Impacts of fertilization rate in nitrate leaching in agricultural soils: Insights from process-based modelling

Nitrogen leaching in agricultural systems is a major environmental risk resulting from irrigation-fertilization practices. Global losses of fertilized agricultural systems are estimated to be about 30% of the applied nitrogen fertilizer. Nitrate leaching, the most predominant form of loss, results in groundwater contamination impacting the quality of drinking water. In the context of climate change, food, and water security, it is imperative to develop strategies that optimize fertilization application (and irrigation) to mitigate adverse environmental effects of nitrogen losses while maximizing grain production. Developing and testing such strategies remains challenging, partly because soil functions strongly depend on pedoclimatic conditions, soil degradation, and crop type; and all these variables may largely differ even for different regions across a same country and for different years. The ongoing Horizon Europe FARMWISE project aims at addressing this challenge by developing a decision support system based on combining: (1) multi-scale data collection and data fusion, (2) development of optimised fertilization-irrigation practices and sensors for monitoring them, (3) process-based modelling of agricultural systems. In this presentation, we introduce how the agroecosystem modelling is integrated in FARMWISE, and discuss the numerical modelling approach to predict crop yield and nitrate leaching as a function of the applied fertilization rate and for different soil types. As part of this, we present a toy-model example simulating winter wheat for 15 fertilization rates from 0 to 150 kg N/ha/yr and for clay contents of 15%, 25%, and 35%. Preliminary simulations predicted a maximum increase in yield of 74%, 97% and 93% achieved at applications rates of 90, 130 and 110 kg N/ha/yr for 15%, 25%, and 35%, respectively. The modelling framework presented in this work, in combination with European observations of current and new agricultural management practices, has the potential on filling information gaps of data for different pedoclimatic conditions and assisting decision making through providing a tool to predict the efficacy of such practices for different conditions and in the context of climate change.