31 .8 38 .3 40 .4 40 .3 37 .3 39 .2 36 .0 39 .0 39 .9 40 .5 38 .9 92 32 .1 34 .5 38 .7 40 .5 41 .3 37 .4 39 .6 41 .0 42 .0 43 .4 44 .4 42 . 1 93 30 .3 36 • 5 37 .6 39 .9 41 .9 37 .2 36 .7 40 .6 39 .2 39 .3 40 .9 39 .4 94 31 .2 36 .9 38 .2 39 .7 41 . 6 37 .5 36 .0 38 .2 39 .5 39 .9 41 .6 39 .0 95 32 .8 36 .0 33 .4 34 . 1 33 . 1 33 .9 36 .5 37 .6 33 . 1 32 .4 32 .0 34 . 3 96 31 .3 36 .5 38 .3 40 .3 42 .2 37 .7 34 .8 38 .8 39 .3 40 . 1 42 .1 39 .0 97 32 .5 33 .9 31 .5 39 .5 42 .9 36 .1 38 . 3 38 .6 35 . 1 39 .9 43 .3 39 .0 98 28 .5 34 . 1 34 .2 32 . 1 38 .8 33 .5 31 .4 34 .5 33 .5 29 .8 36 .7 33 .2 99 35 .6 32 .7 33 .5 36 .1 38 .5 35 .3 40 .3 38 .9 36 .3 36 .7 38 .2 38 . 1 100 31 . 0 36 . 1 39 . 3 38 .5 4 0 .3 37 . 0 42 . 0 43 . 3 41 .7 41 .7 41 .2 42 . 0 101 32 .4 36 .5 39 .0 40 .7 42 .5 38 .2 41 . 1 44 . 0 43 .0 43 . 3 43 . 3 42 .9 102 30 . 1 34 .7 35 . 1 30 .8 27 .4 31 .6 38 .9 40 . 3 38 .8 33 .7 26 .5 35 .6 103 30 .2 33 .8 39 .2 39 .4 42 .6 37 .0 36 .8 39 .4 40 .2 39 .6 42 .3 39 .7 104 35 .0 36 .3 35 .8 39 .2 39 .6 37 .2 39 .9 40 .8 41 . 1 43 .4 43 .0 41 . 6 105 35 . 3 34 .0 38 .0 38 .2 42 .9 37 .7 40 .3 43 .4 42 .9 42 . 3 46 .0 43 .0 106 30 .7 38 .3 37 .6 40 .4 31 .9 35 .8 36 .6 42 .8 43 .2 42 .3 45 .7 42 . 1 107 30 .9 38 . 1 38 .4 39 .5 40 .3 37 .5 40 . 1 42 .1 43 .3 42 .8 41 .9 42 .0 108 31 .3 34 .8 38 .3 39 .6 42 .2 37 .2 39 . 1 41 .5 41 .6 42 . 1 43 .8 41 .6 109 24 .6 26 .1 35 .9 42 .2 41 .9 34 .1 33 . 3 35 .4 41 .3 43 .9 43 .6 39 .5 110 29 .8 36 .5 37 .6 37 .2 39 .5 36 . 1 38 .3 40 .5 40 .4 39 .7 40 .2 39 .8 111 32 . 1 37 .0 38 .6 40 .7 40 .8 37 .8 39 .6 42 . 6 42 . 6 43 .8 42 . 3 42 .2 112 33 . 2 34 .2 30 .2 34 . 0 22 .3 30 .8 38 . 3 39 . 8 37 .2 38 .4 23 . 6 35 .5 113 30 .9 38 .1 39 .7 38 . 3 41 .3 37 .7 38 .6 44 .2 44 .9 42 .9 43 .4 42 .8 114 32 .9 38 .4 38 .4 39 . 6 40 .5 38 .0 40 .7 43 .2 42 . 6 42 .5 44 .0 42 . 6 115 33 .6 38 .3 37 .5 25 .6 37 .7 34 .5 40 .3 41 .9 42 .2 41 .4 41 .4 41 .5 227 AUG05 AUG19 % v/v % v/v •— Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 116 33.0 38.4 38.0 39.4 42.1 38.2 40.0 43.8 41.4 41.9 43.1 42.0 117 35.0 36.9 36.4 36.2 39.2 36.7 40.2 41.8 41.5 41.0 40.6 41.0 AUG29 SEP09 % v/v— % v/v Si t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 34 .5 39 .4 38 .4 39 .4 41 .4 38 .6 34 .9 39 .1 37 .2 38 .2 41 .7 38 .2 81 32 .9 37 .5 38 .7 39 .5 42 . 1 38 . 1 32 .5 36 .6 37 . 1 40 . 2 42 . 5 37 .8 82 32 .9 37 .5 38 .7 39 .2 41 .8 38 . 0 31 .9 37 .4 38 . 3 39 .1 42 . 1 37 .7 83 33 .1 38 .6 38 . 1 39 .7 43 .5 38 .6 32 .4 38 .0 37 .8 40 .4 43 .3 38 .4 84 32 .0 36 .0 36 .2 31 .5 30 .7 33 .3 31 .6 36 .3 35 .2 30 .7 32 . 1 33 .2 85 32 .5 32 .0 19 .8 11 .2 20 .8 23 .3 31 .3 29 .9 16 .3 9 .7 19 .2 21 .3 86 26 .1 34 .7 35 .1 35 .4 36 .5 33 .6 26 .0 33 .8 35 .9 35 .0 35 .0 33 .1 87 32 .4 28 .9 34 .3 34 .0 26 .6 31 .2 32 .8 28 .4 33 . 1 33 .2 23 .4 30 .2 88 13 .0 10 .7 14 .0 19 .1 36 .3 18 .6 12 .3 9 .6 12 .3 17 .8 31 .4 16 .7 89 32 .5 39 .4 37 .3 34 .8 24 .0 33 .6 33 .5 39 .4 37 . 3 33 .3 22 .5 33 .2 90 30 .7 38 .6 40 .1 38 .8 44 .4 38 .5 30 . 1 37 .5 39 . 3 34 .6 42 .9 36 .9 91 32 .2 28 . 2 36 .9 39 .7 39 .7 35 .3 30 .9 26 .6 35 .5 38 .8 38 .9 34 . 1 92 34 .2 35 .5 39 .5 40 .7 42 . 3 38 .5 34 .2 35 . 1 39 . 1 40 .4 41 .5 38 . 1 93 29 .7 36 .4 36 .6 37 .8 39 . 8 36 .1 29 .2 36 . 1 36 .8 37 . 0 38 .4 35 .5 94 29 .0 34 .0 36 .9 39 .0 40 .9 36 .0 27 .9 33 .3 36 .7 39 .4 41 .5 35 .7 95 29 .8 32 .5 28 .7 28 .7 30 .5 30 .0 29 . 3 31 .7 26 . 3 26 .7 29 .0 28 .6 96 28 .5 33 .7 38 .1 38 .7 40 .7 35 .9 27 .9 32 .8 36 .3 39 • 3 39 .8 35 .2 97 32 .0 32 .3 31 .4 38 .5 43 .1 35 .5 32 .3 31 .5 30 .0 39 .3 42 .9 35 .2 98 24 .1 30 .8 30 .3 27 .9 35 .6 29 .7 24 .3 28 .6 3 0 .7 27 .7 33 .5 29 .0 99 36 .0 32 .5 32 .6 34 .4 37 .4 34 .6 35 .9 31 .2 31 .2 33 .9 37 .8 34 . 0 100 35 .0 38 .5 39 .8 39 .2 40 .7 38 .6 35 .5 37 .2 38 .6 39 .0 38 .8 37 . 8 101 34 .2 37 .7 39 .2 40 .3 41 .8 38 .7 32 .9 36 .4 38 .7 40 .5 41 .4 38 .0 102 34 .0 36 .3 35 .5 29 .5 25 .0 32 .0 33 .2 35 .7 33 .8 28 . 1 23 .1 3 0 . 8 103 30 . 4 33 .7 39 . 3 39 .4 41 .8 36 .9 29 . 1 32 .4 38 .5 38 .6 42 .4 36 . 2 104 37 .4 37 .1 36 .7 40 .3 41 .0 38 .5 37 .7 36 .8 36 .4 38 .6 40 . 0 37 .9 105 35 .5 35 .6 39 .0 39 .6 43 .0 38 .5 36 .0 32 .9 37 .9 38 .5 43 .3 37 .7 106 29 .4 37 .8 37 .5 40 . 0 30 .7 35 .1 30 .5 37 . 6 36 .7 39 .2 29 .6 34 .7 107 33 .3 39 .1 40 .2 39 .9 40 .8 38 .7 32 . 1 38 . 6 39 .6 40 .0 39 .6 38 . 0 108 33 .1 37 .2 39 .2 40 • 0 42 .8 38 .5 31 .2 35 .5 37 .8 40 .4 42 .5 37 .5 109 24 .4 26 .6 36 .7 41 .6 42 .2 34 .3 22 .5 23 .2 34 .6 42 .3 41 . 1 32 .7 110 32 .2 37 .1 37 .8 36 .5 38 .7 36 .5 30 .3 36 .9 37 .5 35 .5 36 .7 35 .4 111 33 .2 37 .0 39 .4 41 .3 40 .6 38 . 3 31 .4 36 . 0 39 .3 40 . 1 40 .0 37 .4 112 34 .5 34 .3 30 . 1 34 .7 22 .0 31 . 1 33 .8 33 .9 28 .9 33 .4 21 .2 30 .2 113 33 .3 40 . 1 40 .9 38 .8 42 .2 39 .0 31 .6 38 .2 39 .5 37 .4 41 .8 37 .7 114 35 .2 39 .7 39 .2 40 .1 40 .3 38 .9 34 .9 39 .9 39 .4 39 .7 39 .2 38 .6 115 35 .9 39 .2 38 .9 39 .0 38 .6 38 .3 35 .8 39 .7 38 .9 38 .5 39 .3 38 .4 116 34 .8 39 . 1 38 .6 40 .3 42 . 1 39 .0 33 .3 39 . 1 38 . 1 39 .8 41 . 1 38 .2 117 35 .7 38 .4 38 .2 37 .5 37 .9 37 .5 35 .7 38 .3 36 .9 35 .9 37 . 1 36 .8 SEP20 OCT07 % v/v % v/v S i t e 15cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 39 .6 42 .2 42 .9 42 .1 43 .7 42 . 1 41 .0 43 .2 42 .5 42 .3 42 .5 42 .3 81 40 .7 42 .1 40 .9 42 .6 42 .3 41 .7 41 .3 43 .3 42 . 1 43 .0 43 .3 42 .6 82 40 . 1 43 .7 42 .6 42 .8 44 .4 42 .7 41 .3 43 .9 42 .2 42 .2 43 .6 42 .6 83 41 .6 45 .7 43 .2 42 .6 44 .1 43 .4 43 .5 45 .3 43 .5 42 .7 44 .0 43 .8 84 39 .2 40 .3 40 .4 37 . 6 34 .4 38 .4 40 .4 41 .5 40 . 3 38 .2 39 .1 39 .9 85 37 .6 39 .0 27 .4 14 .6 22 .9 28 .3 39 .2 38 .8 30 . 1 20 .7 37 .9 33 .3 86 39 .3 41 .5 39 .9 39 .4 39 .1 39 .9 41 .6 42 .9 40 .4 39 .8 41 .8 41 .3 87 38 .7 36 .8 37 .9 38 .3 32 .2 36 .8 38 .6 37 .9 38 .1 38 .1 35 .0 37 .5 88 24 .9 20 .7 22 .1 20 . 6 35 .7 24 .8 27 .4 27 .6 32 . 1 30 .9 39 .7 31 .5 89 38 .9 43 .2 41 .9 39 .9 30 .7 38 .9 37 .7 42 .3 41 .5 40 .9 29 .5 38 . 4 90 39 .6 43 .4 44 .1 43 .3 45 . 0 43 . 1 40 .6 43 .9 44 . 1 42 .0 45 .4 43 .2 91 40 .8 39 .6 40 .7 40 .8 39 .9 40 .3 42 .8 41 .4 42 .9 43 . 1 41 .7 42 .4 92 40 .9 43 .0 42 .9 43 .1 44 .1 42 .8 42 .7 42 .4 42 .9 43 .0 44 .5 43 .1 93 38 .6 40 .7 38 .2 37 .8 37 .9 38 .6 41 .0 42 . 1 41 .2 42 . 1 42 .1 41 .7 94 38 .9 42 .0 42 .0 42 .2 42 .2 41 .5 42 .4 42 .4 41 .9 42 .7 42 .6 42 .4 95 36 .5 36 .5 29 .2 26 .3 28 .2 31 .3 40 .3 40 .7 39 .1 38 .3 32 .5 38 .2 96 39 .2 42 .7 43 .7 43 .1 44 .4 42 . 6 39 .7 43 .0 42 .7 43 .4 43 .6 42 .5 97 40 .4 40 .1 38 .7 41 .3 44 .0 40 .9 41 .0 40 .3 38 .5 41 .8 44 .0 41 . 1 98 37 .8 39 .7 38 .6 33 .9 35 .6 37 . 1 38 .4 39 .8 40 .0 38 .1 42 .2 39 .7 99 41 . 8 40 .0 40 . 5 40 .8 41 .4 40 .9 41 .8 39 . 9 41 . 1 40 .9 41 .2 41 .0 100 41 .6 43 .3 41 .8 42 .1 43 .0 42 .4 42 .5 43 . 3 42 .0 41 .7 42 .2 42 . 3 101 40 .7 44 .2 43 . 3 43 .4 44 . 1 43 .2 41 .9 43 . 1 43 . 1 43 . 1 43 .4 42 .9 102 39 .5 40 .2 38 .9 34 .3 26 .2 35 .8 41 .0 40 .9 39 .3 37 .2 36 .5 39 .0 103 40 .3 42 .7 42 .7 42 .3 44 .7 42 . 6 39 .3 42 .5 42 .5 42 .5 43 .7 42 . 1 104 40 .9 40 .7 40 .8 42 .2 43 .5 41 . 6 40 .5 40 . 6 40 .7 42 .3 43 . 1 41 .5 105 41 .7 43 .6 43 .3 41 .9 46 .3 43 .4 42 .0 43 . 1 42 .3 41 .5 45 .5 42 .9 106 40 .3 42 .2 42 .6 41 .7 36 .0 40 .5 40 .7 42 .9 41 .7 42 .4 36 .0 40 .8 107 40 .9 42 .2 43 .0 42 .1 41 .5 41 .9 42 .2 42 . 6 42 .9 42 .1 42 .2 42 .4 108 41 .4 42 .0 41 .7 42 .0 43 .7 42 .2 41 .7 42 .6 41 .5 41 .3 42 .9 42 .0 109 33 .2 33 .4 39 .3 42 .0 41 .8 37 .9 35 .7 37 . 1 41 .9 43 .5 42 .4 40 .1 110 38 .3 41 .5 41 .3 39 .9 41 .7 40 .6 39 .8 41 .3 40 . 8 39 .4 41 .5 40 .6 111 40 .4 43 . 1 43 .1 43 . 1 43 .2 42 .6 40 .7 42 .5 43 . 1 42 .5 41 .9 42 .2 112 39 .8 39 . 6 37 .7 38 .5 24 .2 36 . 0 40 .2 39 .8 37 .9 39 .1 34 .1 38 .2 113 39 .3 44 . 3 45 .0 43 . 1 43 .4 43 .0 41 .4 44 .6 44 . 6 42 .9 43 .5 43 .4 114 40 . 6 43 . 1 42 .0 42 .7 43 .2 42 .3 41 . 6 43 .3 42 .4 42 .6 43 .1 42 .6 115 41 .5 43 . 3 42 .2 41 .5 41 .8 42 . 1 42 .5 42 . 6 42 . 4 40 .7 41 . 1 41 . 8 116 42 . 0 44 .2 42 .0 42 . 1 43 .5 42 .8 42 .2 43 .8 41 .8 43 .0 44 .1 43 .0 117 41 .4 42 .3 42 .0 42 .3 42 .5 42 . 1 41 .3 42 .0 41 .9 41 .2 42 .5 41 .8 Theta values f o r 76 dryland s i t e s , 1987 APRIL29 MAY26 . % v / v _ _ % v / v  S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 30 .8 29 .2 28 .9 29 .3 27 .0 29 .0 30 .2 29 .6 29 .5 30 .0 25 .9 29 .1 2 32 .2 28 .7 28 .2 28 .9 29 .4. 