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A method for developing soil management units Zweck von Zweckenburg, Elizabeth Maria Erna 1981

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A METHOD FOR DEVELOPING SOIL MANAGEMENT UNITS by ELIZABETH MARIA ERNA^WECK VON ZWECKENBURG B.A., The Un i v e r s i t y .of Western Ontario, 1979. A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTERS OF SCIENCE in . THE FACULTY OF GRADUATE STUDIES (Department of S o i l Science) We accept t h i s thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA JUNE, 1981 © ELIZABETH MARIA ERNA ZWECK VON ZWECKENBURG I n p r e s e n t i n g t h i s t h e s i s i n p a r t i a l f u l f i l m e n t o f t h e r e q u i r e m e n t s f o r an advanced degree a t the U n i v e r s i t y o f B r i t i s h C o l u m b i a , I agree t h a t t h e L i b r a r y s h a l l make i t f r e e l y a v a i l a b l e f o r r e f e r e n c e and s t u d y . I f u r t h e r agree t h a t p e r m i s s i o n f o r e x t e n s i v e c o p y i n g o f t h i s t h e s i s f o r s c h o l a r l y p u r p o s e s may be g r a n t e d by t h e head o f my department o r by h i s o r h e r r e p r e s e n t a t i v e s . I t i s u n d e r s t o o d t h a t c o p y i n g o r p u b l i c a t i o n o f t h i s t h e s i s f o r f i n a n c i a l g a i n s h a l l n o t be a l l o w e d w i t h o u t my w r i t t e n p e r m i s s i o n . Department o f g ^ u S O i e ^ C J E -The U n i v e r s i t y o f B r i t i s h C o l u m b i a 2075 Wesbrook P l a c e V ancouver, Canada V6T 1W5 I 9 / 7 Q ^ I i ABSTRACT The purpose of the present study was to develop a quantitative methodology to define optimum land use systems. S o i l survey data were assessed for c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y uses to e s t a b l i s h i n t e r p r e t a t i v e s o i l u nits for which planning and management recommenda-tions could be made. The study was a g r i c u l t u r a l l y oriented due to the a v a i l a b i l i t y of a g r i c u l t u r a l p r o d u c t i v i t y data for s u i t a b i l i t y assessments from four sources: farmer survey, d i r e c t estimate by expert consensus, research s t a t i o n data and p l o t t r i a l s -S o i l s were grouped using c l u s t e r analysis on the basis of permanent inherent s o i l properties. The technique did not group the s o i l s s a t i s -f a c t o r i l y f o r management purposes due to s t a t i s t i c a l l i m i t a t i o n s of the procedure i n assessing overlapping and interdependent v a r i a b l e s , such as s o i l c h a r a c t e r i s t i c s , and r e s t r i c t i o n s imposed by the s o i l s data set which was neither adequately large nor diverse to form multimember s o i l groups. Stepwise discriminant analysis was more successful i n assessing the i n t e r p r e t a t i v e s o i l s data and i n i d e n t i f y i n g discriminant s o i l parameters. The Canada Land Inventory derived c a p a b i l i t y classes were separated by drainage, the q u a n t i t a t i v e l y defined s u i t a b i l i t y classes were separated by parent material and the socioeconomically defined f e a s i b i l i t y groups were separated by pH and coarse f r a c t i o n . Comparison of the interpreta-:. ... t i v e s o i l s c l a s s i f i c a t i o n s revealed that the c a p a b i l i t y ratings over-estimated actual measured y i e l d and that current land use did not r e a l i z e the f u l l a g r i c u l t u r a l p o t e n t i a l of the land. F e a s i b i l i t y , unlike c a p a b i l i t y and s u i t a b i l i t y , stressed parameters other than s o i l properties i n land evaluation. i i i The s u i t a b i l i t y assessment based upon ac t u a l observed p r o d u c t i v i t y data measured under r e a l market conditions was recommended as the most quantitative land evaluation approach. Other s o i l s can be added to the open ended system and optimal use can be made of a l l s o i l s using guide-l i n e s developed by key farmers under r e a l market conditions f o r s o i l s s u i t a b i l i t y groups. i v -TABLE OF CONTENTS ABSTRACT TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES LIST OF APPENDICES ACKNOWLEDGMENT CHAPTER 1 - INTRODUCTION Introduction and Purpose Basic Concepts Scope and.Aims Methodology Description:.of Study Area CHAPTER 2 - LITERATURE REVIEW S o i l Survey Land Evaluation 1. Land C a p a b i l i t y Assessment CHAPTER 3 - METHODS S o i l s Data 1. Sources of S o i l s Data a. S o i l s Data Bank b. Collected Data 2. S e l e c t i o n of Parameters 3. Sampling 4. Laboratory Analysis -Page i i i v v i v i i v i i i i x 1 1 2 3 3 8 12 13 a. USDA Land C a p a b i l i t y C l a s s i f i c a t i o n b. United Kingdom c. Canada Land Inventory 15 15 d. Use of Land C a p a b i l i t y Assessments 2. Land S u i t a b i l i t y Assessment a. P r o d u c t i v i t y Assessments i . Analogue Method i i . S i t e Factor Method i i i . Systems Analysis b. Behavioural Assessment c. Use of Land S u i t a b i l i t y Assessment Summary and Conclusions 17 18 19 20 20 21 21 22 24 24 26 26 26 29 29 V Page S o i l Survey Interpretative C l a s s i f i c a t i o n s 30 1. Land C a p a b i l i t y Data 30 2. Land S u i t a b i l i t y Data 31 a. Farmer Survey 33 b. Di r e c t Estimate by Expert Consensus 33 c. Plot T r i a l s 34 d. Research Station Data 34 3. Land F e a s i b i l i t y Data 34 Numerical Analysis 35 1. Cluster Analysis 35 2. Factor Analysis 37 3. Stepwise Discriminant Analysis 37 4. Mann-Whitney Rank Sum Test 38 Assessment of S o i l s Groups and Interpretative S o i l s Units 38 1. Comparison of S o i l s Groups 39 2. Comparison of Interpretative S o i l s Groups 39 3. Comparison of S o i l s Groups and Interpretative S o i l s Units 42 CHAPTER 4 - RESULTS AND DISCUSSIONS Numerical Analysis of S o i l s Data 43 1. S o i l s Data 43 2. D i r e c t Cluster Analysis 44 3. Indirect Cluster Analysis 47 a. Factor Analysis 47 b. Cluster Analysis 47 Numerical Analysis of Interpr e t a t i v e S o i l s Data 52 1. Ca p a b i l i t y Data 52 a. C a p a b i l i t y Groups 52 b. Stepwise Discriminant Analysis f o r C a p a b i l i t y 55 2. S u i t a b i l i t y Data 58 a. S u i t a b i l i t y Groups 58 b. Stepwise Discriminant Analysis f o r S u i t a b i l i t y 64 3. F e a s i b i l i t y Data 68 a. F e a s i b i l i t y Groups 68 b. Stepwise Discriminant Analysis f o r F e a s i b i l i t y 70 Assessment of S o i l s Groups and Interpretative Land Manage-ment Units 74 1. Comparison of S o i l s Groups 74 2. Comparison of Interpretative S o i l s C l a s s i f i c a t i o n 74 3. Comparison of S o i l s Groups and Interpretative S o i l s Units 78 CHAPTER 5 - CONCLUSIONS Numerical Techniques 81 Comparison of Interpretative C l a s s i f i c a t i o n s 82 S u i t a b i l i t y Assessment 82 Recommendations 83 BIBLIOGRAPHY 84 APPENDICES 88 v i LIST OF TABLES Table 1.1 Study Area Climate Table 2.1 D e f i n i t i o n of C a p a b i l i t y Classes A to G on a Comparative Basis Table 3.1 S o i l Series Used i n Study Table 3.2 S o i l Parameters Table 3.3 Survey Questionnaire Table 4.1 Dir e c t S o i l s Groups Table 4.2 S i g n i f i c a n t Discriminant Parameters of D i r e c t S o i l s Groups I d e n t i f i e d by Mann-Whitney Rank Sum Test Table 4.3 Sorted Rotated Factor Loadings Table 4.4 In d i r e c t S o i l s Groups Table 4.5 S i g n i f i c a n t Discriminant Parameters of Indirect S o i l s Groups I d e n t i f i e d by Mann-Whitney Rank Sum Test Table 4.6 S o i l s C a p a b i l i t y Groups Table 4.7 Corrected S o i l s C a p a b i l i t y Groups Table 4.8 C l a s s i f i c a t i o n Matrix of S o i l s C a p a b i l i t y Groups Table 4.9 S i g n i f i c a n t Discriminant Parameters of S o i l s C a p a b i l i t y Groups I d e n t i f i e d by Mann-Whitney Rank Sum Test Table 4.10 Drainage C h a r a c t e r i s t i c s of the Ca p a b i l i t y Groups Table 4.11 Pro d u c t i v i t y Data Table 4.12 S o i l s S u i t a b i l i t y Groups Table 4.13 Corrected S o i l s S u i t a b i l i t y Groups (Based on Corn Index) Table 4.14 C l a s s i f i c a t i o n Matrix of S o i l s S u i t a b i l i t y Groups (Based on Corn Index) Table 4.15 S i g n i f i c a n t Discriminant Parameters of S o i l s S u i t a b i l i t y Groups I d e n t i f i e d by Mann-Whitney Rank Sum Test Table 4.16 Parent Material of the S o i l s S u i t a b i l i t y Groups Table 4.17 S o i l s F e a s i b i l i t y Groups Table 4.18 Corrected S o i l s F e a s i b i l i t y Groups Table 4.19 Ranges of Discriminant Parameters of S o i l s F e a s i b i l i t y Groups Table 4.20 C l a s s i f i c a t i o n Matrix of S o i l s F e a s i b i l i t y Groups Table 4.21 S i g n i f i c a n t Discriminant Parameters of S o i l s F e a s i b i l i t y Groups I d e n t i f i e d by Mann-Whitney Rank Sum Test Table 4.22 Comparison of Interpretative S o i l s C l a s s i f i c a t i o n s Table 4.23 S o i l s Group Separation by S i g n i f i c a n t Discriminant Parameters of Interpretative C l a s s i f i c a t i o n s Page 7 13 27 28 32 44 46 48 49 51 54 55 56 57 58 60 64 65 65 66 67 70 71 71 72 72 75 79 v i i LIST OF FIGURES Page Figure 1.1 Study Methodology 4 Figure 1.2 Location of Study Area 5 Figure 2.1 Frequency of Requests f or S o i l Interpretations at Various Depths i n Comparison with the Depth Limits of Several Diagnostic S o i l Taxonomy C r i t e r i a Figure 3.1 Geographical D i s t r i b u t i o n of S o i l s 25 Figure 3.2 Clustering Procedure Figure 3.3 Graph Procedure Figure 3.4 Uneven Case D i s t r i b u t i o n 36 40 41 Figure 4.1 D i r e c t Grouping Dendrogram 45 Figure 4.2 Indirect Grouping Dendrogram 50 Figure 4.3 Comparison of Grass P r o d u c t i v i t y Data from Farmer Survey and D i r e c t Estimate 63 Figure 4.4 Comparison of Corn Pr o d u c t i v i t y Data from Farm Survey and D i r e c t Estimate Figure 4.5 Abbotsford Area Land Use Figure 4.6 Comparison of C a p a b i l i t y and S u i t a b i l i t y Groups 77 Figure 4.7 Comparison of C a p a b i l i t y and F e a s i b i l i t y Groups 77 Figure 4.8 Comparison of S u i t a b i l i t y and F e a s i b i l i t y Groups 77 63 69 v i i i LIST OF APPENDICES Page Appendix A Appendix B Appendix C Categorical Parameters S o i l s Data S o i l Limitations f o r A g r i c u l t u r e 88 89 90 i x ACKNOWLEDGMENTS Funding for the present study from B r i t i s h Columbia Science Council Grant #35 i s g r a t e f u l l y acknowledged. I would l i k e to thank my Thesis Committee, and e s p e c i a l l y the Head of my Committee, Professor H. Schreier, f o r h i s continuous enthusiasm and frequent advice. Several people were active i n the present study and t h e i r p a r t i -c i p a t i o n and cooperation was greatly appreciated. H. Luttmerding (Research Analysis Branch, Kelowna) i d e n t i f i e d s o i l s e r i e s c e n t r a l concept l o c a t i o n s . R. Bertrand and C. Wood (Ministry of Ag r i c u l t u r e , Cloverdale) provided a g r i c u l t u r a l management information. R. Bertrand, C. Wood (Ministry of Ag r i c u l t u r e , Cloverdale), B. Peters (Ministry of Ag r i c u l t u r e , Abbotsford) and S. Loewen (.Chilliwack Cooperative) i d e n t i f i e d the key farmers to be interviewed for p r o d u c t i v i t y and management information. The cooperation of the farmers was also appreciated. P r o d u c t i v i t y data was also provided from research data by G. Kowalenko (Agriculture Canada, Agassiz) and Drs. Dobney and Wright (Agriculture Canada, Vancouver) and through expert consensus from H. Luttmerding (.Research Analysis Branch, Kelowna). M. Lagzdins (Matsqui Municipal O f f i c e ) and L. Giverson (.Central Fraser V a l l e y Regional D i s t r i c t ) supplied current land use information. S. MacDonald, B. Grant and Y. St i c h are g r a t e f u l l y acknwledged for t h e i r help i n s o i l sample c o l l e c t i o n and laboratory a n a l y s i s . The a s s i s -tance of V. Miles, P. Carbis and T.D. Nguyen i n the lab was appreciated. I would l i k e to thank my parents for t h e i r continued support and Carol Jones and Frank K e l l i h e r f o r t h e i r encouragement throughout the X project. And l a s t , but not l e a s t , I would l i k e to thank those people who made my stay i n the S o i l Science Department a pleasurable experience. 1 CHAPTER 1 INTRODUCTION Introduction and Purpose An increasing population and a r i s i n g standard of l i v i n g are creat-ing s t e a d i l y increasing demands for most natural resources. The greatest resource demand i s for land to be used for a wide v a r i e t y of purposes including a g r i c u l t u r e s f o r e s t r y , commerce, industry, transportation and urban services. In B r i t i s h Columbia, the population i s concentrated i n a l i m i t e d number of densely s e t t l e d regions, where land i s i n short supply, highly valued and has p o t e n t i a l for several a l t e r n a t i v e uses. There are l i m i t e d remaining opportunities for land settlement and the main thrust of planning and development i s to reorganize land use i n already occupied areas. The present basis for land use planning i n B r i t i s h Columbia i s the Canada Land Inventory (CLI) c l a s s i f i c a t i o n . Despite i t s advantages, the Canada Land Inventory has l i m i t a t i o n s which do not allow f u l l r e a l i z a t i o n of land p o t e n t i a l . The purpose of the present study was to develop a better methodology to define optimum • land use systems. S o i l survey data was to be q u a n t i t a t i v e l y assessed for c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y uses to e s t a b l i s h i n t e r p r e t a t i v e s o i l u n i t s f o r which planning and management recommendations could be made. A comparison of the c l a s s i -f i c a t i o n systems was to reveal the optimal land assessment methodology. Evaluation of the i n t e r p r e t a t i v e s o i l s c l a s s i f i c a t i o n s was conducted i n a small study area i n the Lower Fraser V a l l e y i n B r i t i s h Columbia. Basic Concepts G.A. H i l l s proposed that s o i l s i n t e r p r e t a t i v e c l a s s i f i c a t i o n schemes 2 are of three types: c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y ( H i l l s and Portelance, 1960; Belknap and Furtado, 1967). C a p a b i l i t y r e f e r s to the po t e n t i a l of an area to produce goods and services of various kinds under s p e c i f i e d types of economic and technological controls ( H i l l s i n G i r t , 1977) . The land c a p a b i l i t y l e v e l i s defined., by the degree of l i m i t a t i o n to s p e c i f i c use. S u i t a b i l i t y describes the r e l a t i v e a b i l i t y of a s p e c i f i c area to produce s p e c i f i c goods and services ( H i l l s , i n G i r t , 1977). Differences i n the degree of s u i t a b i l i t y of an area are determined by the performance or behaviour of the land i n response to s p e c i f i c input l e v e l s (Brinkman and Smythe, 1972). F e a s i b i l i t y incorporates socio-economic considerations by weighing the r e l a t i v e advantages of alternate changes i n land use having regard to the conservation of renewable natural resources and to human needs and welfare ( G i r t , 1977). The land f e a s i b i l i t y l e v e l i s defined by such para-meters as land tenure, l o c a t i o n , distance to markets, economic rent, land rent, market value and farm use value (Steele, 1967; G i r t , 1967; Beek, 1978) . In the present study, i n t e r p r e t a t i v e s o i l s c l a s s i f i c a t i o n s were to be developed at each l e v e l of H i l l s System to i d e n t i f y the best method of defining optimum land use. Scope and Aims The s p e c i f i c objectives of the present study were as follows: 1. To compile a v a i l a b l e s o i l survey information and i d e n t i f y s o i l properties useful i n c l a s s i f y i n g some s o i l s of the Lower Fraser Valley for management purposes. 2. To group s o i l s using selected s o i l parameters to form s o i l analogues. 3 3. To derive i n t e r p r e t a t i v e s o i l s u n i t s from c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y assessments of s o i l survey data using numerical c l a s s i f i c a t i o n . 4. To compare s o i l s groups and i n t e r p r e t a t i v e s o i l s units to i d e n t i f y the best land evaluation approach to optimize land use. Methodology The present study involved the following: 1. C o l l e c t i o n of s o i l s data. 2. C a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y assessments of s o i l survey data for a g r i c u l t u r a l purposes. 3. Numerical analysis to quantify i n t e r p r e t a t i v e assessments of s o i l survey data and to develop i n t e r p r e t a t i v e s o i l s u n i t s . 