Multi environment stability analysis of maize (Zea mays) inbreds using AMMI and GGE biplot models

This study investigates the impact of heat stress on the stability and adaptability of maize genotypes across three environments Odisha, Jharkhand and Bihar during 2022-2023. A total of 54 maize inbred lines, along with two check varieties (56 genotypes), were evaluated using an alpha-lattice design with two replications. Data were recorded for 18 quantitative traits and stability analysis was conducted using both Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype plus Genotype × Environment interaction (GGE) biplot models. Pooled ANOVA revealed significant effects for genotypes and environments across all traits, except anthesis-silking interval and brown husk. The mean squares due to genotypes were notably high for grain yield per plant (94058.26), grain weight (23656.84), plant height (1962.3), ear height (691.99), kernel per row (245.06) and chlorophyll content (53.8). Similarly, environments showed significant contributions, with grain weight (11034.5), grain yield per plant (1522), kernel per row (737.15), anthesis-silking interval (603.26), plant height (411.6) and brown husk (350.26) exhibiting substantial variation. The G × E interaction was highly significant for most traits, with grain weight (453.3), grain yield per plant (282.3), brown husk (24.99), plant height (22.76), days to silking (16.91) and days to anthesis (14.36) being particularly important contributors. Partitioning of G × E revealed that IPCA I was significant for traits such as days to 50 % anthesis, days to silking, ears per plant, chlorophyll content, plant aspect, grain yield per plant, cob length, grain weight, kernels per row and kernel rows per cob, while IPCA II was non-significant across traits (values ranging from 0.018 for brown husk to 59.63 for grain weight) and PC3 was zero for all traits. GGE biplot analysis identified genotypes VL1010764, KL155991, KL156009 and KL155979 as high performing under heat stress conditions, whereas VL143892, VL143905, KL155989, KL156003, VL13853, KL155739, VL18333, VL18334, VL143891 and KL153072 demonstrated consistent stability across all environments. These findings provide critical insights into the identification of heat-resilient and stable maize genotypes. The integration of AMMI and GGE biplot models strengthens the precision of selection under heat stress, thereby supporting breeding programs aimed at enhancing climate resilience and ensuring maize productivity in the face of rising global temperatures.