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Assessment of spatial variability of silage corn quality and biomass using remote sensing and GIS techniques Ryan, Andrea L. 1991

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ASSESSMENT OF SPATIAL VARIABILITY OF SILAGE CORN QUALITY AND BIOMASS USING REMOTE SENSING AND GIS TECHNIQUES By ANDREA L. RYAN B.Sc, The University of B r i t i s h Columbia, 1986 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE i n THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF SOIL SCIENCE We accept t h i s thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA A p r i l 1991 ®Andrea L. Ryan, 1991 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. 1 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 of Soil Science The University of British Columbia Vancouver, Canada Date fyril oe, mi DE-6 (2/88) ABSTRACT The Matsqui area of the Lower Fraser Valley exhibits extreme s o i l heterogeneity, as the a l l u v i a l s o i l s i n the area have been deposited by the Fraser River as a series of coarse-textured ridges and finer-textured depressional areas. This v a r i a b i l i t y poses some obvious problems with respect to a g r i c u l t u r a l management. The main aim of t h i s study was to evaluate s o i l s p a t i a l v a r i a b i l i t y i n four f i e l d s , and to r e l a t e t h i s s o i l v a r i a b i l i t y to corn production and q u a l i t y . S i t e conditions, topography, and s o i l chemical and physical va r i a b l e s were related to corn biomass and nutrient concentrations using conventional correlation/regression analyses, and more s p a t i a l l y representative techniques such as those provided by remote sensing and geographic information systems. Variations i n such biophysical variables as s o i l moisture, elevation, and bulk density had consistent impacts on corn p r o d u c t i v i t y , although these e f f e c t s varied from f i e l d to f i e l d , being influenced by inherent s o i l properties and i n d i v i d u a l f i e l d management. Good relationships were found between p i x e l brightness values extracted from d i g i t i z e d colour infra-red photos and corn q u a l i t y variables. In three out of four f i e l d s , near i n f r a - r e d p i x e l values gave good estimates of t o t a l corn crude protein content. S i g n i f i c a n t relationships were also found between p i x e l brightness values and corn phosphorus and calcium contents i i i i n c e r t a i n f i e l d s . The s p a t i a l v a r i a b i l i t y of corn qu a l i t y and biomass could be quantified using image analysis c l a s s i f i c a t i o n techniques. The r e s u l t i n g c l a s s i f i e d images indicate to the farm operator where high vs low q u a l i t y corn i s being produced, and thereby provide a tool for s e l e c t i v e l y managing and harvesting the f i e l d s . The relationships and q u a n t i f i c a t i o n of corn productivity and q u a l i t y in the f i e l d s can further be improved through incorporation of the image data with the biophysical data base using GIS techniques. A multiple regression equation showing a s i g n i f i c a n t r e l a t i o n s h i p between elevation and p i x e l brightness values, and t o t a l corn phosphorus concentration was incorporated within the GIS to produce a quantitative corn q u a l i t y map f o r the f i e l d exhibiting t h i s r e l a t i o n s h i p . The GIS overlay c a p a b i l i t y f a c i l i t a t e s the c l a s s i f i c a t i o n of several corn va r i a b l e s , and allows the r e s u l t s to be displayed i n a s p a t i a l manner. For example, corn biomass and q u a l i t y maps were ov e r l a i n using GIS techniques, to produce a combination map which then r e f l e c t e d both the qu a l i t y and quantity of corn found i n the f i e l d . Through integration of remote sensing and GIS techniques, s o i l and crop v a r i a b i l i t y can be displayed i n a s p a t i a l manner. The output from such procedures can a i d farm operators i n making s e l e c t i v e f i e l d management and harvesting decisions. i v TABLE OF CONTENTS CHAPTER PAGE ABSTRACT i i LIST OF SYMBOLS i x LIST OF TABLES x i LIST OF FIGURES x i i i ACKNOWLEDGEMENTS xiv 1. INTRODUCTION AND METHODS 1 1.1 Aims and Objectives 2 1.2 Background 3 1.2.1 S o i l / s i t e v a r i a b i l i t y and crop production 3 1.2.2 Remote sensing of crop q u a l i t y and biomass 6 1.2.3 Remote sensing and GIS 11 1.3 S i t e Description 12 1.3.1 Location and topography 12 1.3.2 Climate 12 1.3.3 S o i l o r i g i n s and descriptions 13 1.3.4 Land use and management 16 1.4 Methods 18 1.4.1 F i e l d methods 18 1.4.2 S o i l analysis methods 21 1.4.3 Corn analysis methods 22 1.4.4 Remote sensing and image analysis methods , 2 3 1.4.5 Geographical information system methods 24 1.4.6 S t a t i s t i c a l methods 24 1.5 Overview of Research 26 2. VARIABILITY BETWEEN AND WITHIN FIELDS 28 2.1 S t a t i s t i c a l Analyses 28 2.2 S o i l V a r i a b i l i t y 2.2.1 S o i l v a r i a b i l i t y : o v e r a l l and within i n d i v i d u a l f i e l d s 2.2.2 S o i l differences between f i e l d s 2.3 Corn V a r i a b i l i t y 2.3.1 Corn v a r i a b i l i t y : o v e r a l l and within i n d i v i d u a l f i e l d s 2.3.2 Corn differences between f i e l d s 2.4 Pixel Brightness Value V a r i a b i l i t y 2.4.1 P i x e l value v a r i a b i l i t y : o v e r a l l and within i n d i v i d u a l f i e l d s 2.4.2 P i x e l value differences between f i e l d s RELATIONSHIPS BETWEEN CROP VARIABLES AND SOIL PROPERTIES 3.1 Overall Relationships 3.1.1 Overall corn biomass relationships 3.1.2 Overall corn q u a l i t y relationships 3.2 Individual F i e l d Relationships 3.2.1 Biomass relationships within i n d i v i d u a l f i e l d s 3.2.2 Corn quality relationships within i n d i v i d u a l f i e l d s 3.3 Summary of Main Factors Affecting Corn Production on an Individual F i e l d Basis RELATIONSHIPS BETWEEN REMOTE SENSING, SOIL, AND CROP VARIABLES 4.1 Relationships between S o i l and Remote Sensing 4.1.1 Overall relationships 4.1.2 Comparison of Overall F i e l d Relationships with Individual F i e l d Relationships 4.2 Relationships between Crop Variables and Remote Sensing 4.2.1 Overall relationships 4.2.2 Comparison of Overall F i e l d Relationships with Individual F i e l d Relationships 4.2.2.1 Relationships between corn and untransformed p i x e l values 4.2.2.2 Relationships between corn and transformed p i x e l values 4.3 Evaluation of Remote Sensing Results CLUSTER ANALYSIS 5.1 Overall F i e l d Cluster Analysis 5.1.1 S i g n i f i c a n t l y d i f f e r e n t c l u s t e r variables 5.1.2 S i g n i f i c a n t l y d i f f e r e n t associated variables 5.1.3 Individual f i e l d s i t e groupings within the overall cluste groups 5.2 600 F i e l d Cluster Analysis 5.2.1 S i g n i f i c a n t l y d i f f e r e n t c l u s t e r variables and associated corn variables 5.2.2 S i g n i f i c a n t l y d i f f e r e n t associated variables COMPARISON OF LINEAR CORN PRODUCTIVITY RELATIONSHIPS AND SPATIAL CORN PRODUCTIVITY RELATIONSHIPS 6.1 Comparison of Relationships on an Overall F i e l d Basis 6.1.1 Comparison of s o i l and s i t e variables 6.1.2 Comparison of p i x e l brightness values 6.2 Comparison of Relationships i n the 600 F i e l d 6.2.1 Comparison of s o i l and s i t e variables v i i CHAPTER PAGE 6.2.2 Comparison of p i x e l brightness values 94 7. SPATIAL ANALYSIS OF CROP CHARACTERISTICS USING IMAGE ANALYSIS AND GIS TECHNIQUES 96 7.1 Image C l a s s i f i c a t i o n 96 7.1.1 Image c l a s s i f i c a t i o n methods 96 7.1.2 300 f i e l d image c l a s s i f i c a t i o n 102 7.1.3 400 f i e l d image c l a s s i f i c a t i o n 107 7.1.4 600 f i e l d image c l a s s i f i c a t i o n 111 7.2 GIS Techniques 114 7.2.1 GIS methods 114 7.2.2 300 f i e l d GIS r e s u l t s 116 7.2.3 400 f i e l d GIS r e s u l t s 126 7.3 Evaluation of Image C l a s s i f i c a t i o n and GIS Results 132 8. CONCLUSIONS AND RECOMMENDATIONS 134 8.1 Corn-Soil/Site and Remote Sensing Relationships 134 8.1.1 Relationships between corn variables, and s o i l moisture and elevation 134 8.1.2 Relationships between corn variables and other s o i l v ariables 135 8.1.3 Relationships between corn variables and p i x e l brightness values extracted from d i g i t a l a e r i a l images 13 6 8.2 Spatial D i s t r i b u t i o n of Corn Quality and Biomass using Image Analysis and GIS Techniques 137 8.3 Recommendations 138 LITERATURE CITED 141 v i i i CHAPTER PAGE APPENDICES 147 1. S o i l chemical and physical data 147 2. S o i l and corn p i x e l value data 151 3. Corn biomass and qual i t y data 153 4. S o i l moisture data 165 IX LIST OF SYMBOLS A v a r i e t y of abbreviations have been used throughout t h i s text i n order to minimize s o i l and corn variable descriptions. The following i s a l i s t of the abbreviations and uni t s used for the various s o i l chemical and physical, s o i l moisture, remote sensing, and corn variables studied. S o i l chemical and physical properties, and bare s o i l p i x e l  values PH pHCa C N P S o i l pH S o i l pH (CaCl 2) Total carbon (%) Total nitrogen (%) Available phosphorus (ppm) Na } Ca } Mg } K } CEC Exchangeable bases (meq/lOOg) Cation exchange capacity (meq/lOOg) BD1 } BD2 } BD3 } AVBD } Bulk density of the (0-8.5 cm), (8.5-15 cm), and (15-23.5 cm) depths, and the average of a l l 3 depths, respectively (g/cm3) ELEV Elevation (cm) Gl RI NIR1 Bare s o i l green p i x e l values Bare s o i l red p i x e l values Bare s o i l near infra-red p i x e l values S o i l moisture variables S o i l moisture measurements throughout the growing season are designated by an "np" pr e f i x (neutron probe), followed by a number (1-7) indi c a t i n g the date of measurement, and a l e t t e r (a-e) i n d i c a t i n g the s o i l depth of the measurement. Dates and depths of measurements are l i s t e d below, as well as some s p e c i f i c examples of the abbreviations. X 1 = June 15 a = 15 cm depth 2 = June 30 b = 30 cm depth 3 = J u l y 17 c = 45 cm depth 4 = J u l y 29 d = 60 cm depth 5 = August 17 e = 90 cm depth 6 = August 26 7 = September 10 or 16 eg. npla = s o i l moisture (cm3 H20/cm3 s o i l ) on June 15, at the 15 cm depth np4c = s o i l moisture on July 29, at the 45 cm depth Corn va r i a b l e s , and corn p i x e l values Corn biomass was recorded on the basis of s t a l k , cob, and t o t a l plant weights. Corn nutrient concentrations were also determined on a s t a l k , cob, and t o t a l plant basis. In addition, nutrient contents were reported on an unweighted (%) basis, and on a weighted (g) basis. The prefixes "S", "C", or "T" were used to denote s t a l k , cob or t o t a l corn variables, respectively; the s u f f i x "g" was used to indicate weighted corn nutrients. The biomass and corn nutrient variables measured are shown below, as are some s p e c i f i c examples. SWT Stalk weight (g) CWT Cob weight (g) TWT Total weight (g) CP Corn crude protein P phosphorus Ca calcium DN d i g e s t i b l e nutrients DE d i g e s t i b l e energy eg. SCP = s t a l k crude protein, unweighted (%) CCag = cob Ca, weighted (g/cob) TDNg = t o t a l d i g e s t i b l e nutrients, weighted (g/plant) G2 Corn green p i x e l values R2 Corn red p i x e l values NIR2 Corn near infra-red p i x e l values NIR/R Near infra-red to red p i x e l value r a t i o (corn pixels) ND Normalized difference (see Chapter 4) x i LIST OF TABLES PAGE 1. Comparison of s o i l types found i n 4 study f i e l d s . 14 2. Salient features of dominant s o i l s located i n study f i e l d s . 14 3. Pertinent management features of 4 study f i e l d s . 18 4. Descriptive s t a t i s t i c s for s o i l variables for the 4 study f i e l d s combined. 29 5. Descriptive s t a t i s t i c s for s o i l variables for each of the 4 study f i e l d s . 30 6. S o i l and s i t e variables e x h i b i t i n g s i g n i f i c a n t differences between the 4 study f i e l d s . 3 3 7. Descriptive s t a t i s t i c s for corn variables for the 4 study f i e l d s combined. 35 8. Descriptive s t a t i s t i c s for corn variables for each of the 4 study f i e l d s . 37 9. Corn variables exhibiting s i g n i f i c a n t differences between the 4 study f i e l d s . 38 10. Descriptive s t a t i s t i c s for p i x e l values for the 4 study f i e l d s combined. 43 11. Descriptive s t a t i s t i c s for p i x e l values for each of the 4 study f i e l d s . 4 3 12. Best s o i l regressions for p r e d i c t i o n of corn qu a l i t y (overall f i e l d r e l a t i o n s h i p s ) . 50 13. Comparison of o v e r a l l f i e l d (corn-soil) relationships with i n d i v i d u a l f i e l d r e l a t i o n s h i p s . 51 14. Best regressions for p r e d i c t i o n of corn productivity from s o i l variables (200 f i e l d ) . 58 15. Best regressions for p r e d i c t i o n of corn productivity from s o i l variables (300 f i e l d ) . 58 16. Best regressions for p r e d i c t i o n of corn productivity from s o i l variables (400 f i e l d ) . 59 17. Best regressions for p r e d i c t i o n of corn productivity from s o i l variables (600 f i e l d ) . 59 18. S i g n i f i c a n t relationships between near infra-red p i x e l values brightness and s o i l variables. 64 19. Relationships between untransformed p i x e l values and corn vari a b l e s . 69 20. Best consistent p r e d i c t i v e equations for individual (and overall) f i e l d r e l ationships, using untransformed p i x e l values. 72 21. Relationships between transformed p i x e l values and crop variables. 73 22. Best p r e d i c t i v e equations for o v e r a l l and i n d i v i d u a l f i e l d r e l a tionships, using mainly transformed p i x e l values. 7 6 23. Variables e x h i b i t i n g s i g n i f i c a n t differences between c l u s t e r groups ( a l l f i e l d s combined). 81 x i i PAGE 24. Means and standard deviations for variables e x h i b i t i n g s i g n i f i c a n t differences between c l u s t e r groups ( a l l f i e l d s combined). 82 25. Variables exhibiting s i g n i f i c a n t differences between c l u s t e r groups (600 f i e l d ) . 87 26. Means and standard deviations f o r variables e x h i b i t i n g s i g n i f i c a n t differences between c l u s t e r groups (600 f i e l d ) . 88 27. Predictive equations used to set p i x e l brightness c l a s s l i m i t s for the image c l a s s i f i c a t i o n s . 98 28. Variables exhibiting s i g n i f i c a n t differences between pMap cob CP groups (300 f i e l d ) . 118 29. Means and standard deviations f o r variables e x h i b i t i n g s i g n i f i c a n t differences between pMap cob CP groups (300 f i e l d ) . 119 30. Variables exhibiting s i g n i f i c a n t differences between pMap cob weight groups (300 f i e l d ) . 122 31. Means and.standard deviations for variables e x h i b i t i n g s i g n i f i c a n t differences between pMap cob weight groups (300 f i e l d ) . 123 32. Cob weight-cob CP c l a s s i f i c a t i o n (300 f i e l d ) . 126 33. Variables exhibiting s i g n i f i c a n t differences between pMap corn t o t a l P groups (400 f i e l d ) . 128 34. Means and standard deviations f o r variables e x h i b i t i n g s i g n i f i c a n t differences between pMap corn t o t a l P groups (400 f i e l d ) . 128 x i i i LIST OF FIGURES PAGE 1. Locations of the four f i e l d s studied during the project. 12 2. Sampling design of four f i e l d s studied. 20 3. An overview of the research project. 27 4. Overall relationships between corn biomass and qual i t y , and s o i l / s i t e variables. 47 5. Soi l - c o r n relationships within the in d i v i d u a l study f i e l d s . 53 6. Strongest crop - remote sensing relationships f o r a l l f i e l d s combined. 67 7. Regression l i n e used to estimate class guidelines for supervised image c l a s s i f i c a t i o n (300 f i e l d ) . 100 8. Regression l i n e used to estimate class guidelines for supervised image c l a s s i f i c a t i o n (300 f i e l d ) . 100 9. Regression l i n e used to estimate class guidelines for supervised image c l a s s i f i c a t i o n (400 f i e l d ) . 101 10. Regression l i n e used to estimate class guidelines for supervised image c l a s s i f i c a t i o n (400 f i e l d ) . 101 11. Regression l i n e used to estimate class guidelines for supervised image c l a s s i f i c a t i o n (600 f i e l d ) . 102 12. C l a s s i f i e d image for cob weight (300 f i e l d ) . 105 13. C l a s s i f i e d image for cob crude protein (300 f i e l d ) . 106 14. C l a s s i f i e d image for t o t a l corn P (400 f i e l d ) . 109 15. C l a s s i f i e d image for t o t a l corn CP (400 f i e l d ) . 110 16. C l a s s i f i e d image for t o t a l corn CP (600 f i e l d ) . 113 17. GIS c l a s s i f i c a t i o n f or unweighted cob CP (300 f i e l d ) . 117 18. GIS c l a s s i f i c a t i o n for cob weight (300 f i e l d ) . 120 19. GIS c l a s s i f i c a t i o n of cob weight and cob CP combined (300 f i e l d ) . 125 20. GIS c l a s s i f i c a t i o n f or unweighted t o t a l corn P (400 f i e l d ) . 127 21. GIS c l a s s i f i c a t i o n f or unweighted t o t a l corn P (using both p i x e l values and elevation data) (400 f i e l d ) . 131 xiv ACKNOWLEDGEMENTS I would l i k e to express my thanks to the s t a f f and students of the S o i l Science Department who assisted me throughout the course of my thesis, p a r t i c u l a r l y Dr. Hans Schreier for h i s guidance and enthusiasm throughout the project, and Eveline Wolterson, P a t t i Carbis, and Bernie von Spindler for t h e i r assistance i n the laboratory. In addition, I would l i k e to thank Elizabeth Freyman and Theresa Duynstee for t h e i r invaluable assistance i n the f i e l d . I also wish to thank my supervisor Paul Whitfield, and my fr i e n d and co-worker Bev McNaughton, of the Water Quality Branch, for t h e i r understanding and encouragement through the f i n a l phases of the project. A s p e c i a l thank-you goes to my parents, William E. and E l i n o r E. Ryan, whose continual f a i t h and support made t h i s a l l possible (yes, I r e a l l y am finished at last!) 1 CHAPTER 1 INTRODUCTION AND METHODS The Matsqui area of the Lower Fraser Valley exhibits extreme s o i l heterogeneity, as a re s u l t of the o r i g i n and depositional mode of the s o i l s present. They are f l u v i a l i n o r i g i n , having been deposited as a series of meander patterns which form ridges and hollows on the Fraser River floodplain. As i s c h a r a c t e r i s t i c a l l y the case with such deposits, the ridges are composed of coarser-textured sandy s o i l s , while f i n e r -textured s i l t and clay s o i l s are found i n the depressions. This s o i l v a r i a b i l i t y poses some obvious problems with respect to a g r i c u l t u r a l management. The sandier s o i l s forming the ridges have r e l a t i v e l y low nutrient and water-holding capacities, and often ex h i b i t droughtiness i n the l a t t e r part of the growing season. By contrast, the finer-textured s o i l s i n the depressional locations may exhib i t poor drainage and intermittent ponding, and are affected by flu c t u a t i n g water tables (Luttmerding, 1980). Consequently, crops grown on these s o i l s may experience very d i f f e r e n t types of moisture stresses, at d i f f e r e n t times during the growing season. This s o i l and topographic v a r i a b i l i t y can obviously have profound e f f e c t s on crop growth and qual i t y . This study w i l l examine some of these e f f e c t s , i n an e f f o r t to improve the understanding of some of the factors which are influencing crop production i n Matsqui, and perhaps i n other areas where s i m i l a r s i t e conditions p r e v a i l . 2 1.1 Aims and Objectives The primary objectives of t h i s study were the following: 1. To investigate the e f f e c t s of water stresses (excesses and d e f i c i t s ) on the quality and quantity of s i l a g e corn produced i n the Matsqui region of the Lower Fraser Valley. 2. To evaluate the influences of selected i n d i v i d u a l s o i l chemical and physical properties on corn y i e l d and quality, and to examine the relationships between these variables. 3 . To evaluate the effectiveness of using topography as a predictor of s o i l v a r i a t i o n and corn production v a r i a b i l i t y . 4. To r e l a t e s o i l v a r i a b i l i t y to corn y i e l d and q u a l i t y i n i n d i v i d u a l f i e l d s , through the use of remote sensing and geographical information system techniques. 5. To suggest ways i n which s e l e c t i v e s o i l and crop management within the f i e l d s studied could be f a c i l i t a t e d . 3 1.2 Background 1.2.1 S o i l / s i t e v a r i a b i l i t y and crop production As mentioned, the s p a t i a l v a r i a b i l i t y of s o i l s i n the Matsqui area i s very high. The finer-textured depressional s o i l s and the coarser-textured ridge s o i l s have very d i f f e r e n t moisture regimes, and consequently crop growth can be affected i n a number of ways. At very low moisture contents, plant uptake of s o i l nutrients i s retarded, and plant growth i n h i b i t e d as a r e s u l t of reduction in both c e l l d i v i s i o n and c e l l elongation (Tisdale, Nelson and Beaton, 1985). A study by Eck (1986) investigated the e f f e c t s of water d e f i c i t s on various aspects of corn growth. I t was found that water d e f i c i t s imposed 41 days a f t e r planting reduced leaf, s t a l k and ear y i e l d s . D e f i c i t s imposed during vegetative growth decreased kernel numbers, and d e f i c i t s occurring during grain f i l l i n g reduced ear y i e l d s . Alvino and Zerbi (1986) found that for unirrigated grain maize, the number of s t e r i l e plants approached 40% when the water table was deep (i e . 1.7 m) . They also noted that average seed weight was l i n e a r l y related to water table depth, and that the number of seeds per ear was higher when the water table was shallower. Overall, leaf senescence increased and grain y i e l d decreased as water table depth increased. At the other end of the scale, excessive s o i l moisture i s also undesirable, as i t i n h i b i t s root r e s p i r a t i o n and a l t e r s s o i l temperature. In addition, excess water brings about changes 4 i n s o i l b i o l o g i c a l , chemical and physical processes, which may in turn cause n u t r i t i o n a l imbalances i n plants (Lai and Taylor, 1969) . These researchers found that intermittent flooding decreased corn grain y i e l d , and reduced plant uptake of nitrogen and zinc. Applications of N, Zn and Cu did not o f f s e t these e f f e c t s . Chaudhary et a l . (1975) reported s i m i l a r r e s u l t s , suggesting that frequent s o i l submergences, and submergences for greater than 1 day were most detrimental to grain y i e l d . In addition, prolonged s o i l submergence s i g n i f i c a n t l y decreased N, P and K grain concentrations. Sheard and Leyshon (1976) found that short-term flooding of s o i l s (periods of up to 12 days), resulted i n s i g n i f i c a n t reductions i n dry weight accumulation and P concentrations i n corn. The growth of younger plants was most severely affected. In the Matsqui area, both extremes of moisture stress occur during the growing season. Few studies have looked at the v a r i a t i o n i n crop y i e l d and q u a l i t y r e s u l t i n g from these extremes. Crop quality, i n addition to y i e l d , i s important i n t h i s region, as much of what i s grown i s used as sil a g e for dairy c a t t l e . Information r e l a t i n g to crop p r o d u c t i v i t y - s o i l water rel a t i o n s h i p s could indicate to a farm operator i f , when, and how much he should i r r i g a t e . In addition, i f i r r i g a t i o n i s necessary, perhaps only s e l e c t i v e areas ( i e . the sandy ridges) need to be i r r i g a t e d . The topography and s o i l s of the area may also be a f f e c t i n g s o i l nutrients, and hence crop production. In a study i n eastern 5 Colorado, a shortgrass steppe composed of sandy s o i l s on the ridgetops and clay loams on the lower slopes was found to vary i n nutrient a v a i l a b i l i t y with respect to slope p o s i t i o n (Schimel et a l . , 1985). The researchers found that N and P a v a i l a b i l i t y increased downslope, as did organic C, N and P. Such v a r i a b i l i t y could influence crop growth, and perhaps even influence the amount and composition of the f e r t i l i z e r which a farm operator should add to a f i e l d . In the past, a g r i c u l t u r a l f i e l d s have been managed i n a "blanket" fashion, with l i t t l e regard given to s o i l s p a t i a l v a r i a b i l i t y . As f e r t i l i z e r and farm equipment costs increase, and environmental concerns become greater, s e l e c t i v e f i e l d management i n r e l a t i o n to s o i l v a r i a b i l i t y i s becoming of i n t e r e s t . I t i s conceivable that i n some areas, a g r i c u l t u r a l f i e l d s could be s e l e c t i v e l y f e r t i l i z e d i n accordance with topography and s o i l type. A number of other studies have looked at the relationships between topography and crop growth, sometimes with varying r e s u l t s . Spratt and Mclver (1972) studied wheat yi e l d s on a toposequence of Black Chernozemic and G l e y s o l i c s o i l s i n southern Saskatchewan. They discovered that wheat y i e l d s (both with and without f e r t i l i z e r ) were lowest at the summit position, and progressively increased downslope. They concluded that the a r i d conditions of the upper slopes probably l i m i t e d t h e i r y i e l d p o t e n t i a l . Furthermore, because f e r t i l i z e r s d i d not increase the y i e l d p o t e n t i a l of the summit and upper slope positions to that of the lower slopes, they suggested that the basic pedological 6 and microclimatological factors of the toposequence apparently-affected wheat y i e l d s more than s o i l f e r t i l i t y . Ferguson and Gorby (1967) found somewhat opposite r e s u l t s i n t h e i r study of a catena of Chernozemic s o i l s i n Manitoba. They discovered that wheat grew well on the higher, more well-drained s o i l s , and grew more poorly i n the depressed g l e y s o l i c areas. I t i s clear, then, that topographic and s o i l v a r i a b i l i t y i n the Matsqui region may be r e s u l t i n g i n s i g n i f i c a n t crop v a r i a b i l i t y , both i n terms of crop biomass and q u a l i t y . A more thorough understanding of the r e l a t i o n s h i p s between these components could aid farm operators i n making more economical and b e n e f i c i a l crop and s o i l management decisions. 1.2.2 Remote sensing of crop q u a l i t y and biomass Remote sensing involves obtaining information about an object or scene from a distance (ie. without being i n contact with the object or scene). Remote sensing techniques may be employed to measure spectral reflectance from vegetation canopies, thereby allowing prediction of vegetative productivity and condition. Reflectance of green vegetation i s r e l a t i v e l y low i n the v i s i b l e region of the spectrum, because most of t h i s l i g h t i s absorbed by l e a f pigments. Chlorophyll absorbs most of the incident blue and red l i g h t (centred at approximately 0.45 and 0.67 urn, r e s p e c t i v e l y ) , while r e f l e c t i n g incident green l i g h t (centred at about 0.54 urn) (Bauer et a l . , 1981). Reflectance of green vegetation exhibits a large increase i n the near i n f r a - r e d 7 region of the spectrum. According to Gausman (1974) , near i n f r a -red l i g h t i s r e f l e c t e d from leaves by r e f r a c t i v e index d i s c o n t i n u i t i e s . Most important are the d i s c o n t i n u i t i e s between hydrated c e l l walls and i n t e r c e l l u l a r a i r spaces ( i e . within the spongy mesophyll). Of secondary importance are d i s c o n t i n u i t i e s among i n t e r c e l l u l a r constituents ( i e . membranes vs cytoplasm). Spectral reflectance of vegetation can change markedly depending on a number of factors, such as vegetation type (Tueller et a l . , 1988), maturation and senescence (Hinzman et a l . , 1986), canopy geometry (Jackson and Ezra, 1985) and s o i l background (Huete, 1987) . Other s i g n i f i c a n t factors are vegetation moisture content, photosynthetic a c t i v i t y and vegetation form (Esselink and van G i l s , 1985). The o p t i c a l properties of plants are (also influenced by stresses such as nutrient d e f i c i e n c i e s , disease and pest i n f e s t a t i o n s , and drought stress. One of the major problems i n using spectral reflectance measurements f o r assessing crop production and condition, p a r t i c u l a r l y over incomplete canopies, i s the contribution of the s o i l background to reflectance (Huete, 1987) . S o i l colour and moisture have been suggested as being important factors (Kollenkark et a l . , 1982). To counteract s o i l reflectance, a number of "greenness transformations" have been proposed. The most common transformations are known as " r a t i o indices", which involve taking the r a t i o of a l i n e a r combination of the near i n f r a - r e d and red bands and another l i n e a r set of the same bands (Huete et a l . , 1985). The most commonly-used r a t i o indices are 8 the near infra-red/red r a t i o , and the normalized difference ( [ n i r - r e d ] / [ n i r + red]). These r a t i o s are often found to be e f f e c t i v e i n normalizing s o i l reflectance variations. A major area of study i s the use of reflectance data to assess and predict crop productivity. This method o f f e r s an a l t e r n a t i v e to the time-consuming and destructive t r a d i t i o n a l method of harvesting and weighing samples; i t also allows repeated measurements of the same plots (Esselink and van G i l s , 1985). Steven et a l . (1981), for example, found that the near infra-red/red r a t i o increased as % ground cover of sugar beets increased. The measurements taken were used to determine further rates of growth, and to predict the y i e l d of an independently grown crop to within 6% accuracy. Bedard and Lapointe (1987) were able to estimate dry green biomass i n hayfields using the near infra-red/red r a t i o and the normalized difference. With t h e i r models they were able to explain between 74% and 90% of the variance i n dry green biomass. Hinzman et a l . (1986) determined that green LAI (green leaf area/dry weight ratio) for winter wheat could be successfully estimated using multispectral data. As LAI increased, near infra-red reflectance also increased. Their reasoning for t h i s finding was that as LAI becomes larger, the spongy mesophyll of the plant becomes larger, and a i r s p a c e - c e l l wall interfaces consequently increase; as a r e s u l t near infra-red reflectance also increases. Remote sensing has also been shown to aid i n the detection of nutrient d e f i c i e n c i e s and other plant stresses. Hinzman et 9 a l . (1986) found that higher N f e r t i l i z a t i o n rates led to plants with higher chlorophyll concentrations, higher leaf t o t a l N concentrations, and higher LAI. Consequently, reflectance i n the v i s i b l e region decreased with increasing f e r t i l i z a t i o n , while near in f r a - r e d reflectance i n c r e a s e d W a l b u r g et a l . (1982) found v i r t u a l l y the same re s u l t s when studying the e f f e c t s of N f e r t i l i z a t i o n on corn spectral reflectance. Hinzman and h i s co-workers noted, however, that these relationships were non-l i n e a r , and appeared to approach asymptotes for LAI values greater than 3.0. Vickery et a l . (198 0) looked at P f e r t i l i z a t i o n e f f e c t s on spectral reflectance properties of improved pasture. They determined that v i s i b l e reflectance decreased, while near in f r a - r e d reflectance increased, with increasing P f e r t i l i z a t i o n . The researchers suggested that pasture reflectance data could perhaps be used to c l a s s i f y and map areas of improved pasture that require additional f e r t i l i z e r . Remote sensing techniques have also been employed to aid i n water stress detection. Kamat et a l . (1985), fo r example, found that the near infra-red/red r a t i o was always higher for an i r r i g a t e d wheat crop than for an unirrigated one. Curran and Milton (1983) determined that under water stress, curled cress (Lepedium sativum L.) exhibited increased red and near i n f r a - r e d reflectance, and a decreased near infra-red/red r a t i o . They at t r i b u t e d these reflectance changes to the decreased LAI which occurred as the leaves shrank and wilted, which i n turn increased exposure of the dry substrate to the sensor. In many studies, researchers are now recording vegetative reflectance i n a more permanent manner - i e . through the use of colour infra-red a e r i a l photographs. When a colour or colour i n f r a - r e d (CIR) photo i s taken, the reflectance of d i f f e r e n t l i g h t wavelengths from the scene i s recorded on 3 separate dye layers of the f i l m . The response of these dye layers i s measured i n terms of density, which occurs as a r e s u l t of the absorption c h a r a c t e r i s t i c s of the dyes (Estes et a l . , 1983). There i s a r e l a t i o n s h i p between the spectral reflectance of c e r t a i n objects and the densities that they produce on f i l m (McDowell and Specht, 1974); measurements of these densities y i e l d quantitative data that can a s s i s t i n image analysis (Estes et a l . , 1983). Stoner et a l . (1976) related reflectance from a maize canopy to LAI and % ground cover v i a microdensity scanning and d i g i t i z i n g of the 3 emulsion layers of colour and CIR photos. They concluded that t h i s process provided a quantitative technique for analyzing f i l m dye layer density differences, which could then be related to green vegetation and s o i l components within a given frame of the d i g i t i z e d photography. E v e r i t t et a l . (1987) used reflectance data and CIR photography to detect drought stress i n buffelgrass (Cenchrus c i l i a r i s L.). They found that the near infra-red/red r a t i o of the d i g i t i z e d data was d i r e c t l y related to water and c h l o r o p h y l l . The researchers were able to delineate areas of low, medium, and high i r r i g a t i o n on the CIR photo. 