4. Comparison of s o i l s groups and i n t e r p r e t a t i v e s o i l s u n i t s . A d e t a i l e d o u t l i n e of the analyses employed i n the present study i s presen-ted i n a flow diagram i n Figure 1.1. The study was conducted at two l e v e l s : 1. Evaluation of s o i l survey data. 2. Analysis of i n t e r p r e t a t i v e assessments of s o i l survey data. The r e s u l t i n g s o i l s groups and the i n t e r p r e t a t i v e s o i l s units were compared to i d e n t i f y the best land evaluation approach to land use optimization. Description of Study Area The study area was located i n the Abbotsford area of the Lower Fraser V a l l e y i n southwestern B r i t i s h Columbia (Figure 1.2). The area was described by Comar and Kelley, 1962; Runka and Kelley, 1964 and Luttmerding (in press). I t i s composed of four d i s t i n c t i v e regions including lowland areas (Matsqui P r a i r i e and Sumas P r a i r i e ) and upland areas (Langley Uplands, Abbotsford outwash). The lowland Matsqui P r a i r i e i s comparatively f l a t and i s found at elevations of l e s s than 8 meters. Dykes along the south bank S o i l Survey Data Direct Grouping of S o i l s by Cluster Analysis Using a l l Variables Grouping of Soi l s by Cluster Analysis Using Factors I d e n t i f i c a t i o n of S o i l Parameters S i g n i -f i c a n t i n Separating S o i l s Groups By Kruskal Wallis One Way Analysis of Variance and Mann-Whitney Rank Sum- Test Comparison of S o i l s Gi Rank Sum Test roups by Mann-Whitney Grouping- of Soils.Using C a p a b i l i t y , S u i t a b i l i t y and F e a s i b i l i t y Data Evaluation and R e c l a s s i f i c a t i o n of Interpretative Land Management Units; I d e n t i f i c a t i o n of S o i l Parameters S i g n i f i c a n t i n Separating Land Management Units by Stepwise Discrim-inant Analysis V e r i f i c a t i o n of S i g n i f i c a n t Discrimin-ant Parameters by Kruskal-Wallis One Way Analysis of Variance and Mann-Whitney Rank Sum Test Comparison of Interpretative Land Management Units by Graph Method Tabular Comparison of S o i l s Groups and Interpretative Land Management Units  Optimal Approach to Land Evaluation Figure 1.1 - Study Methodology FIGURE 1 .2 -LOCATION OF STUDY AREA 6 of the Fraser River prevent flooding of the area during the freshet of the Fraser River. S o i l forming deposits i n Matsqui P r a i r i e are of post g l a c i a l age and include the l a t e r a l l y accreted sediments of the Fraser f l o o d p l a i n . The Sumas Valley to the southeast i s a gently undulating l a c u s t r i n e deposit of post g l a c i a l age with old beach l i n e s or s p i t s 2 to 5 metres above the general l e v e l . The upland areas are composed of g l a c i a l deposits of Pleistocene Age which have r o l l i n g surfaces up to 140 metres above the sea l e v e l . The Abbotsford outwash i s a g l a c i o f l u v i a l recessional deposit with a v a r i a b l e lcfess,'capping up to 1 metre i n depth. The Whatcom glacio-marine deposit of the Langley Upland was formed when part of an i c e sheet was pushed out over the sea. As the ice was melted by the underlying water, basal debris was released and sank to the sea bottom. The climate of the study area i s inshore maritime and strongly i n -fluenced by the coast mountains to the north and by the Cascade Range to the south. R a i n f a l l occurs mainly from November to February and the summer months, p a r t i c u l a r l y July and August, usually experience^ 'a drdught. Temperature i s f a i r l y constant across the study area (Table 1.1) and the area boasts the longest f r o s t - f r e e period i n Canada (169 days). The area was chosen as the study region for three reasons: 1. The region i s composed of many d i f f e r e n t s o i l forming deposits r e s u l t i n g i n a wide range of s o i l properties. 2. The area supports a wide v a r i e t y of a g r i c u l t u r a l crops. 3. There i s growing pressure from the town of Clearbrook and the c i t y of Abbotsford to convert the surrounding a g r i c u l t u r a l land to non-agricultural uses. The thesis i s presented i n the following manner: Chapter 2 provides a summary of background l i t e r a t u r e pertinent to the study; Chapter 3 7 describes the methods used to c o l l e c t and analyze the data; Chapter 4 presents and discusses the results of the analyses; and Chapter 5 provides the conclusions of the study. Table 1,1 Study Area. Climate Temperature (°C) Elevation Years of Station Maximum i Minimum Annual (m) Record Aldergrove 36 -17 9 84 4 Abbotsford 38 -24 9 60 16 Chilliwack 38 -18 10 79 10 Mission 38 -14 10 56 10 8 CHAPTER 2 LITERATURE REVIEW Increasing population numbers and a wide range of human a c t i v i t i e s i s d i r e c t l y a f f e c t i n g our land resource base. A s t a t i c supply of land i s i n demand for ag r i c u l t u r e , f o r e s t r y , hydrology, transportation, waste disposal and urban development. S o i l s are one of the most important e l e -ments of the natural resource base influencing development of an area. D e f i n i t i v e information regarding the geographic l o c a t i o n of various kinds of s o i l s , the p h y s i c a l , chemical and b i o l o g i c a l properties of the s o i l s and the c a p a b i l i t y of the s o i l s to support a wide range of land uses should serve as the basis f o r any land use decis i o n . Two l e v e l s of s o i l s informa-t i o n - the s o i l survey and i n t e r p r e t a t i v e s o i l s information - are a v a i l a b l e for t h i s purpose. The standard s o i l survey provides an inventory of s o i l resources of an area. Interpretative s o i l s information i s used to r e l a t e s o i l c h a r a c t e r i s t i c s and i n t r i n s i c behaviour of the s o i l to the user (Bauer, 1979; Beatty et a l . , 1979; M i l l e r and Nichols, 1979). S o i l Survey The basic objectives of s o i l survey are to: (1) measure and observe properties and behaviour of s o i l s i n t h e i r natural state, and (2) delineate areas of s i m i l a r s o i l s . S o i l c h a r a c t e r i s t i c s i n d i c a t i v e of s o i l behaviour form the basis of the s o i l survey which i s p r a c t i c a l l y oriented toward planning a l t e r n a t i v e uses of the s o i l and defining a l t e r n a t i v e management practices f o r each use ( B a r t e l l i , 1979; M i l l e r and Nichols, 1979). S o i l s i n the area are mapped at the s o i l series l e v e l and a d e s c r i p t i o n of the central concept of each s o i l series and i t s reaction to management are 9 provided by the s o i l survey ( M i l l e r and Nichols, 1979). Standard s o i l surveys have c e r t a i n inherent l i m i t a t i o n s , p a r t i -c u l a r l y with respect to v a r i a b i l i t y and mapping unit p u r i t y . S o i l s vary across the landscape v e r t i c a l l y and l a t e r a l l y due to such factors as parent material, climate, topography, phy s i c a l and chemical processes, vegetation and b i o l o g i c a l a c t i v i t i e s . S o i l survey attempts to group s o i l s into mapping units which are less v a r i a b l e than the s o i l s population as a whole (Clarke, 1951; Webster and Beckett, 1968; Beckett and Webster, 1971; Bie and Beckett, 1971; Galloway and Yahn'er 1978; M i l l e r , 1978). In many cases, the v a r i a b i l i t y of s o i l properties i s as great within classes of s o i l s as i t i s between them (Beckett and Webster, 1971). An increasing number of studies are being performed to assess the accuracy and r e l i a -b i l i t y of s o i l survey data for use i n i n t e r p r e t a t i o n and p r e d i c t i o n of s o i l s u i t a b i l i t y f o r various uses. (Beckett and Webster, 1971; M i l l e r and Nichols, 1979). The p u r i t y of the mapping unit i s important i n s o i l survey. The s o i l series i s defined for a n a t u r a l l y occurring body, but large areas of homogeneous s o i l s are r a r e l y found and most mapping units contain i n c l u -sions of s o i l s not named i n the mapping u n i t . I t was o r i g i n a l l y thought that mapping units are 80 - 85% pure; however, s o i l map delineations r a r e l y comprise 50 - 70% of the s o i l designated i n the mapping unit name ( M i l l e r , 1976; M i l l e r and Nichols, 1979). Inclusions with s i m i l a r l i m i t a -tions and response to management do not a f f e c t the usefulness of the s o i l survey; however 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 s within the mapping u n i t have behavioural and management implications ( M i l l e r and Nichols, 1979). This problem i s compounded i n s o i l complexes i n which the dimensions, proportions and i n t e r r e l a t i o n s of d i f f e r e n t s o i l types are not s p e c i f i e d (Webster and 10 Beckett, 1968). Map legends should record l i k e l y i n c l u s i o n s i n simple units and should provide aids f o r p r e d i c t i n g the l o c a t i o n of d i f f e r e n t s o i l classes within a mapped compound u n i t . The p r a c t i c a l a p p l i c a t i o n of s o i l survey data i s further r e s t r i c t e d by a number of factors including a basic o r i e n t a t i o n toward a g r i c u l t u r e , the time lapse between the actual s o i l survey and p u b l i c a t i o n , the form of s o i l survey data presentation and the lack of s o i l s data i n t e r p r e t a t i o n . S o i l s were o r i g i n a l l y surveyed to provide information about s p a t i a l v a r i a t i o n of s o i l properties i n an area to help people se l e c t s o i l s that were responsive to farm management systems (Riecken, 1963; Beckett and Bie;;, 1978; M i l l e r , 1978). Since that time, the u t i l i t y of s o i l s information fo r numerous non-agricultural applications has been r e a l i z e d , but s o i l survey s t i l l maintains i t s a g r i c u l t u r a l o r i e n t a t i o n . Engineers often require s o i l s data which are not c o l l e c t e d i n the standard s o i l survey. Important engineering s o i l s parameters include permeability, surface runoff, shrink-swell p o t e n t i a l , water table, density, porosity, bearing capacity, cohesion, shearing and compression. In addition, engineering s o i l s informa-t i o n requirements often extend beyond the standard two metre s o i l p r o f i l e section depth representing the a g r i c u l t u r a l root control zone (Figure 2.1; M i l l e r , 1978) (USDA Cons. Serv.; 1971; Johannsen et a l . 1978). The lag period between the s o i l survey and the p u b l i c a t i o n of s o i l survey date can be as great as 15 years. During t h i s time, knowledge and concepts of the s o i l s and user requirements of s o i l survey data may change and the s o i l survey data may be out of date before i t i s published. Once published, s o i l survey data are presented i n report and map form. The report provides a c e n t r a l concept d e f i n i t i o n and d e s c r i p t i o n f or FIGURE 2.1- FREQUENCY OF REQUESTS FOR SOIL INTERPRETATIONS AT VARIOUS DEPTHS IN COMPARISON WITH THE DEPTH LIMITS OF SEVERAL DIAGNOSTIC SOIL TAXONOMY CRITERIA ( M i l l e r , 1979) 12 each s o i l series which appears on the map. The mapping units which are usually defined by place name do not give any i n d i c a t i o n of s o i l q u a l i t y or s o i l s u i t a b i l i t y f o r s p e c i f i c use (Clarke, 1951). This form of s o i l survey data presentation i s often c r y p t i c to the non-soil s c i e n t i s t and a need exists f o r a more p r a c t i c a l expression of s o i l survey data which indicates] s o i l q u a l i t y and s o i l s u i t a b i l i t y i n a form that map users can understand. The t r a n s l a t i o n of s o i l survey data to simple expressions of s o i l behaviour i s known as i n t e r p r e t a t i v e s o i l s information. To date, too l i t t l e s o i l s i n t e r p r e t a t i o n has been c a r r i e d out. Interpretation requires an intimate knowledge of the behaviour responses of s o i l s and quite often involves supplementing the s o i l s data with a d d i t i o n a l information. S o i l s i n t e r p r e t a t i o n may be c a r r i e d out for a number of d i f f e r e n t purposes; however, they generally f a l l into three categories: c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y . These are discussed i n the following section. Land Evaluation S o i l survey i s an inventory of s o i l resources which does not contain any element of evaluation or i n t e r p r e t a t i o n i n resource terms (Young, 1973). Since s o i l s information i s required by a wide range of users, including farmers, ranchers, f o r e s t e r s , community decision makers, hydrologists, engineers, and teachers, most of whom have no background i n s o i l science, i n t e r p r e t a t i v e maps, which r e l a t e s o i l c h a r a c t e r i s t i c s and i n t r i n s i c behaviour of the s o i l to the user, are becoming more popular. The concept i s not new. In the 1930's farmers were provided with s o i l maps that showed land c a p a b i l i t y classes i n colours ( M i l l e r and Nichols, 1979) . Since that time i n t e r p r e t a t i v e evaluations of s o i l survey data have been developed 13 f o r a wide range of a g r i c u l t u r a l and non-agricultural uses. These interpretations represent the current understanding of s o i l character-i s t i c s - • and how they react to management. G. Angus H i l l s developed an o v e r a l l framework which incorporates a l l i n t e r p r e t a t i v e schemes at three l e v e l s : c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y ( H i l l s and Portelance, 1960; Belknap and Furtado, 1967). D e f i n i t i o n s of c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y were provided i n Chapter 1. Descriptions of some-inter-preta t i v e c l a s s i f i c a t i o n at each l e v e l of H i l l ' s system follow. 1. Land C a p a b i l i t y Assessment Land c a p a b i l i t y i s defined by the degree of l i m i t a t i o n to s p e c i f i c use and by the r e l a t i v e e f f o r t to obtain and maintain high i n t e n s i t y use (Table 2.1, Belknap and Furtado, 1967). Land c a p a b i l i t y systems have been TABLE 2.1 D e f i n i t i o n of C a p a b i l i t y Classes A to G on a Comparative Basis (Belknap and Furtado, 1967) Class Level of Ca p a b i l i t y Relating Intensity of Use Po t e n t i a l (1) Degree of L i m i t a t i o n Relative E f f o r t To Obtain and Maintain a High Intensity Of Use A Very High Very High Very Low Not S i g n i f i c a n t B High High Low Very Low C Mod. High Mod. High Mod. Low Low, D Moderate Moderate Moderate Moderate E Mod. Low Mod. Low Mod. High High F Low Low High Very High G Very Low Very Low Very High Prohibitory 14 developed f o r single purpose and f o r multi-purpose assessments of the land. The United States Department of Agri c u l t u r e (USDA) Land C a p a b i l i t y C l a s s i f i c a t i o n ( K l i n g e b i e l , 1958; Steele, 1967; Robertson et a l . 1968; Young, 1973; Singer, 1978), the United Kingdom (UK) A g r i c u l t u r a l Land Service Scheme and the Land-Use C a p a b i l i t y C l a s s i f i c a t i o n of the United Kingdom s o i l survey (Young, 1973) are examples of sing l e purpose capa-b i l i t y evaluations oriented toward a g r i c u l t u r e . The Canada Land Inventory System i s a multi-purpose scheme which incorporates c a p a b i l i t y assessments for a g r i c u l t u r e , f o r e s t r y , w i l d l i f e and recreation (Environment Canada, 1972; Young, 1973; Coombs and Thie, 1979). Descriptions of the- USDA, U.K* CLI c a p a b i l i t y c l a s s i f i c a t i o n s are given below. a. USDA Land C a p a b i l i t y C l a s s i f i c a t i o n The USDA Land C a p a b i l i t y C l a s s i f i c a t i o n i s an i n t e r p r e t a t i v e system for a g r i c u l t u r e based on s o i l properties and climate. Thirteen c r i t e r i a are used to place s o i l s i n eight classes depending upon t h e i r permanent l i m i t a t i o n s f o r a g r i c u l t u r e and r i s k s of s o i l damage ( K l i n g e b i e l , 1958; Steele, 1967; Robertson et a l . 1968; Young, 1973; S i n g e r 1 9 7 8 ) . The system.assumes a f a i r l y high l e v e l of management one that i s within the a b i l i t y of most farmers but i t does not indicate the kind of management necessary (Steele, 1967; Robertson et a l . 1968). The c a p a b i l i t y c l a s s i -f i c a t i o n does not suggest the most p r o f i t a b l e use of s o i l s nor do distance to markets, kinds of roads, s i z e and shape of s o i l areas, locations within f i e l d s or farms, s k i l l s or resources of i n d i v i d u a l operators and other c h a r a c t e r i s t i c s of land ownership on land management practices influence c a p a b i l i t y grouping (Steele, 1967). 15 b. United Kingdom 1. The Agr i c u l t u r a l Land Service Scheme. 2. The Land Use Capability C l a s s i f i c a t i o n of the United Kingdom S o i l Survey. The Ag r i c u l t u r a l Land Service Scheme and the Land-Use Capability C l a s s i f i c a t i o n of the.UlK.soil survey are oriented toward agriculture. The former i s a system developed to advise on the release of a g r i c u l t u r a l land for urban development and divides s o i l s into f i v e classes describing the degree of l i m i t a t i o n to a g r i c u l t u r a l use (Young, 1973). The l a t t e r scheme closely follows the USDA Land Capability C l a s s i f i c a t i o n but reduces the number of classes to seven and specifies the range of each l i m i t a t i o n permitted within each cap a b i l i t y class (Young, 1973). c. Canada Land Inventory The Canada Land Inventory i s a comprehensive survey of land capa-b i l i t y and use designed to provide a basis for resource and land use planning, c a p a b i l i t y i s assessed for agriculture, forestry, recreation and w i l d l i f e and an overall analysis i s made of land capability by over-laying each of the special purpose maps (Env. Can., 1972; Young, 1973; Coombs and Thie, 1979). All"components of the overall c a p a b i l i t y evaluation are similar i n the following respects: 1. A l l are interpretative systems which group s o i l types into 7 classes. 2. A l l systems, except recreation which presents positive features of the landscape, l i s t the l i m i t i n g factors which progressively r e s t r i c t the land from class 1 to class 7. 3. A l l systems are compatible on a nation wide basis. 4. A l l s i t e factors are used to assess the capability rating. 