Geostatistical Applications for Precision Agriculture

Transcription

1 Geostatistical Applications for Precision Agriculture

2

3 M.A. Oliver Editor Geostatistical Applications for Precision Agriculture 123

4 Editor M.A. Oliver Visiting Professor in Soil Science Department of Soil Science The University of Reading Whiteknights, Reading RG6 6DW United Kingdom ISBN e-isbn DOI / Springer Dordrecht Heidelberg London New York Library of Congress Control Number: Springer Science+Business Media B.V No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, microfilming, recording or otherwise, without written permission from the Publisher, with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Cover legend: Harvesting a variable-rate nitrogen experiment on Cashmore Field, Silsoe, England. Photograph provided by Silsoe Research institute. Printed on acid-free paper Springer is part of Springer Science+Business Media (

5 Preface This book brings together two dynamic subjects, precision agriculture and geostatistics, that have spatial variation at their core. Geostatistics is applied to many aspects of precision agriculture (PA) including sampling, prediction, mapping, decisionmaking, variable-rate applications, economics and so on. Contributions from experts in several fields of study illustrate how geostatistics can and has been used to advantage with data such as yield, soil, crops, pests, aerial photographs, remote and proximal imagery. Geostatistical techniques applied include variography, ordinary-, disjunctive-, factorial-, indicator-, regression-, simple-, space-time- and co-kriging, and geostatistical simulation. This book was requested by participants at the Sixth European Conference on Precision Agriculture in Skiathos, 2007 because the link between geostatistics and PA will increase as more intensive information on the soil and crops becomes available from sensors and on-the-go technology. This is not a recipe book, but is intended to guide readers in the use of appropriate techniques for the types of data and needs of the farmer in managing the land. All chapters include one or more case studies to illustrate the techniques. Chapter 1 sets the scene for the two main topics of the book. The two core techniques of geostatistics, variography and kriging, are described, together with examples of how they can be applied. Sampling for geostatistics is an important issue because it underpins sound results. Chapter 2 considers the importance of spatial scale in sampling, the use of ancillary data, a nested survey and existing variograms of soil or crop properties to guide sampling. Chapter 3 demonstrates the potential to optimize the design of soil sampling schemes if the variation of the target property is represented by a linear mixed model. Chapter 4 describes how calibrated yield data from monitors can be used to target crop and soil investigations and nutrient applications, and for on-farm experiments. This chapter uses spatial statistics rather than only geostatistics because it lends itself better to econometrics. Many environmental variables that are relevant to precision agriculture, such as crop and soil properties and climate, vary in both time and space; Chapter 5 explains the basic elements of space-time geostatistics. Chapter 6 provides an overview of mobile proximal sensors, such as those used to measure apparent soil electrical conductivity (EC a /, and how geostatistics can be used to direct soil sampling to create site-specific management units. Three geostatistical methods to incorporate secondary information into the mapping of soil and crop attributes to improve v

6 vi Preface the accuracy of their predictions are the topic of Chapter 7. For soil and crop properties that require costly sampling and analysis, there are often insufficient data for geostatistical analyses and Chapter 8 shows how management zones can provide an interim solution to more comprehensive site-specific management. Weeds and plant-parasitic nematodes occur in patches in agricultural fields; Chapter 9 describes how standard geostatistical methods have been used successfully to analyse counts of both weed seedlings and nematodes in the soil and to map their distributions from kriged predictions. Chapter 10 shows how geostatistics can play an important role in analysing experiments for site-specific crop management. Two broad classes of experimental design for precision agriculture (management-class experiments and local-response experiments) are considered and how each may be analysed geostatistically. Geostatistical simulation provides a means to mimic the spatial and or temporal variation of processes that are relevant to precision agriculture, and Chapter 11 shows how it can incorporate uncertainty into modelling to obtain a more realistic impression of the variation. The book has raised several issues, ideas and questions, which are summarized in Chapter 12. Geostatistics needs to be tailored better to the needs of the various groups involved; farmers, advisors and researchers which have their own particular requirements. The potential for geostatistics and precision agriculture for the rest of the twenty-first century appears great. The Appendix gives examples of software that can be used for geostatistical analyses, and there are brief descriptions of GenStat, VESPER and SGeMS. Reading, United Kingdom Margaret A. Oliver