11 1.2.3 Remote sensing and GIS An e x c i t i n g new area i n the remote sensing f i e l d involves the incorporation of remote sensing information into geographic information systems (GIS). Geographic information systems are e s s e n t i a l l y systems which enable the processing of s p a t i a l information. A GIS can be defined as a powerful set of tools for c o l l e c t i n g , storing, r e t r i e v i n g at w i l l , transforming, and di s p l a y i n g s p a t i a l data from the r e a l world for a p a r t i c u l a r set of purposes (Burrough, 1989). As an example, Wright and Morrice (1988) combined s a t e l l i t e imagery and map-derived information using a GIS, to aid i n evaluation of potato crop conditions i n Scotland. The researchers integrated Landsat imagery (which was used to c l a s s i f y the potato crop) with s o i l and po t e n t i a l water d e f i c i t data, which enabled them to produce s t a t i s t i c a l information on the amounts of potato crop which were growing on drought-susceptible s o i l s . In t h i s t h esis, the relationships between d i g i t i z e d reflectance data from CIR a e r i a l photographs and corn qu a l i t y were determined for four a g r i c u l t u r a l f i e l d s . These rel a t i o n s h i p s were then used to c l a s s i f y the CIR photos i n terms of corn productivity. The downloading of t h i s data into a GIS allows us to predict corn quality f o r the f i e l d s - e s s e n t i a l l y producing corn q u a l i t y "maps", which depict the v a r i a b i l i t y of corn productivity within the f i e l d s i n a s p a t i a l manner. 12 1,3 S i t e Description 1.3.1 Location and Topography The four t e s t f i e l d s investigated i n t h i s study are located on Matsqui P r a i r i e , between Mission and Abbotsford, i n the Lower Fraser V a l l e y of B r i t i s h Columbia (see Figure 1). The topography of the s i t e s consists of a ser i e s of ridges and hollows (approximately 20-30 m i n wavelength, and 3 m i n height), which transverse the widths of the f i e l d s . The topographic class of these areas varies from gently undulating, to gently r o l l i n g (Luttmerding, 1980). Figure 1. Locations of the 4 f i e l d s studied during the project. 1.3.2 Climate The Matsqui s i t e s are located i n the P a c i f i c Climatic Region, which generally experiences warm, rainy winters and r e l a t i v e l y cool, dry summers (Hare and Thomas, 1979; as c i t e d by Luttmerding, 1980). In the winter, frequent low pressure systems moving eastward from the P a c i f i c Ocean produce some of the 13 c l o u d i e s t and r a i n i e s t conditions i n Canada. In the summer, however, long periods of sunny weather often occur as high pressure c e l l s extend over the coast. The Abbotsford a i r p o r t c l i m a t i c data indicates that the study areas receive approximately 1500 mm of p r e c i p i t a t i o n annually. The mean annual temperature for the area i s 9.5 °C, with a record high of 37.8 °C and a low of -21.2 °C. During the growing season (May - September), average seasonal p r e c i p i t a t i o n i s 306 mm, while pot e n t i a l evapotranspiration i s 381 mm. Consequently, drought conditions are l i k e l y to occur i n the area during the growing season, p a r t i c u l a r l y where coarser-textured s o i l s are present. 1.3.3 S o i l Origins and Descriptions The s o i l s found i n the area are f l u v i a l i n o r i g i n , representing a meander pattern of a serie s of ridges and hollows across the Fraser River floodplain. As mentioned previously, the ridges are r e l a t i v e l y sandy, while the hollows are f i n e r i n texture. Descriptions of the s o i l types found i n the study area are included i n the tables below, and were taken from B.C. S o i l Survey information (Luttmerding, 1980). Table 1 compares the s o i l s found within the 4 study f i e l d s (referred to as the 200, 300, 400 and 600 f i e l d s throughout t h i s t h e s i s ) . Table 2 contains the s a l i e n t features of these s o i l s , as they apply to t h i s study. 14 Table 1. Comparison of s o i l types found i n four study f i e l d s F i e l d S o i l Series S o i l C l a s s i f i c a t i o n 200 F a i r f i e l d Monroe Page Eluviated Melanic Brunisol Eluviated E u t r i c Brunisol Orthic Gleysol 300 Monroe F a i r f i e l d Matsqui Eluviated E u t r i c Brunisol 400 F a i r f i e l d Monroe Hjorth Orthic Humic Gleysol 600 Monroe F a i r f i e l d Matsqui Table 2. Sal i e n t features f i e l d s of dominant s o i l s located i n study S o i l Series Dominant S o i l Texture* Physiography Drainage Monroe s i l / s Ridges Mod. well-well Matsqui s i l / s Ridges Well-mod. well F a i r f i e l d s i l / s , Is Lower slopes, shallow depress. 1 Imperfect • Upper slopes, lower ridges 2 Page s i l , s i c l / s , Is Depressions Poor-mod. poor Hjorth s i l , s i c l / s , Is Depressions Poor-mod. poor Most common s o i l texture(s)/subsoil texture(s), where: s i = s i l t c = clay 1 = loam s = sand 1 When associated with Monroe s o i l s 2 When associated with Page s o i l s 15 As the above tables indicate, the 300 and 600 f i e l d s contain the same s o i l complexes of Monroe - F a i r i e l d - Matsqui s o i l s . The Monroe and Matsqui s o i l s are both well-suited for most a g r i c u l t u r a l crops, although t h e i r adverse topography may be a l i m i t i n g factor i n some areas. In addition, these s o i l s may e x h i b i t droughtiness i n the l a t t e r part of the growing season; i r r i g a t i o n i s therefore often necessary to maintain good production during such periods. F a i r f i e l d s o i l s are some of the best i n the Fraser Valley with respect to ag r i c u l t u r e ; they are however affected by a fluctuating water table, and consequently are not suitable for crops which are very s e n s i t i v e to t h i s . In addition, they have a low bearing strength, which may make them susceptible to compaction by farm vehicles. On the basis of personal observation when taking s o i l samples from these f i e l d s , there appeared to be units of g l e y s o l i c s o i l s (probably the Page series) i n these f i e l d s as well. These very low depressional areas were ponded early on i n the growing season to the extent that growth was patchy in the 600 f i e l d , and planting was a c t u a l l y delayed f o r approximately 2 weeks where t h i s s o i l was present i n the 300 f i e l d . The 200 f i e l d i s mapped as a complex of F a i r f i e l d - Monroe -Page s o i l s . Page s o i l s are s i m i l a r to F a i r f i e l d s o i l s , although they tend to be finer-textured. Through most of the winter and freshet period of the Fraser River, the water table i s at or near the s o i l surface. Surface ponding often occurs during heavy r a i n f a l l as a r e s u l t of slow i n f i l t r a t i o n and percolation rates, and runoff from higher, adjacent areas. In terms of agriculture, Page s o i l s are r e s t r i c t e d by high water tables and poor drainage. Most perennial crops are i n h i b i t e d during the winter, and high water tables hinder c u l t i v a t i o n and crop production during the spring freshet period. The 400 f i e l d i s primarily mapped as a F a i r f i e l d - Monroe complex, although some Hjorth s o i l s may also be present. Hjorth s o i l s are very s i m i l a r to Page s o i l s i n terms of a g r i c u l t u r a l r e s t r i c t i o n s . They are r e l a t i v e l y f e r t i l e , and can be quite productive i f the water table i s co n t r o l l e d . The 200, 300 and 600 f i e l d s are a l l c l a s s i f i e d topographically as being undulating to gently r o l l i n g ( >2 - 9% slope). The topography of the 400 f i e l d i s similar, although where the Hjorth s o i l s are present, i t i s s l i g h t l y less severe, ranging from gently undulating to undulating ( >0.5 - 2% slope). 1.3.4 Land Use and Management The four study f i e l d s are under c u l t i v a t i o n for s i l a g e corn (Zea mays L.) production, to be used as feed for dairy c a t t l e i n the area. For t h i s type of usage, the crops i n t h i s area are rotated between corn and forage, usually following a 3-4 year forage/1 year corn rotation pattern. In general, corn i s a fast-growing crop which produces the highest y i e l d s under conditions of moderate temperatures (75-85 °F) , and a p l e n t i f u l water supply (Aldrich and Leng, 1972) . Maize w i l l grow on a wide variety of s o i l types, as long as s o i l pH i s 6.0 or higher; deep, well-drained loams with large amounts of 17 organic matter are suggested as being most ide a l (Carr and Hough, 1978). Consequently, the poorly-drained s o i l s previously described would not be expected to be as productive, p a r t i c u l a r l y early on i n the growing season. On the other hand, the more well-drained s o i l s may experience drought conditions l a t e r i n the season. In a l l four f i e l d s , the corn hybrid used was Dekalb 24. Approximate planting dates, f e r t i l i z e r and i r r i g a t i o n schedules are outlined i n Table 3. 18 Table 3. Pertinent management features of the four study f i e l d s F i e l d Planting F e r t i l i z e r I r r i g a t i o n Date (Approx) 200 May 20 Pre-plant: Early Sept. Broadcast 30-0-20 Some effects § 420 lbs/acre; f r o m a neighboring With seed 14-29-5 f i e l d . @ 260 lbs/acre, +5 (S), 3 (Mg), 0.1 (B), 0.2 (Zn). 300 June 05 Pre-plant: None June 12- Manured heavily; (wet area) 9-40-4 @ 180 lb/acre. 400 June 05 Pre-plant: None Manured Broadcast 37-0-8 @ 440 lb/acre With seed 20-22-5 § 220 lb/acre 600 June 05 Pre-plant: June 3 0 Broadcast 30-0-20 § 350-400 lbs/acre; With seed 22-22-5 @ 200 lbs/acre. 1.4 Methods 1.4.1 F i e l d methods Four f i e l d s i n the Matsqui region of the Lower Fraser Valley were chosen as study areas on the basis of the following c r i t e r i a : (1) they displayed the prominent ridge-and-hollow topography desired for the project, and (2) they would be planted under s i l a g e corn for the season under study. Study s i t e s within each f i e l d were located at regular i n t e r v a l s along transects taken across the f i e l d s , i n the 19 d i r e c t i o n of the corn rows. The distances between the s i t e s were determined by the frequency of the ridges and hollows, as representative s i t e s for each topographic position (ridge, sideslope, and hollow) were required for the project. The sample designs f o r each f i e l d are shown i n Figure 2. At each s i t e , s o i l samples were c o l l e c t e d at four depths (0-25, 25-50, 50-75, and 75-100 cm), to be analyzed for a var i e t y of chemical and physical properties. An aluminum neutron probe access tube was i n s t a l l e d at each s i t e , within the corn rows. This allowed monitoring of the s o i l water content at 5 depths (15, 30, 45, 60, and 90 cm) every 10 - 14 days over the entire growing season. The neutron probe (Campbell P a c i f i c Nuclear Model 503) was c a l i b r a t e d over the range of moisture contents encountered v i a gravimetric sampling adjacent to the access tubes (Greacen, 1981). The samples were obtained i n 15 cm depth increments which corresponded to the probe 1s v e r t i c a l range of influence. Every 10 - 14 days, the heights of s i x corn plants per s i t e (three on each side of the access tube, excluding those closest to the tube) were measured. Two of these plants per s i t e were then harvested and dried at the end of the growing season for biomass measurement and chemical (quality) analysis. The plants c o l l e c t e d were the two i n the middle of each group of three on e i t h e r side of the access tube. For data analysis, only the r e s u l t s from the t a l l e s t of these plants was used, as combining the r e s u l t s for of both corn plants yielded poorer corn-remote Ftold 200 1— 230 220 1 210 • 221 a 219 a 2M • 221 a 210 a 2M • 227 a 217 a 207 a 221 a 216 a 206 • 225 a 215 a 205 • 224 a 214 a 204 • 223 a 213 a 203 • 222 a 212 a 202 • 221 a a 211 201 A ta.tr tat N o-20 40 to 90 100 120 140 a F*ld 300 Control HirUf N a M l 1 a 315 a a«3M 302 » 3 a 311 • aajii 303 J U a 317 a mjn 304 311 a 318 a 305 a 310 a 319 a 306 a 3M a 320 a 307 a 308 a 321 25 SO 130 140* 220 a 14S 105 45 Field 400 • N 120 1 100 a 420 a 419 a 411 a 417 •0 a 416 a 415 a 414 a 413 CO a 412 a 411 a 410 a 409 40 a 408 a 407 a 406 a 405 20 a 404 a 403 a 402 a 401 Voxtr Post 109 80 60 40 20 O a Raid 600 m • . .* too 630 616 413 608 604 • • • • • • 75 619 61) 611 607 60) • • • • a 50 611 614 410 606 603 • • 0 • • 35 617 613 609 60S 601 0 • 100 60 40 40 30 0* Figure 2. Sampling design of 4 f i e l d s studied. 21 sensing relationships. 1.4.2 S o i l analysis methods S o i l chemical analyses were carr i e d out for the 0-25 cm sample co l l e c t e d from each s i t e . They were previously dried and ground to pass through a 2 mm sieve. Descriptions of the analyses used can be found i n McKeague (1978). Total nitrogen was determined using the Kjeldahl method, followed by analysis on the Technicon Autoanalyzer. Available phosphorus was determined with the Bray PI extraction method, with measurements c a r r i e d out on the Turner Spectrophotometer. Total carbon was obtained using a Leco Induction Furnace (Model 521) with a t o t a l carbon analyzer (Model 572). Cation exchange capacity (CEC) and exchangeable cations were determined v i a the ammonium acetate replacement method, with cation concentrations being measured on the Atomic Absorption Spectrophotometer (Perkin Elmer, Model 306), and CEC measured using a Technicon Autoanalyzer. S o i l pH was measured i n both water and 0.01 M CaCl 2 Bulk density was measured v i a the core sampling method for 3 depths: 0-8.5, 8.5-17, and 17-25.5 cm. Elevation for each s i t e was determined using 1:2,000 scale topographic maps of the area, which had contour i n t e r v a l s of 25 cm. These maps were further used to obtain elevation values f o r a portion of one f i e l d on a 4m x 4m g r i d s i z e . This data was l a t e r used i n conjunction with image analysis and GIS methods, i n an attempt to further improve the crop quality "maps" being produced (see Section 1.4.4). 22 1.4.3 Corn analysis methods The harvested corn plants were dried, and the stalk, cob and t o t a l weights of each were determined. Cob and s t a l k samples were then ground, and digested separately using the Parkinson and A l l e n (1975) digestion method. Corn nitrogen (N) and phosphorus (P) contents were measured on the autoanalyzer, while f o l i a r calcium (Ca) concentrations were determined using the atomic absorption spectrophotometer. Crude protein (CP) was derived from t o t a l N following Raymond (1969): CP = 6.25 x Total N Acid detergent f i b r e (ADF) was determined using the method of Waldern (1971). The values obtained were then u t i l i z e d to estimate d i g e s t i b l e energy (DE) as per McQueen and Martin (1981): DE (Mj/kg DM) = (-0.0495 X ADF + 4.297) X 4.814 The ADF values were also used to calculate t o t a l d i g e s t i b l e nutrients (TDN) as per Goodrich (personal communication, 1991): TDN (%) = 77.44 - (0.453 X ADF). Because DE and DN are both calculated from ADF, they e x h i b i t s i m i l a r relationships with s o i l and remote sensing va r i a b l e s (see Chapters 3 and 4) . For consistency, TDN w i l l mainly be 23 re f e r r e d to throughout t h i s thesis. 1.4.4 Remote sensing and image analysis methods Colour i n f r a r e d (CIR) a e r i a l photographs (23 x 23cm format) of the four study f i e l d s were taken both i n May, when the s o i l was bare, and i n September, when the corn crops were f u l l y mature. An RC-10 photogrammetric camera was used to obtain the photos. Large white markers (1 m2 i n size) were placed along the edges of the f i e l d s at the ends of two rows, to provide measurements of scale for locating the sampling s i t e s l a t e r on the d i g i t i z e d images. The CIR p o s i t i v e transparencies were then scanned using an Optronics C-4500 f i l m scanner to obtain p i x e l brightness values for the three colour-sensitive dye layers of the f i l m . A 50 urn r e s o l u t i o n was used for three of the f i e l d s ; a 100 urn resolution was u t i l i z e d for the 400 f i e l d , due to scanning problems encountered with t h i s image. The images scanned at 50 urn had a resultant ground resolution p i x e l s i z e of 20 x 20 cm, while the 400 f i e l d image had a p i x e l s i z e of 40 x 40 cm. The scanned images were loaded into the image analysis software package Earthprobe (Version 1.2), and for each f i e l d p i x e l values for the sampling s i t e s were extracted (for both the bare s o i l and corn images). The white markers placed at the end of the rows i n each f i e l d , i n addition to other known reference points, allowed the s i t e s to be accurately located on the images. Average p i x e l values were obtained for a i m 2 area at each s i t e ; t h i s allowed a more representative area of each s i t e to be covered. The application of t h i s method i s described i n Chapter 7. 1.4.5 Geographical information system methods The GIS software package pMap (Version 2.0) was u t i l i z e d for f i e l d c l a s s i f i c a t i o n s , for two main reasons. F i r s t l y , i t has the c a p a b i l i t y to overlay crop quality c l a s s i f i c a t i o n s or "maps", thereby producing combined corn q u a l i t y maps, and secondly, i t allows us to combine remote sensing data with s o i l s data, to further improve our f i e l d c l a s s i f i c a t i o n . P i x e l values from the f i e l d s of in t e r e s t were downloaded using the "imask" program within Earthprobe. The data (in ASCII format) can subsequently be imported into Lotus 1-2-3, where i t can be further processed and imported into pMap for the production of overlay and combination corn q u a l i t y maps. The application of t h i s process i s described i n Chapter 7. 1.4.6 S t a t i s t i c a l methods In order to quantify the central tendencies and v a r i a b i l i t y of a l l of the variables measured, the means and standard deviations for each variable were calculated, on both an i n d i v i d u a l f i e l d basis, and on an o v e r a l l basis ( i e . a l l s i t e s i n a l l f i e l d s combined). These s t a t i s t i c s were calculated f o r a l l s o i l , corn and remote sensing variables measured. The c o e f f i c i e n t s of v a r i a t i o n (CV(%) = (s.d./mean) x 100) were also c a l c u l a t e d for a l l variables for a l l f i e l d s , i n order to compare s o i l , remote sensing and corn v a r i a b i l i t y between the four 25 f i e l d s . To determine i f s i g n i f i c a n t differences existed between f i e l d s i n terms of the variables measured, the Mann-Whitney U-Test (two-tailed, at the p = 0.01 level) was employed. This t e s t was chosen as most of the variables measured were not normally d i s t r i b u t e d , and i t i s l i k e l y that variances were not homogeneous. This t e s t was also u t i l i z e d to determine i f s i g n i f i c a n t differences existed between groups obtained v i a c l u s t e r analysis (see below), and between corn q u a l i t y classes determined using image analysis and GIS methods (Chapter 7). Correlation analysis was c a r r i e d out to determine i f r e l a t i o n s h i p s existed between s o i l , remote sensing, and corn pro d u c t i v i t y variables, and i f so, to determine t h e i r magnitude and d i r e c t i o n . This was done on both an o v e r a l l and i n d i v i d u a l f i e l d basis. For a l l s i t e s combined (ie. 94 sites) a Pearson r value of approximately 0.40 was considered as being s i g n i f i c a n t at a p r o b a b i l i t y l e v e l of 0.05. For the i n d i v i d u a l f i e l d s , r values of 0.36, 0.40, and 0.44 were chosen as being s i g n i f i c a n t for f i e l d s with 30, 24, and 20 s i t e s , respectively. In order to obtain p r e d i c t i v e equations for corn biomass and qu a l i t y , regression analysis was also u t i l i z e d . Only the best c o r r e l a t i o n s were chosen for regression analysis; at a p r o b a b i l i t y of 0.01, regressions were c a r r i e d out for r values greater than 0.46, 0.51, and 0.56, for f i e l d s with 30, 24, and 20 s i t e s , respectively. Multiple regressions were also employed to further improve p r e d i c t i v e c a p a b i l i t y . Cluster analysis, a les s conventional s t a t i s t i c a l procedure, was also used to determine i f any s p a t i a l relationships existed between corn quality and s o i l / s i t e and remote sensing variables. I t also served to further elucidate previously-determined r e l a t i o n s h i p s i n a non-linear manner. Cluster analysis e s s e n t i a l l y measures the degree of s i m i l a r i t y found among the vari a b l e s which are chosen to be used i n the clu s t e r i n g procedure. The "C-Group" clu s t e r i n g program on the mainframe computer at UBC was used; the average distance c l u s t e r analysis method (Ward, 1963) i s u t i l i z e d by t h i s program. The Mann-Whitney U-Test was then used to determine i f the c l u s t e r groups found were s i g n i f i c a n t l y d i f f e r e n t with respect to accessory variables which were not used i n the c l u s t e r i n g procedure. 1.5 Overview of Research Figure 3 provides an outline of t h i s project, and gives an overview of the research and how i t was c a r r i e d out. of T n t Areata C h a r a c t e r i s t i c s of Tast Areas Comparison of Test areas Relat ionships between Variables Point data -Pie l d datai S o i l , s i t s , corn biomass C quality variables Spatial data -Remote Sensing data: d i g i t i r e d CIR imagery -Elevation data -Overall v a r i a b i l i t y - V a r i a b i l i t y within f i e l d s -Differences between f i e l d s S o i l fi sit e variables Remote sensing variables Predictions of Corn Qua l i t y fi Biomass Corn biomass & quality variables J Z I Linear predictions of com biomass fi qual i t y from s o i l fi remote sensing data, using single and multiple re-gressions Categoric predictions of s o i l fi s i t e conditions from corn variables, using cluster analysis S p a t i a l a n a l y s i s Image analysis for corn biomass and quality by Clas s i f y i n g pixel values to produce corn productivity "maps" Combining remote sensing and s i t e data with a GIS to produce corn productivity "maps" F i g u r e 3 . An overview of the research p r o j e c t . 28 CHAPTER 2 VARIABILITY BETWEEN AND WITHIN FIELDS 2.1 S t a t i s t i c a l Analyses The means and c o e f f i c i e n t s of v a r i a t i o n were c a l c u l a t e d f o r a l l of the corn, s o i l , remote sensing and moisture v a r i a b l e s f o r a l l f i e l d s combined, as w e l l as on an i n d i v i d u a l f i e l d b a s i s . The v a l u e s c a l c u l a t e d are l i s t e d i n a number of t a b l e s throughout the chapter. For s i m p l i f i c a t i o n , only s p e c i f i c examples of the moisture data are i n c l u d e d . The a c t u a l values f o r a l l v a r i a b l e s , at a l l s i t e s i n each of the four f i e l d s are t a b u l a t e d i n Appendices 1 - 4. The Mann-Whitney U-Test was employed t o t e s t f o r s i g n i f i c a n t d i f f e r e n c e s (p = 0.01) between the f i e l d s i n terms of the above-mentioned v a r i a b l e s . 2.2 S o i l V a r i a b i l i t y 2.2.1 S o i l v a r i a b i l i t y : o v e r a l l and w i t h i n i n d i v i d u a l f i e l d s Tables 4 and 5 t a b u l a t e the means and CVs of the s o i l p r o p e r t i e s f o r a l l f i e l d s combined, and f o r the four i n d i v i d u a l f i e l d s , r e s p e c t i v e l y . A l s o included i n these l i s t s are s e v e r a l s o i l moisture v a r i a b l e s . W i l d i n g and Drees (1978) have c a t e g o r i z e d s e l e c t e d s o i l p r o p e r t i e s as being: (a) l e a s t v a r i a b l e (CV = < 15%) , (b) moderately v a r i a b l e (CV = 15-35%), and (c) most v a r i a b l e (CV > 35%). As shown i n Table 4, Ph, bulk d e n s i t y and e l e v a t i o n are a l l l e a s t v a r i a b l e , w h i l e a v a i l a b l e P, exchangeable c a t i o n s , 29 and s o i l moisture contents l a t e r i n the growing season tend to be most variable. The rest of the variables ( t o t a l C, N and CEC) are a l l moderately variable. These re s u l t s agree well with the findings of Wilding and Drees, i n addition to those of a number of other researchers. Table 4. Descriptive s t a t i s t i c s f or s o i l v ariables for the four study f i e l d s combined. Variable 1 X CV. (%) (n=94) pH 5.8 7 pH(CaCl 2) 5.3 7 Total C 2.65 24 Total N 0.250 25 Available P 8.61 42 Exch. Na 0.10 61 Exch. Ca 9.21 39 Exch. Mg 1.63 38 Exch. K 1.25 60 CEC 25.74 23 Average BD 1.15 9 BD1 1.12 10 BD2 1.15 11 BD3 1.19 11 Elevation 542 14 NP3A 0.41 20 NP3C 0.33 35 NP5A 0.32 36 NP6A 0.29 39 NP6C 0.26 47 Units for s o i l variables shown i n Appendix 1. The differences i n the degrees of v a r i a b i l i t y found are l i k e l y management-related. Beckett and Webster (1971), i n t h e i r review of a large number of s o i l v a r i a b i l i t y papers, found that s o i l properties most affected by management were also those which were most variable. Management practices such as liming, 30 manuring and f e r t i l i z a t i o n tend to increase the heterogeneity of a g r i c u l t u r a l lands r e l a t i v e to uncultivated lands (Leo, 1963). Table 5. Descriptive s t a t i s t i c s for s o i l variables f o r each of the 4 study f i e l d s . Var. 1 200 300 400 600 X CV X CV X CV X CV (n=30) (n=24) (n=20) (n=20) pH 5.9 7 6.0 4 6.1 4 5.2 3 pHCa 5.3 9 5.3 4 5.7 5 4.9 2 Tot.C 2.78 23 2.75 12 2.97 24 2.05 20 Tot.N 0.247 24 0.264 19 0.262 23 0.211 28 Av.P 9.39 30 9.98 40 8.95 35 5.01 42 Ex. Ca 9.75 37 8.55 18 12.34 21 6.10 58 Ex.Mg 1. 19 25 2.04 11 1.09 14 3.40 141 Ex.K 1.13 40 2.16 36 1.05 28 0.57 60 Ex.Na 0. 05 40 0.14 28 0.06 17 0.17 35 CEC 25.82 19 27. 30 16 29.25 22 20.26 22 AVBD 1.13 7 1.16 8 1.10 9 1.24 9 BD1 1. 08 10 1. 14 11 1.11 11 1.21 7 BD2 1.14 9 1. 16 9 1.07 11 1.25 10 BD3 1. 18 8 1.19 12 1.14 9 1.26 12 ELEV 593 12 573 7 475 8 500 15 NP3A 0.475 12 0. 381 19 0.433 12 0.352 28 NP3C 0.340 28 0. 355 32 0.368 21 0.258 59 NP5A 0.370 24 0.297 23 0.417 19 0.214 66 NP6A 0. 313 28 0.280 22 0.392 24 0.200 74 NP6C 0.279 35 0.280 37 0.305 36 0.211 66 Units for s o i l variables shown i n Appendix 1. "Tot" = t o t a l , "Av." = available, "Ex." = exchangeable. Table 5 l i s t s the means and c o e f f i c i e n t s of v a r i a t i o n of s o i l and s i t e variables for a l l of the f i e l d s i n d i v i d u a l l y . Many of the trends which were evident on an o v e r a l l basis are also seen on an in d i v i d u a l f i e l d basis. S o i l pH, bulk d e n s i t i e s and elevation are a l l l e a s t variable, as they were o v e r a l l . Cation exchange capacity and t o t a l N are moderately v a r i a b l e on an i n d i v i d u a l f i e l d basis; t o t a l C i s also moderately v a r i a b l e i n a l l f i e l d s but the 300 f i e l d , i n which i t i s lea s t v a r i a b l e . S o i l properties which were most variable on an o v e r a l l basis d i f f e r somewhat on an i n d i v i d u a l f i e l d basis. Available P, for example, i s c l a s s i f i e d as most variable i n the 300 and 600 f i e l d s , and i s moderately variable i n the 200 f i e l d ; the 400 f i e l d i s borderline between being moderately v a r i a b l e and most va r i a b l e with respect to available P. Exchangeable cations may be categorized as most variable i n the 200 and 600 f i e l d s , whereas in the 3 00 and 400 f i e l d s , they tend to be i n the least or moderately variable classes. The 600 f i e l d i s most variable i n terms of s o i l moisture contents; the other f i e l d s tend to be moderately variable i n t h i s respect. These differences are not s u r p r i s i n g , as s p a t i a l v a r i a b i l i t y i s s o i l and s i t e s p e c i f i c (Wilding and Drees, 1978). The d i f f e r i n g degrees of v a r i a b i l i t y of s o i l f e r t i l i t y parameters are l i k e l y a r e s u l t of inherent s o i l v a r i a b i l i t y and management practices. The amounts of manure, f e r t i l i z e r and lime added to the f i e l d s would be influencing factors, as would the uniformity of the d i s t r i b u t i o n of these substances on the d i f f e r e n t f i e l d s . Other management pract i c e s , such as i r r i g a t i o n , would also a f f e c t nutrient d i s t r i b u t i o n s found within the f i e l d s . A l l of these factors, i n addition to "natural" s o i l v a r i a t i o n , could lead to d i f f e r i n g degrees of s o i l property v a r i a b i l i t y within the four f i e l d s . Much of t h i s s o i l v a r i a b i l i t y may be systematic, and fundamentally re l a t e d to topography. The s o i l t e x t u r a l differences as they r e l a t e to elevation i n these f i e l d s have previously been discussed. In regions of r o l l i n g topography, s o i l s exhibit s p a t i a l v a r i a b i l i t y because lower areas generally accumulate water runoff and sediment o r i g i n a t i n g from topographically higher surrounding areas. In addition, depressional areas may be affected by f l u c t u a t i n g water tables (Birkeland, 1984), as i s the case here. Topography-related s o i l v a r i a b i l i t y appears to be a factor i n t h i s study, as a number of s o i l variables - notably t o t a l C and N, exchangeable K and Mg, and s o i l moisture - are i n most cases negatively r e l a t e d to elevation i n the study f i e l d s . 