A precipitação é um elemento meteorológico de grande importância, e seu conhecimento histórico torna-se relevante para o monitoramento de impactos causados pelo seu excesso ou falta prolongados. Objetivou-se, no presente trabalho, analisar a distribuição da precipitação mensal e anual e os níveis de probabilidade de ocorrência de chuvas, utilizando-se o modelo probabilístico de distribuição Gama incompleta, para os dados de precipitação pluviométrica de Cáceres (MT). Utilizou-se uma série de dados de 26 anos, disponibilizada pelo Instituto Nacional de Meteorologia, o qual possui uma estação em Cáceres. A precipitação anual teve grande variabilidade, com mínima de 972,9 mm, em 1985, e máxima de 1.624,1 mm, em 1998. Observou-se, por meio dos dados mensais, que existem duas estações bem definidas, a seca (maio a setembro) e a chuvosa (outubro a abril). As menores médias mensais ocorreram nos meses de junho, julho e agosto, com 16,59 mm, 17,90 mm e 20,09 mm, respectivamente. As estimativas do parâmetro α variaram de 0,9, em junho e agosto, a 13,4, em março. O parâmetro β variou de 13,2, em março, a 33,1, em janeiro. O período com maior probabilidade de precipitação vai de dezembro a março, enquanto junho, julho e agosto foram os meses em que a probabilidade de ocorrência de precipitação foi mais baixa.

Geostatistics as a methodology for studying the spatiotemporal dynamics of Ramularia areola in cotton crops. Geostatistics is a tool that has been used to study plant pathology, by modeling the spatiotemporal pattern of diseases, generating hypotheses about their epidemiological aspects in order to use tactics and strategies of rational control. The objective of this study was to use geostatistics to study the spatiotemporal dynamics of Ramularia areola in cotton crops. The experiment was conducted at the experimental area of Mato Grosso State University-Tangará da Serra campus, and arranged in a 2 × 3 factorial design, with randomized blocks, with two spaicngs (0.45 and 0.90 cm) and three conditions of soil coverage (no cover, P. glaucum and C. spectabilis). Geostatistical analysis of data was performed using data from temporal and spatial progress of R. areola, obtained through assessments of the incidence and severity of the disease in plants, and spatial dependence, and analyzed using semivariogram fittings. Through the isotropic exponential semivariogram model, it was possible to check the distribution pattern and spatial dependence of Ramularia leaf spot. Spatial dependence was observed for the disease-moderate to strong for most data evaluated. The pathogen spread from the primary source of inoculum, from the center

The objective of this work was to evaluate the progress of the areolate mildew of cotton under different soil cover and spacing conditions. The experiment was carried out using randomized blocks and a 2 × 3 factorial design, with two spacings (0.45 m and 0.90 m) and three soil covers (no cover, Pennisetum glaucum and Crotalaria spectabilis) with four replications. The plants were inoculated with R. areola, sixty DAS. A total of 14 evaluations of disease severity were performed. At the lower, middle and upper thirds of plants, a diagram scale with nine levels of severity was used and the resulting data were converted into the AUDPC. Gompertz, logistic, and monomolecular mathematical models were tested in the disease severity curves for each third. Agronomics characteristics were evaluated as well. Significant differences of AUDPC were found for the cotton plants thirds, and the middle third was the highest AUDPC. Significant difference for the lower and upper thirds, whose AUDPC were highest on 0.90 m spacing, was observed too. The disease progress curves of the thirds did not fit the tested models. Significant results to the both covers situations, where the treatments grown on crotalária cover and without cover had highest AUDPC, were evidenced. The treatments with C. spectabilis cover were taller than other treatments. Significant data were observed for the cover crops used and in the treatments grown at 0.90 m spacing, to residual cover and crop yield, respectively.