Abstract
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.
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Notes
- 1.
Reprinted from Computers and Electronics in Agriculture, 63/2, S. de Bruin, G.B.M. Heuvelink and J.D. Brown, Propagation of positional measurement errors to agricultural field boundaries and associated costs, pp 247–248, Copyright (2008), with permission from Elsevier.
- 2.
Gebbers, R., Herbst, R., & Wenkel, K.-O. (2009). Sensitivity analysis of soil nutrient mapping. In E. J. van Henten, D. Goense, & C. Lokhorst (Eds.), Precision Agriculture ’09. Proceedings of the 7th European Conference on Precision Agriculture (pp. 513–519). Wageningen, The Netherlands: Wageningen Academic Publishers.
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Gebbers, R., de Bruin, S. (2010). Application of Geostatistical Simulation in Precision Agriculture. In: Oliver, M. (eds) Geostatistical Applications for Precision Agriculture. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-9133-8_11
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