Seleção de variáveis para definição de unidades de manejo através da lógica fuzzy C-means

Abstract

The agricultural production area has important information to understand the response of a crop yield. Precision agriculture helps the farmers on acquiring and treating in correct way the variations found in the area. Management zones (MZ) provide the precision agriculture implementation in a viable and relatively economical way, since they represent homogeneous regions, which can be treated differently. Thus, this trial aimed at generating MZ with different numbers of variables using fuzzy C-means algorithm, based on attributes spatially correlated with crop yield. The objective was to evaluate the relative efficiency of each management zone and verify if the different ways of variable compositions differ in the generation of MZ. Fifteen designs of MZ were generated and divided from two to five classes, based on attributes as: copper, silt, clay and altitude, which were correlated with crop yield. The copper variable showed the highest spatial correlation with crop yield, which promoted the generation of MZ with good results of relative efficiency and good separation of data among the classes. The relative efficiency did not present in an orderly manner, increasing or decreasing according to the number of variables used in division of MZ. The delineated MZ was based on variables as copper and clay and divided into five classes, so, it showed the highest relative efficiency