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Geostatistical Applications for Precision Agriculture

:	M.A. Oliver
:	Margaret A. Oliver
:	Geostatistical Applications for Precision Agriculture
:	Springer-Verlag
:	9789048191338
:	1
:	CHF 189.80
:

:	"Landwirtschaft, Gartenbau; Forstwirtschaft, Fischerei, Ernährung"
:	English

:	331
:	Wasserzeichen
:	PC/MAC/eReader/Tablet
:	PDF

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.

	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