Modelo agroclimático na estimativa de produtividade da soja no sudeste de Mato Grosso

• Autor(a) principal: Feletti, Rômulo Caique Gonçalves
• Data de Publicação: 2020
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da UFMT
• Texto Completo: http://ri.ufmt.br/handle/1/5353
• topic: Glycine max; Modelagem agrometeorológico; Balanço hídrico; Modelos de produtividade; CNPQ::CIENCIAS AGRARIAS::AGRONOMIA; Glycine max; Agrometeorological modelist; Hydric balance; Productivity models

The cultivation of soybean has great economic importance for Brazil and especially for the state of Mato Grosso. The southeastern region of Mato Grosso has huge relevance in the state, but it has characteristics that limit production, the main factor that limits the productivity of the crop is the water deficit. Thus, crop management strategies must be evaluated, seeking to identify productivity estimates in advance, reduce climate risks and increase profitability. In addition to climatic conditions, specific characteristics of cultivars, these strategies can also be used in the management of soybean culture. Therefore, the climatic conditions and tolerance to the water deficit of soybean cultivars can be considered in models for estimating productivity, helping to define the preferred sowing times. Based on this, the objective of this study was to calibrate an agrometeorological model to estimate soybean productivity. For that, the Agroecological Zone (FAO) model was used to estimate the potential productivity of the soybean crop, which was subsequently penalized due to the water deficit, obtaining the attainable productivity. The reference evapotranspiration was estimated using the Penman-Monteith method and daily water balances were determined with a program developed especially for this purpose. The adjustment of the coefficients and factors of the agrometeorological models was performed using the least squares method, in simple regression analyzes. Most of the subperiods of the phenological stages registered water deficit and surplus for those in the evaluated sites. The best regression analyzes were obtained when the correction was considered due to the sensitivity to water deficit for the entire period, and when the values for the harvest coefficient were corrected. The adjustment of coefficients and factors proved to be important for obtaining more accurate productivity estimates through agrometeorological models.