Abstract: Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10 to 38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change.

Abstract: Wheat yields in Europe have shown stagnating trends during the last two decades, partly attributed to climate change. Such developments challenge the needs for increased production, in particular at higher latitudes, to meet increasing global demands and expected productivity reductions at lower latitudes. Climate change projections from three General Circulation Models or GCMs (UKMO-HadGEM1, INM-GM3.0 and CSIRO-Mk3.1) for the A1FI SIZES emission scenario for 2000 to 2100 were downscaled at a northern latitude location (Foulum, Denmark) using LARS-WG5.3. The scenarios accounted for changes in temperature, precipitation and atmospheric CO2 concentration. In addition, three temperature-variability scenarios were included assuming different levels of decreased temperature variability in winter and increased in summer. Crop yield was simulated for the different climate change scenarios by a calibrated version of AFRCWHEAT2 to model several combinations of genotypes (varying in crop growth, development and tolerance to water and nitrogen scarcity) and management (sowing dates and nitrogen fertilization rate). The simulations showed a slight improvement of grain yields (0.3-1.2 Mg ha(-1)) in the medium-term (2030-2050), but not enough to cope with expected increases in demand for food and feed. Optimum management added up to 1.8 Mg ha(-1). Genetic modifications regarding winter wheat crop development exhibit the greatest sensitivity to climate and larger potential for improvement (+3.8 Mg ha(-1)). The results consistently points towards need for cultivars with a longer reproductive phases (2.9-7.5% per 1 degrees C) and lower photoperiod sensitivities. Due to the positive synergies between several genotypic characteristics, multiple-target breeding programmes would be necessary, possibly assisted by model-based assessments of optimal phenotypic characteristics.

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.