Corn plant dry mass accumulation considering the previous crop by non-linear models

Abstract

Corn is the most produced cereal in the world, used both in human and animal nutrition. This study aimed to compare the fit of the Logistic, Gompertz, and von Bertalanffy non-linear models to data on the accumulation of total dry mass, dry mass of stems, leaves, and ears in corn plants grown with straw mulch from common bean, millet, and Brachiaria brizantha in relation to the days after plant emergence. The assumptions of normality, homoscedasticity, and independence of residuals were checked by Shapiro-Wilk, Breusch-Pagan, and Durbin-Watson tests, respectively. The models were adjusted by the least squares method using the Gauss-Newton algorithm in the R software. The quality of the fit was evaluated based on the values of the coefficient of determination (R2), the residual standard deviation (RSD), the Akaike Information Criterion (AIC), and Bates and Watts curvature measures. The Logistic model presented the best fit for the dry mass of stems and ears, and the Gompertz model for the dry mass of leaves and total dry mass, based on the quality evaluators used.