No abstract

O balanço hídrico é uma ferramenta de caraterização temporal da dinâmica de água no solo de determinada região. Objetivou-se estimar o balanço hídrico climático e a classificação do clima do município de Turiaçu-MA. Foram utilizados séries de dados históricos entre os anos de 1961 a 2016, de precipitação pluviométrica e temperatura mensais, sendo excluídos dados omitidos. Para o cálculo do balanço hídrico climatológico, foi adotado o valor de 100 mm para a capacidade de água disponível (CAD). A classificação climática foi obtida por meio dos valores do índice hídrico (Ih), índice de aridez (Ia) e índice de umidade (Iu). A evapotranspiração potencial atingiu valores médios anuais de 1765,3 mm. A deficiência hídrica total anual verificada foi de 535,3mm, distribuído em sua totalidade ao longo do período de estiagem da região (agosto a dezembro). A fórmula climática obtida foi B1sA’a’, isto é, clima úmido, Megatérmico, com deficiência hídrica moderada no verão e 26,6 % da evapotranspiração anual concentrada no trimestre mais quente do ano.

The monitoring and determination of peanut maturity are fundamental to reducing losses during digging operation. However, the methods currently used are laborious and subjective. To solve this problem, we developed models to access peanut maturity using images from unmanned aerial vehicles (UAV) and satellites. We evaluated an area of approximately 8 hectares in which a regular grid of 30 points was determined with weekly evaluations starting at 90 days after sowing. Two Artificial Neural Networking (ANN) were used with Radial Basis Function (RBF) and Multilayer Perceptron (MLP) to predict the Peanut Maturity Index (PMI) with the spectral bands available from each sensor. Several vegetation indices were used as input to the ANN, with the data being split 80/20 for training and validation, respectively. The vegetation index, Normalized Difference Red Edge Index (NDRE), was the most precise coefficient of determination (R2 = 0.88) and accurate mean absolute error (MAE = 0.06) for estimating PMI, regardless of the type of ANN used. The satellite with Normalized Difference Vegetation Index (NDVI) could also determine PMI with better accuracy (MAE = 0.05) than the NDRE. The performance evaluation indicates that the RBF and MLP networks are similar in predicting peanut maturity. We concluded that satellite and UAV images can predict the maturity index with good accuracy and precision.

No abstract

The protection conferred via chemical treatment of seeds is indispensable to the normal development of crops, with a view to the best use of its productive potential. The objective of this study was to evaluate the soybean crop response, cultivate ‘FTS Paragominas RR’, to seed treatment. The study was conducted in an experimental area of the Center of Agrarian and Environmental Sciences of the Federal University of Maranhão, in Chapadinha (MA), from February to June 2018. A randomized complete block design was used, with split-plot in time. The plots consisted of five seed treatments: thiophanate-methyl + fluazinam fungicides, fludioxonil, carbendazim + thiram, the insecticide fipronil and the absence of the application. Throughout the crop cycle the agronomic characteristics were verified: plant height, stem diameter, and leaf area. And, at the time of harvesting, grain yield, the height of insertion of the first pod, the total number of pods and weight of 1000 grains. Seed treatments induced very variable responses on the growth and development of soybean ‘FTS Paragominas RR’. The best performances were obtained with the use of thiophanate-methyl + fluazinam fungicides (dose 198 mL) and fludioxonil (dose 200 mL). The application of carbendazim + thiram and fipronil, both at a dose of 200 mL, presented adverse effects throughout the vegetative and reproductive phases of soybean ‘FTS Paragominas RR’. None of the products provided significant increases in grain yield.