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: The purpose of this report is to propose a general procedure for sensitivity analysis when used to evaluate system sensitivity to climate change, including uncertainty information. While sensitivity analysis has been largely used to evaluate how uncertainties in inputs or parameters propagate through the model and manifest themselves in uncertainties in model outputs, there is much less experience with sensitivity analysis as a tool for studying how sensitive a system is to changes in inputs. This report should help make clear the differences between these two uses of sensitivity analysis, and provide guidance as to the procedure for using sensitivity analysis for evaluating system sensitivity to climate change. No Label

Abstract: Rather than on crop modelling only, climate change impact assessments in agriculture  need to be based on integrated assessment and farming systems analysis, and account for  adaptation at different levels. With a case study for Flevoland, the Netherlands, we  illustrate that 1) crop models cannot account for all relevant climate change impacts and  adaptation options, and 2) changes in technology, policy and prices have had and are likely  to have larger impacts on farms than climate change. While crop modelling indicates  positive impacts of climate change on yields of major crops in 2050, a semi-quantitative  and participatory method assessing impacts of extreme events shows that there are  nevertheless several climate risks. A range of adaptation measures are, however, available  to reduce possible negative effects at crop level. In addition, at farm level farmers can  change cropping patterns, and adjust inputs and outputs. Also farm structural change will  influence impacts and adaptation. While the 5th IPCC report is more negative regarding  impacts of climate change on agriculture compared to the previous report, also for  temperate regions, our results show that when putting climate change in context of other  drivers, and when explicitly accounting for adaptation at crop and farm level, impacts may  be less negative in some regions and opportunities are revealed. These results refer to a  temperate region, but an integrated assessment may also change perspectives on climate  change for other parts of the world. No Label