Univariate and multivariate nonlinear models in productive traits of the sunn hemp

Cláudia Marques De Bem,Alberto Cargnelutti Filho,Rafael Vieira Pezzini,Fernanda Carini

doi:10.5935/1806-6690.20200018

Abstract

Multivariate analysis helps to understand the relationships between dependent variables; this methodology has great potential in several areas of knowledge. The aim of this study was to adjust and compare the univariate and multivariate 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 94 days of observations (four plants per day): the fresh mass of leaves (FML), the fresh mass of stem (FMS), and the fresh mass of the aerial parts (FMAP). The Gompertz and Logistic univariate models were adjusted for each productive trait. To adjust the multivariate models, the errors covariance matrix was calculated. The p matrix (Cholesky factor) was obtained for each trait, and the multivariate Gompertz (GG) and Logistic (LL) nonlinear models were generated, together with the combination of 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 achieved satisfactory results to describe the productive traits of sunn hemp.

Highlights

Sunn hemp (Crotalaria juncea L.) is a fastgrowing legume, especially under high temperatures (LEAL et al, 2012)
We modeled several sunn hemp productive traits separately using the Gompertz and Logistic univariate nonlinear models (BEM et al, 2018)
For the criterion of residual standard deviation (RSD), the lowest values found corresponded to the trait fresh mass of leaves (FML) adjusted by the Gompertz model and the Logistic model

Summary

Introduction

Sunn hemp (Crotalaria juncea L.) is a fastgrowing legume, especially under high temperatures (LEAL et al, 2012). This crop is being increasingly used to suppress weeds development (TIMOSSI et al, 2011). A promising approach to analyze crop behaviors is the use of nonlinear regression analysis, more precisely, the application of nonlinear regression models (LÚCIO; NUNES; REGO, 2015). The need to understand the relationships between multiple variables makes nonlinear regression a tool of paramount importance, which can assist in the understanding of biological interactions and the achievement of practical solutions while allowing the characterization of crops’ behavior (REIS et al, 2014). The use of nonlinear regression models provides a comprehensive viewpoint, which may increase the inferences obtained regarding the productive behavior of a given crop throughout its life cycle

Objectives

Methods

Results

Conclusion