Geostatistical Applications for Precision Agriculture

pp 269-303


Application of Geostatistical Simulation in Precision Agriculture

  • R. GebbersAffiliated withDepartment of Engineering for Crop Production, Leibniz-Institute for Agricultural Engineering Email author 
  • , S. de BruinAffiliated withLaboratory of Geo-Information Science and Remote Sensing, Wageningen University

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Geostatistical simulation provides a means to mimic spatial and or temporal variation of processes that are relevant to precision agriculture. Simulation by computer models aids decision making when it is too difficult, time consuming, costly or dangerous to perform real-world experiments. Spatio-temporal processes are often considered as uncertain because it is impossible to make accurate and comprehensive observations. Geostatistical simulation incorporates uncertainty into modelling to obtain a more realistic impression of the variation. This chapter provides a short introduction to the background of geostatistical simulation and explains sequential Gaussian simulation in more detail because it is the method most commonly applied. Three case studies demonstrate the application of geostatistical simulation in precision agriculture. They deal with the risk of under- and over-liming because of uncertainty about the accuracy of a pH map, the economic costs of GPS errors and the identification of factors that are most relevant to the accuracy of mapping.


Sequential Gaussian simulation Stochastic modelling Conditional simulation Unconditional simulation Scenarios Spatio-temporal variability Smoothing Positional error Error propagation Error model Prediction error Uncertainty Risk assessment