16 5. Only inherent physical characteristics are assessed. 6. A l l systems are oriented toward planning rather than management,(Coombs and Thie, 1979). The U.K. capability c l a s s i f i c a t i o n for agriculture follows the USDA capability c l a s s i f i c a t i o n . Mineral s o i l s are grouped into , seven classes according to thei r potentials and lim i t a t i o n s for a g r i -c u l t u r a l use. Class 1 shows no s i g n i f i c a n t l i m i t a t i o n s for common f i e l d crops; class 7 shows no a g r i c u l t u r a l potential (Env. Can. 1972; Runka, 1973; B.C. Land Comm, 1975; B.C. Min. Env., 1979). The capability c l a s s i f i c a t i o n for forestry i s based upon producti-v i t y classes. Productivity i s assessed according to the mean annual increment of the best species or group of species adapted to the s i t e at, or near, rotation age. Class 1 lands have no important l i m i t a t i o n s to the growth of commercial forests with productivity over 7.8 m3/ha per annum; class 7 lands are not suitable for the growth of commercial forests (Coombs & Thie, 1979). Land cap a b i l i t y for w i l d l i f e i s divided into two categories: ungulate and waterfowl. In both cases, the capability class l e v e l i s assessed on the a v a i l a b i l i t y of food, protective cover and space to survive, grow and reproduce. The capability c l a s s i f i c a t i o n for ungulates assesses the a b i l i t y of the land to support or produce w i l d l i f e ; the c l a s s i f i c a t i o n for waterfowl considers both land and water surfaces. The land c a p a b i l i t y c l a s s i f i c a t i o n for outdoor recreation simultaneously assesses the quality of the land for recreation and the a b i l i t y of the land to sustain recreational a c t i v i t i e s per unit area per year under perfect market conditions. Areas which can sustain intensive use have high capability ratings ( i . e . Class 1 to 3); those which lack 17 natural attractiveness OE present severe l i m i t a t i o n s to recreational use have the lowest capability ratings. The map overlay representing the f i v e land capability c l a s s i f i c a t i o n results i n a comprehensive resource data base which can be used i n making land use decisions. d. Use of Land Capability Assessments Evaluation of land ca p a b i l i t y i s useful to a wide range of users in providing a data base for broad-scale resource and land planning based .. upon an understanding of the physical nature of the land's resource capacity to sustain and support various a c t i v i t i e s . Despite i t s advant-ages, the land ca p a b i l i t y c l a s s i f i c a t i o n has inherent lim i t a t i o n s which r e s t r i c t i t s u t i l i t y . These include the following: 1. The capa b i l i t y c l a s s i f i c a t i o n i s based upon l i m i t a t i o n s , mostctf<whichtean.be modified or overcome by management practices. 2. Capability assessments are made at the reconnaissance le v e l and are designed for planning rather than management purposes. 3. Single purpose maps are use spe c i f i c and are of limited use in an overall planning process; the combination of single purpose maps to form a multi-purpose approach i s r e s t r i c t e d by the different set of c r i t e r i a used i n each interpretative scheme. 4. Land cap a b i l i t y assessments are quali t a t i v e and bear l i t t l e c o r r e l a t i o n to measured productivity and performance. A more quantitative assessment of land potential can be made using measured y i e l d or performance data. This type of evaluation i s known as land s u i t a b i l i t y . 2. Land S u i t a b i l i t y Assessment Land s u i t a b i l i t y can be defined either by the productivity of the land expressed by b i o l o g i c a l y i e l d or by s o i l behavioural characterist ICS 18 which affect potential land use. Productivity data generally form the basis for a g r i c u l t u r a l or forestry related s u i t a b i l i t y assessments due to the a v a i l a b i l i t y of y i e l d data. Engineering applications rely upon behavioural interpretations. a. Productivity Assessments Productivity assessments are made for s p e c i f i c crops i n r e l a t i o n to defined sets of management practices and levels of management (Pierre, 1958; Aandahl, 1960; Steele, 1967; B a r t e l l i , 1979; M i l l e r and Nichols, 1979) . Many attempts have been made to relate productivity to s o i l para-meters (Clarke, 1951; Nikolayev, 1975; Allgood and Gray, 1978; Mackney i n Tru d g i l l and Briggs, 1979), but there have been three major d i f f i c u l t i e s . F i r s t l y , requirements of plants and, i n p a r t i c u l a r , edaphic needs of spec i f i c crops are poorly understood. Secondly, soil-crop relationships vary with changes i n plant environments i n response to seasonal climatic patterns (Trudgill and Briggs, 1979). Thirdly, s o i l i s only a small component of b i o l o g i c a l productivity; management i s far more important. Management refers mainly to crop cha r a c t e r i s t i c s , s o i l and water manage-ment practices, crop protection and management s k i l l , rather than to considerations of s o i l and climate (Steele, 1967). The relationships between s o i l parameters and productivity w i l l never be perfect due to the effects of management, but better relationships than those currently i n existence should be developed for different crops i n different areas. T r a d i t i o n a l l y , s o i l productivity studies have stressed s o i l chemical properties, p a r t i c u l a r l y potassium, nitrogen and phosphorus (Cooke, 1979), largely to the exclusion of s o i l physical properties. Recently the s i g n i -ficance of s o i l physical properties has been realized and emphasis has 19 shifted to measurement of such parameters as s o i l moisture, s o i l texture and s o i l structure to observe interactions between structural factors, c u l t i v a t i o n and water management as controls upon crop growth. Cooke argues that available water storage capacity i s the most important single physical measurement that i s relevant to crop production and that the ' i n a b i l i t y to understand, measure and control s o i l structure through c u l t i v a t i o n , and to quantify i t s effect on supply of nutrients and water, i s a major factor in.vthe common f a i l u r e to achieve consistently large cereal y i e l d s ' (Cooke, i n T r u d g i l l and Briggs, 1979).' Nix (1968) suggested three approaches to land s u i t a b i l i t y assess-ment: analogue method, s i t e factor method and systems analysis. Descrip-tions of the methods follow. i . Analogue Method The analogue method i s most commonly used. Productivity data i s collected from experimental s i t e s and extrapolated to analogous s i t e s defined by land or s o i l , c l a s s i f i c a t i o n based on the premise that s i m i l a r s o i l s w i l l show si m i l a r responses for any specified form of use (Nix, 1968; Hoffman, 1971). S o i l s are evaluated using measured or observed s o i l properties and consequently, an a p r i o r i knowledge of the functional relationships between the s o i l parameters and productivity i s not re-quired. Shortcomings of the analogue method include the following: 1. A poor relationship has been observed by several authors including Avery, Butler and Gibbons ( i n Hoffman, 1971) .between ...treatment,,. ;:so,il; parameters , arid t yield;-. (Nix,' 1968; Hoffman, 1971). 2. Productivity studies must be carried out over a long period of time to obtain: r e l i a b l e estimates of productivity (Nix, 20 3. P r o d u c t i v i t y studies are l i m i t e d to a r b i t r a r i l y defined s o i l s classes and are often r e s t r i c t e d by the e x i s t i n g land use (Nix, 1968). ia.. S i te Factor Method The s i t e f a c t o r method r e l a t e s key properties of a s i t e to y i e l d within a given environment. S e l e c t i v e p h y s i c a l and chemical parameters which.directly influence s i t e p r o d u c t i v i t y are plo t t e d as independent variables against the dependent v a r i a b l e , y i e l d , i n a multiple regression. The strength of the r e l a t i o n s h i p of independent to dependent variables suggests which of the chosen parameters r e l a t e to y i e l d . I n t e r r e l a t i o n -ships between y i e l d and s i t e factors may also be i d e n t i f i e d i f the s i t e i s under constant management. Si t e f a c t o r analysis has two l i m i t a t i o n s : 1. The analysis i s s i t e and use s p e c i f i c . 2. The r e s u l t s are v a l i d only f o r the crop studied within the chosen range of parameters. Most of the work done using the s i t e f actor method to date has been i n f o r e s t r y . The few a g r i c u l t u r a l studies report concern over the use of a l i m i t e d number of s o i l properties and a large number of management parameters as s i t e f a c t o r s . The use of management factors tends to obscure the influence of the s o i l properties on y i e l d . i i i . Systems Analysis Systems analysis i s a dynamic approach concerned with:. 1. Resolu-tio n of a complex system into a large number of simple component processes, and 2. subsequent synthesis into a mathematical model of the whole system (Nix, 1968; Hoffman, 1972). The model attempts to i d e n t i f y functional pathways and in t e r a c t i o n s within a complex system to help understand, quantify and pr e d i c t behaviour and response of the system. Although the 21 advent of the computer has allowed much progress i n systems analysis, the technique i s s t i l l l i m i t e d by the complexity of b i o l o g i c a l systems which makes quantitative measurement and h o l i s t i c modelling very d i f f i c u l t to develop (Nix, 1968; Hoffman, 1972). b. Behavioural Assessments S o i l s u i t a b i l i t y assessments f o r engineering purposes rate s o i l s on degree of l i m i t a t i o n to use. The most important s o i l properties reported by the Asphalt I n s t i t u t e (1969'in. B a r t e l l i , 1979) are permeability, e l a s t i c i t y , Atterburg l i m i t s , cohesion, sheaving strength, compressibility, shrinkage and swell, and f r o s t s u s c e p t i b i l i t y . These data are generally obtained from highway testing laboratories ( M i l l e r and Nichols, 1979) and are used i n support of s o i l survey data. Engineering interpretations are a v a i l a b l e for many purposes, including b u i l d i n g , maintenance and construction, roads, sanitary f a c i l i t i e s and dwellings. D i f f e r e n t s o i l parameters are important for each use and manuals, such as the Guidelines for the Interpretation of Engineering Uses, o f f e r guide sheets, which i d e n t i f y the pertinent s o i l properties a f f e c t i n g s p e c i f i c uses. c. Use of Land S u i t a b i l i t y Assessments Land s u i t a b i l i t y assessment provides a quantitative measure of the land's resource capacity to produce s p e c i f i c goods and services. The evaluation i s based upon the p h y s i c a l parameters of the land and shows the r e l a t i o n s h i p between inputs and p r o d u c t i v i t y , on performance. The approach r e l i e s upon accurate y i e l d data and management information for p r o d u c t i v i t y assessments and upon the i d e n t i f i c a t i o n of pertinent s o i l properties for the evaluation of land behaviour f o r land use planning and engineering a p p l i c a t i o n s . Dynamic considerations, such as land rent, 22 market value and geographic l o c a t i o n do not influence land s u i t a b i l i t y assessments; therefore, the r e s u l t i n g c l a s s i f i c a t i o n have long term a p p l i c a b i l i t y . 3. Land F e a s i b i l i t y Assessment Land f e a s i b i l i t y assessments measure the a b i l i t y of the s o i l to produce, y i e l d or support an a c t i v i t y at a cost expressed i n economic, s o c i a l and environmental units of value using l a t e s t f e a s i b l e technology ( B a r t e l l i , 1974 i n B a r t e l l i , 1979). One of the e a r l i e s t attempts to model land f e a s i b i l i t y was the Van Thunen model which explained v a r i a t i o n i n land value i n r e l a t i o n to distance to market ( G i r t , 1977). More recent evaluations of f e a s i b i l i t y have appeared as equations f o r s o i l p o t e n t i a l r a t i n g s . Values i n the equations r e f l e c t s o i l properties influencing use, cost of tre a t i n g s o i l l i m i t a t i o n s and the cost of continuing l i m i t a t i o n s i f not removed (Kellogg, 1961; Slusher, 1977 i n B a r t e l l i , 1979). The evaluation of f e a s i b i l i t y has many shortcomings. Among these are the following: 1. The prices of supplies and products upon which the measures are based do not remain constant to one another over time. 2. The selected economic c r i t e r i a may influence f e a s i b i l i t y by a p r i c e d i s t o r t i o n s , such as subsidies and tax breaks which a f f e c t the actual economics of various land uses; b..a d i f f e r e n t set of c r i t e r i a which are important to the land operator, than to the professional economist; c other c r i t e r i a . w h i c h i n d i r e c t l y a f f e c t the f e a s i b i l i t y of land use may or may not be measured with those factors which have d i r e c t e f f e c t s . Summary and Conclusions D e f i n i t i v e s o i l s information f o r land use decisions i s av a i l a b l e at two levels:, s o i l survey data and s o i l s i n t e r p r e t a t i o n s . S o i l survey 23 data i s an inventory of s o i l s resources within an area. Experience has shown that s o i l s information i n s o i l survey form i s complex to a l l but the s o i l s c i e n t i s t and requires t r a n s l a t i o n to simple expressions that the user can understand. S o i l s i n t e r p r e t a t i o n s have been developed based upon d i f f e r e n t assumptions and c r i t e r i a . f o r many purposes, but generally f a l l into three categories: c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y . C a p a b i l i t y i s a q u a l i t a t i v e evaluation of land a b i l i t y to sustain s p e c i f i c uses.. i S u i t a b i l i t y assesses the p o t e n t i a l of the land to produce s p e c i f i c goods and services under defined management practices and l e v e l s . S u i t a b i l i t y i s a quantitative evaluation based eit h e r upon p r o d u c t i v i t y or s o i l performance. F e a s i b i l i t y evaluates land p o t e n t i a l according to socio-economic influences. I t i s the most dynamic of the three i n t e r p r e t a t i o n s . In general, the most e f f i c i e n t use w i l l be made of the s o i l s i n t e r p r e t a t i o n s i f users understand: 1. What s o i l properties are evaluated, described, recorded, named and c l a s s i f i e d i n a s o i l survey; 2. The assumptions involved and the c r i t e r i a established for d i f f e r e n t i n t e r p r e t a t i o n s ; 3. The nature and v a r i e t y of possible i n t e r p r e t a t i o n s that a r i s e from s o i l survey data (Wohletz, 1968). S o i l survey data and some int e r p r e t a t i o n s are examined i n the present study for a small test region i n the Lower Fraser V a l l e y of B r i t i s h Columbia. The methods employed i n the study are outlined i n the following chapter. 24 CHAPTER 3 METHODS So i l s information i s one of the most important considerations i n land evaluation f o r land use decisions. In the present study, s o i l survey data was assessed using c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y to i d e n t i f y the best approach to optimal land use evaluation. The study involved s e l e c t i o n of s o i l parameters; c o l l e c t i o n of s o i l s data; evalu-ation of c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y ; numerical analysis of s o i l s data and s o i l survey i n t e r p r e t a t i o n s and assessment of s o i l s groups and i n t e r p r e t a t i v e - s o i l s u n i t s . The methodology follows. S o i l s Data S o i l i s i n t e g r a l to plant growth and exerts considerable influence on b i o l o g i c a l p r o d u c t i v i t y through inherent f e r t i l i t y and ease of manage-ment. Consequently, land use should be di c t a t e d to c e r t a i n degree by s o i l q u a l i t y . S o i l s information at the s o i l series l e v e l formed the basii u n i t s of the present study. The selected mineral s o i l s e r i e s (Table 3.1) supported a wide range of a g r i c u l t u r a l crops f o r which p r o d u c t i v i t y data was r e a d i l y a v a i l a b l e . The s o i l s evolved from g l a c i o . f l u v i a l deposits, g l a c i a l t i l l , glaciomarine deposits, alluvium, l a c u s t r i n e deposits and aeolian deposits and consequently exhibited a wide range of s o i l proper-t i e s . The geographical d i s t r i b u t i o n of s o i l s i s presented, i n Figure 3.1. 1. Sources of S o i l s Data Basic s o i l s data f o r the chosen series were c o l l e c t e d from two sources:. 25 F I G U R E 3.1 - G E O G R A P H I C A L D I S T R I B U T I O N O F S O I L S 26 1. S o i l survey data bank 2. Sample analysis a. Soils Data Bank The Soils Data Bank established by the Research Analysis Branch (Victoria) was o r i g i n a l l y intended to serve as the s o i l s data base for the present study, but the required information was limited or unavail-able and additional data was needed to establish a complete data set. Consequently the available data was used as a basis to which additional information, such as s o i l moisture and consistency parameters was added. b. Collected Data F i e l d sampling and laboratory analysis were performed to provide data for a l l the morphological parameters required for the study. 