29 .5 31 .7 29 .1 28 .4 28 .9 30 .0 29 .6 3 30 .6 21 .5 19 .9 18 .2 13 .7 20 .8 30 .5 19 .3 17 . 6 16 .5 13 .0 19 .4 4 30 .6 28 .7 28 .5 27 .4 25 .8 28 .2 30 .0 28 .8 28 .2 27 .4 25 .6 28 .0 5 31 .9 30 .8 29 .9 29 .7 28 .2 30 .1 31 .6 30 .2 29 .2 29 .5 28 .9 29 .9 6 31 .3 28 .6 29 .0 28 .5 29 .6 29 .4 31 .2 28 .9 29 . 0 28 .9 30 .6 29 .7 7 30 .3 29 .2 29 .8 30 .7 31 .3 30 .3 30 .6 29 .4 30 .0 31 .4 31 .9 30 .7 8 33 .4 29 .9 29 .6 29 .3 30 .8 30 .6 32 . 3 30 .2 29 .3 30 .2 31 .9 30 .8 9 29 .5 28 .5 29 .4 30 .2 30 .9 29 .7 29 .6 29 .0 29 . 3 30 .5 31 .0 29 .9 10 32 .4 29 .6 30 .0 30 .8 31 .1 30 .8 30 .8 29 .7 30 .2 31 .0 32 . 1 30 .8 11 31 .8 30 .1 29 .7 30 .2 30 .8 30 .5 31 .2 29 .6 30 . 3 30 .8 31 .7 30 .7 12 32 .0 29 .5 28 .9 28 .3 15 .2 26 .8 31 .2 29 . 1 29 . 0 28 .6 15 .0 26 .6 13 31 .9 27 .4 24 .6 14 .7 1.3 .8 22 .5 30 .8 26 .9 24 . 3 13 .8 14 .0 22 .0 14 30 .4 29 . 6 28 .8 30 .0 30 .6 29 .9 31 .7 29 .9 29 .5 30 .2 31 .6 30 . 6 15 33 . 0 28 .9 28 .3 27 .2 19 .6 27 .4 31 .6 28 . 2 27 .6 27 .5 18 .4 26 .7 16 33 .0 27 .7 27 .5 16 .1 15 . 1 23 .9 32 .1 27 .5 27 .7 14 .7 14 .1 23 .2 17 30 . 5 27 . 6 26 .9 26 .9 24 .9 27 .4 30 . 3 27 .4 25 .7 26 . 6 24 .4 26 .9 18 33 .3 29 . 0 29 .0 29 .6 28 .6 29 .9 32 .2 29 .4 29 .7 29 .7 28 .6 29 .9 19 32 .2 28 .8 28 .8 28 . 1 29 .5 29 .5 30 .7 28 .9 28 .7 28 .5 30 .2 29 .4 20 32 .6 28 .9 29 .5 29 .3 29 . 1 29 .9 32 . 2 29 .4 30 .1 29 .9 29 .9 30 .3 21 30 .8 23 .5 20 .5 24 .9 26 .5 25 .2 30 .5 22 .9 20 . 1 25 .7 27 . 1 25 . 3 22 30 • 5 25 .3 13 .8 9 .2 19 . 8 19 .7 30 .7 23 .5 11 .4 8 .3 18 .4 18 .4 23 32 .2 28 .4 27 .5 26 .2 27 .8 28 .4 32 .5 28 .3 27 .8 26 .6 28 .1 28 .7 24 30 .7 25 .5 24 .3 18 .3 9 .4 21 .7 30 .7 25 .0 24 . 1 17 .9 9 .1 21 .4 25 30 .3 28 .4 27 .9 28 . 1 16 .4 26 .2 30 .2 29 .0 28 .6 28 .2 16 .1 26 .4 26 30 .8 30 .0 30 .3 30 .2 30 .7 30 .4 31 .8 29 .8 30 .6 30 .8 31 .1 30 .8 27 31 .2 28 .8 29 .1 28 .7 30 .2 29 .6 30 .9 29 .0 29 .5 29 .2 30 .6 29 .8 28 31 .9 30 . 3 29 .3 30 .4 29 .9 3 0 .4 30 . 6 30 .7 29 .2 30 .5 31 . 0 30 . 4 29 30 .8 28 .6 28 .3 27 . 1 27 .1 28 .4 31 .3 29 .2 28 .9 27 .2 25 .7 28 .5 30 32 .0 30 .0 29 .8 29 .2 29 .8 3 0 .2 32 . 1 30 .5 30 .3 29 .3 30 .6 30 .6 31 14 .3 12 .3 13 .7 19 .5 24 .8 16 .9 11 .5 10 .9 12 .4 17 .6 25 .2 15 .5 32 30 .2 25 .3 19 .3 21 . 1 25 .0 24 .2 30 .1 24 .6 17 .8 ,19 .2 24 .8 23 .3 33 33 .0 30 .3 29 .7 29 .7 28 . 3 30 .2 32 .5 30 .7 29 .6 30 .6 28 .7 30 .4 34 14 .5 11 .3 14 . 6 20 .8 25 . 0 17 . 2 11 .5 10 .4 13 . 3 19 .8 24 .9 16 . 0 35 28 .3 29 .5 28 . 1 20 .6 24 .4 26 .2 27 .4 29 .2 28 .0 19 .8 23 .9 25 .7 36 17 .9 16 .5 21 .1 21,. 0 13 .6 18 .0 16 .5 14 .7 18 .5 19 .9 13 .6 16 .6 37 31 .2 28 .2 29 .0 27 .7 20 .5 27 .3 30 .6 28 . 1 29 .3 27 .9 20 .7 27 .3 38 32 .5 29 .3 28 .9 29 .2 27 .1 29 .4 31 .9 29 .3 29 .2 29 .2 27 .8 29 .5 39 30 .6 29 .4 29 .1 28 .9 27 .3 29 . 1 30 .5 29 .4 29 .4 29 .4 27 .6 29 .3 40 30 .4 30 . 3 29 .8 30 .2 30 .1 30 . 1 29 .9 30 .5 30 .5 30 .9 31 .2 30 .6 41 32 .4 29 .5 30 .1 30 .0 31 .6 30 .7 30 .5 29 .0 29 .5 30 .4 32 .1 30 .3 42 30 .0 28 • 9 29 .0 29 .8 30 .9 29 .7 30 .9 28 .8 29 .4 30 .3 31 .9 30 .3 43 31 . 1 28 .3 29 .4 30 .4 31 . 1 30 .1 31 .1 28 .1 29 .5 30 .9 31 .7 30 .2 2 3 0 APRIL29 — % v/v-MAY2 6 •-% v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 44 32 .2 28 .8 29 .5 30 .2 31 .6 30 .4 32 .0 28 .7 29 .6 30 .4 32 .0 30 .5 45 31 .2 28 .4 29 .3 28 .2 29 .7 29 .3 30 .1 28 .2 28 .7 29 .0 30 .2 29 .2 46 31 . 6 31 .0 29 .0 29 .8 30 .7 30 .4 32 .0 30 .9 29 .0 30 .2 31 .8 30 .8 47 31 .9 28 .0 27 .6 27 .8 27 .6 28 .6 30 .7 27 .5 26 .9 28 .0 28 .1 28 .3 48 29 .7 28 .3 28 .5 29 .0 27 .7 28 .6 28 .5 28 .4 28 .6 28 .9 27 .7 28 .4 49 32 .8 28 .4 28 .3 28 . 1 28 .9 29 .3 32 .4 28 .9 28 .3 28 .1 29 .4 29 .4 50 32 .0 29 .7 28 .6 27 .5 27 .2 29 .0 31 .2 29 .3 29 .0 27 .5 26 .7 28 .7 51 31 .1 30 .0 28 .6 29 .9 30 .1 30 .0 31 .5 29 .3 28 .8 29 .8 30 .2 29 .9 52 30 .2 28 .6 28 .4 29 .7 30 .8 29 .5 29 .1 28 .7 28 .4 29 .6 31 .4 29 .4 53 31 .0 28 .2 28 .4 28 .2 21 .5 27 .5 30 .3 27 .9 28 . 6 28 .4 21 .6 27 .4 54 31 .8 29 .8 29 .3 29 .8 31 .0 30 .3 30 .4 29 .3 29 .7 29 .7 31 .5 30 . 1 55 32 .5 31 . 1 29 .8 29 .4 31 .0 30 .8 31 .1 30 .8 30 . 1 29 .6 31 .9 30 .7 56 32 .7 29 .2 27 .8 22 .7 28 .0 28 . 1 31 .3 29 .0 27 . 6 22 .7 27 .4 27 .6 57 30 . 1 27 .7 28 .3 28 .9 30 .4 29 . 1 29 .7 27 .8 28 .3 29 .3 30 .5 29 . 1 58 32 .2 28 .7 28 .7 29 .2 30 .4 29 .9 31 .7 28 .2 28 .7 29 . 1 30 .5 29 .6 59 31 .8 28 .5 28 .0 30 .0 30 .4 29 .8 31 .5 28 .0 29 .0 29 .6 31 .1 29 .8 60 32 .1 28 .7 28 .6 30 .1 30 .7 30 . 0 30 .8 28 .5 28 .9 30 .0 31 .0 29 .8 61 32 . 1 27 .7 28 . 1 27 .8 26 .4 28 .4 31 .8 27 .4 28 .1 27 .7 25 .8 28 .2 62 28 . 3 27 .8 22 .8 20 .4 14 . 1 22 .7 27 .8 27 .3 22 .4. 19 .7 13 . 6 22 .2 63 30 . 8 30 .5 29 .7 30 .2 30 . 8 30 .4 30 . 5 30 .9 30 . 3 30 . 5 31 .1 30 . 7 64 29 .7 29 .9 31 .2 30 .3 30 .8 30 .4 30 .2 29 .6 31 .6 30 .6 31 .3 30 .7 65 31 .8 23 .9 27 . 6 30 .4 30 . 1 28 .8 31 .4 23 .4 2 8 .2 31 . 3 30 .9 29 .0 66 28 .5 27 .2 28 .4 24 .9 10 .9 24 . 0 27 .7 26 . 8 28 .5 25 .4 10 .7 23 .8 67 32 .9 29 .2 28 .6 29 .7 31 .2 30 .3 31 . 1 28 .7 29 .3 29 .2 31 .9 30 .0 68 32 .7 30 .3 28 .7 29 . 0 31 .6 30 .5 32 .4 30 . 1 29 . 0 29 .9 31 .8 30 .6 69 32 .0 27 .8 29 .4 30 .9 31 .2 30 .3 31 .8 27 .6 29 .2 30 .8 32 .3 30 .3 70 32 .6 29 .3 28 .3 29 .3 31 .2 30 .2 31 .4 29 .2 28 .5 29 .8 31 . 1 30 . 0 71 30 .6 25 .8 27 .7 28 .6 30 .5 28 .7 30 . 1 25 .7 27 .7 29 . 1 30 .4 28 .6 72 31 .7 28 .0 28 .3 28 .7 30 .7 29 .5 30 .7 27 .8 28 . 1 29 .1 30 .9 29 .3 73 30 .6 25 . 3 26 .7 25 .3 28 .5 27 .3 29 .2 25 .1 26 .4 24 .6 29 .3 26 .9 74 29 .9 25 .9 23 .9 21 .0 19 .5 24 .0 29 .4 25 .2 23 .4 20 .3 17 .8 23 .2 75 33 .5 26 .7 26 .5 29 . 0 30 . 6 29 .2 31 .8 26 . 3 26 . 3 28 .9 31 .3 28 .9 76 33 .3 28 .9 27 .9 27 .7 30 .0 29 .6 32 .7 28 .9 27 .7 28 .2 30 .7 29 .6 S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 30.9 29.6 29.0 29.4 26.9 29.2 25.2 27.3 27.6 28.7 22.1 26.2 2 32.7 29.6 28.6 28.4 30.0 29.9 25.9 25.7 25.5 25.4 28.5 26.2 3 30.6 20.7 19.4 16.9 13.2 20.1 22.7 13.5 12.0 11.6 10.8 14.1 4 30.4 28.9 28.4 27.6 26.2 28.3 25.1 26.0 25.3 22.1 22.7 24.3 5 32.1 29.1 28.7 29.1 29.5 29.7 26.9 26.9 26.7 27.0 26.5 26.8 6 31.9 30.3 29.2 29.4 28.0 29.8 27.5 26.7 26.5 27.2 28.5 27.3 7 29.9 28.9 29.6 30.7 31.3 30.1 25.0 26.4 28.0 29.4 31.1 28.0 JUNE09 % v/v-JULY02 % v/v-JUNE09 JULY02 % v/v % v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 8 32 . 1 29 .5 29 .3 29 .8 31 .4 30 .4 28 .3 27 .2 27 .0 27 .3 29 .6 27 .9 9 31 .0 28 .8 29 .7 30 .2 30 .8 30 .1 23 .9 26 .1 27 .4 27 .7 28 .5 26 .7 10 31 .5 29 .3 29 .7 30 .6 31 .9 30 .6 26 .8 28 .3 28 . 1 28 .5 29 .9 28 .3 11 31 .5 29 .4 29 .5 30 .8 31 .7 30 .6 24 .4 26 .8 27 . 6 28 .0 29 .0 27 . 1 12 31 .9 29 .0 29 .3 28 .6 15 .4 26 .8 28 .1 27 . 1 26 .5 25 .5 11 . 1 23 .7 13 31 . 1 27 .3 24 .8 14 .9 10 .9 21 .8 26 .5 22 .5 17 . 0 7 .6 7 .3 16 .2 14 31 .7 30 .1 28 .7 29 .7 31 .2 30 .3 25 .2 27 .5 26 .9 27 .4 31 . 1 27 .6 15 32 .0 28 .2 27 .5 27 .0 18 .1 26 .6 28 .1 25 .2 24 .9 24 .7 14 .9 23 .6 16 32 .8 28 .0 27 .6 15 .8 13 .8 23 .6 27 .8 23 .4 23 . 3 11 .5 10 .8 19 .3 17 30 .7 27 .7 26 .6 26 .7 25 .3 27 .4 26 .6 23 .6 21 . 3 22 .0 19 .0 22 .5 18 32 .7 29 .2 29 .5 29 .4 28 .6 29 .9 26 .0 27 .4 27 .8 27 .5 26 .9 27 . 1 19 31 .8 28 .8 28 .8 28 .2 29 .9 29 .5 27 .9 27 . 1 25 .5 23 .4 30 .0 26 .8 20 32 .7 29 .4 29 .5 29 .5 29 .1 30 .1 25 .7 25 .3 27 .6 27 .3 27 .2 26 .6 21 30 .7 23 .6 20 .8 25 .9 26 .3 25 .5 25 .5 15 .6 14 .2 21 .4 24 .8 20 . 3 22 31 .3 25 . 1 12 .7 8 .9 19 .2 19 .5 23 . 1 15 .4 7 .7 6 .1 13 . 1 13 .1 23 32 .2 28 .4 27 .7 26 .6 28 .1 28 .6 28 .1 24 .8 24 .3 19 .4 25 .6 24 .4 24 30 .7 25 .2 24 .5 18 .7 9 .3 21 .7 24 .9 20 .0 18 . 1 11 .8 14 .2 17 .8 25 30 .7 29 . 1 27 .7 28 .7 16 . 3 26 .5 25 .5 27 . 1 23 . 1 24 .3 13 .4 22 .7 26 31 .7 30 .3 30 .3 30 . 3 30 .8 30 .7 28 .0 27 .0 26 .8 28 .7 29 .7 28 .0 27 30 .4 28 .9 29 .6 28 .4 30 .4 29 .5 25 .4 26 .5 27 .6 26 .8 29 .4 27 .2 28 31 .2 29 .5 29 .6 30 .4 29 .1 30 .0 26 . 0 25 .6 25 .4 28 .5 30 .7 27 .2 29 31 .4 28 .8 28 .6 27 .3 26 .3 28 .5 29 .3 27 .5 27 .2 24 . 0 16 .6 24 .9 30 32 .8 30 .0 29 .5 29 . 4 30 .2 30 .4 27 .2 26 . 6 28 . 3 28 . 5 28 .8 27 .9 31 14 .4 12 . 0 13 . 3 18 . 2 24 .4 16 . 5 3 .7 6 .9 7 .8 11 .8 21 .5 10 .3 32 30 .1 24 .8 18 . 6 20 .2 21 .9 23 . 1 23 .3 18 .8 12 . 