Long-term wheat response to nitrogen in a rainfed Mediterranean environment: Field data and simulation analysis

Appropriate nitrogen management is one of the main challenges of agricultural production and for the environment. The objectives of this study were to evaluate the efficiency of crop N uptake in a long-term wheat crop in a Mediterranean environment of Southern Italy, and to identify optimal N rate for reasonable economic returns and minimum nitrate leaching using SALUS crop simulation model.The study was part of a long-term monoculture wheat system that started in 1991/1992 season, with two levels of nitrogen (0 and 90 kg N ha−1). Simulations of the treatment with no nitrogen (0 N) and 90 kg N ha−1 (90 N) were performed using the SALUS crop model for wheat. The model was tested against measurements of harvested grain yield, final N uptake, soil water content and total soil N. Long-term simulation over 56 years showed that grain yield median value was 3435 kg ha−1 for 0 N and 3876 kg ha−1 for 90 N. Simulation scenarios with different N rates (0, 30, 60, 90, 120, 180 kg N ha−1) showed that yield response was higher for 120 N (3528 kg ha−1), with the 60 and 90 N yields giving the same response, 3010 and 3054 kg ha−1, respectively. The most profitable treatments were 120 N (302 Euro ha−1), followed by the 60 N (220 Euro ha−1). The simulation results showed that nitrate leaching was higher for the N rate of 120 and 180 with a mean annual value of 49 and 81 kg ha−1, respectively. Results suggest that in such environment 60 kg N ha−1 can be the most appropriate as an N fertilization management due to the best trade-off between leaching and economic. Since N fertilization rates are linked to nitrous oxide (N2O) emissions and N leaching, a trade-off between N fertilization rates profit and grain yield should be thought as way to reduce environmental pollution while keeping productivity and profit. The adoption of simulation models to approximate the best N rate for durum wheat in rainfed Mediterranean environment proved to be a useful tool for supporting management decisions through quantifying the temporal variability related to weather uncertainty as it influences on the yield and nutrient dynamics.