Análise uni e multivariada aplicada à qualidade operacional da colheita mecanizada de soja
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2017-12-19
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Universidade Estadual Paulista (Unesp)
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A colheita mecanizada de soja é uma operação fundamental para a finalização de seu ciclo produtivo, porém a ausência de metodologias eficientes para quantificação das perdas, não tem contribuído para melhoria continua deste processo agrícola. Associado a isto, ainda existem inúmeros fatores que podem influenciar a qualidade da operação da colheita de soja, sendo difíceis de serem analisados e interpretados, pois as relações entre as variáveis são complexas. Para tentar suavizar essa complexidade, abordagens multivariadas, como Análise de Componentes Principais (ACP) e Análise Fatorial (AF), podem ser uma alternativa para extrair informações da base de dados gerados durante a colheita. E por fim, para monitorar a operação com base no nível de qualidade que a mesma está sendo realizada, o controle estatístico de processo, com uso de gráficos multivariados torna-se essencial, para as variáveis que possuem correlação. Diante disto, objetivou-se neste trabalho determinar as variáveis que mais afetam a qualidade operacional da colheita mecanizada de soja, por meio de análises uni e multivariada. O trabalho foi realizado em março de 2016, em área agrícola no município de Ribeirão Preto-SP, sendo utilizada uma colhedora da marca John Deere, modelo 1470, com sistema de trilha do tipo tangencial e separação por saca-palhas. O processo foi considerado incapaz de manter as perdas da colheita mecanizada de soja em níveis aceitáveis durante toda a operação para as duas armações. A análise fatorial permitiu a seleção de quatro indicadores da colheita mecanizada de soja, explicando 76,4% da variância total. As cartas de controle multivariadas foram mais eficazes para determinar a não aleatoriedade no monitoramento de processo com variáveis correlacionadas.
The mechanical harvesting of soybeans is a fundamental operation to the end of its productive cycle, but the absence of efficient methodologies to quantify losses has not contributed to the continuous improvement of this agricultural process. Associated with this, there are still many factors that can influence the quality of the soybean harvesting operation, being difficult to analyze and interpret because the relationships among the variables are complex. To attempt to soften this complexity, multivariate approaches such as Principal Component Analysis and Factorial Analysis may be an alternative to extract information from the database generated during harvesting. Finally, to monitor the operation based on the level of quality that is being performed, the statistical process control, using multivariate graphs becomes essential, for the variables that have correlation. Therefore, the aimed of this study was to determine the variables that most affect the operational quality of the soybean mechanized harvest, through uni and multivariate analyzes. The work was carried out in March 2016, in an agricultural area in the city of Ribeirão Preto-SP, using a John Deere brand model 1470 harvester with a tangential type track system and straw picking. The process was found to be unable to keep soybean harvest losses at acceptable levels throughout the operation for the two frames. The factorial analysis allowed the selection of four indicators of the soybean mechanized harvest, explaining 76.4% of the total variance. Multivariate control charts were more effective in determining non-randomness in process monitoring with correlated variables.
The mechanical harvesting of soybeans is a fundamental operation to the end of its productive cycle, but the absence of efficient methodologies to quantify losses has not contributed to the continuous improvement of this agricultural process. Associated with this, there are still many factors that can influence the quality of the soybean harvesting operation, being difficult to analyze and interpret because the relationships among the variables are complex. To attempt to soften this complexity, multivariate approaches such as Principal Component Analysis and Factorial Analysis may be an alternative to extract information from the database generated during harvesting. Finally, to monitor the operation based on the level of quality that is being performed, the statistical process control, using multivariate graphs becomes essential, for the variables that have correlation. Therefore, the aimed of this study was to determine the variables that most affect the operational quality of the soybean mechanized harvest, through uni and multivariate analyzes. The work was carried out in March 2016, in an agricultural area in the city of Ribeirão Preto-SP, using a John Deere brand model 1470 harvester with a tangential type track system and straw picking. The process was found to be unable to keep soybean harvest losses at acceptable levels throughout the operation for the two frames. The factorial analysis allowed the selection of four indicators of the soybean mechanized harvest, explaining 76.4% of the total variance. Multivariate control charts were more effective in determining non-randomness in process monitoring with correlated variables.
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Controle estatístico de processo, Glycine max L., Mecanização Agrícola, Perdas na colheita, Grain harvester, Statistical process control, Glycine max L., Agricultural mechanization, Harvest losses, Colhedora de grãos