8 12 .6 22 .4 18 .0 33 32 .6 30 .4 29 .6 29 .8 28 .8 30 .3 28 .3 26 .6 27 .4 28 .4 27 .4 27 .6 34 14 .0 10 .9 12 .1 20 .0 19 .8 15 .4 1 .3 5 .9 8 .9 15 .1 22 .0 10 . 6 35 28 .2 29 .2 28 .4 19 .9 24 .0 25 .9 20 .3 25 .5 24 . 6 15 . 6 20 .5 21 .3 36 18 .3 16 .6 21 .0 21 .6 13 .2 18 . 1 8 .2 9 .2 12 .3 15 .9 10 .8 11 .3 37 31 .5 28 .3 29 .2 27 .9 21 .4 27 .7 26 .7 26 . 1 27 .3 24 .2 16 .7 24 .2 38 32 .9 29 .5 28 .9 28 .9 27 .5 29 .5 25 .9 25 • 6 26 .2 26 .7 26 .2 26 . 1 39 31 .9 29 .5 29 .5 29 .5 27 .3 29 .5 26 .5 27 .3 28 .4 27 .2 25 .7 27 .0 40 31 . 1 30 .3 29 .9 30 .5 30 .3 30 .4 24 .8 26 .9 27 .7 29 .2 29 .6 27 .6 41 31 .6 28 .3 29 . 0 29 .5 31 . 1 29 .9 24 .7 26 .7 27 .7 28 .7 31 . 1 27 .8 42 30 .4 28 .6 29 .3 29 .8 31 .6 30 .0 24 .5 24 .4 25 .9 28 .7 30 .9 26 .9 43 31 .3 28 . 1 28 .9 30 .5 31 .0 30 .0 26 .3 24 . 1 27 . 1 29 .0 30 .6 27 .4 44 31 . 8 28 .9 29 . 0 29 .8 31 .5 30 . 2 26 . 3 24 . 0 27 . 6 29 .2 31 .4 27 .7 45 30 .9 28 . 2 28 .6 28 .3 30 . 1 29 . 2 25 .2 26 . 1 27 .2 27 .0 28 .5 26 .8 46 31 .8 30 .8 28 .5 29 .7 30 .8 30 .3 27 .0 28 .0 25 .6 28 .6 30 .2 27 .9 47 31 .1 27 .8 27 .6 27 .5 28 .3 28 .5 27 .7 25 . 1 23 .9 25 .9 26 .8 25 .9 48 29 .7 27 .7 27 .6 28 .0 27 .4 28 . 1 26 .1 26 .2 25 .9 26 .1 26 .4 26 .1 49 33 .6 29 .3 28 .1 27 .9 29 .0 29 .6 28 .3 25 .5 26 . 0 25 .7 27 .5 26 .6 50 31 .9 29 .7 28 .6 27 .7 26 .9 29 .0 28 . 0 27 . 6 26 .4 22 .5 24 .0 25 .7 51 32 .3 29 .4 28 .7 30 .0 29 .9 30 .0 26 .5 27 . 1 25 .8 27 .4 28 .3 27 .0 52 30 .8 28 .7 28 .8 29 .5 30 .5 29 .7 24 .3 26 . 6 26 .2 27 .7 28 .4 26 .6 53 30 .9 28 .1 28 .0 27 .9 21 .7 27 .3 25 .3 24 .7 26 . 1 25 .8 14 .6 23 .3 2 32 JUNE09 JULY02 -% V/V • % v / v ~ S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 54 30 .8 29 .2 29 .4 29 .2 31 .2 30 .0 24 .2 26 .6 27 .5 28 .1 30 .3 27 .3 55 31 .7 30 .2 29 .9 29 .2 30 .6 30 .3 27 .3 28 . 1 26 . 1 27 .9 30 .2 27 .9 56 31 .5 28 .9 27 .2 22 . 6 27 .6 27 .6 29 .0 26 .0 25 .4 17 .3 23 .9 24 .3 57 30 .5 27 .9 28 .6 29 . 3 30 .0 29 .2 25 .9 25 .5 26 . 6 27 .7 31 .1 27 .4 58 31 .7 28 . 1 28 .5 28 .7 30 .0 29 .4 28 .2 26 .0 26 .8 27 .0 28 .9 27 .4 59 32 .4 28 .6 28 .4 29 .4 30 .3 29 .8 26 .3 25 . 1 25 .9 28 .0 29 .9 27 . 1 60 32 .2 28 .9 28 .6 30 .2 30 .7 30 .1 27 . 1 26 .2 25 .8 28 .7 30 .0 27 . 6 61 32 .1 27 .4 27 .5 27 .4 26 .5 28 .2 27 .1 23 .7 25 .4 26 .2 21 .6 24 .8 62 28 .3 27 .3 22 .8 20 .3 13 .9 22 .5 22 .5 24 .8 17 . 1 13 .9 10 .5 17 .8 63 31 .0 31 . 1 29 .5 29 .6 30 .7 30 .4 26 .5 28 .3 28 .2 29 .1 31 .3 28 .7 64 29 .9 29 .8 31 .0 30 .2 30 .6 30 .3 22 .6 25 .6 28 .4 29 .1 29 .8 27 . 1 65 32 .2 24 .0 28 .0 31 .3 30 .3 29 .2 26 .2 18 .7 25 .0 29 .9 29 .7 25 .9 66 28 .9 27 .5 28 .6 25 . 1 11 .6 24 .3 22 .7 23 .2 27 .6 21 .0 7 .3 20 .4 67 31 .2 28 .6 28 .8 29 .4 31 .1 29 .8 26 .2 26 . 1 27 .0 27 .1 32 . 1 27 .7 68 32 .2 29 .8 28 .6 29 . 1 31 .5 30 .3 26 .5 27 .8 27 . 1 27 .2 30 .3 27 .8 69 31 .4 28 . 0 29 .4 30 . 6 32 .0 30 .3 25 .8 23 .0 27 .7 30 .5 32 .4 27 .9 70 31 .3 29 . 1 28 .4 29 .0 30 .4 29 .6 28 .5 27 .9 26 .8 26 .4 28 .8 27 .7 71 30 .9 26 .1 27 .8 28 .7 30 .4 28 . 8 26 . 0 21 .6 25 . 1 27 .9 29 . 5 26 . 0 72 31 .8 27 .4 27 .6 28 .8 30 .4 29 . 2 26 .4 23 . 1 24 . 1 26 .8 28 .8 25 .8 73 29 .9 25 .9 26 .5 25 .0 28 .8 27 .2 25 .7 22 . 3 24 .9 20 .6 23 .3 23 .4 74 30 .6 25 .9 23 .7 20 .4 17 .5 23 .6 26 .2 20 .8 19 .9 16 . 1 14 . 1 19 .4 75 31 .8 26 .7 26 .8 28 .9 30 .6 29 .0 27 . 0 23 .5 23 .2 27 .3 27 .7 25 .7 76 32 .7 28 .9 27 .5 27 .6 30 .1 29 .3 28 .1 26 .3 24 . 3 25 .4 27 .8 26 .4 JULY14 AUGUST05 % v/v — % v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 28 .5 27 .7 27 .2 27 .9 20 .6 26 .4 23 .5 26 .3 26 .4 27 .2 18 .0 24 .3 2 29 .7 27 .3 25 .9 24 .9 27 .4 27 .0 26 .2 25 .2 24 .5 23 .3 27 . 1 25 .3 3 25 .9 14 .2 11 .5 10 .7 10 .0 14 .4 18 .9 11 .3 9 .2 9 .5 9 .3 11 .6 4 27 .6 27 .0 25 .5 21 .8 22 .0 24 .8 23 .6 25 .0 23 .3 19 .8 20 .8 22 .5 5 28 .9 27 .9 27 .4 27 . 3 25 .1 27 . 3 26 . 1 26 .4 25 .9 26 .8 23 .3 25 .7 6 30 .6 27 .5 26 .5 27 . 0 28 .0 27 .9 26 .4 26 .2 25 .6 25 .9 26 .6 26 .1 7 28 .1 27 .7 28 .4 28 .9 29 .5 28 .5 24 .2 26 .2 28 .4 28 .7 30 .0 27 .5 8 30 .1 27 .6 27 .4 27 .6 28 .8 28 . 3 26 .5 26 .0 26 . 1 26 .4 28 . 1 26 .6 9 26 .7 26 .7 26 .9 27 .4 28 .1 27 . 1 21 .4 24 .8 25 .6 26 .6 28 .6 25 .4 10 29 .0 28 .2 28 .3 29 .0 29 .1 28 .7 25 .5 27 .7 27 .4 27 .6 28 .8 27 .4 11 27 .2 27 .4 27 .5 28 .7 30 .3 28 .2 23 .6 26 .7 26 .5 27 .4 30 .5 26 .9 12 29 .9 28 . 0 26 .3 24 .7 10 .3 23 .8 25 .3 25 .4 23 .4 21 .6 8 .9 20 .9 13 28 .9 23 .5 17 .0 7 .3 6 .6 16 .7 21 .2 18 .2 12 .7 6 .0 6 . 6 12 .9 14 28 .7 28 .3 27 .3 27 .1 30 .0 28 .3 23 .8 27 .0 25 .4 25 .8 29 .8 26 .4 15 29 .8 26 .0 25 .0 24 .0 14 .1 23 .8 25 .4 24 .2 22 .5 21 .4 12 .7 21 .2 16 29 .9 24 .2 22 .6 11 .0 10 .0 19 .5 24 .0 20 .5 18 .8 9 .6 9 .2 16 .4 17 29 .3 24 .9 21 .6 21 .8 17 .0 22 . 9 25 .1 21 . 6 18 .5 18 . 6 14 .2 19 .6 2 3 3 JULY14 AUGUST05 — % v/v % v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 18 29 . 3 27 .9 27 .1 26 .7 26 .0 27 .4 24 .6 26 .3 26 .7 25 .4 24 .2 25 .4 19 29 .2 27 .7 25 .7 23 .2 28 .8 26 .9 24 .6 25 .3 22 . 6 21 .2 29 .2 24 .6 20 29 .0 26 .4 27 .5 27 .4 26 .4 27 .4 23 .2 24 .2 27 .1 26 .5 25 .7 25 .4 21 28 .7 17 .9 14 .4 20 .5 23 .4 21 .0 22 .4 14 .2 11 .7 17 .7 20 .8 17 .4 22 25 .3 16 .5 7 .4 5 .3 11 .8 13 .3 16 .2 11 .4 5 .7 4 .7 10 .7 9 .8 23 29 .3 25 .8 24 .8 19 .1 24 .3 24 .6 24 .3 22 .1 20 .9 16 .8 22 .7 21 .4 24 27 .2 20 .7 17 .4 10 .2 6 .2 16 .3 20 .2 15 .7 12 .6 8 .2 5 .5 12 .4 25 28 .7 27 .3 22 .7 23 .0 13 .0 23 .0 23 .5 24 .7 19 .6 20 .2 11 .2 19 .9 26 28 .9 28 .0 27 .4 28 . 1 29 . 1 28 .3 24 .2 25 .5 25 .3 27 .7 29 .0 26 .3 27 27 .8 27 .3 27 .8 26 .3 28 .8 27 .6 23 . 1 25 .2 26 .8 25 .3 28 .6 25 .8 28 29 .4 27 .0 25 .9 27 .9 30 .0 28 .1 25 . 1 24 .7 24 .4 27 .9 28 .2 26 . 1 29 29 .3 28 .0 27 .0 22 .8 14 . 1 24 .2 24 .9 26 .2 25 .7 18 .9 11 .4 21 .4 30 30 .4 27 .9 28 .5 27 .4 25 .3 27 .9 25 . 3 25 .8 27 .9 27 .6 21 .7 25 .7 31 8 .3 7 .4 7 .6 11 .3 20 .2 11 .0 1 .5 4 .9 5 .0 8 .2 18 .4 7 .6 32 26 .7 19 .9 11 .9 11 .3 20 .3 18 .0 18 . 6 15 .8 9 .0 8 .8 18 .7 14 .2 33 29 .6 27 .8 27 .0 27 .5 26 .7 27 .7 24 .2 24 .6 25 .9 27 .0 25 .7 25 .5 34 7 .4 7 .0 8 .6 13 .7 20 .4 11 .4 0 .3 4 .6 6 .2 10 .8 17 .8 7 .9 35 23 .8 26 .3 24 .3 15 .5 19 .6 21 .9 17 .0 22 .7 21 .6 13 .0 17 .8 18 .4 36 11 .6 9 .6 11 .6 15 . 1 10 .0 11 . 6 3 .7 6 .3 8 .1 11 .6 9 .1 7 .8 37 30 .4 27 .4 27 .3 23 .9 15 .9 25 .0 24 .5 24 .9 25 . 2 20 .8 14 .0 21 .9 38 29 .7 27 . 1 26 .5 26 .5 25 .8 27 . 1 24 .1 24 .2 25 . 5 25 .8 23 .8 24 .7 39 29 .5 27 .9 27 .8 27 .0 24 .8 27 .4 24 . 1 25 .8 27 . 1 26 .2 23 . 1 25 .3 40 27 .7 27 .2 26 .8 28 .5 28 .5 27 .7 22 .1 24 .8 26 .5 26 .9 26 .6 25 .4 41 28 .3 27. .9 28 .1 28 . 5 30 .5 28 .7 21 .8 26 .4 26 .8 28 .0 30 .5 26 .7 42 26 .1 25 . 4 26 .5 28 . 1 29 .9 27 . 2 20 .4 22 . 0 24 .7 27 .4 30 . 3 25 . 0 43 29 . 1 25 .3 27 .4 28 . 6 30 . 0 28 . 1 23 .5 22 . 2 27 . 1 28 .3 30 .4 26 .3 44 28 .5 25 .9 27 .3 28 . 8 30 .4 28 .2 22 . 6 22 . 2 25 .9 27 .8 30 .4 25 .8 45 28 .7 27 . 1 27 .3 26 .5 28 .0 27 .5 24 .5 25 .3 26 .5 26 . 0 27 .9 26 .0 46 30 .2 28 .8 25 .3 28 .0 29 .6 28 .4 24 .5 25 .7 22 .7 27 .3 29 .5 25 .9 47 30 .4 26 .8 24 .5 25 .6 25 .9 26 .6 25 .5 23 .5 22 . 6 24 .2 24 .2 24 .0 48 28 .8 27 .2 26 .9 26 .2 25 .3 26 .9 24 .9 25 .2 24 . 6 25 .5 22 .9 24 . 6 49 32 .2 28 .0 27 .2 25 .9 26 .8 28 . 0 27 .1 25 .3 25 .3 24 . 1 26 .1 25 .6 50 30 .6 28 .7 27 .2 22 .2 23 .0 26 .4 26 .2 25 .7 23 .9 18 .7 21 .1 23 .1 51 30 .2 28 .2 26 .1 27 .4 27 .9 27 .9 25 .1 26 .4 24 .4 26 . 1 26 .8 25 .8 52 28 .0 27 .3 26 .4 27 .2 27 .8 27 .3 20 .8 24 .8 24 .4 25 .1 25 .5 24 .1 53 28 . 1 25 .7 26 .4 25 . 8 13 .5 23 .9 21 .6 21 .6 22 .3 22 .1 11 .7 19 .8 54 28 . 3 27 .9 27 .8 27 . 6 29 . 3 28 .2 21 .0 24 . 3 26 . 5 27 . 1 29 .9 25 .8 55 30 . 0 28 .7 27 .9 28 . 0 29 .9 28 .9 22 .5 26 . 0 25 . 7 26 . 7 29 .5 26 .1 56 30 .7 27 .9 26 .0 16 .7 22 .5 24 .7 27 . 1 24 .7 22 .4 14 . 0 20 .7 21 .8 57 29 .4 26 .4 26 .7 27 .6 28 .5 27 .7 23 .0 23 .7 24 .9 25 .4 27 .1 24 .8 58 30 .2 27 .4 27 .3 27 .7 28 .6 28 .2 27 .2 24 .9 26 . 6 26 .5 27 .5 26 .5 59 29 .7 26 .8 26 .7 28 .0 29 .3 28 . 1 25 .4 24 .6 25 . 1 27 .1 28 .6 26 .2 60 29 .7 27 .3 26 .7 29 .1 29 . 1 28 .4 25 .4 25 .2 25 .4 28 .3 29 .6 26 .8 61 30 .4 25 .7 25 .6 25 .9 20 .1 25 .5 25 .1 22 . 0 23 .7 22 .8 17 .8 22 .3 62 26 .4 25 .9 17 .4 13 .2 9 .9 18 .5 18 .8 21 . 