2. Selection of Parameters The o r i g i n a l group of variables consisted of t h i r t y f i v e selective parameters representative of permanent s o i l properties which are expected to remain unaltered when subjected to s o i l management practices. The chosen parameters were predominantly s o i l physical parameters due to the v a r i a b i l i t y of chemical properties i n changing environments. Engineering and a g r i c u l t u r a l s o i l s parameters were included to f u l f i l l the o r i g i n a l intention of designing an optimum multi-purpose land-use system. The i n i t i a l t h i r t y f i v e parameters were reduced to nineteen by the removal of variables that: 1. Described the same parameter ( i . e . l i q u i d l i m i t was chosen to represent the Atterburg l i m i t s ) , or 2. Had such a narrow range of values when plotted on a frequency histogram that they were not useful i n s o i l s group separation. TABLE 3.1  S o i l Series Used In Study Symbol AD BT BL BK F HT HD HJ KD LX LK MH MQ M NN PE RD SI VD W Series Abbotsford Bates Beharrel Buckerfield F a i r f i e l d H a l l e r t Hazelwood Hjorth Kennedy Laxton Lickman Marble H i l l Matsqui Monroe Niven Page Ryder Sim Vedder Whatcom 28.: The r e s u l t i n g group of v a r i a b l e s can be found i n Table 3.2. TABLE 3.2 S o i l Parameters Parameter Horizon* Source pH i n CaCl 2 C Measured Coarse Fra c t i o n S,C Measured Sand S Measured S i l t C Measured Clay S,C Measured Organic Matter S Measured Li q u i d Limit S,C Measured Water Storage Capacity S,C Measured CEC S,C Measured Drainage N/A S o i l s Data Bank Slope N/A S o i l s Data Bank Perviousness N/A S o i l s Data Bank Upper Parent Material N/A S o i l s Data Bank Lower Parent Material N/A S o i l s Data Bank *S refe r s to surface horizon (usually an Ap horizon) C r e f e r s to the C horizon. N/A i s not applicable The majority of va r i a b l e s can be represented by measured values (e.g. 76% c l a y ) ; some, such as drainage, perviousness, upper parent material and lower parent material must be represented by ca t e g o r i c a l data. Explanations of the categories are presented i n Appendix A. 29-3. Sampling Mr. H. Luttmerding (Terrestial Studies, Kelowna) i d e n t i f i e d the location of the s o i l p i t s which served as the central concepts of the s o i l series i n the 1964 S o i l Survey Report. P i t s were dug i n these locations to a depth of approximately one metre. Bulk density was measured according to the held method (Blake, 1965) for the surface horizon and for the uppermost C horizon. Rooting depth, stoniness, mottling abundance, structure, slope, aspect and depth to rock, water table on root r e s t r i c t i n g layer were recorded. S o i l samples were were obtained from the surface and C horizons for laboratory analysis. A single surface horizon was sampled because the s o i l s under study are a g r i c u l t u r a l s o i l s and for the most part have Ap horizons rather than Ah horizons. The C horizon was sampled to determine inherent s o i l f e r t i l i t y . 4. Laboratory Analysis The s o i l samples were returned to the laboratory and a i r dried. Aggregates were broken with a wooden r o l l i n g pin with care not to crush the primary sand and gravel p a r t i c l e s . The samples were separated using a 2 mm sieve and the portion greater than 2 mm was weighed and recorded as the percent coarse f r a c t i o n . The remaining analyses were performed using the f r a c t i o n less than two millimetres. pH was measured i n 0,01 M CaCl 2 (Peech,. 1965). S o i l organic matter was calculated from t o t a l carbon as determined using the Leco Analyzer (Lavkulich, 1977). The ammonium acetate method (at pH 7.0) was performed to measure t o t a l cation exchange capacity (Chapman, 1965). S o i l water retention was determined at 3 bar pressure and 15 bar pressure using the porous plate extraction method. 30 The difference i n moisture content at the two pressures comprised the water storage capacity (Richards, 1965). Liquid l i m i t was assessed according to the test for Atterburg l i m i t s (Sowers, 1965). The hydro-meter method was used to determine the p a r t i c l e size d i s t r i b u t i o n (Day, 1965). Readings were taken at 1 minute, 2 minutes, 5 minutes, 10 minutes, 30 minutes, 60 minutes, 120 minutes, 180 minutes, 360 minutes.and 1440 minutes. S o i l Survey Interpretative C l a s s i f i c a t i o n S o i l series were c l a s s i f i e d into interpretative s o i l s units using c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y assessments of s o i l survey data. Details of the assessment and c l a s s i f i c a t i o n procedures follow. 1. Land Capability Data The Canada Land Inventory Capability C l a s s i f i c a t i o n for agriculture delineates area of similar capability for the production of a range of common f i e l d crops. Soils are divided into seven classes that have the same degree of l i m i t a t i o n or hazard. A recent application of the Canada Land Inventory i s the A g r i - . ; .. cu l t u r a l Land Reserve l e g i s l a t i o n i n B r i t i s h Columbia. The l e g i s l a t i o n i s intended to re t a i n prime a g r i c u l t u r a l land for a g r i c u l t u r a l use. Land with Canada Land Inventory A g r i c u l t u r a l capability class 4 or better i s placed into the A g r i c u l t u r a l Land Reserve which i s zoned to l i m i t land use to agriculture, thereby eliminating non- a g r i c u l t u r a l competition for the land. Canada Land Inventory A g r i c u l t u r a l Capability information i s present-ed i n map form. The s o i l c apability map for Sumas (map sheet 926/1) was the source of capa b i l i t y data for the project. The mapped capability data 31 were o v e r l a i n on a soiiki"..- series map and the percentage of each s o i l series within every c a p a b i l i t y class was determined by intensive g r i d dot counting using a 0.01 cm g r i d . The s o i l s were grouped based upon the c a p a b i l i t y c l a s s , or range of c a p a b i l i t y classes, which contained most of the area within pure s o i l series u n i t s . The range i n capa-b i l i t y classes within a s o i l s e r i e s was explained by: 1. V a r i a t i o n i n c a p a b i l i t y due to the complex topography. 2. Cartographic error derived from overlaying a 1:50,000 scale c a p a b i l i t y map on a reduced 1:25,000 scale s o i l series map. The l a t t e r problem was Inevitable., but-, .there was -norecourse if- . e x i s t i n g capa-b i l i t y data were to be used i n the present study. 2. Land S u i t a b i l i t y Data S u i t a b i l i t y assessments generally involve some measure of p r o d u c t i v i t y f or s p e c i f i c crops i n r e l a t i o n to defined sets of manage-ment practices and l e v e l s (Pierre, 1958). Pr o d u c t i v i t y data may be obtained from a number of sources, including farm observations, farm record data, farmer survey, experimental r e s u l t s and f i e l d t r i a l s . Farm observations of crop growth on d i f f e r e n t types of s o i l and under d i f f e r e n t sets of management may be' made by the s o i l surveyor, but these observations are l i m i t e d to areas which are currently c u l t i v a t e d (Steele, 1967). Farm record data and farmer survey provide crop y i e l d information for a wide range of s o i l s , c l i m a t i c conditions and management p r a c t i c e s . Farm record data i s considered preferable because i t does not r e l y upon / the memory of the c u l t i v a t o r (Odell, 1958;. Steele, 1967). Experimental r e s u l t s are often substituted where farm record data are unavailable. These data are more expensive to obtain than crop y i e l d data, but the r e s u l t s are often more preci s e . In general crop y i e l d s tend to be lower 32: Table 3.3 Survey Questionnaire Name t Location i 1 Total Area a) tinder cultivation b) type of use c) a l t * history 2 Soil Series ») type b) proportion 3 Management a) s o i l treatment b) irrigation c) drainage d) t i l e spacing 4 Soil Test a) tine b) parameters 5 Lime Application a) rate b) time c) type 6 Manure Application a) rate b) time c) type d) management 7 Fertiliser Use a) rate b) time c) type 6 Crop Yield a) tons/ acre b) others c) t of cuttings 9 Animal Production a) type b) # of animals C) milk production 4 ) turn-over 10 Crop Rotation 11 Soil Problems 12 Productivity due to differences in s o i l 33 (75% to 95%) on farms than on experimental plots with s i m i l a r s o i l s , climate and management practices (Odell, 1958). Shorter term y i e l d data may be obtained from f i e l d t r i a l s i n which the performance of selected crops under s p e c i f i c management conditions i s measured for one or two seasons (Odell, 1958; Steele, 1967). In general, the l e v e l of accuracy of crop y i e l d estimates depends upon the number of years over which y i e l d data were c o l l e c t e d and upon the number of land t r a c t s i n the estimate (Odell, 1958). Pro d u c t i v i t y data f o r the present study were c o l l e c t e d from four sources: farmer survey, d i r e c t estimates by expert consensus, p l o t t r i a l and research s t a t i o n data. a. Farmer Survey Pr o d u c t i v i t y data and crop management information were c o l l e c t e d f o r a wide range of crops including grass, corn, raspberries, strawberries, beans, peas, b r o c c o l i , cauliflower, brusselvsprouts and rhubarb. Mr. B. Peters ( B r i t i s h Columbia M i n i s t r y of A g r i c u l t u r e ) , Mr. S. Loewen (East Chilliwack Coop) and B.C. M i n i s t r y of A g r i c u l t u r e personnel i d e n t i f i e d t h i r t y one key farmers who c o n s i s t e n t l y attained high p r o d u c t i v i t y l e v e l s and from whom r e l i a b l e y i e l d and management data could be obtained. These farmers were interviewed using a questionnaire (Table 3,3) adapted from a B r i t i s h Columbia M i n i s t r y of A g r i c u l t u r e form developed by Mr. R. ,Bertrand (B.C. M i n i s t r y of A g r i c u l t u r e ) . The questionnaire stressed s i t e h i s t o r y , management p r a c t i c e s , s o i l features and problems, and p r o d u c t i v i t y . b. D i r e c t Estimate by Expert Consensus Y i e l d estimates made by a group of a g r i c u l t u r a l experts i n 1975 headed by H. Luttmerding (personal communication) were obtained f o r a 34 v a r i e t y of berry, vegetable and forage crops f o r 10 series within the study area. These data were used to v e r i f y and supplement the farmer survey information. c. P l o t T r i a l s The only a v a i l a b l e data from p l o t t r i a l s f o r s o i l s within the study area were mean y i e l d data f o r grass, b r o c c o l i and sweet corn crops on Monroe, Matsqui and Ryder s o i l s . These data were obtained from Dr. G. Kowlaenko from A g r i c u l t u r e Canada, Agassiz. d. Research Station Data Raspberry y i e l d data from the Abbotsford s o i l series were the only a v a i l a b l e experimental s t a t i o n p r o d u c t i v i t y data. These were obtained from research pl o t s at the Canada A g r i c u l t u r a l Research Station (Abbots-ford) courtesy of Mr. Dobney (Agriculture Canada). The y i e l d data from a l l sources was pooled and p r o d u c t i v i t y indices were established using y i e l d data f o r those crops which could p o t e n t i a l l y be grown on a l l the s o i l series within the study area. The s o i l series were grouped into low, medium and high p r o d u c t i v i t y classes on the basis of each crop index. The range of y i e l d s f o r each class depended upon the crop. 3. Land F e a s i b i l i t y Data F e a s i b i l i t y c l a s s i f i c a t i o n s integrate socio-economic considerations with land resource information. Current land use i s a r e f l e c t i o n of f e a s i b i l i t y . A report and a serie s of d e t a i l e d land use maps prepared i n 1977 by the Corporations and the D i s t r i c t s of Matsqui and Abbotsford were the most up-to-date source of current land use data a v a i l a b l e . The 35 information on the maps was based upon a pl o t by p l o t d e s c r i p t i o n of the area. A s i n g l e land use map was prepared from the land use data and o v e r l a i n oh. a s o i l s e r i e s base map. Intensive g r i d dot counting using a .1 cm dot g r i d was employed to determine the percentage of each s o i l under every land use. Numerical Analysis Numerical analysis included c l u s t e r analysis , f a c t o r a n a l y s i s , stepwise discriminant analysis and Mann-Whitney Rank Sum Test ( s i g n i f i c a n c e t e s t ) . A l l analyses were performed by the U.B.C. computer using U n i v e r s i t y of C a l i f o r n i a Biomedical Programs (BMD, Brown and Dixon, 1979). 1. Cluster Analysis Cluster analysis i s an agglomerative process which groups cases on a measure of a s s o c i a t i o n , or s i m i l a r i t y , based upon multiple parameters of equal weight. Each case i s i n i t i a l l y considered a c l u s t e r containing one member. The c l u s t e r s are progressively joined i n a stepwise procedure u n t i l a l l cases form one c l u s t e r . An example of t h i s procedure, which i s known as h i e r a r c h i a l average distance linkage (Ward, 1963) i s shown i n Figure 3.2 using two paramaters, % cl a y and l i q u i d l i m i t . Three cases are p l o t t e d i n two-dimensional space. Cases BT and BL are closest to each other and are li n k e d f i r s t by the c l u s t e r i n g procedure. The midpoint of the l i n e connecting BT to BL becomes the centroid of the group and the t h i r d case, AD, i s li n k e d to t h i s centroid. The distance between the c l u s t e r s at linkage describes the s i m i l a r i t y of the cases. A hierarchy, such as the one presented:, i n Figure 3.2, i l l u s t r a t e s the linkage sequence and the degree of s i m i l a r i t y between cases. The c l u s t e r i n g procedure can be extended, from two parameters to multiple parameters using matrix algebra and computer analysis (Ward, 1963; Sneath and Sokal,1973; Dixon and Brown, 36 SOIL SERIES * CLAY LIQUID LIMIT A 0 4 13 BT 39 33 BL 5 2 37 AD 0 10 20 30 40 50 60 LIQUID LIMIT FIGURE 3.2-CLUSTERING PROCEDURE 37 1979; Webster, 1979). 2. Factor Analysis Factor analysis i s a mul t i v a r i a t e s t a t i s t i c a l procedure which combines common var i a b l e s into f a c t o r s . The procedure assumes that only part of the v a r i a t i o n i n a given population i s contained w i t h i n the v a r i -ables used to define that population. Factor analysis u t i l i z e s information from a multiple c o r r e l a t i o n matrix i n which each v a r i a b l e i s regressed against the others. The diagonals of the matrix provide an estimation of the proportion of the variance of a v a r i a b l e that i s hed i n common with a l l other v a r i a b l e s . These are reduced by t h e i r uniqueness to the . communalities, the percentage of v a r i a t i o n due to common f a c t o r s . The communalities replace the diagonals i n the c o r r e l a t i o n matrix and f a c t o r -ing . using a non-deterministic approach known as varimax r o t a t i o n . The o r i g i n a l p r i n c i p a l components, chosen under the assumption that they represent the scatter i n the data, are rotated orthogonally to new axes such that each o r i g i n a l v a r i a b l e contributes strongly to one fa c t o r and l i t t l e to the others. This i s represented by the fa c t o r loadings which are as large as possible and represent the fewest variables possible f o r each factor ( C a t t e l , 1965; Sneath and Sokal, 1973; Yeates, 1974; Brown and Dixon, 1979; Webster, 1979). 3. Stepwise Discriminant Analysis Stepwise discriminant analysis numerically c l a s s i f i e s s o i l s using d i v i s i o n s which are oblique through space and which are based on descrip-t i v e parameters of the group rather than an i n d i v i d u a l s within the groups. In c l a s s i f i c a t i o n s with more than two groups, the discriminant space i s divided into regions, each associated with one of the groups. Each group 38 has a reference score. Midway points, h a l f the distance between reference scores, are calculated for each p a i r of groups and defines the plane which separates the regions. The length of the l i n e between any two reference scores measured i n discriminant function units i s the square root of the Mahalanobis D 2, or the Euclidean distance. This distance i s important i n t e s t i n g whether the group reference scores are s i g n i f i c a n t l y d i f f e r e n t (using the F-test) and also i n assessing the discriminatory power of each v a r i a b l e . Stepwise discriminant analysis i s p r o b a b i l i s t i c i n that i t gives some measure that the c l a s s i f i c a t i o n of an i n d i v i d u a l i s c o r r e c t . Mis-c l a s s i f i e d i n d i v i d u a l s are i d e n t i f i e d and c l a s s i f i e d into t h e i r proper groups. S i m i l a r l y , unknown cases can be entered into the open-ended c l a s s i f i c a t i o n and placed i n an appropriate grouping (Sneath and Sokal, 1973; 1973; Webster, 1973). 4. Mann-Whitney Rank Sum Test The Mann-Whitney Rank Sum Test t e s t s the niillv.