Computational model of prediction of working days for soybean sowing in cities of Mato Grosso

Abstract

Currently, in agriculture, great competitiveness requires farmers to develop strategies to maximize profits and increase productive efficiency. Thus, it is essential in the planning that the producer esit the time available to carry out their operations taking into account, as the main factor, the climatic variables. In this work, we analyzed the probabilities of occurrence of working days and the number of mechanized sets necessary for soybean sowing operations in the municipalities of Diamantino and Sinop of the state of Mato Grosso. For this, meteorological data were obtained from these municipalities and the days favorable to sowing were identified through the precipitation criteria < 5 mm and soil water storage (ARM) between 40 and 90% of the available water capacity (CAD). The number of working days was simulated through three different criteria, being 40%, 50% and 60% of the day being favorable to sowing, then the number of mechanized sets needed for sowing in a given area was estimated. For both municipalities, the values of the probability of the day being favorable to sowing P(F) increased after the onset of the rains. On the other, the demand for machinery varied according to the criterion adopted being lower when considering the criterion of 40% of P(F).