6 13 .5 10 .3 8 .6 14 .6 63 29 .4 29 .5 28 .3 28 .7 29 .2 29 .1 22 .6 26 .4 26 .8 28 .0 28 .6 26 .5 234 JULY14 AUGUST05 % v / v % v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 64 25 .8 27. 7 28 .6 28. 6 29. 2 28. 0 19. 4 23. 9 27. 0 27 .4 28. 5 25. 3 65 28 .4 20. 6 25 .4 29. 4 29. 1 26. 6 21. 0 15. 5 23. 1 29 .4 28. 7 23. 6 66 26 .9 25. 4 27 .4 19. 5 6. 8 21. 2 19. 2 20. 5 24. 5 16 .3 6. 0 17. 3 67 29 .8 27. 9 27 .2 26. 9 30. 4 28. 5 25. 0 26. 2 26. 3 25 .7 29. 9 26. 6 68 29 .4 28. 4 27 .2 27. 0 29. 9 28. 4 23. 6 26. 5 25. 9 26 .0 29. 8 26. 4 69 28 .4 24. 4 28 .4 30. 2 31. 1 28. 5 21. 6 20. 9 26. 8 29 .9 31. 0 26. 0 70 30 .6 28. 4 27 . 1 26. 3 28. 2 28. 1 25. 8 26. 9 25. 1 24 .3 28. 0 26. 0 71 28 .4 23. 8 25 .7 27. 7 28. 6 26. 8 23. 5 19. 5 23 . 6 26 .5 26. 9 24. 0 72 29 .3 25. 1 24 .6 27. 1 27. 8 26. 8 22. 9 21. 4 21. 8 25 .2 25. 4 23. 4 73 28 .1 23. 7 25 . 1 19. 7 21. 7 23. 7 21. 9 19. 8 22. 1 15 .6 18. 7 19. 6 74 28 .9 23. 0 20 .5 16. 0 13. 3 20. 3 20. 7 16. 9 15. 6 12 .8 11. 9 15. 6 75 29 .8 14. 7 23 . 3 26. 7 30. 6 25. 0 25. 1 21. 3 20. 6 24 .8 29. 5 24. 3 76 31 . 3 27. 5 25 .2 25. 3 27. 4 27. 3 25. 4 25. 0 22. 1 23 . 3 25. 9 24. 4 AUGUST21 SEPTEMBER17 % v/v % v/v • S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 1 21 .1 24 .7 25 .2 26 .3 16 .4 22 .7 21 .7 24 .0 25 .5 25 .7 15 .8 22 .5 2 22 .5 23 .9 22 .7 22 .1 25 .8 23 .4 24 .7 24 .3 22 .8 20 .6 23 .5 23 .2 3 15 .5 10 .4 7 .6 7 .7 8 .1 9 .9 18 .8 11 .9 7 .9 7 .7 8 .9 11 .0 4 20 .3 23 .0 20 .9 17 .5 18 . 1 20 .0 24 .1 24 .6 21 . 2 16 . 1 17 .4 20 .7 5 23 .2 25 .1 24 .5 24 .8 20 .1 23 .5 23 .7 24 . 3 23 .7 23 .7 18 .0 22 .7 6 23 .5 24 .4 24 .0 24 .3 25 .5 24 .4 24 .8 24 .0 22 . 6 22 .2 22 . 1 23 . 1 7 23 .2 25 .3 27 .2 28 .5 29 .3 26 .7 25 .7 27 . 3 28 .7 28 .5 29 .9 28 .0 8 24 .3 24 .7 24 .9 25 . 0 26 .9 25 . 1 25 .0 24 .9 25 . 0 25 .2 25 .3 25 .1 9 19 .2 23 .3 25 .5 25 .9 26 .0 24 .0 22 . 0 25 . 1 26 . 6 26 .1 28 .1 25 .6 10 24 .8 27 . 2 27 .2 27 . 1 27 .0 26 .7 25 .7 27 . 4 27 . 7 27 .8 27 .7 27 .3 11 20 . 7 25 .6 26 .3 27 . 1 30 .5 26 .0 22 .9 26 . 0 26 .4 28 .2 29 .8 26 .7 12 22 .6 22 .5 21 .2 19 .4 7 .7 18 .7 23 .2 22 .6 20 . 3 17 .7 6 .9 18 . 1 13 16 .5 15 .5 11 .0 5 .4 5 .8 10 .8 19 .5 17 .2 12 . 0 6 .2 6 .0 12 .2 14 21 .0 25 .7 25 .1 24 .3 29 .4 25 . 1 22 .6 26 .4 26 . 1 24 .9 29 .0 25 .8 15 22 .4 21 .8 20 .2 19 .5 11 .6 19 . 1 21 .5 21 .7 19 .9 18 .0 9 .8 18 .2 16 20 . 1 18 .0 15 .7 8 .2 8 .6 14 . 1 21 .7 18 .0 15 .4 8 .3 7 .1 14 .1 17 19 .9 19 .4 15 .5 14 .9 11 .4 16 .2 21 .0 19 .5 14 .2 12 .4 8 .6 15 . 1 18 22 . 1 25 . 1 25 .4 23 . 2 22 .1 23 . 6 22 . 1 24 .5 24 . 8 22 . 3 20 .4 22 .8 19 22 . 6 23 .9 21 .0 20 .0 28 .3 23 .2 23 .4 24 .9 22 .9 20 .2 27 .9 23 .9 20 20 .5 22 .9 26 .1 25 .7 24 .0 23 .8 22 .7 24 .3 26 .7 25 .1 22 .5 24 .2 21 20 .2 12 .6 9 .8 15 .1 17 . 1 15 .0 21 .0 13 .3 9 .9 12 .3 12 .9 13 .8 22 12 .2 9 .4 4 .7 3 .9 10 .0 8 .0 16 .2 13 .1 6 .3 4 .7 9 .1 9 .9 23 20 .7 19 .3 18 .4 14 .8 20 .8 18 .8 22 .0 19 .9 17 .3 13 .4 18 .0 18 . 1 24 13 .7 12 .7 10 . 1 6 .3 5 .2 9 .6 14 .4 13 .0 10 .5 6 .5 5 .6 10 .0 25 20 .4 22 .4 17 .5 17 .7 10 .6 17 .7 18 .7 21 .2 17 .4 16 .3 10 . 1 16 .7 26 22 .9 23 .6 24 .2 26 .7 28 .4 25 .2 22 .3 22 .8 24 .1 27 .2 27 .8 24 .8 27 20 .2 23 .5 25 .9 24 .1 27 .7 24 .3 22 .8 23 .6 26 .5 24 .2 26 .9 24 .8 AUGUST21 SEPTEMBER17 % v/v % v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 28 22 .7 22 .7 23 . 3 27 . 1 27 .0 24 .6 24 . 1 23 .9 23 .6 26 .9 25 .0 24 .7 29 22 .1 24 .3 23 .6 15 .7 10 .5 19 .2 21 .7 22 .9 22 .4 14 .4 9 .2 18 .1 30 23 .7 24 .0 26 .7 25 .9 19 .4 24 .0 23 . 3 23 .6 26 .4 24 .7 16 .8 23 .0 31 0 .4 4 . 2 4 .4 6 .5 16 .1 6 . 3 5 .9 5 . 2 4 . 8 6 .7 13 .5 7 .2 32 13 .9 13 .3 7 .5 7 .1 15 .7 11 .5 19 .6 14 .9 9 .0 7 .5 13 .4 12 .9 33 21 .6 22 .8 24 .9 26 .4 24 .8 24 . 1 22 .9 23 . 1 25 .2 26 .5 23 .4 24 .2 34 1 .0 4 .3 5 .4 8 .3 15 .7 7 .0 11 .9 8 .9 7 .7 6 .9 ^ 11 .2 9 .3 35 13 .3 19 .6 18 .8 10 .5 15 .7 15 .6 19 .4 23 .4 22 .3 12 .3 14 .3 18 .4 36 2 .5 5 .5 6 .3 7 .8 7 .7 6 .0 10 .8 7 .7 6 .2 7 .4 7 .0 7 .8 37 21 .5 23 .3 23 .0 17 .8 12 .3 19 .6 24 .4 25 .0 24 . 1 16 .2 10 .2 20 .0 38 20 .4 22 .3 23 .9 23 .8 20 .5 22 .2 20 .8 22 . 1 23 .8 24 .2 18 .6 21 .9 39 20 .8 24 .8 25 .6 24 .8 21 .3 23 .4 23 .2 25 . 1 25 .9 24 .2 17 .6 23 .2 40 19 . 1 22 . 6 24 .5 27 . 1 24 .8 23 .6 18 .4 21 .8 24 .8 27 .2 24 .3 23 .3 41 19 .9 25 .2 26 .5 27 .6 28 .4 25 .5 24 .4 26 .7 26 .9 27 .4 25 .5 26 .2 42 17 .5 20 . 0 23 .8 27 . 6 30 .0 23 .7 19 .2 22 .3 25 .1 27 .9 30 . 1 24 .9 43 20 .9 21 .4 26 .2 28 .1 29 .8 25 .3 23 .4 22 .5 26 .7 28 .2 29 .4 26 .0 44 19 .2 20 .0 25 .1 27 . 6 30 .3 24 .4 20 . 1 20 . 3 25 .2 27 .4 30 .8 24 .7 45 22 . 6 24 . 0 25 .5 25 .9 27 .5 25 . 1 25 . 0 24 . 9 25 .5 24 .9 27 .6 25 .6 46 20 .2 22 .9 21 .2 26 .4 28 .3 23 .8 22 .5 24 .6 22 .1 25 .8 27 . 1 24 .4 47 23 .3 21 .2 20 .7 22 .5 21 .4 21 .8 23 .5 22 . 3 20 .7 21 . 0 21 .3 21 .8 48 23 .3 23 .5 22 .7 23 .3 20 .9 22 .8 23 .2 24 .4 23 . 1 23 . 5 21 .5 23 . 1 49 25 . 6 24 .2 23 .8 22 .7 24 .9 24 . 3 24 .3 23 .8 24 .2 23 . 0 25 . 1 24 .1 50 22 .8 23 .4 21 .7 16 .9 18 .9 20 .8 21 .5 22 .2 20 .4 16 .6 19 .1 20 .0 51 22 .9 24 .7 23 .5 24 .3 25 .0 24 .1 24 .2 25 . 1 24 .0 24 .4 23 .8 24 .3 52 18 .4 22 .0 22 .5 22 .8 23 .6 21 .9 18 .2 22 .9 23 .2 21 .6 21 .9 21 .6 53 18 .2 18 .5 19 .3 19 .3 10 .3 17 .1 19 .6 21 .0 20 .6 18 .8 9 .8 17 .9 54 18 .9 22 .5 25 .3 26 .2 29 .0 24 .4 21 .6 23 .7 25 .6 26 .5 28 .6 25 .2 55 21 .2 23 .4 24 .7 26 .4 28 .9 24 .9 22 .5 24 .9 25 .4 26 .5 28 .8 25 .6 56 23 .4 22 .4 19 .8 12 .4 19 . 1 19 .4 23 . 1 22 . 6 22 .5 13 .7 16 .4 19 .6 57 20 .3 22 .2 23 .9 23 .9 24 .6 23 .0 22 .5 23 .4 24 .7 23 .7 21 .9 23 .2 58 25 .4 23 .9 24 .9 24 .5 26 .0 25 .0 27 .7 25 .5 25 .6 24 .3 24 .3 25 .5 59 23 .0 22 .8 23 .7 25 .8 27 . 3 24 .5 25 .4 23 .3 23 . 5 24 . 0 25 . 0 24 .2 60 24 .3 23 .9 23 .6 27 .7 28 .9 25 .7 25 . 3 25 .2 25 .0 28 . 1 28 .0 26 . 3 61 22 .4 20 .9 21 .0 21 .1 20 .6 21 .2 22 .9 20 .5 21 .6 19 . 1 14 .4 19 .7 62 14 .7 18 .4 11 .4 8 .2 7 .7 12 .1 18 .0 18 .2 10 .7 8 .8 8 . 1 12 .8 63 20 .7 24 .8 25 .4 27 .1 27 . 3 25 .1 22 . 1 26 .3 26 .6 26 .4 25 .3 25 . 3 64 16 . 5 22 . 3 25 .2 26 .6 27 . 5 23 . 6 22 . 5 24 . 0 27 . 1 27 .9 27 . 2 25 .7 65 17 .8 13 . 1 21 .7 29 .2 28 .0 22 .0 21 .0 14 .4 22 .5 28 .7 27 .4 22 .8 66 15 .3 17 .8 21 .2 13 .5 5 . 3 14 .6 17 .6 19 .0 18 .5 11 . 1 4 .5 14 .1 67 23 .4' 24 .8 25 .1 24 .5 29 .7 25 .5 26 .4 26 .6 27 .0 26 . 1 29 .4 27 .1 68 19 .8 25 .0 24 .1 24 .8 28 .6 24 .5 22 .9 26 .0 24 .6 24 .2 27 .6 25 .1 69 18 .8 19 .8 25 .7 29 .0 30 .5 24 .8 23 .2 21 .6 25 .8 28 . 6 29 .9 25 .9 70 24 .0 25 .7 23 .5 22 .7 26 .1 24 .4 25 .7 26 .2 24 .4 22 .4 25 .0 24 .7 71 21 .1 18 .0 21 .7 24 .0 25 . 0 21 .9 24 . 0 18 .6 22 . 0 23 .4 24 .2 22 .4 72 20 .2 19 .6 20 .2 23 .8 22 .8 21 .3 22 .1 20 .7 21 .2 24 . 1 22 .0 22 .0 73 17 .7 17 .5 19 .1 13 .8 17 .7 17 .2 20 .1 17 .5 18 . 0 13 .2 16 .4 17 .0 AUGUST21 SEPTEMBER17 % v / v % v / v  S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 74 16.6 14.1 12.9 10.6 10.2 12.9 75 22.2 20.3 19.4 24.0 28.3 22.9 76 23.7 23.3 21.0 21.5 24.7 22.8 19.5 14.5 11.9 10.1 8.4 12.9 25.4 21.9 19.7 22.8 26.8 23.3 25.7 24.1 21.7 21.2 23.6 23.3 OCTOBER02 % v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 1 20 .2 23 .3 25 .2 25 .5 15 .8 22 .0 2 23 .0 23 .6 22 .5 20 .5 23 .5 22 .6 3 14 .5 9 .8 7 .3 7 .1 7 .4 9 .2 4 22 .4 23 .9 20 .9 16 .0 17 .4 20 .1 5 22 . 1 23 .6 23 .4 23 . 5 18 .0 22 .1 6 23 .1 23 .3 22 .4 22 . 1 22 .1 22 .6 7 23 .9 26 .5 28 .4 28 .3 29 .9 27 .4 8 23 .3 24 .2 24 .7 25 . 1 25 . 3 24 . 5 9 20 .5 24 .4 26 .3 25 .9 28 .1 25 .0 10 24 .0 26 .7 27 .4 27 .6 27 .7 26 .7 11 21 .3 25 . 3 26 . 1 28 . 0 29 .8 26 .1 12 21 .6 22 .0 20 .0 17 .6 6 .9 17 .6 13 15 . 1 14 .5 10 .7 5 .6 5 .7 10 .3 14 21 . 1 25 .6 25 .8 24 .8 29 . 0 25 .3 15 19 .9 21 .0 19 .6 17 .9 9 .8 17 .6 16 20 . 1 17 .5 15 .2 8 .2 7 .1 13 .6 17 19 .5 19 .0 14 .0 12 . 3 8 . 6 14 .7 18 20 .5 23 .8 24 .5 22 .1 20 .4 22 .3 19 21 .8 24 .2 22 .7 20 . 1 27 .9 23 .3 20 21 . 1 23 .6 26 .4 24 .9 22 .5 23 .7 21 16 .5 11 .0 8 .8 11 .3 12 .3 12 .0 22 12 .3 10 .9 5 .5 4 .3 8 .7 8 .3 23 20 .5 19 .3 17 . 1 13 .4 18 .0 17 .7 24 10 .7 10 .8 9 .3 5 .9 5 .3 8 .4 25 17 .3 20 .6 17 .2 16 .2 10 . 1 16 .3 26 20 .7 22 . 1 23 .8 27 .0 27 .7 24 .3 27 21 .2 22 .9 26 .2 24 . 1 26 .9 24 .3 28 22 .4 23 .2 23 . 3 26 .8 25 . 0 24 . 1 29 20 . 1 22 .3 22 .1 14 .3 9 .2 17 .6 30 21 .6 22 .9 26 . 1 24 .6 16 .8 22 .4 31 3 .4 4 . 1 4 .1 6 . 1 12 .9 6 . 