ECONOMIC AND ENVIRONMENTAL EVALUATION OF PRECISION FARMING PRACTICES IN IRRIGATED COTTON PRODUCTION MAN YU, B.S., M.S

ECONOMIC AND ENVIRONMENTAL EVALUATION OF PRECISION FARMING PRACTICES IN IRRIGATED COTTON PRODUCTION by MAN YU, B.S., M.S. A DISSERTATION IN AGRICULTUR...
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ECONOMIC AND ENVIRONMENTAL EVALUATION OF PRECISION FARMING PRACTICES IN IRRIGATED COTTON PRODUCTION by MAN YU, B.S., M.S. A DISSERTATION IN AGRICULTURAL AND APPLIED ECONOMICS Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Approved

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ACKNOWLEDGEMENTS

I would like to recognize and express my sincere appreciation to the people who made this work possible. First and foremost, I want to thank Dr. Eduardo Segarra for his insight, guidance, and encouragment in my graduate studies. The time and effort that he put into this project and my education has meant a great deal to me. Special thanks are due to my committee members. Dr. Emmett Elam, Dr. J. Wayne Keeling, and Dr. William J. Oldham. Each committee member provided valuable constmctive criticism and direction. 1 am extremely grateful for the support from Drs. Hong Li, Robert J. Lascano, Charles Chilcutt, L. Ted Wilson, Kevin Bronson, and Steve Searcy at the Texas Agricultural Experimental Station in Lubbock. Also, I would like to thank Mrs. Susan Watson, who performed suggestions and advises on the research. Finally, and most importantly, I would like to thank my parents, my brother and sister-in-law for their patience, understanding, and support. They have been my motivation and foundation. Thank you all.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS

ii

LIST OF TABLES

vi

LIST OF FIGURES

xvi

I. INTRODUCTION

1

1.1 General Problem

1

1.2 Historical Development of Precision Farming

3

1.3 Components of Precision Farming

4

1.3.1 Data Collection

4

1.3.2 Data Analysis

8

1.3.3 Implementation

11

1.4 Specific Problem

11

1.5 Objectives

12

II. LINTERATURE REVIEW

13

2.1 Influence of Applied and Residual Fertilizer on Crop Yield

13

2.2 Economic Evaluation of Precision Farming Practices

14

2.3 Dynamic Optimal Input Decision Research

18

2.4 Cost/Benefit Analysis of Precision Farming

19

2.5 Costs Associated with Adopting Precision Farming Technology

21

2.6 Future of Precision Farming Practices

24

HI

III. CONCEPTUAL FRAMEWORK

27

3.1 Profit Maximization and Environmental Externality

27

3.1.1 Optimal Input and Output for Profit Maximization

27

3.1.2 Welfare Measurement with Negative Environmental Extemality in Production

30

3.2 Technological Progress and Productivity

33

3.3 Productivity Growth and Its Economic and Environmental Implications 3.3.1 Case 1: The Welfare Change under the Same Level of Input with the Adoption of Precision Farming Technology

35 37

3.3.2 Case 2: The Welfare Changes under the Same Level of Output with the Adoption of Precision Farming Technology

39

3.3.3 Case 3: The Welfare Changes between the Same Level of Input and the Same Level of Output with the Adoption of Precision Farming Technology

40

IV. METHODS AND PROCEDURES

43

4.1 Basic Model

44

4.2 Field Experiment and Data Consideration

45

4.3 Estimation of Crop Response and Input Residual Functions

51

4.4 Economic Evaluation and Environmental Implication of Precision Farming Practices

52

4.5 Productivity with the Application of New Technology under Precision Farming

53

IV

4.6 Environmental Policy and Its Influences to Precision Farming Practices

54

V. RESULTS

55

5.1 Estimation of the Cotton Production and Nitrogen Carry-Over Function 5.2 Economic and Environmental Implications Under Different Price Ratios

55 57

5.2.1 Environmental Implications

64

5.2.2 Economic Implications

68

5.2.3 Continuous Optimized Nitrogen Application Equations

77

5.3 Productivity Associated with Precision Farming Practices and Conventional Whole-Field Farming Practices 5.4 Environmental Policy and Its Influence on Precision Farming Practices 5.5 Factors Influencing Precision Farming Practices Adoption VI. SUMMARY AND CONCLUTIONS 6.1 Summary of Dissertation 6.2 Conclusions and Imphcations REFERENCES APPENDIX A. RESULTS OF THE OPTIMIZATION MODELS FOR PRECISION FARMING SCENARIOS B. RESULTS OF THE OPTIMIZATION MODELS FOR CONVENTIONAL WHOLE-FIELD FARMING SCENARIOS C. COMPARISON OF PRECISION FARMING SCENARIOS AND CONVENTIONAL WHOLE-FIELD FARMING SCENARIOS

81 87 94 97 97 102 105

108

146

184

LIST OF TABLES

2.1 Precision Farming Services Provided to Tennessee Producers and Average Rate Charged for Those Services, 1999

23

5.1 Annual Nitrogen Optimal Application Level Associated with Conventional Whole-Field Farming Practices Under a Ten-Year Planning Horizon

60

5.2 Average Yield Change (%>) on a Per-Acre Basis for Precision Farming Practices versus Conventional Whole-Field Farming Practices

62

5.3 Average Net Revenue Change (%>) above Nitrogen and Water Costs on a Per-Acre Basis for Precision Farming Practices versus Conventional Whole-Field Farming Practices

63

5.4 Comparison of Precision Farming Practices and Conventional Whole-Field Farming Practices in Irrigated Cotton Production at Lamesa, Texas, 1998

78

5.5 A Comparison of Productivity between Precision Farming Practices and Conventional Whole-Field Farming Practices under 50%) ET

83

5.6 A Comparison of Productivity between Precision Farming Practices and Conventional Whole-Field Farming Practices under 15% ET

84

5.7 Productivity Change and Average Nitrogen Level Changes Associated with Precision Farming Practices and Conventional Whole-Field Farming Practices

88

5.8 A Comparison of Precision Farming and Conventional Whole-Field Farming Practices under 25%) Nitrogen Fertilizer Restriction for 50%) ET

90

5.9 A Comparison of Precision Farming and Conventional Whole-Field Farming Practices under 25%) Nitrogen Fertilizer Restriction for 15% ET

91

5.10 A Comparison of Precision Farming and Conventional Whole-Field Farming Practices under 50%) Nitrogen Fertilizer Restriction for 50%) ET

92

5.11 A Comparison of Precision Farming and Conventional Whole-Field Farming Practices under 50%) Nitrogen Fertilizer Restriction for 15% ET

93

5.12 Top 5 Locations and Low 5 Locations with Potential to Increase Net Revenue under 50%) and 75%o ET by Using Precision Farming Practices

95

A.l Optimization Model Results for 50%o ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d P„,trogen=$0.35/lb

110

A.2 Optimization Model Resuhs for 15% ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.35/lb

Ill

A.3 Optimization Model Results for 50%o ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

112

A.4 Optimization Model Results for 75% ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

113

A.5 Optimization Model Results for 50%) ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.25/lb

114

A.6 Optimization Model Resuhs for 75%o ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.25/lb

115

A.7 Optimization Model Results for 50%) ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.35/lb

116

A.8 Optimization Model Resuhs for 75%o ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pn,trogen=$0.35/lb

117

A.9 Optimization Model Results for 50%o ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.30/lb

118

A. 10 Optimization Model Resuhs for 15% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.30/lb

119

A.l 1 Optimization Model Resuhs for 50% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.25/lb

vu

120

A. 12 Optimization Model Results for 75% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.25/lb

121

A. 13 Optimization Model Results for 50% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.35/lb

122

A. 14 Optimization Model Results for 75% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.35/lb

123

A. 15 Optimization Model Results for 50%o ET and Pwater'=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

124

A. 16 Optimization Model Results for 75% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

125

A. 17 Optimization Model Resuhs for 75% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.25/lb

126

A. 18 Optimization Model Resuhs for 75% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.25/lb

127

A. 19 Optimization Model Resuhs for 50%o ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.35/lb

128

A.20 Optimization Model Results for 75% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.35/lb

129

A.21 Optimization Model Results for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

