- Research Article
- 10.32388/0jafr0.3
Spatial Analysis of Soil Fertility Using Geostatistical Techniques and Artificial Neural Networks
- May 18, 2025
- Qeios
- Angel Rafael Valera Valera + 1 more +1
Information on the spatial variation of soil fertility attributes is an essential input in precision agriculture for soil management decisions. In this study, soil fertility was evaluated through the spatial distribution of property maps and subsequent integration into fertility classes, as a fundamental basis for the implementation of fertilization plans and amendments adjusted to crop requirements. For the evaluation of fertility, a systematic surface sampling was carried out at 70 sites in the "Agronomy" production field of Romulo Gallegos University, The Castrero sector, Roscio municipality, Guárico state, Venezuela. Ten soil attributes were analyzed: pH, electrical conductivity, organic matter, phosphorus, potassium, calcium, magnesium, and the relative amounts of sand, silt, and clay. Soil property maps were produced by geostatistical analysis and ordinary kriging interpolation, and artificial intelligence techniques based on an artificial neural network classification system, with the FKCN (fuzzy Kohonen clustering network) algorithm, were applied to generate soil fertility classes. The reliability of the maps for each variable was obtained by cross-validation with a reliability of more than 90%. The integration of the maps produced a map composed of five categories. The final soil class model presented a reliability equivalent to 86%, indicating a high degree of homogeneity within the soil classes obtained. This approach overcomes the limitations of traditional methods by integrating multiple variables into a coherent model and is capable of generating information that can be used as a basis for the establishment of experimental plots for research purposes and the specific management of nutrients present in the soil resource of the area under consideration.
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