Synchronizing Water and Nutrient Supply for Crop Production: Physiological Mechanisms, Technological Pathways, and the Case for Context-Specific Management

Synchronizing water and nutrient supply is central to high-yield, resource-efficient agriculture. We define synchronization operationally along three independently assessable dimensions: temporal overlap between supply and the crop demand window, spatial co-location of delivered water and nutrients with the zone of active roots, and matching of the nutrient-release rate to the crop uptake rate. Conventional simultaneous irrigation and fertilization does not necessarily ensure spatial and temporal matching between resource supply and crop demand. This review uses a four-part framework—costs of supply–demand mismatch, root sensing and response, regulatory technologies, and context-dependent variation—to trace the shift from simultaneous application to synchronized supply. Physiologically, drought restricts nutrient uptake via diffusion, mass flow, and active transport, while nutrient deficiency suppresses water-use efficiency through reduced photosynthesis and context-dependent changes in aquaporin regulation, root hydraulics, and hormonal signaling; combined stresses cause context-dependent oxidative and hormonal interactions. Roots integrate architectural plasticity, hydrotropic and nutrient-dependent foraging, hydraulic and chemical signaling, and rhizosphere processes to coordinate resource acquisition. Water–fertilizer technologies are grouped into four complementary technological pathways: fertigation, partial root-zone drying/alternate wetting and drying, sensor- and model-driven real-time control, and controlled-release fertilizer. Their effects diverge with soil physicochemical and hydraulic properties, genotype, seasonal climate, and spatial scale, so no universally optimal scheme is supported by current evidence. Key bottlenecks are weak in situ root–soil observation, lack of coupled multi-process models, and scarce field validation. Future progress requires high-throughput root phenotyping, AI prediction, low-cost sensing, and multi-objective optimization for context-specific management.