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.

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.

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.

Machine harvesting is an essential step of crop production, considering a dynamic operation, and is subject to losses due to several factors that affect its quality. The objective of this study was to evaluate the quality of mechanized peanut pickers in the three soil tillage operations using Statistical Quality Control (SQC) tools. We conducted the experiments in a peanut field located at 21°20′23″ S and 47°54′06″ W of Brazilian peanut farmers. We used Statistic Control Quality (SQC) experimental design to monitor peanut losses during machine harvesting. The treatments evaluated were three soil tillage operations: conventional (CT), rotary tillers (RT), and hoe (RH). The quality indicators were collected inside the picker’s bulk tank. Statistical analyses used were descriptive statistics and SQC tools (run charts, control charts, and the Ishikawa diagram). The process was considered stable for indicators: whole pods (CT, RT, and RH), broken pods (CT, RT, and RH), and hatched pods (CT, RT, and RH), while the other indicators showed points that were out of control. With the application of SQC tools, it was possible to identify the factors that caused the increase of variability in peanut harvesting, listing the points to be improved to support decision-making, always aiming to increase this operation’s quality.

Digital image processing, when applied to the study of leaf area, allows the integration of the direct measurement and non-destructive, and thus preserves the integrity of the plant. The objective was the quantification of the leaf area of soybean, cv. FTS Paragominas RR, submitted to different treatments of seed with the use of the computer program ImageJ, and basic presuppositions of image processing. The experiment was conducted at the Center of Agrarian Sciences and Environmental, Federal University of Maranhão, in Chapadinha (MA), in the period from February to June 2018. The seeds of soybean 'Paragominas RR' were submitted to the technique of seed treatment, consisting of three fungicides of the active ingredients, thiophanate methyl + fluazinam, fludioxonil and carbendazim + tiram, an insecticide active ingredient fipronil and the control. The leaf area was analyzed in the growth phase, through the use of digital camera and ImageJ®. The use of the routines in the computer program ImageJ® were effective for the determination of leaf area of the soybean submitted to different treatments of the seed. The thiophanate methyl + fluazinam in the dose 200 mL per 100 kg of seeds showed beneficial effects on growth of the cv. FTS Paragominas RR, as estimated by the leaf area.

Remote sensing tools are helpful in monitoring and managing crop production. However, each remote sensing technology responds to crop variability differently. In this way, the objective of this work was to compare sensors on airborne and orbital platforms and to observe which one has the best quality to determine the behavior of the peanut (Arachis hypogaea L.) crop variability. The experimental design followed the premises of the statistical quality control (SQC), with samples collected over time. The experimental area was composed of 30 sampling points spaced every 50 m. The multispectral images were acquired with an unmanned aerial system (UAS) consisting of a DJI Matrice quad-copter and a Micasense RedEdge multispectral camera and with the PlanetScope multispectral imaging satellites. It was verified that in all periods evaluated for spectral bands and vegetation indices (VI), satellite images presented better process quality. The enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) generated from satellite images were able to detect the peanut maturation variation better. The behavior of the bands and the VIs generated from the Planet images show quality for peanut crop monitoring. While UAS showed sensitivity to detect the saturation of the bands, making it difficult to visualize the temporal variability.