Canopy spectral reflectance indices correlate with yield traits variability in bread wheat genotypes under drought stress

Abstract

Drought stress is a major issue impacting wheat growth and yield worldwide, and it is getting worse as the world’s climate changes. Thus, selection for drought-adaptive traits and drought-tolerant genotypes are essential components in wheat breeding programs. The goal of this study was to explore how spectral reflectance indices (SRIs) and yield traits in wheat genotypes changed in irrigated and water-limited environments. In two wheat-growing seasons, we evaluated 56 preselected wheat genotypes for SRIs, stay green (SG), canopy temperature depression (CTD), biological yield (BY), grain yield (GY), and yield contributing traits under control and drought stress, and the SRIs and yield traits exhibited higher heritability (H2) across the growing years. Diverse SRIs associated with SG, pigment content, hydration status, and aboveground biomass demonstrated a consistent response to drought and a strong association with GY. Under drought stress, GY had stronger phenotypic correlations with SG, CTD, and yield components than in control conditions. Three primary clusters emerged from the hierarchical cluster analysis, with cluster I (15 genotypes) showing minimal changes in SRIs and yield traits, indicating a relatively higher level of drought tolerance than clusters II (26 genotypes) and III (15 genotypes). The genotypes were appropriately assigned to distinct clusters, and linear discriminant analysis (LDA) demonstrated that the clusters differed significantly. It was found that the top five components explained 73% of the variation in traits in the principal component analysis, and that vegetation and water-based indices, as well as yield traits, were the most important factors in explaining genotypic drought tolerance variation. Based on the current study’s findings, it can be concluded that proximal canopy reflectance sensing could be used to screen wheat genotypes for drought tolerance in water-starved environments.

Keywords

• Multispectral vegetation indices
• Stay green
• Canopy temperature depression
• Phenotyping
• Multivariate analyses