130

A.22 Optimization Model Results for 75%o ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

131

A.23 Optimization Model Resuhs for 50% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.25/lb

132

A.24 Optimization Model Results for 15% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pni,rogen=$0.25/lb

133

A.25 Optimization Model Results for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.35/lb

134

A.26 Optimization Model Resuhs for 15% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.35/lb

Vlll

135

A.27 Optimization Model Results for 50% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb.. a n d Pnitrogen=$0.30/lb

136

A.28 Optimization Model Results for 15% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.30/lb

137

A.29 Optimization Model Resuhs for 50% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.25/lb

138

A.30 Optimization Model Resuhs for 75%o ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.25/lb

139

A.31 Optimization Model Resuhs for 50% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d P„itrogen=$0.35/lb

140

A.32 Optimization Model Results for 15% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.35/lb

141

A.33 Optimization Model Resuhs for 50% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

142

A.34 Optimization Model Resuhs for 15% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

143

A.35 Optimization Model Results for 50%o ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.25/lb

144

A.36 Optimization Model Results for 15% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.25/lb

145

B.l Optimization Model Resuhs for 50% ET and Pv,ater=$2.68/inch, Pcotton=$0.50/lb., a n d Pn,trogen=$0.35/lb

148

B.2 Optimization Model Results for 75% ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.35/lb

149

B.3 Optimization Model Results for 50%) ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

150

B.4 Optimization Model Resuhs for 75%) ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

151

B.5 Optimization Model Resuhs for 50%o ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.25/lb

IX

152

B.6 Optimization Model Results for 75% ET and Pwater=$2.68/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.25/lb

153

B.7 Optimization Model Results for 50% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.35/lb

154

B.8 Optimization Model Resuhs for 75% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.35/lb

155

B.9 Optimization Model Results for 50%) ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.30/lb

156

B.IO Optimization Model Results for 75%o ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.30/lb

157

B.l 1 Optimization Model Results for 50% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.25/lb

158

B.12 Optimization Model Resuhs for 75% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb., a n d Pn,trogen=$0.25/lb

159

B.l3 Optimization Model Resuhs for 50%) ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.35/lb

160

B.14 Optimization Model Results for 15% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.35/lb

161

B.l5 Optimization Model Results for 50% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

162

B.16 Optimization Model Results for 15% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

163

B.l7 Optimization Model Resuhs for 75%o ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.25/lb

164

B.l8 Optimization Model Resuhs for 75%) ET and Pwater=$2.68/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.25/lb

165

B.l9 Optimization Model Resuhs for 50%o ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.35/lb

166

B.20 Optimization Model Results for 75% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.35/lb

167

B.21 Optimization Model Results for 50%o ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

168

B.22 Optimization Model Results for 15% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.30/lb

169

B.23 Optimization Model Results for 50%o ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.25/lb

170

B.24 Optimization Model Resuhs for 15% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb., a n d Pnitrogen=$0.25/lb

171

B.25 Optimization Model Resuhs for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.35/lb

172

B.26 Optimization Model Resuhs for 15% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.35/lb

173

B.27 Optimization Model Resuhs for 50%o ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.30/lb

174

B.28 Optimization Model Results for 75% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pn,trogen=$0.30/lb

175

B.29 Optimization Model Resuhs for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pn,trogen=$0.25/lb

176

B.30 Optimization Model Results for 15% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., a n d Pnitrogen=$0.25/lb

177

B.31 Optimization Model Results for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d P„,trogen=$0.35/lb

178

B.32 Optimization Model Resuhs for 75%o ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pn,trogen=$0.35/lb

179

B.33 Optimization Model Resuhs for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

180

B.34 Optimization Model Resuhs for 15% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., a n d Pnitrogen=$0.30/lb

181

B.35 Optimization Model Results for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., and Pnitrogen=$0.25/lb

182

B.36 Optimization Model Results for 75%o ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., and Pnitrogen=$0.25/lb

183

C.l Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$2.68/inch, Pcotton=$0.50/lb.,P„,trogen=$0.35/lb

185

C.2 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75%o ET and Pwater=$2.68/inch, Pcotton=$0.50/lb.,Pn,trogen=$0.35/lb

186

C.3 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater==$2.68/inch, Pcotton=$0.50/lb., Pnitrogen=$0.30/lb

187

C.4 Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$2.68/inch, Pcotton=$0.50/lb.,Pn,trogen=$0.30/lb

188

C.5 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$2.68/inch, Pcotton-=$0.50/lb.,Pn.trogen=$0.25/lb

189

C.6 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75% ET and Pwater=$2.68/inch, Pcotton=$0.50/lb.,Pn,trogen=$0.25/lb

190

C.7 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb.,Pni,rogen=$0.35/lb

191

C.8 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb.,Pnitrogen=$0.35/lb

192

C.9 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb.,Pn,trogen-$0.30/lb

193

CIO Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb.,Pn|trogen=$0.30/lb

194

c.l 1 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50% ET and Pwater=$2.68/inch, Pcotton=$0.60/lb.,Pnitrogen=$0.25/lb

195

c.l 2 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75%o ET and Pwater=$2.68/inch, Pcotton=$0.60/lb.,Pnitrogen=$0.25/lb

196

c.l 3 Comparison of Precision Farming and Whole Field Farming Scenarios for 50%o ET and Pwater"=$2.68/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.35/lb

197

C.14 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.35/lb

198

C.l 5 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.30/lb

199

C.l 6 Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb.,Pn,trogen=$0.30/lb

200

c.l 7 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$2.68/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.25/lb

201

c.l 8 Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$2.68/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.25/lb

202

c.l 9 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.50/lb.,P„itrogen=$0.35/lb

203

C.20 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75%o ET and Pwater=$3.50/inch, Pcotton=$0.50/lb.,Pnitrogen=$0.35/lb

204

XUl

C.21 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%o ET and Pwater=$3.50/inch, Pcotton=$0.50/lb.,Pnitrogen=$0.30/lb

205

C.22 Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb.,Pnitrogen=$0.30/lb

206

C.23 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.50/lb.,Pnitrogen=$0.25/lb

207

C.24 Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$3.50/inch, Pcotton=$0.50/lb.,Pnitrogen=$0.25/lb

208

C.25 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.60/lb.,Pn,trogen=$0.35/lb

209

C.26 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75%o ET and Pwater=$3.50/inch, Pcotton=$0.60/lb.,Pnitrogen=$0.35/lb

210

C.27 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.60/lb.,Pn,trogen=$0.30/lb

211

C.28 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb.,Pnitrogen=$0.30/lb

212

C.29 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb., Pnitrogen=$0.25/lb

213

C.30 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75% ET and Pwater=$3.50/inch, Pcotton=$0.60/lb.,Pnitrogen=$0.25/lb

214

C.31 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.35/lb

215

xiv

C.32 Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.35/lb

216

C.33 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$3.50/inch, Peotton=$0.70/lb.,Pnitrogen=$0.30/lb

217

C.34 Comparison of Precision Farming and Whole-Field Farming Scenarios for 15% ET and Pwater=$3.50/inch, Pcotton=$0.70/lb., Pnitrogen=$0.30/lb

218

c.35 Comparison of Precision Farming and Whole-Field Farming Scenarios for 50%) ET and Pwater=$3.50/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.25/lb

219

C.36 Comparison of Precision Farming and Whole-Field Farming Scenarios for 75%o ET and Pwater==$3.50/inch, Pcotton=$0.70/lb.,Pnitrogen=$0.25/lb

220

XV

LIST OF FIGURES

1.1 GPS and Its Application to Precision Farming Practices

7

1.2 Spatial Grain Sorghum Yield Map, Halfway, Texas, 1997

9

1.3 Soil Fertility Map (NO3-N Residual Map from 0 to 12 InchesofSoUDepth), Halfway, Texas, 1997

10

3.1 Optimal Input use and Output Production for Profit Maximization

29

3.2 Welfare Changes with Negative Externalities in Production

31

3.3 Production Relationships and Productivity

34

3.4 Productivity Growth and Its Implications

36

3.5 Welfare Changes under the Same Level of Input Use with the Adoption of Precision Farming Technology 3.6 Welfare Changes under the Same Level of Output Produced with the Adoption of Precision Farming Technology

