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: Crop models, used to make projections of climate change impacts, differ greatly in structural detail. Complexity of model structure has generic effects on uncertainty and error propagation in climate change impact assessments. We applied Bayesian calibration to three distinctly different empirical and mechanistic wheat models to assess how differences in the extent of process understanding in models affects uncertainties in projected impact. Predictive power of the models was tested via both accuracy (bias) and precision (or tightness of grouping) of yield projections for extrapolated weather conditions. Yields predicted by the mechanistic model were generally more accurate than the empirical models for extrapolated conditions. This trend does not hold for all extrapolations; mechanistic and empirical models responded differently due to their sensitivities to distinct weather features. However, higher accuracy comes at the cost of precision of the mechanistic model to embrace all observations within given boundaries. The approaches showed complementarity in sensitivity to weather variables and in accuracy for different extrapolation domains. Their differences in model precision and accuracy make them suitable for generic model ensembles for near-term agricultural impact assessments of climate change.

Abstract: The models developed for simulating olive tree and grapevine yields were reviewed by focussing on the major limitations of these models for their application in a changing climate. Empirical models, which exploit the statistical relationship between climate and yield, and process based models, where crop behaviour is defined by a range of relationships describing the main plant processes, were considered. The results highlighted that the application of empirical models to future climatic conditions (i.e. future climate scenarios) is unreliable since important statistical approaches and predictors are still lacking. While process-based models have the potential for application in climate-change impact assessments, our analysis demonstrated how the simulation of many processes affected by warmer and CO2-enriched conditions may give rise to important biases. Conversely, some crop model improvements could be applied at this stage since specific sub-models accounting for the effect of elevated temperatures and CO2 concentration were already developed. (C) 2014 Elsevier Ltd. All rights reserved.