Demanda tractiva en función de la resistencia mecánica del suelo a la penetración

Abstract

RESUMO:No presente estudo, foram elaborados modelos empíricos para determinar a força de tração demandada por arados de discos, escarificadores e semeadoras adubadoras, em função da resistência mecânica do solo à penetração. A média da referida resistência, determinou-se até as profundidades de 25; 35 e 15 cm, em correspondência com as regulagens do arado de discos, escarificador e semeadora adubadora. O ajuste foi verificado de acordo com o coeficiente de determinação, gráficos de dispersão, análise residual e teste t (Student). Com base nessas análises, confirmou-se a normalidade dos resíduos, e foram estabelecidos intervalos de confiança com 95% de probabilidade. Os valores estimados e obtidos experimentalmente não discreparam significativamente. Concluiu-se que as funções exponenciais associadas apresentaram adequada precisão e confiabilidade para predizer a força de tração, considerando-se a resistência mecânica do solo à penetração e que os modelos podem ser uma ferramenta útil para o planejamento e gestão de operações agrícolas mecanizadas em solos com textura similar às desta pesquisa.PALAVRAS-CHAVE: desempenho de máquinas, modelos empíricos, modelos não lineares. TRACTIVE DEMAND AS FUNCTION OF SOIL MECHANICAL RESISTANCE TO PENETRATIONABSTRACT: In this study, empirical models were developed to determine the tensile force demanded by disc plows, harrows and planters fertilizers, as function of soil mechanical resistance to penetration. The mean of the referred resistance was determined until 25, 35 and 15 cm depths, according to the regulations for the disc plow, springs harrow and fertilizer seeder. The adjustment was verified based on the coefficient of determination, dispersion graphics, residual analysis and t test (Student). It was confirm the normality of the residuals and to establish intervals of confidence with 95% of probability. The estimated and experimentally obtained values did not differ significantly. It was concluded that the associated exponential functions presented appropriate precision and reliability to predict the traction force being considered the soil mechanical resistance to penetration and that the models can be a useful tool for planning and management of mechanized farming operations in soils with texture similar to those of this research.KEYWORDS: machine performance; empirical models; nonlinear models. INTRODUCCIÓNPara implantar una cultura agrícola, se puede preparar el terreno convencionalmente o adoptando métodos conservacionistas. En el preparo convencional existe elevada movilización del suelo causada por arados y gradas. Por otro lado, los métodos conservacionistas tienen por finalidad disminuir el número de operaciones, como es el sistema reducido utilizándose un escarificador o aún eliminar totalmente esas actividades como es en el plantío directo, realizado solamente con una sembradora abonadora apropiada. Sin embargo, independientemente del método de preparo, para Pedro H. M. Borges, Aloísio Bianchini, João C. S. Maia et al. Eng. Agríc., Jaboticabal, v.34, n.2, p....

Artificial Neural Networks for Predicting Animal Thermal Comfort

“…The normality of residuals of the neural networks with the best indexes was assessed using the Kolmogorov-Smirnov, Lilliefors, and Shapiro-Wilk tests, and the adjustment between the observed and estimated values was confirmed using the Student's t-test, as suggested by Borges et al (2017). Moreover, taking as reference the studies of Although the presented analytical criteria confirmed the accuracy of the networks in predicting the THI, Borges et al (2014Borges et al ( , 2017 recommend graphically evaluating the characteristics of the residuals to assess the presence of inadequate trends in their distribution. For this purpose, different graphs should be prepared, including the histogram of residuals, a quantile-quantile plot with confidence intervals, a graph with the values of the residuals as a function of the estimated values, a graph with the values of the residuals according to the day of the year (independent variable), and a scatter plot with the observed values and values estimated by each selected network for each day of the year.…”

Artificial Neural Networks for Predicting Animal Thermal Comfort

“…Moreover, taking as reference the studies of Binoti et al (2014a, b), Georgens et al (2014), Soares et al (2014), and Borges et al (2017), the correlation coefficients (r), Willmott's index of agreement (Iw), and performance index (Id) were calculated, and the latter index measures the proximity between the observed and estimated values. Dai et al (2014), Rodrigues et al (2015), Sousa et al (2015), Brighenti et al (2016), Chaves et al (2016), Lopes et al (2016), andMachado et al (2017) recommend evaluating the efficiency of the networks using the Nash-Sutcliffe coefficient (NSC) (Nash & Sutcliffe, 1970) according to equation: Although the presented analytical criteria confirmed the accuracy of the networks in predicting the THI, Borges et al (2014Borges et al ( , 2017 recommend graphically evaluating the characteristics of the residuals to assess the presence of inadequate trends in their distribution. For this purpose, different graphs should be prepared, including the histogram of residuals, a quantile-quantile plot with confidence intervals, a graph with the values of the residuals as a function of the estimated values, a graph with the values of the residuals according to the day of the year (independent variable), and a scatter plot with the observed values and values estimated by each selected network for each day of the year.…”