38 41

4.1 NO3-N Pre-Season Residual Map from 0 to 90 Centimeters of SoU Depth, Lamesa, Texas, 1998

47

4.2 Total Nitrogen Available Map, Lamesa, Texas, 1998

48

4.3 Spatial Cotton Yield Map, Lamesa, Texas, 1998

49

4.4 NO3-N After-Season Residual Map from 0 to 90 Centimeters of SoU Depth, Lamesa, Texas, 1998 5.1 Optimal Levels of Spatial Nitrogen Application Map on a Per-Acre and Per-Year Basis for a Ten-Year Planning Horizon, Lamesa, Texas, 1998

65

5.2 Nitrogen Over or Under Application on a Per Acre and Per-Year Basis for a Ten-Year Planning Horizon, Comparing Precision Farming Practices and Conventional Whole-Field Farming Practices, Lamesa, Texas, 1998

67

50

5.3 Spatial Cotton Lint Yield Map Associated with Precision Farming Practices for a Ten-Year Planning Horizon, Lamesa, Texas, 1998

69

5.4 Spatial Cotton Lint Yield Map For Conventional Whole-Field Farming Practices for a Ten-Year Planning Horizon, Lamesa, Texas, 1998

70

5.5 Yield Change for a Ten-Year Optimization Model (Precision Farming and Conventional Whole-Filed Farming), Lamesa, Texas, 1998

72

5.6 Spatial Net Revenue Above Nitrogen and Water Costs for a Ten-Year Optimization Model For Precision Farming Practices, Lamesa, Texas, 1998

73

5.7 Spatial Net Revenue Above Nitrogen and Water Costs for a Ten-Year Optimization Model For Conventional Whole-Field Farming Practices, Lamesa, Texas, 1998

75

5.8 Spatial Net Revenue Change to Nitrogen Use (Precision Farming and Conventional Whole-Filed Farming), Lamesa, Texas, 1998

76

5.9 Continuous Form of the Optimal Decision Rules of Applied Nitrogen for Different Levels of Initial Nitrogen Residual under 50%) ET with Precision Farming Practices and Conventional Whole-Field Farming Practices

80

5.10 Nitrogen Residual Level at the End of the lO"" Growing Season Associated with Precision Farming Practices, Lamesa, Texas, 1998

86

XVll

CHAPTER I INTRODUCTION

Increased use and improvement of fertilizers, pesticides, and other inputs in agriculture over the past four decades have contributed to the enhancement of the productivity of the agricultural sector (Ahearn et al., 1998). Currently, production agriculture is facing significant challenges, including escalating costs of production, pest resistance to chemicals, and increased public concerns about the impacts of agricultural production practices on the environment. As the trend of world trade liberalization continues, agricultural producers will increasingly compete to produce high quality products at low prices for the world market, while attempting to use production practices that are benign to the environment.

1 • 1 General Problem Traditionally, optimal input use in agriculture has assumed spatial field homogeneity with respect to field characteristics such as soil type, fertility, moisture, acidity, and other factors. That is, optimal input use decision rules have not accounted for these differences within fields. Hence, inputs have been applied at uniform rates across fields based on average yield potential, and average soil characteristics. This farming approach has been denominated as conventional whole-field farming (Snyder, 1996, p.5). However, if uniform levels of inputs are used on a field, there exist differences in potenfial yields within the field. Onken (1996) showed that cotton lint

yields can vary up to 30%, depending upon location within the field when applying the same levels of irrigation water and fertilization inputs across the whole field. In recent years, many new technologies have been developed and are being utilized in agricultural production. These new technologies involve microcomputers, microprocessor based control systems, satellite poshioning technologies, and different kinds of sensors used to record and measure the variability of soil fertility, soil moisture, crop yield, and crop moisture within fields. With the help of these technologies, spatial soil testing, variable rate applicafion of fertilizers, variable rate spraying, and yield mapping are available. Also, Variable-Rate Technology (VRT) has made the varying of amounts of inputs applied based on spatial locafion within fields possible. These new technologies have the potential to radically change agriculture. There are many arguments about how to call this "agricultural revolution," including "Precision Farming," "Prescripfion Farming," "Targeting Farming," and "Site-Specific Farming." The more predominant terms appear to be "Precision Farming" and "Precision Agriculture" (Clark, 1998). The precision farming term will be the one used in this research. There are several definitions of precision farming. Clark (1998) defines it, as a management system designed to optimize farm profit and to minimize the environmental impact by controlling the use of farm inputs on a spatial basis. Robert et al. (1995, p. xii) state that precision farming (or site-specific crop management (SSCM)) is an information and technology based agricultural management system designed to identify, analyze, and manage site-soil spatial and temporal variability within fields for optimum profitability,

sustainability, and protecfion of the environment. Simply speaking, precision farming, precision agricuhure, or site-specific management recognizes field spafial variability and seeks to optimize variable input use within fields (Segarra et al., 1996).

1.2 Historical Development of Precision Farming In the past, farmers collected soil samples from a field, shipped them off for analysis, and received a uniform fertilization recommendation based on the average characteristics for that field. Not much attention was given to the location or how many samples were obtained because it was implicitly assumed that the soil in the field was uniform (Clark, 1998). The concept of precision farming was first proposed as early as 1929 (Oolman, 1995). However, with limitations in technology, it was impossible to quantify and manage differences within fields. As new information technologies were developed, they were applied to production agricultme. For example, several studies have found that soils posses different crop yield potential. That is, given a particular soil type adding more inputs beyond a certain point will not necessarily increase yield under any circumstances. In this case, it would be more efficient to reduce the amount of inputs used, reduce costs, and possibly increase yields. The combinafion of increasing producfivity while saving on inputs costs is one of the goals of precision farming (Johnson, 1996, p. 24). In the early 1990s, civilian use of Global Poshion System (GPS) technologies, the development of sophisticated electronic sensors, and VRT made the use of precision

farming to manage the differences within a field possible. Currently, precision farming practices are being successfully used in grain producfion in the Midwest. In addifion, cotton yield monitoring systems, i.e. cotton fiow sensors, became commercially available recently and are undergoing technical improvements (Clark, 1998).

1.3 Components of Precision Farming Precision farming in its broadest sense is a process for managing variability within fields. The process follows three steps: (1) data collection, (2) data analysis, and (3) implementation (Johnson, 1996, p. 18). This section discusses each step in detail to provide a better understanding of precision farming.

1.3.1 Data Collection In order to manage variability within a field, it is important to measure inherent field characterisfics first. Data collecfion is the key process in measuring differences of key field characteristics. Generally, this process includes the use of GPS (Global Posifion System) and the measurements of yield, soil characterisfics, weed population, insects and other plant related data. The starting point for data collection is spatial yield data. Yield monitors measure the rate of yield flowing into a grain combine's hopper or cotton stripper and display the information in the combine cab. During this process, a yield sensor is used to detect the amount of grain or cotton flowing into the hopper or basket. The sensors and their associated electronics components must be property calibrated in order to compensate for

crop moisture content which affects the measurement. Yield monitors provide the several types of spatial information including moisture-corrected yield in bushels or pounds per acre, average moisture content, acres harvested and several other important pieces of information (Johnson, 1996, p. 19). Another factor needed to be measured in precision farming is the variability of desirable soil properties. In precision farming pracfices, small scale grid soil sampling is usually conducted. Following laboratory analysis, soil fertility of each locafion in the field is measured. One of the key technologies that make precision farming viable is the Global Posifion System (GPS). GPS was first deployed in 1973 by the U.S. Department of Defense. From the mid-1970's unfil 1995, a total of 24 satellites were launched into orbit at a height of 2,200 kilometers above the earth (Johnson, 1996, p. 50). The primary purpose of this system was to provide continuous, worldwide positioning and navigation data to U.S. and allied military forces around the globe. There are two signals broadcasted from each GPS satellite: a Standard Positioning Service (SPS) signal for worldwide civilian use and a Precise Positioning Service (PPS) signal for U.S. and allied military use. Precision farming is only one of several civilian and commercial applications using the SPS signal portion of the GPS system (Snyder, 1996, p. 13). These satellites contain precise atomic clocks, and the exact time is encoded into the signals broadcast from each satellite. A GPS receiver uses this fime information to measure the distance to each satellite from which a signal is being received. With at least four satellite signals, the receiver can use triangulation to

