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: We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. the spatial bias (Delta), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the., especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.

Abstract: The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. No Label