- hypothesis that two independent samples are derived from the same population. Observa-tions from both samples are ranked i n a sequence and the rank of each sample i s determined. The U - s t a t i s t i c represents the number of times a score from one sample precedes a score from the second sample i n the rank-ing (Siegel, 1956). Assessment of S o i l s Groups and Interpretative S o i l s Units The assessment of the s o i l s groups and i n t e r p r e t a t i v e s o i l s groups was performed i n three stages: 1. Comparison of s o i l s groups based upon d i r e c t grouping (d i r e c t s o i l s groups) with s o i l s groups based upon c l u s t e r analysis using factors ( i n d i r e c t s o i l s groups). 39 2. Comparison of soils interpretative units. 3. Comparison of soils groups and soils interpretative units. 1. Comparison of Soils Groups The Mann-Whitney analysis tested corresponding direct and indirect soils groups for significant differences. 2. Comparison of Interpretative Soils Groups Similarities between the interpretative soils classifications was assessed using a graph method which plotted each interpretative classi-fication against the others in a series of three graphs. The graph method is best explained using the examples presented in Figure 3.3. Figure 3.3a illustrates perfect correlation between CEC and yield. The entire soil population is plotted on the diagonal representing on hundred percent agreement. Figure 3.3b illustrates the opposite extreme in which cation exchange capacity bears no relationship to yield. Every point on the graph is represented and random distribution is observed. Figure 3.3c represents the intermediate stage between perfect correlation and random distribution. The cases show a general relation-ship defined by the diagonal but do not f a l l directly upon the line representing perfect agreement. No cases are found in the extreme corners (i.e., low CEC, high yields; high CEC, low yield). An.uneven case distribution toward one axis (see Figure 3.4) indicates that one variable overestimates the other. In Figure 3.4, the distribution indicates that the CEC rating is high in comparison to yield as described by the relation-ship defined by the diagonal. 40 3a - PERFECT DISTRIBUTION O 1 2 3 YIELD 3 b - RANDOM DISTRIBUTION 0 1 2 3 YIELD 3.C.- INTERMEDIATE DISTRIBUTION 0 1 2 3 YIELD FIGURE 3.3-GRAPH PROCEDURE 41 YIELD F I G U R E 3.4- U N E V E N C A S E D I S T R I B U T I O N 42 3• Comparison of S o i l s Groups and Interpretative S o i l Units The s o i l s groups r e f l e c t s i m i l a r i t i e s i n s o i l properties and contain no element of resource evaluation or i n t e r p r e t a t i o n . Inter-p r e t a t i v e s o i l s c l a s s i f i c a t i o n s s t r e s s i n g physical resource parameters should also create s o i l s units that r e f l e c t the s o i l property s i m i l a r i -t i e s evident i n the s o i l s groups. Furthermore, s i m i l a r s o i l parameters should separate the s o i l s groups and s o i l s u n i t s . In the present study, the degree of s o i l s group separation by s i g n i f i c a n t discriminant s o i l parameters of the i n t e r p r e t a t i v e s o i l s c l a s s i f i c a t i o n s determined the re l a t i o n s h i p between the s o i l s groups and the i n t e r p r e t a t i v e s o i l s u n i t s . S i g n i f i c a n t discriminant parameters for c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y were obtained from the stepwise discriminant analysis r e s u l t s and the s i g n i f i c a n c e test., r e s u l t s . Only s o i l parameters which s i g n i -f i c a n t l y separated two or more of the three i n t e r p r e t a t i v e land management units were used. The degree of s o i l s group separation was expressed as the percentage of s o i l s groups separated by each s i g n i f i c a n t discriminant parameters. For example, 80, 72 and 64% separation of s o i l s groups by s i g n i f i c a n t i n t e r p r e t a t i v e s o i l s discriminant parameters indicated strong emphasis on physical parameters i n the s o i l s i n t e r p r e t a t i o n . Conversely, low percentage separation indicated a weak r e l a t i o n s h i p between physical resource parameters and the underlying assumptions of the i n t e r p r e t a t i v e c l a s s i f i c a t i o n . The r e s u l t s of the data analysis are presented and discussed, i n the f o l l o w i n g chapter. '. 43 CHAPTER 4 RESULTS AND DISCUSSIONS In the present study s o i l interpretations at a l l levels of H i l l s ' system of land c l a s s i f i c a t i o n were performed for twenty s o i l series. S o i l survey data were collected and assessed for ca p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y . Numerical analysis, including cluster analysis and stepwise discriminant analysis c l a s s i f i e d s o i l s into s o i l s groups and interpretative s o i l units. Results are presented i n this chapter i n the following order: numerical analysis results are presented f i r s t ; c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y assessments follow; and a compari-son of the s o i l c l a s s i f i c a t i o n s ends the section. Numerical Analysis of Soils Data S o i l survey i s an inventory of s o i l s resources of an area and uses s o i l properties which predict s o i l behaviour. Consequently, s o i l s groups with similar properties should exhibit similar behaviour and management responses. A clustering procedure using average distance linkage, grouped s o i l s on the basis of multi parameter s o i l s data. Cluster analysis was performed i n two stages: 1. direct grouping of a l l variables. 2. indirect grouping following factor analysis. 1. Soils Data Collected data and s o i l s information from the Soils Data Bank for twenty s o i l series were the basic s o i l s data for the present study (Appendix B). Nineteen s o i l parameters influencing plant productivity and representing permanent s o i l properties were selected for analysis. 44 2. D i r e c t Cluster Analysis Twenty s o i l s series were grouped by c l u s t e r analysis using nineteen s o i l parameters (Table 3.2). The r e s u l t i n g dendrogram (Figure 4.1) revealed seven s o i l s groups; s i x contained two members or less (Table 4.1). Sign i f i c a n c e t e s t i n g of s o i l s group pairs indicated that discriminant s o i l parameters varied according to the tested s o i l s group p a i r and that the number of d i s t i n g u i s h i n g parameters was d i r e c t l y r e l a t e d to group siz e (Table 4.2). Table 4.1 Di r e c t S o i l s Groups Group 1 2 3 •4 5 6 I AD RD HD KD HT BL MH LX W NN LK VD BK HJ SI MQ M BT PE F It i s possible that c l u s t e r analysis was not the most e f f e c t i v e method of separating s o i l into classes. The most obvious feature of the s o i l s groups i s that the majority form one large group linked early i n the hierarchy and the remaining s o i l s formed one or two member groups. This phenomenon could be a function of the s o i l s data set which consists only of q u a l i t y s o i l s found.on prime farmland. The range of s o i l proper-t i e s i s possibly so small that the majority of the s o i l s f a l l into one group. A wide range of s o i l s including organics and poorer q u a l i t y s o i l s might have offered better separation into a larger number of groups. It i s also possible that the s t a t i s t i c a l procedure performed by c l u s t e r analysis i s not suited to the nature of s o i l s data. Cluster 45 AMALGAMATED DISTANCE 2.421 2.277 2.335 2.427 2.727 2.920 2.991 2.872 3.680 3.848 4.137 4.180 4.816 4.470 5.452 5.653 5.398 6.243 7.098 AD LX RD W HD NN KD LK HT VD BL PE BK HJ SI F MQ L T 6 4 T -5 FIGURE 4 .1 - DIRECT GROUPING DENDROGRAM Letters r e f e r to s o i l s e r i e s ; numbers re f e r to s o i l s groups Table 4.2 S i g n i f i c a n t Discriminant Parameters of D i r e c t S o i l s Groups I d e n t i f i e d by Mann-Whitney Rank Sum Test cd cd cd cd Group p, . u [n cj A l l 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 5 5 6 CD CD cd co cd cj U g g ^ ^ o c- . . 0 +? .>> 2 >, CD CD •H CO 4-1 CO 4-1 _ >H u M O -a 4-1 >, 5 4 J 3 - H p *rj 5 3 3c5 P. 4J ft ' N r-l CO •— \ / — N o ' — \ /—s ^—' CO CJ — ' 4-1 CJ o CO o '—s CO N — ' v—' ^—' •H r4 T) -  C CD •H 4-J •rH 4J u !>> J N cd 4-i S •rH CD a cd i—1 cd 00 4-1 zr e cr a 4-1 cd I-I •H i—i u cd •i-l <H •I-I •r- l cd oo CJ C/3 CJ o g rJ rH (J I-H 4J W 4J •r-l -H O r-l O IS O r4 O M Cd r l - r-l U Cd -,H -  -,H -,H Cd Cd Cd Cd CD /—\ —^s 60 CO CJ ctf V—' —' c CD •H ft CJ CJ cd o w w r-l r-l CJ CJ o CO CO 4J 4-) CO C c CD <U CD c rJ M CO cd r-l cd rH P H cd (3-1 cd o •I-I •iH •I-I M u r4 u > CD CD CD CD r-l ft 4-1 4-1 CD ft cd o cd P M iD g r J g vs 2 X X X X X X X X X X X vs 3 X X X X X X X X X X X X X vs 4 X X X X X X vs 5 X X X X X X X X X X X X vs 6 X X X X X X X X X X vs 7 X X X X X X X X X X X X X vs 3 X X X X X X X X X X X X X X vs 4 X X X X X X X X X vs 5 X X X X X X X X X vs 6 X X X X X X X X vs 7 X X X X X X X X X X X X X X X vs 4\ X X X X X X X X X X X X X vs 5 X X X X X X vs 6 X X X X X X vs 7 X X X X X X X X X X X vs 5 X X X X X X X X X vs 6 X X X X vs 7 X X X X X X X X X X X X X X X X vs 6 X vs 7 X X X X X X X X X X X X X X vs 7 X X X X X X X X X X X X X 4>» ON X = s i g n i f i c a n t discriminant parameters 47-analysis attempts to assign cases to groups along planes through character space which are orthogonal to the property axes. Overlapping and i n t e r -dependent properties, such as s o i l c h a r a c t e r i s t i c s , do not contribute to orthogonal axes which adequately divide the population into groups (Webster, 1975). 3. Indirect Cluster Analysis The same data set (Appendix B) was subjected to factor analysis to i d e n t i f y parameters which have a s i g n i f i c a n t e f f e c t on t o t a l variance and to determine i n t e r r e l a t i o n s h i p s between the parameters. Cluster analysis was subsequently performed based on the factor scores to c l a s s i f y the s o i l s into s o i l s groups. a. Factor Analysis Factor analysis was performed to eliminate over-emphasis of corre l a t e d parameters and the 19 v a r i a b l e s (Table 3.2) were reduced to six factors (Table 4.3). The f i r s t f a c t o r , describing texture i n the C horizon and drainage c h a r a c t e r i s t i c s accounted f o r 38 percent of the v a r i a t i o n among the parameters. Another 20 percent was explained by the second factor which describes texture and water holding c h a r a c t e r i s t i c s of the surface horizon. The remaining factors described the water storage capacity of the C horizon, parent material, pH and coarse f r a c t i o n of the surface horizon (Table 4.3). Texture and moisture c h a r a c t e r i s t i c s of the s o i l accounted for most of the t o t a l variance (over 70%) within the t o t a l s o i l population. These parameters were described by the f i r s t three f a c t o r s . The remaining s o i l parameters accounted f o r l e s s than 30%. b. Cluster Analysis Cluster analysis was performed based on the fa c t o r scores. The Table 4.3 Sorted Rotated Factor Loadings Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 (Texture i n (Texture and (Water Storage (Parent (pH (Coarse C Horizon and Water Holding Capacity of Material Factor) Fra c t i o n : Variables Drainage Capacity i n S C Horizon Factor S Horizon Factor) Horizon Factor) Factor) Factor) Clay (C Horizon) 0.942 0.0 0.0 0.0 0.0 0.0 Clay (S) 0.909 0.0 0.0 0.0 0.0 0.0 Drainage 0.895 0.0 0.0 0.0 0.0 0.0 Liqui d Limit (C) 0.847 0.0 0.0 0.0 0.0 0.0 CEC (C) 0.797 0.0 - 0.344 0.0 0.0 0.0 Sand (C) - 0.714 - 0.310 0.566 0.0 0.0 0.0 Slope - 0.682 0.0 0.0 0.317 0.0 - 0.294 Perviousness 0.625 ' 0.392 0.0 0.0 - 0.456 - 0.268 CEC (S) 0.554 0.283 0.337 0.0 - 0.479 0.0 S i l t (S) 0.0 0.861 0.0 0.0 0.0 0.0 Organic Material (S) 0.0 0.823 0.0 0.0 0.0 0.0 Water Storage Capacity(S) 0.0 0.782 0.0 0.356 0.0 0.0 Liqui d Limit (S) 0.550 0.663 0.310 0.0 0.0 0.0 Water Storage Capacity(C) 0.0 0.509 - 0.812 0.0 0.0 0.0 Coarse Fraction (C) - 0.293 0.344 0.800 0.0 0.0 0.0 Upper Parent Material 0.0 0.0 0.0 0.925 0.0 0.0 Lower Parent Material - 0.433 0.0 0.0 0.863 0.0 0.0 PH 0.0 0.0 0.0 0.0 0.838 0.0 Coarse Fra c t i o n (S) 0.0 0.0 0.0 0.0 0.0 0.900 VP 6.170 3.243 2.178 2.052 1.507 1.218 Percent Explanation 37.7 19.8 13.3 12.5 11.3 9.1 S = surface horizon C = C horizon 49 r e s u l t i n g dendrogram (Figure 4.2) revealed eight groups, two which contained two members and f i v e which were s i n g l e member classes (Table 4.4). Table 4.4 Indirect S o i l s Groups Group I _ . 3 4 5 6 7 _ _ AD VD BL NN HD RD W MH LX PE HT BK H'J SI LK KD F M MQ PT The s i g n i f i c a n c e of the three multi-member groups was assessed by the Mann-Whitney Rank Sum Test and numerous discriminating parameters., representing a l l factors except the pH-p f a c t o r , were revealed (Table 4.5). A comparison of the r e s u l t s from d i r e c t and i n d i r e c t soils..grouping revealed that the large groups linked early i n the c l a s s i f i c a t i o n (group 6 i n d i r e c t grouping and group 3 i n i n d i r e c t grouping) were s i m i l a r . The s o i l s not contained within the large groups comprised single or double member groups l i n k e d l a t e i n the c l a s s i f i c a t i o n . These groups showed d i f f e r e n t associations at higher l e v e l s i n the hierarchy. The early l i n k -age of the large groups indicated that these s o i l s exhibited the most s i m i l a r s o i l properties and, consequently the most s i m i l a r behaviour and management responses. Optimum use could be made of these s o i l s i f the best management practices and uses were defined by key farmers f o r the large s o i l s groups. The single and double member groups were too small and too 50 AMALGAMATED DISTANCE 1.002 1 045 1.052 0.941 1.439 1.538 1.540 1.560 1.735 1.781 2.609 2.892 2.917 3.037 2.987 3.277 3.406 3.406 4.086 AD LX VD BL PE BK HJ SI LK KD F M MQ BT NN HT HD RD U 2 6 _i FIGURE 4.2- INDIRECT GROUPING DENDROGRAM Letters r e f e r to s o i l s e r i e s ; numbers r e f e r to s o i l s groups fD CO i— 1 Gr < < <1 o cn CO CO up -C- CO X OQ 3 Ml 3 co o l-{ g- Coarse H* 3 Co 3 X X X X X Coarse Fr a c t i o n (S) Fr a c t i o n (C) X x Sand (C) g s i l t (s) 3 fD X x Clay (S) x x Clay (C) Organic Matter (S) Li q u i d Limit (S) L i q u i d Limit (C) Water Storage Capacity (S) Water Storage Capacity (C) X X CEC (S) X X CEC (C) X X X Drainage Slope Perviousness Upper Parent ^ Material X X Lower Parent Material H Cu W I-' fD -C~ Ul o CO H-o CTQ P 3 X) (-•  CO Hi H-M O a. Cu fD 3 3 rt rt H- t) hh H* P- co fD O C- H* 3 H--3 Cu Cu 3 3 rt 3 1 hd Cu -? i-i H- Cu rt 3 3 fD n> rt ^ fD l-i CO Cu 3 o ! ^ i-h C/l M £ 3 B (-H-H H fD fD CO O rt rt CO o H-r—1 CO 52 d i s s i m i l a r to j u s t i f y using the groups for management purposes. In summary, s o i l s groups were formed by d i r e c t grouping of a l l variables and by c l u s t e r analysis using f a c t o r s . The advantages of grouping s o i l s by c l u s t e r analysis using s o i l properties were: 1. The groups were o b j e c t i v e l y defined based upon s o i l properties. 2. No a p r i o r i knowledge of the functional r e l a t i o n s h i p between the s o i l properties and the s o i l s groups was not required since s o i l s with s i m i l a r properties are assumed to exhibit s i m i l a r behaviour and responses to management. 3. The s o i l s groups can be used for management purposes and the best management practices and uses can be defined by key farmers. The disadvantages were: 1. The c l u s t e r analysis r e s u l t e d i n one large group „• comprising V- the most s i m i l a r s o i l s of the entire data set and a number of s i n g l e and double member groups comprising the remaining and less s i m i l a r s o i l s . 2. Only the - large group could be used for management purposes. Numerical Analysis of Interpretative S o i l s Data The r e l i a b i l i t y of the s o i l s groups formed by c l u s t e r analysis was tested by r e l a t i n g c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y evaluations to them. 1. C a p a b i l i t y Data a. C a p a b i l i t y Groups The Canada Land Inventory assesses c a p a b i l i t y for a g r i c u l t u r e using t h i r t e e n l i m i t a t i o n s (Appendix C):and divides s o i l s into classes that have the same r e l a t i v e degree of l i m i t a t i o n or hazard. The study area was c l a s s i f i e d into c a p a b i l i t y classes using the s o i l survey information and the s o i l mapping u n i t s . S o i l c a p a b i l i t y for a l l s o i l s i n the area ranged 53 between classes two and s i x . The r e s u l t s of the intensive g r i d dot counting revealed that i n d i v i d u a l s o i l s e r i e s often ranged over two or three c a p a b i l i t y classes. Three c a p a b i l i t y groups were established based on two or three consecutive c a p a b i l i t y classes and s o i l series were placed into the group defining the c a p a b i l i t y range of most of the area within each s o i l s e r i e s (Table 4.6).. The c a p a b i l i t y groups were assessed using pure s o i l s e r i e s units f o r a l l s o i l s except Monroe and Sim which did not appear as pure units within the study area. C a p a b i l i t y groups for these s o i l s e r i e s were determined from s o i l complexes i n which the named s o i l s e r i e s dominated. High c a p a b i l i t y s o i l s are class 2 and 3 s o i l s and have moderate to moderately severe l i m i t a t i o n s that r e s t r i c t the. range of crops or require moderate conservation pr a c t i c e s (Env. Can., 1972). The s o i l s are deep and can be managed and cropped f a i r l y e a s i l y . R e s t r i c t i o n s on the range of crops usually evolve from a s i n g l e hazard or l i m i t a t i o n . The key s o i l l i m i t a t i o n s f o r high c a p a b i l i t y s o i l s i s excess water r e s u l t i n g " from inadequate s o i l drainage or a high water table but i n some areas the range of crops i s r e s t r i c t e d by undesirable soil structure and/or low per-meability, or a combination of c u l u l a t i v e minor adverse c h a r a c t e r i s t i c s . Medium c a p a b i l i t y s o i l s are c l a s s 3 and 4 s o i l s that have moderately severe to severe l i m i t a t i o n s which r e s t r i c t the range crops, require s p e c i a l conservation p r a c t i c e s , or both. These s o i l s are generally l i m i t e d by two or more r e s t r i c t i v e properties. The l i m i t a t i o n s which r e s t r i c t • c u l t i v a t i o n of medium c a p a b i l i t y s o i l s include combinations of droughtiness, stoniness and topography. Low c a p a b i l i t y s o i l s are class 4,5 and 6 s o i l s . C u l t i v a t i o n i s r e s t r i c t e d by severe l i m i t a t i o n s . Class 4 s o i l s may support a l i m i t e d range of crops under s p e c i a l conservation p r a c t i c e s ; c u l t i v a t i o n i s 54 Table 4.6 S o i l s C a p a b i l i t y Groups % Area Within Assigned Ca p a b i l i t y Grouping S o i l Series C a p a b i l i t y Grouping Low Medium High LX 67 MQ 80 RD 78 W 82 AD 78 KD 81 M* 80 BT 94 BL 100 BK 79 F 90 HT 100 HD 100 HJ 100 LK 86 MH 81 NN 92 PE 90 SI* 100 VD 70 *These s o i l series did not appear as pure units within the study area. Ca p a b i l i t y groupings were assessed from s o i l complexes i n which the named s o i l series dominated. 