calculate its poshion on the ground. However, the calculated posifion is slightly inaccurate because of errors in the satellite signals. Some of these errors are caused by atmosphere interference, which are unavoidable. Another error source is government controlled for security purposes. This intenfional degradafion of the satellite signals is known as selective availability. The inaccuracies can be up to 300 feet from its true position. These inaccuracies are unacceptable for site-specific management. One way to solve this problem is by using another signal to improve positioning accuracy. The additional signal is transmitted from a fixed base station with a precisely known position within a field. The base station receives the satellite signals and compares its calculated position with its exact position. The amount of error is then broadcasted to mobile receivers in the field so that they can correct for the same satellite errors (Figure 1.1). This system is known as differentially corrected GPS. The accuracy of the receivers can be improved to within 5 to 10 feet from the true location, which is acceptable when dealing with a 60-foot swath (Searcy, 1997). With the help of the GPS system, it is easy to determine the location in latitude, longitude, and elevation using a satellite radio receiver in the combine or tractor. This receiver picks up signals from several satellites to determine the locafion, or poshion on an ongoing basis. Another technique that should be mentioned here is remote sensing. Remote sensing includes any type of measurement from a distance. One application of this is the use of aerial photographs and satellite images in analyzing yields and soil conditions.

Figure I.I. GPS and Its Apphcation to Precision Farming Practices. (Source: Searcy, 1996)

Researchers are also trying to correlate the infrared radiation given off by crops at various stages of their development with fertility, salinity, pH, weed and pest infestations, and other factors. Some experts feel that remote sensing holds important keys to effective use of precision farming technologies in the future (Johnson, 1996, p. 21).

1.3.2 Data Analysis Data analysis is the process of making sense of raw data. The goal is to find out the meaning of the data in order to make good management decisions. "Precision farming doesn't tell you how to farm; it gives you information. You do the same thing you've always done, you just do it more accurately" (Johnson, 1996, p. 23). Thus, data analysis is one of the most important processes in precision farming activities. The first step of data analysis is to combine yield data and soil fertility data with the positioning data to produce spatial yield and soil fertility maps. Examples of spatial yield and soil fertility maps are shown in Figures 1.2 and 1.3. The next step is to prescribe the exact amount of inputs needed in each grid of the field, in order to achieve a desired level of yield or other specified goal. Then a prescription map, indicating the areas of the field where specific levels of inputs must be applied, can be created. This dissertation will try to concentrate on this aspect, i.e., it will analyze raw data so that optimal management decisions can be made.

•^80

yield .tab YIELD 0

1055.16

2137.78

3220.39

4303

Units; pounds/acre Figure 1.2. Spatial Grain Sorghum Yield Map, Halfway, Texas, 1997.

nrt.tab NRT 8.91092

44.4397

79.9686

115.497

151.026

Units: pounds Figure 1.3. Soil Fertility Map (NO3-N Residual Map from 0 to 12 Inches of Soil Depth), Halfway, Texas, 1997.

10

1.3.3 Implementation VRT is one of the most important and recent technologies that have been developed recently to accomplish precision farming. After the prescription map has been completed, it is then loaded into the VRT via a memory card, which is used to control the variable rate application equipment in the field. It has been shown that VRT can control the application of seed, fertilizer, and spray of insecticides and herbicides within fields (Johnson, 1996, p. 24).

1.4 Specific Problem The effectiveness of precision farming is based on applying greater amounts of inputs on specific area of fields that exhibit profitable response to them, and on reducing amotmts of inputs in less productive or environmentally sensitive areas of fields (Wolf and Buttel, 1996). Some of the advantages of precision farming include: (1) more efficient use of inputs; (2) potential for increased profits; and (3) potential for reducing the impacts of input use on the environment by using chemicals only where they are needed and in amotmts that are sufficient, but not excessive for a given set of conditions. The acceptance and widespread use of precision farming practices in agricultural production will ultimately depend on both, their economic performance and their environmental implications as compared to current conventional whole-field farming practices. In recent years, there have been many research efforts and publications on precision farming, especially on the introduction of new technologies that have been developed. However, only very few of these have dealt with the profitability issue

11

(Onken, 1996). In this dissertation, the economic performance and environmental implications of precision farming were evaluated.

1.5 Objectives The overall objective of this study was to evaluate the profitability and environmental implications of precision farming practices in irrigated cotton production in the Southern High Plains of Texas, evaluate the change of productivity associated with precision farming practices, and determine the impacts of possible governmental restrictions on input use. More specifically, it included the following specific objectives: 1. To determine within field variability of crop yield response functions to the application of inputs, including fertilizers (nitrogen or phosphorus), pesticide, and irrigation water, and other soil and location characteristics, including elevation, and clay, sand, and silt content in the soil; 2. To evaluate the economic and environmental effectiveness of varying inputs to match yield potential within areas of a field by utilizing spatial input response functions; 3. To derive a continuous form of the optimal decision rule of input application with respect to different input-output price ratios; 4. To compare the level of productivity under conventional whole-field farming and precision farming technology; 5. To evaluate the economic and environmental impacts of governmental restrictions on input use.

12

CHAPTER 11 LITERATURE REVIEW

In order to make input use recommendations, predict crop yields, and evaluate the economic implicafions of opfimal input utilization under different soil types and crop conditions, several areas of related literature are reviewed in this section. These include the influence of applied and residual fertilizer on crop yield, the economic evaluation of precision farming practices, dynamic optimal input decision research, cost/benefit analysis of precision farming, costs associated with adopfing precision farming technology, and the future of precision farming practices.

2.1 Influence of Applied and Residual Fertilizer on Crop Yield Efficient use of fertilizer in crop production is important for increasing crop yields and economic returns to producers, and minimizing potential environmental damage. As fertilizer use in crop production increases, potential accumulation of unused fertilizer residual in the soil is unavoidable. Therefore, one of the most important aspects in improving fertilizer use efficiency is to consider how to use imused fertilizer accumulation in the soil. Onken and Sunderman (1972) performed a three-year multi-rate nitrogen applicafion in a clay loam soil to determine the influence of applied nitrogen and soil nitrogen residual on the yield of irrigated grain sorghum. The purpose of this study was to obtain information about accumulation and depletion patterns of nitrogen. Before the

13

crop growing season, soil samples were taken at depth increments of 0-15, 15-30, 30-61, £ind 61-91 cm and analyzed for nitrogen content. During the crop growing season, nitrogen fertilizer treatments consisted of 0, 45, 90, 134, and 179 kg/ha applied with phosphorus fertilizer at 39 kg/ha, and potassium fertilizer at 74 kg/ha. They found that a highly significant linear relationship existed between the quantities of soil nitrogen measures at upper depths to those measured at lower depths. Also, they found that high nitrogen residual in the previous year was reflected in the high yields of unfertilized plots. By using regression analysis, highly significant multiple correlation coefficients and coefficients of multiple determination, R^, were obtained, which indicated a better fit of the equation when the nitrogen residual level was included in relating grain sorghum yield to nitrogen. Highest correlations were obtained when nitrogen residual measurements from 0 to 30 cm were included in the regression analysis. It appeared that soil samples taken from 0-15 or 0-30 cm would be sufficient for evaluation of residual nitrogen effects. This was an earlier study on fertilizer residual's movement in the soil and its impact on crop yield. As expected, fertilizer residual had a significant impact on crop yield. But because of technology limitations, the research results could not be used in agricultural production to control the unused fertilizer accumulation in 1970s.

