Multi-Environment and Multi-Year Bayesian Analysis Approach in Coffee canephora

André Monzoli Covre; Flavia Alves da Silva; Gleison Oliosi; Caio Cezar Guedes Correa; Alexandre Pio Viana; Fabio Luiz Partelli

doi:10.3390/plants11233274

Multi-Environment and Multi-Year Bayesian Analysis Approach in Coffee canephora

Plants (Basel). 2022 Nov 28;11(23):3274. doi: 10.3390/plants11233274.

Authors

André Monzoli Covre¹, Flavia Alves da Silva², Gleison Oliosi¹, Caio Cezar Guedes Correa³, Alexandre Pio Viana², Fabio Luiz Partelli¹

Affiliations

¹ Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil.
² Laboratório de Melhoramento Genético Vegetal, Universidade Estadual do Norte Fluminense Darcy Ribeiro, Avenida Alberto Lamego, 2000, Campos dos Goytacazes 28013-602, RJ, Brazil.
³ Departamento de Agronomia, Universidade Federal do Espírito Santo, Alto Universitário, S/N Guararema, Alegre 29500-000, ES, Brazil.

Abstract

This work aimed to use the Bayesian approach to discriminate 43 genotypes of Coffea canephora cv. Conilon, which were cultivated in two producing regions to identify the most stable and productive genotypes. The experiment was a randomized block design with three replications and seven plants per plot, carried out in the south of Bahia and the north of Espírito Santo, environments with different climatic conditions, and evaluated during four harvests. The proposed Bayesian methodology was implemented in R language, using the MCMCglmm package. This approach made it possible to find great genetic divergence between the materials, and detect significant effects for both genotype, environment, and year, but the hyper-parametrized models (block effect) presented problems of singularity and convergence. It was also possible to detect a few differences between crops within the same environment. With a model with lower residual, it was possible to recommend the most productive genotypes for both environments: LB1, AD1, Peneirão, Z21, and P2.

Keywords: Markov chain; coffee production; cultivar recommendation; informative priors.

Grants and funding

The authors are indebted to the pioneer breeders, the coffee farmers, who made the first steps in the process of selection of most of the superior genotypes available nowadays. Therefore, the traditional names of the clones were maintained as used among coffee growers. We further acknowledge the support of the Federal University of Espírito Santo (UFES), the National Council of Scientific and Technological Development (CNPq), and the Foundation for Research and Innovation Support of Espírito Santo (FAPES). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.