Bayesian Modelling of the effects of nitrogen doses on the morphological characteristics of braquiaria grass

Luiz Henrique Marra da Silva Ribeiro, Matheus de Souza Costa, Luiz Alberto Beijo, Alberto Frank Lázaro Aguirre, Tatiane Gomes de Araújo, Josiane dos Santos Alves


The Bayesian approach in regression models has shown good results in parameter estimations,
where it can increase accuracy and precision. The objective of the current study was to analyze the application
of Bayesian statistics to the modeling yield for leaf dry matter (LM) and stem (SM), in kg ha-1, leaf ratio (LR),
crude protein content for leaves (CPL) and stem (CPS) (%) of Brachiaria grass as a function of varying N doses
(0; 100; 200 and 300 kg ha-1 yr-1). Simple and two degree polynomial linear regression models were analyzed.
Information for a priori distributions was obtained from the literature. A posteriori distribution was generated
using a Monte Carlo method via Markov chains. Parameters significance was assyed with HPD (Highest
Posteriori Density) with a 95% interval. Model selections was performed using DIC (Deviance Information
Criterion); and adjustment quality estimated with means and 95% HPD for Bayesian R2 distribution ranges.
The models selected for the variables LM, SM and CPS were linear, while for LR and CPL, they were second
level polynomial. The lowest doses that maximize response variables were: LM: 274 ha-1yr-1, SM: 280 ha-1yr-1,
LR: 113 ha-1yr-1, CPL: 265 ha-1yr-1, CPS: 289 ha-1yr-1. The Bayesian approach allowed the inclusion of literatureverified
a priori information, and the identification of evidence optimization range intervals.


Bayes factor. Regression models. A priori information. Optimization.

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Direitos autorais 2018 REVISTA AGRO@MBIENTE ON-LINE

Revista Agroambiente On-line ISSN 1982-8470 (online), E-mail: Licença Creative Commons
Este obra está licenciado com uma Licença Creative Commons Atribuição-SemDerivações-SemDerivados 3.0 Brasil.