2.2.2 S o i l differences between f i e l d s Table 6 l i s t s the s o i l / s i t e variables which 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 four f i e l d s . Of a l l the study s i t e s , the 600 f i e l d i s most d i f f e r e n t from the others i n terms of the s o i l variables examined. Mean s o i l pH, C, N, P, K, Ca and CEC are a l l s i g n i f i c a n t l y lower, and bulk density of the 0-8.5 cm depth i s s i g n i f i c a n t l y higher, i n the 600 f i e l d than i n the other three f i e l d s . S o i l K i s highest i n the 300 f i e l d , while s o i l Mg and Na are s i g n i f i c a n t l y higher i n the 300 and 600 f i e l d s than they are i n the other f i e l d s . 33 Table 6. S o i l and s i t e variables exhibiting s i g n i f i c a n t differences between the four study f i e l d s . FIELD 200 300 400 300 -K, Mg, Na -NP2B, NP3A 400 -Elev -K, Mg, Na -Elev -NP5A, NP6A, NP7A 600 -pH, C, N, P -K, Ca, Mg -Na, CEC -BDl, Elev -NP2B, NP3A, NP5A -pH, C, N, P -K, Ca, CEC -BDl, Elev -pH, C, N, P -K, Ca, Mg -Na, CEC -BDl -NP4A, NP5A -NP6A, NP7A Elevation i n both the 200 and 300 f i e l d s i s s i g n i f i c a n t l y higher than i n the other two f i e l d s . This f i n d i n g i s somewhat misleading, however, as i t i s r e a l l y the v a r i a b i l i t y (in terms of ridges and hollows) within each f i e l d which would be expected to have the greatest e f f e c t s on corn production. The 200 and 600 f i e l d s are most variable i n t h i s respect. S o i l moisture does not exhibit many s i g n i f i c a n t differences between the various f i e l d s . In general, moisture contents of the upper depths of the 200 f i e l d are s i g n i f i c a n t l y higher than i n the other f i e l d s , from mid-summer onwards. This i s l i k e l y because some of the s i t e s i n the 200 f i e l d were affected by i r r i g a t i o n of a neighbouring f i e l d e a r l i e r i n the season, and the 200 f i e l d was i t s e l f i r r i g a t e d l a t e r on i n the summer. The 400 f i e l d also tends to have higher moisture contents i n the upper depths l a t e r i n the growing season; s i g n i f i c a n t l y higher than those i n the 300 and 600 f i e l d s . This may r e f l e c t the presence of the s o i l units with the higher organic matter contents i n the 400 f i e l d . Several selected s o i l moisture means and CVs are included i n Table 5. 2.3 Corn V a r i a b i l i t y 2.3.1 Corn v a r i a b i l i t y : o v e r a l l and within  in d i v i d u a l f i e l d s The means and CVs of the corn biomass variables for a l l of the f i e l d s combined are l i s t e d i n Table 7. If we the apply the same system for c l a s s i f y i n g corn v a r i a b i l i t y as was u t i l i z e d for s o i l v a r i a b i l i t y , i t i s evident that on an o v e r a l l basis, corn biomass, and unweighted CP, P and Ca concentrations tend to be moderate i n terms of v a r i a b i l i t y -the only exception i s cob Ca, which i s highly var i a b l e . Caution should be exercised when interpreting t h i s value however, as the CV i s an i n v a l i d index i f the mean and standard deviation vary; t h i s i s p a r t i c u l a r l y a problem when measured values are within the range of laboratory errors (Wilding and Drees, 1983), as i s the case with the cob Ca values. Weighted Ca and DN values, and weighted stalk P are also high i n terms of v a r i a b i l i t y . D i g e s t i b l e nutrients on an unweighted basis exh i b i t low v a r i a b i l i t y . 35 Table 7. Descriptive s t a t i s t i c s for corn variables for the four study f i e l d s combined. Variable 1 X CV. (%) (n=94) TCP 8.63 11 TP 0.217 12 TCA 0.171 29 TDN 64.0 2 TWT 207 25 TCPg 17.83 26 TPg 0.451 28 TCAg 0.359 40 TDNg 132.9 25 CCP 9.19 16 CP 0.293 14 CCA 0.008 70 CDN 70.4 3 CWT 101 33 CCPg 9.05 27 CPg 0.292 30 CCAg 0.007 67 CDNg 71.9 33 SCP 8.25 18 SP 0. 147 24 SCA 0.331 26 SDN 57.3 6 SWT 105 28 SCPg 8.79 36 SPg 0.159 43 SCAg 0.352 41 SDNg 61.0 31 Unweighted nutrient concentrations are expressed as % Weighted nutrients concentrations (as denoted by the 'g' su f f i x ) expressed as g/stalk, g/cob, or g/plant. Table 8 l i s t s the means and c o e f f i c i e n t s of v a r i a t i o n f o r the four study f i e l d s on an in d i v i d u a l basis. As on an o v e r a l l basis, biomass tends to be moderately v a r i a b l e within the i n d i v i d u a l f i e l d s . The exception i s cob weight, which i s highly v a r i a b l e i n both the 300 and 600 f i e l d s . This trend i s also apparent with respect to corn nutrients. In the 300 and 600 f i e l d s , unweighted cob and stalk CP are moderately variable; i n the other f i e l d s they are low i n terms of v a r i a b i l i t y . Unweighted phosphorus concentrations are moderately to highly v a r i a b l e i n the 600 f i e l d ; corn P v a r i a b i l i t y i s low i n the other f i e l d s . These 2 f i e l d s also tend to exhibit markedly higher v a r i a b i l i t y i n terms of several of the weighted corn nutrient concentrations. The reasons for t h i s increased v a r i a b i l i t y are outlined i n the next section. 37 Table 8. Descriptive s t a t i s t i c s f or corn variables f o r each of the four study f i e l d s . /ariable 1 200 300 400 600 X CV X CV X CV X CV (n=30) (n=24) (n=20) (n=20) TCP 8.61 7 8.65 12 8.16 6 9.11 14 TP 0.21 12 0.23 11 0.21 11 0.21 15 TCa 0.17 22 0.16 28 0.13 17 0.22 26 TDN 64.7 2 62.9 2 63.4 2 64.9 2 TWT 250 17 202 18 161 23 196 23 TCPg 21.5 18 17.3 17 13.0 20 17.8 23 TPg 0.53 22 0.47 24 0.34 24 0.42 25 TCag 0.44 27 0.33 29 0.21 27 0.43 38 TDNg 161.6 17 127.0 18 102.3 22 127.4 22 CCP 8. 15 7 10.15 16 8.67 • 4 10.09 16 CP 0.28 9 0.32 14 0.27 10 0.31 15 CCa 0.002 150 0.012 42 0.007 43 0.013 31 CDN 72.0 9 67.4 3 70.8 . 1 71.3 1 CWT 126 17 88 40 95 28 90 37 CCPg 10.2 19 8.5 29 8.2 28 8.8 30 CPg 0.35 9 0.27 36 0.26 29 0.27 30 CCag 0.003 133 0.009 44 0.006 50 0. 011 36 CDNg 90.5 18 59.3 40 67.2 28 63.8 35 SCP 9.07 12 7.83 21 7.35 13 8.41 17 SP 0.15 21 0.17 19 0.12 16 0.14 23 SCa 0.35 19 0.29 32 0.31 14 0.38 28 SDN 57.4 3 59.1 3 53.0 7 59.5 4 SWT 124 18 114 21 66 21 107 22 SCPg 11.24 22 8.83 23 4.82 21 9.01 21 SPg 0.19 30 0.20 29 0.08 25 0.15 38 SCag 0.43 27 0.32 30 0.20 28 0.42 39 SDNg 71.1 19 67. 6 20 35.2 22 63.6 23 Unweighted nutrient concentrations are expressed as %. Weighted nutrients concentrations (as denoted by the *g* su f f i x ) expressed as g/stalk, g/cob, or g/plant. Cob, s t a l k and t o t a l weights expressed i n grams (g). 2.3.2 Corn differences between f i e l d s 38 Table 9. Corn variables exhibiting s i g n i f i c a n t differences between the four study f i e l d s . FIELD 200 300 400 -HT, TWT, SWT, -HT, TWT, SWT, CWT CWT 300 -- A l l unweighted -CCP, CP, CCa cob nutrients -SCP, SP, SDN -SCP - A l l weighted -SCPg, SPg, SCag t o t a l nutrients -CCPg, CPg, SCPg -HT, TWT, SWT, CWT 400 -CDN - A l l unweighted s t a l k nutrients -CCPg, CPg -SCPg, SPg, SCag - A l l weighted t o t a l nutrients -HT, TWT, SWT, -TWT, SWT, CWT CWT - A l l unweighted -CCP, CP, CCa cob nutrients 600 -SCP -SCP, SCa, SDN -TCP, TCa -TCP, TCa -TCP, TCa -CCPg, CPg -SCPg -SCPg, SPg, SCag -TCPg, TPg, TDNg Table 9 l i s t s the corn variables which exhibit s i g n i f i c a n t d i f f e r e n c e s between the study f i e l d s . In general, the 2 00 f i e l d e x h i b i t s the highest biomass production of a l l the f i e l d s . This i s l i k e l y r e l a t e d to management, as t h i s f i e l d was planted approximately two weeks before the other three f i e l d s , and the corn therefore got an early s t a r t - the corn i n the other f i e l d s never caught up with i t i n terms of biomass production. In addition, t h i s f i e l d was the most heavily f e r t i l i z e d of the four f i e l d s , and was also f e r t i l i z e d with S, B, and Zn, which the other f i e l d s were not (see Table 3) . The farm operator also i r r i g a t e d the high ridge areas of the f i e l d a couple of times during dry periods, which would have further improved production and reduced corn s p a t i a l v a r i a b i l i t y . Corn height i n the 200 f i e l d was s i g n i f i c a n t l y higher than i n the other f i e l d s . The 400 f i e l d was second i n terms of height, being s i g n i f i c a n t l y t a l l e r than the corn i n the 300 f i e l d , and noticeably t a l l e r than that i n the 600 f i e l d . This i s most l i k e l y because there was a portion of the 300 f i e l d which was not planted with the rest of that f i e l d , due excessive water ponding during the i n i t i a l p lanting. The corn i n t h i s area was consequently shorter, r e s u l t i n g i n a lower mean height value for t h i s f i e l d . The 600 f i e l d had a somewhat s i m i l a r problem, i n that the topographically low area of the f i e l d became ponded shortly a f t e r planting, thereby reducing growth i n t h i s area. The 400 f i e l d yielded the lowest s t a l k and t o t a l weights -s i g n i f i c a n t l y lower than those i n the other f i e l d s . This could possibly be due to excess water problems or the weed i n f e s t a t i o n which occurred i n t h i s f i e l d - these factors w i l l be discussed i n Chapter 3. In addition, t h i s f i e l d (as well as the 300 f i e l d ) was harvested approximately 1 week l a t e r than the other f i e l d s , so i t may be that some stalk biomass was l o s t through death and dropping of leaves, which becomes more frequent as harvest i s delayed (Aldrich and Leng, 1972). This would not be as apparent i n the 300 f i e l d , because as previously mentioned, i n some areas growth was delayed by poor s o i l conditions, and some of the plants may therefore have been less mature at harvest time than they were i n the 400 f i e l d . A complicating factor a f f e c t i n g the cob weights i n the 300 and 600 f i e l d s should be mentioned. Although the mean cob weight values f o r these f i e l d s are the lowest, they are close to the values obtained for the 400 f i e l d ; i t i s only the 200 f i e l d that ex h i b i t s weights which are s i g n i f i c a n t l y higher. The within-f i e l d v a r i a b i l i t y of cob weights i n the 300 and 600 f i e l d s i s extremely high. In the compacted area of the 300 f i e l d , and the area which was ponded i n the 600 f i e l d , cob weights tended to be very high; i n some cases 2 cobs per plant were present. This i s because i n these areas, some plants were unable to survive the unfavourable planting conditions ( i e . ponding and compaction), r e s u l t i n g i n lower plant density i n these areas. Studies have found that decreasing plant density leads to increased grain y i e l d (Bunting, 1973). This must be kept i n mind when cob y i e l d r e l a t i o n s h i p s for these f i e l d s are interpreted i n l a t e r chapters. The 200 f i e l d i s also most productive with respect to corn nutrients. On a weighted basis, i t y i e l d s the highest concentrations of cob and t o t a l CP, P and DN, and s t a l k CP. I t i s also s i g n i f i c a n t l y higher than the 300 and 400 f i e l d s i n terms of weighted t o t a l Ca. The 400 f i e l d i s l e a s t productive, e x h i b i t i n g , i n general, the lowest corn nutrient concentrations on both a weighted and an unweighted basis. On an unweighted basis, the 300 and 600 f i e l d s are highest with respect to cob CP, P and Ca. This i s because cob weights i n these f i e l d s are much lower; cob nutrients are therefore more concentrated i n these f i e l d s . I t should be noted that cob Ca concentrations are very low for a l l f i e l d s , which i s normal, as Ca tends to concentrate i n the corn leaves (Pain, 1978) . The 600 f i e l d y i e l d s the highest unweighted t o t a l CP and Ca values -again, however, t o t a l weights are low i n t h i s f i e l d , which leads to a " d i l u t i o n e f f e c t " when the nutrients are examined on an unweighted basis. Overall, i t i s apparent that the 200 f i e l d i s the most productive of the f i e l d s studied, i n terms of both biomass and nutrient contents. This i s because the f i e l d was very well-managed. I t was planted very early i n the growing season, and was also w e l l - f e r t i l i z e d . The fact that the farm operator applied S, Zn and B, i n addition to the standard nutrients, was probably a factor here; the other f i e l d s had only manure and N-P-K f e r t i l i z e r s added. In addition, the high ridge areas of the 200 f i e l d were i r r i g a t e d a couple of times during dry periods, which would have further improved production. The 400 f i e l d , on the other hand, was least productive. This f i e l d had the lowest mean elevation of a l l four f i e l d s . I t also had the highest mean s o i l moisture contents through much of the growing season. I t may be that early corn growth was i n h i b i t e d i n t h i s f i e l d because of s o i l wetness and consequently low s o i l temperatures, and never caught up to corn growth i n the other f i e l d s . The 400 f i e l d also had a f a i r l y severe weed problem; the weeds would have been competing with the corn for nutrients and water, thereby impacting corn production. 42 The 300 and 600 f i e l d s were quite s i m i l a r i n terms of corn growth, both exhibiting intermediate productivity. Interestingly enough, these two f i e l d s are mapped as containing exactly the same s o i l units, which suggests that the basic pedologic features of these f i e l d s may be influencing t h e i r productivity to a large degree. This seems possible, as the 600 f i e l d was managed somewhat d i f f e r e n t l y than the 300 f i e l d , i n terms of f e r t i l i z a t i o n and i r r i g a t i o n . 2.4 P i x e l Brightness Value V a r i a b i l i t y 2.4.1 Pixel value v a r i a b i l i t y : o v e r a l l and within individual  f i e l d s Table 10 l i s t s the means and c o e f f i c i e n t s of v a r i a t i o n for both bare s o i l and corn p i x e l values on an o v e r a l l basis. The near i n f r a - r e d to red r a t i o (NIR2/R2) and the normalized diffe r e n c e (ND) are transformed p i x e l values which are often u t i l i z e d i n an attempt to minimize the influence of s o i l background on o v e r a l l crop reflectance. These values w i l l be explained i n greater d e t a i l i n Chapter 4. I t i s evident that bare s o i l p i x e l values exhibit much greater v a r i a b i l i t y than do those representing corn, p a r t i c u l a r l y with respect to NIR p i x e l values. This indicates that reflectance from the bare s o i l i s much more variable than that from the corn canopy. On an o v e r a l l basis, corn reflectance i s f a i r l y uniform. The reasons for s o i l reflectance v a r i a b i l i t y are discussed f u l l y i n Chapter 4. 43 Table 10. Descriptive s t a t i s t i c s for p i x e l values for the four study f i e l d s combined. Variable 1 X CV. (%) (n=94) Gl 86 26 RI 73 33 NIR1 46 48 G2 134 7 R2 130 7 NIR2 165 3 NIR2/R2 1.27 7 ND 0.12 25 P i x e l values with the s u f f i x "1" are bare s o i l values; those with the s u f f i x "2" denote corn p i x e l values. Table 11. Descriptive s t a t i s t i c s for p i x e l values f o r each of the four study f i e l d s . Variable 1 200 300 400 600 X CV X CV X CV X CV (n=30) (n=24) (n=20) (n=20) Gl 87 18 83 34 67 15 108 10 RI 74 23 69 45 53 21 94 13 NIR1 46 39 46 59 28 39 67 19 G2 132 5 130 8 131 4 143 • 3 R2 132 5 123 8 129 4 138 3 NIR2 162 3 168 3 163 3 166 2 NIR2/R2 1.23 3 1.38 6 1.26 2 1.20 3 ND 0.10 10 0.16 19 0.12 8 0.09 11 P i x e l values with the s u f f i x "1" are bare s o i l values; those with the s u f f i x "2" denote corn p i x e l values. Within the in d i v i d u a l study f i e l d s , bare s o i l p i x e l values e x h i b i t much higher v a r i a b i l i t y than do the corn p i x e l values The 300 f i e l d y i e l d s the highest v a r i a b i l i t y i n terms of bare s o i l p i x e l values. This i s l i k e l y due to the large area of the f i e l d which was ponded when the photo was flown, i n contrast to the other, d r i e r areas of the f i e l d . The 300 f i e l d i s also most va r i a b l e with respect to corn p i x e l values - t h i s may also be att r i b u t e d to v a r i a b i l i t y i n corn productivity r e s u l t i n g from the ponded area. The 600 f i e l d i s the least variable i n terms of both bare s o i l and corn p i x e l values, while the 200 and 400 f i e l d s are intermediate with respect to p i x e l value v a r i a b i l i t y . In general, corn p i x e l value v a r i a b i l i t y follows that of the bare s o i l p i x e l s ( i e . f i e l d s exhibiting high v a r i a b i l i t y of the bare s o i l p i x e l s also exhibit higher corn p i x e l v a r i a b i l i t y ) . 2.4.2 Pix e l value differences between f i e l d s The 600 f i e l d i s most d i f f e r e n t from the other f i e l d s i n terms of bare s o i l p i x e l values, as i t was with respect to s o i l properties. This f i e l d y i e l d s s i g n i f i c a n t l y higher s o i l p i x e l values than the other f i e l d s i n a l l 3 colour bands (green, red, and near i n f r a - r e d ) . The 400 f i e l d exhibits the lowest s o i l r e flectance values i n a l l three wavelengths; s i g n i f i c a n t l y lower than those i n the 200 and 600 f i e l d s - t h i s may again r e f l e c t the presence of areas with higher organic matter concentrations i n the 400 f i e l d . These findings w i l l be discussed i n more d e t a i l i n Chapter 4. As f o r bare s o i l p i x e l values, corn p i x e l s i n the green and red regions of the spectrum are s i g n i f i c a n t l y highest i n the 600 f i e l d . Near infra-red p i x e l brightness values are highest i n the 300 and 600 f i e l d s ; these s i m i l a r values are to be expected, as corn production i n these f i e l d s i s f a i r l y comparable. The 200 and 400 f i e l d s y i e l d lower near infra-red p i x e l values. In the 400 f i e l d , t h i s l i k e l y occurs because of the lower biomass production i n t h i s f i e l d ; more s o i l background would be showing through, and thereby decreasing o v e r a l l reflectance. The 200 f i e l d was planted e a r l i e r than the other f i e l d s , and was therefore more advanced throughout the growing season. Once corn has matured, i t begins to senesce, and more s o i l begins to show through; t h i s may explain the lower mean near i n f r a - r e d p i x e l values found i n t h i s f i e l d . I t i s also possible that i n t h i s f i e l d an asymptote has been reached, such that near i n f r a - r e d reflectance w i l l not increase any further, even i f corn biomass increases. This w i l l be discussed i n more d e t a i l i n Chapter 4. In terms of transformed p i x e l values, the NIR/R r a t i o and the normalized difference are s i g n i f i c a n t l y higher i n the 300 and 400 f i e l d s than they are i n the other two. This suggests that s o i l background i s influencing crop reflectance i n d i f f e r e n t ways and to d i f f e r e n t extents i n the various f i e l d s . These rel a t i o n s h i p s are again discussed more f u l l y i n Chapter 4. 46 CHAPTER 3 RELATIONSHIPS BETWEEN CROP VARIABLES AND SOIL PROPERTIES 3.1 Overall Relationships In t h i s section, o v e r a l l relationships for the four f i e l d s studied w i l l be discussed. A l l s i t e s from a l l f i e l d s were combined (giving a sample number of 94), and relationships then determined for t h i s entire population. The corn biomass vari a b l e s CWT, SWT and TWT, the corn q u a l i t y parameters CP, P, Ca, and DE and DN, and the s o i l chemical and physical variables were compared. The corn quality variables were studied separately with respect to the s t a l k and the cob, and also as t o t a l s ( i e . s t a l k + cob). In addition they were examined on both an unweighted (%) and a weighted (g/plant) basis. 3.1.1 Overall corn biomass relationships Figure 4 depicts the relationships between corn y i e l d and quality, and s o i l and s i t e variables. As indicated, both s t a l k and t o t a l weights are p o s i t i v e l y correlated with elevation. The wet conditions of the fine-textured hollows early i n the season evidently i n h i b i t s t a l k development, and therefore ultimately a f f e c t t o t a l biomass production. This suggestion i s also supported by the s o i l moisture data. Stalk weight i s negatively correlated with water content of the 0-15 cm depth i n mid-June, as well as i n l a t e August arid early September. Cob weight, on the other hand, i s p o s i t i v e l y correlated with water content of the top depth around mid-July. This i s l i k e l y because the ear i s beginning to form at t h i s time, and demand for water and nutrients i s very high (Aldrich and Leng, 1972). These r e l a t i o n s h i p s are a l l r e l a t i v e l y weak ( i e . have low p r e d i c t i v e c a p a b i l i t i e s ) . M SCA np3a np7a A / (-) Son K Son Mg HCCP, CCaj jSONl SWT, WTO Elev TWTi WTO TOTAL NUTRIENTS CON Soil Na Figure 4. Overall relationships between corn biomass and quality, and s o i l / s i t e v a r i a b l e s . 3.1.2 Overall corn quality relationships As a r e s u l t of the relationship between SWT and elevation, weighted s t a l k nutrient concentrations are also p o s i t i v e l y correlated with t h i s variable. Unweighted s t a l k CP also e x h i b i t s t h i s r e l a t i o n s h i p with elevation. Because t o t a l weight was also correlated with elevation, so too are the weighted t o t a l q u a l i t y v a r i a b l e s . Most corn qu a l i t y variables (both weighted and unweighted) are negatively correlated with s o i l moisture over the growing season; only the dates e x h i b i t i n g the strongest 48 r e l a t i o n s h i p s are included i n the above correllelogram. As with SWT, weighted stalk CP, Ca, and DE/DN are negatively correlated with s o i l moisture of the 0-15 cm depth through most of the growing season. P o s i t i v e relationships occur between weighted cob DE/DN and P, and mid-July moisture (top depth), presumably as a r e s u l t of the r e l a t i o n s h i p between CWT and t h i s same measurement. On an unweighted basis, cob CP, P and Ca tend to be negatively correlated with s o i l moisture contents. This may be because these nutrients are negatively correlated with cob weight. I t i s also possible, however, that early wet conditions have ultimately affected mature cob nutrient contents. As t o t a l weight exhibits no relationships with s o i l moisture, unweighted t o t a l nutrients tend to y i e l d the best r e l a t i o n s h i p s with t h i s v a r i a b l e . Total CP i s inversely r e l a t e d to moisture of the upper s o i l depths throughout the growing season, while t o t a l Ca i s negatively related to s o i l moisture at the lower depths early on i n the season, with the r e l a t i o n s h i p becoming stronger through a l l depths from August onwards. A number of s o i l chemical properties are also involved with corn q u a l i t y . S o i l K figures prominently i n several r e l a t i o n s h i p s . This i s hardly surprising, since corn requires large amounts of K for good growth and increased y i e l d s (Aldrich and Leng, 1972) . In t h i s study, i t was found that s o i l K i s p o s i t i v e l y correlated with unweighted s t a l k and cob P, and also weakly correlated with cob CP. The amount of K i n the plant influences the conversion of n i t r a t e into protein (Koch and Mengel, 1974), which i n turn i s thought to a f f e c t n i t r a t e uptake. Claasen and Barber (1977) using s p l i t - r o o t studies showed that increasing K concentrations i n the nutrient media grea t l y increased the n i t r a t e uptake rate. Potassium may also be i n d i r e c t l y a f f e c t i n g P uptake i n t h i s manner; an increase i n N uptake can i n turn increase P uptake. M i l l e r (1974), i n a review of the research on the e f f e c t s of N on P, concluded that the presence of N increased the translocation rate of P to the shoot, which then i n d i r e c t l y affected the P absorption rate. S o i l K was also found to be negatively correlated with both s t a l k and t o t a l Ca. This i s not s u r p r i s i n g , as the addition of K i s known to decrease both Ca and Mg uptake through competition (Barber, 1984). S o i l Mg was also involved i n several corn q u a l i t y r e l a t i o n s h i p s . This element i s important for plant n u t r i t i o n , as i t i s a component of the chlorophyll molecule, and therefore e s s e n t i a l to the process of photosynthesis. I t i s also involved i n a number of physiological and biochemical functions, and i s involved i n the formation of polypeptide chains from amino acids; a Mg deficiency leads to a decrease i n the proportion of protein to nonprotein N (Tisdale et a l . , 1985). I t i s therefore not s u r p r i s i n g that s o i l Mg i s p o s i t i v e l y related to c e r t a i n corn q u a l i t y variables. S i m i l a r relationships were found to occur between s o i l Na, and the same variables (cob CP and Ca, and s t a l k DE/DN) which 50 were related to s o i l Mg. Since Na i s not thought to be e s s e n t i a l f o r corn growth and survival (Pain, 1978), these r e l a t i o n s h i p s may occur because s o i l Mg and Na are highly correlated. A study by Cope et a l . (1953), however, suggested that Na may improve y i e l d i n d i r e c t l y by increasing uptake of s o i l K. Since several of these relationships also e x i s t with s o i l K, t h i s process may be infl u e n c i n g corn quality. The best of the above-mentioned co r r e l a t i o n s were chosen for regression analysis. The regressions and multiple regressions with the greatest predictive c a p a b i l i t i e s are shown i n Table 12. Table 12. Best s o i l regressions for prediction of corn q u a l i t y (overall f i e l d r e l a t i o n s h i p s ) . Corn Predictive Equation R2 S.E. Variable SCPg SCPg = ELEV(0.021) - 2.86 0. 27 2.72 CCa CCa = Na(0.057) + (2.72 x 10"2) 0. 36 0.005 CDN CDN = K(-1.91) + 72.81 0. 46 1.59 SCPg SCPg = ELEV(0.019) - np6a(6.85) + 0. 049 0. 31 2.65 CCa CCa = Na(0.049) - np2c(0.012) + 0.008 0. 42 0.004 TP TP = K(0.037) - np5a(0.177) + 0.452 0. 31 0.055 51 3.2 Individual F i e l d Relationships Table 13. Comparison of overall f i e l d (corn-soil) relationships with i n d i v i d u a l f i e l d r elationships. Corn Variable S o i l Variable R Value 1 A l l 200 300 400 600 SCP SCa SDEg/ SDNg ELEV 0.38 0.39 (-0.20) (0.23) (0.24) • 0.53 0.49 -0.39 -0.43 -0.71 (-0.20) 0.40 0.40 (0.32) (-0.30) -0.46 (-0.40) (-0.40) 0.61 (0.29) (0.28) 0.61 0.50 0.59 0.57 0.64 CCP CP CCa TCP TCa CCP CP CCa CDE/CDN K (0.31) 0.41 -0.63 0.52 0.80 (0.28) -0.60 (0.24) (0.29) (0.30) (0.26) (0.26) 0.43 0.43 CCP CCa Mg 0.44 0.43 0.46 0.37 (0.22) (0.25) TCP TCa Ca 0.33 0. 58 0.57 (0.27) (0.27) (0.23) (0.30) SCP NP2A -0.49 (-0.35) -0.69 CCP CCa NP3A -0.50 -0.51 (0.23) (-0.23) -0.47 -0.57 CP NP5A -0.38 -0.46 (-0.27) -0.45 -0.50 -0.55 TCP TCa NP6A -0.56 -0.43 (-0.22) -0.56 R values i n brackets are not s i g n i f i c a n t , they are only shown for completeness. 52 Table 13 contains some of the best c o r r e l a t i o n c o e f f i c i e n t s for the corn - s o i l relationships found o v e r a l l , and compares them with those found for the individual f i e l d s . Figures 5a-5d i l l u s t r a t e the most consistent, s i m i l a r r e l a t i o n s h i p s found within the i n d i v i d u a l f i e l d s ; these tend to be water and elevation-related. Corn - s o i l f e r t i l i t y r e l a t i o n s h i p s have not been included i n these figures, as they are r e l a t i v e l y inconsistent from f i e l d to f i e l d . Where such re l a t i o n s h i p s are important, however, they w i l l be outlined i n section 3.2.3. 3.2.1 Biomass relationships within i n d i v i d u a l f i e l d s In two of the f i e l d s studied, corn biomass exh i b i t s r e l a t i o n s h i p s with water-related variables. Biomass tends to be negatively related to s o i l moisture, i n d i c a t i n g that corn biomass production i s i n h i b i t e d by excess water - p a r t i c u l a r l y i n the depressional areas - early on i n the growing season. This suggestion i s best i l l u s t r a t e d by biomass r e l a t i o n s h i p s i n the 600 f i e l d , where s t a l k weight i s negatively correlated with s o i l moisture, but p o s i t i v e l y related to elevation. On the other hand, cob weight i n t h i s f i e l d i s p o s i t i v e l y r e l a t e d to s o i l moisture, and negatively correlated with elevation. Evidently early wet conditions i n h i b i t stalk development, while droughty conditions l a t e r i n the season ( p a r t i c u l a r l y i n the ridge areas) retard cob growth. In the 300 f i e l d , t o t a l weight i s negatively r e l a t e d to bulk density, while i n the 600 f i e l d , cob weight i s inversely r e l a t e d to t h i s v a r i a b l e . This suggests that s o i l compaction i s 53 CWT TWT Weighted Total CP, P, DN (-) Weighted Cob CP, P, DN TCP, TP TCA -) bd1 (-) np6a np3c (-) TPg, TDNg TWT TCP, TCA SCP, SCa CCP, CP (-) Figure 5(a). 200 F i e l d Figure 5(b). 300 F i e l d Elev (-) TP (-) SCA h—1 SP (-) bd2 np4b np4a np3d (-) TDN SDN CP, CCA SWT CCA, TCA Elev CWT (-) (-) CCP, CP; TCP, TP bd Soil moisture (upper depths) (-) (-) Soil moisture (lower depths) (-) Figure 5(c). 400 F i e l d Figure 5(d). 600 F i e l d Figure 5(a-d). S o i l - corn relationships within the i n d i v i d u a l study f i e l d s . 54 corn development i n these f i e l d s . This i s not surprising i n the 300 f i e l d , as i t contains the large compacted area which was planted l a t e , and exhibited poorer corn growth. 