Univariate and multivariate nonlinear models in productive traits of the sunn hemp (Revista Ciência Agronômica, 2019)
Multivariate analysis helps to understand the relationships between dependent variables; this methodologyhas great potential in several areas of knowledge. The aim of this study was to adjust and compare the univariate andmultivariate Gompertz and Logistic nonlinear models to describe the productive traits of sunn hemp (Crotalaria juncea L.).Two uniformity trials were performed, and the following productive traits were analyzed in 376 sunn hemp plants along 94days of observations (four plants per day): the fresh mass of leaves (FML), the fresh mass of stem (FMS), and the fresh massof the aerial parts (FMAP). The Gompertz and Logistic univariate models were adjusted for each productive trait. To adjustthe multivariate models, the errors covariance matrix was calculated. The matrix (Cholesky factor) was obtained for eachtrait, and the multivariate Gompertz (GG) and Logistic (LL) nonlinear models were generated, together with the combinationof both models (GL and LG). To define the best model, the residual standard deviation (RSD), the determination coefficient(R2), the Akaike information criterion (AIC), the mean absolute deviation (MAD), and the measures of intrinsic nonlinearity(INL) and parametric nonlinearity (PNL) were calculated. The nonlinear multivariate model LL was adequate and achievedsatisfactory results to describe the productive traits of sunn hemp.