The goal of this work was to estimate stability and adaptability parameters using a Bayesian approach to Eberhart and Russel's method and to assess the efficiency of using an a priori distribution. The information from assessing the popping expansion and grain yield of 16 popcorn genotypes was used in randomized block experiments implemented in five environments in the North and Northeast regions of the State of Rio de Janeiro, Brazil. The Bayesian methodology was implemented using the free software package R with the MCMCregress function of the MCMCpack package. Eberhart and Russel's method using a Bayesian technique was found to be efficient in recommending cultivars to more or less favorable environments. The incorporation of a priori information provided greater accuracy in estimating the stability and adaptability parameters. In the comparison of a priori distributions, the BayesFactor function indicated the informative a priori as the most effective for obtaining reliable estimates.
The green bean (Phaseolus vulgaris L.) is a nutrient-rich vegetable much appreciated; although, little studied, in Brazil. The aim of the current study was to investigate the nature of traits of interest, as well as to select plants for the green bean breeding program based on genotype vs. trait biplot analysis. The experiment followed a randomized block design, with 4 repetitions and 17 genotypes. Analysis of variance, principal component analysis and biplot charts were performed to analyze the number of pods per plant, the number of seeds per pod, the number of seeds per plant, seed weight per plant, 100-seed weight, as well as grain and pod yields. The analysis of variance showed genetic variability between genotypes. Grain yield, pod yield and seed weight per plant were highly correlated. The number of seeds per pod was negatively correlated with pod weight, grain weight and with seed weight per plant. Lines Feltrin and UENF 14-30-3 were indicated to increase gains in variables such as grain yield and pod yield.
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