3.2.2 Corn quality relationships within individual f i e l d s Elevation figures prominently i n a number of corn q u a l i t y r e l a t i o n s h i p s . When examining the f i e l d s on an i n d i v i d u a l basis, unweighted nutrients y i e l d the best r e s u l t s , since biomass -elevation relationships within the separate f i e l d s are r e l a t i v e l y weak. Elevation relationships with corn q u a l i t y are strongest i n the f i e l d s which exhibit the largest elevation and s o i l differences. These are p r i m a r i l y the 300 f i e l d , which contains the low, compacted area which was ponded early on i n the growing season, and the 600 f i e l d , which also contains a low area that exhibits ponding and waterlogging problems under wet conditions. In addition, both of these f i e l d s (most notably the 600 f i e l d ) also contain large, sandy ridge areas. Stalk and t o t a l CP and Ca are p o s i t i v e l y correlated with elevation i n both the 300 and 600 f i e l d s . Cob nutrients tend to be negatively related to elevation i n the 300 f i e l d , but p o s i t i v e l y correlated with t h i s v a r i a b l e i n the 600 f i e l d . In the 4 00 f i e l d , cob Ca and t o t a l P are both negatively correlated with elevation. These r e s u l t s indicate that early on i n the growing season when the s o i l s ( p a r t i c u l a r l y i n the hollows) are very wet, nutrient uptake i s i n h i b i t e d . At t h i s time water tables, p a r t i c u l a r l y i n the hollows where the F a i r f i e l d and Page s o i l s 55 are present, may be at or very near the s o i l surface. Fausey et a l . , (1985) found that when water tables were high (at 0.30 m), nitrogen uptake by sweet corn was reduced from that which occurred when water tables were lower. In a s i m i l a r study, Shih and Rosen (1985) determined that high water tables resulted i n s i g n i f i c a n t l y lower sweet corn dry biomass, and TKN and Mg concentrations. Chaudhary et a l . , (1975) found that prolonged s o i l submergence resulted i n decreased corn grain contents of N, P, and K. Suggested reasons for decreased nutrient uptake by corn under wet conditions include l i m i t a t i o n s i n the plant rooting system, predominance of reducing s o i l conditions, d e f i c i e n c y of s o i l oxygen, and excess carbon dioxide (Lai and Taylor, 1970). In addition, nitrogen d e f i c i e n c i e s could occur as a r e s u l t of leaching, d e n i t r i f i c a t i o n , or v o l a t i l i z a t i o n of t h i s element (Kanwar et a l . , 1988). In the l a t t e r part of the growing season, the sandy ridges dry out and tend to become less productive. This i s supported by the f a c t that several cob nutrients are negatively correlated with elevation i n 2 of the f i e l d s studied. These relationships are p o s i t i v e i n the 600 f i e l d , however. This may be p a r t i a l l y because the cob nutrients i n t h i s f i e l d are strongly negatively c o r r e l a t e d with CWT, which i s i n turn inversely related to elevation. I t i s also possible, however, that nutrient uptake i n the wet areas of t h i s f i e l d was i n h i b i t e d to such an extent e a r l i e r i n the growing season, that cob nutrient content was affec t e d l a t e r on. Chaudhary et a l . , (1975) determined that s o i l submergence during early growth was more damaging than during l a t e growth. They also noted that for submergence exceeding 2 days, grain N and P concentrations were decreased s i g n i f i c a n t l y , i n d i c a t i n g that prolonged wetness affected the translocation of N and P from the stem and leaves to the grain. Soil iroisture also y i e l d s good relationships with corn quality. In a l l four f i e l d s , s o i l moisture tends to be negatively correlated with corn q u a l i t y variables. In many cases, these relationships are found at a p a r t i c u l a r depth through much of the growing season; for s i m p l i c i t y , only the best relationships are shown. The fact that s t a l k , cob and t o t a l nutrient concentrations a l l tend to be negatively related to s o i l water contents indicates that wet conditions i n the spring i n h i b i t corn nutrient uptake to such an extent that the ultimate quality of the mature corn i s affected. There are some exceptions to t h i s , however. In the 400 f i e l d , for example, t o t a l P, and stalk and t o t a l DN are p o s i t i v e l y correlated with s o i l moisture, and t o t a l P i s negatively related to elevation. These relationships indicate some s i g n i f i c a n t drought stress effects i n t h i s f i e l d ; the 300 and 600 f i e l d s also e x h i b i t some of these e f f e c t s . Bulk density i s another variable which has s i g n i f i c a n t e f f e c t s on corn productivity. Bulk density i s negatively related to t o t a l P concentrations i n the 200, 300 and 400 f i e l d s . I t i s known that P uptake i s dependent on s o i l structure, as roots are forced to feed mainly on the outside of dense s o i l blocks. The larger and more compact these blocks are, the less t o t a l volume 57 a v a i l a b l e for the roots to take up P (Aldrich and Leng, 1972). In addition, higher bulk densities are related to lower t o t a l CP, t o t a l DE/DN and cob Ca i n the 200, 300 and 400 f i e l d s , r e spectively. In the 600 f i e l d , cob and t o t a l CP and P are p o s i t i v e l y correlated with bulk density, l i k e l y because of i t s r e l a t i o n s h i p with elevation. Relationships between corn quality and other s o i l variables e x h i b i t l i t t l e consistency between f i e l d s . In general, corn q u a l i t y - s o i l f e r t i l i t y relationships e x i s t i n f i e l d s where s o i l concentrations of c e r t a i n nutrients are lowest, or where s o i l nutrients are related to other variables of importance. For example, i n f i e l d s where corn qu a l i t y i s related to elevation, and elevation i s i n turn related to s o i l f e r t i l i t y variables (C, N, exchangeable cations), then corn quality i s also related to these variables. Such relationships e x i s t i n the 300 and 600 f i e l d s and may be " i n d i r e c t " relationships, or they may a c t u a l l y i n d i c a t e elevation-related nutrient d e f i c i e n c i e s i n the sandy ridge areas of these f i e l d s . The best relationships. found within the four f i e l d s are l i s t e d as p r e d i c t i v e equations i n Tables 14 - 17. 58 Table 14. Best regressions for prediction of corn productivity from s o i l variables (200 f i e l d ) . Corn Predictive Equation R2 S.E. Variable CWT CWT = = K(-26 .90) + 156.14 0.28 19.71 CCPg CCPg = K(-2 .41) + 12.97 0.29 1.71 Table 15. Best regressions for prediction of corn productivity from s o i l variables (300 f i e l d ) . Corn Predictive Equation R2 S.E. Variable SCP SCP = ELEV(0.021) - 4.00 0. 28 1.45 SCP SCP = K(-0.25) + 4.18 0. 30 0.084 CP CP = = ELEV(-7.57 X 10"4) + 0.752 0. 51 0.033 CP CP = = K(0.046) + 0.218 0. 63 0.028 CDN CDN = ELEV(0.028) + 51.35 0. 39 1.52 CDN CDN = C(-3.76) + 77.76 0. 43 1.48 TCP TCP = Ca(0.873) + 0.991 0. 34 0.262 TCa TCa = Ca(0.017) + 0.018 0. 33 0.039 CP CP = ELEV(-6.89 X 10"4) + Na(0.537) + 0.637 0.69 0. 026 59 Table 16. Best regressions for prediction of corn productivity from s o i l variables (400 f i e l d ) . Corn Predictive Equation R2 S.E. Variable SWT SWT = = BD2(62.37) - 0.234 0. 28 12.53 SP SP = pH(0.052) - 0.194 0. 30 0.018 CP CP = Ca(7.74 X 10*3) + 0.179 0. 46 0.022 CP CP = NP4C(-0.182) + 0.337 0. 31 0.025 TP TP = BD2(-0.100) + 0.319 0. 27 0.020 TP TP = pH(0.068) - 0.205 0. 42 0.018 TDN TDN = = NP4B(0.038) - 2.06 0. 42 0.060 CP CP = NP1D(-0.114) + Ca(0.006) + 0.244 0. 53 0.022 TP TP = pH(0.059) -Mg(0.042) - 0.103 0. 50 0.017 Table 17. Best regressions for prediction of corn variables from s o i l variables (600 f i e l d ) . Corn Predictive Equation R2 S. E. Variable SWT SWT = Ca(4.04) + 82.17 0. 36 19. 40 CWT CWT = C(44.14) - 0.861 0. 30 28. 19 SCP SCP = ELEV(0.012) + 2.20 0. 37 1. 24 SCP SCP = NP2A(-10.05) + 12.57 0. 48 1. 13 CP CP = BD1(0.335) - 0.092 0. 39 0. 040 CCa CCa = ELEV (3.47 x 10"5) - 0.004 0. 34 0. 004 CDN CDN = N(-8.04) + 72.98 0. 35 0. 666 TCP TCP = ELEV(9.82 x 10"3) +4.71 0. 33 1. 08 TCP TCP = NP2B(-6.24) + 11.14 0. 41 1. 01 TCA TCA = ELEV(4.78 X 10"4) - 0.024 0. 41 0. 044 TCA TCA = NP3D(-0.285) + 0.290 0. 57 0. 037 SCPg SCPg = ELEV(0.014) + Ca(0.370) -0.273 0. 51 1. 98 CCa CCa = K(0.003) + ELEV(2.96 X 10"5) -0.003 0.40 0.004 60 3.3 Summary of Main Factors A f f e c t i n g Corn Production on an  Individual F i e l d Basis This section w i l l include a b r i e f summary of the major underlying factors which influence corn production i n the four study f i e l d s . The 200 f i e l d i s the most productive f i e l d . I t was planted e a r l i e r than the others, and had the largest amounts of f e r t i l i z e r applied. In addition to the major nutrients, some micronutrients were also added, which was not the case i n the other f i e l d s . Although t h i s f i e l d did not exhib i t elevation differences which were as large as some of the other f i e l d s , i r r i g a t i o n was applied several times during the growing season, which evidently further improved production. I t does appear that early wet conditions i n h i b i t biomass production i n t h i s f i e l d , and that s o i l compaction may be impeding nutrient uptake. The 400 f i e l d i s the least productive f i e l d . S o i l f e r t i l i t y seems to be a problem, as corn qu a l i t y i s d i r e c t l y related to a number of s o i l f e r t i l i t y parameters. Again there are indications that wet s o i l conditions are r e s t r i c t i n g growth; t h i s f i e l d has a much lower r e l a t i v e elevation than the others which may make i t more susceptible to a fluctuating water table. This may well be the case, as s o i l moisture values i n t h i s f i e l d tend to be higher than i n the other f i e l d s . A weed i n f e s t a t i o n of t h i s f i e l d may also have contributed to poor productivity. The 300 and 600 f i e l d s are quite s i m i l a r to each other. They show extreme elevational differences and are mapped as containing the same s o i l units. Consequently, both have very sandy ridge areas and extremely low, fine-textured depressional areas which were ponded early i n the growing season. As a r e s u l t , i n both f i e l d s there i s evidence that wet depressional conditions early i n the growing season i n h i b i t both biomass production and nutrient uptake,, while l a t e r i n the summer, droughty ridge conditions hinder corn productivity. Basically, then, these f i e l d s are influenced to a large degree by s o i l moisture conditions, and elevation i s a r e l a t i v e l y good i n d i c a t o r of these conditions, p a r t i c u l a r l y i n the 600 f i e l d . S o i l f e r t i l i t y parameters also play a r o l e i n these two f i e l d s ; the f e r t i l i t y variables which y i e l d good rel a t i o n s h i p s are generally r e l a t e d to elevation. As i s often the case in studies looking at s o i l / s i t e - c r o p p r o d u c t i v i t y relationships, no single v a r i a b l e e i t h e r water-rela t e d or f e r t i l i t y - r e l a t e d , can be found which s a t i s f a c t o r i l y explains corn production v a r i a b i l i t y i n a p r e d i c t i v e manner. Although some s i m i l a r i t i e s do exist, i t i s apparent that the influences of c e r t a i n variables and combinations of variables are f i e l d - s p e c i f i c . Certain s i m i l a r l i n e a r relationships do e x i s t within the four f i e l d s ; elevation and other, often related v a r i a b l e s , are influencing corn p r o d u c t i v i t y i n the f i e l d s . However, the degrees of t h e i r influences, and the e f f e c t s of these influences, vary from f i e l d to f i e l d . D i f f e r e n t i a l f i e l d management and v a r i a t i o n s i n the basic pedologic features of the f i e l d s are factors which modify the influences of elevation and other s o i l and s i t e variables. 62 CHAPTER 4 RELATIONSHIPS BETWEEN REMOTE SENSING, SOIL, AND CROP VARIABLES This chapter w i l l deal with the reflectance data obtained from the f i e l d s of f u l l y mature corn, as well as from the bare s o i l . One of the most well-studied areas of remote sensing (as i t r e l a t e s to vegetation) i s the use of reflectance data to assess and predict crop productivity. Remote sensing o f f e r s an al t e r n a t i v e to the labour-intensive and time consuming t r a d i t i o n a l method of hand sampling for monitoring vegetation condition and productivity over large areas (Weiser et a l . , 1986) . Reflectance data can also be used to assess c e r t a i n s o i l c h a r a c t e r i s t i c s , and consequently can also o f f e r an alt e r n a t i v e to, or at le a s t supplement and thereby lessen, intensive s o i l sampling. The s o i l - r e f l e c t a n c e and corn-reflectance relationships w i l l be studied i n t h i s chapter using the conventional c o r r e l a t i o n and regression approach. Supplemental chapters w i l l deal with t h i s data by using more non-conventional s p a t i a l s t a t i s t i c s , i n addition to image analysis and GIS techniques. 4.1 Relationships between S o i l and Remote Sensing  4.1.1 Overall Relationships In a manner si m i l a r to the analysis of o v e r a l l s o i l - c o r n r e l a t i o n s h i p s , a l l s i t e s from a l l f i e l d s were combined, and re l a t i o n s h i p s determined for t h i s e n t i r e group. The remote sensing v a r i a b l e s used were the p i x e l brightness values for the t e s t s i t e s , obtained from the d i g i t i z e d images of the f i e l d s p r i o r to planting ( i e . the bare s o i l images). For the bare f i e l d s combined, green, red and near inf r a - r e d p i x e l brightness values were a l l found to be negatively correlated with s o i l C, N and CEC. Many studies have found that as s o i l organic matter (and hence s o i l C) increases, s o i l s p e c t r a l reflectance decreases (Leger et a l . , 1979; Stoner and Baumgardner, 1981; Zheng and Schreier, 1988). Zheng and Schreier (1988) also noted that % t o t a l N shows the same rel a t i o n s h i p , through i t s association with organic C. Cation exchange capacity has also often been found to be negatively correlated with reflectance. Myers (1983) suggests that t h i s i s a secondary e f f e c t , occurring because CEC i s la r g e l y determined by organic matter, and clay amounts and composition - variables which are a l l negatively correlated with s o i l reflectance. In addition, t h i s researcher notes that finer-textured s o i l s may also e x h i b i t lower reflectance due to t h e i r increased moisture content; moisture tends to darken s o i l colour, and hence decrease reflectance (Leger et a l . , 1979; Zheng and Schreier, 1988). 64 4.1.2 Comparison of Overall F i e l d Relationships with  Individual F i e l d Relationships In t h i s section, s o i l - remote sensing re l a t i o n s h i p s within the i n d i v i d u a l f i e l d s w i l l be compared with those observed for the combined f i e l d s . In general, near infra-red p i x e l brightness values f o r individual f i e l d s yielded the best r e s u l t s ; s o i l r e l a t i o n s h i p s with t h i s variable w i l l therefore be concentrated on. S i g n i f i c a n t relationships between s o i l variables and NIR p i x e l values for a l l f i e l d s combined, as well as each i n d i v i d u a l f i e l d , are shown i n Table 18, along with t h e i r corresponding r values. As t h i s table indicates, many of the o v e r a l l r e l a t i o n s h i p s are echoed within individual f i e l d s . Table 18. S i g n i f i c a n t relationships between near i n f r a - r e d p i x e l brightness values and s o i l variables. S o i l R Value Variable A l l 200 300 400 600 C -0.42 -0.52 -0.53 N -0.41 -0.44 -0.62 CEC -0.50 -0.42 -0.54 ELEV (0.26) 0.88 0.64 0.42 The most notable remote sensing-soil r e l a t i o n s h i p i s that with elevation. In three of the four f i e l d s , NIR p i x e l values are p o s i t i v e l y correlated with t h i s variable. This r e l a t i o n s h i p occurs as a r e s u l t of the differences i n s o i l texture found between the ridges and hollows. The coarser-textured ridges are l i g h t e r i n colour, p a r t i c u l a r l y since they have a lower moisture content than the hollows. The finer-textured hollows, on the other hand, would be darker as a r e s u l t of higher water content, as well as higher C content (Myers, 1983). Hence, a higher elevation results i n a higher reflectance, and vic e versa. In a l l of the f i e l d s , elevation i s negatively correlated with s o i l C, N, and CEC. As a consequence, i n the f i e l d s where elevation i s affe c t i n g reflectance, these s o i l variables would also be expected to be related to NIR reflectance. In the 300 and 400 f i e l d s , where elevation i s most strongly related to NIR p i x e l values, s o i l C, N, and CEC are a l l negatively correlated with t h i s same variable, as predicted. In the 600 f i e l d , where the elevation - p i x e l value r e l a t i o n s h i p i s weaker, and i n the 200 f i e l d , where i t i s non-existent, these other r e l a t i o n s h i p s do not hold true. In addition, i n the 600 f i e l d , mean t o t a l C content i s r e l a t i v e l y low. I t has been found that organic C only exerts an strong influence on s o i l spectral reflectance when percent carbon levels are > 2 % (Zheng and Schreier, 1988) . The mean t o t a l C value i n the 600 f i e l d (as well as % N and CEC) i s s i g n i f i c a n t l y lower (by almost 1%) than i n the other f i e l d s . I t i s therefore also possible that s o i l C values i n t h i s f i e l d are too low to influence reflectance. The relationships discussed above indicate that the use of CIR photos of bare s o i l may have some predictive c a p a b i l i t y f o r several of the f i e l d s studied, with respect to c e r t a i n s o i l v a r i a b l e s . The major p o s s i b i l i t i e s include areas of extreme topographic and s o i l v a r i a b i l i t y . Under such conditions, v a r i a b l e s which are strongly correlated with elevation (and/or s o i l C, clay content, etc.) could also be estimated. 66 4.2 Relationships between Crop Variables and Remote Sensing  4.2.1 Overall Relationships Using the f i e l d images of mature corn, the 94 s i t e s from a l l f i e l d s were combined, and re l a t i o n s h i p s between crop variables and remote sensing were examined. The remote sensing variables tested were obtained from the d i g i t i z e d images of the corn crops obtained at the end of the growing season (several days p r i o r to harvest) . Pixel brightness values for the 3 dye layers of the image (green, G2; red, R2; near i n f r a - r e d , NIR2) were extracted for each of the test s i t e s , and correlated with crop biomass and q u a l i t y parameters. In addition, various combinations of these p i x e l values were also included i n the cor r e l a t i o n s . These combinations w i l l be referred to as "transformed" p i x e l values, and were used i n an attempt to lessen s o i l background influence. The "transformed" values u t i l i z e d were the following: near in f r a - r e d to red r a t i o (NIR/R) normalized difference (ND); ([NIR2 - R2]/[NIR2 + R2]) differences between corn and s o i l p i x e l values: [G2 - G l ] , [R2 - RI], and [NIR2 - NIR1]. Figure 6 depicts some of the best remote sensing - crop r e l a t i o n s h i p s found o v e r a l l . Although near infra-red p i x e l brightness values were correlated with a number of the crop v a r i a b l e s examined, several of the transformations outlined above yielded stronger r e l a t i o n s h i p s . Biomass was most strongly cor r e l a t e d with the [NIR2 - NIR1] transformation. Consequently, SWT, TWT 67 (NIR2-NIR1) i d (G2-G1) Weighted: Stalk CP, CA; 1=1 Total CA, DE/DN Unweighted: Cob CP, CA NIR/R (-) (-) Unweighted: Cob DE/DN 1=1 R2 Figure 6. Strongest crop - remote sensing r e l a t i o n s h i p s f o r a l l f i e l d s combined. weighted s t a l k CP and Ca, and weighted t o t a l Ca and DE/DN were also c o r r e l a t e d with t h i s transformation. Cob nutrients y i e l d e d better r e l a t i o n s h i p s when unweighted. Cob CP and Ca were cor r e l a t e d with the [G2 - Gl] transformation, while cob DE and DN were more c l o s e l y r e l a t e d to the r a t i o of NIR/R corn re f l e c t a n c e . The f a c t that the transformed p i x e l brightness values produce stronger r e l a t i o n s h i p s than the o r i g i n a l values suggests that s o i l background i s i n t e r f e r i n g with crop ref l e c t a n c e patterns i n some of the f i e l d s . The con t r i b u t i o n of s o i l background to crop s p e c t r a l reflectance, p a r t i c u l a r l y over incomplete canopies (such as corn), i s a major problem i n these types of studies (Huete, 1987). The NIR/R r a t i o and the ND are often used to lessen s o i l background influences; i t appears that subtraction of s o i l reflectance from crop re f l e c t a n c e ( i f both sets of data are available) i n c e r t a i n bands may also be e f f e c t i v e i n reducing these influences. 68 The reasons for the correlations found between biomass and CP, and remote sensing data, were discussed i n the l i t e r a t u r e review found i n the Chapter 1. Other f o l i a r q u a l i t y variables (Ca, DE/DN) are l i k e l y related to remotely sensed data through t h e i r c o r r e l a t i o n with CP and/or biomass. This area has not been the focus of much research, but the p o t e n t i a l to predict a number of crop qu a l i t y variables v i a remote means, when they are cor r e l a t e d with parameters such as CP and biomass, appears promising. 4.2.2 Comparison of Overall F i e l d Relationships with  Individual F i e l d Relationships 4.2.2.1 Relationships between corn and untransformed  p i x e l values Table 19 l i s t s the c o r r e l a t i o n r values for the best r e l a t i o n s h i p s found between crop variables and the untransformed p i x e l brightness values on an o v e r a l l basis and on an in d i v i d u a l f i e l d basis. The best relationships occur with near infra-red p i x e l values. Stalk, cob and t o t a l CP are strongly correlated with near infra-red p i x e l brightness values i n three of the four f i e l d s studied. Other studies have found s i m i l a r r e l a t i o n s h i p s . Hinzman et a l . , (1986) determined that increased N f e r t i l i z a t i o n of winter wheat led to higher chlorophyll concentrations, higher l e a f t o t a l N concentrations, and higher LAI, leading to increased near i n f r a - r e d reflectance. Walburg et a l . , (1982) found v i r t u a l l y the same results for corn. This r e l a t i o n s h i p may not show up i n the fourth f i e l d due to the uniformly high 69 Table 19. Relationships between untransformed p i x e l values and corn variables. Corn R.S. R Value Variable Variable A l l 200 300 400 600 SCP NIR2 -0.39 -0.44 (-0.39) -0.73 CCP (-0.21) -0.43 -0.54 TCP (-0.28) -0.50 -0.57 -0.75 TCa — — — — -0. 38 -0.50 CDE/CDN R2 0.59 0.36 0.50 0.48 CWT G2 0.37 0.48 TWT 0.39 0.45 CPg 0.41 0.54 CDEg/CDNg 0.40 0.47 TDEg/TDNg (0.21) 0.40 0.47 — — — — ~~ p r o d u c t i v i t y found there. In the 200 f i e l d , the corn was planted very early, and as a r e s u l t biomass production was s i g n i f i c a n t l y higher there than i n the other three f i e l d s . Other studies have suggested that relationships between reflectance and biomass are asymptotic - once a ce r t a i n point i n biomass production i s reached, a further increase in production does not lead to a corresponding increase i n reflectance (Hinzman et a l . , 1986; Ripple, 1985). This phenomenon could perhaps be a f f e c t i n g the r e l a t i o n s h i p between reflectance and crop q u a l i t y as well. In addition, corn CP concentrations i n t h i s f i e l d are not too var i a b l e , which may also p a r t i a l l y explain why a l i n e a r r e l a t i o n s h i p i s not apparent. Walburg et a l . (1982) found that reflectance differences due to varying corn N contents could only be distinguished i n more extreme cases ( i e . w e l l - f e r t i l i z e d vs n o n - f e r t i l i z e d corn) . Consequently, i f corn CP (or other nutrients) are not variable within a . f i e l d , large spectral 70 differences would not be expected to show up. Total Ca i s also s i g n i f i c a n t l y correlated with near i n f r a -red p i x e l values i n two of the four f i e l d s . This i s l i k e l y because i n these two f i e l d s , TCA i s correlated with TCP, and hence also with near infra-red reflectance. Total Ca i s also highly variable i n both of these f i e l d s , which may also be a reason why the relationships are strong. I t should be noted here that the r e l a t i o n s h i p s found between ear i n f r a - r e d p i x e l values and corn q u a l i t y are negative; normally these would be expected to be p o s i t i v e . In the f i e l d s where these relationships occur, however, corn biomass and q u a l i t y are negatively correlated. In areas of lower corn biomass (and consequently higher nutrient concentrations), more s o i l shows through, leading to a decrease i n near i n f r a - r e d reflectance. This i n turn produces the negative relationships found between corn quality and near in f r a - r e d p i x e l values i n c e r t a i n f i e l d s . O verall near infra-red relationships with the above-mentioned corn nutrients are r e l a t i v e l y weak or non-existent. I t appears that i f one of the four f i e l d s shows no r e l a t i o n s h i p with p i x e l values, then the o v e r a l l r e l a t i o n s h i p i s decreased s i g n i f i c a n t l y . Red p i x e l values are s i g n i f i c a n t l y correlated with cob DE and cob DN i n three out of four f i e l d s . They are also strongly c o r r e l a t e d with these variables i n terms of the o v e r a l l f i e l d r e l a t i o n s h i p s . This relationship occurs because an increase i n DE suggests a healthier plant, presumably a higher LAI, and consequently a higher chlorophyll content. As t h i s pigment increases i n concentration, reflectance of red l i g h t decreases, as c h l o r o p h y l l absorbs l i g h t i n t h i s region of the spectrum (Hinzman et a l . , 1986). The relationship with the p i x e l values i s again the opposite of that expected - presumably due again to biomass-soil exposure relationships, as discussed previously. The Relationship between red p i x e l values and cob DE/DN i s not found i n the 400 f i e l d ; t h i s may be a t t r i b u t e d to s o i l background interferences, and the uniformly low biomass production found i n t h i s f i e l d . In general, biomass relationships with near infra-red p i x e l brightness values are weak and inconsistent. This may be a r e s u l t of the asymptotic relationship found between biomass and near i n f r a - r e d reflectance, as previously mentioned. Green p i x e l values are s i g n i f i c a n t l y correlated with cob weight, and therefore also with t o t a l weight, i n the 200 and 300 f i e l d s . The most consistent predictive equations for the i n d i v i d u a l f i e l d s , using the untransformed p i x e l values, are shown i n Table 20. Where possible, t o t a l nutrient contents are predicted; i n most of these cases stalk and/or cob nutrients are also related to the remote sensing variable i n question. This table also contains several multiple regressions which include s o i l v a r i a b l e s , and thereby improve p r e d i c t i v e c a p a b i l i t i e s . 72 Table 20. Best consistent p r e d i c t i v e equations for indi v i d u a l (and overall) f i e l d relationships, using untransformed p i x e l values. F i e l d Equation R2 S. E. 300 TCP = NIR2(-0.099) + 25.32 0. 25 0. 920 400 TCP = NIR2(-0.050) + 16.29 0. 33 0. 328 600 TCP = NIR2(-0.218) + 45.35 0. 57 0. 863 300 TCP = NIR2(-0.106) + P(0.012) + 3.05 0. 32 0. 898 400 TCP = NIR2(-0.053) + ELEV(0.002) + 15.96 0. 34 0. 396 600 TCP = NIR2(-0.053) + P(0.111) + 41.75 0. 64 0. 813 400 TP = NIR2(-2.37 X 10"3) + 0.598 0. 29 0. 020 400 . TP = NIR2(-2.06 X 10"2) - ELEV(1.86 X 10"A) + 0.635 0. 37 0. 019 600 CP = NIR2(-6.24 X 10*3) +1.35 0. 31 0. 042 A l l CDN = R2(0.147) + 51.20 0. 37 1. 72 600 CDN = R2(0.121) + 54.58 0. 36 0. 658 300 CDN = R2(0.077) + ELEV(0.025) + 43.69 0. 55 1. 34 300 CDN = R2(0.069) + C(3.24) + 67.87 0. 55 1. 34 600 CDN = R2(0.115) + C(0.739) + 56.91 0. 50 0. 598 4.2.2.2 Relationships between corn and transformed  p i x e l values Table 21 presents the res u l t s obtained when transformed p i x e l brightness values are correlated against crop variables. As t h i s table indicates, using transformed p i x e l values tends to improve r e l a t i o n s h i p s between remote sensing and crop variables i n terms of o v e r a l l f i e l d r e lationships. By taking the NIR2/R2 r a t i o , we get a stronger relationship with cob DE and DN. The r e l a t i o n s h i p i s also improved for the 300 f i e l d , which i s the f i e l d with bare s o i l areas in i t . . Table 21. Relationships between transformed p i x e l values and crop variables. Corn Variable R.S. Variable A l l 200 R Value 300 400 600 CDE/CDN NIR2/R2 -0.78 -0.67 (-0.29) CCP [G2-G1] -0.50 (-0.30) (-0.33) (-0.23) SWT SCP SCPg SCag [NIR2 -NIR1] -0.46 -0.56 -0.65 -0.53 -0.72 -0.53 -0.41 -0.44 -0.51 -0.62 (-0.35) CDE/CDN -0.54 -0.50 (-0.30) : TCP TCPg TCa TCag TDEg/TDNg (-0.28) -0.61 -0.51 -0.56 -0.66 (-0.29) -0.52 -0.40 (-0.33) (-0.30) -0.55 -0.44 -0.53 (-0.36) U t i l i z i n g the [G2 - Gl] transformation, the o v e r a l l f i e l d c o r r e l a t i o n for cob CP i s improved, as i s that i n the 200 f i e l d ; r e l a t i o n s h i p s i n other f i e l d s are not improved. Use of the [NIR2 - NIR1] transformation i s generally most successful for improving o v e r a l l f i e l d r elationships, p a r t i c u l a r l y i n terms of weighted s t a l k and t o t a l nutrients, as t h i s v a r i a b l e i s correlated with both s t a l k and t o t a l weights o v e r a l l . Overall f i e l d relationships for most of the corn va r i a b l e s outlined previously are improved as are relationships i n the 300 f i e l d , again i n d i c a t i n g the influence of s o i l background on near infra-red reflectance. S o i l moisture and colour are factors which have been suggested to influence both v i s i b l e and near infra-red reflectance from crop canopies (Kollenkark et a l . , 1982). In the 300 f i e l d , the unweighted crop v a r i a b l e s provide better relationships, as stalk and t o t a l weights are not related to the [NIR2 - NIR1] transformation i n t h i s f i e l d . One other i n t e r e s t i n g transformation to note i s the subtraction of green corn p i x e l values from near i n f r a - r e d corn p i x e l values [NIR2 - G2]. This transformation y i e l d s s i g n i f i c a n t r e l a t i o n s h i p s with cob and t o t a l weights, and with cob CP on a weighted basis, i n three of the four f i e l d s . In general, using transformed p i x e l values improves the r e l a t i o n s h i p s found between corn productivity and remote sensing, presumably by reducing interference from background s o i l reflectance. S o i l properties which could conceivably a f f e c t crop reflectance i n the f i e l d s studied include p a r t i c l e s i z e (Myers, 1983; Al-Abbas et a l . , 1971), ir o n content (Karmanova, 1981; Coleman and Montgomery, 1981) and/or mineralogy (daCosta, 1979; as c i t e d by Myers, 1983). In addition, f i e l d management can a f f e c t crop reflectance. For example, Kollenkark et a l . (1982) found that v i s i b l e reflectance decreased with decreasing row width; s o i l colour and moisture were suggested to be the important factors influencing reflectance. Table 22 outlines the best p r e d i c t i v e equations f o r o v e r a l l and i n d i v i d u a l f i e l d s using transformed remote sensing v a r i a b l e s . As with the biophysical relationships, only a few good multiple regressions were obtained. Only regressions explaining 30 % or more of the v a r i a b i l i t y i n corn properties are presented. Some of the equations l i s t e d i n the above table i l l u s t r a t e the same relationships shown i n Table 20. Other equations are d i f f e r e n t , however, because i n c e r t a i n f i e l d s the best r e l a t i o n s h i p s found were p a r t i c u l a r to that f i e l d . In the f i e l d s where s o i l background reflectance i n c e r t a i n wavelengths appears to be i n t e r f e r i n g with crop reflectance, c e r t a i n transformations y i e l d the best relationships (eg. [NIR2 - G2] i n the 200 f i e l d , NIR/R2 r a t i o i n the 300 f i e l d ) . In the other f i e l d s , untransformed p i x e l values generally y i e l d the best r e s u l t s . 