Coulibaly, M., Bodjrenou, G., Fassinou Hotègni, N. V., Akohoue, F., Agossou, C. A., Azon, C. F., Matro, X., Bello, S., Adjé, C. O. A., Sanou, J., Batieno, B. J., Sawadogo, M., & Achigan-Dako, E. G. (2024). Genotype by environment interaction and stability analysis of three agronomic traits in Kersting’s groundnut (Macrotyloma geocarpum) using factor analytic modeling and environmental covariates. Crop Science, 1–21. https://doi.org/10.1002/csc2.21249
Understanding genotype by environment interaction (GEI) represents a challenge in Kersting’s groundnut [Macrotyloma geocarpum (Harms) Maréchal and Baudet] breeding for selecting high-performing and stable lines across environments. Here, we investigated GEI and stability in Kersting’s groundnut using factor analytic (FA) based linear mixed models and environmental covariates. A total of 375 accessions were evaluated across 3 years (2017, 2018, and 2019) and two locations (Sékou and Savè) in Benin, generating five environments (E1, E2, E3, E4, and E5). The traits measured included days to 50% flowering (DFF), grain yield (YLD), and 100-seed weight (HSW). The study generated multi-environment values for grain yield and its components in Kersting’s groundnut. The genetic correlations between pairs of envi- ronments ranged from −0.71 to 0.99. The genetic correlations between YLD and HSW indicated positive and moderate to high correlations in all environments. The FA analysis revealed that FA2 structure accounted for 93.9% of the genetic variability in DFF with factor 1 accounting for more than 90% of the environments variations. Two factors explained 87% of the genetic variance in grain yield, and 70% of the environments variability were clustered by factor 1. For HSW, two factors explained 85% of the genetic variance of the environments, and factor 1 accounted for 72.7%. Combining environmental covariates to FA models revealed that precipitation, tem- perature, and growth cycle duration were highly correlated to the environmental loadings of factor 1. Relative humidity and solar radiation showed moderate to high correlations with factor 2 loadings. Those covariates explained the high GEI among environments clustered by a given factor. Precipitations and temperatures affected the variations in grain yield. Finally, based on latent regression analysis, the accessions AF202, AF221, AF223, AF225, and AF256 were identified as accessions combin- ing best performance for grain yield, early flowering, and 100-seed weight, showing adaptability across environments and stability to some environments.