1 32 18 .1 14 .4 8 .9 7 .4 13 .4 12 .4 33 21 .3 22 .4 24 .9 26 .3 23 .4 23 .7 34 8 .7 7 .3 6 .7 6 .3 10 .7 7 .9 35 18 .0 22 .7 22 .1 12 .3 14 .3 17 .9 36 7 .6 6 .3 5 .4 6 .7 6 .6 6 .5 37 22 .7 24 .3 23 .8 16 . 1 10 .2 19 .4 OCTOBER02 % v / v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 38 19 .3 21 .5 23 .5 24 .1 18 .6 21 .4 39 21 .6 24 .4 25 .6 24 .0 17 .6 22 .6 40 17 .1 21 .2 24 .5 27 .0 24 .3 22 .8 41 22 .3 25 .6 26 .2 26 .9 25 . 1 25 .2 42 17 .4 21 .3 24 .5 27 .4 29 .7 24 . 1 43 21 .4 21 .6 26 . 1 27 . 6 29 . 1 25 .1 44 18 .2 19 .5 24 .6 26 .9 30 .4 23 .9 45 22 .9 23 .9 24 .9 24 .4 27 .2 24 .6 46 20 .5 23 .6 21 .5 25 .3 26 .7 23 .5 47 21 .5 21 .3 20 .2 20 .6 21 .0 20 .9 48 21 .2 23 .4 22 .6 23 .0 21 .2 22 .3 49 22 .2 22 .8 23 .6 22 .5 24 .7 23 .2 50 19 .5 21 .3 19 .9 16 .3 18 .8 19 .2 51 22 . 1 24 .0 23 .4 24 .0 23 .4 23 .4 52 16 .5 22 .0 22 .6 21 .2 21 .6 20 .8 53 17 .8 20 .1 20 .0 18 .4 9 .7 17 .2 54 19 .7 22 .7 24 .9 26 . 0 28 .2 24 .3 55 20 .5 23 .9 24 . 8 26 .0 28 .4 24 .7 56 21 . 1 21 .6 21 .9 13 .4 16 . 1 18 .8 57 20 .6 22 .4 24 .1 23 .2 21 . 6 22 .4 58 25 .5 24 .4 25 .0 23 .9 23 .9 24 .5 59 23 .3 22 .3 22 .9 23 .5 24 .6 23 .3 60 23 .1 24 .2 24 .3 27 .6 27 .6 25 .4 61 20 .9 19 .6 21 .0 18 .7 14 .2 18 .9 62 13 .6 15 .1 9 .4 8 .0 7 .6 10 .7 63 20 .2 25 .2 25 .9 25 .9 24 .9 24 .4 64 20 .5 23 .0 26 .4 27 .4 26 .9 24 .8 65 19 .1 13 . 8 22 . 0 28 .2 27 .0 22 . 0 66 13 .2 15 .7 16 .4 10 .1 4 .2 11 .9 67 24 .2 25 .5 26 .3 25 .6 29 .0 26 . 1 68 20 .9 24 .9 24 .0 23 .7 27 .2 24 .2 69 21 .2 20 .7 25 .2 28 . 1 29 .5 24 .9 70 23 .5 25 .1 23 .8 21 .9 24 .7 23 .8 71 21 .9 17 .8 21 .5 23 .0 23 .8 21 .6 72 20 .1 19 .8 20 .7 23 .6 21 .7 21 .2 73 18 .2 16 .7 17 .5 12 .9 16 .2 16 .3 74 14 .9 12 .0 10 .5 9 .2 7 .8 10 .9 75 23 .3 20 .9 19 .2 22 .4 26 .4 22 .4 76 23 .5 23 . 1 21 . 1 20 .8 23 .3 22 .4 Theta values f o r 38 i r r i g a t e d s i t e s , 1987 APRIL29 MAY26 _. . % v / V ; • % v / v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 32 .1 29 .5 30 .0 29 .5 30 .6 30 .4 31 .7 29 .1 29 .4 29 .6 30 .9 30 .2 81 32 .6 29 .0 29 .1 29 .8 30 .4 30 .2 31 .5 28 .9 29 .2 30 .5 31 .0 30 .2 82 31 .8 29 .7 29 .5 29 .9 30 .3 30 .2 30 .5 29 .2 29 .3 30 .6 31 .5 30 .2 83 34 .2 31 .1 30 .1 30 .6 30 .9 31 .4 33 .1 31 .4 30 .2 30 .7 31 .5 31 .4 84 30 .7 28 .1 28 .0 26 .2 26 .9 28 .0 29 .8 27 .2 28 . 3 27 . 1 27 .7 28 .0 85 30 .1 26 .3 20 .4 14 .4 25 .4 23 .3 29 .4 26 .4 19 .9 13 .3 23 .8 22 .6 86 32 .7 28 .4 28 . 1 27 .8 29 .0 29 .2 31 .3 28 .7 28 .5 28 .4 29 .7 29 .3 87 32 .1 29 .0 28 .0 27 .7 28 .9 29 .1 30 .6 24 .9 26 .4 26 .8 23 .7 26 .5 88 21 .4 18 .0 21 .6 23 .0 28 .6 22 .5 19 .7 16 .7 20 .0 22 .5 29 .2 21 .6 89 30 .1 29 . 1 28 .7 28 . 1 21 .4 27 .5 29 .5 28 .9 28 .7 27 .6 21 .2 27 .2 90 30 .7 29 .6 30 .0 29 .5 32 .1 30 .4 31 . 1 30 .3 3 0 .5 29 .9 32 .9 30 .9 91 32 .5 27 .7 29 .2 30 . 1 29 .6 29 .8 33 . 1 27 .4 29 . 6 30 .8 29 .8 30 . 1 92 32 .0 28 .7 28 .8 29 .7 30 .7 30 .0 31 .5 28 .4 30 .0 30 .3 31 .3 30 .3 93 30 .6 28 .1 28 .0 29 .2 29 .7 29 . 1 30 .5 28 .3 28 .7 30 .0 30 .4 29 . 6 94 31 .8 28 .7 28 .8 29 .4 29 .8 29 .7 30 .7 28 .8 29 .2 30 . 1 30 .2 29 .8 95 31 .8 27 .8 27 .5 27 .6 26 .7 28 . 3 30 .5 28 .2 27 .7 27 .5 26 .9 28 .2 96 31 .4 28 .4 29 .5 29 .4 30 .8 29 .9 30 .0 28 .5 29 . 6 30 . 0 31 .5 29 .9 97 31 .7 27 .7 25 .8 29 . 0 31 .6 29 .2 31 .4 27 .5 26 .1 28 .9 32 .6 29 . 3 98 29 .3 26 .7 27 .3 27 .0 29 .2 27 .9 28 .9 26 . 6 27 .4 26 .7 29 .9 27 .9 99 33 .3 27 .5 27 .9 28 .0 28 . 1 29 .0 33 .0 27 . 1 27 . 3 28 .7 28 .9 29 .0 100 32 .7 29 .3 29 .2 28 .7 29 .6 29 .9 31 .1 29 .2 29 . 1 29 .2 30 .7 29 .9 101 32 . 6 29 .5 29 . 1 29 . 6 30 .5 30 .3 31 . 0 29 . 2 29 . 6 29 .9 31 .4 30 .2 102 31 .5 27 .2 26 .8 26 .0 26 .2 27 .6 30 .4 27 .5 26 .9 25 .9 25 .5 27 .2 103 31 .6 28 .0 29 .0 29 .5 30 .2 29 .7 31 .5 28 . 3 30 . 0 30 . 1 31 .7 30 .3 104 32 .3 27 .6 27 .5 29 .3 29 .6 29 .3 31 .9 28 .0 28 .5 29 .6 30 .4 29 .7 105 33 .3 28 .9 29 .1 29 .2 31 .3 30 .4 32 .5 28 .7 29 .7 29 .2 32 . 1 30 .4 106 30 .4 28 .8 28 .1 29 .6 25 .2 28 .4 29 .2 28 .9 29 .0 30 .4 25 .4 28 .6 107 31 .7 28 .6 29 .7 29 .9 29 .2 29 .8 31 . 1 29 .1 29 .9 30 .3 29 .6 30 .0 108 31 .6 28 .5 28 .7 29 .4 30 .5 29 .7 31 .2 28 .2 29 .2 30 .0 31 .3 30 .0 109 26 .3 25 .5 28 .0 30 . 1 30 .3 28 .1 26 . 5 24 .8 28 .8 30 .6 30 .7 28 .3 110 30 .3 28 .2 28 .0 28 .1 28 .5 28 .6 30 . 1 28 .6 28 .8 28 .3 28 .8 28 .9 111 32 .0 29 .4 29 .4 29 .8 30 .1 30 .1 31 .2 28 .8 29 .4 29 .8 30 .4 29 .9 112 31 . 3 26 .9 26 .3 27 . 1 23 .0 26 .9 31 .5 28 .4 27 . 1 25 .4 23 .6 27 .2 113 32 . 1 31 .0 31 .0 30 .3 31 .3 31 .1 31 .7 30 .9 30 .7 30 .3 31 .4 31 .0 114 31 . 5 29 . 5 29 .0 29 .0 30 . 0 29 .8 30 . 3 29 . 0 29 . 3 29 .7 30 . 1 29 .7 115 32 .5 28 .7 29 .2 28 .8 28 .8 29 .6 31 .3 28 .8 29 .5 29 . 3 29 .7 29 .7 116 32 .8 30 .4 28 .9 29 .9 31 .0 30 .6 30 . 6 29 .9 29 .2 30 . 1 31 . 6 30 .3 117 32 . 6 28 .8 28 .8 28 .6 29 .0 29 .6 32 . 3 28 .6 28 .6 28 .0 29 .5 29 .4 JUNE09 JULY02 % v/v % v/v Sitel5cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 31 .9 29 .4 29 .3 29 .2 29 .9 30 .0 29 .7 28 . 1 27 . 1 28 .2 30 .6 28 .7 81 31 .4 28 .9 28 .7 30 .1 30 .0 29 .8 26 .3 27 . 1 26 .9 28 .6 30 .2 27 .8 82 30 .9 29 .4 29 .3 29 .0 30 .6 29 .8 25 .6 26 .2 27 .5 28 .5 29 .6 27 .5 83 32 .3 30 .6 29 .9 30 .5 31 .2 30 .9 27 . 0 27 .2 27 .5 29 .4 31 .1 28 .4 84 30 .8 27 .8 28 .4 26 .5 27 .6 28 .2 28 .2 26 .2 26 .1 23 .8 24 .4 25 .7 85 29 .7 26 .9 20 .6 14 .0 25 .2 23 .3 26 .2 22 .3 14 .8 9 .5 17 .2 18 .0 86 31 .2 28 .6 28 .2 27 .7 29 .4 29 .0 26 .3 26 .5 26 .6 26 . 1 27 .2 26 .5 87 31 .1 25 .3 26 .5 26 .7 24 . 1 26 .7 27 .6 21 . 1 24 .5 23 .8 18 .1 23 .0 88 21 .3 17 .9 21 .9 23 .4 28 .9 22 .7 14 .5 11 .3 13 .7 17 .2 27 .9 16 .9 89 30 .7 28 .8 28 .5 27 .9 21 .3 27 .4 26 .8 27 .5 26 . 3 22 .8 17 .4 24 .2 90 31 . 1 30 .1 30 .5 29 .3 32 .1 30 .6 26 .3 27 .2 28 . 1 25 .7 31 .3 27 .7 91 32 .2 28 .1 29 .5 30 .4 29 .7 30 .0 27 .3 21 .8 26 .9 28 .6 28 .4 26 .6 92 32 . 1 28 .1 29 .3 29 .9 30 .8 30 . 0 26 .2 24 .2 27 .4 28 .2 29 .4 27 . 1 93 30 .7 28 .5 28 .4 29 .8 30 .2 29 .5 24 .2 26 .7 26 .8 28 . 3 28 .9 27 .0 94 31 .2 28 .0 28 .4 29 .7 30 . 1 29 .5 24 .4 24 .5 26 .5 28 .4 29 .5 26 .7 95 31 .0 27 .8 27 .7 27 .7 26 .4 28 .1 27 .7 25 .5 23 .5 23 .9 23 .7 24 .8 96 30 .3 28 .5 29 .0 29 .6 30 .7 29 .6 24 .2 25 . 1 26 .9 28 .5 29 .6 26 .9 97 31 .7 27 .7 26 .2 28 . 1 31 .8 29 . 1 27 .6 24 . 0 22 .5 27 .6 32 .0 26 .7 98 28 .7 27 . 0 27 .1 26 . 6 30 .0 27 .9 24 .8 24 .7 24 . 3 22 .6 27 .2 24 .7 99 33 .5 27 .1 27 .3 28 .5 28 .2 28 .9 29 .5 23 . 0 23 .9 25 .5 27 .5 25 .9 100 31 .6 29 .0 28 .5 29 . i 29 .8 29 .6 25 .9 25 .9 27 .5 27 .3 28 .4 27 .0 101 32 .4 29 .7 29 .6 29 .6 30 .0 30 .3 24 .5 24 .9 27 .5 28 .7 29 . 0 26 .9 102 30 .9 27 .6 27 .6 25 .9 25 .8 27 .6 26 .7 25 . 0 25 . 1 22 .2 20 .2 23 .8 103 31 .5 28 .0 29 .4 29 .5 30 .7 29 .8 25 .6 24 .2 28 .1 28 .2 30 . 1 27 .2 104 32 .5 27 .6 28 .0 29 .1 29 .8 29 .4 28 .5 25 • 6 25 .5 27 .2 28 .0 27 .0 105 32 .3 29 .1 29 .4 29 .0 31 . 3 30 .2 27 . 6 23 .8 26 . 6 27 .2 30 .7 27 .2 106 30 .7 29 .3 28 .5 29 .8 25 .5 28 .8 23 .6 26 .9 26 .9 28 . 1 22 .9 25 .7 107 32 .0 28 .9 29 .8 29 .6 29 .2 29 .9 24 .5 26 .2 27 .7 28 .2 28 . 3 27 .0 108 31 .0 28 .9 28 .5 29 .3 30 .5 29 • 6 24 . 4 24 .4 26 .9 28 .4 29 .6 26 .8 109 26 . 1 24 .7 27 .8 30 . 1 30 . 0 27 .7 17 . 0 17 .5 24 .7 30 . 0 29 .4 23 .7 110 30 .0 26 .8 28 . 3 27 .3 28 .2 28 .2 25 . 1 26 .6 26 .9 26 .4 27 .5 26 .5 111 31 .8 28 .9 28 .9 29 .7 30 .0 29 .9 24 .2 25 .5 27 .5 28 . 1 28 .6 26 .8 1.12 30 .6 27 .2 25 .6 27 .0 23 .6 26 .8 28 . 3 24 .5 21 .5 25 .1 18 .2 23 .5 113 31 .9 30 .6 31 .1 29 .7 30 .8 30 .8 27 .0 27 .3 28 . 5 27 . 0 29 .9 28 .0 114 30 .4 28 .8 28 .6 29 .3 29 .6 29 .3 25 .5 27 . 1 27 .5 27 . 6 27 .3 27 .0 115 31 .3 28 .9 28 .4 28 .5 29 .1 29 .3 27 .1 26 .7 26 . 6 26 .6 27 .1 26 .8 116 31 .4 29 . 4 29 . 3 29 .4 30 . 6 30 .0 26 . 0 26 . 5 26 . 8 28 . 3 29 .7 27 .5 117 32 .0 29 .1 28 .2 28 .1 28 .7 29 .2 30 .0 26 .5 25 .8 25 .4 26 .6 26 .9 JULY14 AUGUST05 % v / V % v/v Sitel5cm 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 32.3 29.6 29.7 30.1 31.2 30.6 32.1 29.0 29.0 28.9 30.6 29.9 81 32.6 29.6 29.4 30.1 30.4 30.4 31.4 29.0 29.0 29.7 30.5 29.9 JULY14 AUGUST05 % v/v % v/v • S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 82 32 .3 30 .2 29 .7 29 .9 31 .3 30 .7 31 .2 29 .3 29 . 