76 Table 22. Best p r e d i c t i v e equations for o v e r a l l and ind i v i d u a l f i e l d relationships, using mainly transformed p i x e l values. F i e l d Equation R2 S. E. A l l TWT = [NIR2-NIR1](-0.643) + 268.76 0. 26 45. 03 200 CWT = [NIR2-G2](-3.51) + 232.07 0. 32 19. 12 200 TWT = [NIR2-G2](-6.38) + 443.10 0. 28 37. 89 300 CWT = R2(2.08) - 167.71 0. 35 28. 83 A l l TCPg = [NIR2-NIR1](-0.068) + 24.38 0. 37 3. 70 200 CCPg = [NIR2-G2](-0.312) + 19.69 0. 33 1. 66 300 CCP = NIR2/R2(13.91) -9.07 0. 68 0. 941 300 TCP = Gl(0.027) + 6.37 0. 53 0. 726 300 CP = NIR2/R2(0.341) - 0.154 0. 52 0. 032 A l l CCa = [G2-G1] (-8.35 X 10'5) + 0.014 0. 30 0. 005 A l l TCag = [NIR2-NIR1] (-1.82 X 10"3) - 0.154 0. 26 0. 126 300 TCa = [NIR2-NIR1] (-8.01 X 10"4) + 0.263 0. 27 0. 041 300 TCa = [NIR2-NIR1] (-4.72 X 10"4) + Ca(0.012) + 0.117 0. 40 0. 038 600 TCa = [NIR2-NIR1] (-1.91 X 10"3) + 0.404 0. 29 0. 048 600 TCa = [NIR2-NIR1] (-9.55 X 10"4) + ELEV(3.69 X 10"4) + 0.125 0. 46 0. 043 A l l CDN = NIR2/R2(-19.54) + 95.23 0. 66 1. 27 A l l TDNg = [NIR2-NIR1](-0.020) - 0.154 0. 32 1. 19 200 CDNg = [NIR2-G2](-6.38) + 443.10 0. 28 37 . 89 300 CDN = NIR2/R2(-13.54) + 86.13 0. 46 1. 43 4.3 Evaluation of Remote Sensing Results From these r e s u l t s , i t i s apparent that the remote sensing data generally y i e l d s more consistent and stronger r e l a t i o n s h i p s with corn quality variables than does the s o i l data. Near i n f r a -red p i x e l values give reasonable estimates of t o t a l CP i n three of the four f i e l d s studied. No singl e s o i l v ariable gave such consistent relationships. Good relationships were also obtained between t o t a l corn P and Ca, and near infra-red reflectance i n c e r t a i n f i e l d s . In f i e l d s where s o i l reflectance appeared to i n t e r f e r e with corn reflectance, c e r t a i n transformations of the remote sensing variables further improved re l a t i o n s h i p s . The success of such transformations was f i e l d - s p e c i f i c however, perhaps as a r e s u l t of d i f f e r e n t inherent s o i l c h a r a c t e r i s t i c s and/or varying management practices within the f i e l d s . The obvious advantage of using p i x e l values to predict crop q u a l i t y (apart from the consistency of the relationships from f i e l d to f i e l d ) i s that much less e f f o r t i s required to obtain large amounts of data. From one a e r i a l photograph for example, several thousand p i x e l values (in each of the green, red and near infra-red regions of the spectrum) may become available, allowing us to predict t o t a l CP for an e n t i r e f i e l d . To go out into the f i e l d and c o l l e c t t h i s many s o i l samples would be extremely time-consuming and expensive, and the relationships obtained would probably not be as consistent. Since f i e l d management (eg. f e r t i l i z a t i o n ) tends to vary, so too w i l l the r e l a t i o n s h i p s found between s o i l f e r t i l i t y variables and crop q u a l i t y . In the same vein, inherent s o i l and s i t e properties w i l l vary between f i e l d s , again leading to inconsistent r e l a t i o n s h i p s between the d i f f e r e n t f i e l d s . Crop reflectance, however, w i l l be related to c e r t a i n crop q u a l i t y variables i n any f i e l d where quality v a r i a b i l i t y i s s u f f i c i e n t l y high, regardless of the underlying reason for that v a r i a b i l i t y . I t should be noted, however, that s o i l s data can compliment remote sensing data and further improve our p r e d i c t i v e c a p a b i l i t y , as 78 indicated by the above multiple regressions. Perhaps the most e x c i t i n g development with respect to remotely-sensed data within recent years i s the c a p a b i l i t y to incorporate i t into a GIS, thereby portraying the information i t y i e l d s i n a s p a t i a l manner. This process i s the topic of Chapter 7. One of the main disadvantages of using remote sensing data i s that only f i e l d s which exhibit r e l a t i v e l y high crop v a r i a b i l i t y y i e l d good relationships with reflectance. In addition, at the present time, a number of factors can negatively a f f e c t reflectance data, and l i m i t i t ' s usefulness. The most obvious factor i s s o i l reflectance, p a r t i c u l a r l y where incomplete canopies e x i s t . Other "natural" factors include canopy geometry (Jackson and Ezra, 1985), and in-canopy shadowing (Everitt et a l . , 1987). Quantifiable spectral information from d i g i t i z e d photography may also be l i m i t e d as a r e s u l t of exposure and f i l m processing v a r i a b i l i t y between frames, broad wavelength s e n s i t i v i t y , and possible vari a t i o n s i n view angle, a l t i t u d e , and sun elevation angle (Stoner et a l . , 1976). Some of these l i m i t a t i o n s w i l l l i k e l y be overcome as the remote sensing technology advances. Stoner and h i s co-workers also found that CIR f i l m was best for determining maize canopy density when the s o i l background was dark; t h i s could be part of the reason why no strong reflectance-biomass re l a t i o n s h i p s were found i n the f i e l d s studied here. In the present study, colour i n f r a - r e d photos were taken at the end of the growing season, when s o i l s were d r i e r , and therefore somewhat l i g h t e r i n colour. E v e r i t t et a l . (1987) also found that d i g i t a l reflectance data i s somewhat lim i t e d i n terms of crop r e l a t i o n s h i p s , because t h i s data encompasses a much larger area than actual reflectance measurements do, and therefore combines s o i l background e f f e c t s and in-canopy shadowing on a much larger scale, leading to increased v a r i a b i l i t y . 80 CHAPTER 5 CLUSTER ANALYSIS The c l u s t e r analyses were carried out on the basis of t o t a l nutrient concentrations. The variables chosen fo r the analysis were t o t a l CP, Ca and DN - variables which tended not to be r e l a t e d to one another. Five separate c l u s t e r analyses were c a r r i e d out: one for a l l of the f i e l d s combined ( i e . a l l 94 s i t e s ) , and one for each separate f i e l d . Using the dendrograms, the s i t e s were separated into obvious groupings and tested for s i g n i f i c a n t differences i n terms of the t o t a l corn variables used i n the analysis. Once these differences had been established, variables not used i n the c l u s t e r i n g procedure (independent corn, s o i l , s i t e and remote sensing variables) were tested to determine which of these also exhibited s i g n i f i c a n t differences between the groups. The o v e r a l l f i e l d c l a s s i f i c a t i o n and the grouping within the 600 f i e l d produced good re s u l t s . These w i l l therefore be discussed i n d e t a i l ; r e s u l t s for the other f i e l d s w i l l be outlined b r i e f l y where appropriate. 5.1 Overall F i e l d Cluster Analysis 5.1.1 S i g n i f i c a n t l y d i f f e r e n t c l u s t e r variables The o v e r a l l f i e l d c l u s t e r analysis of a l l 94 s i t e s from the combined data set of the four f i e l d s produced three d i s t i n c t groups. Table 23 l i s t s the variables which 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 three classes. These va r i a b l e s are separated into two groups of variables: those which were used i n the c l u s t e r i n g procedure, and associated variables ( i e . those which were not used i n the procedure). Table 24 l i s t s the means and standard deviations for each of these variables. Table 23. Variables exhibiting s i g n i f i c a n t differences 1 between c l u s t e r groups ( a l l f i e l d s combined). Group 2 Group 3 Group 1 Cluster Variables: Cluster Variables: TCP, TCA, (TDN) Assoc. Variables: TCP, TCA Assoc. Variables: TCPg, TCAg, TDNg, (TWT) Elev, bd2, bd3, cec, npla, np2a nirl, r2, nir2/r2, [nir2 - nir 1 ] TCAg, (TDNg) bd1, (cec) np3a, np3c nir 1 Group 2 Cluster Variables: TCP, TCA Assoc. Variables: TCPg, (TDNg) Elev, bd1, bd2 bd3, cec, np3a, np3c nir 1, r2, nir2/r2, [nir2 - nir 1] Variables without brackets are s i g n i f i c a n t l y d i f f e r e n t at the p = 0.01 l e v e l ; those within brackets are s i g n i f i c a n t l y d i f f e r e n t at the p = 0.05 l e v e l . 82 Table 24. Means and standard deviations for variables exhibiting s i g n i f i c a n t differences between c l u s t e r groups ( a l l f i e l d s combined). Variable Group 1 Group 2 Group 3 Mean S.D. Mean S.D. Mean sn (Cluster Variables) TCP 8.67 0.72 8.08 0.68 9.54 094 TCa 0.165 0.033 0.134 0.019 0.247 0.03 (Associated Variables) TWT 217 53 198 51 202 45 TCPg 18.87 5. 04 15.86 3.81 19.12 3.69 TCag 0.370 0.130 0.264 0.070 0.505 0.14 TDNg 44.0 1.4 43.1 1.4 43.3 1.2 ELEV 557 87 515 62 559 53 NP2A 0.40 0.08 0.45 0.07 0.40 0.09 NP3A 0.44 0. 08 0.42 0.07 0.36 0.03 NP3C 0.34 0.12 0.37 0.08 0.25 0.13 CEC 25.39 5.32 28.17 5.01 22.14 6.03 BD1 1.11 0.12 1.10 0.09 1.19 0.11 BD2 1.16 0.13 1.10 0.10 1.20 0.11 NIR1 48 21 33 19 66 11 R2 132 7 127 9 132 10 NIR2/R2 1.24 0.05 1.31 0.10 1.25 0.11 NIR2-NIR1 76 39 119 40 101 27 Total CP and Ca, which were i n i t i a l l y used i n the c l u s t e r i n g procedure, are s i g n i f i c a n t l y d i f f e r e n t between a l l 3 c l u s t e r groups; i n most cases on both a weighted and unweighted basis. Group 3 tends to be the most productive group i n terms of nutrient concentrations, containing s i t e s with much higher CP and Ca contents than the other groups. Group 1 i s also quite productive, as i t contains s i t e s with the highest t o t a l weights and weighted t o t a l DN concentrations. Group 2 i s lea s t productive, exhibiting the lowest mean nutrient concentrations. 83 5.1.2 S i g n i f i c a n t l y d i f f e r e n t associated variables Group 2 i s l e a s t productive, containing s i t e s with the lowest unweighted and weighted t o t a l CP and Ca concentrations; t o t a l weights i n t h i s group are also low. Elevation i s s i g n i f i c a n t l y lower, and CEC s i g n i f i c a n t l y higher i n t h i s group than i n the other groups - i e . the majority of s i t e s i n the l e a s t productive group are located i n the depressional areas. Evidently, wetter conditions that would have been prevalent i n the lower areas early i n the growing season resulted i n poor ear l y growth, which ultimately affected the mature corn quality. This suggestion i s supported by the s o i l moisture data from the upper depths i n l a t e June (np2a), which are s i g n i f i c a n t l y highest i n t h i s group. S o i l and corn p i x e l brightness values are s i g n i f i c a n t l y lowest i n group 2; t h i s w i l l be discussed i n more d e t a i l l a t e r . Group 3, which i s most productive i n terms of nutrient concentrations, exhibits the lowest s o i l moisture contents and CEC's, which supports the suggestion that the higher s i t e s are more productive. However, t h i s group also y i e l d s a low mean t o t a l weight; a r e s u l t that l i k e l y occurs due to drought e f f e c t s i n the l a t t e r part of the growing season. I t appears, therefore, that there are 2 water-related mechanisms whereby corn p r o d u c t i v i t y can be i n h i b i t e d : (1) poor early growth brought on by wet s o i l conditions i n the hollows, and (2) poor l a t e growth r e s u l t i n g from droughty conditions ( p a r t i c u l a r l y i n the ridge areas). Of these, the f i r s t seems to a f f e c t corn productivity 84 most, as i n i t i a l nutrient uptake i s i n h i b i t e d - r e s u l t i n g i n lower nutrient concentrations i n the mature corn. Bulk density also exhibits s i g n i f i c a n t differences between the o v e r a l l c l u s t e r groups. Higher bulk d e n s i t i e s , p a r t i c u l a r l y of the lower s o i l depths, tend to be found within the higher-p r o d u c t i v i t y groups. This i s probably related to the higher water-holding capacities associated with t h i s property. I t i s in t e r e s t i n g to note, however, that group 3, which has the highest bulk densities, also has r e l a t i v e l y low mean weight and P content. This suggests that higher bulk de n s i t i e s may be advantageous during dry periods, when such a c h a r a c t e r i s t i c leads to higher water contents; when bulk d e n s i t i e s are too high, however, they i n h i b i t corn growth and nutrient ( p a r t i c u l a r l y P) uptake. In the c l u s t e r analyses of the in d i v i d u a l f i e l d s , s i m i l a r trends were found. In the 300 and 400 f i e l d s , c l u s t e r i n g produced groups i n which high bulk densities were linked with lower t o t a l P contents. In the 200 and 600 f i e l d s , higher bulk densities were grouped with lower t o t a l weights and t o t a l nutrient concentrations. Several other s o i l variables, including C, N, P and K also exhibited some differences between the groups - primarily through t h e i r r elationships with elevation. Bare s o i l p i x e l brightness values i n the green, red and near i n f r a - r e d bands are s i g n i f i c a n t l y d i f f e r e n t between a l l three c l u s t e r groups (for s i m p l i c i t y only the near i n f r a - r e d values are l i s t e d , however). The lowest s o i l p i x e l values are found i n 85 the lowest productivity, lowest elevation group. This i s obviously because the lower elevation s i t e s were wetter when the a e r i a l photos were flown - consequently s o i l reflectance i n these areas was lower. Group 3, containing the highest elevation s i t e s , exhibited the highest s o i l p i x e l values. Similar r e s u l t s were obtained v i a the individual c l u s t e r analyses for the 300 and 600 f i e l d s . This indicates that s o i l reflectance at the s t a r t of the growing season may be a good predictor of ultimate corn q u a l i t y , i n f i e l d s where elevation (and consequently s o i l moisture) differences are large. Corn p i x e l brightness values do not y i e l d r e s u l t s which are quite as consistent. Red pix e l values are s i g n i f i c a n t l y lower i n the low pr o d u c t i v i t y group than i n the other two, while the near infra-red/red r a t i o and the [NIR2 - NIR1] transformation are s i g n i f i c a n t l y higher i n t h i s group than i n the other two. Again i t should be noted that crop quality differences must be quite large i n order to produce large differences i n crop reflectance. 5.1.3 Individual f i e l d s i t e groupings within the ov e r a l l  c l u s t e r groups Another way of interpreting these findings i s through the determination of which f i e l d s tend to predominate within each of the o v e r a l l c l u s t e r groups. The le a s t productive group (group 3) i s composed of about 75% of the 400 f i e l d s i t e s , approximately h a l f of the 300 f i e l d s i t e s , and several 200 f i e l d s i t e s . The 400 f i e l d i s generally the least productive of the four f i e l d s , and as previously discussed, i s r e l a t i v e l y low i n elevation. 86 Most of the 300 f i e l d s i t e s i n t h i s group are also low i n elevation; the s i t e s which were located i n and around the section of t h i s f i e l d affected by ponding and compaction are included i n t h i s group. Of the 200 f i e l d s i t e s i n group 3, one i s of very low elevation, and the other two were affected by i r r i g a t i o n of a neighboring f i e l d . Group 3 which was more productive, on the other hand, contains s i t e s of higher elevation from a l l f i e l d s but the 400. Several of the 600 f i e l d s i t e s i n t h i s group were very high and sandy, and noticeably droughty l a t e r i n the summer, which p a r t i a l l y accounts for the lower mean weights found i n t h i s group. Group 1, which has the highest mean t o t a l weight, contains about 75% of the 200 f i e l d s i t e s , as well as about a t h i r d of the 600 f i e l d s i t e s , and several 300 and 400 f i e l d s i t e s . Most of the 200 f i e l d s i t e s are r e l a t i v e l y moderate to high i n terms of elevation, as are the 300 and 400 f i e l d s i t e s - such s i t e s tended to be most productive within these f i e l d s . Most of the s i t e s from the 600 f i e l d , however, are lower i n elevation, i n d i c a t i n g that within t h i s f i e l d , some lower s i t e s were quite productive. This w i l l be dealt with more f u l l y when the 600 f i e l d c l u s t e r i s discussed. 5.2 600 F i e l d Cluster Analysis Cluster analysis of the 600 f i e l d , again u t i l i z i n g TCP, TCa and TDN as the c l u s t e r variables, yielded three d i s t i n c t c l u s t e r groups. Table 25 l i s t s the c l u s t e r variables and associated v a r i a b l e s which were s i g n i f i c a n t l y d i f f e r e n t between these groups; Table 26 indicates the means and standard deviations of 87 these variables within each group. 5.2.1 S i g n i f i c a n t l y d i f f e r e n t c l u s t e r variables and  associated corn variables On an unweighted basis, t o t a l CP i s highest i n group 2, t o t a l Ca i s highest i n group 1, and t o t a l DN i s highest i n group 3. On a weighted basis, however, i t i s obvious that group 1 i s the most productive, and group 3 the least productive, of the three c l u s t e r groups. Biomass and t o t a l weighted nutrients are a l l highest i n group 1; weighted nutrients are lowest i n group 3. Biomass tends to be lowest i n group 2, but weighted CP and Ca are s t i l l quite high ( r e l a t i v e to group 3). Table 25. Variables exhibiting s i g n i f i c a n t differences between c l u s t e r groups (600 f i e l d ) .  Group 2 Group 3 Group 1 Cluster Variables: Cluster Variables: TDN Assoc. Variables: TCA, (TDN) Assoc. Variables: TCAg, TDNg, TWT, CWT, SWT, (TCP) bd1, (bd2), np2a np2c (nir2) TCAg, TDNg,(TWT) CWT, SWT,(TCPg) (elev), np2d Group 2 Cluster Variables: TCA Assoc. Variables: TCAg Elev, np2a, np2c np2d nir 1, nir2 88 Table 26. Means and standard deviations for variables e x h i b i t i n g s i g n i f i c a n t differences 1 between c l u s t e r groups (600 f i e l d ) . Variable Group 1 Group 2 Group 3 Mean S.D. Mean S.D. Mean S.D. (Cluster Variables) TCP 8.40 0.47 9.65 0.68 8.63 1.27 TCa 0.270 0.017 0.247 0.022 0.152 0.013 TDN 64.6 0.2 64.9 0.6 65.3 1.3 (Associated Variables) TWT 251 28 176 34 192 37 CWT 126 24 72 29 90 23 SWT 126 14 105 20 101 25 TCPg 21.08 2.75 17.05 3.59 16. 62 4.44 TCag 0.682 0.115 0.433 0.079 0.294 0.066 TDNg 44.8 0.7 42.8 0.9 43.3 1.0 ELEV 510 38 535 56 449 68 NP2A 0.47 0.02 0.35 0.08 0.46 0.09 NP2C 0.26 0.10 0.15 0.10 0.37 0.14 BDl 1.15 0.03 1.25 0.08 1.16 0.07 BD2 1.14 0.06 1.27 0.07 1.27 1.15 NIR1 68 6 74 6 58 14 NIR2 168 4 162 4 168 3 5.2.2 S i g n i f i c a n t l y d i f f e r e n t associated variables In terms of s o i l and s i t e variables, elevation i s s i g n i f i c a n t l y lower, and s o i l moisture higher, i n group 3 than i n the other groups. A l l of the very low s i t e s i n t h i s f i e l d (including the low area which was ponded early i n the season) are included i n group 3. As was found i n the o v e r a l l f i e l d c l u s t e r , nutrient uptake i n these lower, wetter s i t e s was obviously i n h i b i t e d early i n the growing season, p a t i c u l a r l y with respect to Ca. Biomass growth at t h i s time was evidently also r e s t r i c t e d , as stalk and therefore t o t a l weights i n t h i s 89 group are also r e l a t i v e l y low. Group 2, which i s composed of s i t e s y i e l d i n g the lowest mean cob and t o t a l weights, i s also the group with the highest mean elevations and lowest s o i l water contents. This again suggests that the droughtiness of such s i t e s l a t e r i n the growing season i n h i b i t s biomass production. E a r l i e r i n the season, however, these higher s i t e s are l i k e l y more productive than the low ones, as evidenced by the fac t that CP and Ca are higher i n t h i s group than i n group 3. Group 1 i s composed of s i t e s which are more moderate with respect to elevation and s o i l moisture, i n d i c a t i n g that s i t e s which are les s extreme are the most productive. Bulk density also exhibited some s i g n i f i c a n t differences between the cl u s t e r groups. Group 2, which contains s i t e s with the lowest t o t a l weights and P concentrations, also has the highest mean bulk d e n s i t i e s . This group, which contains some very high s i t e s , also contains some lower s i t e s . I t may be, therefore, that 2 mechanisms are l i m i t i n g corn productivity i n t h i s group: (1) droughtiness of the higher elevation s i t e s l a t e r i n the growing season, and (2) compaction (and perhaps related excess water) problems i n the lower s i t e s . Bulk density of the second depth i s also very high i n group 3, which i s also a les s productive group. In terms of remote sensing, once again, the lowest elevation s i t e s tend to y i e l d the lowest s o i l p i x e l values, while the highest elevation s i t e s produce the highest s o i l p i x e l values. Group 2, which has the highest unweighted CP concentrations, i s s i g n i f i c a n t l y lower with respect to corn near infra-red p i x e l values. This i s i n agreement with the c o r r e l a t i o n analysis, which indicated that t o t a l CP was negatively correlated with near i n f r a - r e d reflectance i n t h i s f i e l d . The r e s u l t s of the c l u s t e r analyses serve to further i l l u s t r a t e the relationships between corn productivity, and s o i l and s i t e conditions. Moreover, c l u s t e r i n g allows the r e l a t i o n s h i p s to be portrayed i n a s p a t i a l manner, which further c l a r i f i e s the correlations found previously. This i s p a r t i c u l a r l y evident where low productivity i s occurring both at very high and very low elevations, due to both droughty and excessively wet conditions. Once again, the best r e s u l t s were obtained i n f i e l d s where the largest elevation differences prevailed, although the o v e r a l l f i e l d c l u s t e r also i l l u s t r a t e d corn p r o d u c t i v i t y - s i t e relationships quite well. In the Chapter 7, these relationships w i l l be displayed v i s u a l l y , with the aid of image analysis and GIS techniques. 91 CHAPTER 6 COMPARISON OF LINEAR CORN PRODUCTIVITY RELATIONSHIPS AND SPATIAL CORN PRODUCTIVITY RELATIONSHIPS The l i n e a r relationships discussed i n Chapters 3 and 4 on an o v e r a l l f i e l d and 600 f i e l d basis agree quite well i n some cases with those obtained from the c l u s t e r analysis. The clus t e r analysis, however, also i l l u s t r a t e d some additional r e l a t i o n s h i p s which the l i n e a r c o r r e l a t i o n procedures f a i l e d to produce. In addition, c l u s t e r analysis also allowed further i n t e r p r e t a t i o n of some of the r e s u l t s previously observed. 6.1 Comparison of Relationships on an Overall F i e l d Basis  6.1.1 Comparison of s o i l and s i t e variables P o s i t i v e l i n e a r relationships had been found between t o t a l weight and t o t a l weighted nutrients, and elevation; c l u s t e r analysis groups which 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 these same corn variables, also showed some s i g n i f i c a n t differences between them with respect to elevation. The advantage of the cluster analysis, however, was that by grouping s i t e s of s i m i l a r productivity together, i t became evident that although the higher elevation s i t e s tended to have higher nutrient contents, they also tended to have lower biomass production. This indicated that nutrient uptake early i n the season i s higher at higher elevation s i t e s , but biomass production w i l l be in h i b i t e d l a t e r i n the growing season. This became even more evident when the 600 f i e l d was clustered on an in d i v i d u a l basis (see next section). In addition, bulk de n s i t i e s exhibited s i g n i f i c a n t differences between the c l u s t e r groups, suggesting compaction impacts on corn productivity, and perhaps further water-related influences. No l i n e a r r elationships were found between BD, and biomass or any t o t a l nutrient concentrations on an o v e r a l l f i e l d basis. The o v e r a l l f i e l d c l u s t e r analysis also allowed i d e n t i f i c a t i o n of the most productive and the least productive s i t e s found within a l l the f i e l d s . This i n turn l e t s us pinpoint more s p e c i f i c a l l y why these s i t e s e x h i b i t d i f f e r i n g p r o d u c t i v i t i e s . The least productive group contained most of the 400 f i e l d s i t e s , as would be expected since t h i s f i e l d was i d e n t i f i e d as being s i g n i f i c a n t l y less productive than the other f i e l d s (Chapter 2). I t also contained the 300 f i e l d s i t e s which were affected by compaction and ponding, and the 200 f i e l d s i t e s which were affected by i r r i g a t i o n of a neighboring f i e l d . One of the more productive groups, on the other hand, contained mostly 200 f i e l d s i t e s , and several s i t e s of moderate to high elevations from the other f i e l d s . The 200 f i e l d was i d e n t i f i e d as being most productive i n Chapter 2, and l i n e a r correlations had indicated that higher s i t e s were more productive within some f i e l d s . 6.1.2 Comparison of p i x e l brightness values On an o v e r a l l f i e l d basis, the c l u s t e r groups yielded s i g n i f i c a n t l y d i f f e r e n t mean s o i l p i x e l values i n a l l 3 bands. Lower s o i l p i x e l values were found i n the groups containing the lower elevation s i t e s . Such s i t e s also tended to have higher t o t a l C concentrations, and higher CEC's. Although negative l i n e a r r e l a t i o n s h i p s were found between s o i l p i x e l values, and t o t a l C and CEC o v e r a l l , no s i g n i f i c a n t l i n e a r r e l a t i o n s h i p with elevation was found. In terms of corn p i x e l brightness values, the NIR/R r a t i o exhibits some s i g n i f i c a n t differences between c l u s t e r groups; no l i n e a r r e l a t i o n s h i p between t h i s r a t i o and t o t a l corn weight or nutrient contents had been observed. The [NIR2 - NIR1] transformation also was s i g n i f i c a n t l y d i f f e r e n t i n the lowest pr o d u c t i v i t y group than i t was i n the other two. This i s i n agreement with the l i n e a r relationships found between t h i s transformation and t o t a l weighted nutrient concentrations on an o v e r a l l f i e l d basis. 6.2 Comparison of Relationships i n the 600 F i e l d  6.2.1 Comparison of s o i l and s i t e v a r i a b l e s Linear r e l a t i o n s h i p s exist between t o t a l corn nutrient concentrations, and elevation, s o i l moisture, and BD. These re l a t i o n s h i p s were also confirmed by the c l u s t e r analysis. Again, however, the clu s t e r i n g of s p e c i f i c s i t e s into groups further c l a r i f i e d some of the relationships previously observed. The s i t e s i n the very low area of the 600 f i e l d a l l clustered into 1 group - the group containing lower nutrient concentrations, and r e l a t i v e l y low biomass production. On the other hand, the group containing the highest elevation s i t e s and r e l a t i v e l y high nutrient contents, also had the lowest mean cob and t o t a l weights. These findings indicated the e f f e c t s of the d i f f e r e n t water stresses at d i f f e r e n t times during the growing season. Linear relationships had indicated that although the higher elevation s i t e s were more productive i n terms of nutrient uptake, they could also be less productive i n terms of cob production l a t e r i n the growing season. No s i g n i f i c a n t l i n e a r r e l a t i o n s h i p s between biomass and elevation and/or s o i l moisture, however, had been found. The c l u s t e r analysis indicated, i n f a c t , that s i t e s which were more moderate i n terms of elevation were most productive i n terms of both corn biomass and q u a l i t y . Correlations had indicated that unweighted t o t a l CP was p o s i t i v e l y r e l a t e d to BD; the c l u s t e r analysis also shows t h i s . However, the c l u s t e r i n g provides additional information, i n that i t indicates that higher BD i s linked to lower t o t a l weights, and as a r e s u l t , also to lower weighted nutrient concentrations. 