Classification of areas associated with soybean yield and agrometeorological variables through fuzzy clustering

Abstract

This study aimed to apply an approach based on fuzzy clustering for the classification of areas associated with soybean yield combined with the following agrometeorological variables: rainfall, average air temperature and average global solar radiation. The study was conducted with 48 municipalities in the western region of Parana State, Brazil, with data from the crop-year 2007/2008. Through the fuzzy c-means algorithm, it was possible to form groups of municipalities that were similar in soybean yield using the Method of Decision by the Higher Degree of Relevance ( MDMGP ) and Method of Decision by Threshold β ( β MDL ). Subsequently, the identification of the appropriate number of clusters was obtained using Modified Partition Entropy ( MPE ). To measure the degree of similarity for each cluster, the Cluster Similarity Index ( ISC l ) was constructed and implemented. From the perspective of this study, the method used was adequate, allowing the identification of clusters of municipalities with degrees of similarities between 63 and 94%. Este trabajo tuvo como objetivo aplicar un enfoque basado en el analisis de agrupamiento fuzzy para la clasificacion de areas asociadas con la productividad de la soya, juntamente con las variables meteorologicas: nivel de precipitaciones, temperatura media del aire y la media de la radiacion solar. El estudio se llevo a cabo con la participacion de 48 municipios de la region oeste del Estado de Parana, Brasil, con los datos de la temporada de cultivo del ano 2007/2008. Mediante el algoritmo Fuzzy C-Means , fue posible formar grupos de municipios similares al rendimiento de la soya, utilizando el metodo de decision de mayor grado de relevancia ( MDMGP ) y el metodo de decision por Threshold β (MDL β ) . Seguidamente, se obtuvo la identificacion del numero apropiado de conglomerados utilizando la entropia de particiones modificada. Para medir el grado de similitud de cada grupo, se definio el Indice de Similitud de Agrupamiento ( ISC l ). Dentro de la perspectiva de este estudio, el metodo utilizado se presento adecuado, lo que permitio identificar grupos de municipios con grados de similitudes en el orden de 63 a 94%.