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Integrating Drought Stress Signaling and Smart Breeding for Climate-Resilient Crops: Regulatory Mechanisms and
Mingyu Wang1, Yuwei Zhao1, Yaqian Huang1
1Key Laboratory of Biology and Genetic Improvement of Oil Crops, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China.
Abstract:
The escalating frequency and severity of drought events pose significant threats to agricultural productivity and food security. Drought stress not only restricts crop growth and yields but also destabilizes agricultural ecosystems. Over evolutionary timescales, plants have developed intricate adaptive strategies, encompassing drought escape (accelerated phenology), avoidance (water-conserving morphology) and tolerance (cellular protection), which involve complex biological mechanisms spanning molecular signaling, metabolic reprogramming and organ morphological remodeling. To mitigate drought risks, breeding drought-tolerant and water-efficient crops is imperative. Currently, drought resistance breeding is undergoing a paradigm shift, transitioning from traditional phenotypic selection toward genomics-assisted selection, molecular design and artificial intelligence (AI)-driven predictive modeling. This review provides a comprehensive analysis of drought stress response mechanisms in crops, integrating three key dimensions: physiological/biochemical adaptations, hormonal signaling networks and morphological/structural modifications. Furthermore, it critically evaluates recent advances in genetic improvement approaches for drought resistance, such as marker-assisted selection, transgenic technology and gene editing. It also explores the integration of multi-omics data and AI to enhance precision molecular breeding and overcome the inherent trade-off between drought resistance and yield potential. By synthesizing advancements in molecular breeding and smart agriculture, this work provides a roadmap for developing climate-resilient crops optimized through synergistic trait engineering and intelligent environmental sensing.
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