6.2.2 Comparison of p i x e l brightness values A p o s i t i v e l i n e a r relationship e x i s t s between elevation and near i n f r a - r e d s o i l p i x e l values. This r e l a t i o n s h i p i s echoed by the c l u s t e r i n g , i n which the lowest elevation group i s s i g n i f i c a n t l y d i f f e r e n t from the highest elevation group i n terms of near i n f r a - r e d s o i l p i x e l values. The c l u s t e r group containing the highest unweighted t o t a l CP also y i e l d s the lowest mean corn near i n f r a - r e d p i x e l values. This i s i n agreement with the l i n e a r r e l a t i o n s h i p found between these two v a r i a b l e s . Overall, the c l u s t e r analysis c l a r i f i e s previously-outlined r e l a t i o n s h i p s , by presenting them i n a more s p a t i a l manner. In addition, i t indicates relationships which may not be l i n e a r , but do occur s p a t i a l l y . CHAPTER 7 SPATIAL ANALYSIS OF CROP CHARACTERISTICS USING IMAGE ANALYSIS AND GIS TECHNIQUES In previous chapters, the r e l a t i o n s h i p s between corn p r o d u c t i v i t y and s o i l / s i t e and remote sensing v a r i a b l e s have been examined through more conventional means: i n a l i n e a r fashion (through c o r r e l a t i o n s and regressions), and i n a more s p a t i a l manner (through c l u s t e r a n a l y s i s ) . In t h i s chapter, some of these r e l a t i o n s h i p s w i l l be presented i n more non-conventional s p a t i a l manners, with the a i d of image c l a s s i f i c a t i o n and GIS. These t o o l s allow us to v i s u a l i z e the r e l a t i o n s h i p s , thereby further i l l u s t r a t i n g and c l a r i f y i n g them, and presenting them i n a fashion which i s more p r a c t i c a l from a f i e l d management point of view. 7.1 Image C l a s s i f i c a t i o n 7.1.1 Image c l a s s i f i c a t i o n methods U t i l i z i n g the p i x e l brightness values obtained from the sampling s i t e s within each f i e l d as " t r a i n i n g areas", the corn images were c l a s s i f i e d into high and low p r o d u c t i v i t y categories. The p i x e l brightness values from each s i t e had previously been used i n regression equations to p r e d i c t crop q u a l i t y and biomass v a r i a b i l i t y within several f i e l d s . The r e l a t i o n s h i p s y i e l d i n g the best p r e d i c t i o n s of crop q u a l i t y or biomass from p i x e l brightness values within these f i e l d s were chosen to be used as the basis f o r the image c l a s s i f i c a t i o n s . These r e l a t i o n s h i p s were p l o t t e d g r a p h i c a l l y , using the software package Grapher (Version 1.75); they were then v i s u a l l y s p l i t into high and low categories of the corn v a r i a b l e of i n t e r e s t . In t h i s way, the p i x e l brightness value c l a s s l i m i t s to be used as guidelines f o r the supervised image c l a s s i f i c a t i o n s were set. I t was decided to c l a s s i f y the images on the basis of the r e l a t i o n s h i p s which y i e l d e d the best r e s u l t s within each f i e l d , rather than those which were consistent between f i e l d s . The 300 f i e l d was c l a s s i f i e d i n terms of cob weight, cob CP and P rather than t o t a l nutrient concentrations, and the NIR/R r a t i o was used; these r e l a t i o n s h i p s were very strong, and allowed c l a s s i f i c a t i o n f o r more than one corn q u a l i t y parameter. The 600 f i e l d was c l a s s i f i e d f o r t o t a l CP, using the NIR p i x e l values, as t h i s was the strongest corn q u a l i t y - remote sensing r e l a t i o n s h i p f o r t h i s f i e l d . The 400 f i e l d was also c l a s s i f i e d using the NIR p i x e l values, f o r the p r e d i c t i o n of t o t a l P. In addition, there was a c u r v i l i n e a r r e l a t i o n s h i p between NIR p i x e l values and t o t a l CP f o r the 400 f i e l d . Using only the l i n e a r portion of t h i s curve, a new regression was obtained f o r the p r e d i c t i o n of t o t a l CP, t h i s equation was then used to set p i x e l c l a s s l i m i t s f o r t h i s c l a s s i f i c a t i o n . The equations representing the above-mentioned r e l a t i o n s h i p s are l i s t e d i n Table 27. The graphical representations of these equations, which were used to set the p i x e l brightness c l a s s l i m i t s f o r the image c l a s s i f i c a t i o n s , are shown i n Figures 7 - 1 1 . 98 Table 27. P r e d i c t i v e equations used to set p i x e l c l a s s brightness l i m i t s f o r the image c l a s s i f i c a t i o n s . F i e l d Equation r 2 S.E. 300 CCP = NIR/R (13.91) - 9.07 CP = NIR/R (0.341) - 0.154 CWT = NIR/R (-206) + 372 0. 0. 0. 68 52 32 0.941 0. 032 30 400 ( A l l s i t e s ) : TP = NIR (-2.37 X 10~3) + 0.598 0. 29 0. 020 400 (Linear s i t e s only): TCP = NIR(-0.179) + 36.75 0. 53 0.378 600 TCP = NIR (-0.218) + 45.35 0. 57 0.863 It was necessary to set c l a s s l i m i t s i n t h i s manner, rather than more o b j e c t i v e l y , as the number of t e s t s i t e s within each f i e l d i s r e l a t i v e l y small. The e n t i r e range of possible corn and p i x e l values f o r the f i e l d s are not covered by these t e s t populations; c l a s s i f i c a t i o n s based on the means and standard deviations of these populations are therefore not possible, as the range covered i s quite small. In addition, a c l a s s i f i c a t i o n based on c r i t i c a l n u t r ient l e v e l s f o r dairy c a t t l e i s also not possible, as maize contains lower amounts of nutrients than other forage crops (Wilkinson, 1978), and the corn nutrient contents of a l l of the f i e l d s studied are l e s s than those suggested by ADAS (1975) to be c r i t i c a l l e v e l s f o r animal forage. Once the classes had been decided upon, the image analysis software package Earthprobe (Version 1.2) was u t i l i z e d to carry out supervised c l a s s i f i c a t i o n . For each f i e l d , the representative s i t e s from each c l a s s were used as t r a i n i n g areas as a basis f o r c l a s s i f i c a t i o n of each e n t i r e image. At each t r a i n i n g s i t e , an area of 1 m2 ( i e . 5 p i x e l s x 5 pix e l s ) was used to obtain an average p i x e l value f o r the s i t e . This procedure was used i n order to cover a lar g e r representative area at each t e s t s i t e , while at the same time expanding the p i x e l database somewhat to get a wider d i s t r i b u t i o n of values f o r the f i e l d , rather than covering only one 20 cm p i x e l per t e s t s i t e . A f t e r c l a s s i f i c a t i o n , the images were f i l t e r e d using a coarse f i l t e r (within the Earthprobe system) to remove background noise and enhance corn c l a s s patterns. I t should be noted that some of the areas c l a s s i f i e d i n the images f a l l outside of the actual ranges of the p i x e l values covered by the t r a i n i n g s i t e s . This occurs because the Earthprobe program automatically c l a s s i f i e s p i x e l s which are higher or lower than those found at the t r a i n i n g s i t e s which determine the classes, into e i t h e r the higher or lower category, r e s p e c t i v e l y . Class 1 Class 2 0.45 0.25 r-0.20 I 1 ' 1 1 1 1 1  1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 NIR/R Ratio Class 1 Class 2 200 , — — 150 ^ 100 o u 50 1.20 1.25 1.30 1.35 1.40 1.45 1.50 1.55 1.60 1.65 NIR/R Ratio Figures 7 & 8. Regression l i n e s used to estimate c l a s s guidelines f o r supervised image c l a s s i f i c a t i o n (300 f i e l d ) . Class 1 Class 2 7.00 I 1 1 1 1 1 1 ' 1 ' 154 155 156 157 158 159 160 161 162 163 NIR pixel values igures 9 & 10. Regression l i n e s used to estimate c l a s guidelines f o r supervised image c l a s s i f i c a t i o n (400 f i e l d ) . 102 Class 1 Class 2 13.00 i 1 6.00 I 1 1 1 1 1 1 150 155 160 165 170 175 180 NIR pixel values Figure 11. Regression l i n e used to estimate c l a s s guidelines for supervised image c l a s s i f i c a t i o n (600 f i e l d ) . 7.1.2 300 f i e l d image c l a s s i f i c a t i o n U t i l i z i n g both the near i n f r a - r e d and red p i x e l values, the 300 f i e l d was f i r s t l y c l a s s i f i e d i n terms of cob weight, and then i n terms of cob crude protein. Figures 11 and 12 are the r e s u l t i n g outputs from these c l a s s i f i c a t i o n s . Figure 11 represents the p i x e l values which have been c l a s s i f i e d i n terms of cob weight. The green areas of the image are the p i x e l values which in d i c a t e regions of high cob weight, while the red areas represent corn with low cob weights. Regions of higher cob weight comprise 48% of the f i e l d , while those of low cob weight make up 52% of the f i e l d . I t i s evident that the late-planted area of the f i e l d contains a large portion of the 103 low cob weight c l a s s ; t h i s i s due to the l a t e planting, as well as the wet and compacted conditions found here. Small areas within t h i s region do e x h i b i t some higher cob weights. This r e s u l t s from reduced plant growth, leading to lower plant d e n s i t i e s , and consequently higher cob weights, as previously discussed. The black area i n the middle of the f i e l d i s an area of bare s o i l , where corn did not grow. The upper l e f t part of the image i s also a region of lower cob weights. The s i t e s here are r e l a t i v e l y high i n terms of elevation; suggesting that droughty conditions i n such areas would appear to i n h i b i t cob growth i n the l a t t e r part of the growing season. There was some i n d i c a t i o n of t h i s seen i n s o i l -cob n u trient c o r r e l a t i o n s f o r t h i s f i e l d (Chapter 3). In general, the s i t e s which are moderate with respect to elevation produce the higher cob weights. This has previously been indicated by both the c o r r e l a t i o n and c l u s t e r analyses. Image analysis and c l a s s i f i c a t i o n , however, allow us to a c t u a l l y v i s u a l i z e t h i s r e l a t i o n s h i p . In t h i s f i e l d , the low, wet, compacted area i s causing poor cob production i n at l e a s t 3 0% of the f i e l d , while on the other hand, droughty conditions of the ridges cause decreased cob production i n another 15-2 0% of the f i e l d . Because cob d i g e s t i b l e nutrients (and DE) are highly c o r r e l a t e d with cob weight i n t h i s f i e l d , the approximate d i s t r i b u t i o n of cob DE/DN within the f i e l d i s also i l l u s t r a t e d by t h i s image. In terms of management, there i s probably not much that the 104 farm operator can do about the compacted, wet area. The droughty ridge areas, however, should be i r r i g a t e d l a t e r on i n the growing season, to improve cob production. The cob i s the most important part of the corn plant, i n terms of both biomass and qu a l i t y . By maturity, the cob may comprise up to 60% of the t o t a l corn DM (Aldrich and Leng, 1972), and contain nearly two-t h i r d s of t o t a l corn N, and 70% of t o t a l corn P (Pain, 1978). Based upon the r e s u l t s of t h i s t h e s i s , d i g e s t i b l e nutrients are also much higher i n the cob than they are i n the s t a l k . I t i s apparent then that every e f f o r t should be made to maximize cob production. Figure 12 i s the c l a s s i f i e d image f o r unweighted cob CP. The green areas of the image represent areas of high CP, while the red areas are lower with respect to cob CP. The areas of higher unweighted cob CP comprise 64% of the f i e l d , while regions of lower unweighted cob CP make up 36% of the f i e l d . I t i s evident that i n many ways, t h i s c l a s s i f i e d image i s the opposite of that f o r cob weight. In the late-planted area of t h i s f i e l d , where cob weights are generally low, cob CP i s high. As mentioned i n Chapter 3, t h i s i s e s s e n t i a l l y a d i l u t i o n e f f e c t . In actual f a c t , while t h i s area i s c l a s s i f i e d as containing high cob CP, on a weighted basis i t i s a c t u a l l y the le a s t productive area. The upper l e f t part of the f i e l d i s also an area of high unweighted cob CP, and t h i s again corresponds to an area of lower cob weights as discussed above. 105 Legend Class 1: Green p i x e l s = High CWT (48% of f i e l d ) Class 2: Red p i x e l s = Low CWT (52% of f i e l d ) Figure 12. C l a s s i f i e d image f o r cob weights (300 f i e l d ) . 106 Legend Class 1: Green pixels = High unweighted cob CP (64% of f i e l d ) Class 2: Red pi x e l s = Low unweighted cob CP (36% of f i e l d ) Figure 13. C l a s s i f i e d image for unweighted cob crude protein (300 f i e l d ) . 107 Cob phosphorus follows much the same pattern as cob CP, since the two are highly correlated. The c l a s s i f i e d image for cob CP i s therefore somewhat representative of cob P d i s t r i b u t i o n s f o r the f i e l d . The areas of high unweighted cob CP are a c t u a l l y low i n terms of cob biomass, and therefore low also i n terms of weighted n u t r i e n t s . Such images can be useful to the farm operator, as they i n d i c a t e regions of higher vs lower nutrient concentrations; the farmer could conceivably harvest and e n s i l e these areas separately. The s i l a g e from the higher q u a l i t y areas would consequently require l e s s mineral supplementation than that from lower q u a l i t y areas. 7.1.3 400 f i e l d image c l a s s i f i c a t i o n Using the graphs shown i n Figures 9 and 10 as guidelines for p i x e l c l a s s l i m i t s , the 400 f i e l d was c l a s s i f i e d f or representation of unweighted t o t a l phosphorus and t o t a l crude protein d i s t r i b u t i o n s , using the near i n f r a - r e d p i x e l values from the image. Figure 14 i s the c l a s s i f i e d image de p i c t i n g t o t a l P concentrations, while Figure 15 depicts t o t a l CP concentrations. According to these c l a s s i f i c a t i o n s , 23% of the corn i s high i n terms of unweighted t o t a l P concentration, while corn i n the re s t of t h i s f i e l d (77%) i s low with respect to t h i s n utrient. Eight percent of the f i e l d i s c l a s s i f i e d as corn with high unweighted t o t a l CP contents, while 92% of the corn i n t h i s f i e l d i s low i n terms of unweighted CP. 108 The patterns of high nutrient concentrations on both images are very s i m i l a r , although they show up more strongly on the t o t a l P image. In general, these patterns of high unweighted t o t a l n u t r i e n t s follow areas of low elevation, i n d i c a t i n g that depressional s i t e s tend to be more productive than the higher el e v a t i o n s i t e s i n t h i s f i e l d . Previous c o r r e l a t i o n s had also suggested t h i s . These findings w i l l be investigated and discussed more f u l l y i n the GIS section of t h i s chapter. I t should be noted that i n 1987, when t h i s study was c a r r i e d out, no actual ponding occurred i n t h i s f i e l d . However, i n the spring of 1990, the most prominent low area i n t h i s f i e l d was observed to be ponded (Schreier, 1990; personal communication). These low areas under such conditions would c e r t a i n l y i n h i b i t corn p r o d u c t i v i t y i n t h i s f i e l d . 109 Legend Class 1: Green p i x e l s = High Unweighted Total P (23% of f i e l d ) Class 2: Red Pixels = Low Unweighted Total P (77% of f i e l d ) Figure 1 4 . C l a s s i f i e d image f o r unweighted t o t a l phosphorus (400 f i e l d ) . 110 Legend Class 1: Green p i x e l s = High Unweighted Total CP (8% of f i e l d ) Class 2: Red P i x e l s = Low Unweighted Total CP (92% of f i e l d ) Figure 15. C l a s s i f i e d image f o r unweighted t o t a l crude protein (400 f i e l d ) . I l l 7.1.4 600 f i e l d image c l a s s i f i c a t i o n The 600 f i e l d was c l a s s i f i e d f o r a representation of unweighted t o t a l CP d i s t r i b u t i o n , using the near i n f r a - r e d p i x e l values from the image. The r e s u l t a n t c l a s s i f i e d image i s shown i n Figure 16. Forty-eight percent of the f i e l d i s c l a s s i f i e d as being high with respect to unweighted t o t a l CP (green areas), while the r e s t (52%) i s c l a s s i f i e d as being low i n terms of t h i s n u t r i e n t (red areas). The black area shown on the image depicts bare s o i l , and i l l u s t r a t e s the area of the f i e l d which was ponded e a r l y i n the growing season, r e s u l t i n g i n v i r t u a l l y no corn growth there. This image i l l u s t r a t e s the problem which can be encountered when using remote sensing f o r row crops. The corn rows i n t h i s f i e l d were planted s l i g h t l y f a r t h e r apart than rows i n the other f i e l d s ; as a r e s u l t , inter-row s o i l exposure i s i n f l u e n c i n g the o v e r a l l c l a s s i f i c a t i o n of the f i e l d . A study by Kollenkark et a l . (1982) suggested that row width of a soy bean crop produced di f f e r e n c e s i n percent s o i l cover and LAI, which i n turn a f f e c t e d the s p e c t r a l r eflectance of the soy bean canopy. S o i l moisture and colour were suggested as the fac t o r s of major influence. As a r e s u l t of the inter-row e f f e c t s , CP d i s t r i b u t i o n for t h i s f i e l d i s very scattered, and patterns are not as e a s i l y d i s c e r n i b l e as i n the other f i e l d images. In addition, i n t h i s f i e l d , p r o d u c t i v i t y d i s t r i b u t i o n s are quite c l o s e l y r e l a t e d to elevations; corn growth being i n h i b i t e d i n areas of both very 112 high and very low elevations. This was apparent from the c l u s t e r a n a l y s i s , i n which unweighted t o t a l CP was found to be highest i n groups with the highest and the lowest elevations. T o t a l weights i n these groups, however, were low - consequently t o t a l weighted CP i n these areas was also low, i e . areas of very high and very low elevation were the l e a s t productive areas of the f i e l d . This was supported by the f a c t that both very low and very high s o i l moisture contents were associated with c l u s t e r groups with the lowest weighted t o t a l nutrients concentrations. In general, as has been suggested before, s i t e s located i n areas of moderate elevation tend to be the most c o n s i s t e n t l y productive. In t h i s f i e l d s e l e c t i v e management would be d i f f i c u l t , given the scattered patterns of the corn q u a l i t y d i s t r i b u t i o n s . Image analysis and c l a s s i f i c a t i o n allows us to v i s u a l i z e corn p r o d u c t i v i t y i n a s p a t i a l manner. I t can also i n d i c a t e to the farm operator where he i s ge t t i n g high vs low production, and give i n d i c a t i o n s as to why these d i f f e r e n c e s occur. In addition, i t can allow a farm operator to see where areas of corn which are nutrient-enriched e x i s t , (even though they may be areas of lower biomass production), thus allowing the p o s s i b i l i t y of s e l e c t i v e harvesting. U t i l i z a t i o n of GIS techniques can further c l a r i f y these r e s u l t s , and a i d i n confirming what the underlying reasons f o r p r o d u c t i v i t y v a r i a b i l i t y are. In addition, the use of GIS allows us to overlay d i f f e r e n t maps, and further enhance the usefulness of 113 Legend Class 1: Green p i x e l s = High Unweighted Total CP (48% of f i e l d ) Class 2: Red Pixels = Low Unweighted Total CP (52% of f i e l d ) Figure 16. C l a s s i f i e d image f o r unweighted t o t a l crude protein (600 f i e l d ) . 114 the s p a t i a l data which has been obtained. 7.2 GIS Techniques In t h i s section, p i x e l brightness values from the 3 00 and 400 f i e l d images were converted into biomass and crop q u a l i t y values using regression equations, and presented v i s u a l l y using a GIS. Crop biomass and q u a l i t y f o r the 300 f i e l d were then combined int o a s i n g l e "map" using the GIS overlay method. P i x e l brightness values from the 400 f i e l d image were combined with el e v a t i o n data using a multiple regression equation to improve pr e d i c t i o n s of crop q u a l i t y v a r i a b i l i t y within t h i s f i e l d . 7.2.1 GIS Methods P i x e l values were downloaded from the 300 and 400 f i e l d images, using the "imask" program within Earthprobe. The downloaded data i s comprised of p i x e l values i n ASCII format, which were imported into a Lotus 1-2-3 spreadsheet. In order to make t h i s data compatible with the elevation data which had been extracted f o r the 400 f i e l d , every tenth near i n f r a - r e d p i x e l value was used from the 400 f i e l d image ( i e . one p i x e l every four meters) , so that the two data sets could be used i n combination l a t e r . For consistency, every tenth near i n f r a - r e d and red p i x e l value were therefore used from the 3 00 f i e l d image (i e . one p i x e l every three meters). Near i n f r a - r e d and red p i x e l brightness values from the 300 f i e l d were imported into Lotus 1-2-3. The near i n f r a - r e d to red r a t i o s were converted into cob weight, and unweighted cob crude 115 pro t e i n and phosphorus values, using the following regression equations: CCP = NIR/R (13.91) - 9.07 r 2 = 0.68 S.E. = 0.941 CP = NIR/R (0.341) - 0.154 r 2 = 0.52 S.E. = 0.032 CWT = NIR/R (-206) + 372 r 2 = 0.32 S.E. =30 The near i n f r a - r e d p i x e l brightness values from the 400 f i e l d were converted into unweighted t o t a l P values i n a s i m i l a r manner. In addition, elevation data corresponding to each p i x e l l o c a t i o n was extracted from a topographic map of the area. Total unweighted P values were then c a l c u l a t e d f o r a l l s i t e s using a multiple regression equation based on p i x e l brightness values and elevation data, thereby further improving corn P pr e d i c t i o n s f o r the f i e l d . The regression equations used were the following: TP = (-2.37 X 10"3) ..+ 0.598 r 2 = 0.29 S.E. = 0.020 TP = NIR(-2.06 X 10"2) - ELEV(1.86 X 10"4) + 0.635 r 2 = 0.37 S.E. = 0.019. The predicted values corn biomass and q u a l i t y values were then used to produce corn p r o d u c t i v i t y maps f o r both f i e l d s , u t i l i z i n g the GIS pMap. The predicted u n i t s were c l a s s i f i e d within pMap into three crop p r o d u c t i v i t y ( i e . biomass and/or quality) classes to produce corn q u a l i t y "maps", using the following c l a s s i f i c a t i o n scheme based on the predicted values for the f i e l d s : 116 High biomass or q u a l i t y (unweighted CP or P) = > (mean + 1 S.D.) Moderate biomass or q u a l i t y (unwt'd CP or P) = (mean +/- 1 S.D.) Low biomass or q u a l i t y (unwt'd CP or P) = < (mean - 1 S.D.). 7.2.2 300 f i e l d GIS r e s u l t s Figure 17 displays the unweighted cob CP c l a s s i f i c a t i o n f o r the 3 00 f i e l d as three classes: high, moderate, and low. A d i s p l a y of unweighted cob P was also c l a s s i f i e d i n a s i m i l a r manner, but since the r e s u l t s were very s i m i l a r to Figure 17 (as cob CP and P are c l o s e l y related) the f i g u r e w i l l not be displayed here. Once the f i e l d had been c l a s s i f i e d i n terms of cob CP, i t was determined which of the o r i g i n a l t e s t s i t e s f e l l i nto these cl a s s e s . These s i t e s were then used as a t e s t population, to determine i f s i g n i f i c a n t differences with respect to other corn nutrients and s o i l v a r i a b l e s (those not used i n the c l a s s i f i c a t i o n ) also existed between these groups. Tables 28 and 29 l i s t v a r i a b l e s which 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 cob CP classes, and the means and standard deviations of these v a r i a b l e s , r e s p e c t i v e l y . The s i g n i f i c a n t l y d i f f e r e n t s o i l moisture v a r i a b l e s shown are only examples; i n most cases more s o i l moisture di f f e r e n c e s e x i s t between the groups, but are not shown for s i m p l i c i t y (in t h i s and other c l a s s i f i c a t i o n s ) . Group 1, although i t contains s i t e s with the highest unweighted cob CP, as well as other unweighted cob nutrients (not shown), i s i n f a c t the l e a s t productive group i n terms of both cob and t o t a l weighted nutrient concentrations. Several of 117 Figure 17 GIS c l a s s i f i c a t i o n f o r unweighted, cob CP (300 f i e l d ) . LEGEND Symbol Group Description # C e l l s Map t;i'i't 1 High cob CP (>12.16%) 1352 3 Low cob CP (<8.56%) 401 2 Mod cob CP (8.56-12.16%) 1647 40 12 48 118 the s i t e s i n t h i s group are located i n and around the large unproductive wet area i n t h i s f i e l d that was planted l a t e ; they are a l l low elevation s i t e s e x h i b i t i n g high s o i l water contents, and have the highest depth 1 bulk d e n s i t i e s . Group 3, which i s most productive i n terms of cob and t o t a l biomass, and weighted nutrients, contains s i t e s which are moderate terms of elevation and s o i l moisture. I t also contains a couple of s i t e s i n the late-planted area of t h i s f i e l d . Corn growth around these s i t e s was patchy; as explained previously, some plants consequently had large cobs, thereby g i v i n g them abnormally high weighted nutrient contents. Table 28. Variables e x h i b i t i n g s i g n i f i c a n t d i f f e r e n c e s between pMap cob CP groups (3 00 f i e l d ) . Group 2 Group 3 Classif. Variables: Classif. Variables: CCP CCP Assoc. Variables: Assoc. Variables: Group 1 CP, CDN, CWT (CCPg, CPg), CDNg TDN All unwt'd + all wt'd cob nutr's (TWT), (TPg), TDN bd2, elev, np2c,4c NIR1, NIR/R (elev), np4c, np6d NIR/R Classif. Variables: Group 2 CCP Assoc. Variables: (CCa), CWT, (CCPg), CPg, CDNg TCa, TWT, (TPg, TDNg) np6d, NIR 1, ND 119 Group 2 i s moderate i n terms of p r o d u c t i v i t y ; s i t e s within t h i s group tend to e x h i b i t moderate to high elevations, and low s o i l water contents. This again indicates that although e a r l y wet conditions i n h i b i t corn growth, the higher areas of the f i e l d are at times also l e s s productive. As expected, near i n f r a - r e d p i x e l values from the bare s o i l images are lowest i n the lower elevation groups, as s i t e s within these groups would have been wetter when the a e r i a l photos were flown. The near i n f r a - r e d to red corn p i x e l value r a t i o also e x h i b i t s s i g n i f i c a n t d i f f e r e n c e s between groups - most strongly between the high vs low p r o d u c t i v i t y groups. Table 29. Means and standard deviations f o r v a r i a b l e s e x h i b i t i n g s i g n i f i c a n t d i f f e r e n c e s between pMap cob CP groups (300 f i e l d ) . V ariable Group 1 Group 2 Group 3 Mean SD Mean SD Mean SD CCP 13 . 07 0.79 9 . 84 0. 67 8.00 0.41 CWT 50 11 86 25 142 28 CCPg 6. 57 1.28 8.45 2.26 11. 39 2.25 CPg 0.197 0. 040 0.261 0. 075 0.425 0. 098 CCag 0. 007 0.000 0 . 009 0. 003 0.011 0. 000 CDNg 32 . 6 7.2 58 . 8 17.4 97.9 7.9 TWT 183 26 197 28 252 45 TPg 0.429 0. 075 0.448 0. 094 0. 627 0. 113 TDNg 112 . 6 15.7 124 .4 17.9 160. 3 27.4 NP2C 0.45 0. 01 0.34 0. 11 0.41 0. 04 NP4C 0.42 0. 01 0.30 0.13 0.36 0. 06 BD1 1. 16 0. 04 1. 14 0. 14 1.11 0. 04 BD2 1. 05 0.05 1.18 0. 10 1. 12 0. 02 ELEV 525 3 588 38 550 28 NIR1 25 20 54 23 24 24 NIR/R 1.5 0.0 1.3 0.0 1.2 0.0 120 Figure 18 " = * = £ • • 4 4 44=>4=4t +=4= = » = » fs» * 4=: 444444=4===4=44 ^ 4 4 = = Q s * » = >H = tSt^ = 4 4444 »TS i = i t = ^ S 4 = » i 4=4 44=4 S3 • » i » » » C ^ J — • 4==4==>s» * ~+ 4 4 = 4B4£E 4 I | = 4^ 4^ =4 4>4J»»fa. • = tiir. f Bt tit *=!*£*=t» 4=l=f 4 = » »U? J4 4f^Si4 4tif I = 4TS4=J4^i4U4ii»j • i = ± E * ±l±^ i I * * t== » i 4 i * = ± = * = i £ = = ± t = * » ± = = » = *Sl*S==. V 4 4 4.4 + 444 ~ 4 4 4 ===4 = 4 4 44C^4 = 44 = 4 4 44 = 4==4 4=*=> GIS c l a s s i f i c a t i o n f o r cob weight (3 00 f i e l d ) . LEGEND Symbol Group Description # C e l l s % Map D ™ 1 High cob wt (>118g) 213 6 = 3 Low cob wt (<60g) 1466 43 ••• 2 Mod cob wt (60-118g) 1721 51 Cob weight was also c l a s s i f i e d within pMap, and the r e s u l t i s shown i n Figure 18. As f o r the previous c l a s s i f i c a t i o n , representative t e s t s i t e s which f e l l within the c l a s s i f i e d groups were used to t e s t f o r s i g n i f i c a n t l y d i f f e r e n t v a r i a b l e s , i n c l u d i n g independent ones ( i e . those which were not used to c l a s s i f y the f i e l d ) . Group 1 was found to be most productive, containing the highest cob weights, and the highest weighted cob nutrient concentrations. In addition, t o t a l weights and t o t a l weighted nutrient contents also tended to be highest i n group 1. Several s i t e s which f e l l into the high and low p r o d u c t i v i t y groups i n t h i s c l a s s i f i c a t i o n were s i m i l a r l y grouped i n the cob CP c l a s s i f i c a t i o n . In terms of s o i l v a r i a b l e s , group 1 contained s i t e s with the lowest mean s o i l water contents and BD3, suggesting once again that the wetter, and/or more compacted areas of t h i s f i e l d are the l e a s t productive. The near i n f r a - r e d to red r a t i o exhibited the l a r g e s t d i f f e r e n c e s between the high and low p r o d u c t i v i t y groups. Table 3 0 contains the v a r i a b l e s which e x h i b i t s i g n i f i c a n t d i f f e r e n c e s between the classes, while Table 31 l i s t s the means and standard deviations of these v a r i a b l e s within the three classes. 122 Table 30. Variables e x h i b i t i n g s i g n i f i c a n t d i f f e r e n c e s between pMap cob weight groups (3 00 f i e l d ) . Group 2 Group 3 Group 1 Classif. Variables: Classif. Variables: CWT Assoc. Variables: CCPg, CPg, CDNg TWT, TPg, TDNg avbd, bd3 (R1) CWT Assoc. Variables: CCPg, CPg, CDNg TWT, TCPg, TPg, TDNg (NIR/R) Group 2 Classif. Variables: CWT Assoc. Variables: CCPg, CPg, CCag, CDNg TCPg, TCag, np2d R1, (NIR/R) 123 Table 31. Means and standard deviations f o r v a r i a b l e s e x h i b i t i n g s i g n i f i c a n t d i f f e r e n c e s between pMap cob weight groups (3 00 f i e l d ) . V a riable Group 1 Group 2 Group 3 Mean SD Mean SD Me an SD CWT 13 4 21 84 14 43 7 CCP 8. 94 0.87 9. 94 0. 94 12 . 15 1. 73 CP 0. 302 0. 02 0. 304 0. 034 0. 370 0. 05 CCa 0. 009 0. 003 0. 011 0. 004 0. 016 0. 003 CDN 67 •8 0.4 68 .0 1. 7 65 .4 2 . 0 CCPg 11 .79 1. 03 8. 22 1. 10 5. 31 1. 40 CPg 0. 404 0. 068 0. 0 0. 039 0. 160 0. 036 CCag 0. 012 0. 003 0. 009 0. 004 0. 007 0. 001 CDNg 90 . 6 14 . 3 57 . 0 10 .8 28 . 0 4 . 4 TWT 24 2 34 19 3 22 17 6 26 TCP 7. 99 0.95 9. 15 0. 84 8. 13 0. 76 TDN 63 .2 0. 74 63 . I 1. 12 61 . 9 0. 17 TCPg 19 . 09 1.76 17 . 64 2 . 29 14 .44 3 . 27 TPg 0. 574 0. 101 0. 446 0. 089 0. 394 0. 081 TCag 0. 333 0. 079 0. 360 0 . 100 0. 249 0. 058 TDNg 15 3 .0 21. 6 12 2 . 0 14 . 7 10 8. 6 15 . 9 NP2D 0. 35 0.15 0. 35 0. 13 0 . 40 0. 10 BD3 1. 07 0. 04 1. 24 0. 12 1. 18 0. 14 NIR/R 1. 3 0.1 1. 4 0. 1 1. 5 0. 1 Because i t would be more useful to the farm operator to v i s u a l i z e the s p a t i a l d i s t r i b u t i o n of cob weight r e l a t i v e to that of unweighted cob CP, the overlay c a p a b i l i t y of pMap was u t i l i z e d to obtain a combined c l a s s i f i c a t i o n f o r both corn v a r i a b l e s . The cob weight map was ov e r l a i n by the cob CP map, and the res u l t a n t combination map was r e - c l a s s i f i e d . The output from t h i s operation i s depicted i n Figure 19. The combination of these two maps would t h e o r e t i c a l l y y i e l d nine possible cob weight-cob CP combinations; some of these 124 combinations d i d not occur, however, and only s i x combinations were obtained. Some of the smaller classes were further combined to produce a map with three main cob weight-cob CP classes. The cl a s s containing moderate cob weights i n conjunction with moderate CP concentrations i s the lar g e s t , comprising 46% of the f i e l d . The c l a s s containing areas with low cob weights and high cob CP i s also very large, making up almost 40% of the f i e l d . As described previously, most of the low cob weight-high unweighted cob CP c l a s s occurs i n and around the late-planted area of t h i s f i e l d , which exhibited ponding and compaction problems. U t i l i z a t i o n of the overlaying technique within pMap allows us to represent t h i s f i n d i n g both v i s u a l l y and i n a quan t i t a t i v e manner. Table 31 outl i n e s the four combination classes of cob weight and CP, to further c l a r i f y the r e s u l t s . 