1 29 .5 30 .6 29 .9 83 33 .2 31 .4 30 .0 30 .5 30 .4 31 .1 31 .8 30 .9 29 .9 30 .0 31 .0 30 .7 84 31 .5 28 .5 28 .7 26 .0 25 .4 28 .0 31 .6 27 .9 27 .7 25 .8 24 .8 27 .6 85 29 .8 27 .0 20 .7 14 .2 23 .5 23 .0 30 .5 26 .8 19 .6 12 .9 21 .6 22 .3 86 32 .6 29 .9 28 .5 28 .0 28 .1 29 .4 30 . 1 28 .0 26 .9 26 .6 26 .6 27 .6 87 30 .7 25 .7 26 .3 25 .2 19 .7 25 .5 29 .0 22 .8 24 . 3 23 .7 17 .7 23 .5 88 20 .6 16 .7 18 .7 18 .7 27 .4 20 .4 19 .6 15 .4 16 .8 18 .2 27 .5 19 .5 89 30 .6 29 .5 29 .0 28 .1 19 .9 27 .4 30 .8 28 .6 28 .5 28 . 1 20 .8 27 .4 90 31 .2 30 .3 30 .9 29 .9 32 .6 31 .0 31 .8 28 .9 28 .3 27 .9 29 .2 29 .2 91 33 .4 28 .7 30 .1 29 .8 28 .9 30 .2 31 .8 28 .9 28 .3 27 .9 29 .2 29 .2 92 33 .0 28 .8 29 .4 29 .9 30 .5 30 .3 32 .2 26 .5 28 .2 28 .7 28 .3 28 .8 93 31 .4 29 .0 28 .3 28 .4 29 .1 29 .3 29 .8 28 .2 27 .7 28 .3 28 .8 28 .6 94 31 .0 29 .2 28 .9 28 .7 29 .6 29 .5 30 .1 27 .8 28 .2 28 .9 29 .1 28 .8 95 31 .0 28 .1 26 .9 25 .5 24 .1 27 . 1 29 .6 27 .2 24 . 6 23 .8 22 .9 25 .6 96 31 .0 28 .9 29 .6 29 .5 29 .6 29 .7 28 .8 28 . 6 29 .5 30 . 1 30 .4 29 .5 97 31 .9 28 .2 26 .6 29 .7 32 . 1 29 .7 31 .5 26 .8 24 .7 28 . 3 31 .7 28 .6 98 30 .0 27 .4 28 .0 25 . 6 28 .2 27 .8 29 .2 26 .8 27 .2 24 . 3 27 .5 27 . 0 99 33 . 1 27 .2 27 .0 27 . 1 27 .2 28 .3 33 . 1 26 .7 26 .9 27 .3 27 .4 28 .3 100 31 .4 29 .3 29 .0 28 .9 28 . 8 29 .5 31 . 3 28 .9 29 .7 29 . 3 29 .9 29 .8 101 32 .2 29 .9 30 .1 30 . 3 31 . 0 30 .7 32 . 6 29 .2 29 .8 29 .8 31 . 0 30 .5 102 31 .3 27 .8 27 .1 24 . 6 21 .4 26 .5 30 .2 26 .8 25 . 3 22 . 1 18 .7 24 .6 103 30 .4 28 .7 29 .1 28 .6 29 .8 29 .3 31 .8 28 .5 29 .4 29 .6 31 .6 30 .2 104 32 .2 28 .0 28 .3 29 .4 30 .1 29 .6 31 .5 27 .8 27 .7 29 .1 29 .7 29 .1 105 33 .5 30 . 0 29 .4 29 .1 31 .6 30 .7 32 .2 29 .7 29 .5 28 .8 31 .6 30 .3 106 31 .4 29 . 6 29 .4 30 .2 25 . 1 29 .1 30 .6 29 . 1 28 . 3 29 .9 25 .5 28 .7 107 32 .0 29 .4 29 .9 29 .9 29 .4 30 . 1 31 .0 28 . 9 29 . 7 30 . 0 29 .2 29 .8 108 31 .7 28 .9 29 .0 29 .6 30 .5 30 .0 31 .4 28 .5 28 .4 29 .2 30 .4 29 .6 109 27 .0 25 .1 28 .8 30 .8 30 .8 28 .5 26 .2 24 .4 28 . 3 30 .3 30 .0 27 .9 110 31 .0 28 .9 28 .6 27 .7 28 .0 28 .8 30 .8 28 .8 28 .4 28 . 1 29 .0 29 .0 111 31 .4 29 .2 29 .9 30 .3 30 . 1 30 .2 31 .4 28 .7 29 .4 29 .8 30 .0 29 .8 112 31 .4 27 .8 26 .7 27 .5 21 .4 27 .0 31 . 6 27 .9 26 .4 27 .9 24 .3 27 .6 113 31 .9 31 .2 31 .0 30 .3 31 .2 31 .1 32 .1 31 .0 30 .9 30 . 1 31 .3 31 . 1 114 30 .9 29 .7 29 .4 29 . 1 29 .3 29 .7 30 .5 29 . 1 29 .2 29 . 3 29 .7 29 .6 115 32 .9 29 .5 29 .5 29 .4 29 .8 30 .2 32 .0 28 .9 29 . 1 28 .6 29 .4 29 .6 116 32 .6 30 .4 29 .8 30 .3 31 .2 30 .9 31 .4 29 .7 29 .2 29 .4 30 .9 30 .1 117 33 . 1 29 .4 28 .8 28 .7 29 .2 29 .8 32 .5 29 . 1 28 .8 28 .6 29 .2 29 .7 AUGUST21 SEPTEMBER17 % v/v % v/v S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 80 33.3 29.9 29.5 29.6 30.8 30.6 34.2 30.0 29.7 29.2 30.6 30.7 81 32.4 29.6 30.1 30.5 30.7 30.6 32.8 29.6 29.3 29.5 30.7 30.4 82 33.9 30.8 30.5 30.6 31.0 31.4 33.3 29.7 29.2 29.2 30.5 30.4 83 33.9 31.4 30.4 30.5 31.2 31.5 33.7 30.7 29.4 30.2 31.1 31.0 241 AUGUST21 SEPTEMBER17 % v/v % v/v- • S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 15cm 30cm 45cm 60cm 90cm Avg. 84 32 .2 28 .8 28 .5 26 . 1 24 .8 28 . 1 32 .6 28 .7 28 . 1 25 .8 25 .3 28 .1 85 31 .0 27 .6 21 .7 14 .7 25 .9 24 .2 32 .8 26 .9 19 .5 12 .1 20 .3 22 .3 86 32 .0 28 .7 27 .8 27 .8 26 .9 28 .6 32 .4 28 .5 27 .8 27 .5 27 .6 28 .8 87 28 .3 23 .9 25 .1 23 .3 17 .1 23 .5 31 .6 22 .9 24 .4 23 .2 16 .7 23 .8 88 20 .7 17 .2 19 . 0 18 .8 27 .8 20 .7 22 .2 14 .1 14 .3 17 .0 27 .3 19 .0 89 31 .8 30 .2 29 .4 28 .7 20 .9 28 .2 32 .6 29 .3 28 .9 27 .0 18 .7 27 .3 90 32 .3 31 .0 31 . 1 30 .7 32 .4 31 .5 32 .1 30 .7 30 .1 29 . 1 32 .0 30 .8 91 33 .0 27 .7 28 .6 28 .8 28 .6 29 .3 33 .7 26 .9 28 .6 29 .3 28 .8 29 .4 92 33 .9 29 .8 30 .5 30 .7 31 .3 31 .2 34 . 6 29 . 3 29 .4 29 . 7 31 .1 30 .8 93 31 .9 29 .5 29 .1 29 .3 29 .8 29 .9 32 .3 29 . 1 28 .4 29 .2 29 .8 29 .7 94 32 .0 29 .0 28 .4 29 . 1 29 .6 29 .6 32 .2 28 .4 28 .3 28 .7 29 .2 29 .4 95 32 .3 28 .1 26 .9 24 .9 23 .2 27 . 1 32 .3 27 .5 25 . 0 24 .0 22 .8 26 .3 96 31 .4 29 .1 29 .9 29 .3 29 .7 29 .9 32 .4 28 .5 29 .0 29 .2 30 .0 29 .8 97 32 .7 28 .1 26 .4 28 .9 32 .4 29 .7 33 .5 26 .5 24 .2 28 .6 32 . 1 29 .0 98 28 . 6 26 .4 26 . 1 23 .5 27 .2 26 . 3 29 .0 26 . 6 25 .3 22 .5 26 .7 26 .0 99 33 . 6 27 .4 26 .8 26 .9 27 .4 28 .4 33 . 1 25 .5 24 .4 25 . 1 27 .3 27 . 1 100 33 .1 30 .1 30 .0 29 .6 29 .9 30 .6 33 .1 28 .9 28 .7 28 . 6 28 .4 29 .5 101 33 .6 30 .7 30 . 6 30 .4 31 . 1 31 . 3 34 .2 29 .9 29 . 5 29 . 6 30 .3 30 .7 102 31 .8 27 .5 26 .0 22 .7 18 . 1 25 .2 32 .5 27 .9 25 . 6 21 . 3 17 .5 25 .0 103 31 .9 29 .0 29 .4 29 .2 30 .5 30 . 0 30 . 3 27 .4 29 . 0 29 .0 30 .0 29 .2 104 33 .6 29 .2 29 .1 29 .9 30 .1 30 .4 34 .1 28 .3 28 . 1 28 .9 29 .5 29 .8 105 34 .0 30 . 1 30 .2 29 .6 32 .6 31 .3 34 .4 28 .9 27 .7 28 .2 32 .1 30 .3 106 32 .7 30 .2 29 .8 30 . 3 30 .2 30 .6 33 .0 29 .2 28 . 2 29 .5 24 .2 28 .8 107 33 .2 29 .7 30 .6 30 .8 31 .3 31 . 1 33 .6 28 .9 29 . 5 28 .9 29 .8 30 . 1 108 32 .4 29 .2 29 .1 30 .0 31 .1 30 .4 33 .8 29 . 0 28 .4 29 .3 30 .6 30 .2 109 27 .9 25 .7 29 .4 30 .6 29 .7 28 .7 27 .0 22 . 6 27 .5 29 .9 29 .1 27 .2 110 32 . 1 29 . 1 29 .4 28 .3 28 .6 29 .5 31 .2 28 .7 28 .1 26 .9 27 .7 28 .5 111 32 .7 29 .2 30 . 1 30 .4 30 . 1 30 .5 32 .7 28 . 6 28 .7 29 .5 28 .9 29 .7 112 32 .0 28 .2 26 .6 27 .7 22 .3 27 .4 32 .8 27 .9 25 . 1 26 .8 19 .8 26 .5 113 33 . 1 31 .5 31 .8 30 .4 30 .8 31 .5 32 .5 30 .5 29 .9 28 .5 29 .7 30 .2 114 33 .0 29 .9 29 .7 29 . 6 30 .3 30 .5 32 .8 29 . 4 29 . 0 29 . 1 28 .7 29 .8 115 33 .0 30 .1 29 .7 29 .2 28 .7 30 .1 33 .8 29 .2 29 .0 28 .5 28 .6 29 .8 116 33 .0 30 .4 29 .8 29 .9 3.0 .7 30 .7 32 .9 30 .0 29 . 2 29 . 3 30 .4 30 .3 117 32 .9 30 .3 29 .3 28 .7 29 . 1 30 .1 34 .6 29 .6 28 .6 27 .5 27 .9 29 .6 OCTOBER02 % V / V S i t e l 5 c m 30cm 45cm 60cm 90cm Avg. 80 33.2 29.8 29.6 29.2 30.2 30.4 81 31.9 29.4 29.3 29.5 30.4 30.1 82 32.4 29.5 29.2 29.1 30.1 30.1 83 32.7 30.5 29.4 30.1 30.8 30.7 84 31.4 28.5 28.0 25.6 23.9 27.5 85 31.6 25.7 17.6 11.3 18.5 20.9 OCTOBER02 — % v / v  Sitel5cm 30cm 45cm 60cm 90cm Avg. 86 31 .5 28 .3 27 .7 27 .5 27 .2 28 .5 87 30 .7 22 .7 24 .4 23 .2 16 .5 23 .5 88 21 .5 14 .0 14 .3 16 .9 26 .9 18 .7 89 31 .6 29 . 1 28 .8 27 .0 18 .4 27 .0 90 31 .2 30 .5 30 .1 29 . 1 31 .6 30 .5 91 32 .7 26 .7 28 .6 29 . 3 28 .4 29 . 1 92 33 .6 29 .1 29 .4 29 .7 30 .8 30 .5 93 31 .3 28 .9 28 .3 29 .2 29 .4 29 .4 94 31 .3 28 .2 28 .2 28 .7 28 .9 29 .1 95 31 .4 27 .3 25 .0 23 .9 22 .5 26 .0 96 31 .4 28 .3 28 .9 29 .2 29 .7 29 .5 97 32 .6 26 .3 24 .2 28 .6 31 .7 28 .7 98 28 .2 26 .4 25 .3 22 .5 26 .4 25 .7 99 32 .2 25 .3 24 .4 25 .1 26 .9 26 .8 100 32 .1 28 .7 28 .7 28 .5 28 .1 29 .2 101 33 .2 29 .7 29 .5 29 . 6 29 .9 30 .4 102 31 .5 27 .7 25 .6 21 .3 17 .3 24 .7 103 29 .4 27 .2 29 . 0 29 . 0 29 . 6 28 .8 104 33 .2 28 . 1 28 . 1 28 .8 29 . 1 29. .5 105 33 .4 28 .7 27 .7 28 .2 31 .7 29 .9 106 32 .0 29 .0 28 .1 29 .5 23 .9 28 .5 107 32 .6 28 .7 29 .5 28 .8 29 .4 29 .8 108 32 .8 28 .8 28 .4 29 .3 30 .2 29 .9 109 26 .1 22 .4 27 .4 29 .9 28 .7 26 .9 110 30 .3 28 .5 28 .0 26 .9 27 • 3 28 .2 111 31 .7 28 .4 28 .7 29 .5 28 .6 29 .4 112 31 .8 27 .7 25 .0 26 .8 19 .5 26 .2 113 31 .5 30 .3 29 .9 28 . 5 29 .3 29 .9 114 31 .8 29 .2 29 .0 29 . 1 28 .4 29 .5 115 32 .9 29 . 0 28 .9 28 .4 28 . 3 29 .5 116 31 .9 29 .8 29 .2 29 .3 30 .0 30 .0 117 33 .6 29 .4 28 . 6 27 .5 27 .5 29 .3 243 Spectral reflectance (%) for selected s i t e s , August, 1987. S i t e 550 630 670 870 900 1050 1200 1600 2200 725 No nm nm nm nm nm nm nm nm nm nm 3 3 .8 1 .8 1 • 2 46 .2 45 . 1 39 .4 49 .5 20 .0 15 .8 16 .5 9 4 .6 2 .3 0 .6 42 .8 41 .7 35 .8 35 .6 17 .5 14 .4 13 .2 10 4 .0 0 .9 0 .5 49 .3 48 .1 39 .6 39 .0 17 .8 16 .3 13 .9 12 5 .6 1 .5 2 .1 49 .5 50 .4 44 .2 42 .9 21 .5 17 .4 15 .7 14 5 .8 3 .5 2 .8 50 .2 49 .4 43 .3 32 .6 21 .7 17 .2 16 .9 22 6 .0 3 .6 2 .7 58 .3 59 .7 48 .0 47 .7 24 .6 18 .8 19 .9 31 5 .7 4 .0 3 .1 17 .0 35 .6 37 .2 37 .6 26 .5 40 . 1 17 .2 34 5 . 1 3 .9 3 .3 15 .0 24 .2 27 .2 14 .2 17 .8 29 .6 13 .4 36 6 .4 8 .1 2 .4 63 .0 63 . 1 48 .9 52 .3 28 .1 18 .3 23 .0 56 4 .2 2 .4 1 .5 44 .2 45 .0 38 .9 39 .5 21 .4 15 .5 14 • 3 65 4 .7 2 .4 1 .3 14 .2 19 .9 17 .0 16 .5 14 .4 19 .2 17 .9 66 4 .0 2 .6 2 .1 50 .1 49 .8 42 .7 42 . 1 21 .9 17 .2 14 .2 69 3 .7 1 .5 1 .2 43 .2 43 .0 36 .9 38 .4 21 .7 14 .8 13 .7 74 5 .4 2 .3 1 .8 52 .1 51 .7 45 .7 43 .6 21 .7 17 .4 15 .5 76 4 .4 2 .0 1 .7 58 .1 57 .5 49 .3 45 .8 22 . 1 19 . 3 18 .2 82 3 .2 0 . 