Optimization Of The Sampling Scheme For Maps Of Physical And Chemical Properties Estimated By Kriging [esquemas De Otimização Amostral Em Mapas De Atributos Físicos E Químicos Do Solo, Estimados Pela Krigagem]

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 georeferenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and mediumscale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.37511281135Aarts, E., Korst, J., (1990) Simulated annealing and Boltzmann machines-a stochastic approach to combinatorial optimization and neural computing, p. 272. , New York, John WileyAtkinson, P.M., Lloyd, C.D., Non-stationary variogram models for geostatistical sampling optimization: An empirical investigation using elevation data (2007) Comput. 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