7 Contents 1 An Overview of Geostatistics and Precision Agriculture... 1 M.A. Oliver 1.1 Introduction A Brief History of Geostatistics A Brief History of Precision Agriculture A Brief History of Geostatistics in Precision Agriculture The Theory of Geostatistics Stationarity The Variogram Geostatistical Prediction: Kriging Case Study: Football Field Summary Statistics Variography Kriging Conclusions References Sampling in Precision Agriculture R. Kerry, M.A. Oliver and Z.L. Frogbrook 2.1 Introduction The Importance of Spatial Scale for Sampling How Can Geostatistics Help? How can the Variogram be Used to Guide Sampling? Variograms to Guide Sampling Nested Survey and Analysis: Reconnaissance Variogram Variograms from Ancillary Data Use of the Variogram to Guide Sampling for Bulking Case Study The Variogram to Guide Grid-Based Sampling The Variogram and Kriging Equations Half the Variogram Range Rule of Thumb as a Guide to SamplingInterval vii

8 viii Contents 2.5 Variograms to Improve Predictions from Sparse Sampling Residual Maximum Likelihood (REML) VariogramEstimator Standardized Variograms Conclusions References Sampling in Precision Agriculture, Optimal Designs from Uncertain Models B.P. Marchant and R.M. Lark 3.1 Introduction The Linear Mixed Model: Estimation, Predictions and Uncertainty The Model Estimation Prediction Uncertainty Optimizing Sampling Schemes by Spatial Simulated Annealing Spatial Simulated Annealing Objective Functions from the LMM Optimized Sample Scheme for Single Phase Geostatistical Surveys Adaptive Exploratory Surveys to Estimate the Variogram A Case Study in Soil Sampling Conclusions References The Spatial Analysis of Yield Data T.W. Griffin 4.1 Introduction Background of Site-Specific Yield Monitors Concept of a Yield Monitor Calibration and Errors Common Uses of Yield Monitor Data Profitability of Yield Monitors Quantity and Quality of Product Managing Yield Monitor Data Quality of Yield Monitor Data Challenges in the Use of Yield Data for Decision Making Aligning Spatially Disparate Spatial Data Layers Spatial Statistical Analysis of Yield Monitor Data Explicit Modelling of Spatial Effects...101

9 Contents ix Spatial Interaction Structure Empirical Determination of Spatial Neighbourhood Structure Case Study: Spatial Analysis of Yield Monitor Data froma Field-Scale Experiment Case Study Data Data Analysis Case Study Results Case Study Summary Conclusion References Space Time Geostatistics for Precision Agriculture: A Case Study of NDVI Mapping for a Dutch Potato Field G.B.M. Heuvelink and F.M. van Egmond 5.1 Introduction Description of the Lauwersmeer Study Site and Positional Correctionof NDVIData Exploratory Data Analysis of Lauwersmeer Data Space Time Geostatistics Characterization of the Trend Characterization of the Stochastic Residual Application of Space Time Geostatistics to the LauwersmeerFarm Data Characterization of the Trend Characterization of the Stochastic Residual Space Time Kriging Discussion and Conclusions References Delineating Site-Specific Management Units with Proximal Sensors D.L. Corwin and S.M. Lesch 6.1 Introduction The Need for Site-Specific Management Definition of Site-Specific Management Unit (SSMU) Proximal Sensors Objective Directed Sampling with a Proximal Sensor Complexity of Proximal Sensor Measurementsandthe Role of Geostatistics Practical Consideration of Differences in Support Delineation of SSMUs with a Proximal Sensor Geostatistical Mixed Linear Model...146

10 x Contents Soil Sampling Strategies Based on Geo-ReferencedProximalSensorData Applications of Geostatistical Mixed Linear Models to ProximalSensor Directed Surveys Case Study Using Apparent Soil Electrical Conductivity (EC a / San JoaquinValley,CA Materials and Methods Results and Discussion Conclusion References Using Ancillary Data to Improve Prediction of Soil and Crop Attributes in Precision Agriculture P. Goovaerts and R. Kerry 7.1 Introduction Theory Variogram and Cross-Variogram Cokriging Simple Kriging with Local Means Kriging with an External Drift Case Study 1: The Yattendon Site Site Description and Available Data Data Preparation Variograms Leave-One-Out Cross-Validation Patterns of Variation How Small Can the Sample Size of Primary Data be when Secondary Data are Available? Case Study 2: The Wallingford Site Site Description and Available Data Leave-One-Out Cross-Validation Using Grid SampledData Patterns of Variation Conclusions References Spatial Variation and Site-Specific Management Zones R. Khosla, D.G. Westfall, R.M. Reich, J.S. Mahal and W.J. Gangloff 8.1 Introduction Quantifying Spatial Variation in Soil and Crop Properties Site-Specific Management Zones Soil Properties, Crops and Geographic Distributionof ManagementZones Techniques of Delineating Management Zones...202