3 0 .2 52 .8 52 . 1 44 .9 52 . 6 18 .3 9 . 8 16 .7 83 5 .3 1 .3 1 .4 59 .4 57 .1 49 .4 45 .6 20 .4 10 . 0 17 .2 85 7 . 1 2 .4 0 .3 66 .1 61 .8 59 .4 50 . 1 26 .8 13 .9 21 .7 86 4 .9 0 .3 0 .0 63 .2 59 .1 51 .6 55 .0 20 .9 12 .7 17 .0 88 3 .9 1 .5 0 .7 52 .8 50 .7 45 .9 41 .9 21 .6 11 .5 20 .3 91 6 .0 2 .5 1 .6 63 . 1 64 .8 54 .4 49 .6 24 .8 13 .5 19 .8 96 6 .5 2 .6 1 .6 63 .7 63 .5 55 .0 52 .5 27 .6 15 .3 18 .3 102 6 .2 2 .3 1 .3 68 .3 66 . 1 70 .4 54 .4 28 .2 15 • 0 24 .3 105 5 .5 1 .0 0 .2 10 .8 62 .9 49 .1 48 .4 23 .0 11 .5 21 . 0 112 5 .5 0 .3 0 .2 71 .7 70 .6 52 .5 47 .5 21 .4 11 .7 19 . 1 Spectral reflectance Red - Infrared r a t i o s for selected s i t e s August, 1987 630/ 630/ 630/ 670/ 670/ 670/ S i t e 870 1050 725 870 1050 725 No nm nm nm nm nm nm 3 0. 03 0. 04 0. 10 0. 03 0. 03 0. 07 9 0. 05 0. 06 0. 16 0. 01 0. 02 0. 05 10 0. 01 0. 02 0. 06 0. 01 0. 01 0. 03 12 0. 03 0. 03 0. 09 0. 04 0. 05 0. 13 14 0. 06 0. 07 0. 20 0. 06 0. 07 0. 17 22 0. 06 0. 07 0. 18 0. 05 0. 06 0. 14 31 0. 23 0. 10 0. 23 0. 18 0. 08 0. 18 34 0. 25 0. 14 0. 28 0. 22 0. 12 0. 25 36 0. 12 0. 16 0. 35 0. 04 0. 05 0. 10 56 0. 05 0. 06 0. 17 0. 03 0. 04 0. 10 244 630/ 630/ 630/ 670/ 670/ 670/ S i t e 870 1050 725 870 1050 725 No nm nm nm nm nm nm 65 0. 16 0. 13 0. 13 0. 09 0. 08 0. 07 66 0. 05 0. 06 0. 18 0. 04 0. 05 0. 15 69 0. 03 0. 04 0. 10 0. 03 . 0. 03 0. 09 74 0. 04 0. 05 0. 14 0. 03 0. 04 0. 11 76 0. 03 0. 04 0. 11 0. 03 0. 03 0. 09 82 0. 00 0. 00 0. 01 0. 00 0. 01 0. 01 83 0. 02 0. 02 0. 07 0. 02 0. 03 0. 08 85 0. 03 0. 04 0. 11 0. 01 0. 01 0. 02 86 0. 00 0. 00 0. 01 0. 00 0. 00 0. 00 88 0. 02 0. 03 0. 07 0. 01 0. 01 0. 03 91 0. 03 0. 04 0. 12 0. 02 0. 03 0. 08 96 0. 04 0. 04 0. 14 0. 03 0. 03 0. 09 102 0. 03 0. 03 0. 09 0. 02 0. 02 0. 06 105 0. 09 0. 02 0. 04 0. 02 0. 00 0. 01 112 0. 00 0. 00 0. 01 0. 00 0. 00 0. 01 Physiographic p o s i t i o n , elevation and e f f e c t i v e rooting depth (ERD) for 7 6 dryland s i t e s 1986 1987 1986 1987 J i t e Phys. E l e v . 3 ERD ERD S i t e Phys. E l e v . ERD ERD No Posn. cm m m No Posn. cm m m 1 1 120 1.00 1.00 39 1 100 1.00 1. 00 2 1 102 1.00 1.00 40 1 74 0. 50 1. 00 3 1 134 1.00 1.00 41 1 49 0.25 1. 00 4 2 88 1.00 1. 00 42 3 53 0.50 1.00 5 1 112 1.00 1. 00 43 3 61 0.50 1.00 6 2 119 0.75 1. 00 44 3 56 0.75 1.00 7 3 40 0.50 1.00 45 1 80 0.75 1. 00 8 2 80 1.00 1.00 46 1 52 0.75 1.00 9 3 16 0.75 1.00 47 1 57 1.00 1. 00 10 3 20 0.50 1.00 48 2 73 1. 00 1. 00 11 3 18 0.75 1.00 49 3 109 0.75 1.00 12 1 146 1.00 1.00 50 3 133 1.00 1.00 13 1 61 1.00 1.00 51 2 121 1.00 1.00 14 3 24 0.75 1. 00 52 2 85 1. 00 1. 00 15 2 60 1.00 1.00 53 1 130 1.00 1. 00 16 2 101 0.75 1.00 54 3 110 0.50 1.00 17 2 100 1.00 1.00 55 2 125 0.50 1.00 18 2 95 1.00 1.00 56 1 195 1.00 1.00 19 2 81 0.75 1.00 57 2 149 1.00 1.00 20 2 62 1.00 1.00 58 3 108 1.00 1. 00 21 2 112 1.00 1.00 59 2 115 1.00 1.00 3 E l e v a t i o n above lowest p o i n t i n study area. 1986 1987 S i t e Phys.Elev. ERD ERD No Posn. cm m m 1986 1987 S i t e Phys.Elev. ERD ERD No Posn. cm m m 22 1 153 0.75 1. 00 60 1 133 0.50 1.00 23 2 104 1.00 1. 00 61 1 153 1.00 1.00 24 3 100 1.00 1. 00 62 2 119 0.75 1.00 25 3 60 1.00 0. 75 63 1 147 1.00 1.00 26 2 149 0.50 1. 00 64 2 133 0.50 0.75 27 3 42 0.75 1. 00 65 3 36 0.50 1.00 28 3 53 0.50 1. 00 66 2 41 1.00 1.00 29 2 149 1.00 1. 00 67 3 30 0.75 1.00 30 3 60 1.00 1. 00 68 3 41 0.75 1.00 31 1 196 1.00 1. 00 69 2 98 0.50 1. 00 32 1 128 1.00 1. 00 70 2 117 1.00 1.00 33 3 58 1.00 1. 00 71 2 155 1.00 1.00 34 1 193 1.00 1. 00 1 72 2 126 1.00 1.00 35 2 133 1.00 1. 00 73 1 163 1.00 1.00 36 1 144 1.00 1. 00 74 1 181 1.00 1.00 37 2 92 1.00 1. 00 75 2 141 1. 00 1.00 38 3 109 1. 00 1. 00 76 1 163 1. 00 1.00 Physiographic p o s i t i o n , elevation and e f f e c t i v e rooting depth (ERD) for 38 i r r i g a t e d s i t e s 1986 1987 1986 1987 S i t e Phys.Elev. ERD ERD S i t e Phys.Elev. ERD ERD No Posn. cm m m No Posn. cm m m 80 2 108 0.75 1.00 99 2 157 0.75 1.00 81 3 44 0.50 1.00 100 2 130 0.50 1.00 82 3 34 0.50 1.00 101 3 80 0.50 1. 00 83 3 52 0.75 1.00 102 1 161 1. 00 1. 00 84 1 134 1.00 1.00 103 3 93 0.50 1. 00 85 1 186 0.75 1.00 104 1 85 0.50 1.00 86 3 57 0.75 1.00 105 3 57 0.50 1.00 87 3 110 0.50 1. 00 106 1 96 0.50 1.00 88 1 151 0.75 0.75 107 3 52 0.75 1.00 89 2 96 1.00 1.00 108 3 80 0.75 1. 00 90 3 68 0.75 1.00 109 3 96 0.50 1. 00 91 2 42 0.75 1.00 110 2 88 0.50 0.75 92 3 80 0.50 1.00 111 2 98 0.50 1.00 93 1 101 0.50 1.00 112 2 155 1.00 1.00 94 2 69 0.75 1.00 113 3 74 0.75 1,00 95 1 158 1.00 1.00 114 3 61 0.50 1.00 96 3 51 0.50 1. 00 115 3 45 0.75 1. 00 97 2 100 0.75 1.00 116 3 62 0.50 1. 00 98 1 181 0.75 1.00 117 1 126 1. 00 1. 00 246 APPENDIX 4 Central tendency s t a t i s t i c s for selected biophysical v a r i a b l e s . S o i l chemical properties for 56 dryland s i t e s S p r i n g Depth Mean SD Range V a r i a n c e CV Skewness 1986 cm % pH 0-25 5.1 0.28 4.5 _ 5.9 0.08 6 0 .38 C a C l 2 25-50 5.2 0.24 4.5 - 6.0 0.06 5 0. ,69 C 0-25 2.1 0.48 0.6 - 3.0 0.23 23 -0 .88 % 25-50 1.4 0.52 0.3 - 2.5 0.26 36 -0 .06 Tot N 0-25 0.19 0.06 0. 05 - 0.28 0. 004 34 -0 .43 % 25-50 0.18 0.06 0.00 - 0.31 0. 004 35 -0 .64 Bray P 0-25 40.1 16.8 9.0 - 83.0 284 42 0 .49 ppm 25-50 16.4 9.6 2.0 - 52.0 92 58 1 .45 Ca 0-25 9.7 3.5 1.3 - 21.6 12.3 36 0 . 34 m. e. % 25-50 8.6 2.7 1.8 - 18.1 7.2 31 0 .13 Mg 0-25 1.49 0.43 0.20 - 2.60 0.18 29 -0 .76 m.e.% 25-50 1.52 0.43 0.40 - 2.50 0.19 29 -0 .78 K 0-25 0.6 0.49 0.13 - 2.49 0.24 79 2 .24 m.e.% 25-50 0.5 0.4 0.04 - 2.17 0.16 80 1 .76 BS 0-25 54 . 0 15.6 12.0 - 100.0 245 29 0 .04 % 25-50 56.4 12.9 19.0 - 100. 0 168 23 0 .04 CEC 0-25 21.8 3 . 8 8.2 - 30.1 14.9 18 -1 .38 m.e.% 25-50 19.0 4.4 6.1 - 35.7 19.3 23 -0 . 16 Tot S 0-25 0.027 0.005 0.008 - 0.037 0.000 19 -1 .15 % 25-50 0.016 0.005 0. 005 — 0. 028 0. 000 31 0 . 11 F a l l 1986 pH 0-25 4.9 0.22 4.4 — 5.6 0.05 5 0 .37 C a C l 2 25-50 4.9 0.18 4.6 - 5.5 0. 03 4 0. ,58 Tot N 0-25 0.22 0.04 0.06 - 0.29 0. 002 20 -1 .59 % 25-50 0.13 0.05 0.02 - 0.25 0.003 41 0 . 08 Bray P 0-25 113 43 38.0 - 233 . 0 1817 38 0 .58 ppm 25-50 42 24 10.0 - 115.0 . 577 57 1 .20 Ca 0-25 8.5 1.8 2 .1 - 11.2 3 . 3 21 -1 .30 m.e.% 25-50 7.8 1.7 2.2 - 13.9 3 .1 22 -0 .17 Mg 0-25 1.52 0.33 0.30 - 2.20 0.11 22 -1 .20 m.e.% 25-50 1.58 0.35 0.50 - 2.20 0.12 22 -0 .97 K 0-25 0.5 0.35 0.06 - 1.76 0.12 72 1 .66 m.e.% 25-50 0.4 0.29 0.04 - 1.57 0. 08 77 1 .88 BS 0-25 49.0 7.5 26.0 - 64.0 56 15 -0 .22 % 25-50 53.1 9.4 30.0 - 97.0 88 18 1 .27 Tot S 0-25 n.d. n.d. n .d • n.d. n.d. n .d. % 25-50 n.d. n.d. n .d n.d. n.d. n .d. 247 S o i l chemical properties for 28 i r r i g a t e d s i t e s S p r i n g Depth Mean SD Range V a r i a n c e CV Skewness 1986 cm % pH 0-25 5.3 0.24 5.0 - 6.0 0.06 5 1 .01 C a C l 2 25-50 5.3 0.21 4.9 - 5.9 0. 05 4 0. ,70 C 0-25 2.2 0.31 1.3 - 2.7 0.09 14 -0 .62 % 25-50 1.3 0.47 0.3 - 2.3 0.22 35 0 .22 Tot N 0-25 0.14 0.06 0.0 - 0.27 0.004 42 0 .12 % 25-50 0.21 0.05 0.07 - 0.28 0.002 22 -1 .18 Bray P 0-25 44.8 14.7 23.0 - 83.0 217 33 0 .56 ppm 25-50 15.4 7.3 6.0 - 39.0 53 47 1 .16 Ca 0-25 10.7 1.6 7.9 - 14.6 2.67 15 0 .35 m.e.% 25-50 9.2 1.8 3.9 - 13.1 3 .19 19 -0 .34 Mg 0-25 1.34 0.18 1.0 - 1.8 0.03 14 0 .52 m.e.% 25-50 1.41 0.28 0.6 - 2.2 0.08 20 0 . 36 K 0-25 0.43 0.17 0.13 - 0.82 0.03 41 0 .22 m.e.% 25-50 0.31 0.18 0.06 - 0.67 0. 04 60 0 .40 BS 0-25 56.2 6.19 44.0 - 71.0 38. 30 11 0 .69 % 25-50. 56.5 7.54 36.0 - 71.0 56.80 13 -0 .48 CEC 0-25 22.4 2.77 14.8 - 27.9 7.71 12 -0 .52 m.e.% 25-50 19.5 3.39 13.2 - 24.9 11.49 17 0 .02 Tot S 0-25 0. 027 0. 004 0. 017 - 0. 034 0.000 15 -0 .25 % 25-50 0.016 0. 006 0. 005 — 0. 031 0.000 38 0 .69 F a l l 1986 PH 0-25 4.9 0.22 4.7 — 5.4 0.05 4 0 .67 C a C l 2 25-50 4.9 0.20 4.7 - 5.4 0.04 4 0. ,80 Tot N 0-25 0.21 0.37 0.11 - 0.27 0.001 17 -0 .88 % 25-50 0.12 0.05 0.03 - 0.22 0.002 38 0 .20 Bray P 0-25 157 66 55.0 - 405.0 4329 42 1 .32 ppm 25-50 52 24 23.0 - 125.0 556 45 1 .12 Ca 0-25 9.9 1.90 3.4 - 14.2 3.63 19 -0 . 60 m.e.% 25-50 8.5 1.39 4.5 - 11.2 1.94 16 -0 .64 Mg 0-25 1.04 0.51 0.20 - 1.80 0.26 49 -0 .40 m.e.% 25-50 1.02 0.54 0.10 - 1.80 0.29 53 -0 .22 K 0-25 0. 34 0.15 0.10 - 0.70 0. 02 42 0 . 51 m.e.% 25-50 0.26 0.15 0.08 - 0.65 0.02 58 0 .92 BS 0-25 51.4 8.28 34.0 - 77. 0 7.71 16 0 .90 % 25-50 51.2 7.50 36. 0 - 68.0 56 17 -Q .07 Tot S 0-25 n.d. n.d. n.d n.d. n.d. n .d. % 25-50 n.d. n.d. n • d n.d. n.d. n .d. 248 S o i l physical properties and elevation for 56 dryland s i t e s Depth Mean SD Range V a r i a n c e CV Skewness cm % AWSC 0-25 55.6 7.3 30. 