Assuring the quality of mechanical soybean harvesting is crucial to ensure that investment in other stages of crop production is converted into profit for the farmer. Because the quality of this operation is related to decreased losses, which may be affected by the combine header used and by the harvesting speed, the aim of the present study was to assess the quality of mechanical soybean harvesting in the Southern Cone of Rondônia, using harvesters with different combine headers and travel speeds via statistical process control. The experiment was performed during the 2015/16 harvest in an agricultural area of the municipality of Cabixi, RO, located at the geographical coordinates 13° 27’ S and 60° 41’ W. The study was designed based on the premises of the statistical process control, sampling 80 points 100 m apart. Combine header, internal, total, and cutting height losses were assessed. Quality of the harvesting process was assessed using the draper header. Travel speed had little effect on process quality. Crop losses were higher than the acceptable limit, indicating the need to improve the process.

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Using UAV and multispectral images has contributed to identifying field variability and improving crop management through different data modeling methods. However, knowledge on application of these tools to manage peanut maturity variability is still lacking. Therefore, the objective of this study was to compare and validate linear and multiple linear regression with models using artificial neural networks (ANN) for estimating peanut maturity under irrigated and rainfed conditions. The models were trained (80% dataset) and tested (20% dataset) using results from the 2018 and 2019 growing seasons from irrigated and rainfed fields. In each field, plant reflectance was collected weekly from 90 days after planting using a UAV-mounted multispectral camera. Images were used to develop vegetation indices (VIs). Peanut pods were collected on the same dates as the UAV flights for maturity assessment using the peanut maturity index (PMI). The precision and accuracy of the linear models to estimate PMI using VIs were, in general, greater in irrigated fields with R2 > 0.40 than in rainfed areas, which had a maximum R2 value of 0.21. Multiple linear regressions combining adjusted growing degree days (aGDD) and VIs resulted in decreased RMSE for both irrigated and rainfed conditions and increased R2 in irrigated areas. However, these models did not perform successfully in the test process. On the other hand, ANN models that included VIs and aGDD showed accuracy of R2 = 0.91 in irrigated areas, regardless of using Multilayer Perceptron (MLP; RMSE = 0.062) or Radial Basis Function (RBF; RMSE = 0.065), as well as low tendency (1:1 line). These results indicated that, regardless of the ANN architecture used to predict complex and non-linear variables, peanut maturity can be estimated accurately through models with multiple inputs using VIs and aGDD. Although the accuracy of the MLP or RBF models for irrigated and rainfed areas separately was high, the overall ANN models using both irrigated and rainfed areas can be used to predict peanut maturity with the same precision.

The intensifying use of machines in agriculture to increase operational capacity requires investments in more powerful and automated machines capable of working at higher speeds to meet the demands of agricultural activities. The objective of this study was to evaluate the sowing quality of a second crop maize using a pneumatic sowing machine equipped with two seed-metering devices at different displacement speeds. The statistical design was a randomized block design arranged in 6 × 2 factorial, with 4 replications, totaling 48 experimental plots. Where it was tested two seed-metering mechanisms from different manufacturers denominated A and B, and 6 displacement speeds of approximately 2.0; 4.7; 6.5; 9.1; 10.3 and 12.3 km h -1. The seed-metering mechanisms were compared by mean test while displacement speeds were compared by regression plots. The initial and final plant populations, seed depth, seedling longitudinal distribution (normal, faulty and double spacing) and grain yield were also evaluated. Displacement speed and seed-metering devices showed significant interaction only for the percentages of normal, faulty, and double spacings. The initial and final population presented an isolated effect for both the seed-metering devices and velocities. The seed depth showed an isolated velocity effect. The grain yield showed a significant isolated effect from the analyzed seed-metering devices. The seed-metering device B operating at lower speeds had better performance in the sowing process.

The variation in population density in a corn crop can positively, negatively or neutrally affects plant productivity depending on the productive potential of the area. The aim of this work was to evaluate the effects of the variation in corn seed dosage on crop yield, and define from which percentage of variation the productivity of the sown line is affected negatively. The experiment was installed at FCAV-UNESP, in Jaboticabal (SP), Brazil. Twelve variations on plant population were evaluated, ranging from -27% to 27%, varying with a frequency of 4.5%. The morphological and productive characteristics of each treatment were evaluated through regression analysis. Each 1% of negative variation on seed dosage was lost 1.06% in corn yield. Positive variations, however, presented changes that were 0 to -2.59%. Negative variations on seed dosage reduced corn productivity by up to 28%. The positive variation affects the productivity of the crop in a less accentuated way, with a reduction in productivity that reaches 2.59% in the largest variations and may even cause positive productivity results depending on the maximum potential of the area.

KEYWORDS Bioindicators Invasive plants Microbial respiration rate Metabolic quotient PALAVRAS-CHAVE Bioindicadores Plantas invasoras Taxa de respiração microbiana Quociente metabólico ABSTRACT:The objective of this study was to evaluate the impact of different glyphosate-based herbicide formulations on microbial activity of soils from Carajás National Forest. We tested three formulations of glyphosate, i.e., Roundup Original  , Roundup Ultra  and Roundup WG  that were applied in five doses: 0; 240; 480; 720 and 1440 g of active ingredient in acid equivalent ha -1 , with four replications. Herbicides were sprayed on pots containing 500 g of soil derived from the 0-10 cm layer of the study site. We determined the carbon from microbial biomass (C-MB), microbial respiration rate (MR) and metabolic quotient (qCO 2 ) at one and 28 days after herbicide application. No treatment affected the C-MB and MR at one and 28 days of incubation. There was no difference for qCO 2 at any dose of Roundup Ultra  and WG  formulations at one and 28 days of incubation. However, the qCO 2 was inhibited by the Roundup Original  at one day post treatment. This parameter was normalized at 28 days after herbicide application. The data indicate that no one of the treatments tested cause significant impact on soil microorganisms of the Carajás National Forest, suggesting that herbicide-based invasive weed control could be used. RESUMO: Objetivou-se com este estudo avaliar o impacto de diferentes formulações de herbicidas à base de glyphosate na atividade microbiana de solo da Floresta Nacional de Carajás. Foram testadas três formulações comerciais de glyphosate (Roundup Original