2.2 Economic Evaluation of Precision Farming Practices In the 1990s, as technologies become available and are used in precision farming practices, economic evaluation is one of the most important aspects that will help decide

14

the future of precision farming practices utilization. Snyder (1996) pointed out that the economic feasibility of precision farming will play a major role in determining if precision agricultural technologies are widely adopted. The objective of this study was to evaluate whether precision farming can be applied to improve economic efficiency on continuous com production under center pivot irrigation in central Kansas. Hence, the economic impacts of variable and uniform nitrogen management were compared. This study chose two farms on which to perform the experiment. In each farm, the field was randomly divided into two parts, one for precision farming nitrogen management, the other for uniform nitrogen management. A yield goal for each field was found according to the soil conditions and historical data. Then, the exact amounts of recommended nitrogen under the uniform nitrogen management were calculated, using the yield goal for each field. The amount of nitrogen application was decided by using recommended nitrogen minus the nitrogen residual in the soil (0 to 6 inches). Under the variable nitrogen management, the field was divided into 180 by 180 ft. grids. The amount of nitrogen application for each grid were calculated by using the same method as above. After application of the recommended amounts of nitrogen fertilizer on the uniform and variable nitrogen management fields, the yields of com in both scenarios were measured at the end of the experiment. Then, a cost/benefit analysis evaluation of precision farming nitrogen management was performed. Also, using different prices of nitrogen fertilizer and corn, the addhional return ($/acre) due to precision farming nitrogen management over uniform nitrogen management was calculated. The additional

15

per acre cost associated with precision farming nitrogen management was measured when the experiment was performed. The results of this study showed that less total nitrogen fertilizer was used with variable nitrogen management than with uniform nitrogen management in com production. The results of this study also indicated, that additional retums from precision farming nitrogen management resulted when compared to uniform nitrogen management. The cost/benefit analysis in this study showed that the benefit/cost ratio was greater than one. Bongiovanni and Lowenberg-DeBoer (1998) evaluated the profitability of variable rate technology for lime as a stand-alone activity. Soil acidity is commonly indicated by soil pH, a measure of hydronium ion activity in a soil suspension. It has been be recognized as one of the most important reasons for a soil to become unproductive. Liming to correct soil acidity has been practiced for a long time. If soil pH is 7.0 or higher, no lime is needed. Most crops require some lime if pH falls below 5.0. As soil pH is one of the most manageable soil characteristics, variable rate application of lime is often considered a good place to start precision farming practices. The objective of this study was to evaluate the profitability of custom-operated variable rate application for lime as a stand-alone activity, using data for Indiana farms that produce grain in a com-soybean rotation. This study used a spreadsheet to build a fouryear model by setting the marginal value product equals to marginal factor cost (MVP = MFC) to determine the optimal rate of lime application. The study found that on average,

16

site-specific management can increase annual returns of $7.91 per acre or 4.82%, as compared to whole field management. These two studies are some of the few studies that have evaluated the economics of precision farming practices. Given that precision farming technology is feasible in cotton production, it is important to evaluate whether it would be profitable for cotton producers, especially for producers in the Southem High Plain of Texas (SHPT), to adopt this production technology. Yu et al. (2000) derived and evaluated nitrogen optimal decision mles for irrigated cotton in the SHPT using a single-year optimization model. The objective of this study was to evaluate the economic and environmental implications of precision farming practices with respect to nitrogen use in cotton production in the short-run. This analysis revealed that precision spatial utilization of nitrogen fertilizer, as compared to whole-field farming practices, would resuh in a 2.29%) yield increase on a per acre basis. The associated increase in net revenues above nitrogen fertilizer cost was found to be 1.69% on a per acre basis. Most importantly, the study revealed that nitrogen fertilizer could be used more efficiently, implying higher yields and net revenue, and lower potential environmental damage under precision farming practices, as compared to conventional whole-field farming practices. This is one of few studies that has addressed both, the economic and environmental implications of precision farming practices. The above three studies on the economics of precision farming practices have concluded that higher net retums above variable costs (either nitrogen fertilizer or lime) from the adoption of precision farming practices are possible. The limitations of these

17

studies are that these models are short-mn in nature, and do not consider the costs associated with the adoption of precision farming (or variable rate application) technology.

2.3 Dynamic Optimal Input Decision Research In order to evaluate the economic and environmental implications of precision farming in cotton production in the SHPT, it is important to identify the effects of the dynamic relationship between input residual and cotton yield. Segarra et al. (1989) derived and evaluated nitrogen fertilizer optimal decision rules for irrigated cotton in the SHPT. In order to achieve this objective, a dynamic optimization model of nitrogen utilization, which introduced an inter-temporal nitrate-nitrogen carry-over function in the optimization procedure, was developed. Their study showed that a single-year optimal decision mle of nitrogen application for cotton, which ignores the dynamic nature of the problem, would lead to suboptimal nitrogen application levels, implying inefficiencies in irrigated cotton production. It was also shown, however, that adoption of a multi-period optimal decision mle of nitrogen utilization would not significantly increase net present value of retums. The optimal decision rules derived from the dynamic model which considered the nitratenitrogen residual impacts were found to be critically influenced by both, the initial condition on nitrate-nitrogen residual and cotton and nitrogen price ratios. Their study proved that nitrogen residual has a significant influence on the long-term optimal nitrogen application decision rules in cotton production.

18

2.4 Cost/Benefit Analysis of Precision Farming Theoretically, it is assumed that the advantage of precision farming practices may include higher average yields, lower farm input costs, and environmental benefits from fewer input applications. Roberts, English, and Mahajanashetti (2000) pointed out that the net economic benefit a farmer receives from variable rate input application is determined by "variability" within a field. More specifically, there are two variability factors. The first is the degree of spatial variability of inherent characteristics within the field, or the proportions of the field in potential management zone. The second is the yield response variability among the management zones. The objectives of this research were: (1) to illustrate the potential economic benefits of using variable nitrogen services compared with the cost of hiring those services, and (2) to illustrate the impacts of changes in input and output prices, spatial variability of field's inherent characteristics, and yield response variability on potential net retums and potential use of custom-hired precision farming services. Their study assumed a hypothetical case that a com field contained only two land types, which corresponded to a possible high and low yield. It is further assumed that these two land types have the same proportions across the field, i.e., 50%o for high yield and 50%) for low yield. Nitrogen fertilizer was the only variable input considered in the analysis. Two hypothetical quadratic com yield response functions (concave) that were associated with the two land types were assumed. Given the above information, a

19

uniform rate response function was derived, which was simply an average of the above two production functions. In this study, two scenarios, i.e., uniform rate technology and variable rate technology, were analyzed. The first scenario centered around the assumption of uniform rate technology utilization and the uniform yield response function was used. The second scenario assumed variable rate technology utilization and two yield functions were associated with the two types found. They found that by using the ten-year (1986-1995) average com ($2.42 /bushel) and nitrogen prices ($0.22/pound), utilization of uniform rate technology resulted in an average nitrogen fertilizer of 165.02 pounds per acre, obtained an average yield of 97.63 bushels per acre, and gained an average retum above nitrogen cost of $199.97 per acre. Under the assumption of equal separation of the field according to land types, variable rate technology used an average nitrogen fertilizer of 156.35 pounds per acre, obtained an average yield of 98.54 bushels per acre, and gained an average retum above nitrogen cost of $204.07 per acre. That is, average retum above nitrogen cost per acre for variable rate technology was $4.10 per acre more than under uniform rate technology. Their study also surveyed the cost associated with the use of variable rate technology and uniform rate technology, ft was found that additional charges for variable rate technology services above uniform rate technology services were between $2.50 and $3.00 per acre. Thus, given that the average retum above nitrogen cost per acre for variable rate technology was $4.10 per acre more than under uniform rate technology, it

20

was expected that variable rate technology would be economically beneficial to farmers on the order of $1.10 to $1.60 per acre. Their study also found that the return above nitrogen cost depended on com and nitrogen prices, and the proportion of each land type. After performing sensitivity analysis, they concluded that economic incentives for farmers to use variable rate technology increase (decrease) as prices of both input and output increase (decrease), and as the cost of services decrease (increase). This study showed the advantages of precision farming practices, i.e., higher average yields, lower farm input costs, and potential environmental benefits from lower input use, as compared to whole-field farming practices. However, this study assumed two quadratic com yield response functions (concave) to represent high and low yield scenarios under variable rate technology and an average of the two functions for uniform rate technology. In reality, it would be almost impossible to obtain such perfect production functions.

