Computational system for geostatistical analysis

Abstract

Geostatistics identifies the spatial structure of variables representing several phenomena and its use is becoming more intense in agricultural activities. This paper describes a computer program, based on Windows Interfaces (Borland Delphi), which performs spatial analyses of datasets through geostatistic tools: Classical statistical calculations, average, cross-and directional semivariograms, simple kriging estimates and jackknifing calculations. A published dataset of soil Carbon and Nitrogen was used to validate the system. The system was useful for the geostatistical analysis process, for the manipulation of the computational routines in a MS-DOS environment. The Windows development approach allowed the user to model the semivariogram graphically with a major degree of interaction, functionality rarely available in similar programs. Given its characteristic of quick prototypation and simplicity when incorporating correlated routines, the Delphi environment presents the main advantage of permitting the evolution of this system.

Spatial variability of soil properties and cotton yield in the Brazilian Cerrado

“…Models were fitted to the experimental semivariograms using the best-adjusted model with a smaller root mean square (RMS) and validated by the jack-knifing method (Vendrusculo et al, 2004;Vieira et al, 2010). The number of rotten bolls and open bolls presented a pure nugget effect (absence of spatial dependence in the sampling spacing used).…”

“…The nugget effect (C0) represents non-explained variance, frequently caused by errors in measurement or by variations of properties not detected in the sampling scale (Vendrusculo et al, 2004). Cambardella et al (1994) proposed that spatial dependence degree (SDD) be verified by the relationship between the nugget effect (C0) and the sill (C0+C) being classified as weak for values greater than 75%; moderate between 25 and 75%; and strong less than 25%.…”

“…The range defines the maximum radius from which neighboring samples are drawn for interpolation by kriging, since samples are spatially related (Vendrusculo et al, 2004). The closerthe samples are, the more important they are in the interpolation and, because of that, the semivariograms must be more accurate, specially at small distances (Vieira et al, 2010).…”

“…The analysis of spatial dependence was conducted by fitting a model to the experimental semivariograms, following the regionalized variables theory, using the software GEOST . Vendrusculo et al (2004) recommended the jack-knifing auto validation procedure to verify the error of estimation in parameters of the models fitted. In this technique, each measured value is interpolated using the kriging method, and the estimated value is successively eliminated during calculation, then an errors study is performed, and it is possible to estimate values with different models fitted to the semivariograms.…”

Kriging on comparison of Original and Outlier-free data

“…Semivariogram is the main tool of regionalized variable theory which quantifies the size and intensity of spatial variation. Semivariogram provide a basis for the optimum interpolation through of kriging method [1].…”