Modelagem aplicada aos fatores de produtividade do milho em função de atributos químicos do solo

Abstract

The use of mathematical models to predict non-sampled values or to optimize variables has been very common in agriculture, since in agriculture, there are several factors that influence cultivars. The physical and chemical quality of the soil, for example, are essentially important factors in plant productivity, and can be corrected through appropriate soil management, or applications of fertilizers and other correctives. It is important to understand these factors for satisfactory production in the field. Therefore, the aim of this work is to obtain a model, through data regression analysis, that relates the chemical attributes of the soil that most contribute to the components of corn productivity. To build the model, the most important chemical variables were defined, and linear multiple regression was subsequently used to build the mathematical model. The research took place in western Paraná, with the Morgan MG-593 PWU variety. In this research, phosphorus and potassium were the nutrients that most influenced corn productivity components. And aluminum together with magnesium had a harmful effect on the weight of the grains.