125 Symbol Group Description # C e l l s % Map A Low cwt, high cob CP 1352 40 B Mod--high cwt, low cob CP 363 11 = C Low cwt, low-mod cob CP 114 4 D Mod cwt, mod cob CP 1571 46 126 Table 32. Cob weight-cob CP c l a s s i f i c a t i o n (300 f i e l d ) CLASS CLASSIFICATION % OF AREA COB WEIGHT COB CP A Mod Mod 46 B Low Low - Mod 3 C Mod - High Low 11 0 Low High 40 7.2.3 400 f i e l d GIS r e s u l t s Figure 2 0 represents the unweighted t o t a l P d i s t r i b u t i o n for the 400 f i e l d , as predicted from near i n f r a - r e d p i x e l values, and c l a s s i f i e d using pMap. Low t o t a l P and high t o t a l P classes each comprise about 30% of the c l a s s i f i e d area. The r e s t of the f i e l d (approximately 40%) i s moderate i n terms of t o t a l P contents. The classes were tested f o r s i g n i f i c a n t d i f f e r e n c e s as described previously, and the r e s u l t s are displayed i n Tables 32 and 33. The patterns which were evident i n the c l a s s i f i e d images are even more pronounced i n the GIS output. There are d i s t i n c t areas of a l t e r n a t i n g high and low t o t a l P, which would appear to follow the ridge and hollow topography of the f i e l d . This suggestion i s confirmed i n Table 33, which in d i c a t e s that higher unweighted t o t a l P values are associated with lower elevation s i t e s . 127 Figure 20. GIS c l a s s i f i c a t i o n f o r unweighted t o t a l corn P (400 f i e l d ) . 1 rJ i° 'J ~ tv- V-4 4 y-f 3 r>vM • 4"?nH <-4 r-fcV^/^^^-Alf^ , , f x x T T y4 Vlr-4 44 444 y-4 4 44 4 4 4 ^ / / ^ ^ ^ > / ' W ^ / M w^^//^/r^4 LEGEND Symbol Group Description # C e l l s % Map - w" v 1 High t o t a l P (>0.234%) 434 27 *** 2 Mod t o t P (0.190-0.234%) 669 42 3 Low t o t a l P (0.190%) 496 31 128 Table 33. Variables e x h i b i t i n g s i g n i f i c a n t d i f f e r e n c e s between pMap corn t o t a l P groups (400 f i e l d ) . Group 2 Group 3 Group 1 Claaslf. Variables: Claaslf. Variables: TP TP Assoc. Variables: elev, avbd, bd2 cec, C, N, Ca NIR2 Qroup 2 Claaslf. Variables: CCP Assoc. Variables: elev, (avbd, bd2) bd3, cec, (C, P, Ca), N (NIR2) Table 34. Means and standard deviations f o r va r i a b l e s e x h i b i t i n g s i g n i f i c a n t d i f f e r e n c e s between pMap t o t a l P groups (4 00 f i e l d ) . V ariable Group 1 Group 2 Group 3 Mean SD Mean SD Mean SD TP 0.246 0.012 0. 215 0. 011 0.179 0. 007 TCP 8.44 0. 52 8.20 0.43 7.83 0.19 ELEV 462 14 467 46 510 16 AVBD 1. 05 0. 04 1. 09 0.09 1.20 0. 11 BD2 0 .96 0. 09 1. 06 0. 10 1. 18 0. 09 BD3 1.13 0.03 1.11 0. 09 1.23 0 .08 CEC 32.30 2.90 30.41 6. 07 23 . 27 4 . 11 C 3.44 0.27 3 .10 0 . 67 2 .21 0. 65 N 0.319 0.288 0 .288 0. 063 0. 214 0. 05S Ca 15.59 3 . 06 12 . 28 1.79 10.07 1.26 NIR2 158 2 162 4 168 4 129 The higher elevation s i t e s i n t h i s f i e l d tend to be l e s s productive i n terms of corn t o t a l P contents - a f i n d i n g which was previously echoed by both c o r r e l a t i o n a n a l y s i s and image an a l y s i s . I t i s l i k e l y that although e a r l y wet conditions could have i n h i b i t e d corn growth, droughtiness of the ridges l a t e r i n the growing season also hindered p r o d u c t i v i t y . Corn t o t a l P v a r i a b i l i t y i s also bulk d e n s i t y - r e l a t e d . Bulk density values are extremely high i n the group containing s i t e s with low corn t o t a l P, and they decrease as corn t o t a l P increases. I t i s l i k e l y that s o i l compaction - p a r t i c u l a r l y i n group 3 - i s having a negative impact on P uptake i n some areas of t h i s f i e l d . Previous c o r r e l a t i o n r e s u l t s echoed t h i s f i n d i n g . Lower bulk d e n s i t i e s (and higher corn t o t a l P values) are found i n the depressional areas of the f i e l d , which l i k e l y r e f l e c t s the presence of the s o i l u nits with higher organic matter concentrations. This suggestion i s supported by the f a c t that higher s o i l C values are also found i n these areas. I t i s therefore possible that s o i l f e r t i l i t y - as influenced by elevation and s o i l organic matter content - may also be playing a r o l e . The lower elevation-higher corn P s i t e s are also much higher i n terms of s o i l N, P, Ca and pH. As expected, near i n f r a - r e d p i x e l values exhibited some s i g n i f i c a n t d i f f e r e n c e s between groups; most noticeably between the high and low corn t o t a l P groups. In an attempt to further c l a r i f y the unweighted t o t a l P d i s t r i b u t i o n s found i n the 400 f i e l d , a portion of the f i e l d was 130 c l a s s i f i e d on the basis of t o t a l P values predicted from both near i n f r a - r e d p i x e l values and el e v a t i o n data. This c l a s s i f i c a t i o n i s i l l u s t r a t e d i n Figure 21. The r e s u l t s were quite s i m i l a r to the previous c l a s s i f i c a t i o n and d i d not provide any a d d i t i o n a l information. This c l a s s i f i c a t i o n does, however, account f o r approximately 10% more of the v a r i a b i l i t y i n unweighted t o t a l P than that based on p i x e l values alone, and would consequently be more accurate. 131 Figure 21. GIS c l a s s i f i c a t i o n f o r unweighted t o t a l corn P (using both p i x e l values and ele v a t i o n data) (400 f i e l d ) . BtfHu ( . T T , T - T T — " * f LEGEND Symbol Group Description # C e l l s Map 1 High t o t a l P (>0.240%) 243 2 Mod t o t a l P (0.188-0.240%) 450 3 Low t o t a l P (<0.188%) 207 2 7 50 23 132 7.3 Evaluation of Image C l a s s i f i c a t i o n and GIS Results Image c l a s s i f i c a t i o n allowed us to v i s u a l i z e corn q u a l i t y and biomass d i s t r i b u t i o n s within several f i e l d s i n a s p a t i a l manner. U t i l i z a t i o n of the f i e l d t e s t s i t e s as t r a i n i n g areas allowed c l a s s i f i c a t i o n of each e n t i r e image i n terms of c e r t a i n corn biomass or q u a l i t y v a r i a b l e s . The main problems encountered with t h i s method included the accuracy of analysis of p i x e l values outside of the t e s t s i t e p i x e l value ranges, and s o i l background r e f l e c t i v e interferences. The inter-row reflectance i n the 600 f i e l d , f o r example, influenced the c l a s s i f i c a t i o n process, making i t d i f f i c u l t to a c t u a l l y see corn q u a l i t y patterns within the f i e l d . Nevertheless, the method worked well f o r the 300 and 400 f i e l d s , i l l u s t r a t i n g the s p a t i a l v a r i a b i l i t y of corn p r o d u c t i v i t y i n r e l a t i o n to c e r t a i n s o i l and s i t e v a r i a b l e s , such as elevation and bulk density. The use of a GIS further enhanced the s p a t i a l analysis of corn p r o d u c t i v i t y . Through the use of the t e s t s i t e s , s i g n i f i c a n t d i f f e r e n c e s between the c l a s s i f i c a t i o n groups, fo r v a r i a b l e s not used i n the o r i g i n a l c l a s s i f i c a t i o n , were determined. These differences indicated more c l e a r l y the underlying reasons fo r corn production v a r i a b i l i t y . The GIS method also allowed overlaying of corn q u a l i t y and biomass maps, thereby creating combination maps which represent both v a r i a b l e s . In addition, the method permits incorporation of both s o i l / s i t e and remote sensing data, to further improve the p r e d i c t i o n of corn s p a t i a l v a r i a b i l i t y . 133 These image analysis and GIS c a p a b i l i t i e s could provide the farm operator with a useful t o o l which may a i d i n s e l e c t i v e f i e l d management and harvesting. 134 CHAPTER 8 CONCLUSIONS AND RECOMMENDATIONS 8.1 Corn-Soil/Site and Remote Sensing Relationships 8.1.1 Relationships between corn v a r i a b l e s , and s o i l  moisture and elevation Indications are that s o i l moisture plays a large r o l e i n corn production i n the study f i e l d s . There appear to be two mechanisms whereby corn p r o d u c t i v i t y can be adversely a f f e c t e d by water stresses: (1) poor early growth brought about by wet s o i l conditions, p a r t i c u l a r l y i n the very low areas, and (2) poor l a t e growth r e s u l t i n g from droughty conditions, p a r t i c u l a r l y on the sandy ridge areas. These e f f e c t s were indicated by s i n g l e l i n e a r regressions between s o i l moisture and elevation, and by c l u s t e r analysi s , which c l a s s i f i e s multiple v a r i a b l e s . These water stresses adversely a f f e c t both corn biomass and corn nutrient concentrations, although the re l a t i o n s h i p s are complex, and are not consistent within a l l of the f i e l d s studied. Of these 2 mechanisms, r e s u l t s i n d i c a t e that e a r l y wet conditions cause the most severe damage, p a r t i c u l a r l y i n f i e l d s which have very low areas which are susceptible to waterlogging and ponding. On the other hand, cob production on the sandy ridge.areas of some f i e l d s may be i n h i b i t e d l a t e r on i n the growing season, at a time when the depressional areas may s t i l l be productive. I t i s apparent that elevation d i f f e r e n c e s within the f i e l d s (and t h e i r associated s o i l differences) can lead to v a r i a b i l i t y 135 i n corn production. The extent of t h i s v a r i a b i l i t y , however, can be c o n t r o l l e d to some degree by f i e l d management, and i s also dependent on the s e v e r i t y of the elevation and t e x t u r a l d i f f e r e n c e s found within the i n d i v i d u a l f i e l d s . The f i e l d s with the most severe elevation differences exhibited the strongest e l e v a t i o n - r e l a t e d e f f e c t s . Because there are two separate mechanisms whereby water stress occurs, e l e v a t i o n does not produce many strong l i n e a r r e l a t i o n s h i p s with corn v a r i a b l e s -i t s influence becomes more apparent when more s p a t i a l types of data analyses ( i e . c l u s t e r analysis) are employed. In f i e l d s where elevation differences are most prominent, s o i l moisture i s inversely r e l a t e d to elevation, i n d i c a t i n g that i n f i e l d s where elevation d i f f e r e n c e s are large, s o i l moisture v a r i a b i l i t y w i l l also be high. This suggests that i n such f i e l d s , e l evation may be used as a surrogate v a r i a b l e for p r e d i c t i n g corn q u a l i t y on the basis of t h i s r e l a t i o n s h i p . Under these conditions, where f i e l d management i s not an i n f l u e n c i n g f a c t o r , v a r i a b i l i t y i n corn production i s r e l a t e d to s o i l water status, and elevation i s an easily-measured v a r i a b l e which r e f l e c t s t h i s status. 8.1.2 Relationships between corn v a r i a b l e s and other s o i l  v a r i a b l e s Other s o i l v a r i a b l e s also figured prominently i n a number of r e l a t i o n s h i p s found within the f i e l d s . These included bulk density, which was found to be negatively r e l a t e d to corn nutrient status ( p a r t i c u l a r l y phosphorus) i n several f i e l d s , h i s indicates that s o i l compaction (whether i t be management-related or pedologically-related) i s i n h i b i t i n g nutrient uptake i n some areas of the f i e l d s . On the other hand, however, higher bulk d e n s i t i e s of the lower depths showed some p o s i t i v e r e l a t i o n s h i p s with corn biomass production, i n d i c a t i n g that as long as they are not so high as to be causing compaction problems, higher BDs of the lower depths may be advantageous from a water status point of view. In f i e l d s which exhibited r e l a t i v e l y low f e r t i l i t y s t a t u s 1 , many r e l a t i o n s h i p s existed between corn biomass and q u a l i t y , and s o i l f e r t i l i t y parameters. In addition, where s o i l v a r i a b l e s were strongly c o r r e l a t e d with elevation, and ele v a t i o n was re l a t e d to corn v a r i a b l e s , then these s o i l v a r i a b l e s were also r e l a t e d to the corn v a r i a b l e s i n question. Such r e l a t i o n s h i p s may be i n c i d e n t a l , although i t i s l i k e l y that e l e v a t i o n - r e l a t e d s o i l n u t r i e n t d e f i c i e n c i e s occur within some f i e l d s as we l l . On the whole, s i n g l e s o i l v a r i a b l e s d i d not y i e l d c o n s i s t e n t l y strong c o r r e l a t i o n s with corn p r o d u c t i v i t y i n the f i e l d s studied. 8.1.3 Relationships between corn v a r i a b l e s and p i x e l  brightness values extracted from d i g i t a l a e r i a l  images P i x e l values from the d i g i t i z e d CIR a e r i a l photos of the corn f i e l d s y i e l d e d r e l a t i v e l y consistent r e l a t i o n s h i p s . In three of the four f i e l d s studied, near i n f r a - r e d p i x e l values gave reasonable estimates of t o t a l corn unweighted crude protein content. No s i n g l e s o i l v a r i a b l e gave such consistent r e l a t i o n s h i p s . Good r e l a t i o n s h i p s were also obtained between t o t a l corn P and Ca, and near i n f r a - r e d p i x e l values i n c e r t a i n f i e l d s . In f i e l d s where s o i l background r e f l e c t a n c e appeared to be i n t e r f e r i n g with corn reflectance, some of the remote sensing transformations further improved these r e l a t i o n s h i p s . This was f i e l d - s p e c i f i c , however, depending perhaps upon inherent s o i l c h a r a c t e r i s t i c s within the i n d i v i d u a l f i e l d s , and/or d i f f e r i n g management p r a c t i c e s . The f i e l d s e x h i b i t i n g low to moderate corn production tended to y i e l d the best corn-pixel value r e l a t i o n s h i p s . Relationships between biomass and p i x e l brightness values were r e l a t i v e l y poor, perhaps because much of the biomass production i n the f i e l d s had reached the c r i t i c a l stage at which a further increase i n biomass would not r e s u l t i n a corresponding increase i n re f l e c t a n c e . 8.2 S p a t i a l D i s t r i b u t i o n of Corn Quality and Biomass using Image  Analysis and GIS Techniques The s p a t i a l d i s t r i b u t i o n of corn q u a l i t y and biomass can r e a d i l y be q u a n t i f i e d using image c l a s s i f i c a t i o n techniques, i n f i e l d s where good corn p r o d u c t i v i t y - p i x e l brightness value r e l a t i o n s h i p s e x i s t . Using such techniques, allowed the c l a s s i f i c a t i o n of the 300 f i e l d i n terms of both cob biomass and crude p r o t e i n . Image c l a s s i f i c a t i o n of the 400 and 600 f i e l d s produced c l a s s i f i e d images of t o t a l corn phosphorus and crude protein, r e s p e c t i v e l y . The combination of image analysis with a GIS allows further 138 s p a t i a l a n a lysis of corn p r o d u c t i v i t y , through the a b i l i t y of a GIS to allow map overlays, and the incorporation of both remote sensing and s o i l s data within i t . Using the GIS i n conjunction with image analysis data, cob weight and crude p r o t e i n "maps" fo r the 3 00 f i e l d were produced, and then combined to provide a dual c l a s s i f i c a t i o n of both cob weight and CP f o r the f i e l d . Use of a GIS also allowed the combination of image analysis and elevation data f o r the 400 f i e l d , to produce a t o t a l corn P map of greater accuracy than image analysis data alone produced. 8.3 Recommendations I t i s evident from t h i s study that there are probably no u n i v e r s a l r e l a t i o n s h i p s between corn p r o d u c t i v i t y , and s o i l / s i t e v a r i a b l e s and remote sensing. Although some s i m i l a r i t i e s e x i s t , i n d i v i d u a l f i e l d management and inherent s o i l and s i t e properties combine to a l t e r the magnitude and d i r e c t i o n of the r e l a t i o n s h i p s found. The f i e l d s e x h i b i t i n g the highest degree of s o i l and elevation s p a t i a l v a r i a b i l i t y are the f i e l d s which produce the strongest r e l a t i o n s h i p s . In f i e l d s where strong e l e v a t i o n / s o i l d i f f e r e n c e s occur, they do influence corn production v a r i a b i l i t y . Where these e l e v a t i o n - s o i l units are s u f f i c i e n t l y d i s t i n c t and large, they should be managed i n s e l e c t i v e ways. For example, the lower areas should not be worked when they are wetter, while the higher areas at t h i s time are l i k e l y s t i l l t r a f f i c a b l e . Later i n the growing season, p a r t i c u l a r l y during cob formation and growth, the sandy ridge areas should be i r r i g a t e d to maximize 139 cob production. Some of the farm operators i n the Matsqui area are already s e l e c t i v e l y i r r i g a t i n g the ridge areas at c e r t a i n times of the year, and apply manure i n a s e l e c t i v e manner i n some of t h e i r f i e l d s . Remote sensing r e l a t i o n s h i p s with corn v a r i a b l e s are more consistent, but are strongest i n f i e l d s which e x h i b i t high corn s p a t i a l v a r i a b i l i t y . S o i l background r e f l e c t i v e interference from the corn inter-rows i s also a problem, making corn a more d i f f i c u l t crop to study i n t h i s manner. I t may soon be f e a s i b l e to "extract" the s o i l background from a corn f i e l d image; i f t h i s could be accomplished, p i x e l value-corn p r o d u c t i v i t y r e l a t i o n s h i p s would be improved dramatically. The i n t e g r a t i o n of remote sensing, image analysis and GIS techniques to produce crop p r o d u c t i v i t y "maps" i s promising, p a r t i c u l a r l y i n f i e l d s where high corn s p a t i a l v a r i a b i l i t y occurs. Improvement of remote sensing technology, i n addition to the use of a more powerful and f l e x i b l e GIS would gr e a t l y improve t h i s procedure. In p a r t i c u l a r , removal of the s o i l background from crop images would gr e a t l y enhance the r e s u l t s obtained. The crop q u a l i t y maps produced could then conceivably be used to s e l e c t i v e l y harvest areas of high q u a l i t y corn vs low q u a l i t y corn, i f such areas are large enough to make t h i s f e a s i b l e . This would allow the farm operator to store high q u a l i t y s i l a g e separately from low q u a l i t y s i l a g e , and then e i t h e r mix the two to produce a feed of more uniform q u a l i t y , or supplement the feed only as much as i s necessary f o r each batch. 140 This i s quite conceivable, p a r t i c u l a r l y as many farm operators are now using "Ag-Bags" for s i l a g e storage. Recommendations f o r future study include looking at long term trends within the f i e l d s studied, f o r s i l a g e corn and other crops, with respect to elevation and water e f f e c t s on crop q u a l i t y and biomass production. The consistency of these r e l a t i o n s h i p s i n addition to remote sensing r e l a t i o n s h i p s , both between f i e l d s and within f i e l d s from year to year should be assessed. A part of t h i s study could include t e s t i n g of the p r e d i c t i v e accuracy of the crop q u a l i t y maps from year to year, by s e l e c t i n g c e r t a i n c l a s s i f i e d areas of the maps, c o l l e c t i n g samples from these areas the following year, analyzing them, and thereby determining how c l o s e l y the predicted classes follow actual f i e l d n utrient concentration d i s t r i b u t i o n s . I f consistent, long term r e l a t i o n s h i p s between s o i l / s i t e v a r i a b l e s and/or remote sensing v a r i a b l e s , and crop p r o d u c t i v i t y are observed, then some more d e f i n i t i v e statements about s e l e c t i v e f i e l d management on a yearly basis could be made. 141 LITERATURE CITED ADAS. 1975. 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APPENDIX 1 - SOIL CHEMICAL AND PHYSICAL DATA SITE pH pH Tot.C Tot.N Av.P Na Ca Mg K CEC AVBD BD1 BD2 BD3 ELEV (Ca) (%) (%) (ppm) (meq/100g) (g/cm3) (cm) 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 7.1 6.6 6.2 6.1 6.3 6.2 6.5 5.8 5.6 5.4 5.8 5.8 5.7 6.1 6.3 5.8 6.1 5.7 5.6 5.6 6.2 5.8 6.1 5.7 5.9 6.1 6.1 5.7 5.4 4.9 6.8 5.8 5.6 5.5 5.9 5.6 5.6 4.7 4.8 4.8 5.1 5.3 5.2 5.7 5.7 5.4 5.7 4.8 5.0 5.0 5.6 5.2 5.5 5.1 5.3 5.6 5.6 5.0 4.8 4.4 3.45 2.08 3.71 3.36 3.19 3.44 3.14 1.95 1.86 2.52 2.61 2.23 3.09 3.65 3.07 2.62 4.13 1.15 2.62 2.25 2.07 2.61 3.28 3.20 2.31 2.36 3.26 2.50 2.78 2.80 0.275 0.199 0.309 0.232 0.249 0.262 0.314 0.157 0.141 0.197 0.237 0.194 0.306 0.302 0.278 0.237 0.406 0.097 0.244 0.289 0.205 0.237 0.294 0.325 0.216 0.222 0.278 0.216 0.214 0.291 9.5 14.2 13.5 7.7 9.6 8.4 9.0 12.2 8.3 8.0 10.9 8.8 14.3 9.9 9.1 10.2 9.6 4.3 8.0 4.6 8.0 11.9 13.1 15.5 6.7 8.0 9.5 5.9 8.1 5.0 0.04 0.05 0.06 0.05 0.06 0.06 0.05 0.02 0.02 0.05 0.05 0.06 0.08 0.05 0.05 0.06 0.06 0.03 0.03 0.06 0.06 0.05 0.05 0.06 0.07 0.08 0.05 0.03 0.03 0.04 21.96 10.42 10.54 10.10 12.67 11.41 11.79 4.17 4.42 6.30 .8.30 9.23 10.48 13.97 12.85 10.54 14.35 4.64 7.73 9.11 10.23 8.23 11.85 8.86 9.17 10.67 10.92 7.49 5.64 4.38 1.09 1.23 2.14 1.32 1.46 1.54 1.07 0.78 0.66 1.03 1.38 1.15 1.15 1.23 1.38 1.19 1.30 0.84 1.32 1.11 1.11 1.40 1.56 1.36 1.03 1.27 1.17 0.95 0.97 0.62 1.30 1.39 2.13 1.07 1.27 1.65 0.98 0.63 0.50 0.37 1.33 1.25 1.60 1.63 1.47 1.09 1.60 0.48 1.05 0.74 1.12 1.50 1.57 1.47 1.16 1.18 0.93 0.54 0.54 0.38 34.52 23.87 35.58 24.89 29.46 29.53 27.80 16.99 18.18 27.03 24.36 23.51 35.34 32.42 29.82 26.83 27.80 14.85 23.80 26.02 23.90 24.81 24.18 27.18 25.36 22.24 24.94 25.42 19.01 24.97 1.13 1.04 1.10 1.17 1.23 1.14 0.98 1.27 1.14 1.16 1.18 1.09 1.24 0.90 1.08 1.03 1.15 1.05 1.12 1.07 1.15 1.17 1.19 1.23 1.13 1.23 1.07 1.20 1.25 1.13 1.08 0.99 1.06 1.19 1.21 1.09 0.89 1.33 1.09 1.08 1.13 1.07 1.21 0.87 0.90 0.92 1.12 1.01 1.07 0.99 1.11 1.12 1.12 1.16 1.08 1.10 0.95 1.23 1.16 1.08 1.14 1.06 1.12 1.10 1.14 1.12 0.97 1.13 1.14 1.15 1.21 1.20 1.34 0.89 1.17 1.06 1.13 0.96 1.08 1,14 1.19 1.17 1.25 1.26 1.16 1.26 1.13 1.22 1.28 1.14 1.18 1.06 1.12 1.21 1.34 1.20 1.07 1.34 1.18 1.25 1.21 1.00 1.29 0.90 1.18 1.12 1.19 1.18 1.20 1.08 1.14 1.20 1.20 1.27 1.15 1.33 1.14 1.15 1.30 1.18 670 570 610 575 680 560 445 670 675 625 630 610 580 540 605 580 440 625 670 620 650 610 560 500 580 660 425 600 670 560 SITE pH pH Tot.C (Ca) (%) 301 5.9 5.3 2.57 302 5.8 5.2 2.44 303 5.9 5.3 2.90 304 6.2 5.6 2.82 305 5.9 5.2 2.29 306 6.2 5.5 2.37 307 6.2 5.6 3.03 308 6.2 5.5 3.30 309 6.3 5.5 2.60 310 6.1 5.3 3.51 311 6.1 5.5 2.57 312 6.1 5.5 3.19 313 6.1 5.4 2.80 314 6.0 5.3 2.99 315 5.5 4.8 2.71 316 5.6 5.1 2.87 317 5.5 4.9 2.83 318 6.2 5.3 2.53 319 5.7 5.2 2.21 320 5.8 5.2 2.60 321 5.9 5.3 2.37 322 6.1 5.5 2.57 323 6.2 5.5 3.19 324 5.8 5.4 2.80 Tot.N Av.P Na (%) (ppm) 0.267 9.3 0.12 0.261 8.0 0.11 0.267 9.5 0.08 0.281 9.3 0.07 0.259 8.3 0.11 0.259 8.1 0.14 0.303 9.8 0.18 0.319 0.0 0.15 0.273 9.0 0.11 0.308 7.6 0.15 0.308 11.2 0.16 0.308 13.5 0.23 0.286 18.8 0.19 0.298 9.6 0.16 0.209 15.4 0.14 0.258 12.7 0.13 0.270 12.6 0.13 0.092 4.0 0.17 0.213 16.5 0.16 0.253 11.3 0.15 0.233 10.2 0.15 0.310 9.1 0.15 0.182 4.6 0.16 0.308 11.0 0.08 Ca Mg K CEC AVBD BD1 BD2 BD3 ELEV —(meq/100g) (g/cm3) (cm) 8.86 2.04 1.60 35.81 1.09 1.12 0.97 1.17 571 8.92 2.08 1.57 27.85 1.17 1.16 1.08 1.24 581 10.35 1.83 1.47 25.12 1.14 1.07 1.17 1.17 584 12.72 1.79 1.44 26.95 1.10 1.06 1.16 1.08 586 9.54 2.04 1.21 27.22 1.19 1.16 1.14 1.28 589 9.79 2.26 2.11 26.64 1.30 1.42 1.28 1.20 594 10.85 2.57 2.05 30.82 1.22 1.01 1.18 1.46 594 8.61 2.10 3.20 27.27 1.10 1.17 1.08 1.06 523 8.79 2.36 3.10 25.80 1.09 1.10 1.08 1.09 525 8.05 2.26 3.23 27.60 1.04 1.19 0.96 0.97 530 8.17 2.38 3.39 28.23 1.14 1.11 1.07 1.23 520 8.23 2.18 2.91 37.87 1.21 1.20 1.11 1.33 522 6.74 2.20 3.04 26.97 1.13 0.81 1.25 1.33 525 7.24 2.04 3.13 29.33 1.07 1.05 1.03 1.14 525 6.14 1.56 1.41 21.50 1.38 1.33 1.36 1.44 625 8.67 2.01 1.41 25.11 1.33 1.24 1.35 1.40 625 8.48 2.01 1.31 24.07 1.19 1.31 1.24 1.03 606 8.79 1.89 1.73 20.84 1.28 1.30 1.36 1.18 625 7.30 1.89 1.95 20.50 1.20 1.12 1.18 1.31 625 8.77 1.91 1.39 24.78 1.10 1.10 1.19 1.03 629 9.92 1.79 1.25 30.02 1.18 1.22 1.17 1.15 638 7.73 1.81 2.40 30.00 1.08 1.04 1.12 1.09 550 7.05 2.22 3.16 22.59 1.10 1.10 1.14 1.10 532 5.39 1.78 2.46 32.34 1.06 1.07 1.08 1.04 528 03 SITE pH pH Tot.C Tot.N (Ca) (%) (%) 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 6.0 6.2 6.1 5.7 6.3 6.2 5.8 6.0 6.3 6.3 6.1 5.9 6.0 5.8 5.9 6.0 6.5 6.4 6.3 5.9 5.7 5.8 5.7 5.2 6.2 5.6 5.3 5.6 5.9 5.8 5.7 5.5 5.7 5.5 5.6 5.6 6.2 6.1 5.9 5.4 2.71 2.99 2.51 1.19 1.50 2.72 3.82 3.82 3.58 2.90 3.45 2.99 2.94 2.17 2.52 3.18 3.31 3.19 4.27 3.64 0.274 0.286 0.175 0.118 0.142 0.278 0.357 0.345 0.330 0.279 0.321 0.294 0.275 0.221 0.243 0.299 0.318 0.293 0.377 0.330 Av.P (ppm) 8.3 8.6 5.3 3.2 3.9 7.3 9.4 13.5 11.1 9.1 11.7 9.9 8.3 4.8 8.5 9.6 7.1 11.2 14.3 13.9 Na Ca Mg K (meq/100g)— CEC AVBD BD1 BD2 (g/cm3) BD3 ELEV (cm) 0.06 0.04 0.04 0.05 0.08 0.06 0.05 0.07 0.05 0.06 0.06 0.07 0.09 0.05 0.05 0.05 0.08 0.07 0.06 0.08 12.48 14.10 10.29 8.61 12.48 12.04 10.23 11.85 14.60 12.60 12.60 10.17 11.29 9.04 11.35 11.60 19.34 15.59 10.23 16.22 0.97 1.09 1.27 1.42 1.23 1.23 0.97 1.21 1.05 1.01 0.99 0.90 1.27 1.01 1.23 1.09 0.84 0.99 0.99 1.03 0.96 1.02 0.80 0.29 0.67 1.34 1.02 1.28 1.41 1.05 0.99 1.15 1.44 0.61 1.12 1.18 0.80 1.05 1.28 1.47 29.31 30.28 21.13 16.48 20.06 34.80 32.98 36.30 38.86 26.44 42.40 27.39 26.61 23.52 26.49 27.10 29.96 30.34 33.01 31.54 1.05 1.03 1.19 1.38 1.3 1.04 0.92 1 1.06 1.11 1.06 1.11 1.12 1.17 1.11 1.14 1.1 1.06 1.04 1.08 1.02 1.07 1.06 438 1.10 1.00 0.98 .438 1.23 1.14 1.20 500 1.48 1.31 1.36 520 1.26 1.31 1.34 540 1.05 0.96 1.10 512 0.97 0.85 0.95 462 1.07 0.84 1.09 450 1.01 1.02 1.08 425 1.09 1.09 1.16 425 1.03 1.03 1.12 450 1.15 1.06 1.10 462 0.98 1.17 1.21 483 1.21 1.17 1.15 512 1.07 1.06 1.21 525 1.08 1.15 1.18 511 1.16 1.00 1.15 481 1.09 1.04 1.16 455 1.02 1.03 1.08 438 1.07 1.06 1.11 475 ID SITE pH pH Tot.C Tot.N Av.P Na Ca Mg K CEC AVBD BD1 BD2 BD3 ELEV (Ca) (%) (%) (ppm) (meq/100g) (g/cm3) (cm) 601 5.3 5.0 2.21 0.227 5.5 0.28 3.22 3.17 0.83 21.56 1.13 602 5.4 5.0 1.86 0.187 2.8 0.10 1.20 2.38 0.35 17.31 1.38 603 5.3 4.9 1.69 0.177 1.9 0.13 1.51 3.33 0.19 23.89 1 604 5.2 5.0 1.97 0.207 3.4 0.15 1.75 2.47 0.26 22.77 1.2 605 5.2 4.9 2.28 0.222 6.4 0.16 1.83 2.34 0.32 20.34 1.18 606 5.2 4.9 1.85 0.210 3.7 0.18 2.11 2.53 0.35 20.94 1.21 607 5.2 4.9 2.62 0.396 3.3 0.14 1.63 3.21 0.54 28.15 1.19 608 5.2 4.9 2.07 0.213 1.9 0.21 9.98 3.62 0.45 25.74 1.2 609 5.1 4.8 2.11 0.219 4.2 0.13 8.79 1.81 0.35 20.73 1.25 610 5.4 5.1 2.54 0.265 4.5 0.17 11.79 2.30 0.35 22.91 1.12 611 5.2 4.9 2.28 0.230 4.7 0.21 10.17 2.55 0.61 26.64 1.23 612 5.2 4.9 2.90 0.285 5.8 0.27 10.85 2.22 0.80 25.81 1.17 613 5.0 4.7 1.85 0.186 6.8 0.13 6.55 1.67 0.64 15.51 1.34 614 5.0 4.8 1.53 0.179 7.6 0.15 6.05 1.54 0.38 14.42 1.39 615 5.0 4.9 2.13 0.220 15.4 0.26 7.80 2.20 0.96 17.19 1.39 616 5.4 5.0 1.74 0.203 9.5 0.13 8.97 2.08 0.48 18.11 1.23 617 5.6 5.2 1.77 0.111 6.6 0.15 6.25 1.64 1.04 13.96 1.42 618 5.5 4.9 1.27 0.131 3.2 0.09 5.75 1.52 0.52 12.80 1.23 619 5.5 5.2 2.59 0.184 8.4 0.30 7.44 2.18 1.61 19.01 1.39 620 5.2 4.9 1.71 0.166 4.4 0.11 8.31 1.91 0.36 17.41 1.16 1.13 1.07 1.19 459 1.19 1.39 1.57 390 1.03 1.06 0.92 416 1.12 1.20 1.28 425 1.13 1.15 1.25 497 1.24 1.14 1.25 475 1.16 1.23 1.18 405 1.10 1.17 1.32 396 1.21 1.24 1.30 567 1.16 1.10 1.09 518 1.16 1.27 1.24 483 1.18 1.15 1.18 451 1.26 1.39 1.37 600 1.34 1.41 1.43 600 1.27 1.51 1.40 515 1.26 1.29 1.14 479 1.27 1.51 1.47 600 1.23 1.23 1.23 625 1.45 1.35 1.38 561 1.21 1.24 1.03 525 H O APPENDIX 2-SOIL AND SITE G1 R1 NIR1 G2 R2 NIR2 NIR27 R2 ND 201 51 41 10 119 118 153 1.30 0.13 202 85 72 37 122 121 158 1.31 0.13 203 78 69 33 130 130 160 1.23 0.10 204 84 77 44 134 134 164 1.22 0.10 205 90 81 52 134 135 163 1.21 0.09 206 98 88 58 138 139 168 1.21 0.09 207 77 64 37 138 139 165 1.19 0.09 208 93 85 58 136 138 163 1.18 0.08 209 73 66 35 135 137 159 1.16 0.07 210 87 78 57 137 133 161 1.21 0.10 211 61 52 14 118 119 156 1.31 0.13 212 84 74 36 124 124 161 1.30 0.13 213 89 77 42 133 134 163 1.22 0.10 214 96 86 58 136 137 168 1.23 0.10 215 105 94 68 136 137 166 1.21 0.10 216 100 41 63 138 140 167 1.19 0.09 217 76 61 33 140 140 167 1.19 0.09 218 104 93 64 142 143 169 1.18 0.08 219 77 68 37 136 137 165 1.20 0.09 220 80 67 43 134 132 162 1.23 0.10 221 50 39 8 115 116 151 1.30 0.13 222 80 69 33 118 119 155 1.30 0.13 223 109 94 62 126 127 156 1.23 0.10 224 93 79 49 135 135 166 1.23 0.10 225 101 93 63 132 133 165 1.24 0.11 226 83 73 44 138 139 167 1.20 0.09 227 83 69 37 135 136 164 1.21 0.09 228 98 83 56 135 133 165 1.24 0.11 229 126 115 92 133 131 163 1.24 0.11 230 100 89 59 139 134 165 1.23 0.10 CORN PIXEL VALUE DATA SITE G1 R1 NIR1 G2 R2 NIR2 NIR2/ ND R2 301 94 81 55 136 131 172 1.31 0.14 302 104 89 60 133 129 170 1.32 0.14 303 107 91 62 132 129 169 1.31 0.13 304 102 87 58 126 124 165 1.33 0.14 305 104 88 59 130 124 167 1.35 0.15 306 108 95 66 132 128 169 1.32 0.14 307 110 98 67 119 115 161 1.40 0.17 308 78 63 37 122 106 168 1.58 0.23 309 91 77 52 118 104 165 1.59 0.23 310 58 38 14 124 109 168 1.54 0.21 311 35 11 0 128 108 166 1.54 0.21 312 21 1 0 133 118 171 1.45 0.18 313 53 38 18 133 118 173 1.47 0.19 314 72 56 28 135 125 173 1.38 0.16 315 102 90 67 125 122 165 1.35 0.15 316 102 92 69 127 124 168 1.35 0.15 317 99 87 62 121 123 164 1.33 0.14 318 101 91 70 129 124 167 1.35 0.15 319 109 98 77 131 130 169 1.30 0.13 320 104 94 75 114 112 157 1.40 0.17 321 101 92 76 129 125 165 1.32 0.14 322 62 41 14 144 131 174 1.33 0.14 323 41 25 4 152 140 178 1.27 0.12 324 44 29 10 157 144 182 1.26 0.12 H Ul o r— O CM CM CM o CD o cn oo en CO O o o O O o o o O d d d d d d d d d d d d d d d d d d d d CO CO CM O CO cn 00 in CD cn m CM m CM CM CM CM CM CM CM CM CM CM CM CM T—' T—• CO CO co O CD CD Is* CO m 00 CM T— in CD in cn CO co CD CO CD CO CD co CD CD CD CO co in CO m o CO CM r-~ co CD CM CM o CD CO CO co CO CO co co CO CO co CO CO •«* CO CO to oo CD •<*• co O cn T— m CO CM O co CO O rr co •d- co co CO in co co cn CO oo r- CM o co o T  oo CM co oo CO CO in CD Tj- LO -<* m CO CO CD in CO cn cn co CO o cn o oo m r- cn cn CD 00 o o co oo o r- T— CD o cn cn oo o 00 CD i — *~ T " T ~ 1 — T ~ Ol CD CM cn in CM co CD T— r~~ 00 CD cn LO CM 1™ O co CD o T— CM CO CD o T— CM CM T— T— o o cn T- o T— T— T- T- T ~ T— T— T ~ T— *~ T— T_ CM CO CO co 00 CD o T— CM CO in CO CO O) o o O O o o o o o O 1— 1— T— T— T— CM CO CO CO CO CO CO CO CO CO CO CO CO CD CO CO CO CD CO CO CO CO CO CM CM co CM CM CM CM 1— CO CM o CM O o o o O O o o o O o O O o o O O o O O o o O CD O co m CD 00 oo f» cn N- CO CM co CM in CM co CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM CM •t CD CO CD in r~~ in CD m cn CO m CO co co m in in in m CD CO co CD CD in CO m co CM m o CM CO i — CO 00 oo CM o CO CM CM co CO CM CM CM CM CM CO CO CO CM co CO co CM CO co CO CM h- CM CO m CO m CO CO in CM o CM cn rr CO CM cn LO CNJ CM CO CO CM CM CM CM CM CO CO CO CO co CM CO CM CO CM CD co 00 CM o cn o cn cn h- cn CO cn CM CO CO _^ T—' CM CM co * — CM CM co CM CM CO CO T— CM CO CO CD 00 O 00 cn CM CO m o T— CM CO CO co CO co in in CD in in CD CO m LO CD CM oo CM CO CO 00 cn CD f- oo co CD o o CO oo 00 co in in m CD CD CD m CD CM co •<* m CO 00 cn o CM co m CO co CD o o O o o o o o o o T— i — i — , — , — i — T - CM Ti- -<* APPENDIX 3-CORN SITE CWT CCP CP CCA CTDN CDE (g) (%) (%) (%) (%) (MJ/kg) 201 135 7.06 0.270 0.005 71.46 18 202 124 7.50 0.267 0.010 71.52 18 203 133 8.10 0.282 0.000 72.30 18 204 114 7.23 0.213 0.000 72.06 18 205 158 7.91 0.253 0.002 71.73 18 206 119 8.21 0.279 0.003 71.49 18 207 115 7.56 0.259 •0.003 71.96 18 208 115 7.61 0.253 0.000 71.64 18 209 142 7.88 0.261 0.000 71.29 17 210 184 7.69 0.258 0.000 70.84 17 211 110 7.50 0.257 0.005 71.76 18 212 79 8.79 0.307 0.000 71.50 18 213 99 8.26 0.268 0.005 72.50 18 214 126 8.26 0.258 0.000 72.90 18 215 133 8.38 0.263 0.003 71.32 17 216 123 8.40 0.299 0.000 71.87 18 217 113 7.79 0.318 0.000 72.35 18 218 146 9.51 0.317 0.000 72.54 18 219 122 8.04 0.304 0.008 71.84 18 220 153 9.29 0.338 0.003 72.42 18 221 128 7.88 0.257 0.000 ' 71.32 17 222 73 9.14 0.282 0.003 70.86 17 223 109 8.43 0.292 0.008 72.62 18 224 130 8.09 0.279 0.003 73.40 19 225 117 8.45 0.273 0.003 72.32 18 226 106 8.21 0.282 0.000 72.70 18 227 119 8.91 0.310 0.005 71.53 18 228 141 7.51 0.224 0.003 72.24 18 229 150 8.23 0.283 0.005 72.64 18 230 156 8.78 0.277 0.013 71.92 18 CCPG CPG CCAG CTDNG CDEG (MJ/cob) 9.53 0.365 0.007 96.47 2.4 9.30 0.331 0.012 88.68 2.2 10.77 0.375 0.000 96.16 2.4 8.24 0.243 0.000 82.14 2.0 12.50 0.400 0.004 113.34 2.8 9.77 0.332 0.003 85.08 2.1 8.70 0.298 0.003 82.75 2.0 8.75 0.291 0.000 82.38 2.0 11.18 0.371 0.000 101.23 2.5 14.16 0.475 0.000 130.35 3.2 8.25 0.283 0.005 78.93 1.9 6.95 0.243 0.000 56.48 1.4 8.17 0.265 0.005 71.78 1.8 10.40 0.325 0.000 91.85 2.3 11.14 0.350 0.003 94.85 2.3 10.33 0.368 0.000 88.40 2.2 8.81 0.359 0.000 81.75 2.0 13.88 0.463 0.000 105.90 2.6 9.81 0.371 0.009 87.65 2.2 14.22 0.517 0.004 110.80 2.8 10.06 0.328 0.000 91.03 2.2 6.70 0.207 0.002 51.93 1.3 9.19 0.318 0.008 79.16 2.0 10.52 0.363 0.003 95.42 2.4 9.89 0.319 0.003 84.61 .2.1 8.70 0.299 0.000 77.06 1.9 10.61 0.369 0.006 85.12 2.1 10.59 0.316 0.004 101.86 2.5 12.35 0.425 0.008 108.97 2.7 13.69 0.432 0.020 112.20 2.8 SITE CWT CCP CP (g) (%) (%) CCA (%) 301 68 10.94 0.299 0.010 302 78 8.99 0.281 0.010 303 88 9.30 0.299 0.010 304 74 9.99 0.271 0.010 305 103 8.20 0.282 0.010 306 73 10.03 0.309 0.013 307 100 9.66 0.299 0.000 308 68 12.13 0.386 0.010 309 51 13.36 0.354 0.013 310 46 14.23 0.422 0.015 311 67 10.78 0.360 0.018 312 37 12.61 0.415 0.023 313 49 11.31 0.374 0.015 314 116 10.19 0.315 0.015 315 74 9.63 0.312 0.020 316 102 10.16 0.254 0.010 317 117 9.46 0.290 0.010 318 32 9.28 0.287 0.015 319 108 9.27 0.318 0.013 320 124 9.26 0.269 0.005 321 83 10.13 0.288 0.013 322 119 8.95 0.336 0.010 323 166 8.38 0.315 0.007 324 159 7.43 0.292 0.007 CTDN CDE CCPG CPG CCAG CTDNG CDEG (%) (MJ/ (g/ (g/ (g/ (g/ (MJ/ kg) cob) cob) cob) cob) cob) 67.32 15 7.44 0.203 0.007 45.78 1.0 70.02 17 7.02 0.219 0.008 54.62 1.3 66.23 15 8.18 0.263 0.009 58.28 1.3 67.34 15 7.40 0.201 0.007 49.83 1.1 70.40 17 8.45 0.290 0.010 72.51 1.7 68.98 16 7.32 0.226 0.009 . 