Abstract: Data quality is a key factor in determining the quality of model estimates and hence a models’ overall utility. Good models run with poor quality explanatory variables and parameters will produce meaningless estimates. Many models are now well developed and have been shown to perform well where and when good quality data is available. Hence a major limitation now to further use of models in new locations and applications is likely to be the availability of good quality data. Improvements in the quality of data may be seen as the starting point of further model improvement, in that better data itself will lead to more accurate model estimates (i.e. through better calibration), and it will facilitate reduction of model residual error by enabling refinements to model equations. This report sets out why data quality is important as well as the basis for additional investment in improving data quality. No Label

Abstract: Climate change poses fundamental challenges on agriculture. It triggers autonomous adaptation responses of famers and thereby impacts the success of climate change mitigation. Integrated modelling frameworks (IMF) on land use serve as decision support instruments under such conditions by considering climate signals and accounting for combined mitigation and adaptation policies. We apply an IMF at the farm level in two contrasting grassland and cropland dominated landscapes in Austria to analyze climate change impacts on land use as well as impacts from mitigation and adaptation policies on the abiotic and biotic environment and the landscape. Results show that the impacts on farm gross margins and the abiotic and biotic environment are substantial either directly from climate change (e.g. changing erosion levels) or triggered via adaptation responses (i.e. land use and management change). Average gross margins increase between 1% and 12% depending on the case study landscape, the climate change scenario, and the policy scenario. With respect to biodiversity indicators, land use changes in the adaptation scenario decrease plant species diversity on farmland by 13% on average and losses are up to 80% for some farms. These changes are driven by policies in the adaptation scenario as responses on climate change in the absence of policies are modest with minor impacts on biodiversity. Results indicate the effectiveness of climate change adaptation in increasing farm incomes and the need to coordinate mitigation and adaptation policies to manage environmental outcomes. The IMF turns out to be effective in revealing heterogeneity of climate change impacts among farms and regions and linkages among adaptation and mitigation policies. No Label

Abstract: ConclusionsIncreasing productivity can increase intensification pressuresThreatened permanent (extensive) grasslands and landscape elements, butsubject to resource constraints, costs and prices andfuture production potential to increase global food supplyFuture RDP and environmental policy design (e.g. WFD) should take changing productivity into accountHeterogeneity matters at farm and regional levelChanging relative competitiveness of farmsFuture research: analyze uncertainties No Label