0 - 70.0 54 13 -1. 37 mm 25-50 54.2 8.7 15. 0 - 70.0 77 16 -2.10 50-75 5.6 11.1 15. 0 - 78.0 124 22 -0.74 75-100 44.4 15.8 8. 0 - 78.0 251 36 -0.61 0-100 203.8 34.0 95. 0 - 267.0 1159 17 -0.86 PSC 1 ERD 171.9 46.1 58. 0 - 255.0 2133 27 -0.37 0-25 3.8 - 1 - 5 — - -25-50 3.8 - 1 - 5 — - -50-75 3.7 — 1 - 5 — — -75-100 3.2 - 1 - 5 - - -0.03MPa 0-25 39.3 4.7 19. 0 - 45.0 22.1 12 -2.83 % v/v 25-50 36.7 6.0 10. 0 - 45.0 36.2 16 -2.53 50-75 32.8 7.3 9. 0 - 42 . 0 53 .7 22 -1.35 75-100 27.8 9.7 5. 0 - 40.0 95.2 35 -0.77 1.5MPa 0-25 17.1 2.5 7. 0 - 23.0 6.6 15 -1.80 % v/v 25-50 15.1 3.4 4. 0 - 20.0 11.8 23 -1.60 50-75 12.7 4.3 3 . 0 - 20.0 18.6 34 -0. 39 75-100 10.1 4.3 2. 0 - 19.0 18.7 43 -0.02 ELEV. 100.3 45.1 16. 0 - 196. 0 2033 45 -0. 04 cm S o i l physical properties and elevation for 28 i r r i g a t e d s i t e s Depth Mean SD Range V a r i a n c e CV Skewness cm % AWSC 0-25 54.8 5.9 38.0 68.0 35.2 11 -0.27 mm 25-50 53.1 8.3 23.0 - 65.0 70.1 16 -1.68 50-75 53.0 7.4 28.0 - 65.0 55.5 14 -2.02 75-100 53.0 7.0 35.0 - 63.0 49.0 1.3 -0.85 0-100 213.5 23.4 130. 0 - 250. 0 551 11 -1.36 PSC 1 ERD 144.6 42.2 86.0 - 235.0 1785 29 0.54 0-25 4.1 - 2 - 5 - - -25-50 4.3 - 1 - 5 - - -50-75 4.4 - 1 - 5 — - -75-100 3.7 - 1 - 5 — — — 0.03MPa 0-25 40. 3 2.9 27.0 - 44.0 8.7 7 -2.96 % v/v 25-50 38.1 5.5 15. 0 - 44.0 30.1 14 -2.51 50-75 36.0 6.0 15.0 - 43. 0 36.4 17 -1.99 75-100 33.8 5.7 19.0 - 42.0 32.6 17 -0.77 1.5MPa 0-25 18.4 2.2 12.0 - 23.0 5.2 12 -0.69 % v/v 25-50 16.9 3.3 6.0 - 21.0 10.7 19 -1.59 50-75 14.8 3.6 4.0 - 19.0 12.6 24 -1.27 75-100 12.6 3.5 5.0 - 20.0 12.9 28 -0.13 249 Depth Mean SD Range V a r i a n c e CV Skewness cm % ELEV. 95.2 41.5 34.0 - 186.0 1725 44 0.61 cm 1 PSC 1 = sandy, 2 = coarse loamy, 3 = f i n e loamy, 4 = f i n e s i l t y , 5 = f i n e c l a y e y . Dry matter, f o l i a r elements and energy components f o r 56 dryland s i t e s Cut Mean SD Range V a r i a n c e CV Skewness 1986 No. % DM 1 6.0 1.1 3.8 — 8.7 1.8 22 1.57 T/ha 2 4.0 0.6 2.6 - 5.5 0.4 16 0.33 3 2.8 0.5 1.5 - 4.3 0.3 20 0.01 4 1.1 0.3 0.4 - 1.7 0.1 25 0. 07 5 1.6 0.5 0.5 - 2.7 0.3 32 0.33 Ca 1 0.21 0.11 0.12 - 0.81 0. 01 52 3.89 % 2 0.43 0.16 0.21 - 0.96 0. 02 37 1.36 3 0.42 0.14 0.25 - 0.97 0. 02 32 1. 64 4 0.55 0.14 0.28 - 0.99 0.02 25 0.56 5 0.52 0.15 0.18 - 0.95 0. 02 29 0.45 Mg 1 0.13 0.02 0.08 - 0.25 0.001 19 2.13 % 2 0.18 0.03 0.10 - 0.27 0.001 18 -0.11 3 0.20 0.03 0.16 - 0.31 0. 001 13 1.19 4 0.25 0.03 0.22 - 0.34 0. 001 11 0.87 5 0.26 0.03 0.21 - 0.36 0.001 13 0.70 K 1 3.16 0.39 2 . 03 - 4 .33 0.15 13 0.16 % 2 3.22 0.65 1.63 - 4.48 0.43 20 -0.41 3 3.37 0.78 1.58 - 4.75 0. 61 23 -0.89 4 3.07 0.73 1.38 - 4.25 0.54 24 -0.83 5 2.83 0.49 1.58 - 3.80 0.25 18 -0.62 N 1 2.34 0.29 1. 57 - 3 .18 0.08 13 0.24 % 2 2.28 0.28 1.80 - 3.06 0.08 12 0.74 3 3.02 0.33 2.29 - 3.70 0.11 11 0.28 4 3.68 0.27 2.98 - 4.62 0.07 7 0.08 5 3.47 0.29 2.93 - 4.40 0.08 8 0.45 P 1 0.36 0. 02 0.31 - 0.44 0. 001 6 0.82 % 2 0.36 0.03 0.32 - 0.60 0. 001 10 4.51 3 0.33 0.03 0.27 - 0.43 0. 001 9 0.56 4 0.32 0. 03 0.25 - 0.37 0. 001 8 -0.19 5 0.29 0.07 0.15 - 0.43 0.006 26 -0.08 Zn 1 17.5 2.64 12.0, - 26.0 6.97 15 1.36 ppm 2 16.2 3.10 12.0 - 28.0 9. 61 19 1.22 3 17.6 2.60 13.0 - 26.0 6.78 15 0.67 4 23.7 2.77 17.0 - 33.0 7.70 12 0.71 5 25.2 4.29 17.0 - 38.0 18.45 17 0.44 250 Cut Mean SD Range V a r i a n c e CV Skewness 1986 No. % Fe 1 112.1 115.2 35.0 — 505.3 13280 103 2. 24 ppm 2 133.6 64.1 57.0 - 347.0 4111 48 1. 41 3 117.6 97.7 50.0 - 670.0 9554 83 4. 03 4 293.5 368.9 130.0 - 3100.0 136154 126 6. 24 5 720.6 800.1 100.0 - 5000.0 640145 111 2. 75 A l 1 0.008 0.013 0.0 - 0.05 0. 000 163 2. 01 % 2 0.008 0.008 0.0 - 0.03 0. 000 100 0. 85 3 0.006 0.012 0.0 - 0.07 0.000 200 3. 52 4 0.027 0.042 0.0 - 0.34 0.002 156 5. 84 5 0.082 0.084 0.01 - 0.47 0.007 102 2. 14 Mn 1 66.8 15.1 30.0 - 106.0 228 23 0. 27 ppm 2 114.9 26.2 40.0 - 178.0 686 23 -0. 38 3 93.8 17.7 45.0 - 128.0 316 19 -0. 21 4 108.8 20.9 70.0 - 163.0 440 19 0. 28 5 123.9 26.4 63.0 - 192.0 698 21 0. 27 ADF 1 34.7 3.1 24.8 - 39.1 9.63 9 -0. 17 % 2 34.9 1.52 28.8 - 39.1 2.32 4 -0. 55 3 30.3 1.62 26.0 - 33.2 2.63 5 -0. 45 4 29.1 2.51 18.0 - 36.4 6.33 9 -1. 11 5 29.6 2.65 23.5 - 35.0 7. 06 9 -0. 12 DE 1 12.4 0.75 10.8 - 14.8 0.56 6 0. 17 Mj/kg DM 2 12.3 0.36 11.4 - 13.8 0.13 3 0. 61 3 13.4 0.39 12.8 - 14.5 0.15 3 0. 45 4 13.7 0. 60 12.0 - 16.4 0.36 4 1. 11 5 13.6 0.63 12.3 - 15.1 0.40 5 0. 13 DMTOT 15.7 1.99 11.9 - 22.7 3 .95 13 0. 84 T/ha Dry matter, f o l i a r elements and energy components for 28 i r r i g a t e d s i t e s Cut Mean SD Range V a r i a n c e CV Skewness 1986 No. % DM 1 5.3 1.1 3.7 — 7. 5 1. 12 20 0. 42 T/ha 2 4.9 1.1 3.1 - 8. 9 1. 25 23 1. 25 3 2.4 0.4 1.5 - 3 . 3 0. 15 16 0. 12 4 1.4 0.3 1.0 - 2. 2 0. 06 18 0. 51 5 1.1 0.2 0.8 - 1. 7 0. 09 26 -0. 77 Ca 1 0.21 0. 03 0.15 - 0. 26 0. 001 14 . 0. 31 % 2 0.44 0. 09 0.26 - 0. 71 0. 009 23 0. 87 3 0.47 0.7 0.28 - 0. 64 0. 006 18 0. 83 4 0.51 0.1 0.39 - 0. 88 0. 01 19 0. 89 5 0.54 0.14 0.20 - 0. 82 0. 019 27 0. 10 251 1986 Cut No. Mean SD Range V a r i a n c e CV % Skewnei Mg 1 0.11 0.02 0.09 — 0.15 0.000 13 -0.33 % 2 0.27 0.02 0.15 - 0.26 0.001 13 0.68 3 0.23 0.03 0.16 - 0.27 0. 001 15 -0.04 4 0.32 0.03 0.20 - 0. 32 0.001 12 0.04 5 0.35 0.03 0.21 - 0.33 0.001 12 0.37 K 1 3.20 0.3 2.63 - 3.73 0.08 9 -0.39 % 2 3.04 0.5 1.73 - 4.10 0.21 15 -0.40 3 3.03 0.7 1.58 - 3.90 0.51 24 -0.37 4 3.07 0.7 1.53 - 3.90 0.42 21 -0.67 5 2.94 0.6 1.80 - 3.78 0.32 20 -0.45 N 1 2.31 0.2 1.74 - 2.78 0.05 10 -0.48 % 2 2.48 0.3 1.82 - 3.07 0.06 11 0.47 3 3.16 0.4 2.57 - 3.69 0.12 11 -0.08 4 4.13 0.4 3.34 - 4.81 0.12 9 -0.24 5 4.16 0.3 3.10 - 4.63 0.11 8 -1.01 P 1 0.42 0.02 0.32 - 0.41 0.000 5 0.06 % 2 0.47 0.02 0.31 - 0.40 0. 001 6 -0.06 3 0.32 0.03 0.27 - 0.43 0. 001 10 0.63 4 0.34 0.03 0.25 - 0.44 0. 001 11 -0.23 5 0.41 0. 04 0. 32 - 0.47 0.001 9 0.17 Zn 1 16.57 1.6 12.0 - 20.0 2.41 9 -0.46 ppm 2 16.34 2.4 13 . 0 - 22.0 5. 65 14 0.23 3 19.17 2.0 16. 0 - 23.0 4 .17 11 0.05 4 22.81 2.3 18.0 - 28.0 5.43 10 -0.11 5 22.75 2.2 20.0 28.0 4 . 63 9 0.46 Fe 1 52.93 43.9 35.0 - 295. 0 1925 83 4.91 ppm 2 260.1 273.6 77.0 - 1407.0 74865 105 3.00 3 73.7 18.8 50.0 - 150. 0 354 26 1.95 4 375.3 211.7 150.0 - 1400.0 44836 56 3.32 5 1245.8 839.1 300.0 - 3350.0 704079 67 1.18 A l 1 0.001 0. 004 0.0 - 0. 02 0.000 400 3.99 % 2 0.020 0.032 0.0 - 0.15 0. 001 160 2.84 3 0. 001 0.002 0.0 - 0.01 0. 000 200 4.17 4 0.014 0. 023 0.0 - 0.13 0. 001 164 3.96 5 0.094 0.086 0.02 - 0.36 0.007 91 1.73 Mn 1 65.1 11.9 42.0 - 98.0 141 18 0.17 ppm 2 116.1 17.6 82.0 - 166.0 310 15 0.48 3 88.9 11.0 65.0 - 108.0 121 12 -0.28 4 97.6 11.7 70.0 - 122.0 136 12 -0. 09 5 93.2 15.3 65.0 - 132.0 235 16 0.36 ADF 1 34.5 1.7 31.4 - 39.3 2.7 5 0.77 % 2 34.8 0.9 32.8 - 37.5 0.9 3 0.41 3 31.7 1.7 28.1 - 35.6 2.7 5 -0.01 4 28.7 2.4 20.5 - 36.3 5.6 8 0.10 5 25.9 2.4 22.3 - 31.6 4.4 8 0. 83 252 Cut Mean SD Range V a r i a n c e CV Skewness 1986 No. % DE 1 12. .4 0. .39 11. .3 - 13. .2 0. . 16 3 -0. ,76 Mj/kg DM 2 12. .3 0. .25 11. .7 - 12. .9 0. .06 2 -0. .48 3 13. .1 0. .39 12. .2 - 14. .0 0. .15 3 -0. .01 4 13. .8 0. .57 12. .0 - 15. .8 0. .33 4 -0. ,22 5 14. .5 0. .51 13, .2 - 15. .4 0. .26 3 -0. ,75 DMTOT 15. .1 1.8 12. .1 - 19. .5 3. .20 12 0. .39 T/ha Dry matter, f o l i a r elements and energy components for 56 dryland s i t e s 1987 Cut No. Mean SD Range V a r i a n c e CV % Skewness DM 1 3.5 0.6 2.5 — 5.5 0.36 17 0.93 T/ha 2 3.7 0.6 2.7 - 5.5 0. 38 17 0.84 3 2.6 0.4 1.8 - 3.8 0.18 16 0.29 4 1.3 0.4 0.4 - 2.1 0.14 30 -0.27 5 0.7 0.3 0.1 - 1.2 0.09 42 -0.36 Ca 1 0.44 0.1 0.14 - 0.84 0.02 39 0.72 % 2 0.62 0.3 0.28 - 1.59 0.07 43 1. 64 3 0. 67 0.2 0.30 - 1.19 0.04 33 0.46 4 0.93 0.2 0.46 - 1.48 0.05 24 0.08 5 0.87 0.2 0.39 - 1.40 0.05 29 0.49 Mg 1 0.15 0.02 0.12 - 0.22 0.00 14 1.01 % 2 0.21 0.03 0.14 - 0.29 0.001 15 0.17 3 0.25 0.03 0.20 - 0.32 0.001 12 0.21 4 0.36 0.04 0.21 - 0.37 0.001 13 0.08 5 0.34 0.05 0.18 - 0.41 0.002 17 0.21 K 1 3.31 0.5 2.30 - 4.65 0.25 15 0.54 % 2 3.21 0.7 1.73 - 4.85 0.44 21 -0.34 3 3 . 22 0.7 1.58 - 4 . 63 0.46 21 -0.43 4 2.86 0.8 1.35 - 4.80 0.64 28 -0.14 5 2.52 0.8 1.18 - 4.05 0.57 30 0.01 N 1 3.28 0.3 2 . 69 - 3.92 0. 08 9 0.23 % 2 3.37 0.3 2.84 - 3 .94 0. 08 8 -0.12 3 3.53 0.3 2.76 - 4.09 0. 09 8 -0.42 4 3.74 0.2 3.27 - 4.28 0.05 6 0.23 5 3.97 0.3 3.23 - 4.89 0.07 7 0.33 P 1 0.31 0.03 0