2.5 Costs Associated with Adopting Precision Farming Technology Precision farming is an information driven system. It requires specific equipment for measuring and documenting the spatial location of crop yields and soil fertility, and for applying different levels of inputs on fields. It uses this information to produce detailed crop yield and soil characteristics maps. Another type of information that is important in determining the economic feasibility of precision farming is the economic costs and benefits associated with precision farming.

21

The costs associated with precision farming are easier to estimate than the benefits. The costs include money spent on GPS receivers, crop yield monitors, and annual soil testing, mapping, and application services. In 1995, it was estimated that the cost of a yield monitor was $3,100, a GPS receiver cost $2,400, and per year to the co-op for soil testing, mapping, and application services cost $500 (Leidner, 1995). However, in Leinder's estimation, he assumed that VRT equipment was too expensive and farmers would rather use VRT services from an independent contractor. In 1995, the estimated cost of VRT application equipment was about $250,000 (Christensen and Krause, 1995). As discussed above, because of high costs of the equipment associated with precision farming technology, it would be expected that most users of precision farming practices would choose to use the services from off-farm service providers. In 1999, Roberts, English, and Sleigh (2000) surveyed the cost of precision farming services in Tennessee. Table 2.1 lists the types of services by service providers and their cost. It should be noticed that spreading charges for variable rate P and K application average $5.75/acre. Comparing this rate to uniform rate P and K application costs, at an average charge of $3.85/acre, variable rate P and K application adds $1.90/acre in cost. As precision mapping techniques, yield sensing and soil analysis equipment, and VRT application equipment are improved, costs may decline in the future. Christensen and Krause (1995) estimate that within 10 years, roughly 50 percent of all the crop acreage in the U.S. will be farmed using precision farming practices, ft is likely that

22

Table 2.1. Precision Farming Services Provided to Tennessee Producers and Average Rate Charged for Those Services, 1999. Precision Farming Services Provided

Average Cost ($/acre)

Precison soil sampling: Grid soil sampling, lab analysis, and fertility reconnmendations, 2.5-acre grid

7.05

Grid soil sannpling, lab analysis, and fertility recommendations. 5-acre grid

7.00

Grid soil sampling, lab analysis, and fertility recommendations, 10-acre grid

7.00

Grid soil sampling, lab analysis, and fertility recommendations, 15-acre grid

4.00

Grid soil sampling only, 2.5-acre grid

2.00

Processing and analyzing geo-referenced data: Fertility mapping, recommendations and application prescriptions

0.63

Yield mappling from yield monitor data

0.50

Geo-referenced yield mappming and measurement

1-65

GPS data processing and analysis {$/tiour)

30.00

Variable rate input application: Variable rate P and K application (spreading fee)

5-75

Variable rate lime application (spreading fee)

4.70

Other services: Service on yield monitor ($/tiour)

30.00

Sources: Roberts, English, and Sleigh, "Precision Farming Services in Tennessee: Resuhs of a 1999 Survey of Precision Farming Service Providers." Research Report 00-06, Department of Agricultural Economic and Rural Sociology, The Tennessee Agricuhural Experiment Station, The University of Tennessee, March, 2000.

23

large farms and input-intensive crops such as potatoes and sugar-beets will initially adopt precision farming technology on a wide scale basis. However, costs are likely to decline with increased adoption, encouraging broader based use.

2.6 Future of Precision Farming Practices Because of high costs associated with the application of precision farming technology, it is important to evaluate the likelihood of precision farming practices use in agricultural production. In 1999, Roberts, English, and Sleigh (2000) conducted a survey of precision farming service providers in Tennessee. The objective of this survey was to document the types, costs, and location of precision farming services available to Tennessee farmers from off-farm farm service providers. In this survey, information was gathered from 23 farm service dealers, crop consultants, and custom applicators who provided precision farming services within Tennessee. The following information was collected from the individuals who responded: 1) the counties where precision farming services were provided; (2) the provided precision farming services and the rates charged for these services; (3) the number of farmers using those services with and without GPS, (4) the crops and acreage for which precision farming services were provided; (5) the number of additional or fewer customers that are expected to use precision farming services in the following year (2000) and the next five years (2000 to 2004); (6) the additional precision farming services planned to offer in the future; and (7) the types of equipment and computer applications that were used to provide precision farming services.

24

The results of the survey indicated that the numbers of Termessee producers using some types of precision farming technology (grid soil sampling, and variable rate fertilizer and/or lime application) accounted for less than 0.5%) of the total number of farms (56,016 farms) and less than 1% of the farms with sales over $10,000 (21,228 farms). Precision farming services provided by farm service dealers, crop consultants, and custom applicators included: (1) precision soil sampling, including grid soil sampling with and without lab analysis and fertility recommendations; (2) processing and analyzing geo-referenced data which included soil fertility mapping, fertility recommendations and application prescriptions, yield mapping from yield monitor data, geo-referenced field mapping and measurement, and GPS data processing and analysis service; and (3) variable rate input application including variable rate application using GPS technology; and (4) other services including yield monitor repairs and aerial application using GPS technology to apply seed, pesticide, and fertilizer. The prices associated with these services are discussed in Section 2.5. Respondents reported that a total of 110,225 acres used grid soil sampling services. The crops produced using this services included: cotton (40%o) com (21%), soybeans (19%), wheat (13%), sorghum (less than l%o), rice (less than \%), hay, and pastureland (less than 1%). Furthermore, acreage of cotton, com, soybeans, and wheat which used grid soil sampling services accounted for 9%, A%, 2%, and 5%o, respectively, of the total acreage of cotton, com, soybeans, and wheat harvested in Tennessee. Variable rate application from service provided totaled 30,805 acres.

25

The anticipated growth rates between 1999 and 2004 for precision farming services were 118%o for grid soil sampling, and 221% for variable rate fertilizer and/or lime application. The anticipated percentage of farmers receiving variable rate input application services compared to those receiving grid-soil-sampling services was 66% for 2000 and 89% for 2004, compared with 28% in 1999. Also, the survey indicated that about 61%) of the service providers planned to offer additional services to the customers in the future. These services include: sale of precision farming equipment, field mapping, yield mapping, 5-acre and 10-acre grid soil sampling, aerial photography with geo-referencing, variable rate input application including seed, lime, fertilizer, herbicide, and insecticide, geo-referenced crop scouting, experimental use of remote sensing, measurement of soil elector-conductivity, and a complete precision package, including decisions on input and variety use, and insect and weed control.

26

CHAPTER III CONCEPTUAL FRAMEWORK

High productivity has been the key element that has kept the U.S. farm sector competitive in the world market for many decades. Consequently, high levels of productivity has benefited producers, consumers, and society as a whole, because it has resulted in higher levels of production per unit of input and lower prices of commodities. Adoption of new technologies is a key factor that can improve productivity. In this section, the adoption of new precision farming technology and its relationship to productivity, as well as the economic and environmental implications of increased productivity are discussed.

3.1 Profit Maximization and Environmental Extemality In order to address the economic and environmental implications of precision farming, some basic production economics concepts including optimal input use, profit maximization, and environmental extemalhies, need to be discussed first.

3.1.1 Optimal Input and Output for Profit Maximization A production function expresses the relationship between the maximum amount of output, given different levels of input under a specific level of producfion technology. The production technology describes how inputs are transformed into output. More specifically, it influences the efficiency of this transformafion.

27

An illustration of a production function is depicted in panel (a) of Figure 3.1. In this case, a single output (Y) is produced with a single input (X). ft should be noted that in reality there are usually more than one input used in the production of an output. For simplification, it is assumed that only one input (X) is variable, and other inputs are fixed. Assuming perfectly competitive markets and assuming that the production function is Y = /(X), the associated profit fiinction can be expressed as: 7r = P Y Y - P x X - B ,

(3-1)

where n represents profit, Y is the amount of output, X is the amount of input, Py is the price of the output, Px is the price of the input, and B is the fixed cost of production. In order to obtain the maximum profit, the first order condition with respect to input use must equal to zero. It can be expressed as follows:

dX i.e.,

dX

| ^ = P Y M P P X - P X = 0,

oX or

MPPX = PX/PY,

or

VMPx=Px,

(3-2)

(3-3)

dY where MPPx (or — ) represents the marginal physical product of input X, which gives dX the exact rate of change of the total product function, Y, for an infinitesimal change in input X; VMPx represents the value of marginal physical product of input X; Px and

28

(a) Y = /(X)

(b)

Figure 3.1. Optimal Input Use and Output Production for Profit Maximization.

