Geostatistical Applications for Precision Agriculture (eBook)

Margaret A. Oliver (Herausgeber)

eBook Download: PDF

2010 | 2010
XIV, 331 Seiten
Springer Netherland (Verlag)
978-90-481-9133-8 (ISBN)

Infos

The aim of this book is to bring together a series of contributions from experts in the field to cover the major aspects of the application of geostatistics in precision agriculture. The focus will not be on theory, although there is a need for some theory to set the methods in their appropriate context. The subject areas identified and the authors selected have applied the methods in a precision agriculture framework. The papers will reflect the wide range of methods available and how they can be applied practically in the context of precision agriculture. This book is likely to have more impact as it becomes increasingly possible to obtain data cheaply and more farmers use onboard digital maps of soil and crops to manage their land. It might also stimulate more software development for geostatistics in PA.

Inhalt

Preface 6
Contents 8
Contributors 14
Chapter 1: An Overview of Geostatistics and Precision Agriculture 16
1.1 Introduction 16
1.1.1 A Brief History of Geostatistics 17
1.1.2 A Brief History of Precision Agriculture 18
1.1.3 A Brief History of Geostatistics in Precision Agriculture 21
1.2 The Theory of Geostatistics 22
1.2.1 Stationarity 23
1.2.1.1 Intrinsic Variation and the Variogram 24
1.2.2 The Variogram 24
1.2.2.1 Estimating the Variogram 24
1.2.2.2 Features of the Variogram 25
1.2.2.3 Modelling the Variogram 26
1.2.3 Geostatistical Prediction: Kriging 27
1.2.3.1 Ordinary Kriging 28
1.2.3.2 Kriging Weights 30
1.2.3.3 Other Types of Kriging 30
1.2.3.4 Disjunctive Kriging 31
1.3 Case Study: Football Field 33
1.3.1 Summary Statistics 34
1.3.2 Variography 35
1.3.3 Kriging 41
1.3.3.1 Ordinary Kriging 41
1.3.3.2 Disjunctive Kriging 44
1.3.3.3 Factorial Kriging 45
1.3.4 Conclusions 46
References 47
Chapter 2: Sampling in Precision Agriculture 50
2.1 Introduction 51
2.1.1 The Importance of Spatial Scale for Sampling 52
2.1.2 How Can Geostatistics Help? 53
2.1.3 How can the Variogram be Used to Guide Sampling? 54
2.2 Variograms to Guide Sampling 55
2.2.1 Nested Survey and Analysis: Reconnaissance Variogram 55
2.2.1.1 Unequal Sampling 55
2.2.2 Variograms from Ancillary Data 58
2.2.2.1 Case Study 58
2.3 Use of the Variogram to Guide Sampling for Bulking 62
2.3.1 Case Study 63
2.4 The Variogram to Guide Grid-Based Sampling 66
2.4.1 The Variogram and Kriging Equations 66
2.4.1.1 Case Study 66
2.4.2 Half the Variogram Range `Rule of Thumb' as a Guide to Sampling Interval 69
2.5 Variograms to Improve Predictions from Sparse Sampling 70
2.5.1 Residual Maximum Likelihood (REML) Variogram Estimator 70
2.5.1.1 Case Study 71
2.5.2 Standardized Variograms 74
2.6 Conclusions 76
References 77
Chapter 3: Sampling in Precision Agriculture, Optimal Designs from Uncertain Models 79
3.1 Introduction 79
3.2 The Linear Mixed Model: Estimation, Predictionsand Uncertainty 81
3.2.1 The Model 81
3.2.2 Estimation 82
3.2.3 Prediction 84
3.2.4 Uncertainty 85
3.3 Optimizing Sampling Schemes by SpatialSimulated Annealing 86
3.3.1 Spatial Simulated Annealing 86
3.3.2 Objective Functions from the LMM 87
3.3.3 Optimized Sample Scheme for Single Phase Geostatistical Surveys 91
3.3.4 Adaptive Exploratory Surveys to Estimatethe Variogram 92
3.4 A Case Study in Soil Sampling 95
3.5 Conclusions 99
References 100
Chapter 4: The Spatial Analysis of Yield Data 102
4.1 Introduction 102
4.2 Background of Site-Specific Yield Monitors 103
4.2.1 Concept of a Yield Monitor 106
4.2.2 Calibration and Errors 107
4.2.3 Common Uses of Yield Monitor Data 108