55 r e s t r i c t e d to perennial forage crops on class 5 and 6 s o i l s . Limitations of low c a p a b i l i t y s o i l s include combinations of topography, droughtiness, inundation by streams or lakes, excess water, cumulative minor adverse c h a r a c t e r i s t i c s and adverse s o i l c h a r a c t e r i s t i c s , b. Stepwise Discriminant Analysis f o r Ca p a b i l i t y Stepwise discriminant analysis was performed on the three c a p a b i l i t y groups of s o i l s to determine parameters responsible f o r separating low, medium and high c a p a b i l i t y classes. The Resource Analysis Branch (Ministry of the Environment, 1979) s p e c i f i e s poor drainage and flooding as the single most important l i m i t a -t i o n i n s u b s t a n t i a l parts of the Lower Fraser Valley. Drainage was i d e n t i f i e d by stepwise discriminant analysis as the d i s t i n g u i s h i n g c h a r a c t e r i s t i c of the c a p a b i l i t y c l a s s i f i c a t i o n f o r s o i l s w i t h i n the study area. Four m i s c l a s s i f i e d s o i l s were placed into t h e i r appropriate groups (Table 4.7). The accuracy l e v e l s of the groups p r i o r to r e c l a s s i f i c a t i o n are presented i n Table 4.8. Table 4.7 Corrected Soils. C a p a b i l i t y Groups Group:... Low Medium . High LK AD BT MQ KD BL M LX BK RD MH F ">vW HT HD HJ NN PE SI VD 56 Table 4.8 C l a s s i f i c a t i o n Matrix of. S o i l s C a p a b i l i t y Groups Group. _% :Correct 1 .• -f Number, of. S o i l s C l a s s i f i e d . into Group i Low Medium High Low 84.6 11 1 1 Medium 66.7 0 2 1 High 75.0 0 1 3 TOTAL 80.0 11 4 5 S i g n i f i c a n c e t e s t i n g of the corrected c l a s s i f i c a t i o n revealed that three s o i l parameters - drainage, CEC (C. horizon) and clay (C horizon) -separated a l l three groups. These s o i l parameters were common var i a b l e s representing the texture and drainage f a c t o r (Table 4.3). Drainage c h a r a c t e r i s t i c s of the three groups (Table 4.10) i n d i c a t e than drainage problems are most severe i n high c a p a b i l i t y s o i l s , l i m i t e d to the lower part of the s u b s o i l i n low c a p a b i l i t y s o i l s and absent i n medium c a p a b i l i t y s o i l s . In summary, three c a p a b i l i t y groups based upon c a p a b i l i t y classes assigned by the Canada Land Inventory were tested by stepwise discriminant analysis to determine. . the s o i l parameters s i g n i f i c a n t i n separating the c a p a b i l i t y groups. Drainage was revealed as the only d i s t i n g u i s h i n g v a r i a b l e . High c a p a b i l i t y s o i l s were characterized by severe ^drainage problems throughout the p r o f i l e , medium c a p a b i l i t y s o i l s exhibited good drainage and low c a p a b i l i t y s o i l s were l i m i t e d by imperfect drainage i n the lower s u b s o i l . The advantages of using s o i l s c a p a b i l i t y groups f o r land planning and management included: 1. .VLand p o t e n t i a l i s measured based upon the p h y s i c a l aspects of the land and consequently, the c l a s s i -f i c a t i o n has r e l a t i v e l y long-term v a l i d i t y . 57 Table 4.9 S i g n i f i c a n t Discriminant Parameters of  S o i l s C a p a b i l i t y Groups I d e n t i f i e d by  Mann-Whitney Rank Sum Test Medium Low Coarse F r a c t i o n (C), Clay (S & C), L i q u i d Limit (S & C), Water Storage Capacity (S), CEC (S & C), Drainage, Slope Clay (C), Water Storage Capacity (C), CEC (C), Drainage Coarse F r a c t i o n (C), Clay (S & C) L i q u i d Limit (S & C), Water Storage Capacity (S), CEC (S & C), Drainage, Slope Medium 58 Table 4.10 Drainage C h a r a c t e r i s t i c s of the S o i l s  C a p a b i l i t y Groups Grouping L i m i t a t i o n High 1. Ground water table at or near surface during winter 2. Ground water table at or near surface during freshet of Fraser River 3. Surface ponding during heavy, prolonged rains :Affected S o i l Series BT,BL,BK,F,HT,HD,HJ,NN,PE,SI,VD F,HJ,PE BL,BK,HT,HD,HJ,NN,PE,SI,VD Medium N i l Low 1. ground water table i n lower LK,MQ,M part of subsoil during high stream or r i v e r l e v e l s 2. Ground water table i n lower LK,MQ,M,W part of subsoil during heavy, prolonged rains 3. Temporary perched water tables W above compact dense subsoil during heavy, prolonged rains 4. L a t e r a l seepage above compact, RD dense subsoil during heavy, prolonged rains 5. Flooding of areas outside • \ .. MQ,M dykes during freezing 59 2. The c a p a b i l i t y groups can be used for land management and the best management p r a c t i c e s and uses can be defined by key farmers. 3. The established groups can be used as a data t r a i n i n g set f o r the grouping of a d d i t i o n a l unknown s o i l s . The disadvantages include: 1. The c a p a b i l i t y ratings assigned by the Canada Land Inventory are based upon l i m i t a t i o n s which can often be modified or overcome through management. 2. C a p a b i l i t y i s assessed at the reconnaissance l e v e l and i s designed f o r planning rather than management purposes. 3. C a p a b i l i t y i s a q u a l i t a t i v e assessment of land p o t e n t i a l . 4. C a p a b i l i t y assessments are use s p e c i f i c and are not e a s i l y integrated for multipurpose evaluations. 5. The c a p a b i l i t y ratings may be of l i m i t e d value due to cartographic error. 2. S u i t a b i l i t y Data a. S u i t a b i l i t y Groups S u i t a b i l i t y was assessed using an approach intermediate between the analogue method and s i t e f actor a n a l y s i s . P r oductivity data was c o l l e c t e d from experimental s i t e s to be extrapolated to analogous s i t e s according to the analogue approach. The s i t e f a c t o r method was followed to deter-mine the r e l a t i o n s h i p s between key properties of the s i t e and y i e l d within the study area. The r e s u l t i n g assessment assumes an,;. a ' p r i o r i knowledge of the functional r e l a t i o n s h i p s between the s o i l parameters and p r o d u c t i v i t y and allows the extrapolation of t h i s information to analogous s i t e s i f a l l ; the s i t e parameters which d i r e c t l y influence p r o d u c t i v i t y i n both the t r a i n i n g area and the test area have been incorporated i n the i n i t i a l a n a l y s i s . In the present study, c l i m a t i c f a c tors were ignored since climate was assumed to be f a i r l y constant over the small study area; however, these parameters would have to be considered i n studies incorporating larger s f f s SB SC SC T I « 03 w XD s !* X b O H tr1 H OJ OJ U l OJ U l *- U i U l U l U l U l O N U l O N OJ • 1 O N 1 i 1 1 • I U l 1 U l I 1 1 • NJ U l U l »J U l U l 1—' - j r - 1 U l • • • NJ NJ U l U l U l S3 NJ NJ NJ NJ NJ NJ NO NJ r-> NJ r-» NJ NJ NJ r—* o o I- 1 NJ U l NJ o NJ oo r - 1 1 O 1 •vj 1 1 1 1 | 1 1 1 1 U l 1 I 1 U l NJ NJ NJ NJ NJ N J NJ OJ NJ OJ U l *- 4> U l NJ U l U l o NJ U l U l I U l O N I O N U l I z CO U l I U l O N I U i S o i l Series Grass Corn Straw-berries H 03 ro 4> TJ I-I o Cu c o Ul I U l U l I O N U l U l z CO z CO U l I U l I U l I 4> U l Rasp-berries a OJ U l 4> U l I U l U l I • O I z CO U l NJ I OJ U l U l Beans NJ NJ NJ U l I I I I I I NJ NJ • I U l OJ I NJ I NJ V © NJ NJ • I I • • U l NJ NJ NJ Peas > I I I I I I I I I I I I I I I I U l I 00 Brocoli I I I I i i i i i i i i I I I I I I U l C a u l i -flower I O I P I I I I I I i o I I I U l I I I I I O N ]ole ^rops i i i I T l I O I I I W I I I I I I I T l I M l I I U i I oo Tt NJ Brussel Sprouts Rhubarb i i O N o oo O N I r- 1 O N NJ I I O N ~ J O N O N O N O N O N O N O N ~~J NJ O o o »J - ~ J NJ U l 00 NJ oo oo o NJ NJ NJ • O N O N • NJ I O N 1 O N 1 1 oo 1 1 NJ 1 1 NJ 1 1 1 1 ~ - J 1 O N 1 O N 1 1 «-J O N - J NJ I- 1 oo 00 vO oo vO U l O N o o O N OJ o OJ O N NJ OJ OJ NJ O N OJ O N I I I Milk 7? 00 VS H T l O T l ro 3 o ro rr I-I H 1 H - tn co W cn C a ro o> vs a T I T l T l 01 o> T l T l pi pj ro 3 3 3 3 3 ro ro ro ro ro rt H n I-I I-I w CO C O co CO CO CO c c c c c I f »1 •1 •1 < < < < < S ro ro ro ro ro 03 vs vs vs VS T l T l a T l a T l a T l a T l 50 O 03 03 03 03 03 03 ro 3 3 I-I 11 3 1 3 1 3 CO H B a ro 9 ro 3 ro B ro 3 ro ro ro ro o ro o ro o ro o ro PJ o i rt n rt i rt H rt i i i rt C O CO m CO P J CO P I CO P I CO \ * zr P J c C CO c CO c CO c CO c CO >1 rt >1 rr 11 rt n rt CO rt <J < < H- <J H- < H- < rt ro ro s ro B ro 3 ro a ro 03 a vs vs 03 vs 03 vs 03 03 vs rt 03 rt rt rt rt rt ro ro ro ro O ro T l 03 ro •I c '.3 ro v: Data Source 09 rd 53 3 M Z OJ Oo On On IO On On 1 • • • 1 On On On On On On ro ho to (—* o to I 1 1 1 tO to to On On On s: < oo p a h o a s s o i l Series II II II M O X O Cu O N OO w . .,, W l 2 S _• <^  i i M • O N *- i • • i Grass ro a. i-t r-" oo i- o i- 1 ro 3 to rt r o t o to , , ^ „ • I i i —• i i i i M i Corn Oo o * » On • u i i i I O N I i i i o n i Strawberries On O N OO oo ^ 0 1 1 1 i i " ^ i i i on i Raspberries u i i i i i o n i i i Beans On to to r-> • I i i i • i i i i - i Peas On oi »j ' u i i i i i T i i i i B r o c o l i On »vl 1 u i i i i i .> i i I I I Cauliflower I I i i On " « I i i i o i Cole Crops U l I I I I I I I I I I Brussel Sprouts ° 1 1 1 ' ^ i i i I O I Rhubarb O N O N O N O 00 O 00 O 00 O N OO O N ' ' i r l 1 1 1 ^ 1 ' ^  Milk r-1 r-> O N O N to to O N O N V O ~ J I - 1 o o> i o • I I -vl i O N 1 i 1 -~J M to to *rJ >TJ a CD to I—1 P ' £0 rj o M 3 rt ro g ro CD n ro l-t H rt i-t CO CO P- w CO c c Di 0) c I-I rt < < P- < ro ro 3 ro CO a ^ Uj nj hjj O 1] TJ iTjOhrj P- CU fu CU l-'H'CU CO to r-*H'(U 3 3 3 g i g g g 3 S S g o ro ro ro o r o r o r o nro rt i-l H i-l H f 4 H H H r t i - l M oo co co H n co oo oo P. M O O D a t a Source coC C C to w C C e to cn e rt H n i-t H M ^ i-t p r t i i H- < < < p . < <J <j H . <J B r o r o r o B r o r o r o Bro to ^< ^ ^ too-v-^ cu ^  ro rt ro T9 62 test regions. The advantages of such a system are that:. 1. i t i s an open-ended c l a s s i f i c a t i o n which w i l l accept and group unknown cases, and 2. r e l a t i o n s h i p s between s i t e factors and p r o d u c t i v i t y can be determined. The approach requires knowledge and incorporation of s i t e and y i e l d parameters over the en t i r e f i n a l test area to overcome the l i m i t a t i o n of s i t e s p e c i f i c i t y . P r o d u c t i v i t y data for the present study from a l l information sources are presented i n Table 4.11. The farmers survey data showed marginal agreement with the l i m i t e d research s t a t i o n data and p l o t t r i a l estimates. The r e l a t i o n s h i p between farmer survey data and d i r e c t e s t i -mates by expert consensus for corn and grass are presented i n Figures 4.3 and 4.4. The farmer survey data revealed a wide range of y i e l d s , but the majority of the data lay w i t h i n a narrow band of the diagonal representing perfect c o r r e l a t i o n between the data sources. Some farmer reports of grass p r o d u c t i v i t y data were higher than the d i r e c t estimates by expert consensus. In general, the estimates of the experts were representative of actual y i e l d . Y i e l d data from farmer survey and d i r e c t estimate by expert consen-sus were used to e s t a b l i s h s o i l groups representative of s o i l p r o d u c t i v i t y l e v e l s . S o i l s were placed into three groups representing low, medium and high p r o d u c t i v i t y based upon crop y i e l d indices which r e f l e c t e d y i e l d l e v e l s f or s p e c i f i c crops. Only crops which had a v a i l a b l e y i e l d data for every s o i l series were used for crop in d i c e s . Two indices, a corn index and a grass index were developed. Wherever possib l e , both sources of y i e l d data were used to assess the p r o d u c t i v i t y l e v e l ; i n ten cases (HT, HD, HJ, KD, LX, LK, NN, PE, SI and VD) only farmers survey data were av a i l a b l e and 63 0 1 2 3 4 5 6 7 8 9 10 11 12 DIRECT ESTIMATE (T/ACRE) FIGURE 4.3-COMPARISON OF GRASS PRODUCTIVITY DATA FROM FARMER SURVEY AND DIRECT ESTIMATE 0 10 20 30 40 DIRECT ESTIMATE ( T / A C R E ) FIGURE 4.4-COMPARISON OF CORN PRODUCTIVITY DATA FROM FARMER SURVEY AND DIRECT ESTIMATE 64 i n one case (W), d i r e c t estimate by expert consensus was used. Y i e l d ranges f o r each p r o d u c t i v i t y l e v e l were as follows: P r o d u c t i v i t y Y i e l d (Tons/Acre) Index Grass Corn Low 3-5 10-20 Medium 5.5-6.5 21-23 High >6.5 >24 S o i l grouping based upon the grass..and corn indices are presented i n Table 4.12. b. Stepwise Discriminant Analysis f o r S u i t a b i l i t y Stepwise discriminant analysis f o r s u i t a b i l i t y was assessed using both the grass index and the corn index to determine what d i f f e r e n t i a t e d low, medium and high p r o d u c t i v i t y s o i l s . The assessment of s u i t a b i l i t y by stepwise discriminant analysis using the grass index indicated that Table 4.12 S o i l s S u i t a b i l i t y Groups Index Grass Corn Low AD F HJ LX MH M MQ PE RD AD LX MH RD VD W Medium BT BL HT HD KD NN W BL F HT HD HJ KD LK MQ M NN PE SI High BK LK SI VD BT BK 65 the s o i l s were not separated by the s o i l parameters. Grass crops require l i t t l e preparation or input f o r growth and can be found growing over a wide range of s o i l s with l i t t l e v a r i a t i o n i n y i e l d . Corn i s more i n -herently suited to c e r t a i n s o i l types and shows v a r i a t i o n i n p r o d u c t i v i t y i n response to s o i l conditions. Stepwise discriminant analysis based upon the corn index revealed separation by one parameter, lower parent material. Three m i s c l a s s i f i e d s o i l s were placed into t h e i r appropriate groups (4.13). The accuracy of the groups p r i o r to r e c l a s s i f i c a t i o n i s presented i n Table 4.14. Table 4.13 Corrected S o i l s S u i t a b i l i t y Groups (Based on Corn Index Low Medium High AD BT BK LX BL KD MH F VD RD HT W HD HJ LK MQ M NN PE SI Table 4.14 C l a s s i f i c a t l b r i Matrix of S o i l s S u i t a b i l i t y Groups (Based on Corn Index) Group % Correct Number of S o i l s C l a s s i f i e d into Groups Low Medium High Low 83.3 5 0 1 Medium 91.7 0 11 1 High 50.0 0 1 1 TOTAL 85.0 5 12 3 66 Sig n i f i c a n c e t e s t i n g of s o i l s groups revealed that lower parent material s i g n i f i c a n t l y separated a l l s o i l s groups (Table 4.15). Table 4.15 S i g n i f i c a n t Discriminant Parameters of S o i l s  S u i t a b i l i t y Groups I d e n t i f i e d by Mann-Whitney  Rank Sum Test. 2 3 Coarse F r a c t i o n (C) Sand (C), Clay (S & C), CEC (C), Drainage, Slope, Upper and Lower Parent Material Sand (C), Clay (S) Lower Parent Material Upper and Lower Parent Material 2 S o i l s from l a c u s t r i n e parent materials showed highest y i e l d s (based on corn index); alluvium-based s o i l s exhibited moderate y i e l d s and s o i l s developed from g l a c i a l deposits with aedian capping provided lowest y i e l d s (Table 4.16) . In summary, s o i l s u i t a b i l i t y was assessed using an approach i n t e r -mediate between the analogue method and s i t e f actor a nalysis. Y i e l d data from farmer survey and d i r e c t estimate by expert consensus were the bases of two indices used to place s o i l s into p r o d u c t i v i t y classes. The grass index did not s i g n i f i c a n t l y separate the s o i l s according to prod u c t i v i t y ; the corn index revealed separation by lower parent material. High p r o d u c t i v i t y s o i l s developed from l a c u s t r i n e deposits, medium pr o d u c t i v i t y , s o i l s were alluvium based and low p r o d u c t i v i t y s o i l s evolved from g l a c i a l deposits with aeolian cappings. Advantages of using s o i l s s u i t a b i l i t y groups for land planning and management included: 67 Table 4.16 Parent Materials of the S o i l s S u i t a b i l i t y Groups Grouping 1 (Low) (Medium) 3 (High) S o i l Series AD LX MH RD W BT BL F HT HD HJ LK MQ M NN PE SI BK KD VD Parent Material Aeolian veneer over g l a c i o f l u v i a l deposits Aeolian deposits Aeolian veneer over g l a c i o f l u v i a l deposits Aeolian veneer over morainal deposits Glaciomarine deposits Local stream deposits Flood p l a i n deposits Flood p l a i n deposits Flood p l a i n deposits Flood p l a i n deposits Flood p l a i n deposits Local stream deposits Flood p l a i n deposits Flood p l a i n deposits Flood p l a i n deposits Flood p l a i n deposits Local stream deposits Lacustrine deposits Lacustrine deposits Lacustrine deposits 1. The c l a s s i f i c a t i o n has r e l a t i v e l y long term a p p l i c a b i l i t y since i t was developed using physical parameters. 