A spatially explicit scenario-driven model of adaptive capacity to global change in Europe

Traditional impact models combine exposure in the form of scenarios and sensitivity in the form of parameters, providing potential impacts of global change as model outputs. However, adaptive capacity is rarely addressed in these models. This paper presents the first spatially explicit scenario-driven model of adaptive capacity, which can be combined with impact models to support quantitative vulnerability assessment. The adaptive capacity model is based on twelve socio-economic indicators, each of which is projected into the future using four global environmental change scenarios, and then aggregated into an adaptive capacity index in a stepwise approach using fuzzy set theory. The adaptive capacity model provides insight into broad patterns of adaptive capacity across Europe, the relative importance of the various determinants of adaptive capacity, and how adaptive capacity changes over time under different social and economic assumptions. As such it provides a context for the implementation of specific adaptation measures. This could improve integrated assessment models and could be extended to other regions. However, there is a clear need for a better theoretical understanding of the adaptive capacity concept, and its relationship to the actual implementation of adaptation measures. This requires more empirical research and coordinated meta-analyses across regions and economic sectors, and the development of bottom-up modelling techniques that can incorporate human decision making.     

Analysis of farm performance in Europe under different climatic and management conditions to improve understanding of adaptive capacity

The aim of this paper is to improve understanding of the adaptive capacity of European agriculture to climate change. Extensive data on farm characteristics of individual farms from the Farm Accountancy Data Network (FADN) have been combined with climatic and socio-economic data to analyze the influence of climate and management on crop yields and income and to identify factors that determine adaptive capacity. A multilevel analysis was performed to account for regional differences in the studied relationships. Our results suggest that socio-economic conditions and farm characteristics should be considered when analyzing effects of climate conditions on farm yields and income. Next to climate, input intensity, economic size and the type of land use were identified as important factors influencing spatial variability in crop yields and income. Generally, crop yields and income are increasing with farm size and farm intensity. However, effects differed among crops and high crop yields were not always related to high incomes, suggesting that impacts of climate and management differ by impact variable. As farm characteristics influence climate impacts on crop yields and income, they are good indicators of adaptive capacity at farm level and should be considered in impact assessment models. Different farm types with different management strategies will adapt differently.