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

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.

Adaptability and stability of Coffea canephora to dynamic environments using the Bayesian approach.

The objective of this work was to use the Bayesian approach, modeling the interaction of coffee genotypes with the environment, using a bisegmented regression to identify stable and adapted genotypes. A group of 43 promising genotypes of Coffea canephora was chosen. The genotypes were arranged in a randomized block design with three replications of seven plants each. The experimental plot was harvested four years in the study period, according to the maturation cycle of each genotype. The proposed Bayesian methodology was implemented in the free program R using rstanarm and coda packages. It was possible to use previous information on coffee genotypes as prior information on parameter distributions of an Adaptability and Stability model, which allowed obtaining shorter credibility intervals and good evidence of low bias in the model by the determination coefficient. After fine adjustments in the approach, it was possible to make inferences about the significant GxE interaction and to discriminate the coffee genotypes regarding production, adaptability, and stability. This is still a new approach for perennials, and since it allows more accurate estimates it can be advantageous when planning breeding programs. The Z21 genotype is recommended to compose part of selected genetic material for highly technical farmers, as it responds very well to the favorable environment, being one of the most productive and with excellent stability.