Abstract: During the past few years, several studies have compared the performance of crop simulation models to assess the uncertainties in model-based climate change impact assessments and other modelling studies. Many of these studies have concentrated on cereal crops, while fewer model comparisons have been conducted for grasses. We compared the predictions for timothy grass (Phleum pratertse L.) yields for first and second cuts along with the dynamics of above-ground biomass for the grass simulation models BASGRA and CATIMO, and the soil -crop model STICS. The models were calibrated and evaluated using field data from seven sites across Northern Europe and Canada with different climates, soil conditions and management practices. Altogether the models were compared using data on timothy grass from 33 combinations of sites, cultivars and management regimes. Model performances with two calibration approaches, cultivar-specific and generic calibrations, were compared. All the models studied estimated the dynamics of above-ground biomass and the leaf area index satisfactorily, but tended to underestimate the first cut yield. Cultivar-specific calibration resulted in more accurate first cut yield predictions than the generic calibration achieving root mean square errors approximately one third lower for the cultivar-specific calibration. For the second cut, the difference between the calibration methods was small. The results indicate that detailed soil process descriptions improved the overall model performance and the model responses to management, such as nitrogen applications. The results also suggest that taking the genetic variability into account between cultivars of timothy grass also improves the yield estimates. Calibrations using both spring and summer growth data simultaneously revealed that processes determining the growth in these two periods require further attention in model development.

Abstract: Highlights • A framework to study climate and policy uncertainty in agriculture is presented. • Combining both sources of uncertainty has ambiguous effects on agriculture. • Uncertainty needs to be highlighted in modelling tools for policy analysis. Abstract Farmers are exposed to climate change and uncertainty about how that change will develop. As farm incomes, in Norway and elsewhere, greatly depend on government subsidies, the risk of a policy change constitutes an additional uncertainty source. Hence, climate and policy uncertainty could substantially impact agricultural production and farm income. However, these sources of uncertainty have, so far, rarely been combined in food production analyses. The aim of this study was to determine the effects of a combination of policy and climate uncertainty on agricultural production, land use, and social welfare in Norway. Output yield distributions of spring wheat and timothy, a major forage grass, from simulations with the weather-driven crop models, CSM-CERES-Wheat and, LINGRA, were processed in the a stochastic version Jordmod, a price-endogenous spatial economic sector model of the Norwegian agriculture. To account for potential effects of climate uncertainty within a given future greenhouse gas emission scenario on farm profitability, effects on conditions that represented the projected climate for 2050 under the emission scenario A1B from the 4th assessment report of the Intergovernmental Panel on Climate Change and four Global Climate Models (GCM) was investigated. The uncertainty about the level of payment rates at the time farmers make their management decisions was handled by varying the distribution of payment rates applied in the Jordmod model. These changes were based on the change in the overall level of agricultural support in the past. Three uncertainty scenarios were developed and tested: one with climate change uncertainty, another with payment rate uncertainty, and a third where both types of uncertainty were combined. The three scenarios were compared with results from a deterministic scenario where crop yields and payment rates were constant. Climate change resulted in on average 9% lower cereal production, unchanged grass production and more volatile crop yield as well as 4% higher farm incomes on average compared to the deterministic scenario. The scenario with a combination of climate change and policy uncertainty increased the mean farm income more than a scenario with only one source of uncertainty. On the other hand, land use and farm labour were negatively affected under these conditions compared to the deterministic case. Highlighting the potential influence of climate change and policy uncertainty on the performance of the farm sector our results underline the potential error in neglecting either of these two uncertainties in studies of agricultural production, land use and welfare.

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.