Spectral reflectance indices and multivariate analyses to identify stable durum wheat genotypes across different environments

Abstract

Durum wheat is a critical crop in the Arab region, yet it faces severe challenges, primarily water scarcity and high temperatures, which adversely affect yield. Sustainable farming requires the development of adapted varieties that combine climate resilience with high productivity. To achieve this, the present study evaluated six durum wheat genotypes (GENs) across six different environments (ENVs) in Saudi Arabia [comprising control (optimal), drought stress, and heat stress conditions over two seasons, totaling six ENVs]. We used multivariate analysis techniques [SRA (stepwise regression analysis), PCA (principal component analysis), AMMI (additive main-effects and multiplicative interaction), stability value (ASV), WAASB (weighted average of absolute scores), and biplots] for the reliable and effective disclosure of the magnitude of the variance among GENs, GENs: ENVs with different ENVs. Joint ANOVA revealed that GEN, ENV, and (GEN: ENV) significantly contributed to GY variability and five non-destructive SRIs (spectral reflectance indices). AMMI analysis detected pronounced crossover GENs: ENVs, highlighting the capacity for adaptation for GENs. The combined biplots (PCA, AMMI, WAASB, and WAASB-GY) revealed that GEN (K9.3) was the best performer and stable with six different ENVs. Furthermore, the prediction accuracy for K9.3 exceeded 98.00%. Consequently, K9.3 represents a promising genetic resource for enhancing productivity and stability in wheat breeding programs under abiotic stress conditions. Using these high-accuracy predictive models in an integrated manner provides robust support for plant breeders in improving recommendation precision.