29

PY are defined as above. That is, in order to achieve maximum profit, optimal input use can be obtained by equating the marginal physical product of input (MPPx) to the ratio of input price to product price (Equation (3-2)), assuming perfect competition in both product and factor markets. This can also be expressed as equating the value of marginal product of input X (VMPx) to input price (Px) (Equation (3-3)). The relationship of Equation (3-3) is depicted in panel (b) of Figure 3.1. Given the price of input X (Px), the optimal level of input, X,* can be found. Combining the panel (a) and (b) together, the optimal level of output, Y,* can also be found. Hence, point O on the production function represents the optimal point at which producers make their production decision.

3.1.2 Welfare Measurement with Negative Environmental Extemality in Production The basic concepts of welfare measurement are consumer surplus and producer surplus. Consumer surplus can be defined as the gain to consumers from paying a lower price than what they would be willing to pay. Producer surplus can be defined as the gain to producers from receiving a higher price than what they would be willing to take. In panel (a) of Figure 3.2, the consumer surplus and the producer surplus can be represented by the areas a and b, respectively. Thus, without considering extemalities, social welfare equals area a+b. It should be noted that welfare measures must be evaluated under general market equilibrium conditions.

30

(a)

(b) E = E(Q)

Figure 3.2. Welfare Changes with Negative Extemalities in Production.

31

Externalities can be viewed as a cost or benefit to society that is not intemalized in input and output markets. In this analysis, environmental extemalities can be viewed as the cost to society in terms of the environmental degradation caused by input use, such as fertilizers, pesticides, and other inputs used in agricultural producfion. The environmental cost or extemality can be expressed as a function of the quantity of output, i.e., E = E(Q). This relafionship is depicted in panel (b) of Figure 3.2. Eo is the autonomous level of extemality, which is present even before production takes place. In this analysis, it is assumed that Eo remains the same under both, whole-field farming technology and precision farming technology. Given an existing level of technology, which corresponds to the environmental impact resulting from production, the associated negative impacts perceived to accme as a result of the Q* level of production is represents by area c. The environmental externality function depends on the existing level of technology. That is, the function will shift (or rotate) upwards if new information reveals that the environmental damages are greater that they were thought to be, and will shift (or rotate) downwards if new information reveals that previous estimates were overstated or technological change has reduced the environmental impacts of production activities. When considering the environmental negative extemalities associated with agricultural production, the total social welfare can be expressed as area a+b-c (Segarra et al. 1991, p. 3). Thus, negative environmental extemalhies decrease societal welfare by areac.

32

3.2 Technological Progress and Productivity Productivity, in its broadest sense, describes the efficiency of a production process. In economics, it is commonly expressed as total factor productivity (TFP), which is a ratio of total output to total inputs. TFP can be measured in an index form (Aheam et al., 1998). If the ratio is increasing, this implies that productivity has improved, i.e., more output can be obtained from a given level of inputs. Productivity, or TFP, captures the growth in output not accounted for by the growth in production inputs. The analysis of productivity, or TFP, is usually based on the economic production theory. That is, the basic concept used is the production function. An illustration of technological change and its influence on production function is depicted in Figure 3.3. Curve Yi in Figure 3.3 represents the production relationship between input and output under whole-field farming technology. Curve Y2 represents the same relationship, but under precision farming technology. Any input-output combination below curve Yi, for example point A, represents a "technically inefficienf level of production, since more output (Y) could be produced with the same level of input (Xi). With a positive production technological progress, i.e., from whole-field farming to precision farming, as depicted in Figure 3.3, the production function shifts from Yi to Y2. That is, for a given level of input use, more output can be produced with precision farming technology

33

Y = /(X) —

Y2

^^^^ •

Y*

Y,

7 ^^"•""^^^'^ 1

TA X,

X

Figure 3.3. Production Relationships and Productivity.

34

represented by Y2 than with whole-field farming technology represented by Yi. For example, given an input level of Xi under whole-field farming technology, output level Y* is possible. If precision farming technology is adopted, production would be increased to Y** using the same input level. That is, given a level of input, productivity or TFP, increases when precision farming technology is adopted in the production process.

3.3 Productivity Growth and Its Economic and Environmental Implications Increased productivity improves society's general standard of living by producing more output with less inputs. Its direct effects could include three aspects: (1) increase of agricultural producers' profits, i.e., improved economic performance; (2) lower commodity prices which benefit consumers; and (3) possible benefits to society due to positive environmental improvement or increase of producer and consumer surplus. Figure 3.4 depicts possible production choices faced by producers and society at large. As before, production ftinctions Yi and Y2 correspond to the production functions associated with whole-field farming technology and precision farming technology, respectively. With whole-field farming technology, a producer uses input X* to produce Y* at point A. If precision farming technology is adopted, then this producer would face two extreme cases at points B and C, and any point between B and C with respect to input use and the associated new level of production. These altematives are discussed below.

35

Y-/(X)

X**

X*

Figure 3.4. Productivity Growth and Its Implications.

36

3.3.1 Case 1: The Welfare Change under the Same Level of Input with the Adoption of Precision Farming Technology Producers can make their production decision at a point such as B, maintaining the same level of input use at X* before production. In this case, output increases from Y* to Y.** In the short-run, with the adoption of the precision farming technology and maintaining the same level of input, output level (Y) will increase. The profit function is sfill equal to TI = PY Y - Px X. Assuming that the cost of adoption of the precision farming technology is lower than the benefits derived from its adoption, profits for those producers adopting the precision farming technology will increase, i.e., TCB > TIA, where 7iB and TiA represent the profits at points B and A, respectively. It is reasonable to expect that more and more producers will adopt precision farming technology in order to pursue higher profits. This will result in an increase in output inducing a shift in the market supply function from S to S', which in tum will reduce market equilibrium price from P to P' (panel [a] of Figure 3.5). The price will decrease until long-run profit is equal to zero, i.e., TTB = TTA- Those producers who do not adopt the new precision farming technology will be forced out of the market, because they will not be able to cover their production costs at the new equilibrium price. Consumers would benefit from the decrease of equilibrium price. Consumer surplus will increase from area a to area a+b+c+d. Producer surplus will change from area b to area e+f Hence, without considering environmental externality, societal

37

(a)

(b)

Figure 3.5. Welfare Changes Under the Same Level of Input Use with the Adoption of Precision Farming Technology.

38

welfare increases from area a+b to area a+b+c+d+e+f with the adoption of precision farming technology. However, with the application of precision farming technology, the environmental externality function will be expected to move from E to E' (panel [b] of Figure 3.5). The negative environmental extemality will change from the area g+h to area h+i. Thus, societal welfare changes from area a+b-g-h to area a+b+c+d+e+f-h-i. Hence, with the adoption of a precision farming production technology, if producers maintain a constant level of input use, in the short-mn producers who adopt precision farming technology could increase profits; in the long-mn producers will benefit from the higher yield, consumers will benefit from the lower equilibrium prices, negative environmental extemality will decrease (assuming |i| < |g|), and society's welfare will improve.

3.3.2 Case 2: The Welfare Changes under the Same Level of Output with the Adoption of Precision Farming Technology Producers adopting precision farming technology could choose to move from A to C (Figure 3.4), in order to maintain the same level of output (Y*). In this case, the input use decreases from X* to X.** The profit function can still be expressed as 7t = PY Y - Px X. With other variables unchanged and the level of X decreasing, the profit of producers will increase, i.e. Ttc > TTA, where TTC and TTA represent profit at points C and A, respectively. In this case, there is no increase of product supply to the market. Market price remains at P*

39

(panel (a) of Figure 3.6). Thus, consumer surplus and producer surplus will remain at areas a and b, respectively. Without considering environmental extemality, societal welfare will remain at area a+b. However, with the applicafion of precision farming technology, the environmental extemality curve will shift from E to E' (panel (b) of Figure 3.6). Thus, the negative environmental extemality will decrease from area c+d to area d. Therefore, societal welfare will increase from area a+b-c-d to area a+b-d. Hence, neither producers nor consumers will directiy benefit in this case. However, there will be positive environmental impacts due to the reduction of input use. Thus, society as a whole would benefit.