4.2.4 Profitability of Yield Monitors 109
4.2.5 Quantity and Quality of Product 110
4.3 Managing Yield Monitor Data 110
4.3.1 Quality of Yield Monitor Data 110
4.3.2 Challenges in the Use of Yield Data for Decision Making 113
4.3.3 Aligning Spatially Disparate Spatial Data Layers 113
4.4 Spatial Statistical Analysis of Yield Monitor Data 114
4.4.1 Explicit Modelling of Spatial Effects 114
4.4.2 Spatial Interaction Structure 116
4.4.3 Empirical Determination of Spatial Neighbourhood Structure 117
4.5 Case Study: Spatial Analysis of Yield Monitor Data from a Field-Scale Experiment 120
4.5.1 Case Study Data 120
4.5.2 Data Analysis 123
4.5.3 Case Study Results 125
4.5.4 Case Study Summary 125
4.6 Conclusion 126
References 126
Chapter 5: Space-Time Geostatistics for Precision Agriculture: A Case Study of NDVI Mapping for a Dutch Potato Field 130
5.1 Introduction 130
5.2 Description of the Lauwersmeer Study Site and Positional Correction of NDVI Data 132
5.3 Exploratory Data Analysis of Lauwersmeer Data 133
5.4 Space--Time Geostatistics 138
5.4.1 Characterization of the Trend 139
5.4.2 Characterization of the Stochastic Residual 139
5.5 Application of Space--Time Geostatistics to the Lauwersmeer Farm Data 141
5.5.1 Characterization of the Trend 141
5.5.2 Characterization of the Stochastic Residual 143
5.5.3 Space--Time Kriging 144
5.6 Discussion and Conclusions 147
References 149
Chapter 6: Delineating Site-Specific Management Units with Proximal Sensors 151
6.1 Introduction 152
6.1.1 The Need for Site-Specific Management 152
6.1.2 Definition of Site-Specific Management Unit (SSMU) 153
6.1.3 Proximal Sensors 153
6.1.4 Objective 156
6.2 Directed Sampling with a Proximal Sensor 157
6.2.1 Complexity of Proximal Sensor Measurements and the Role of Geostatistics 157
6.2.2 Practical Consideration of Differences in Support 158
6.3 Delineation of SSMUs with a Proximal Sensor 158
6.3.1 Geostatistical Mixed Linear Model 158
6.3.2 Soil Sampling Strategies Based on Geo-Referenced Proximal Sensor Data 160
6.3.3 Applications of Geostatistical Mixed Linear Models to Proximal Sensor Directed Surveys 162
6.4 Case Study Using Apparent Soil Electrical Conductivity (ECa) -- San Joaquin Valley, CA 163
6.4.1 Materials and Methods 163
6.4.1.1 Study Site 163
6.4.1.2 ECa-Directed Soil Sampling Protocols for Site-Specific Management 163
6.4.1.3 Yield Monitoring and ECa Survey 165
6.4.1.4 Sample Site Selection, Soil Sampling and Soil Analyses 166
6.4.1.5 Statistical and Spatial Analyses 167
6.4.2 Results and Discussion 167
6.4.2.1 Correlation Between Crop Yield and ECa 167
6.4.2.2 Exploratory Statistical Analysis 167
6.4.2.3 Crop Yield Response Model Development 169
6.4.2.4 Site-Specific Management Units 171
6.5 Conclusion 173
References 173
Chapter 7: Using Ancillary Data to Improve Prediction of Soil and Crop Attributes in Precision Agriculture 178
7.1 Introduction 178
7.2 Theory 180
7.2.1 Variogram and Cross-Variogram 180
7.2.2 Cokriging 181
7.2.3 Simple Kriging with Local Means 182
7.2.4 Kriging with an External Drift 183
7.3 Case Study 1: The Yattendon Site 183
7.3.1 Site Description and Available Data 183
7.3.2 Data Preparation 185
7.3.3 Variograms 187
7.3.4 Leave-One-Out Cross-Validation 189
7.3.5 Patterns of Variation 192
7.3.6 How Small Can the Sample Size of Primary Data be when Secondary Data are Available? 195
7.4 Case Study 2: The Wallingford Site 199
7.4.1 Site Description and Available Data 199
7.4.2 Leave-One-Out Cross-Validation Using Grid Sampled Data 200
7.4.3 Patterns of Variation 201
7.5 Conclusions 203
References 204
Chapter 8: Spatial Variation and Site-Specific Management Zones 206
8.1 Introduction 207
