Mulberry Drought Diagnosis: Integrating Proximal Sensing and Metabolomics for Remote Monitoring

Drought is the most severe natural hazard threatening agricultural production. Mulberry (Morus alba L.) is an important crop for the sericulture industry, and its drought tolerance has been extensively studied. In this study, the phenotypic and physiological responses of two different mulberry tree genotypes (711 and NS8) to drought stress were investigated, with the aim of screening potential nondestructive traits and understand interrelationships. The significant reductions of digital biomass (DB), leaf area (LA), and projected leaf area (PLA) in morphological traits indicated that drought led to a decrease in mulberry yield. The change of color traits R_FarRed and R_NIR were associated with pigments and leaf morphology. Vegetation indexes were also significantly affected by drought stress. Due to their had high correlation coefficients and good linear relationships with yield, DB and LA can be used as yield proxy traits for this measure. Drought-sensitive traits were identified using PCA and correlation analysis, and the results showed that greenness (GR) was a proxy predictor of drought stress. For antioxidant defenses, CAT activity and phenolic compound content were significantly decreased. Metabolomics analysis revealed that genotype 711 exhibited 1691 differential metabolites under drought stress; these mainly comprised amino acids, lipids, and phenolic acids, which were mainly enriched in secondary metabolism and flavonoid biosynthesis. Drought also reprogrammed carbohydrate, secondary compounds, and amino acid metabolism. The results revealed that the phenotypic response of two mulberry trees to drought, as well as the integration of phenotypic traits with metabolic traits, could help us to understand drought tolerance mechanisms and benefit efficient selection and breeding of fitter genotypes.