3.3.3 Case 3: The Welfare Changes between the Same Level of Input and the Same Level of Output with the Adoption of Precision Farming Technology In reality, with increased concems about the improvement of the environment and more competition in the world food market, producers may choose to produce at a point between B and C on the Y2 function (Figure 3.4). That is, in this case their decision will increase yield, which enables producers to get higher profit in the short-mn; lower prices, which benefit consumers in the long-mn; and decrease the level of input application, which will benefit the environment. As a whole, under this scenario society will benefit from the adoption of precision farming technology. The exact point producers choose will ultimately depend on government environmental regulations and the prices of inputs and outputs.

40

(a)

(b)

Figure 3.6. Welfare Changes Under the Same Level of Output Production with the Adoption of Precision Farming Technology.

41

Given the above analysis, it can be concluded that the adoption of precision farming technology would in general be expected to improve the productivity and lead to a more efficient use of inputs in agricultural production. Hence, there will be positive environmental implications with the adoption of precision farming technology.

42

CHAPTER IV METHODS AND PROCEDURES

This section is divided into six sub-secfions: (1) basic model, (2) field experiments and data considerations, (3) estimation of crop response and input carry-over functions, (4) economic evaluation and envirormiental implication of precision farming, (5) productivity with the application of new technology under precision farming, and (6) environmental policy and its infiuence to precision farming practices. The first subsection presents the basic model that will be used in this research. The second subsection describes the specific crop and input, field experiments and available data. The third sub-section addresses the estimation of the crop response function, in order to determine within field variability of crop yield response functions to the application of inputs, input residual in the soil and other soil and location variables, and the input carryover function which can update input residual levels for continuous years. In the fourth sub-section, the models to conduct the economic evaluation and environmental implications of precision farming are presented. The productivity of precision farming technology is addressed in the fifth sub-secfion. The last sub-section discusses the government's environmental policy and its potential influence on precision farming practices.

43

4.1 Basic Model This research addresses the impacts of input application and residual on crop yields under different levels of soil fertility. Hence, a dynamic optimization model is developed to evaluate optimal decision mles of input use and input residual levels. At a given time, crop yield is a function of the level of input use and input residual level. Input residual level at a given time is a function of previous input applications and previous levels of input residual. Given these relationships, the general form of the optimization model is: n

Max Z = Z {[ Pf Yt(XTO - CPt • XA, ]-(l+r)-'}

(4-1)

t=o

Subject to: XT, = XAt+XRt,

(4-2)

XRt+i=/,[XAt,XRtJ,

(4-3)

XRo = XR(0),

(4-4)

and XAt, XRt > 0 for all t. Where Z is the per-acre net present value of retums to risk, management, overhead, and all other inputs in the production of a crop ($/acre); n is the length of the decisionmaker's planning horizon (years); Pt is the price of the crop in year t ($/lb.); Yt is the crop yield fiinction in year t (Ibs./acre); XTt is the total amount of input available to be used by the crop in year t (Ibs./acre); CPt is the price of input in year t ($/lb.); XAt is the amount of input applied in year t (Ibs./acre); XRt is the amount of input residual in year t (Ibs./acre); and r is the discount rate. Equation (4-1) represents the objective function, or performance measure, of the optimization model. Equation (4-2) is an equality constraint which adds up the amount

44

of applied input during crop growing season and residual input at the beginning of crop growing season at time t (or season t), and h is being used in Equation (4-1) to calculate the crop yield in year t. Equation (4-3) is the equation of motion, which updates input residual levels, which is indirectiy used (through Equafion (4-2) and variable XTt) in equation (4-1) to compute contemporary crop yield. So, Equation (4-3) depicts the relationship between input residual level at time t+1 and input application and residuals at time t. Equation (4-4) is the initial condition on the level of input residual at the begirming of the planning horizon.

4.2 Field Experiments and Data Considerations The crop considered in this research is cotton, and the variable input considered is nitrogen. The field experiments used to derive key relationships in the formulation of the above model were conducted at the Agricultural Complex for Advanced Research and Extension Systems (AG-CARES) farm in Lamesa, Texas, in 1998. At the beginning of the experiment, 104 locations within the field were chosen. Because of data missing, only 100 locations will be considered in this research. At each location, the nitrogen residual level in the soil at a depth of 0 to 90 centimeters was measured on June 3, 1998. Using Maplnfo, a desktop mapping software that can provide a mapping technique for calculating and displaying the trends of data which vary over geographic space (Vertical Mapper Manual), the 100 locations and their pre-season

45

nitrogen residual levels are shown in Figure 4.1. As depicted in that figure, the nitrogen residual levels in the top soil at a depth of 0 to 90 centimeters ranged from 0 to 283.14 pounds per acre at the beginning of the season. In the experiment, the whole field was treated equally, except for irrigation water, which was applied at two different levels of evapotranspiration (ET), 50%o ET and 75% ET, and nitrogen fertilizer, which was applied at three different rates (0, 80, and 120 pounds per acre). Other production inputs, such as pesticides, phosphoms fertilizer, and herbicides, were applied at the same rates across the whole field. Total nitrogen available to the crop at each of the 100 locations, which is equal to the sum of applied nitrogen and nitrogen residual in the top 0 to 90 centimeters of soil, was obtained and is depicted in Figure 4.2 using Maplnfo. It ranged from 0 pounds per acre to 352.79 pounds per acre. At the end of the growing season, a cotton stripper equipped with sensors and a Global Position System (GPS) was used. Then, data were downloaded into a computer and analyzed using Maplnfo. Cotton lint yields associated with the 100 locations were obtained. Figure 4.3 shows the cotton lint yield map for the field. As depicted in Figure 4.3, cotton lint yield in this field ranged from 392.63 pounds per acre to 1086.67 pounds per acre. After the cotton was harvested, the nitrogen residual level in the soil at a depth of 0 to 90 centimeters was measured for each of the 100 locations on November 19, 1998. The data was analyzed and is shown in Figure 4.4 using Maplnfo. ft ranged from 19.01 pounds per acre to 407.67 pounds per acre.

46

«22C.

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Figure 4.1. NO3-N Pre-Season Residual Mapfrom0 to 90 Centimeters of Soil Depth, Lamesa, Texas, 1998.

47

1 ^ . ^ S»'1A " «14A

NA 20.5937

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Figure 5.1. Optimal Levels of Spatial Nitrogen Apphcation Map on a Per-Acre and PerYear Basis for a Ten-Year Planning Horizon, Lamesa, Texas, 1998.

65

But at some locations with a low nitrogen residual level at the beginning of the season, such as 4A and 4B, no additional nitrogen fertilizer should be applied to maximize net revenue. When assuming conventional whole-field farming practices under the same prices as above for water, cotton, and nitrogen fertilizer, the optimal nitrogen application rates are 46.70 pounds per acre per year for the 50%) ET water application scenario, and 84.17 pounds per acre per year for the 15% ET water application field. When comparing the optimal nitrogen application amount under precision farming practices (Figure 5.1) to those under conventional farming practices, the over- or under- application of nitrogen fertilizer in this field are depicted in Figure 5.2. It was found that nitrogen fertilizer overapplicafion accounts for 5A% of the 50% ET field and 46%) of the 15% ET field. Taken together, nitrogen fertilizer over-application accounts for 50%) of the whole field. The average amount of over-applied nitrogen fertilizer in these portions of the field is 9.46 pounds per acre. The maximum over-applied amount is 31.42 pounds per acre. These additional amounts of nitrogen fertilizer could cause both environmental damage and reduced profits. In some parts of the field, nitrogen fertilizer application was not high enough, reducing crop yield and producers' profits. These portions of the field make up approximately 50% of the whole field (46% in the 50%o ET scenario and 54%) in the 75% ET scenario). The average under-application nitrogen fertilizer amount is 13.99 pounds per acre. The maximum under-applied amount is 54.23 pounds per acre. These amounts of nitrogen fertilizer under-application could cause a decrease in net revenue.

66

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