In order to assess agricultural adaptation to climate impacts, new methodologies are needed. The translog distance function allows assessing interactions between different factors, and hence the influence of management on climate impacts. The Farm Accountancy Data Network provides extensive data on farm characteristics of farms throughout the EU15 (i.e. the 15 member states of the European Union before the extension in 2004). These data on farm inputs and outputs from 1990¿2003 are coupled with climate data. As climate change is not the only change affecting European agriculture, we also include effects of subsidies and other changes on inputs and outputs of farms throughout Europe. We distinguish several regions and empirically assess (1) climate impacts on farm inputs and outputs in different regions and (2) interactions between inputs and other factors that contribute to the adaptation to these impacts. Changes in production can partly be related to climatic variability and change, but also subsidies and other developments (e.g. technology, markets) are important. Results show that impacts differ per region, and that `actual impacts¿ cannot be explicitly separated into `potential impacts¿ and `adaptive capacity¿ as often proposed for vulnerability assessment. Farmers adapt their practices to prevailing conditions and continuously adapt to changing conditions. Therefore, `potential impacts¿ will not be observed in practice, leaving it as a mainly theoretical concept. Factors that contribute to the adaptation also differ per region. In some regions more fertilizers or more irrigation can mitigate impacts, while in other regions this amplifies impacts. To project impacts of future climate change on agriculture, current farm management strategies and their influence on current production should be considered. This clearly asks for improved integration of biophysical and economic models.
|Journal||Mitigation and Adaptation Strategies for Global Change|
|Publication status||Published - 2009|
- land-use change
- adaptive capacity
- technical efficiency
- ricardian analysis
- future scenarios