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.

Bayesian ridge regression shows the best fit for SSR markers in Psidium guajava among Bayesian models.

Markers are an important tool in plant breeding, which can improve conventional phenotypic breeding, generating more accurate information outcoming better decision making. This study aimed to apply and compare the fit of different Bayesian models BRR, BayesA, BayesB, BayesB (setting the value from very low to [Formula: see text] = [Formula: see text]), BayesC and Bayesian Lasso (LASSO) for predictions of the genomic genetic values of productivity and quality traits of a guava population. The models were fitted for traits fruit mass, pulp mass, soluble solids content, fruit number, and production per plant in the genomic prediction with SSR markers, obtained through the CTAB extraction method with 200 primers. The Bayesian ridge regression model showed the best results for all traits and was chosen to predict the individual's genomic values according to the cross-validation data. A good stabilization of the Markov and Monte Carlo chains was observed with the mean values close to the observed phenotypic means. Heritabilities showed good predictive accuracy. The model showed strong correlations between some traits, allowing indirect selection.

Impact of Bayesian Inference on the Selection of Psidium guajava.

Perennial breeding species demand substantial investment in various resources, mainly the required time to obtain adult and productive plants. Estimating several genetic parameters in these species, in a more confidence way, means saving resources when selecting a new genotype. A model using the Bayesian approach was compared with the frequentist methodology for selecting superior genotypes. A population of 17 families of full-siblings of guava tree was evaluated, and the yield, fruit mass, and pulp mass were measured. The Bayesian methodology suggest more accurate estimates of variance components, as well as better results to fit of model in a cross-validation. Proper priori for Bayesian model is very important to convergency of chains, mainly for small datasets. Even with poor priori, Bayesian was better than frequentist approach.