Abstract: Future crop yields in northern Europe are subject to many factors and uncertainties, according to recent agro-ecological studies. Based on our farm level analysis, we concluded that prices of agricultural products are the primary drivers in the adaptation to climate change. They, as well as the policy conditions, affect the level of fertilization and the use of other inputs, land use and the intensity and the volume of agricultural production. We outlined 5 main scenarios of agricultural adaptation in Finland, and used an agricultural sector model to assess the impacts of the 5 scenarios on total production and land use in the whole country and in its four main regions. In the scenarios with unchanged product prices in the real terms, we find that a small increase or decrease in crop yields is possible. Significantly higher yields would require also 20-30% higher prices of crop products. Our sector modeling results suggest that avoiding decreases in crop yields is important for agricultural income in the long-term, even if livestock production in also maintained by national subsidies. Decreasing yields will result in increasing nutrient surplus and most likely in increased nutrient leaching, while increasing crop yields, even slightly, would significantly decrease nutrient surplus and increase farm income. Significant increases in crop yields and prices, however, are required before production clearly increases in Finland. Interestingly, cereals production would increase relatively more than livestock production, in the case of high future prices. This is explained by the abundant land resources, as well as the high opportunity cost of labor and policy systems maintaining current livestock production. No Label

Abstract: The effects of aggregating soil input data on modelling crop yields at regional scale have been explored within the MACSUR- Crop M – WP3 scaling exercise for an ensemble of crop models 1. The models were run for the North Rhine-Westphalia region in Germany with an average climate time-series (30 years) and soil data at resolution 1 km to 100 km. Aggregation effects showed substantial differences between the models 1. This could be linked to differences in model structure and concepts and to different procedures for the parameterization of soil properties. A further analysis of the sensitivity of the outputs to key soil properties, for each ‘model – method of parameterization’, could help in understanding differences observed within the model ensemble. In this study, we explored the relationship between winter wheat yields, water and N-stress indexes and simple key-soil properties, based on the COUP-model 2 simulations. Soils were grouped into classes according to selected parameters (i.e. soil depth, soil texture and soil organic content). Preliminary results show that some of those soil classes are clearly associated with high water and / or N-stress and lower yields or with high inter-annual variation of the yield. As such they represent key factors explaining the spatial pattern of the simulated yield at the different resolutions. In addition we identified differences in the fractional area of those soil classes between high and low spatial resolutions (‘inherent errors’ due to data aggregation). How this may influence soil data aggregation effects on simulated yields will be further analyzed. No Label

Abstract: Adaptation can reduce climate change risks to crop production and is best analyzed at local scales considering regional specificities. Uncertainty inherent in modelling adaptation options is due to climate projections, downscaling and imperfections of crop models. The challenge of making effective adaptation decisions requires powerful approaches for exploiting the potential of genotype by environment by management interactions, and for generating projections informed with uncertainty.Here we present a methodology that constructs impact response surfaces (IRSs) from an ensemble of crop models and applies these to explore the adaptation potential of rainfed winter wheat at Lleida (NE Spain) in a water-limited environment. The simulation experiment includes: 1) a systematic sensitivity analysis to changes to baseline temperature and precipitation (1981-2010) through a delta change approach that accounts for seasonal differences, 2) three levels of CO2 representing present-day and future conditions until 2050 (A1B scenario), and 3) soil profiles representative for the variable conditions around Lleida. The adaptation simulations represent adjusted management practices about sowing, supplementary irrigation, and the thermal and vernalisation requirements of cultivars used.A pre-selection of the adaptation options was done iteratively, in ranges supported by literature review of crop adaptation in the Mediterranean (e.g. shifts from current sowing date between -30 and +45 days). This procedure allowed to identify a limited number of effective and feasible adaptations to be evaluated combining IRSs and probabilistic projections of climate change. No Label