Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection - Singamsetti, Ashok; Zaidi, Pervez H.; Seetharam, Kaliyamoorthy; Vinayan, Madhumal Thayil; Olivoto, Tiago; Mahato, Anima; Madankar, Kartik; Kumar, Munnesh; Shikha, Kumari (Frontiers in Plant Science, 2023)
Unpredictable weather vagaries in the Asian tropics often upturn the risk of a series of abiotic stresses in maize growing areas, hindering the efforts to reach the projected demands. Breeding climate-resilient maize hybrids with a cross-tolerance to drought and waterlogging is necessary yet challenging since the presence of genotype and environment interaction (GEI) and lack of an efficient multi-trait based selection technique. The present study aimed at estimating the variance components, genetic parameters, inter-trait relations and expected selection gains (SGs) across the soil moisture regimes through genotype selection obtained based upon the novel multi-trait genotype-ideotype distance index (MGIDI) for a set of 75 tropical pre-released maize hybrids. Twelve traits including grain yield and other secondary characters for experimental maize hybrids were studied at two locations. Positive and negative SGs were estimated across moiture regimes, including drought, waterlogging and optimal moisture conditions. Hybrid, moisture condition and hybrid-by-moisture condition interaction effects were significant (p≤0.001) for most of the traits studied. Eleven genotypes were selected in each moisture condition through MGIDI by assuming 15% selection intensity where two hybrids viz., ZH161289 and ZH161303 found to be common across all the moisture regimes, indicating their moiture stress resilience, a unique potential for broader adaptation in rainfed stress-vulnerable ecologies. The selected hybrids showed desired genetic gains such as positive gains for grain yield (almost 11% in optimal and drought; 22% in waterlogging) and negative gains in flowering traits. The view on strengths and weaknesses as depicted by the MGIDI assists the breeders to develop the maize hybrids with desired traits, such as grain yield and other yield contributors under specific stress condition. The MGIDI would be a robust and easy-to-handle multi-selection process under varied test environments with minimal multicollinearity issues. It was found to be a powerful tool to develop better selection strategies and optimizing the breeding scheme, thus contributing to the development of climate-resilient maize hybrids.