Bresler, Eshel, Inst of Soils & Water, Israel

Laufer, Asher, Inst of Soils & Water, Israel

Laufer, Asher, Inst of Soils & Water, Israel

The conditional multivariate normal (CMVN) method is systematically described and applied to 24 spatial random functions. These include: 12 soil properties, four soil variables, and two yield components of each of four field crops (wheat, vetch, corn and peanut). The statistical parameters characterizing the joint probability density function in a two-dimensional field were estimated, from 20 to 60 measurements, by the maximum likelihood procedure. An estimation procedure for three parameters of four covariance models constant mean, and linear drift, is described. It was found that the covariance function of all the 24 spatial functions can be represented by the four models and that there is a significant two-dimensional linear drift of the mean. Study results are discussed. Estimations of conditional expectation, conditional covariance, the variance, and the variance of the estimated variance were made and demonstrate that the CMVN method can be applied t real field data.

Statistical inferences of soil properties and crop yields as spatial random function

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Bresler, Eshel, Inst of Soils & Water, Israel

Laufer, Asher, Inst of Soils & Water, Israel

Laufer, Asher, Inst of Soils & Water, Israel

Statistical inferences of soil properties and crop yields as spatial random function

The conditional multivariate normal (CMVN) method is systematically described and applied to 24 spatial random functions. These include: 12 soil properties, four soil variables, and two yield components of each of four field crops (wheat, vetch, corn and peanut). The statistical parameters characterizing the joint probability density function in a two-dimensional field were estimated, from 20 to 60 measurements, by the maximum likelihood procedure. An estimation procedure for three parameters of four covariance models constant mean, and linear drift, is described. It was found that the covariance function of all the 24 spatial functions can be represented by the four models and that there is a significant two-dimensional linear drift of the mean. Study results are discussed. Estimations of conditional expectation, conditional covariance, the variance, and the variance of the estimated variance were made and demonstrate that the CMVN method can be applied t real field data.

Scientific Publication

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