Genetic Progress of Seed Yield and Nitrogen Use Efficiency of Brazilian carioca Common Bean Cultivars Using Bayesian Approaches

AMMI-Bayesian perspective in the selection of pre-cultivars of carioca beans in Agreste-Sertão of Pernambuco, Brazil

The productivity of beans is greatly influenced by the different edaphoclimatic conditions in the Agreste-Sertão region, requiring the identification of adapted and stable genotypes to minimize the effects of the interaction between genotypes per environments (GxE). The objective of this work was to analyze the adaptability and stability of carioca bean pre-cultivars in three municipalities in the Agreste-Sertão of Pernambuco using the AMMI model in its Bayesian version BAMMI and compare the results with the frequentist approach. According to the results, the BAMMI analysis showed better predictive capacity, as well as better performance in the study of adaptability and stability. The cultivar BRS Notável stood out in terms of main effect and stability. Adaptability of genotypes to specific locations was also observed, enabling the use of the positive effect of the GxE interaction, which was more evident with the BAMMI model. From this work, the flexibility of BAMMI model to deal with data resulting from multi-environmental experiments can be seen, overcoming limitations of the standard analysis of the AMMI model.

Morpho-agronomic, biochemical and molecular analysis of genetic diversity in the Mesoamerican common bean panel

The common bean (Phaseolus vulgaris L.) is of great importance to the food and nutritional security of many populations, and exploitation of the crop's genetic diversity is essential for the success of breeding programs. Thus, the aim of the present study was to evaluate the genetic diversity of 215 common bean accessions, which included cultivars, obsolete cultivars, improved lines, and landraces using morpho-agronomic and biochemical traits, and amplified fragment length polymorphism markers (AFLP). Genetic parameters, box plots, Pearson's correlation analysis, and Ward's hierarchical clustering were used to analyze the data. The Jaccard similarity coefficient and neighbour-joining clustering method were used for molecular analysis. A wide variability among the accessions was observed for morpho-agronomic and biochemical traits. Selective accuracy (Ac) and broad-sense heritability (h2) values were high to intermediate for all traits, except seed yield. Ward's hierarchical clustering analysis generated six groups. AFLP analysis also revealed significant differences among the accessions. There was no correlation between the differences based on genetic markers and those based on morpho-agronomic and biochemical data, which indicates that both datasets are important for elucidating the differences among accessions. The results of the present study indicate great genetic diversity among the evaluated accessions.