Procedures for identification of superior progenies in successive generations of evaluation in common bean

Abstract

When breeding the common bean in Brazil, the best progenies are chosen, normally, from solely the generation under analysis at the conclusion of the evaluation, without considering what occurred in the past. However, a number of recently published studies show that if an evaluation were to consider all relevant generations, the gain from selection could be higher, especially when an index that involves information from the population that gave rise to the progenies is used. Thus, the aim of this study was to compare three selection procedures in the evaluation of successive generations and to discuss the implications of the progeny × environment interaction in terms of success of selection. Cycle XV progenies from a bean recurrent selection program were used. The traits evaluated were grain yield, plant architecture and grain type. Analysis of variance was carried out and the variance components and heritabilities were estimated. The same analyses were made using mixed models. A selection index weighted by the effect of populations and progenies within populations (WSI) was also obtained. We estimated the correlations between the classification of the progenies using the three procedures and the coincidence of the best progenies evaluated in S 0:4 with the progenies in the previous generations. We found that the classification of the progenies by the BLUP's and WSI did not expressively differ from that obtained when using only the mean, even when a number of generations were considered in the selection. None of the procedures used effectively mitigated the effect of the progeny × environment interaction.

A half century of a bean breeding program in the South and Alto Paranaíba regions of Minas Gerais

“…The S 0:2 progenies selected were evaluated once more for their grain yield and type in three locations. The 20 best S 0:3 progenies were recombined as described in Lemos et al (2020), and the S 0 population for the first cycle (C-I) of the RS was obtained. The selected progenies and other progenies with superior performance were evaluated up to the S 0:6 or S 0:7 generation.…”

Akaike Information Criterion (AIC) and stepwise regression method to select the areca nut population as across parent material

“…According to [2], the selection index is obtained from the preferences of participatory plant breeding consumers towards the desired character in a commodity. The selection index is carried out in the selection of pea plants to assemble a hybrid [3],. Selection activities for cross-pollinated plant genotypes used the index selection method by involving several characters as the selection criteria, and the genotypes were arranged based on the selection index.…”

“…The use of index selection has the advantage that it produces genotypes with several characters at once that are balanced according to the breeder's goals [4]. The assembly of the M4 mutant maize hybrid has the advantage of using index selection, namely after using index selection, agronomic performance exceeds all hybrids tested [3].…”

Accounting for heterogeneous genotypic variance of progenies within populations in autogamous plant breeding: An evaluation in common bean

“…In the context of early generation testing, this approach aims to identify the superior progenies, especially for quantitative traits such as yield, and after the subsequent selfing generations, to obtain inbred lines from the best homozygous plants within these progenies (Fehr, 1991). This scenario in which inbred progenies from several populations are evaluated also occurs in breeding programs of self‐pollinated crops, for example, common bean ( Phaseolus vulgaris L.; Alves et al., 2015; Batista et al., 2017; Lemos et al., 2020), that use multipopulation recurrent selection (MRS) as a strategy to obtain superior inbred lines.…”