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Process-oriented agro-ecosystem models are increasingly applied to assess crop management options or impacts of climate change on agricultural production, food security and ecosystem services. Thereby, the aggregation of initial soil and climate information is a widely used approach for performing simulations at larger scales such as regions, nations or even globally. In this context, the ability of models to respond to different site conditions is essential for high quality impact assessment through the use of modelling tools. As part of a model inter-comparison the present study investigated models' yield response on variable site conditions using data sets from two well-documented fields, one located in Germany and one in Italy. The fields were sampled at 60 and 100 grid points, respectively, and soil and crop variables were recorded at varying intensity for the entire simulation period covering three growing seasons. The data was provided successively to the participating modelling groups in three calibration steps (a, b, and c) and the first growing season was considered for calibration. Model validation was based on these steps and each growing season as well as on the entire simulation period considering the soil state variables mineral nitrogen and water content (N, WC) as well as crop yield, biomass, and leaf area index (LAI). The WC was best depicted by the models, resulting in high correlation coefficients (r) up to 0.81 between simulated and observed values. The root mean square error (RMSE) of simulated N ranged from 20 kg ha(-1) to 1072 kg ha(-1) regarding all steps and growing seasons. The annual within-field variability of yields was better simulated by the models when observed subsoil information was provided. However, the RMSE ranged from 0.5 t ha(-1) to 3.5 t ha(-1) at the German field, and from 0.6 t ha(-1) to 5.9 t ha(-1) at the Italian field, respectively. It was found that intensified calibration did not necessarily lead to improved model output. Furthermore, single models showed specific inconsistencies in their algorithms when, for example, underestimated WC was associated with overestimated yields. In total, the sensitivity of models to spatially variable site conditions differed considerably. The importance of quality-assured soil and yield information for model improvement was highlighted.

Future climatic changes may have profound impacts on cropping systems and affect the agronomic and environmental sustainability of current N management practices. The objectives of this work were to i) evaluate the ability of the SALUS crop model to reproduce experimental crop yield and soil nitrate dynamics results under different N fertilizer treatments in a farmer’s field, ii) use the SALUS model to estimate the impacts of different N fertilizer treatments on NO3- leaching under future climate scenarios generated by twenty nine different global circulation models, and iii) identify the management system that best minimizes NO3- leaching and maximizes yield under projected future climate conditions. A field experiment (maize-triticale rotation) was conducted in a nitrate vulnerable zone on the west coast of Sardinia, Italy to evaluate N management strategies that include urea fertilization (NMIN), conventional fertilization with dairy slurry and urea (CONV), and no fertilization (N0). An ensemble of 29 global circulation models (GCM) was used to simulate different climate scenarios for two Representative Circulation Pathways (RCP6.0 and RCP8.5) and evaluate potential nitrate leaching and biomass production in this region over the next 50 years. Data collected from two growing seasons showed that the SALUS model adequately simulated both nitrate leaching and crop yield, with a relative error that ranged between 0.4% and 13%. Nitrate losses under RCP8.5 were lower than under RCP6.0 only for NMIN. Accordingly, levels of plant N uptake, N use efficiency and biomass production were higher under RCP8.5 than RCP6.0. Simulations under both RCP scenarios indicated that the NMIN treatment demonstrated both the highest biomass production and NO3- losses. The newly proposed best management practice (BMP), developed from crop N uptake data, was identified as the optimal N fertilizer management practice since it minimized NO3- leaching and maximized biomass production over the long term.

Estimation of nitrate leaching from agricultural systems is critical to environment impact studies. Simulation models can help to assess and understand the dynamics of nitrates in the soil and in relation to fertilizer types and agronomical management. The main hypothesis of this study was that the long term annual application of mineral and organic N fertilizers, in irrigated sandy soil areas increases dramatically the quantity of NO 3 − in the soil for potential nitrate leaching. The main objectives of this research were then to quantify inorganic N concentration in soil layers affected by mineral and organic N (manure and slurry) fertilizers; and to test the SALUS crop simulation model to identify the best N management strategy able to minimize NO 3 − leaching without compromising crop growth and yields. A first set of rotational simulations were carried out for the 2007 and 2008 experiments, with the goal of comparing measured and simulated results. A second set of rotational simulations were carried out for a long term assessment (50 years) of the two treatments in comparisons. Additional long term rotational simulation runs were carried out considering two different fertilization strategies based on the maize crop uptake in 2007 (276 kg N ha −1 ) and on the PUA (“Best Nitrogen Management with manures”) which defines the fertilization practices adopted in the nitrate vulnerable zones (no more than 170 kg N ha −1 year −1 ). Measured data showed that in the manure treatment, the highest nitrate-N concentration was 627.3 mg kg −1 at 0.10 m depth on August 2008, while the lowest was 2.50 mg kg −1 at 1.40 m depth on July 2007. In the slurry treatment, the maximum nitrate N concentration was 344.6 mg kg −1 at 0.10 m depth on August 2008 and the minimum was 8.9 mg kg −1 at 0.60 cm depth on September 2007. The model was able to closely reproduce the measured results. The simulation results of the inorganic soil N were 1228 kg ha −1 and 1370 kg ha −1 for the manure and slurry treatment respectively versus the measured soil inorganic N content at the beginning of the experiment of 1267 kg ha −1 for the manure treatment and 1323 kg ha −1 for the slurry treatment. The simulated cumulative amount of nitrate leaching was 9251 kg ha −1 for the manure treatment and 11,101 kg ha −1 for the slurry treatment for the 50 years simulations. The simulations results also showed that when the inorganic N was used alone, the amount of leaching and soil N were significantly reduced.