Fuzzy Classification in the Determination of Input Application Zones

ABSTRACT Correctly interpreting soil fertility and its spatial distribution within an area helps to lessen losses and environmental effects associated with agriculture, to optimize fertilization and liming practices. This study is aimed at using concepts and methods from spatial and temporal analyses to soil fertility and to develop a fuzzy classification methodology in an effort to define input application zones in three conilon coffee harvests. An irregular network with georeferenced points was built in the central region of the farm. Soil samples were collected at 0.00-0.20 m depth within the projections of tree canopies. Geostatistical analysis was used to set up maps in which the variables were shown. In such maps, input and output fuzzy sets were created and applied, as well as rules of inference and determination of to-be-applied logical operators. Fuzzy classification of the area was performed in the three harvests so as to define whether or not inputs were needed. Our main findings show that the N-P-K requirement was spatially dependent in all harvests. By classifying the area using fuzzy logic, it was possible to analyze soil fertility and to indicate the regions having the smallest and greatest needs for N-P-K and liming.