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A multi-model ensemble-based system for seasonal-to-interannual prediction has been developed in a joint European project known as DEMETER (Development of a European Multimodel Ensemble Prediction System for Seasonal to Interannual Prediction). The DEMETER system comprises seven global atmosphere–ocean coupled models, each running from an ensemble of initial conditions. Comprehensive hindcast evaluation demonstrates the enhanced reliability and skill of the multimodel ensemble over a more conventional single-model ensemble approach. In addition, innovative examples of the application of seasonal ensemble forecasts in malaria and crop yield prediction are discussed. The strategy followed in DEMETER deals with important problems such as communication across disciplines, downscaling of climate simulations, and use of probabilistic forecast information in the applications sector, illustrating the economic value of seasonal-to-interannual prediction for society as a whole. The DEMETER project has been funded by the European Union under the Contract EVK2-1999-00024.

Accounting for more than half of the European Union's (EU) territory, agriculture ensures food production, manages important natural resources and supports socio-economic development of rural areas. Moreover, it is estimated that 50% of all plant and animal species (including some of that are listed in the EU Habitat Directive) depend on agricultural practices. The continuation of appropriate agricultural land management is essential to ensure these primary functions. Avoidance of farmland abandonment is therefore an important rationale for the EU's Common Agricultural Policy which requires improved knowledge of this phenomenon at the European level. This study assesses the risk of farmland abandonment in the 27 EU Member States. It summarizes the work performed by an expert panel of European scientists and national representatives which aimed to identify the main drivers of farmland abandonment in Europe, to define indicators for assessing its risk of occurrence and to test the value of European-wide data sources to achieve these aims. Drivers were identified under two rationales: low farm stability and viability, and negative regional context. Indicators were defined using recent socio-economic farm data and geospatial datasets. Some indicators were then combined to make a composite risk indicator. Regions with higher risk of farmland abandonment are located in Portugal, Spain, Italy, Greece, Latvia, Estonia, Finland, Sweden and Ireland. This paper demonstrates the challenges of performing a European-wide assessment of a phenomenon influenced by drivers whose effects vary at local levels. Other problems encountered are data heterogeneity in terms of spatial resolution and quality, as well as access to micro-data (local level data). High spatial resolution European datasets measuring farmland abandonment are needed to validate the defined indicators as well as to benchmark the methodology. Furthermore, such data could be used to establish a weighting system for the drivers.