Geostatistical theory and application to variability of some agronomical properties
AuthorsS. R. Vieira
J. L. Hatfield
D. R. Nielsen
J. W. Biggar
Authors AffiliationsS. R. Vieira was a former graduate research assistant; presently Soil Scientist, Institute Agronomico de Campinas, Campinas, Brazil; J. L. Hatfield was Associate Professor of Meteorology and Land, Air and Water Resources and Associate Biometeorologist in the Experiment Station, University of California, Davis; D. R. Nielsen was Professor, Soil and Water Science and Land, Air and Water Resources and Water Scientist in the Experiment Station, University of California, Davis; J. W. Biggar was Professor, Land, Air, and Water Resources, University of California, Davis.
Hilgardia 51(3):1-75. DOI:10.3733/hilg.v51n03p075. June 1983.
In agronomic problems the sampling procedure may create some confusion and bias in the analysis. Geostatistics provides a method for the analysis of the spatial and temporal properties in a data set and a method of interpolation between selected points. This paper describes the theory of geostatistics and its application to selected agronomic problems. Geostatistics considers a set of data collected in either space or time at discrete intervals. These samples may be correlated with each other to provide some unique information about the parameters which would not be detected in the classical statistical methods. Through the application of geostatistics to this type of problem, we can estimate the spatial or temporal dependence of samples and from this knowledge arrive at an estimation of the sampling procedures or structure at a field. The application of these techniques is shown for air temperature, surface temperature, yield, clay content, and fertilizer content in various fields and reveals the versatility of the techniques.
Geostatistics also allows for the evaluation of the dependence between two parameters in either time or space. From this information it is possible to develop sampling procedures which would allow the more costly or time consuming variable to be sampled less frequently and estimated from the other variable by the method of kriging. This report summarizes all of these techniques and provides several different examples of their utilization. Examples of the computer code are provided for the reader wishing to apply these techniques.
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