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Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions. [ABSTRACT FROM AUTHOR]

The increasing severity of drought has become a significant threat to global crop production. Early season drought in corn produces poor plant stand and grain yield. Thus, identifying corn hybrids for drought tolerance during the early season is important. Nineteen corn hybrids commonly grown in the Midsouthern US were assessed for drought tolerance using mini-hoop structures. Plants grown under non-stress conditions were exposed to three moisture levels at 100% (0.17 m3 m−3 soil; control), 66% (mild drought; DS1), and 33% (moderate drought; DS2) of the control from one to five leaf stages (V1 to V5). The physiological and morphological traits of corn hybrids were measured to assess variability in drought tolerance. When averaged across the hybrids, shoot parameters declined by 51% and 59% under DS1 and DS2 conditions, respectively, compared to the control. A decline in root traits was noticed under drought stress (38% under DS1 and 48% under DS2) compared to the control, revealing the shoot system sensitivity under drought conditions. In the principal component analysis, the first two principal components accounted for 66% of the phenotypic variation among the corn hybrids under drought stress. Total, shoot, leaf dry weights, root surface area, and root volume captured most of the phenotypic variation among the corn hybrids under drought. The results of the principal component analysis and drought stress response indices complimented the identification of 'A6659' and 'D57VP51' as drought-tolerant hybrids during the early seedling stage. These hybrids can be used as source material in developing drought-tolerant cultivars. Also, the tolerant hybrids will perform best under rainfed environments prone to early-season drought. [ABSTRACT FROM AUTHOR]