2. S u i t a b i l i t y i s a quantitative assessment based upon p r o d u c t i v i t y data. 3. I t i s the most precise and accurate estimate of land producti-v i t y p o t e n t i a l . 4. S o i l s s u i t a b i l i t y groups are useful f o r management since p r o d u c t i v i t y data are gathered at the l e v e l of the i n d i v i d u a l farm or f i e l d . 5. The assessment i s based upon p o s i t i v e aspects of management rather than l i m i t a t i o n s to land use. 6. The best management practices and uses can be defined by key farmers f o r each s o i l s u i t a b i l i t y group. 7. Management practices are r e a l i s t i c and p r a c t i c a l since they were defined under r e a l economic market conditions. 68 8. Interpretative s o i l s units can be used as a data t r a i n i n g set and unknown cases can be c l a s s i f i e d into t h e i r appropriate groups. A disadvantage was: 1. S o i l s s u i t a b i l i t y groups are defined by subjective pre-defined categories. 3. F e a s i b i l i t y Data a. F e a s i b i l i t y Groups F e a s i b i l i t y v a r i e s from c a p a b i l i t y and s u i t a b i l i t y i n that, crops are not n e c e s s a r i l y grown on the most capable or the most s u i t a b l e s o i l s , but rather i n areas which derive the most economic or s o c i a l b e n e f i t s . A r e l a t i o n s h i p between land use and the p h y s i c a l a t t r i b u t e s of the land e x i s t s due to s o i l requirements of the crop, but i t i s not always obvious due to the influence of socio-economic parameters i n a l l o c a t i n g land to various uses. Current land use i s a r e f l e c t i o n of f e a s i b i l i t y . Land use data from a se r i e s of maps and reports prepared for the : D i s t r i c t s of Matsqui and Abbotsford were transcribed to a s i n g l e land use map (Figure 4.5) and used to e s t a b l i s h f e a s i b i l i t y classes. Nine land use categories c o n s i s t i n g of pasture and forage, hobby farms, market gardening, specia 1-l i z e d a g r i c u l t u r e , berry farms, f o r e s t and r e c r e a t i o n a l land, urban and urban r e l a t e d uses, represented a l l the a c t i v i t i e s within the study area. Intensive g r i d dot counting established how much df each s o i l s e r i e s was contained: within each land use category. A g r i c u l t u r a l land uses showing a d i r e c t r e l a t i o n s h i p with the p h y s i c a l land resource base were used as the basis of f e a s i b i l i t y classes. Consequently, s p e c i a l i z e d a g r i c u l t u r e was excluded as a category as i t con-s i s t e d of a c t i v i t i e s such as poultry farms and greenhouses which do not:rely upon s o i l requirement f o r t h e i r l o c a t i o n . Hobby, farms were also omitted 69 PASTURE AND FORAGE 1*^ 1 BERRIES lllllill MARKET GARDENING (VEGETABLES) SPECIALIZED AGRICULTURE HOBBY FARIAS FIGURE 4.5- ABBOTSFORD AREA LAND USE (BASEDOW 1977 SURVEY DATA CENTRAL FRASER VALLEY REGION AND MATSQUI DISTRICT PLANNING! 70 since they were not considered serious a g r i c u l t u r a l undertakings. The r e s u l t i n g f e a s i b i l i t y classes consisted of combinations of three a g r i -c u l t u r a l a c t i v i t i e s : pasture and forage operations ( i . e . dairy farms), market ( i . e . vegetables), gardening and berry farms. S o i l s were c l a s s i f i e d by the combination of land uses they supported (Table 4.17). Table 4.17 S o i l s F e a s i b i l i t y Groups Pasture/ Pasture/ Vegetable/ Group Pasture Vegetable Berry BL BT AD BK KD MH F LK RD HT VD HD HO LX MQ M NN PE SI W b. Stepwise Discriminant Analysis f o r F e a s i b i l i t y F e a s i b i l i t y groups were assessed by stepwise discriminant analysis to i d e n t i f y d i s t i n g u i s h i n g s o i l features of areas of d i f f e r e n t land use. Two v a r i a b l e s , pH and coarse f r a c t i o n (C horizon) were revealed as s i g n i -f i c a n t discriminant parameters. Discriminant parameter ranges of the corrected f e a s i b i l i t y groups (Table 4.18) are presented i n Table 4.19. The accuracy l e v e l s of the groups p r i o r to r e c l a s s i f i c a t i o n are presented i n Table 4.20. Sign i f i c a n c e t e s t i n g revealed that none of the s o i l para-meters distinguished a l l three groups (Table 4.21). No r e l a t i o n s h i p was evident between a g r i c u l t u r a l p o t e n t i a l defined.by :the number of crops Table 4.18 Corrected S o i l s F e a s i b i l i t y Groups  Group Pasture Pasture/Vegetable Pasture/Vegetable/Berry BL BT BK KD F MQ HT NN HD RD HJ SI LX VD LK M PE W Table 4.19 Ranges of Discriminant Parameters of  S o i l s " F e a s i b i l i t y Groups Parameter. : .Pasture Pasture/Vegetable Pasture/Vegetable/Berry pH 4.8 -5.1 4.8-5.5 4.6-4.7 Coarse F r a c t i o n (C) 0-3% 0-1% 19.57 - 62.61% 72 Table 4.20 C l a s s i f i c a t i o n Matrix of S o i l s F e a s i b i l i t y  Groups Group % Correct Pasture Pasture/ Vegetable Pasture/ Vegetable/ Berry TOTAL 76.9 75.0 66.7 35.0 Number of Cases C l a s s i f i e d Into Group Pasture/ Vegetable/ Pasture Vegetable Berry  10 0 11 Pasture/ Table 4.21 S i g n i f i c a n t Discriminant Parameters of S o i l s  F e a s i b i l i t y Groups I d e n t i f i e d by Mann-Whitney Rank Sum Test Medium pH Coarse Frac t i o n (C), pH pH, Coarse, Frac t i o n (C), Sand (C), Clay (C), Water Storage Capacity (C), CEC (C) Low Medi 73 currently grown on the s o i l and the r e s u l t i n g - f e a s i b i l i t y c l a s s i f i c a t i o n does not represent optimal use of the physical resource base and s o i l c h a r a c t e r i s t i c s ( i . e . greater s o i l property v a r i a t i o n was observed between dairy/market garden land and dairy/berry/market garden land than between dairy land and land supporting a l l crops) due to socioeconomic influences which have encouraged attempts to grow crops on s o i l s to which they are not inherently suited. Consequently, current land use patterns do not r e f l e c t optimum use of the physical resource base. In summary, three s o i l s f e a s i b i l i t y classes based on a g r i c u l t u r a l land use were subjected to stepwise discriminant analysis to i d e n t i f y discriminant s o i l parameters of land use groups, pH and coarse f r a c t i o n (C horizon) were s i g n i f i c a n t . The advantages of using s o i l s f e a s i b i l i t y groups f o r land planning and management are: 1. F e a s i b i l i t y i s a dynamic assessment of land p o t e n t i a l based upon current market values. 2. I t i s a quantitative assessment which considers socio-economic influences. 3. Interpretative f e a s i b i l i t y units can be used as a data t r a i n i n g set for the c l a s s i f i c a t i o n of a d d i t i o n a l unkown cases. The disadvantages are: 1. F e a s i b i l i t y c l a s s i f i c a t i o n has r e l a t i v e l y short term a p p l i c a b i l i t y due to changing market conditions. 2. I t i s d i f f i c u l t to measure prices of supplies and products which do not remain constant to each other over time. 3. I t i s important to select economic c r i t e r i a ! which do not a f f e c t f e a s i b i l i t y . 4. An inventory of current land use i s expensive and d i f f i c u l t to e s t a b l i s h and maintain. 5. Current land use did not r e f l e c t a r e l a t i o n s h i p between crops grown and land a t t r i b u t e s . 74 Assessment of S o i l s Groups and Interpretative Land  Management Units D i f f e r e n t c r i t e r i a and assumptions underlay the s o i l s grouping and the i n t e r p r e t a t i v e land management u n i t s . Consequently, each c l a s s i f i c a -t i o n has s p e c i f i c advantages f or various objectives. Quantitative . i d e n t i f i c a t i o n of the optimal land use c l a s s i f i c a t i o n was performed by: 1. S o i l s group comparison 2. Interpretative s o i l s c l a s s i f i c a t i o n comparison 3. S o i l s group and i n t e r p r e t a t i v e s o i l s c l a s s i f i c a -t i o n comparison. 1. Comparison of S o i l s Groups The major s o i l s groups (Group 6 ( d i r e c t grouping) and Group 3 ( i n d i r e c t grouping))were subjected to the Mann-Whitney Rank Sum Test to determine i f the groups were from the same population. The test revealed that the groups were not s i g n i f i c a n t l y d i f f e r e n t based on an absence of discriminant parameters. The smaller groups were not tested f o r s i g n i f i -cance because they did not form e f f e c t i v e units for management due to membership s i z e and s o i l property d i s s i m i l a r i t y . 2. Comparison of Interpretative S o i l s C l a s s i f i c a t i o n s Three i n t e r p r e t a t i v e s o i l s c l a s s i f i c a t i o n s were developed i n the present study using c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y data. The inte r p r e t a t i o n s were s i m i l a r i n the following respects: 1. A l l were developed from one s o i l s data base 2. A l l measured land p o t e n t i a l 3. A l l placed s o i l s into three cl a s s e s representing high, medium and low land p o t e n t i a l . The land use categories of the f e a s i b i l i t y c l a s s i f i c a t i o n can be converted to l e v e l s of land p o t e n t i a l i f one assumes that each a d d i t i o n a l crops grown 75 represents increasing a g r i c u l t u r a l p o t e n t i a l ( i . e . pasture-low, pasture/ vegetables/berries-high). Differences i n the underlying assumptions of each s o i l s i n t e r -p r e t a t i o n were r e f l e c t e d i n the s o i l parameters that s i g n i f i c a n t l y separated the s o i l s groups (Table 4.22). Drainage discriminate the s o i l s c a p a b i l i t y groups; parent material separated the s o i l s s u i t a b i l i t y u n i t s ; and pH and coarse f r a c t i o n , i n combination, distinguished the s o i l s f e a s i b i l i t y classes. Although drainage separated the s o i l c a p a b i l i t y groups, i t was not the sole l i m i t i n g f a c t o r d e f i n i n g c a p a b i l i t y l e v e l s . The s o i l s group with the highest c a p a b i l i t y r a t i n g had the most severe drainage problems, but the lower c a p a b i l i t y s o i l s groups had an increased number or degree of l i m i t a t i o n s . The parameters l i m i t i n g the s o i l s groups at each l e v e l were not ne c e s s a r i l y the same and consequently did not appear as discriminant parameters. Table 4.22 Comparison of Interpretative S o i l s C l a s s i f i c a t i o n s Interpretation C a p a b i l i t y S u i t a b i l i t y F e a s i b i l i t y Parameters Drainage Lower Parent Material A - pH B - Coarse F r a c t i o n (C) R A N G E Low Moderately well to well G l a c i a l and Aeolian A B 4.3 - 5.1 0 - 3% Medium Well to rapid A l l u v i a l 4.8 - 5.5 0 - 1% High Imperfect to very poor Lacustrine 4.6 - 4.7 20 - 63% Each i n t e r p r e t a t i v e s o i l s c l a s s i f i c a t i o n was useful f o r s p e c i f i c purposes r e l a t i n g to the underlying objectives f o r that use. Quantitative i d e n t i f i c a t i o n of the s o i l s i n t e r p r e t a t i o n defining optimal land use was performed using the graph method. The following r e s u l t s were revealed. 76 1. S o i l s groups were d i f f e r e n t f o r each i n t e r p r e t a t i o n and consequently, a perfect c o r r e l a t i o n did not e x i s t between the i n t e r p r e t a -t i v e s o i l s c l a s s i f i c a t i o n s . 2. A l l interpretations had a common objective of measuring land p o t e n t i a l using the same s o i l s data base. Consequently, a r e l a t i o n s h i p was evident among the c l a s s i f i c a t i o n s and the random d i s t r i b u t i o n (Figure 3.3b) was not observed. 3. The c a p a b i l i t y / s u i t a b i l i t y comparison (Figure 4.6) revealed an intermediate d i s t r i b u t i o n (Figure 3.3c) biassed toward c a p a b i l i t y . Sixty percent of the s o i l s f e l l above the diagonal i n d i c a t i n g that the c a p a b i l i t y r a t i n g assigned to the majority of the s o i l s overestimated t h e i r actual s u i t a b i l i t y based upon the corn index. 4. The c a p a b i l i t y / f e a s i b i l i t y comparison (Figure 4.7) revealed an intermediate d i s t r i b u t i o n tending toward ' randomness. Sixty percent of the cases f e l l above the diagonal and t h i r t y f i v e percent represented the extreme case of high c a p a b i l i t y - low pr o d u c t i v i t y . The overemphasis of c a p a b i l i t y indicated that the s o i l s were capable of supporting a wider range of crops than were currently being grown. 5. The s u i t a b i l i t y / f e a s i b i l i t y comparison (Figure 4.8) revealed an intermediate d i s t r i b u t i o n tending toward randomness. Both extremes were represented - f i v e percent of the cases represented high s u i t a b i l i t y / l o w p r o d u c t i v i t y and ten percent represented low s u i t a b i l i t y / h i g h p r o d u c t i v i t y . The d i s t r i b u t i o n was weighted toward s u i t a b i l i t y ( f i f t y f i v e percent of the cases f e l l above the diagonal) i n d i c a t i n g that current land use did not r e a l i z e the f u l l a g r i c u l t u r a l p o t e n t i a l of the land based upon actual p r o d u c t i v i t y l e v e l s . 77 78 In summary, a p o s i t i v e r e l a t i o n s h i p existed between the i n t e r -p r e t a t i v e s o i l s c l a s s i f i c a t i o n due to the common objective of evaluating land p o t e n t i a l using s o i l s data. The c a p a b i l i t y and s u i t a b i l i t y i n t e r -pretation were most c l o s e l y r e l a t e d i n underlying assumptions and the in t e r p r e t a t i v e s o i l s units were s i m i l a r . The c a p a b i l i t y / s u i t a b i l i t y comparison revealed that c a p a b i l i t y optimized land p o t e n t i a l by over-estimating actual observed y i e l d . F e a s i b i l i t y underestimated c a p a b i l i t y and s u i t a b i l i t y revealing that current land use did not r e a l i z e f u l l a g r i c u l t u r a l p o t e n t i a l . S u i t a b i l i t y offered the most precise and accurate evaluation of land p o t e n t i a l using p r o d u c t i v i t y data. 3. Comparison of S o i l s Groups and Interpretative S o i l s Units S o i l s groups and i n t e r p r e t a t i v e s o i l s units were developed from a single s o i l s data base. Difference i n the c l a s s i f i c a t i o n s arose from the underlying assumption of the evaluations: s o i l s groups were based upon s o i l property, s i m i l a r i t i e s and i n t e r p r e t a t i v e s o i l s units were developed from s o i l survey data assessments. The degree to which i n t e r p r e t a t i v e assessments were based upon the physical parameters was r e f l e c t e d i n the s i m i l a r i t y between the i n t e r p r e t a t i v e land management un i t s and the s o i l s groups, and i n the number of common discriminant parameters. The percentage of the s o i l s groups separated by the s i g n i f i c a n t discriminant parameters of the i n t e r p r e t a t i v e s o i l s units (Table 4.23) indicated the degree to which the i n t e r p r e t a t i v e c l a s s i f i c a t i o n s were based upon physical parameters. The i n t e r p r e t a t i v e c a p a b i l i t y units were separated by the greatest number of s o i l parameters. Nine of the ten parameters explained from f i f t y two to eighty one percent of the s o i l s group separation; only one parameter . explained less than 25%. Seventy f i v e percent of the s i g n i f i c a n t d i s c r i m i n -ant parameters of the s u i t a b i l i t y c l a s s i f i c a t i o n explained from s i x t y seven 79 Table 4.23 Soils Group Separation Significant Discriminant  Parameters or Interpretative C l a s s i f i c a t i o n s Parameters pH* Coarse Fraction (C)* Clay (C) Liquid Limit (S) Liquid Limit (C) Water Storage Capacity (S) CEC (S) CEC (C) Drainage* Slope Clay (S) Lower Parent Material* Upper Parent Material Sand (C) % of Clusters Separated by Significant Capability Discriminant Parameters 24 81 52 62 67 57 81 67 57 71 % of Clusters Separated by Significant S u i t a b i l i t y Discriminant Parameters 71 67 33 67 % of Clusters Separated by Significant F e a s i b i l i t y Discriminant Parameters 14 24 *Signi f i c a n t discriminant s o i l p discriminant analysis. arameters id e n t i f ied by stepwise 80 to severity one percent of the s o i l s group separation; one parameter separated only t h i r t y three percent of the s o i l s groups. The i n t e r -pretative f e a s i b i l i t y units were discriminated by two s o i l parameters. Each explained less than twenty f i v e percent of the s o i l s group separation. The high degree of s o i l s group separation by the capa b i l i t y and s u i t a b i l i t y discriminant parameters indicated strong emphasis onv. physical parameters i n evaluation of land poten t i a l . Conversely, the poor s o i l s group separation based upon f e a s i b i l i t y discriminant parameters indicated emphasis on parameters other than s o i l properties. In summary, capa b i l i t y and s u i t a b i l i t y emphasized s o i l parameters i n land quality assessment. F e a s i b i l i t y stressed other parameters, such as s o c i a l and economic factors. Optimum land potential i s best defined using physical parameters and consequently, capability and s u i t a b i l i t y are better assessments of land quality than i s f e a s i b i l i t y . 