Adaptability and stability of soybean [Glycine max (L.) Merrill] strains in Central-West Brazil

Abstract

Soybean (Glycine max (L.) Merril) is one of the most important seed legumes in the world due to its high protein and vegetable oil content, being widely used in the food industry and animal feed. However, environmental changes, especially those related to rising global temperatures due to CO2 emissions, are impacting plant productivity, including soybeans. In this context, genetic improvement programs have been fundamental to develop cultivars that are more resistant to abiotic stresses, such as droughts and intense rains. To evaluate the adaptability and stability of soybean genotypes in different environments, statistical methods such as AMMI (Multiplicative Model of Interpretation and Intersection) and BLUP (Best Linear Unbased Prediction) have been widely used. The AMMI model is used to analyze the interaction between genotype and environment, while the BULP considers random genetic effects, providing a more accurate estimate of genetic value. Furthermore, the weighting between stability (WAASBY) (Weighted Average WAASB) index has been used to identify stable, high-performance genotypes, combining stability and yield characteristics. In the research carried out, eleven improved soybean lines were evaluated in five municipalities in the central-western region of Brazil. Data were analyzed using statistical techniques such as linear mixed model, GGE biplot and AMMI and BLUP models. The results indicated that soybean lines showed significant variations in productivity in different environments, highlighting specific genotypes for each location. The productivity prediction analysis showed that the BLUP model was more accurate compared to the AMMI model. Furthermore, the GGE biplot identified the most suitable genotypes for each environment, considering both average performance and stability. Finally, the combination of characteristics from the AMMI and BLUP techniques, using the WAASBY index, made it possible to identify genotypes with high potential for yield and stability. In summary, the results of this research contribute to the development of soybean cultivars more adapted to variable environmental conditions, providing valuable information for genetic improvement programs and agricultural practices in the central-western region of Brazil. The integration of different statistical methods and evaluation indices has been fundamental to improving the selection of genotypes with high performance and stability, contributing to food security and the sustainability of agricultural production.