Items related to Geostatistical Applications for Precision Agriculture
Synopsis
Preface 1 An Overview of Geostatistics and Precision Agriculture; M. A. Oliver Abstract; 1.1 Introduction; 1.2 The Theory of Geostatistics; 1.3 Case study: Football Field 2 Sampling in Precision Agriculture: Part I; R. Kerry, M. A. Oliver and Z. L. Frogbrook Abstract; 2.1 Introduction; 2.2 Variograms to guide sampling; 2.3 Use of the variogram to guide sampling for bulking; 2.4 The variogram to guide grid-based sampling; 2.5 Variograms to improve predictions from sparse sampling; 2.6 Conclusions; References 3 Sampling in Precision Agriculture, Optimal Designs from Uncertain Models; B. P. Marchant and R. M. Lark Abstract; 3.1 Introduction; 3.2 The linear mixed model: estimation, predictions and uncertainty; 3.3 Optimizing sampling schemes by spatial simulated annealing; 3.4 Conclusions; References 4 The Spatial Analysis of Yield Data; T. W. Griffin Abstract; 4.1 Introduction; 4.2 Background of site-specific yield monitors; 4.3 Managing Yield Monitor Data; 4.4 Spatial statistical analysis of yield monitor data; 4.5 Case study: Spatial analysis of yield monitor data from a field-scale experiment; 4.6 Conclusion; References 5 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 Abstract; 5.1 Introduction; 5.2 Description of the Lauwersmeer study site and positional correction of NDVI data; 5.3 Exploratory data analysis of Lauwersmeer data; 5.4 Space-time geostatistics; 5.5 Application of space-time geostatistics to the Lauwersmeer farm data; 5.6 Discussion and Conclusions; References 6 Delineating Site-specific Management Units with Proximal Sensors; D. L. Corwin and S. M. Lesch; Abstract; 6.1 Introduction; 6.2 Directed Sampling with a Proximal Sensor; 6.3 Delineation of SSMUs with a Proximal Sensor; 6.4 Case Study Using Apparent Soil Electrical Conductivity (ECa) - San Joaquin Valley, CA; 6.5 Conclusion; References 7 Using Ancillary Data to Improve Prediction of Soil and Crop Attributes in Precision Agriculture; P. Goovaerts and R. Kerry Abstract; 7.1 Introduction; 7.2 Theory; 7.3 Case study 1: the Yattendon site; 7.4 Case Study 2: the Wallingford site; 7.5 Conclusions; References 8 Spatial Variation and Site-specific Management Zones; R. Khosla, D. G. Westfall, R. M. Reich, J.S. Mahal and W. J. Gangloff Abstract; 8.1 Introduction; 8.2 Quantifying spatial variation in soil and crop properties; 8.3 Site-specific management zones; 8.4 Statistical evaluation of management zone delineation techniques: A case study; 8.5 Conclusions; References 9 Weeds, Worms and Geostatistics; R. Webster Abstract; 9.1 Introduction; 9.2 Weeds; 9.3 Nematodes; 9.4 The future of geostatistics in precise pest control; References 10 The Analysis of Spatial Experiments; M.J. Pringle, T.F.A. Bishop, R.M. Lark, B.M. Whelan and A.B. McBratney Abstract; 10.1 Introduction; 10.2 Background; 10.3 Management-class experiments; 10.4 Local-response experiments; 10.5 Alternative approaches to experimentation; 10.6 Issues for the future 10.7 Conclusions; References 11 Application of Geostatistical Simulation in Precision Agriculture; R. Gebbers and S. de Bruin Abstract; 11.1 Introduction;11.2 Case study I: uncertainty of a pH map; 11.3 Case study II: uncertainty in the position of geographic objects; 11.4 Case study III: uncertainty propagation in soil mapping; 11.5 Application of geostatistical simulation in precision agriculture: summary; References 12 Geostatistics and Precision Agriculture: A Way Forward; J. K. Schueller Abstract; 12.1 Introduction; 12.2 Weather, time, and space; 12.3 Farmers, advisors and researchers; 12.4 Issues, ideas and questions; 12.5 Past, present, and future; References Appendix: Software Index
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From the Back Cover
This book brings together in one place two dynamic subjects, precision agriculture and geostatistics, that have spatial variation at their core. Geostatistics is applied in precision agriculture (PA) to sampling, prediction, mapping, decision-making, economics, designed experiments, variable-rate applications and so on. Contributions from experts in both fields illustrate how geostatistics can and has been used to advantage with PA data such as yield, soil, crop, pests, aerial photograph, remote and proximal imagery. Geostatistical techniques include variography, ordinary-, disjunctive-, factorial-, indicator-, regression-, simple-, space-time- and co-kriging, and geostatistical simulation. 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.
Review
From the reviews:
“Aims to illustrate the link between geostatistics and PA ... . Experts in the subject area were each asked to write a chapter on a topic. ... the editor has done a very good job in harmonizing the chapters and makes sure that they cover a sufficiently broad range of techniques. ... written by geostatisticians offering techniques to a PA audience ... . Does this book fulfil a need? Yes, I definitely believe so. ... value for communities of both geostatisticians and PA practitioners.” (M. Van Meirvenne, European Journal of Soil Science, Vol. 62, August, 2011)"About this title" may belong to another edition of this title.
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