Abstract: We adapted a large area crop model, MCWLA-Wheat, to winter wheat Triticum aestivum L. and spring wheat in Finland. We then applied Bayesian probability inversion and a Markov Chain Monte Carlo technique to analyze uncertainties in parameter estimations and to optimize parameters. Finally, a super-ensemble-based probabilistic projection system was updated and applied to project the effects of climate change on wheat productivity and water use in Finland. The system used 6 climate scenarios and 20 sets of crop model parameters. We projected spatiotemporal changes of wheat productivity and water use due to climate change/variability during 2021-2040, 2041-2070, and 2071-2100. The results indicate that with a high probability wheat yields will increase substantially in Finland under the tested climate change scenarios, and spring wheat can benefit more from climate change than winter wheat. Nevertheless, in some areas of southern Finland, wheat production will face increasing risk of high temperature and drought, which can offset the benefits of climate change on wheat yield, resulting in an increase in yield variability and about 30% probability of yield decrease for spring wheat. Compared with spring wheat, the development, photosynthesis, and consequently yield will be much less enhanced for winter wheat, which, together with the risk of extreme weather, will result in an up to 56% probability of yield decrease in eastern parts of Finland. Our study explicitly para meterized the effects of extreme temperature and drought stress on wheat yields, and accounted for a wide range of wheat cultivars with contrasting phenological characteristics and thermal requirements.

Abstract: Integrated land use models (ILM) featuring agronomic and economic drivers of land use are frequently applied to serve the high information demand of stakeholders. This presentation results from collaboration among bio-economic farm modelers across the MACSUR regional pilot studies (www.macsur.eu) and shall compare and finally reveal good practice examples on the representation of climate change adaptation in bio-economic farm models. First results show a considerable diversity of approaches employed in the MACSUR regional pilot studies. All are programming models that optimize more or less elaborated forms of utility. All consider or plan to consider crop yield impacts from bio-physical crop models based on daily-resolution climate data. While some models include pest and diseases or livestock impacts, none take climate change impacts on market prices or interactions among farms into account so far. Clearly, adaptation options determine the solution space and are mainly expert-based in the regional case studies. Overall, the models are normative and analyze economically rational and optimal land use and management at the farm level, capable of showing the likely direction of differences in future management as a response to exogenous parameter changes (prices, yields, disease pressure, changed policy conditions, etc.). Such detailed models and their results may be applied in stakeholder interaction. Integrating the different direct and indirect effects of climate change, including the policy dimension, is the main contribution of farm level modelling of agricultural systems in the domain of climate change adaptation research. No Label