8.2 Quantifying Spatial Variation in Soil and Crop Properties 208
8.3 Site-Specific Management Zones 210
8.3.1 Soil Properties, Crops and Geographic Distribution of Management Zones 211
8.3.2 Techniques of Delineating Management Zones 213
8.4 Statistical Evaluation of Management Zone Delineation Techniques: A Case Study 220
8.5 Conclusions 226
References 227
Chapter 9: Weeds, Worms and Geostatistics 231
9.1 Introduction 231
9.2 Weeds 232
9.3 Nematodes 238
9.3.1 Lives of Nematodes 238
9.3.2 Geostatistical Applications 239
9.3.3 Case Study 241
9.3.4 Economics 244
9.4 The Future for Geostatistics in Precise Pest Control 249
References 250
Chapter 10: The Analysis of Spatial Experiments 252
10.1 Introduction 253
10.2 Background 254
10.3 Management-Class Experiments 256
10.3.1 Case Study I: REML-Based Analysis of a Management-Class Experiment 259
10.4 Local-Response Experiments 262
10.4.1 Case Study II: Analysis of a Local-ResponseExperiment 266
10.5 Alternative Approaches to Experimentation 270
10.6 Issues for the Future 272
10.7 Conclusions 273
References 274
Chapter 11: Application of Geostatistical Simulation in Precision Agriculture 277
11.1 Introduction 278
11.1.1 Basics of Geostatistical Simulation 279
11.1.2 Theory 282
11.1.2.1 Spatial Random Variable and Spatial Random Function 282
11.1.2.2 Stochastic Simulation 282
11.1.2.3 Overview of Methods for Geostatistical Simulation 282
11.1.3 Sequential Gaussian Simulation 283
11.1.4 Transformation of Probability Distributions 285
11.2 Case Study I: Uncertainty of a pH Map 286
11.2.1 Introduction 286
11.2.2 Materials and Methods 286
11.2.3 Results and Discussion 288
11.2.3.1 Simulation 289
11.2.3.2 Comparison of Kriging and Simulation 290
11.2.3.3 Prediction Error of the Interpolated Map 292
11.2.3.4 Probability that a pH Value is Outside the Optimal Range 293
11.2.4 Summary and Conclusions 294
11.3 Case Study II: Uncertainty in the Positionof Geographic Objects 295
11.3.1 Introduction 295
11.3.2 Methods 296
11.3.2.1 Definition of Positional Error Model1 296
11.3.2.2 Identification of the Model 297
11.3.2.3 Application 298
11.3.2.4 Data and Scenarios 299
11.3.3 Study Site 299
11.3.3.1 Software 300
11.3.3.2 Results and Discussion 300
11.3.4 Conclusions 304
11.4 Case Study III: Uncertainty Propagation in Soil Mapping 304
11.4.1 Introduction 304
11.4.2 Materials and Methods 305
11.4.3 Results and Discussion 306
11.4.4 Conclusions 308
11.5 Application of Geostatistical Simulation in Precision Agriculture: Summary 308
References 309
Chapter 12: Geostatistics and Precision Agriculture: A Way Forward 312
12.1 Introduction 312
12.2 Weather, Time and Space 313
12.3 Farmers, Advisors and Researchers 315
12.4 Issues, Ideas and Questions 317
12.5 Past, Present and Future 319
References 319
Appendix: Software 320
A.1 Geostatistics in GenStat 320
A.2 VESPER 322
A.2.1 Background 322
A.2.2 The Software 323
A.2.3 Applications 327
A.3 SGeMS and Other Software 328
A.3.1 SGeMS 328
A.3.2 Other Software 329
References 329
Index 331

Produkt-Details

• Erscheint lt. Verlag: 27.7.2010
• Zusatzinfo: XIV, 331 p.
• Verlagsort: Dordrecht
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Studium ► 1. Studienabschnitt (Vorklinik) ► Biochemie / Molekularbiologie
• Themenwelt: Naturwissenschaften ► Biologie ► Ökologie / Naturschutz
• Themenwelt: Naturwissenschaften ► Geowissenschaften ► Geologie
• Themenwelt: Technik
• Themenwelt: Wirtschaft
• Themenwelt: Weitere Fachgebiete ► Land- / Forstwirtschaft / Fischerei
• Schlagworte: Geostatistics • Kriging • paper • Precision Agriculture • Simulation • Site-specific Management • Variogram
• ISBN-10: 90-481-9133-5 / 9048191335
• ISBN-13: 978-90-481-9133-8 / 9789048191338