RESUMO O direcionamento automático de máquinas agrícolas via sinal GNSS (Global Navigation Satellite System) tem sido adotado cada vez mais pela comunidade agrícola na última década. Produtores de amendoim estão adotando tal tecnologia a fim de melhorar o paralelismo das operações face às dificuldades principalmente encontradas no arranquio mecanizado, onde as perdas são, todavia, um grande problema. Objetivou-se avaliar as operações mecanizadas de semeadura e de arranquio de amendoim controlados manualmente e com piloto automático, juntamente com a qualidade das operações. Os tratamentos consistiram de duas operações de escavação, com e sem piloto automático, e duas velocidades de deslocamento (4,5 e 6,0 km h-1). O experimento foi realizado em solo de textura média, sob delineamento inteiramente casualizado disposto em faixas, utilizando-se das variáveis analisadas como indicadores de qualidade. Na semeadura foi avaliado o paralelismo entre as passadas do conjunto trator-semeadora em 120 pontos de cada sistema (direcionamento manual e automático). Foram avaliadas as perdas de arranquio nos dois sistemas de direcionamento sob duas velocidades de deslocamento (4,5 e 6,0 km h-1), em 15 pontos de cada tratamento. Verificou-se que o paralelismo entre as passadas do conjunto trator-semeadora foi melhor quando se utilizou do piloto automático, além da maior qualidade da operação. A velocidade de deslocamento não influenciou as perdas de arranquio. Não houve diferenças nas perdas visíveis de arranquio, porém quando o arranquio foi realizado com o piloto automático obteve-se maior qualidade. Menores perdas invisíveis e totais de arranquio foram obtidas quando o arranquio foi realizado com o direcionamento automático, e qualidade superior foi obtida na velocidade de 4,5 km h-1; porém, a qualidade foi afetada negativamente sob operação manual. Portanto, o uso do piloto automático se mostrou eficaz na melhoria da precisão e da qualidade das operações.

ABSTRACT Over the past decade, automatic guidance of agricultural machinery via Global Navigation Satellite System (GNSS) signals has been increasingly adopted by the farming community. Peanut farmers are adopting such technology in order to improve the parallelism of operations to address difficulties that occur in mechanical digging, where losses are a big problem. This study aimed to evaluate mechanized operations of peanut sowing and digging controlled manually and by autopilot, along with their quality. The treatments consisted of two digging operations, with and without autopilot, and two displacement speeds (4.5 and 6.0 km h-1). The experiment was conducted in a medium-textured soil in a completely randomized design arranged in bands, using the analyzed variables as quality indicators. At sowing, parallelism between passes of the tractor-sower set, was evaluated at 120 points of each system (manual and automatic routing). The digging losses were evaluated in the two guidance systems, under two displacement speeds (4.5 to 6.0 km h-1) at 15 points per treatment. It was verified that the parallelism between the passes of the tractor-sower set was better when using the autopilot, which improved the operation quality. Displacement speed did not influence the digging loss. There were no differences in the visible digging loss, but higher quality was obtained when the operation was performed using autopilot. Minor invisible and total digging losses were obtained when the digging was performed with automatic routing, and superior quality was found at a speed of 4.5 km h-1; however, quality was adversely affected under manual operation. Thus, the use of autopilot is effective for improving the accuracy and the quality of the operations.

RESUMO A qualidade da colheita mecanizada de soja é essencial para garantir que todo investimento realizado nas demais etapas do processo produtivo da cultura seja convertido em lucro para o produtor. Considerando que a qualidade dessa operação está relacionada com menores perdas e que a plataforma de corte utilizada e a velocidade de colheita podem influenciar nisso, objetivou-se avaliar a qualidade da colheita mecanizada da soja, no Cone Sul de Rondônia, em colhedoras com diferentes plataformas de corte e velocidades de deslocamento por meio do controle estatístico de processo. O experimento foi realizado na safra 2015/16 em uma área agrícola no município de Cabixi, RO, localizada nas coordenadas geográficas 13º 27’ S e 60º 41’ W. O delineamento experimental foi estabelecido de acordo com as premissas do controle estatístico de processo, sendo amostrados 80 pontos distanciados a cada 100 m. Avaliaram-se as perdas na plataforma, nos mecanismos internos e totais e a altura de corte. A melhor qualidade do processo de colheita foi obtida na plataforma de esteira, com pouca influência da velocidade de deslocamento. O nível de perdas na colheita foi acima do limite aceitável, indicando necessidade de melhorias do processo.

ABSTRACT 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.

ABSTRACT The peanuts harvesting mechanization is affected by the soil physical characteristics and it may increase the losses due to the production of pods in subsurface. The objective of the experiment was to identify the clusters through multivariate exploratory approaches from similarity in six soil textures (very clayey, clayey, silty clayey loam, clayey loam, sandy loam and sandy) in the state of São Paulo, Brazil, determining the main agronomic variables that most influenced the clustering division to assist the decision-making process in peanuts mechanized harvesting. The data were analyzed by the multivariate exploratory that is performed to simplify the description of a set of interrelated variables, using: yield, maturity, soil and pod moisture content, windrow width and height, visible and invisible digging losses, and gathering losses, as agronomic indicators of quality. The low and high clay content were grouped into clusters I and III, respectively, according to the agronomic traits of the peanut crop. The principal components analysis (PC) allowed a single distribution of accesses since only two eigenvalues were higher than “one”: the highest eigenvalues of 4.51 and 1.79, resulted in a Biplot that explained 70% of the original variability, 50.11% and 19.89% of which in the PC1 and PC2, respectively. The multivariate analysis indicated that high peanut yields in soils with low clay are correlated with the losses during the peanut mechanized harvesting operation.