11 Contents xi 8.4 Statistical Evaluation of Management Zone DelineationTechniques:A Case Study Conclusions References Weeds, Worms and Geostatistics R. Webster 9.1 Introduction Weeds Nematodes Lives of Nematodes Geostatistical Applications Case Study Economics The Future for Geostatistics in Precise Pest Control References The Analysis of Spatial Experiments M.J. Pringle, T.F.A. Bishop, R.M. Lark, B.M. Whelan and A.B. McBratney 10.1 Introduction Background Management-Class Experiments Case Study I: REML-Based Analysis of a Management-ClassExperiment Local-Response Experiments Case Study II: Analysis of a Local-Response Experiment Alternative Approaches to Experimentation Issues for the Future Conclusions References Application of Geostatistical Simulation in Precision Agriculture R. Gebbers and S. de Bruin 11.1 Introduction Basics of Geostatistical Simulation Theory Sequential Gaussian Simulation Transformation of Probability Distributions Case Study I: Uncertainty of a ph Map Introduction Materials and Methods Results and Discussion Summary and Conclusions...286

12 xii Contents 11.3 Case Study II: Uncertainty in the Position of GeographicObjects Introduction Methods Study Site Conclusions Case Study III: Uncertainty Propagation in Soil Mapping Introduction Materials and Methods Results and Discussion Conclusions Application of Geostatistical Simulation in Precision Agriculture:Summary References Geostatistics and Precision Agriculture: A Way Forward J.K. Schueller 12.1 Introduction Weather, Time and Space Farmers, Advisors and Researchers Issues, Ideas and Questions Past, Present and Future References Appendix: Software A.1 Geostatistics in GenStat A.2 VESPER A.2.1 Background A.2.2 The Software A.2.3 Applications A.3 SGeMS and Other Software A.3.1 SGeMS A.3.2 Other Software References Index...325

13 Contributors Thomas F.A. Bishop Australian Centre for Precision Agriculture, Faculty of Agriculture, The University of Sydney, 1 Central Avenue, Australian Technology Park, Eveleigh, NSW 2015, Australia, t.bishop@usyd.edu.au Dennis L. Corwin USDA-ARS U.S. Salinity Laboratory, 450 West Big Springs Road, Riverside, CA , USA, Dennis.Corwin@ars.usda.gov Sytze de Bruin Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, P.O. Box 47, 6700 AA Wageningen, The Netherlands, sytze.debruin@wur.nl Fenny M. van Egmond The Soil Company, Leonard Springerlaan 9, 9727 KB Groningen, The Netherlands, fenny@medusa-online.com Zoë L. Frogbrook Environment Agency Wales, Ty Cambria, 29 Newport Road, Cardiff CF24 0TP, United Kingdom, zoe.frogbrook@environment-agency.wales.gov.uk William J. Gangloff Department of Soil & Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA, billgangloff@gmail.com Robin Gebbers Department of Engineering for Crop Production, Leibniz-Institute for Agricultural Engineering, Max-Eyth-Allee 100, D Potsdam, Germany, rgebbers@atb-potsdam.de Pierre Goovaerts BioMedware Inc3526 W Liberty, Suite 100, Ann Arbor, MI 48104, USA, goovaerts@terraseer.com Terry Griffin Division of Agriculture, University of Arkansas, 2301 S University Avenue, Little Rock, AR 72204, USA, tgriffin@uaex.edu Gerard B.M. Heuvelink Environmental Sciences Group, Wageningen University and Research Centre, PO Box 47, 6700 AA Wageningen, The Netherlands, Gerard. Heuvelink@wur.nl Ruth Kerry Department of Geography, Brigham Young University, 690 SWKT, Provo, UT 84602, USA, ruth kerry@byu.edu xiii

14 xiv Contributors Raj Khosla Department of Soil & Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA, R. Murray Lark Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom, Scott M. Lesch Riverside Public Utilities, Resource Division, th Street, Riverside, CA 9250, USA, Alex B. McBratney Australian Centre for Precision Agriculture, The University of Sydney, John Woolley Building, Sydney, NSW 2006, Australia, Jaskaran S. Mahal Department of Farm Power and Machinery, Punjab Agricultural University, Ludhiana , India, Ben P. Marchant Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom, Budiman Minasny Australian Centre for Precision Agriculture, John Woolley Building, The University of Sydney, NSW 2006, Australia, Margaret A. Oliver Department of Soil Science, The University of Reading, Whiteknights, Reading RG6 6DW, United Kingdom, Matthew J. Pringle Department of Environment and Resource Management, QCCCA Building, 80 Meiers Road, Indooroopilly, QLD 4068, Australia, matthew. Robin M. Reich Department of Forest, Range and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523, USA, John K. Schueller Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL , Richard Webster Rothamsted Research, Harpenden, Hertfordshire, AL5 2JQ, United Kingdom, Dwayne G. Westfall Department of Soil & Crop Sciences, Colorado State University, Fort Collins, CO 80523, USA, Brett M. Whelan Australian Centre for Precision Agriculture, The University of Sydney, John Woolley Building, Sydney, NSW 2006, Australia,