50.35 1.2 68.63 16 9.66 0.299 0.000 68.63 1.6 64.04 14 8.25 0.262 0.007 43.54 0.9 65.63 14 6.81 0.181 0.006 33.47 0.7 64.33 14 6.54 0.194 0.007 29.59 0.6 67.21 15 7.22 0.241 0.012 45.03 1.0 64.15 14 4.66 0.154 0.008 23.74 0.5 63.67 13 5.54 0.183 0.007 31.20 0.7 67.72 16 11.82 0.365 0.017 78.55 1.8 67.48 15 7.13 0.231 0.015 49.93 1.1 68.68 16 10.37 0.259 0.010 70.05 1.6 68.32 16 11.06 0.339 0.012 79.94 1.9 69.07 16 2.97 0.092 0.005 22.10 0.5 70.16 17 10.01 0.343 0.014 75.78 1.8 67.75 16 11.49 0.334 0.006 84.01 1.9 67.79 16 8.41 0.239 0.010 56.27 1.3 67.04 15 10.65 0.400 0.012 79.78 1.8 67.93 16 13.91 0.523 0.012 112.76 2.6 68.14 16 11.82 0.464 0.012 108.34 2.5 SITE CWT CCP CP CCA CTDN CDE CCPG CPG CCAG CTDNG CDEG (g) (%) (%) (%) (o/o) (MJ/ (g/ (g/ (g/ (g/ (MJ/ kg) cob) cob) cob) cob) cob) 401 80 8.81 0.258 0.015 69.48 402 128 8.46 0.266 0.010 67.79 403 140 8.81 0.260 0.000 69.59 404 131 8.63 0.227 0.005 70.91 405 80 8.63 0.280 0.003 71.20 406 64 9.84 0.295 0.003 72.08 407 63 8.46 0.272 0.008 71.51 408 115 8.93 0.279 0.008 71.44 409 58 8.76 0.285 0.005 71.20 410 95 8.08 0.294 0.008 71.05 411 78 8.78 0.276 0.013 70.81 412 82 8.44 0.282 0.010 72.17 413 120 8.27 0.215 0.008 71.16 414 83 8.28 0.246 0.005 71.22 415 95 8.63 0.234 0.010 70.46 416 86 8.78 0.276 0.008 71.18 417 62 8.78 0.314 0.008 71.17 418 146 8.61 0.331 0.008 70.65 419 118 8.44 0.290 0.005 70.57 420 76 8.98 0.319 0.015 70.61 16 7.05 0.206 0.012 55.58 1.3 16 10.83 0.340 0.013 86.78 2.0 17 12.33 0.364 0.000 97.42 2.3 17 11.31 0.297 0.007 92.89 2.3 17 6.91 0.224 0.002 56.96 1.4 18 6.30 0.189 0.002 46.13 1.1 18 5.33 0.171 0.005 45.05 1.1 18 10.27 0.321 0.009 82.16 2.0 17 5.08 0.165 0.003 41.29 1.0 17 7.68 0.279 0.007 67.50 1.6 17 6.84 0.215 0.010 55.23 1.3 18 6.92 0.231 0.008 59.18 1.5 17 9.92 0.258 0.009 85.39 2.1 17 6.87 0.204 0.004 59.11 1.4 17 8.19 0.222 0.010 66.94 1.6 17 7.55 0.237 0.006 61.22 1.5 17 5.44 0.195 0.005 44.13 1.1 17 12.57 0.483 0.011 103.15 2.5 17 9.96 0.342 0.006 83.28 2.0 17 6.83 0.242 0.011 53.66 1.3 SITE CWT CCP CP CCA (g) (%) (%) (%) 601 145 7.23 0.217 0.015 602 93 7.58 0.337 0.010 603 72 7.94 0.265 0.010 604 111 7.90 0.256 0.008 605 85 11.42 0.298 0.010 606 98 10.73 0.313 0.013 607 87 11.96 0.352 0.013 608 96 11.09 0.310 0.010 609 132 8.63 0.277 0.013 610 140 7.96 0.257 0.004 611 91 10.04 0.278 0.015 612 116 10.76 0.298 0.015 613 44 11.12 0.334 0.023 614 66 12.13 0.406 0.015 615 107 10.03 0.313 0.015 616 92 9.87 0.273 0.015 617 39 12.17 0.348 0.023 618 68 12.54 0.406 0.018 619 93 10.78 0.370 0.013 620 16 10.03 0.313 0.015 CTDN CDE CCPG CPG CCAG CTDNG CDEG (%) (MJ/ (g/ (gl (gl (gl (MJ/ kg) cob) cob) cob) cob) cob) 71.07 17 10.48 0.315 0.022 103.05 2.5 72.04 18 7.05 0.313 0.009 66.99 1.7 72.25 18 5.72 0.191 0.007 52.02 1.3 71.46 18 8.77 0.284 0.008 79.32 1.9 71.68 18 9.71 0.253 0.009 60.93 1.5 70.67 17 10.51 0.307 0.012 69.26 1.7 69.19 16 10.41 0.306 0.011 60.20 1.4 70.37 17 10.64 0.298 0.010 67.56 1.6 71.37 17 11.39 0.366 0.017 94.20 2.3 70.54 17 11.15 0.360 0.006 98.75 2.4 72.18 18 9.14 0.253 0.014 65.69 1.6 70.60 17 12.48 0.346 0.017 81.90 2.0 71.85 18 4.89 0.147 0.010 31.62 0.8 72.24 18 8.00 0.268 0.010 47.68 1.2 70.92 17 10.73 0.335 0.016 75.88 1.8 72.31 18 9.08 0.251 0.014 66.53 1.7 70.96 17 4.75 0.136 0.009 27.67 0.7 71.21 17 8.53 0.276 0.012 48.42 1.2 71.93 18 10.03 0.344 0.012 66.90 1.7 70.92 17 1.61 0.050 0.002 11.35 0.3 SITE SWT S C P (g) (%) SP SCA STDN S D E S C P G SPG SCAG STDNGSDEG (%) (%) (%) (MJ/ (g/ (g/ (g/ (g/ (MJ/ . stk) stk) stk) stk) stk) stk) 201 129 8.49 0.158 202 130 8.84 0.125 203 140 9.34 0.169 204 94 9.78 0.168 205 134 8.69 0.135 206 135 9.19 0.144 207 109 9.57 0.129 208 106 8.16 0.117 209 132 9.63 0.134 210 168 7.31 0.110 211 110 11.43 0.121 212 101 7.15 0.129 213 124 8.76 0.112 214 147 9.04 0.123 215 133 10.40 0.174 216 127 9.81 0.197 217 128 8.00 0.186 218 146 9.48 0.245 219 124 10.06 0.148 220 156 9.89 0.191 .221 143 10.27 0.137 222 31 8.81 0.157 223 116 9.02 0.135 224 122 9.48 0.130 225 122 10.95 0.205 226 106 7.55 0.160 227 123 9.38 0.205 228 120 8.52 0.135 229 124 7.94 0.131 230 141 7.08 0.126 0.438 56.3 10 10.96 0.293 57.7 10 11.50 0.328 57.0 10 13.08 0.403 58.3 11 9.19 0.360 58.9 11 11.65 0.385 59.4 11 12.41 0.415 57.9 10 10.43 0.250 58.0 10 8.63 0.340 57.8 • 10 12.71 0.243 57.5 10 12.29 0.308 55.5 9 12.57 0.253 57.0 10 7.22 0.469 56.5 10 10.86 0.335 56.6 10 13.28 0.430 57.8 10 13.83 0.335 57.2 10 12.45 0.260 56.0 9 10.24 0.500 54.3 9 13.81 0.398 57.9 10 12.47 0.370 60.6 12 15.42 0.370 56.9 10 14.68 0.268 58.0 10 2.73 0.380 58.5 11 10.43 0.400 57.8 10 11.55 0.338 57.9 10 13.30 0.350 59.1 11 8.04 0.310 58.5 11 11.49 0.365 56.5 10 10.22 0.274 58.4 11 9.89 0.270 51.5 7 9.98 0.204 0.564 72.6 1.2 0.163 0.380 75.0 1.3 0.237 0.459 79.8 1.4 0.158 0.378 54.8 1.0 0.181 0.482 79.0 1.5 0.194 0.520 80.1 1.5 0.141 0.452 63.1 1.1 0.124 0.264 61.3 1.1 0.177 0.449 76.3 1.4 0.185 0.407 96.7 1.7 0.133 0.338 61.0 1.0 0.130 0.255 57.5 1.0 0.139 0.581 70.1 1.2 0.181 0.492 83.1 1.4 0.231 0.572 76.9 1.4 0.250 0.425 72.5 1.3 0.238 0.333 71.7 1.2 0.357 0.728 79.1 1.2 0.184 0.493 71.8 1.3 0.298 0.577 94.5 1.8 0.196 0.529 81.4 1.4 0.049 0.083 18.0 0.3 0.156 0.439 67.6 1.2 0.158 0.487 70.5 1.3 0.249 0.410 70.4 1.3 0.170 0.373 62.9 1.2 0.251 0.380 71.7 1.3 0.162 0.438 67.7 1.2 0.163 0.341 72.7 1.3 0.178 0.381 72.7 1.0 SITE SWT SCP SP SCA STDN S D E S C P G SPG SCAG STDNGSDEG (g) (%) '(%) (%) (%) (MJ/ (g/ (gy (g/ (g/ (MJ/ stk) stk) stk) stk) stk) stk) 301 134 7.58 0.174 0.218 62.0 302 81 10.51 0.170 0.253 57.2 303 110 10.20 0.174 0.365 59.3 304 89 8.79 0.140 0.410 58.8 305 87 9.49 0.209 0.248 59.1 306 100 9.48 0.159 0.418 57.7 307 124 8.96 0.182 0.425 58.6 308 118 9.66 0.194 0.240 59.4 309 166 7.48 0.169 0.203 60.5 310 138 .7.01 0.195 0.188 60.9 311 120 4.92 0.195 0.193 62.2 312 107 4.72 0.137 0.149 61.4 313 132 6.99 0.208 0.180 61.5 314 118 6.86 0.116 0.384 58.6 315 108 8.24 0.150 0.323 60.5 316 108 6.91 0.112 0.380 58.3 317 69 7.51 0.141 0.315 57.7 318 120 7.74 0.165 0.183 59.8 319 133 8.43 0.259 0.260 59.5 320 117 8.37 0.182 0.285 56.8 321 114 9.76 0.160 0.453 56.4 322 108 7.14 0.141 0.330 58.8 323 102 6.80 0.210 0.318 53.2 324 138 4.41 0.151 0.175 59.2 13 •10.15 0.233 0.291 83.1 1.7 10 8.49 0.137 0.204 46.2 0.8 11 11.20 0.191 0.401 65.1 1.2 11 7.79 0.124 0.363 52.1 1.0 11 8.25 0.182 0.215 51.3 1.0 10 9.52 0.160 0.419 58.0 1.0 11 11.09 0.225 0.526 72.6 1.3 11 11.39 0.229 0.283 70.1 1.3 12 12.42 0.281 0.337 100.4 2.0 12 9.68 0.269 0.260 84.0 1.7 13 5.90 0.234 0.231 74.7 1.5 12 .5.05 0.147 0.159 65.7 1.3 12 9.23 0.275 0.238 81.1 1.6 11 8.10 0.137 0.453 69.2 1.3 12 8.90 0.162 0.348 65.3 1.3 11 7.47 0.12l' 0.410 63.0 1.1 10 5.18 0.097 0.217 39.8 0.7 11 9.29 0.198 0.219 71.8 1.4 11 11.24 0.345 0.347 79.4 1.5 10 9.79 0.213 0.333 66.5 1.2 10 11.12 0.182 0.516 64.3 1.1 11 7.71 0.152 0.356 63.5 1.2 8 6.94 0.214 0.324 54.2 0.8 11 6.08 0.208 0.242 81.6 1.5 SITE SWT SCP (g) (%) SP (%) SCA STDN SDE SCPG SPG SCAG STDNGSDEG (%) (%) (MJ/ (gl (g/ (g/ (g/ (MJ/ stk) stk) stk) stk) stk) stk) 401 78 6.87 0.129 402 68 6.44 0.116 403 66 5.24 0.080 404 79 7.37 0.113 405 75 7.81 0.150 406 49 8.34 0.153 407 38 7.98 0.129 408 61 6.99 0.130 409 39 8.94 0.136 410 80 7.44 0.125 411 73 8.04 0.138 412 56 8.13 0.124 413 8.7 7.08 0.107 414 54 6.72 0.095 415 61 6.36 0.083 416 75 7.60 0.110 417 54 9.29 0.156 418 75 6.07 0.129 419 80 7.86 0.138 420 81 6.61 0.126 0.337 56.4 10 5.36 0.373 56.0 9 4.38 0.350 52.9 8 3.46 0.355 55.4 9 5.82 0.285 49.6 6 5.86 0.327 50.0 6 4.09 0.243 55.0 9 3.03 0.263 47.8 5 4.27 0.275 56.5 10 3.49 0.272 55.1 9 5.96 0.245 56.4 10 5.84 0.205 47.4 5 •4.52 0.347 47.9 5 6.13 0.366 49.6 6 3.60 0.309 57.7 10 3.89 0.285 54.0 8 5.71 0.315 55.9 9 5.01 0.335 45.8 4 4.54 0.330 55.6 9 6.30 0.295 55.1 9 5.34 0.101 0.263 44.0 0.7 0.079 0.253 .38.1 0.6 0.053 0.231 34.9 0.5 0.089 0.280 43.8 0.7 0.113 0.213 37.2 0.5 0.075 0.160 24.5 0.3 0.049 0.092 20.9 0.3 0.079 0.160 29.1 0.3 0.053 0.107 22.0 0.4 0.100 0.218 44.1 0.7 0.100 0.178 41.0 0.7 0.069 0.114 26.3 0.3 0.093 0.300 41.5 0.4 0.051 0.196 26.6 0.3 0.051 0.189 35.3 0.6 0.083 0.214 40.6 0.6 0.084 0.170 30.1 0.5 0.096 0.251 34.3 0.3 0.111 0.265 44.6 0.7 0.102 0.238 44.5 0.7 SITE SWT SCP SP SCA (g) (%) (%) (%) 601 109 8.09 602 88 5.76 603 53 7.73 604 99 6.77 605 124 6.74 606 91 8.13 607 77 9.13 608 117 6.25 609 121 8.52 610 148 9.55 611 110 7.20 612 127 7.75 613 67 8.89 614 89 11.88 615 124 10.39 616 113 9.73 617 126 7.56 618 130 10.01 619 99 9.14 620 124 9.08 0.108 0.585 0.158 0.298 0.116 0.333 0.111 0.368 0.090 0.410 0.116 0.445 0.173 0.263 0.137 0.270 0.125 0.579 0.199 0.562 0.078 0.388 0.142 0.288 0.107 0.413 0.159 0.453 0.172 0.252 0.162 0.438 0.174 0.213 0.179 0.415 0.108 0.440 0.140 0.304 STDN SDE SCPG SPG SCAG STDNGSDEG (%) (MJ/ (gl (gl (gl (gl (MJ/ stk) stk) stk) stk) stk) stk) 56.6 10 8.82 0.118 0.638 61.7 1.1 62.4 13 5.07 0.139 0.262 54.9 1.1 57.1 10 4.10 0.061 0.176 30.3 0.5 55.7 9 6.70 0.110 0.364 55.2 0.9 60.2 12 8.35 0.112 0.508 74.6 1.4 59.8 11 7.40 0.106 0.405 54.4 1.0 60.9 12 7.03 0.133 0.202 46.9 0.9 58.0 10 7.31 0.160 0.316 67.9 1.2 57.0 10 10.31 0.151 0.701 69.0 1.2 58.5 11 14.13 0.295 0.832 86.6 1.6 60.1 12 7.92 0.086 0.426 66.2 1.3 59.3 11 9.84 0.180 0.365 75.2 1.4 60.2 12 5.96 0.072 0.277 40.3 0.8 55.4 9 10.57 0.142 0.403 49.3 0.8 64.0 14 12.89 0.213 0.313 79.3 1.7 57.7 10 10.99 0.183 0.495 65.2 1.2 62.8 13 9.52 0.219 0.268 79.1 1.6 60.2 12 13.02 0.233 0.540 78.3 1.5 59.0 11 9.05 0.107 0.436 58.4 1.1 64.3 14 11.26 0.174 0.378 79.8 1.7 SITE SWT SCP SP SCA STDN SDE SCPG SPG SCAG STDNGSDEG (g) (%) (%) (%) (%) (MJ/ (g/ (gj (gj (gj (MJ/ stk) stk) stk) stk) stk) stk) 601 109 8.09 0.108 602 88 5.76 0.158 603 53 7.73 0.116 604 99 6.77 0.111 605 124 .6.74 0.090 606 91 8.13 0.116 607 77 9.13 0.173 608 117 6.25 0.137 609 121 8.52 0.125 610 148 9.55 0.199 611 110 7.20 0.078 612 127 7.75 0.142 613 67 8.89 0.107 614 89 11.88 0.159 615 124 10.39 0.172 616 113 9.73 0.162 617 126 7.56 0.174 618 130 10.01 0.179 619 99 9.14 0.108 620 124 9.08 0.140 0.585 56.6 10 8.82 0.298 62.4 13 5.07 0.333 57.1 10 4.10 0.368 55.7 9 6.70 0.410 60.2 12 8.35 0.445 59.8 11 7.40 0.263 60.9 12 7.03 0.270 58.0 10 7.31 0.579 57.0 10 10.31 0.562 58.5 11 14.13 0.388 60.1 12 7.92 0.288 59.3 11 9.84 0.413 60.2 12 5.96 0.453 55.4 9 10.57 0.252 64.0 14 12.89 0.438 57.7 10 10.99 0.213 62.8 13 9.52 0.415 60.2 12 13.02 0.440 59.0 11 9.05 0.304 64.3 14 11.26 0.118 0.638 . 61.7 1.1 0.139 0.262 54.9 1.1 0.061 0.176 30.3 0.5 0.110 0.364 55.2 0.9 0.112 0.508 74.6 1.4 0.106 0.405 54.4 1.0 0.133 0.202 46.9 0.9 0.160 0.316 67.9 1.2 0.151 0.701 69.0 1.2 0.295 0.832 86.6 1.6 0.086 0.426 66.2 1.3 0.180 0.365 75.2 1.4 0.072 0.277 40.3 0.8 0.142 0.403 49.3 0.8 0.213 0.313 79.3 1.7 0.183 0.495 65.2 1.2 0.219 0.268 79.1 1.6 0.233 0.540 78.3 1.5 0.107 0.436 58.4 1.1 0.174 0.378 79.8 1.7 SITE TWT TCP TP TCA (g) .(%) (%) (%) 301 202 8.71 0.216 0.148 302 159 9.77 0.225 0.133 303 198 9.80 0.230 0.207 304 163 9.34 0.200 0.228 305 190 8.79 0.249 0.119 306 173 9.71 0.222 0.247 307 224 9.27 0.234 0.235 308 186 10.56 0.264 0.156 309 217 8.86 0.212 0.158 310 184 8.82 0.252 0.145 311 187 7.02 0.254 0.130 312 144 6.75 0.208 0.116 313 181 8.16 0.253 0.135 314 234 8.51 0.215 0.201 315 182 8.81 0.216 0.200 316 210 8.49 0.181 0.200 317 186 8.74 0.235 0.123 318 152 8.06 0.191 0.147 319 241 8.80 0.285 0.149 320 241 8.83 0.227 0.141 321 197 9.91 0.214 0.267 322 227 8.09 0.243 0.162 323 268 7.78 0.275 0.125 324 297 6.03 0.226 0.085 TDN TCPG TPG TCAG TDNG TDEG (%) (gl (gl (gl (gl (MJ/ pit) pit) pit) pit) pit) 63.8 17.59 63.5 15.51 62.4 19.38 62.7 15.19 65.2 16.69 62.5 16.85 63.1 20.76 61.1 19.64 61.7 19.23 61.8 16.22 64.0 13.13 62.1 9.71 62.1 14.77 63.1 19.92 63.3 16.03 63.4 17.83 64.4 16.25 61.8 12.25 64.3 21.25 62.4 21.28 61.2 19.53 63.1 18.36 62.3 20.85 64.0 17.90 0.437 0.298 0.357 0.212 0.454 0.410 0.325 0.371 0.472 0.225 0.385 0.429 0.524 0.526 0.491 0.290 0.461 0.343 0.463 0.267 0.475 0.243 0.300 0.167 0.458 0.245 0.502 0.470 0.393 0.363 0.380 0.421 0.437 0.229 0.290 0.224 0.689 0.360 0.547 0.340 0.421 0.527 0.552 0.368 0.737 0.336 0.673 0.253 128.9 2.7 100.8 2.1 123.4 2.5 102.0 2.1 123.9 2.7 108.3 2.2 141.2 2.9 113.7 2.2 133.9 2.7 113.6 2.3 119.7 2.5 89.4 1.8 112.3 2.3 147.7 3.1 115.3 2.4 133.1 2.8 119.7 2.6 93.9 1.9 155.2 3.3 150.5 3.1 120.5 2.4 143.3 3.0 167.0 3.4 190.0 4.0 SITE TWT TCP TP TCA (g) (o/o) (%) (o/o) 401 158 7.85 0.194 0.174 402 196 7.76 0.214 0.136 403 206 7.66 0.202 0.112 404 210 8.16 0.184 0.137 405 155 8.24 0.217 0.139 406 113 9.19 0.233 0.143 407 101 8.28 0.218 0.096 408 170 8.55 0.235 0.099 409 97 8.83 0.225 0.114 410 175 7.79 0.217 0.129 411 151 8.42 0.209 0.125 412 138 8.31 0.218 0.089 413 207 7.77 0.170 0.150 414 137 7.66 0.187 0.147 415 156 7.74 0.175 0.127 416 161 8.23 0.199 0.137 417 116 9.02 0.241 0.151 418 221 7.75 0.263 0.118 419 198 8.21 0.228 0.137 420 157 7.76 0.220 0.159 TDN T C P G TPG TCAG TDNG TDEG (%) (g/ (gl (gl (gj (MJ/ pit) pit) pit) pit) pit) 63.0 12.40 63.7 15.21 64.2 15.79 65.1 17.13 60.8 12.76 62.5 10.39 65.3 8.36 63.2 14.54 65.3 8.57 63.8 13.63 63.8 12.69 62.2 11.44 61.4 16.05 62.7 10.47 65.5 12.08 63.2 13.26 64.1 10.45 62.2 17.11 64.5 16.27 62.6 12.16 0.307 0.275 0.419 0.266 0.417 0.231 0.387 0.287 0.337 0.215 0.264 0.162 0.220 0.097 0.400 0.169 0.218 0.110 0.379 0.225 0.316 0.188 0.300 0.122 0.351 0.309 0.255 0.201 0.273 0.199 0.320 0.220 0.279 0.174 0.580 0.261 0.453 0.271 0.344 0.250 99.6 2.1 124.9 2.6 132.3 2.8 136.7 3.0 94.2 1.8 70.6 1.4 65.9 1.4 111.3 2.3 63.3 1.4 111.6 2.4 96.2 2.0 85.5 1.7 126.9 2.5 85.7 1.8 102.2 2.2 101.8 2.1 74.3 1.6 137.4 2.8 127.9 2.8 98.1 2.0 SITE TWT T C P TP (g) (%) (%) TCA (%) 601 254 7.60 0.170 0.260 602 181 6.70 0.250 0.150 603 125 7.85 0.202 0.147 604 210 7.37 0.188 0.177 605 209 8.64 0.175 0.247 606 189 9.48 0.218 0.221 607 164 10.63 0.268 0.130 608 213 8.43 0.215 0.153 609 253 8.58 0.204 0.284 610 288 8.78 0.227 0.291 611 201 8.49 0.168 0.219 612 243 9.19 0.216 0.157 613 111 9.78 0.197 0.258 614 155 11.98 0.264 0.266 615 231 10.23 0.237 0.142 616 205 9.79 0.212 0.248 617 165 8.65 0.215 0.168 618 198 10.88 0.257 0.278 619 192 9.94 0.235 0.233 620 140 9.19 0.160 0.271 TDN T C P G TPG TCAG TDNG TDEG (%) (g/ (g/ (g! (gj (MJ/ pit) pit) pit) pit) pit) 64.9 19.29 67.4 12.12 65.8 9.81 64.0 15.47 64.9 18.06 65.5 17.91 65.3 17.43 63.6 17.96 64.5 21.70 64.4 25.28 65.6 17.06 64.7 22.32 64.8 10.85 62.6 18.57 67.2 23.62 64.3 20.07 64.7 14.27 64.0 21.54 65.3 19.08 65.1 12.87 0.432 0.659 0.452 0.271 0.252 0.183 0.394 0.372 0.365 0.517 0.412 0.417 0.439 0.213 0.458 0.325 0.517 0.717 0.654 0.837 0.339 0.440 0.526 0.383 0.219 0.287 0.409 0.413 0.548 0.329 0.434 0.509 0.355 0.277 0.509 0.551 0.451 0.447 0.224 0.380 164.7 3.6 121.9 2.8 82.3 1.8 134.5 2.9 135.5 2.9 123.7 2.7 107.1 2.3 135.5 2.9 163.2 3.5 185.3 4.0 131.9 2.9 157.1 3.4 71.9 1.6 97.0 2.0 155.2 3.5 131.7 2.8 106.8 2.3 126.7 2.7 125.3 2.7 91.1 2.0 APPENDIX 4 - SOIL MOISTURE DATA SITE 2NP1 (cm3 H20/cm3 soil) 2NP2 2NP3 2NP4 1 (dpth) 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 201 0.42 0.41 0.40 0.39 0.25 0.37 0.44 0.45 0.44 0.28 0.47 0.36 0.37 0.37 0.20 0.34 0.32 0.33 202 0.43 0.44 0.43 0.44 0.43 0.37 0.48 0.48 0.49 0.49 0.48 0.40 0.39 0.42 0.42 0.36 0.39 0.36 203 0.40 0.42 0.41 0.35 0.25 0.34 0.47 0.46 0.38 0.28 0.49 0.40 0.38 0.27 0.20 0.37 0.36 0.32 204 0.36 0.39 0.37 0.34 0.32 0.34 0.44 0.40 0.40 0.36 0.45 0.37 0.31 0.31 0.28 0.31 0.34 0.26 205 0.31 0.36 0.28 0.28 0.32 0.25 0.39 0.30 0.28 0.35 0.35 0.31 0.21 0.20 0.25 0.21 0.27 0.16 206 0.43 0.40 0.42 0.41 0.32 0.37 . 0.45 0.48 0.47 0.35 0.47 0.36 0.39 0.38 0.25 0.35 0.33 0.35 207 0.48 0.47 0.44 0.43 0.44 0.40 0.50 0.50 0.50 0.51 0.49 0.42 0.42 0.42 0.44 0.37 0.42 0.41 208 0.29 0.24 0.23 0.14 0.24 0.22 0.26 0.25 0.18 0.28 0.31 0.17 0.15 0.10 .0.19 0.37 0.27 0.27 209 0.40 0.42 0.41 0.41 0.41 0.35 0.46 0.46 0.46 0.46 0.46 0.38 0.38 0.38 0.35 0.53 0.44 0.44 210 0.45 0.37 0.28 0.22 0.37 0.46 0.46 0.36 0.44 0.47 0.56 0.36 0.21 0.27 0.36 0.49 0.40 0.31 211 0.48 0.43 0.42 0.42 0.43 0.41 0.48 0.47 0.48 0.49 0.53 0.40 0.39 0.42 0.42 0.14 0.37 0.35 212 0.43 0.42 0.41 0.32 0.33 0.39 0.47 0.48 0.35 0.38 0.49 0.40 0.40 0.25 0.29 0.38 0.38 0.36 213 0.46 0.44 0.42 0.35 0.17 0.42 0.49 0.48 0.36 0.22 0.52 0.41 0.40 0.26 0.14 0.39 0.37 0.34 214 0.42 0.40 0.40 0.42 0.31 0.35 0.45 0.46 0.48 0.35 0.44 0.36 0.36 0.39 0.27 0.30.0.32 0.33 215 0.48 0.43 0.42 0.41 0.43 0.44 0.47 0.48 0.47 0.49 0.53 0.39 0.39 0.40 0.41 0.40 0.37 0.36 216 0.43 0.43 0.42 0.42 0.41 0.37 0.47 0.47 0.50 0.47 0.47 0.39 0.40 0.42 0.39 0.37 0.37 0.37 217 0.54 0.48 0.45 0.45 0.45 0.47 0.52 0.50 0.51 0.53 0.55 0.43 0.43 0.44 0.46 0.41 0.41 0.41 218 0.31 0.28 0.32 0.28 0.11 0.29 0.32 0.33 0.30 0.15 0.38 0.23 0.22 0.21 0.08 0.29 0.23 0.20 219 0.42 0.41 0.23 0.12 0.12 0.39 0.44 0.25 0.16 0.17 0.49 0.36 0.17 0.10 0.09 0.42 0.41 0.23 220 0.40 0.42 0.35 0.25 0.15 0.41 0.50 0.36 0.33 0.22 0.48 0.40 0.26 0.25 0.14 0.44 0.44 0.29 221 0.47 0.43 0.43 0.41 0.41 0.29 0.45 0.48 0.48 0.46 0.50 0.37 0.39 0.40 0.39 0.36 0.34 0.35 222 0.40 0.41 0.41 0.37 0.35 0.37 0.47 0.47 0.42 0.39 0.46 0.39 0.39 0.33 0.31 0.31 0.37 0.36 223 0.39 0.32 0.32 0.20 0.23 0.38 0.35 0.36 0.26 0.28 0.47 0.27 0.27 0.17 0.20 0.30 0.29 0.22 224 0.44 0.45 0.43 0.46 0.45 0.39 0.48 0.47 0.51 0.52 0.47 0.40 0.39 0.42 0.44 0.33 0.36 0.34 225 0.41 0.41 0.38 0.41 0.41 0.39 0.46 0.41 0.46 0.46 0.47 0.38 0.31 0.37 0.37 0.33 0.33 0.27 226 0.46 0.47 0.40 0.36 0.37 0.39 0.51 0.46 0.43 0.39 0.49 0.42 0.38 0.34 0.29 0.36 0.41 0.34 227 0.50 0.53 0.56 0.53 0.49 0.50 0.61 0.63 0.60 0.56 0.59 0.52 0.54 0.53 0.49 0.44 0.49 0.52 228 0.40 0.42 0.38 0.31 0.36 0.34 0.47 0.42 0.34 0.39 0.47 0.40 0.36 0.28 0.32 0.34 0.40 0.33 229 0.32 0.27 0.14 0.11 0.09 0.30 0.28.0.18 0.16 0.15 0.41 0.23 0.11 0.08 0.07 0.26 0.20 0.09 . H 230 • 0.50 0.42 0.43 0.37 0.39 0.53 0.54 0.51 0.47 0.48 0.52 0.42 0.42 0.37 0.39 0.46 0.45 0.44 $ 166 Is-o_ z CM CO Q_ CM LO CM LO oo CO CM T Is-T — CM co T — CM CM CD CM 00 co o CM cn o o CM oo CM CM Is-CM LO o o CO CO in CM CO Is-co CD CM in co CM CO O o d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d co o Is- o T— CM oo CM Is-o co CO Is-CM CD CO LO co co CM CO T— CO Is-co CD o LO o CM Is-co oo CM cn o Is-co co CM oo co 00 CO o oo CO d d d d o d d d d d d d d d d d d d d d d d d d d d d d d d CO CO co Is-co CM CM o CM CM LO CM LO co CD o CO co CM CM co co co CM CM CO CM CO 1— co CD co CD o o Is-CM CD co LO co o co T — CM CM CM co co CM cn o CM d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d CM co CO o CO CM CO CM CM CO CM CO CO CO Is-co r-CO CD CO CM CM CO CO CO CM CM in CO co CO Is- CM CO cn CM o CO cn CO lO CO CM co d d d d d d d d d d d d d d d d d d d d d d d d d d d d d d Is-CO o co CM T— CM co co CM CM CO Is- co co CO co co CO T— CM CD CD CO LO co CO 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d d d d d d d d d d d d d d d d d CM n 0 c o •* c o 00 CM •* 0 c o CD CM c o 0 LO i n xt CO c o LL. -7 T— CM •* ^t •<d- i n i n i n LO xi- c o CO CO xi- Xt xt xt LO xt xt CM O 0 0 0 0 0 0 O 0 0 0 0 O 0 0 O 0 O 0 0 0 O O 0 Xt m i n i n i n CD i n i n CD CD CM t— 0 CD 0 i n • ^ •sj- •* •* xt CM c o y~ CM xt c o d d d d d d d 0 0 O O O 0 O O 0 O 0 CO CO CO •* c o m i n LO c o CO ,_ ,_ CD CD 0 c o •^i- xt 1— CM T CM CO 1— T— soi d d d d d d d 0 0 0 O 0 O O O O O O CO CD CD c o c o CM ^i- i n CM CO 0 CD xj- m O xt CM E CO c o CM xf xf xt 1— 1— CM c o xt c o CM !0/c d d d d d d d d d d d d d d d d d d i \ i X CD CD m m CM c o c o CD CO xi- c o c o T— T - 1— c o CM •sj- •si- •"3- •* xt CM CM CM c o xt Xt xt (cm d d d d d d d d d d d d d d d d d d CO 00 CO 1— CD 0 CM 0 CM CM CM CD CO xi; 00 LO CM i n i n i n m LO xi- xt CO xr x> xt xt xt a. a . d d d d d d d d d d d d d d d d d d 2N LU CM c o •si- m co c o CD 0 CM c o xi- i n CO c o CD 0 T — CM c o xt I— 0 O 0 O 0 O : : O • 0 O T i— •t— T — 1— 1— T — 1— T — 1— CM CM CM CM CM CO CO CO CO c o c o c o c o c o CO CO c o CO c o CO CO c o c o CO CO CO CO CO CO CO SITE 2NP5 1 (dpth) 301 0.33 302 0.16 303 0.35 304 0.32 305 0.33 306 0.32 307 0.39 308 0.22 309 0.26 310 0.28 311 0.41 312 0.34 313 0.30 314 0.27 315 0.22 316 0.15 317 0.37 318 0.33 319 0.41 320 0.34 321 0.26 322 0.28 323 0.22 324 0.27 (cm3 H20/cm3 soil) 2 3 4 0.36 0.27 0.38 0.36 0.38 0.41 0.37 0.40 0.41 0.35 0.38 0.40 0.33 0.38 0.40 0.27 0.13 0.20 0.38 0.30 0.39 0.38 0.38 0.37 0.37 0.34 0.39 0.37 0.40 0.43 0.40 0.37 0.38 0.35 0.35 0.36 0.29 0.31 0.41 0.37 0.39 0.40 0.13 0.05 0.06 0.13 0.10 0.12 0.22 0.17 0.08 0.27 0.21 0.12 0.38 0.39 0.37 0.31 0.22 0.10 0.22 0.10 0.06 0.41 0.41 0.40 0.19 0.37 0.44 0.16 0.25 0.46 2NP6 5 1 2 0.43 0.26 0.34 0.43 0.21 0.35 0.42 0.33 0.35 0.44 0.27 0.33 0.41 0.30 0.31 0.46 0.28 0.24 0.44 0.35 0.35 0.42 0.23 0.34 0.43 0.28 0.22 0.44 0.21 0.34 0.43 0.38 0.39 0.41 0.31 0.34 0.44 0.26 0.27 0.44 0.23 0.34 0.14 0.24 0.12 0.29 0.21 0.17 0.08 0.34 0.19 0.14 0.32 0.25 0.39 0.41 0.36 0.16 0.36 0.32 0.20 0.29 0.23 0.45 0.29 0.38 0.46 0.16 0.16 0,47 0.21 0.12 2NP7 3 4 5 1 2 3 4 5 0.26 0.36 0.40 0.31 0.35 0.27 0.37 0.42 0.36 0.36 0.41 0.15 0.37 0.38 0.39 0.42 0.38 0.39 0.40 0.40 0.40 0.41 0.40 0.40 0.36 0.37 0.42 0.39 0.39 0.40 0.40 0.42 0.36 0.39 0.37 0.36 0.35 0.39 0.40 0.37 0.10 0.22 0.44 0.29 0.29 0.12 0.23 0.44 0.27 0.37 0.41 0.41 0.41 0.31 0.38 0.42 0.34 0.30 0.38 0.18 0.35 0.34 0.29 0.33 0.30 0.34 0.38 0.31 0.38 0.31 0.33 0.36 0.38 0.38 0.40 0.31 0.38 0.40 0.39 0.41 0.35 0.36 0.37 0.44 0.42 0.37 0.35 0.35 0.34 0.34 0.39 0.38 0.36 0.35 0.34 0.37 0.30 0.38 0.41 0.29 0.28 0.30 0.37 0.40 0.37 0.37 0.41 0.31 0.39 0.40 0.39 0.40 0.13 0.06 0.13 0.29 0.17 0.05 0.06 0.13 0.11 0.10 0.26 0.19 0.13 0.07 0.09 0.22 0.13 0.06 0.07 0.29 0.18 0.11 0.06 0.06 0.18 0.10 0.11 0.32 0.25 0.18 0.09 0.09 0.38 0.33 0.34 0.42 0.36 0.38 0.30 0.30 0.31 0.21 0.09 0.34 0.31 0.21 0.09 0.12 0.09 0.05 0.17 0.29 0.22 0.08 0.05 0.15 0.39 0.38 0.42 0.37 0.41 0.41 0.39 0.43 0.33 0.40 0.43 0.21 0.18 0.34 0.41 0.42 0.20 0.42 0.44 0.26 0.18 0.23 0.43 0.44 SITE 2NP1 (cm3 H20/cm3 soil) 2NP2 1 (dpth) 2 3 4 5 1 2 3 4 401 0.48 0.42 0.38 0.36 0.38 0.48 0.42 0.38 0.34 402 0.48 0.45 0.42 0.41 0.43 0.45 0.45 0.41 0.40 403 0.53 0.44 0.43 0.44 0.44 0.54 0.44 0.44 0.45 404 0.54 0.45 0.43 0.43 0.46 0.52 0.46 0.44 0.44 405 0.41 0.28 0.29 0.27 0.31 0.34 0.24 0.27 0.25 406 0.53 0.43 0.41 0.45 0.46 0.51 0.43 0.42 0.46 407 0.52 0.43 0.42 0.43 0.45 0.43 0.43 0.43 0.45 408 0.51 .0.31 0.46 0.45 0.46 0.42 0.29 0.46 0.47 409 0.52 0.44 0.41 0.42 0.45 0.52 0.45 0.42 0.42 410 0.51 0.47 0.35 0.40 0.46 0.47 0.45 0.26 0.37 411 0.39 0.36 0.36 0.35 0.34 0.34 0.35 0.35 0.35 41-2 0.42 0.38 0.29 0.27 0.21 0.42 0.37 0.27 0.25 413 0.50 0.42 0.44 0.44 0.46 0.47 0.43 0.44 0.44 414 0.45 0.40 0.40 0.37 0.29 0.44 0.40 0.40 0.37 415 0.46 0.46 0.43 0.42 0.47 0.46 0.46 0.43 0.44 416 0.52 0.43 0.43 0.43 0.43 0.52 0.43 0.43 0.43 417 0.45 0.41 0.37 0.30 0.32 0.46 0.42 0.38 0.29 418 0.45 0.34 0.26 0.18 0.21 0.44 0.33 0.23 0.16 419 0.50 0.43 0.44 0.43 0.45 0.46 0.43 0.44 0.45 420 0.46 0.41 0.37 0.29 0.16 0.45 0.40 0.36 0.26 2NP3 2NP4 5 1 2 3 4 5 1 2 3 4 5 0.36 0.47 0.41 0.37 0.32 0.34 0.46 0.40 0.33 0.28 0.32 0.43 0.44 0.43 0.39 0.38 0.43 0.44 0.43 0.35 0.35 0.41 0.46 0.48 0.43 0.43 0.44 0.45 0.52 0.43 0.41 0.41 0.46 0.46 0.49 0.45 0.43 0.43 0.46 0.46 0.45 0.42 0.43 0.46 0.31 0.33 0.21 0.23 0.22 0.28 0.30 0.18 0.18 0.19 0.26 0.46 0.47 0.42 0.41 0.44 0.46 0.46 0.41 0.39 0.44 0.46 0.45 0.46 0.42 0.41 0.43 0.45 0.45 0.41 0.39 0.41 0.44 0.47 0.39 0.27 0.45 0.45 0.46 0.36 0.26 0.43 0.44 0.46 0.46 0.50 0.44 0.41 0.40 0.44 0.51 0.43 0.39 0.39 0.41 0.46 0.45 0.42 0.24 0.36 0.42 0.39 0.37 0.21 0.36 0.46 0.33 0.31 0.33 0.33 0.32 0.31 0.31 0.31 0.30 0.29 0.29 0.20 0.38 0.34 0.24 0.23 0.18 0.33 0.28 0.19 0.18 0.17 0.47 0.48 0.42 0.44 0.44 0.46 0.48 0.40 0.42 0.44 0.46 0.27 0.40 0.38 0.39 0.34 0.25 0.42 0.37 0.38 0.30 0.21 0.48 0.42 0.45 0.43 0.42 0.47 0.46 0.44 0.42 0.41 0.45 0.43 0.49 0.42 0.43 0.44 0.45 0.47 0.41 0.43 0.45 0.45 0.30 0.45 0.40 0.35 0.27 0.28 0.45 0.38 0.30 0.23 0.26 0.21 0.40 0.29 0.21 0.15 0.20 0.37 0.23 0.15 0.11 0.19 0.45 0.43 0.40 0.42 0.44 0.44 0.40 0.41 0.40 0.43 0.44 0.15 0.42 0.39 0.36 0.23 0.14 0.41 0.37 0.32 0.20 0.14 t o SITE 2NP5 (cm3 H20/cm3 soil) 2NP6 1 (dpth) 2 3 4 5 1 2 401 0.45 0.39 0.30 0.24 0.26 0.46 0.38 402 0.44 0.41 0.32 0.29 0.38 0.43 0.41 403 0.51 0.42 0.40 0.40 0.44 0.50 0.41 404 0.50 0.44 0.40 0.41 0.45 0.48 0.42 405 0.28 0.15 0.13 0.13 0.17 0.25 0.15 406 0.49 0.41 0.38 0.42 0.44 0.47 0.40 407 0.46 0.41 0.38 0.38 0.41 0.46 0.41 408 0.36 0.25 0.42 0.43 0.43 0.36 0.25 409 0.52 0.43 0.38 0.35 0.37 0.49 0.43 410 0.34 0.34 0.18 0.33 0.43 0.33 0.33 411 0.34 0.28 0.24 0.24 0.24 0.30 0.26 412 0.27 0.23 0.15 0.11 0.12 0.21 0.20 413 0.47 0.41 0.40 0.41 0.42 0.45 0.41 414 0.40 0.34 0.30 0.21 0.16 0.29 0.29 415 0.47 0.44 0.40 0.38 0.40 0.45 0.43 416 0.51 0.42 0.41 0.43 0.43 0.50 0.40 417 0.41 0.34 0.24 0.17 0.21 0.39 0.34 418 0.32 0.20 0.11 0.08 0.15 0.27 0.19 419 0.45 0.41 0.40 0.42 0.41 0.44 0.40 420 0.35 0.30 0.25 0.14 0.11 0.31 0.29 3 4 0.29 0.20 0.32 0.28 0.40 0.39 0.43 0.40 0.13 0.11 0.38 0.42 0.37 0.38 0.43 0.43 0.36 0.35 0.18 0.33 0.23 0.20 0.13 0.09 0.40 0.42 0.27 0.17 0.40 0.37 0.41 0.44 0.22 0.15 0.11 0.07 0.41 0.42 0.23 0.12 2NP7 5 1 0.21 0.46 0.37 0.49 0.44 0.58 0.45 0.55 0.13 0.33 0.44 0.55 0.41 0.52 0.45 0.48 0.36 0.56 0.44 0.41 0.22 0.41 0.10 0.33 0.42 0.53 0.13 0.41 0.39 0.52 0.44 0.57 0.19 0.44 0.11 0.41 0.40 0.51 0.10 0.39 2 3 0.41 0.31 0.44 0.36 0.45 0.44 0.47 0.43 0.17 0.13 0.44 0.41 0.44 0.41 0.30 0.45 0.46 0.40 0.38 0.20 0.34 0.25 0.22 0.13 0.44 0.45 0.34 0.26 0.47 0.43 0.45 0.44 0.37 0.24 0.22 0.12 0.44 0.44 0.30 0.23 4 5 0.20 0.19 0.29 0.37 0.42 0.46 0.44 0.46 0.11 0.12 0.45 0.45 0.41 0.42 0.45 0.45 0.36 0.36 0.34 0.45 0.20 0.20 0.09 0.08 0.45 0.43 0.15 0.12 0.41 0.42 0.45 0.44 0.15 0.16 0.07 0.08 0.44 0.42 0.12 0.09 SITE 2NP1 (cm3 H20/cm3 soil) 2NP2 1 (dpth) 2 3 4 5 1 2 3 4 601 0.50 0.43 0.39 0.40 0.49 0.49 0.45 0.43 0.43 602 0.53 0.46 0.47 0.48 0.50 0.51 0.46 0.47 0.48 603 0.53 0.45 0.43 0.46 0.48 0.50 0.44 0.44 0.47 604 0.53 0.44 0.44 0.45 0.46 0.49 0.44 0.44 0.46 605 0.41 0.38 0.18 0.14 0.25 0.50 0.42 0.24 0.13 606 0.44 0.41 0.38 0.22 0.21 0.43 0.42 0.38 0.22 607 0.58 0.47 0.46 0.48 0.48 0.56 0.48 0.46 0.48 608 0.48 0.43 0.45 0.46 0.49 0.47 0.43 0.45' 0.48 609 0.38 0.25 0.15 0.11 0.16 0.46 0.32 0.16 0.10 610 0.46 0.42 0.22 0.15 0.23 0.45 0.42 0.20 0.13 611 0.46 0.37 0.16 0.15 0.23 0.39 0.36 0.17 0.13 612 0.51 0.44 0.41 0.43 0.35 0.48 0.43 0.42 0.43 613 0.41 0.27 0.14 0.14 0.28 0.48 0.28 0.13 0.14 614 0.30 0.16 0.12 0.18 0.26 0.23 0.15 0.10 0.16 615 0.34 0.13 0.09 0.17 0.28 0.24 0.11 0.08 0.15 616 0.41 0.19 0.13 0.18 0.34 0.36 0.18 0.11 0.16 617 0.32 0.19 0.16 0.27 0.18 0.43 0.24 0.19 0.27 618 0.35 0.20 0.09 0.10 0.20 0.27 0.17 0.09 0.08 619 0.38 0.16 0.09 0.19 0.18 0.25 0.13 0.08 0.17 620 0.37 0.21 0.10 0.10 0.18 0.28 0.18 0.09 0.09 2NP3 2NP4 5 1 2 3 4 5 1 2 3 4 5 0.48 0.40 0.41 0.38 0.40 0.48 0.40 0.40 0.36 0.39 0.47 0.51 0.51 0.47 0.47 0.48 0.50 0.45 0.44 0.47 0.49 0.51 0.49 0.46 0.43 0.43 0.46 0.49 0.45 0.41 0.42 0.46 0.50 0.47 0.46 0.43 0.44 0.46 0.45 0.47 0.42 0.43 0.45 0.46 0.22 0.39 0.36 0.35 0.13 0.21 0.35 0.28 0.13 0.12 0.20 0.16 0.39 0.40 0.37 0.22 0.15 0.41 0.37 0.30 0.19 0.13 0.49 0.52 0.47 0.45 0.47 0.49 0.53 0.48 0.46 0.48 0.49 0.49 0.41 0.42 0.45 0.47 0.49 0.41 0.41 0.44 0.48 0.50 0.15 0.30 0.24 0.13 0.11 0.16 0.30 0.18 0.09 0.09 0.14 0.22 0.42 0.40 0.22 0.15 0.24 0.39 0.34 0.16 0.13 0.22 0.20 0.37 0.33 0.17 0.13 0.20 0.36 0.26 0.12 0.12 0.19 0.34 0.38 0.42 0.42 0.42 0.34 0.39 0.40 0.40 0.40 0.32 0.26 0.31 0.22 0.12 0.15 0.28 0.24 0.16 0.08 0.13 0.26 0.24 0.20 0.14 0.11 0.16 0.26 0.13 0.10 0.08 0.16 0.25 0.26 0.21 0.11 0.09 0.17 0.27 0.15 0.08 0.08 0.15 0.27 0.33 0.33 0.18 0.13 0.18 0.34 0.28 0.13 0.10 0.16 0.33 0.16 0.25 0.17 0.16 0.28 0.18 0.21 0.15 0.14 0.25 0.16 0.16 0.25 0.17 0.09 0.10 0.18 0.18 0.13 0.07 0.08 0.17 0.16 0.23 0.13 0.09 0.20 0.19 0.14 0.09 0.08 0.08 0.17 0.17 0.25 0.17 0.10 0.12 0.19 0.19 0.13 0.08 0.10 0.18 H SITE 2NP5 (cm3 H20/cm3 soil) 2NP6 1 (dpth) 2 3 4 5 1 2 601 0.29 0.30 0.31 0.36 0.44 0.32 0.35 602 0.29 0.37 0.46 0.48 0.50 0.28 0.37 603 0.42 0.37 0.40 0.45 0.48 0.43 0.38 604 0.42 0.37 0.41 0.44 0.44 0.43 0.38 605 0.20 0.20 0.09 0.10 0.17 0.16 0.18 606 .0.31 0.30 0.20 0.12 0.10 0.20 0.25 607 0.44 0.45 0.45 0.46 0.48 0.45 0.43 608 0.39 0.38 0.42 0.45 0.47 0.40 0.39 609 0.19 0.13 0.06 0.07 0.11 0.15 0.12 610 0.24 0.27 0.12 0.09 0.18 0.23 0.25 611 0.19 0.19 0.08 0.09 0.16 0.15 0.16 612 0.34 0.38 0.34 0.27 0.28 0.33 0.36 613 0.10 0.11 0.05 0.09 0.21 0.08 0.09 614 0.01 0.06 0.05 0.13 0.21 0.01 0.06 615 0.04 0.05 0.05 0.13 0.25 0.04 0.04 616 0.13 0.09 0.07 0.14 0.31 0.11 0.08 617 0.09 0.11 0.11 0.23 0.14 0.08 0.10 618 0.06 0.08 0.05 0.07 0.15 0.04 0.08 619 0.05 0.06 0.05 0.16 0.15 0.04 0.06 620 0.08 0.09 0.05 0.07 0.16 0.07 0.08 3 4 0.30 0.36 0.44 0.47 0.40 0.44 0.41 0.44 0.08 0.08 0.17 0.10 0.44 0.46 0.42 0.45 0.06 0.05 0.11 0.05 0.07 0.07 0.32 0.23 0.04 0.08 0.04 0.12 0.04 0.11 0.05 0.12 0.10 0.20 0.04 0.05 0.05 0.16 0.04 0.06 2NP7 5 1 0.40 0.39 0.49 0.44 0.47 0.48 0.45 0.48 0.15 0.15 0.09 0.38 0.48 0.50 0.48 0.44 0.08 0.30 0.13 0.43 0.13 0.26 0.22 0.36 0.16 0.21 0.19 0.24 0.23 0.25 0.29 0.33 0.12 0.20 0.14 0.26 0.13 0.16 0.15 0.22 2 3 0.39 0.35 0.41 0.46 0.42 0.42 0.42 0.42 0.17 0.07 0.33 0.22 0.46 0.45 0.41 0.43 0.17 0.09 0.39 0.19 0.21 0.09 0.37 0.33 0.11 0.04 0.14 0.10 0.10 0.07 0.15 0.08 0.13 0.12 0.15 0.08 0.08 0.06 0.15 0.08 4 5 0.36 0.40 0.47 0.50 0.44 0.49 0.46 0.44 0.07 0.12 0.11 0.09 0.47 0.49 0.45 0.48 0.07 0.10 0.13 0.23 0.08 0.12 0.23 0.20 0.07 0.15 0.14 0.20 0.13 0.25 0.12 0.29 0.22 0.13 0.09 0.15 0.17 0.14 0.08 0.17 

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