Heat stress tolerance indices for identification of the heat tolerant wheat genotypes

Heat stress is one of the major challenges in wheat cultivation because it coincides with the flowering period and limits the crop productivity. This study was conducted for evaluation of 50 wheat genotypes to identify the heat stress tolerant genotypes for improvement of stress tolerance. All genotypes were cultivated for two consecutive years (2018-2020) under normal and late sown conditions. The results of the study revealed that the combined analysis of variance indicated significant variations among genotypes for all the studied stress indices. The reduction in mean grain yield of all genotypes under stress condition as compared to non-stress condition, indicating that the heat stress significantly affect the grain yield. The correlation analysis showed that the negative correlation of tolerance index and stress susceptibility percentage index with the grain yield of genotypes under heat stress condition (Ys) and a highly positive correlation of stress tolerance index, mean productivity, geometric mean, harmonic mean and mean relative performance with grain yield (Yp and Ys) under both conditions, helped accurately to identify the desirable genotypes. From the results obtained from principal component, biplot and cluster analysis, it was reported that HD 2967, WH 1249, HI 1617, WH 1202, WH 1021 and WH 1142 are suitable and good yielding genotypes under both conditions. Thus, above genotypes can be used for cultivation at high temperature or as genetic resources for introducing genetic variations in wheat genotypes to improve stress tolerance.

Automated summary

This study evaluated the heat stress tolerance of 50 wheat genotypes to improve stress tolerance. The results showed a negative correlation between tolerance index and yield, and a positive correlation between stress tolerance index, mean productivity, and other indices with yield. Several suitable and high-yielding genotypes for cultivation in high temperature conditions were identified based on principal component, biplot, and cluster analysis.