Abstract

Information on the spatial variation of soil fertility attributes is an essential input for precision agriculture and soil management decision making. In this study, soil fertility assessment was carried out through the spatial distribution of thematic maps of individual properties and the subsequent integration into a digital mapping model of local fertility classes, as fundamental bases for the implementation of fertilization and amendment plans adjusted to soil status and crop requirements. For the evaluation of fertility, a systematic surface sampling was carried out in 70 sites in the "Agronomy" production field of the National University of the Central Plains "Romulo Gallegos", El Castrero sector, Juan German Roscio municipality, Guárico state, Venezuela. Ten soil variables were analyzed: pH (1:2.5), electrical conductivity (1:5), organic matter, available phosphorus, assimilable potassium, available calcium and magnesium, and the relative amounts of sand, silt and clay. Soil property maps were produced by geostatistical analysis and interpolation by ordinary kriging, and artificial intelligence techniques based on an artificial neural network classification system were applied to generate soil fertility classes using the Fuzzy Kohonen Clustering Network (FKCN) algorithm by interpolating the values of the membership function for each of the classes. The reliability of the individual maps of each soil variable was obtained by cross validation with a reliability level higher than 90%, with the exception of the variables % Clay and % Silt that presented a reliability higher than 85%. The integration of the soil attribute maps and the combination of the values of belonging to each class produced a map integrated by five soil fertility categories. The final model of digital soil fertility classes presented a reliability equivalent to 86%, which indicated a high degree of homogeneity within the soil classes obtained for fertility purposes.

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