Genetic gains in tropical maize hybrids across moisture regimes with multi-trait-based index selection

Abstract

Unpredictable weather vagaries in the Asian tropics often increase 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 because of the presence of genotype-by-environment interaction (GEI) and the 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 on 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 characteristics for experimental maize hybrids were studied at two locations. Positive and negative SGs were estimated across moisture 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, were found to be common across all the moisture regimes, indicating their moisture 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 maize hybrids with desired traits, such as grain yield and other yield contributors under specific stress conditions. The MGIDI would be a robust and easy-to-handle multi-trait selection process under various test environments with minimal multicollinearity issues. It was found to be a powerful tool in developing better selection strategies and optimizing the breeding scheme, thus contributing to the development of climate-resilient maize hybrids.

Stability Analysis and Identification of Superior Hybrids in Pearl Millet [Pennisetum glaucum (L.) R. Br.] Using the Multi Trait Stability Index

“…Pearson’s correlation was performed, and a network plot of correlation was developed as per Singamsetti et al [ 22 ] among all traits using mean over environment data ( Table S6 and Figure 4 ). Figure 4 indicates the positive and significant correlation between DF and DM while GY is positively correlated with PH and PL while, it is negatively correlated with Fe and Zn content.…”

“…In this study, all traits included in the MTSI calculation were highly significant for GEI in the joint ANOVA except plant population at harvest and the number of productive tillers per plant [ 22 ]. Pearson’s correlation matrix was used to generate the WAASBY (Weighted Average of Absolute Scores of Stability with Yield) value and retrieve a high-magnitude relationship that was combined as a common factor.…”

Identification of new stable resistant sources and assessing agro-morphological performance of sponge gourd germplasm against Tomato Leaf curl New Delhi Virus incidence

“…Thus, the GYT biplot based on these traits helps in identification of genotypes DSG-29 (G-3), followed by DSG-7 (G-2) and DSGVRL-18 (G-6) as the most stable and ideal to combine a high level of yield along with other important traits like ToLCNDV resistance, earliness, and more number of fruits per plant under disease incidence. MGIDI is an innovative approach, which has proven to be highly effective in numerous breeding programs, paving the way for more efficient and successful selection strategies ( Nelimor et al., 2020 ; Pour-Aboughadareh et al., 2021 ; Nardino et al., 2022 ; Yue et al., 2022a , b ; Dhand and Garg, 2023 ; Memon et al., 2023 ; Singamsetti et al., 2023 ). According to MGIDI index values, genotypes DSG-29 (G-3), DSG-7 (G-2), and DSGVRL-18 (G-6) have higher mean performance and desirable selection gains for yield-attributing traits and resistance to ToLCNDV .…”

Genetic gains in forage sorghum for adaptive traits for non - conventional area through multi-trait-based stability selection methods

“…Several analytical models and techniques, including analysis of variance (ANOVA), regression analysis viz. Finlay and Wilkinson (1963) and Eberhart and Russel (1966) ; non-parametric methods like, principal component analysis (PCA), additive main effects and multiplicative interaction (AMMI), and genotype and genotype plus environment (GGE) bi-plots, were developed in order to understand the unpredictable effects of genotype, environment, and their interaction ( Kendal, 2016 ; Singamsetti et al., 2023 ). In light of India’s complex climate, breeding climate-resistant and region-specific high-yielding hybrids has emerged as a top priority for sorghum breeders.…”