8i :. CHAPTER 5  CONCLUSIONS A methodology was developed i n the present :study to quantitatively interpret s o i l properties related to s o i l behaviour and performance for a g r i c u l t u r a l c a p a b i l i t y , s u i t a b i l i t y and f e a s i b i l i t y . Conclusions were drawn from the study i n the following areas. Numerical Techniques Cluster and factor analysis have great potential for handling multi-parameter data and quantitatively defining:, s o i l s groups by s o i l properties; however these numerical analysis were p a r t i a l l y l i m i t i n g i n the present study due to: 1. S t a t i s t i c a l l i m i t a t i o n s of the procedure regarding overlapping and interdependent properties, such as s o i l c h a r a c t e r i s t i c s , and ....': / . .: . .:•.•.: :: .:.::. . : .:: .:.:'. ':.' : 2. r e s t r i c t i o n s imposed by the s o i l s data set which was neither adequately large nor diverse to form multimember s o i l s groups. Stepwise discriminant analysis was more successful i n creating interpretative s o i l s units and had the added advantage of c l a s s i f y i n g un-known s o i l s using the i n i t i a l s o i l s data training set. The method was limi t e d by: 1. Subjective prerequisite grouping using a grouping variable, and 2. the reliance upon a l i n e a r relationship for group separation and s o i l r e c l a s s i f i c a t i o n . Neither numerical analysis was singularly successful i n defining optimal land units and sim i l a r studies would merit from a combination of the methods which would remove the subjectively defined s o i l s groups and 82 more s t r i c t l y define c l u s t e r i n g parameters. Interpretative s o i l s groups derived using both the present method and the proposed method could be compared by s i g n i f i c a n c e tests to i d e n t i f y the better evaluation procedure. Comparison of Interpretative C l a s s i f i c a t i o n s Each i n t e r p r e t a t i v e c l a s s i f i c a t i o n r e f l e c t e d d i f f e r e n t underlying assumptions and c r i t e r i a and consequently a d i f f e r e n t set of discriminant s o i l parameters characterized each i n t e r p r e t a t i v e c l a s s i f i c a t i o n . The Canada Land Inventory derived s o i l s c a p a b i l i t y groups were separated by drainage; parent material separated the quantitative s o i l s p r o d u c t i v i t y ( s u i t a b i l i t y ) units and the socio-economically defined f e a s i b i l i t y classes were separated by pH and coarse f r a c t i o n (C horizon). Comparison of the c l a s s i f i c a t i o n s revealed the following: 1. The c a p a b i l i t y ratings optimized land potential by over-estimating actual observed y i e l d . 2. the f e a s i b i l i t y ratings did not r e a l i z e the f u l l a g r i c u l t u r a l p o t e n t i a l of the land, and 3. the s u i t a b i l i t y assessment based upon actual observed y i e l d s was the most quantitative land evaluation method. S u i t a b i l i t y Assessment Pro d u c t i v i t y data was c o l l ected from four sources. The data correlated reasonably well and provided the basis for a corn index upon which the s o i l s s u i t a b i l i t y units were developed. Advantages of the s u i t a -b i l i t y management c l a s s i f i c a t i o n included the following: 1. I t i s the most quantitative assessment of land p o t e n t i a l based upon actual observed p r o d u c t i v i t y data. 2. P r o d u c t i v i t y data are actual observed y i e l d s measured under r e a l market conditions. 3. P r o d u c t i v i t y are r e a d i l y a v a i l a b l e f o r a wide range of crops. 83:". 4. Optimal use can be made of a l l s o i l s using guidelines established by key farmers for s o i l s s u i t a b i l i t y groups. 5. Other s o i l s can be added to the open-ended s o i l s s u i t a b i l i t y c l a s s i f i c a t i o n . A disadvantage of using productivity data to evaluate s o i l s u i t a b i l i t y i s that s u i t a b i l i t y can only be assessed for currently cultivated crops; however, the range of crops can be expanded by research station data and plot t r i a l s . An alternative source of quantitative s o i l s s u i t a b i l i t y information i s behavioural data. To date, work with behavioural data has been l i m i t e d to engineering applications, but the potential exists to develop guide sheets stressing s o i l behaviour characteristics such as s o i l moisture, s o i l structure, testure and depth to bedrock for a wide range of a g r i c u l t u r a l and forest crops. Recommendations. Different assumptions and c r i t e r i a underlie the interpretative c l a s s i f i c a t i o n s and consequently, the interpretations are not i n t e r -changeable. The s u i t a b i l i t y assessment i s recommended as the most quantitative land quality measure. Need&and potential exist to develop the methodology to include a wide range of a g r i c u l t u r a l and forest crops and to evaluate s o i behaviour to develop functional crop-site relationships. The s o i l s s u i t a b i l i t y c l a s s i f i c a t i o n i s open-ended and should be tested by adding unknown cases to the established s o i l s s u i t a b i l i t y data training set. 84 BIBLIOGRAPHY Aandahl, A.R. 1960. S o i l p r o d u c t i v i t y - concept and pr e d i c t i o n s . 7 th Inter. Congress S o i l S c i . V. 51:365 - 370. Allgood, F.P., F. Gray. 1978. U t i l i z a t i o n of s o i l c h a r a c t e r i s t i c s i n computing p r o d u c t i v i t y ratings of Oklahoma s o i l s . S o i l S c i . 125 (6):359 - 366. B a r t e l l i , L.J. 1979. Interpreting s o i l data. In:Beatty, M.T., G.W. Peterson, L.D. Swindale (eds.). Planning the uses and management of land. Agron. 21:91 - 116. Bauer, K.W. 1979. Planning metropolitan land uses i n r e l a t i o n to natural landscape features. In:Beatty, M.T., G.W. Peterson, L.D. Swindale (eds.). Planning the uses and management of land. Agron. 21:91 -116. Beatty, M.T., G.W. Peterson, L.D. Swindale. 1979. Planning the uses and management of land. Agron. 21:91 - 116. Beckett, P.H.T., S.W. Bie. 1978. Use of s o i l and land system maps to provide s o i l information i n A u s t r a l i a . Div. S o i l s Tech Pap 33, CSIRO, A u s t r a l i a , 76. Beckett, P.H.T., R. Webster. 1971. S o i l v a r i a b i l i t y : A review. S o i l s F e r t. 34 (1):1 - 15. Beek, K.J. 1978. Land evaluation f o r a g r i c u l t u r a l development. Intern. Inst, f o r Land Reclamation and Improvement, IRLI. Publ. #23. Wageningen. p. 333. Belknap, R.K., J.G. Furtado. 1967. Three approaches to environmental resource analysis, foundation, Washington, D.C. Bie, S.W., P.H.T. Beckett. 1971. Quality control i n s o i l survey: Intro-duction: 1. The choice of mapping u n i t . J . S o i l S c i . 22 (1):32 -49. Blake, G.R. 1965. Bulk density. In: Black, C A . (ed.). Methods of S o i l s Analysis, Agron. 9, Am. Soc. Agron. Inc., Madison, Wisconsin. Brinkman, R., A.J. Smyth. 1973. Land evaluation f or r u r a l purposes. IRLI Publ. 17, Wageningen. B r i t i s h Columbia Land Commission. 1975. Keeping the options open. Burnaby, B.C. Carmean, N.H. 1975. Forest s i t e q u a l i t y evaluation i n the United States. Adv. Agron. 27:209 - 269. 85 C a t t e l , R.B. 1965. Factor Analysis: Introduction to E s s e n t i a l s . Bio-metrics. 21:190 - 215. Chapman, H.D. 1965. Cation exchange capacity. In: Black, C A . (ed.) Methods of S o i l Analysis, Am. Society Agron. Inc., Madison, Wisconsin. Clarke, G.R. 1951. The evaluation of s o i l s and the d e f i n i t i o n of q u a l i t y classes from studies of the physical properties of the s o i l p r o f i l e i n the f i e l d . J . S o i l . S c i . 2 (1):50 - 60. Comar, V.K., C C Kelley. 1962. S o i l survey of Sumas municipality. Preliminary Report #5 B.C. Dept. of Ag r i c . Cooke, G.W. 1979. Some p r i o r i t i e s f o r B r i t i s h s o i l science. J . S o i l S c i . (30):187 - 214. Coombs, D.B., J . Thie. 1979. The Canada land inventory system. In: Beatty, M.T., G.W. Peterson, L.D. Swindale (eds.). Planning the Uses and Management of Land. Agron. 21:909 - 934. Day, Paul R. 19 . P a r t i c l e f r a c t i o n a t i o n and p a r t i c l e s i z e a n a l y s i s . Agron. #9, Am. Soc. Agron:545 - 567. Department of the Environment. 1972. Canada Land Inventory Report 2: S o i l c a p a b i l i t y c l a s s i f i c a t i o n f o r a g r i c u l t u r e , p. 16. Dixon, W.J., M.B. Brown. 1979. BMDP - 79: Biomedical computer programs P-Series. U n i v e r s i t y of C a l i f o r n i a Press, Berkeley, C a l i f o r n i a . Galloway, H.M., O.E. Yahner. 1978. S o i l survey education: The Indiana program. J . S o i l Water Cons. 33 (3):110 - 114. Geyer, W.A., R.O. Marquard and J.F. Barber. 1980. Black walnut s i t e q u a l i t y i n r e l a t i o n to s o i l and topographic c h a r a c t e r i s t i c s i n North-eastern Kansas. J . S o i l Water Cons. 35 (3):135 - 136. G i r t , J.L. 1977. The evaluation of a l t e r n a t i v e methodologies f o r r u r a l land evaluation. P u b l i c a t i o n 82, Univ. of Guelph. p. 112. Hambridge, A.M. et a l . 1977. Abbotsford-Matsqui Land Use Study - Manual and Summary Report. Matsqui and Abbotsford D i s t r i c t , p.33. H i l l s , G.A., R. Portelance. 1960. A multiple land-use plan f o r the Glackmeyer development area. Ont. Dept. Lands Forests. Hoffman, D.W. 1971. The assessment of s o i l p r o d u c t i v i t y f o r a g r i c u l t u r e . Canada Land Inventory Program. ARDA Rep. #4. Guelph, Ontario, p. 50. Johannsen, C.J., T.W. Barney, A.A. K l i n g e b i e l . 1979. R e s i d e n t i a l , commer-c i a l and l i g h t i n d u s t r i a l land uses. In: Beatty, M.T., G.W. Peterson. L.D. Swindale (eds.). Planning the Uses and Management of Land. Agron. 21:485 - 498. 86 K l l h g e b i e l , A.A. 1958. S o i l survey i n t e r p r e t a t i o n - c a p a b i l i t y groupings. S o i l S c i . Soc. Am. Proc. 22:160 - 163. Lavkulich, L.M. 1977. Organic matter. Methods Manual, Pedology Labor-atory, U n i v e r s i t y of B r i t i s h Columbia, B.C. Low, A.J. 1973. S o i l structure and crop y i e l d . J . S o i l S c i . 24 (2): 249 - 259. Mader, D.L. 1976. S o i l - s i t e p r o d u c t i v i t y f o r natural stands of white pine i n Massachusetts. S o i l S c i . Soc. Am. Proc. 40:112 - 115. M i l l e r , F.P. 1978. S o i l survey under pressure: The Maryland experience. J . S o i l Water Cons. 33 (3):104 - 111. M i l l e r , F.T., J.D. Nichols. 1978. S o i l s data. In: Beatty, M.T., G.W. Peterson, L.D. Swindale (eds.). Planning Metropolitan Land Uses in Relation to Natural Landscape Features. Agron. 21:67 - 90. Min i s t r y of the Environment-Resource Analysis Branch. 1979. A g r i c u l t u r a l Land C a p a b i l i t y i n B r i t i s h Columbia. RAB Resource Data 1, ARDA Project #89077. Nikolayev, A.V. 1975. Main physical s o i l properties i n d i c a t i v e of s o i l p r o d u c t i v i t y . Soviet S o i l S c i . 7:707 - 713. Nix, H.A. 1968. The assessment of b i o l o g i c a l p r o d u c t i v i t y . In: Stewart, G.A. (ed.). Land Evaluation. Macmillan, A u s t r a l i a , pp. 76 - 87. Odell, R.T. 1958. S o i l survey i n t e r p r e t a t i o n - y i e l d p r e d i c t i o n . S o i l Science Soc. Am. Proc. 22:157 - 160. Peech, M. 1965. Hydrogen-ion a c t i v i t y . In: Black, C A . (ed.). Methods of S o i l A n a l y s i s . Am. Soc. Agron. Inc., Madison, Wisconsin. P i e r r e , W.H. 1958. Relationship of s o i l c l a s s i f i c a t i o n to other branches of s o i l science. S o i l S c i . Soc. Am. Proc. 22:167 - 170. Richards, L.A. 1965. Physical condition of water i n s o i l . In: Black, CA. (ed.). Methods of S o i l Analysis, Am. Soc. Agron. Inc., Madison, Wisconsin. Riecken, F.F. 1963. Some aspects of s o i l c l a s s i f i c a t i o n i n farming. S o i l S c i . 96:49 - 61. Robertson, V.C, T.N. Jewitt, A.P.S. Forbes, R. Law. 1968. The assessment of land q u a l i t y f o r primary production. In:. Stewart, G.A. (ed.). Land Evaluation, Macmillan, A u s t r a l i a , pp. 88 - 103. Runka, G.G. 1973. Methodology: Land C a p a b i l i t y f o r A g r i c u l t u r e . B.C. Dept. Ag r i c . 87 Runka, G., C.C. Kelley. 1964. S o i l survey of Matsqui Mu n i c i p a l i t y and Sumas Mountains. Report #6. B.C. Dept. Agric. S i e g e l , S. 1956. Nonparametric S t a t i s t i c s f o r the Behavioural Sciences. McGraw-Hill, New York. Singer, M.J. 1978. The USDA land c a p a b i l i t y c l a s s i f i c a t i o n and Storie Index r a t i n g : A comparison. J . S o i l Water Cons. 33 (4):178 -182. Sneath, P.H.A., R.R. Sokal. 1973. Numerical Taxonomy: The p r i n c i p l e s and p r a c t i c e of numerical c l a s s i f i c a t i o n . W.H. Freeman and Company, San Francisco, pp. 194 - 200, 246 - 248. Sowers, G.F. 1965. Consistency. In: Black, C A . (ed.). Methods of S o i l A nalysis. Am. Soc. Agron. Inc., Madison, Wisconsin. Steele, J.G. 1967. S o i l Survey Interpretat ion and i t s Use. FAO S o i l s B u l l e t i n 8. Rome. T r u d g i l l , S.T., D.J. Briggs. 1979. S o i l and land p o t e n t i a l . Prog. Phys. Geog. 4:262 - 275. USDA S o i l Conservation Service. 1971. Guide f o r Interpreting Engineering Uses of S o i l s . Ward, J. 1963. H i e r a r c h i a l grouping to optimize an objective function. American S t a t i s t i c a l A s sociation. 58:236 - 274. Webster, R. 1979. Quantitative and numerical methods i n s o i l c l a s s i f i c a -t i o n and survey. Clarendon Press, 0xford:159 - 218. Webster, R., P.H.T. Beckett. 1968. Quality and usefulness of s o i l maps. Nature 219:680 - 682. Wohletz, L.R. 1968. Interpretative s o i l maps for land use planning. 9 th Inter. Cong. S o i l S c i . Trans:225 - 234. Yeates, M. 1974. Introduction to Quantitative Analysis i n Human Geography. McGraw-Hill, U.S.A.:209 - 230. Young, A. 1973. Rural land evaluation. In: Dawson, J.A. and J.C. Doornkamp (eds.). Evaluating the Human Environment: Essays i n Applied Geography:5 - 33. 88 APPENDIX A  CATEGORICAL PARAMETERS Drainage Categories 1 rapid 1.5 well to rapid 2 well 2.5 moderately well to well 3 moderately well 4 imperfect 5 moderately poor 5.5 moderately poor to poor 6 poor 6.5 poor to very poor 7 very poor Perviousness Categories 1 rapid 2 rapid to moderate 3 moderate to rapid 4 moderate 5 moderate to slow 6 slow to moderate 7 slow Parent Material Categories 1 colluvium 2 outwash 3 l o c a l stream deposits 3.5 f l o o d p l a i n deposits 4 aeolian 5 l a c u s t r i n e 6 g l a c i o l a c u s t r i n e - g l a c i o f l u v i a l 7 g l a c i a l t i l l 8 glaciomarine 9 marine APPENDIX B - SOILS DATA CTl OO CO CD •H U cu CO •I-I o CO CJ cd CJ 35 ft •fc — \ CO. CJ o CU TH CO .4-1 u o cd cd o u U fn C J CM o CU - H CO 4-1 n o cd cd O M CO CJ CJ nj PP •i-i co cd CJ cd r-l CJ CJ •H r4 (3 CU cr) 4J 60 4-1 r l CD O S r l r l CO N—' Tj •i-l 4-1 3 'rH cr a •rH »rH CU CU 6 0 . — . 60^-N cd co cd CJ U W rH ^ ^ o o a 4-i >> 4J j * . ^ O) u to n TJ -rH -rH • H 4 J rH o u a 3 -rH cu cd cu cd cr B 4J 4J p. • H » H cd cd cd cd rJ rJ S U 2 U AD 4.6 14° 20 §°4 £6 10 4° 6° 3 13 10 1 BT 5.3 0 0 4 65 33 39 5 36 33 10 9 BL 4.7 3 0 1 87 10 52 9 45 37 10 8 BK 4.6 0 0 5 46 42 39 8 38 32 11 12 F 4.9 0 0 3 62 35 28 5 38 30 9 12 HT 4.5 38 0 4 55 27 27 9 39 27 13 14 HD 4.3 5 0 2 20 41 61 7 30 46 8 9 HJ 4.4 3 0 3 51 39 33 13 45 31 15 13 KD 5.2 1 0 25 49 22 9 6 34 21 10 11 LX 4.9 0 0 95 1 4 3 1 20 20 2 2 LK 5.0 0 0 64 37 10 11 7 30 17 9 8 MH 4.7 20 63 94 64 20 5 15 45 18 13 2 MQ 5.1 1 1 27 63 28 17 6 34 22 11 11 M 4.8 0 0 4 64 31 34 6 37 34 14 13 NN 5.2 31 0 0 25 72 48 6 35 41 ,7 10 PE 4.7 4 0 1 44 53 45 6 38 33 7 9 RD 5.3- 5 1 6 79 9 10 15 29 27 15 20 SI 5.1 0 0 1 46 52 37 12 46 32 6 9 VD 5.5 0 1 1 45 54 56 6 42 37 11 8 W 4.9 1 3 28 61 17 13 8 38 30 16 11 CU CU 60 60 CJ CJ cd /—s cd /-N co 4J J3 co X I C J CO CJ CJ ^ CJ ^ CU CU X x • • CJ u W >s W >s CU CO Cd rH 4-> 4-1 60 3 CH Cd CJ TH CJ -rH cd O •rH o a o o CJ CU •rH U U •rH CTj •H cd •rH & > CU CU 4-1 ft 4-> P. cd o U Pi 4-1 cd cd cd co U r-l a) ft. cd C J C J C J o Q CO CH P S ine /lOO^gm % 5.5 2f0 1.5 2.5 2 4.0 6.0 6.0 4.0 5.5 4 3-0 11.5 6.5. 5.5 4.0 6 3.5 10.5 7.0 5.5 2.0 5. 5.0 13.5 4.0 4.0 4.0 4 3.5 7.5 4.0 6.5 1.0 4 3.5 15.0 8.5 6.0 1.5 7 3.5 10.5 5.5 5.5 2.5 4 3.5 7.5 3.0 2.0 12.5 1 5.0 2.5 1.5 2.0 15.0 1 4.0 5.0 3.0 2.5 3.0 3 3.0 14.5 1.0 2.0 9.5 4 4.0 7.0 4.0 2.5 5.5 3 3.5 7.0 4.5 2.5 5.0 4 3.5 11.5 9.5 6.5 1.0 4 . 3.5 15.5 5.5 5.5 2.0 7 3.5 5.5 3.5 2.5 20.0 6 4.0 9.0 5.0 6.0 4.0 4 3.0 6.5 3.0 6.0 1.0 5 5.0 5.0 4.0 25.0 8.5 4 8.0 CJ cu VH Cd i-H CM cd •rH M U CU CU & 4-1 o cd rH S 6.0 ,0 .5 .0 ,5 ,5 3.5 3.5 5. 4. 3. 6. 3.5 3.5 3.5 3.5 7.0 3.0 5.0 8.0 *S = surface horizon C = C horizon 90 APPENDIX C SOIL LIMITATIONS FOR AGRICULTURE (as defined by the Canada Land Inventory) L i m i t a t i o n Symbol Adverse Climate C Undesirable S o i l Structure/or Low Permeability D Erosion E Low F e r t i l i t y F Inundation by Streams or Lakes I Moisture L i m i t a t i o n M S a l i n i t y N Stoniness P Consolidated Bedrock R Adverse S o i l C h a r a c t e r i s t i c s - Includes Two or More of D, F, M, N S Topography T Excessive Water W Cumulative Minor Adverse C h a r a c t e r i s t i c s X 

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