Stability and adaptability of elite upland rice lines using Bayesian-AMMI model

Abstract

Rice is one of the world’s most important crops. The search for genotypes that are more productive and have wide adaptation to different environments is paramount. One of the major breeder’s obstacles faced is identification of superior strains is the presence of Genotype × Environment Interaction (GEI), which motivated the development of countless statistical procedures aiming to offer more efficient studies. In this work we analysed adaptability and stability of 13 upland rice lineages as part of a genetic improvement program in nine different environments, resulting from local combination and years of agriculture. The experiment was conducted in a completely randomized block design, with three replicates. The main variable is the grain storage in kg/ha. The model applied is the Bayesian Main Additive Effects and Multiplicative Interaction (Bayesian-AMMI). Our implementation implies an extra assumption of random effects from genotypes coming from a single population as opposed to previous works in the literature. Credibility regions with maximum posteriori density allowed identification of cultivars with higher average yield. Stable genotypes showed an initial evidence of adaptation to an environment in this rice breeding program. Bayesian-AMMI is flexible, and starts to be more widely used, but our suggestion is promising in making it a more powerful tool.