Beyond Nitrogen Status: Predicting Crop Response to Additional Nitrogen in Intensive Vegetable Production Systems

Crop response to nitrogen (N) fertilizer varies across space and time due to complex interactions among soil properties, weather, water availability, crop characteristics, and management practices, making the response to additional N difficult to predict. This variability presents a major challenge for N management because measurements of soil or crop N status do not necessarily indicate whether additional N will increase crop yield or quality. In intensive vegetable systems, short production cycles, frequent irrigation or fertigation, relatively shallow rooting in some crops, and high-value crops create narrow margins between insufficient and excessive N supply. The objectives of this critical review were to (i) examine how N supply, transformations and losses, water dynamics, root acquisition, and dynamic crop N demand generate variation in N response within genotype (crop) × environment × management (G × E × M) interactions; (ii) evaluate the capacity and limitations of soil and plant diagnostics, remote sensing, process-based models, and data-driven approaches to predict N response; (iii) identify factors limiting the transferability of N-response predictions; and (iv) define research priorities for predicting the marginal crop response to additional N and its uncertainty. This review identifies interactions among indigenous N supply, N transformations, soil water, root acquisition, and dynamic crop demand as major sources of variation in fertilizer response. Although soil and plant diagnostics, remote sensing, process-based models, and data-driven approaches provide valuable information about N status and crop performance, their capacity to predict the marginal yield or quality response to additional N remains constrained by incomplete process representation and limited counterfactual evidence. Prediction transferability is further limited by variation in G × E × M conditions, highlighting the need for response-generating experiments, improved representation of soil–plant–water interactions, explicit quantification of uncertainty, and independent validation across environments.