Review of advancements and challenges in delayed irrigation: Enhancing crop water productivity and sustainable crop production
Run Xue, Yue Jiang, Hong Li, Bin Shi
Abstract
Water scarcity is an inherent challenge for terrestrial plants, driving the evolution of diverse crop adaptive mechanisms. These mechanisms regulate water balance, minimize water loss, and sustain turgor pressure to optimize water retention within plant systems. As an innovative water-saving technique, delayed irrigation (DI) optimizes crop growth conditions by adjusting irrigation timing, promoting root development, increasing yield, enhancing crop water productivity (WP c ), reducing agricultural water waste, and demonstrating significant environmental benefits. However, the efficacy of DI is constrained by crop type, climatic conditions, and soil characteristics. This review systematically examines the impacts of DI on root architecture, growth physiology, yield, WP c , soil greenhouse gas emissions, and crop disease management. In the example of winter wheat, appropriate DI reduced soil moisture evapotranspiration by 3.1 %, increased wheat yield by 7.2 %, and improved WP c by 17.1 %, demonstrating the great superiority of DI in agricultural water-saving technology. However, DI is a double-edged sword for both water saving and yield performance, excessive DI may lead to diminishing returns and even negative impacts. Moreover, DI induces partial stomatal closure, redirecting resource allocation from growth to defense mechanisms by imposing mild drought stress, which enhances disease resistance despite slowing growth. Additionally, DI shows promise in mitigating greenhouse gas emissions, reduces CH 4 and CO 2 emissions, thereby reducing global warming potential. Collectively, DI offers economic benefits through cost savings and reduced disease incidence while balancing water conservation, yield, and quality, which is a critical factor for agricultural success and sustainable water management. Future research should focus on identifying crop-specific water thresholds under diverse environmental conditions, developing multi-objective optimization models (water conservation, emission reduction, and yield enhancement), and integrating interdisciplinary technologies (e.g., IoT and deep learning) to enable precise DI implementation in global agricultural systems, fostering coordinated progress in water resource management and climate action.