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.

Selective mechanized coffee (Coffea arabica L.) harvesting is strategic for producers to add greater quality and value to their production. However, the success of this operation is linked to the strength needed to detach the fruit from the coffee tree. The objective of this work was to evaluate the detachment force of coffee fruits according to the period of the day (TT), as well as the relation between the stages of maturation and exposure to sunlight. The experiment was carried out in a coffee plantation Catuaí Vermelho IAC 144 (in the municipality of Presidente Olegário, Minas Gerais, Brazil) during the 2018-2019 and 2019-2020 harvests. A completely randomized design was used in a time-sub-subdivided plot scheme. The main plot (sun exposure), subplots (fruit maturation stages [TSs]), and sub-subplot (days). The detachment force of the fruits was evaluated using a portable digital dynamometer, with 36 repetitions. Data variance analysis was performed and, when necessary, Tukey's test was applied, both at .05 probability. The force required to remove the fruits from the coffee tree was influenced by the TS, the TT, and the plants' face of sun exposure. It is concluded that the TT and the face of sun exposure influence the detachment strength of the coffee fruits and that considering the detachment strength of the green and ripe fruits, all periods evaluated favor the realization of selective mechanized harvesting of coffee.

Dentre as operações agrícolas, a colheita mecanizada é a etapa final que merece muita atenção por afetar diretamente na produtividade, ou seja, quanto maior for a quantidade de perdas haverá reduções de produtividade e o aumento de custos. Assim análises estatísticas como o Controle Estatístico de Qualidade (CEQ) está sendo aplicado na agricultura e tem demonstrado grande potencial para a melhoria da gestão dos sistemas agrícola bem como nas tomadas de decisão. Com este trabalho, objetivou-se monitorar a qualidade operacional da colheita mecanizada do amendoim, durante o recolhimento, por meio do CEQ, e quantificar as perdas totais com a utilização da armação retangular. O experimento foi realizado, em área comercial, na safra 2019/2020, no município de Ribeirão Preto, estado de São Paulo, localizado nas coordenadas geográficas 21°20'17.55"S e 47°54'7.31"O. O amendoim foi semeado em sistema de Meiosi (Método Inter Ocupacional Simultâneo). O delineamento experimental seguiu as premissas do CEQ, monitorando, ao longo do tempo, 20 pontos amostrais que foram distanciados entre si com 80 m de comprimento. O indicador de qualidade avaliado, durante o recolhimento, foram as perdas totais que foram quantificadas por meio da armação retangular, possuindo as seguintes dimensões 5,4 m de largura por 0,37 m de comprimento. A análise estatística foi executada por meio das ferramentas do CEQ, que foram: cartas de controle de valores individuais, gráficos sequenciais ou run charts e análise descritiva. Concluiu-se que por meio da aplicação das ferramentas de qualidade permitiu o maior acompanhamento e monitoramento da operação, em que não houve presença de causas especiais e nem de padrões de não aleatoriedade.

Soybean is one of the main crops in Brazil, with a significant share of national agribusiness exports. Nonetheless, several factors such as weed competition and soil fertility directly affect soybean yield and productivity. This study aimed to analyse the spatial distribution of weeds as a function of soil fertility and soybean yield in farming fields. We carried out the experiment on a farm located in Brejo, Maranhão state, Brazil, through a geostatistical analysis of 60 sampling points on a regular grid of 10.0 m x 50.0 m. At these points, we collected phytosociological information on the weed community, soil fertility, and soybean yield. We performed principal component analysis (PCA) to determine the most responsive variables and to group them. We determined spatial dependence through geostatistical procedures, with the interpretation and adjustment of variogram components. We identified seven weed species, distributed across seven genera and six botanical families, of which 76.78% were eudicotyledons. In the cluster analysis, we grouped monocotyledonous species separately from eudicotyledons as explained by the morphophysiological contrasts between these botanical classes. Soybean yield did not correlate with soil fertility or weeds. These two factors can be considered only as a share of soybean productivity because their individual variations do not directly influence production factors. The efficient management of weeds and soil fertility should result in a more uniform and potencially more soybean yield when other conditioning factors are also effective