Unravelling Yield and Yield-Related Traits in Soybean Using GGE Biplot and Path Analysis
Tonny Obua | Julius Pyton Sserumaga | Phinehas Tukamuhabwa | Mercy Namara | Bruno Awio | Johnson Mugarra | Geoffrey Tusiime | Godfree Chigeza
Soybean (<i>Glycine max</i>) is a vital crop for food, animal feed, and industrial products. However, its yield performance is significantly affected by genotype-by-environment interaction (GEI), which complicates the selection of high-yielding, stable varieties. This study aimed to evaluate the yield performance and stability of 12 elite soybean varieties across five major production areas in Uganda using GGE biplot and path analysis. The varieties were planted in a randomized complete block design with three replications over two consecutive seasons. Results revealed significant differences in grain yield among the varieties, locations, and their interactions (<i>p</i> < 0.001). The highest-yielding varieties were Maksoy 5N (979 kg ha<sup>−1</sup>), Maksoy 4N (978 kg ha<sup>−1</sup>), Maksoy 3N (930 kg ha<sup>−1</sup>), and Signal (930 kg ha<sup>−1</sup>). GGE biplot analysis grouped the locations into two mega-environments, with the Maksoy varieties exhibiting greater yield stability compared to Seed Co. varieties. Path analysis showed that traits such as the number of lower internodes, central internode length, and filled pods had the highest positive direct effects on grain yield. This study provides insights into soybean breeding in tropical environments, highlighting traits that can be targeted to improve yield and stability. The findings offer a framework for breeding programs in Uganda and similar agro-ecological regions, promoting more resilient and productive soybean varieties. This study also illustrated the potential advantages of employing more complex mathematical techniques like path analysis to uncover yield and yield-related traits in soybean breeding programs.
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