Adaptability and stability evaluation of maize hybrids using Bayesian segmented regression models.

Tâmara Rebecca A Oliveira,Gustavo Hugo F Oliveira,José Luiz S Carvalho Filho,Hélio Wilson L Carvalho,Moysés Nascimento,Emiliano Fernandes N Costa,Geraldo A Gravina,Antonio T Amaral Junior

doi:10.1371/journal.pone.0236571

Abstract

The occurrence of genotype by environment interaction (G x E), which is defined as the differential response of genotypes to environmental variation, is frequently reported in maize cultures, making it challenging to recommend cultivars. Methods allowing to study the potential nonlinear pattern of genotype responses to environmental variation allied to prior beliefs on unknown parameters are interesting to evaluate the phenotypic adaptability and stability of genotypes. In this context, the present study aimed to assess the adaptability and stability of maize hybrids, by using the Bayesian segmented regression model, and evaluate the efficacy of using informative and minimally informative prior distributions for the selection of cultivars. Randomized complete-block design experiments were carried out to study the yield (kg/ha) of 25 maize hybrids, in 22 different environments, in Northeastern Brazil. The Bayesian segmented regression model fitted using informative prior distributions presented lower credibility intervals and Deviance Criterium of Information values, compared to those obtained by fitting using minimally informative distributions. Therefore, the model using informative prior distributions was considered for the adaptability and stability evaluation of maize genotypes. Once most northeastern farmers in Brazil have limited capital, the genotype P4285HX should be considered for planting, due to its high yield performance and adaptability to unfavorable environments.

Highlights

Maize (Zea mays L.) cultures are appreciated worldwide
Considering the results provided by the model M1, which is characterized by the minimally informative prior distributions, most genotypes (14 genotypes) presented the linear regression coefficient related to the unfavorable environments equal to 1, except 30A16HX, 2B707HX, 2B587HX, 30A37HX, 2B604HX, 20A55HR, 20A78HX and DKB370, which presented values higher than 1 and the genotypes P4285HX and BRS2020, which presented values lower than 1 (Table 3)
Once smaller Deviance Information Criterion (DIC) values indicate better data fitting, these results demonstrate that Model 2 (M2) should be considered for the adaptability and stability evaluation of maize genotypes (Table 4)

Summary

Introduction

Maize (Zea mays L.) cultures are appreciated worldwide. it has tremendous relevance due to its several uses and applications in areas ranging from animal feed to technological industries.because maize is grown under different environmental conditions, it interacts with various environments, resulting in varied genotype performances [1]. Maize (Zea mays L.) cultures are appreciated worldwide. It has tremendous relevance due to its several uses and applications in areas ranging from animal feed to technological industries. Because maize is grown under different environmental conditions, it interacts with various environments, resulting in varied genotype performances [1]. Bayesian segmented regression models for adaptability and stability analysis hinder the genotype sealing works given that the best-suited genotype for a specific environment may not be best suited for another environment where such interactions take place. Recommendations for the broad adaptability and stability of cultivars become costly [1]

Methods

Results

Conclusion