Abstract: The identification of site-specific agricultural management practices in order to maximize yield while minimizing environmental nitrogen losses remains in the center of environmental pollution research. Here, we used the biogeochemical model LandscapeDNDC to explore different agricultural practices with regard to their potential to reduce soil N2O emissions and NO3 leaching while maintaining yields. In a first step, the model was tested against observations of N2O emissions, NO3 leaching, soil micrometeorology as well as crop growth for eight European cropland and grassland sites. Across sites, LandscapeDNDC predicts very well mean N2O emissions (r(2)=0.99) and simulates the magnitude and general temporal dynamics of soil inorganic nitrogen pools. For the assessment of site-specific mitigation potentials of environmental nitrogen losses a Monte Carlo optimization technique considering different agricultural management options (i.e., timing of planting, harvest and fertilization, amount of applied fertilizer as well as residue management) was used. The identified optimized field management practices reduce N2O emissions and NO3 leaching from croplands on average by 21% and 31%, respectively. Likewise, average reductions of 55% for N2O emissions and 16% for NO3 leaching are estimated for grasslands. For mitigating environmental loss – while maintaining yield levels – it was most important to reduce fertilizer application rates by in average 10%. Our analyses indicate that yield scaled N2O emissions and NO3 leaching indicate possible improvements of nitrogen use efficiencies in European farming systems. Moreover, the applied optimization approach can be used also in a prognostic way to predict optimal timings and fertilization options (rates and splitting) upon accurate weather forecasts combined with the knowledge of modeled soil nutrient availability and plant nitrogen demand.

Abstract: Process-based models (PBM) for simulation of weather dependent grass growth can assist farmers and plant breeders in addressing the challenges of climate change by simulating alternative roads of adaptation. They can also provide management decision support under current conditions. A drawback of existing grass models is that they do not take into account the effect of winter stresses, limiting their use for full-year simulations in areas where winter survival is a key factor for yield security. Here, we present a novel full-year PBM for grassland named BASGRA. It was developed by combining the LINGRA grassland model (Van Oijen et al., 2005a) with models for cold hardening and soil physical winter processes. We present the model and show how it was parameterized for timothy (Phleum pratense L.), the most important forage grass in Scandinavia and parts of North America and Asia. Uniquely, BASGRA simulates the processes taking place in the sward during the transition from summer to winter, including growth cessation and gradual cold hardening, and functions for simulating plant injury due to low temperatures, snow and ice affecting regrowth in spring. For the calibration, we used detailed data from five different locations in Norway, covering a wide range of agroclimatic regions, day lengths (latitudes from 59 degrees to 70 degrees N) and soil conditions. The total dataset included 11 variables, notably above-ground dry matter, leaf area index, tiller density, content of C reserves, and frost tolerance. All data were used in the calibration. When BASGRA was run with the maximum a-posteriori (MAP) parameter vector from the single, Bayesian calibration, nearly all measured variables were simulated to an overall normalized root mean squared error (NRMSE) <0.5. For many site x experiment combinations, NRMSE was <0.3. The temporal dynamics were captured well for most variables, as evaluated by comparing simulated time courses versus data for the individual sites. The results may suggest that BASGRA is a reasonably robust model, allowing for simulation of growth and several important underlying processes with acceptable accuracy for a range of agroclimatic conditions. However, the robustness of the model needs to be tested further using independent data from a wide range of growing conditions. Finally we show an example of application of the model, comparing overwintering risks in two climatically different sites, and discuss future model applications. Further development work should include improved simulation of the dynamics of C reserves, and validation of winter tiller dynamics against independent data. (C) 2016 Elsevier B.V. All rights reserved.