BAYESIAN INFERENCE OF THE SOIL PROPERTIES SPATIAL DISTRIBUTION GETEROGENIZATION
Abstract
Spatial variation of soil properties within polygon on the agricultural field occupied with corn have been considered in article. Geostatistics parameter estimation had been drawn by Bayesian inference. As spatial model the Matern variogram has been considered. Such approach allowed adding the existing list of the geostatistics parameters with smoothing from this model. Such edaphic parameters as soil density, humidity, temperature, and electrical conductivity have been considered. 100 per cent of variation of investigated property could be explained by nugget-effect that considered as null-alternative. Such situation is observed after bedrock machining. Formation of spatial patterns of edaphic properties was considered as a result of exogenous factors (relief, vegetation, gradient of climatic conditions) and endogenous like an inherent soli ability to self-organization.
Key words: Bayesian inference, geterogenization, soil properties
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References
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McBratney, A.B., Pringle, M.J. (1999). Estimating average and proportional variograms of soil properties and their potential use in precision agriculture. Precision Agriculture. 1, 125– 152.
McCullagh, P., Clifford, D. (2006). Evidence for conformal invariance of crop yields. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Science. 462, 2119–2143.
Minasny, B., McBratney, A.B. (2005). The Matern function as a general model for soil variograms. Geoderma. 128, 192– 207.
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R Core Team. (2013). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org
Stein, M.L. (1999). Interpolation of Spatial Data: Some Theory for Kriging. New York: Springer.
Webster, R., Oliver, M.A. (2001). Geostatistics for Environmental Scientists. Chichester. – John Wiley & Sons.
Whittle, P. (1954). On stationary processes in the plane. Biometrika. 41, 434– 449.
Zimmerman, D.L., Zimmerman, M.B. (1991). A comparison of spatial semivariogram estimators and corresponding ordinary kriging predictors. Technometrics. 33, 77–91.
Copyright (c) 2015 A. V. Zhukov, K. V. Andrusevich, A. Yu. Pokusa, E. V. Lapko
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