GGE Biplot and REML/BLUP based-analysis of yield stability and adaptability for common beans in multi-environment trials

Abstract

Prior to releasing new varieties, one of the most common aims in breeding is to study genotype by environmental interactions. Using the analysis obtained from these initial processes, a multitude of varieties based on their relative performance through different environments is produced. Herein we assessed the link-up between GGE Biplot and REML/BLUP basedmethods in a commercial panel of 32 bean genotypes, which included 17 carioca bean genotypes and 15 black bean genotypes. The assessment was conducted during 2009 in four different locations of Pernambuco State, Brazil. Regarding the locations, São João and Araripina showed to be highly representative and non-discriminatory, while Arcoverde was the most discriminatory and well-represented favoring the selection of widely adapted genotypes. The selection of genotypes was consistent by using both methods and the final conclusion shows outstanding performance. Considering a high yield stability and adaptability, the following genotypes showed outstanding performance: the black types included CNFP (11995, 11983, 11985 and 11991), and the carioca types are CNFC (11951 and 11954). Understanding genotype by environmental interactions enabled us to identify potential genotypes for releasing.

Adaptability and stability of early carioca beans by mixed models

“…In this context, genotypes with higher yield in an environment tend to exhibit distinct performances in several environments, in addition to unpredictability and low adaptation to environmental variations. Consequently, low values of genotypic correlation were obtained in the different environments, which caused changes in the order of the genotypes selected (SOUZA et al, 2018;SANTOS et al, 2019).…”

“…This finding implies that the simultaneous selection criterion based on adaptability, stability, and yield is effective in selecting the best genotypes according to their genotypic value, with the penalization of unstable genetic material and capitalization of the adaptability of the most stable ones (ROSADO et al, 2012;SANTOS et al, 2017;SANTOS et al, 2019).…”

“…The recommendation of the most stable and best-adapted genotypes to the different environments under analysis becomes inherent to the capitalization of the interaction. Among the methods evaluated, this was the one that least penalizes the predicted genotypic values since it capitalizes on the effects of the genotype x environment interaction of each environment (SANTOS et al, 2019;SILVA et al, 2013).…”

“…It also generates results in the unit itself or the scale of the trait evaluated, which may be directly interpreted as breeding values that are not allowed by other methods. This model also allows additional inferences, such as the selection of specific genotypes for each environment; selection of stable genotypes (high predictability) in different environments and years; selection of genotypes (high adaptability) responsive (high adaptability) to the improvement of the environment; and simultaneous selection for yield, stability, and adaptability (CARVALHO et al, 2016;GONÇALVES et al, 2014;SANTOS et al, 2017;SANTOS et al, 2018;SANTOS et al, 2019;SOUZA et al, 2018).…”

Simultaneous selection of peach rootstocks by mixed models

“…Several studies have applied the HMRPGV methodology in annual crops, such as corn (Mendes et al, 2012), rice (Colombari et al, 2013), beans (Santos et al, 2019) and soybean (Freiria et al, 2018). However, studies focusing on perennial crops such as peach trees, more specifically to evaluate the rootstock, are unpublished and need more development.…”