Nearly 50% of the European Union (EU) land area is agricultural. However, the ecosystem services (ESS) provided by these agro-ecosystems – including food, bioenergy, water, carbon storage and biodiversity – are threatened by processes such as land-use intensification and changing climate. European, national and regional policy makers must hence rethink and redesign rural policy to enhance the sustainability of agricultural landscapes while ensuring farmers’ livelihoods at the same time. However, the policy impact assessment models currently used by the European Commission (EC) ignore the complexity of farmers’ decision making, potentially leading to incorrect predictions of policy outcomes. Furthermore, existing models focus on narrow aspects of agricultural economics (e.g. income), ignoring policy impacts on rural natural, social and cultural assets. BESTMAP will develop a new modelling framework using insights from behavioural theory, linking existing economic modelling with individual-farm Agent-Based Models. Using these new modular and customizable tools BESTMAP will quantitatively model, map and monitor co-designed policy scenarios’ impacts on the environment, climate system, delivery of ESS, as well as socio-economic metrics (e.g. jobs). BESTMAP outputs will improve and contribute to existing tools used by the EC such as the Modular Applied GeNeral Equilibrium Tool (MAGNET) and Common Agricultural Policy Regionalised Impact model (CAPRI). Finally, BESTMAP will use a range of external communication and dissemination methods, including online policy dashboard, workshops and training, to help build capacity for EC staff and policy makers at EU institutions, national, regional and local decision makers and expert personnel, as well as other researchers.

The capabilities of the latest generation of Earth observation satellites to collect large volumes of diverse and thematically rich data are unprecedented. For exploiting these valuable data sets, many research and industry groups have started to shift their processing into the cloud. Although the functionalities of existing cloud computing solutions largely overlap, there are all custom-made and tailored to the specific data infrastructures. This lack of standards not only makes it hard for end users and application developers to develop generic front-ends, but also to compare the cloud offerings by running the same analysis against different cloud back-ends. To solve this, a common interface that allows end- and intermediate users to query cloud-based back offices and carry out computations on them in a simple way is needed. The openEO project will design such an interface, implement it as an open source community project, bind it to generic analytics front-ends and evaluate it against a set of relevant Earth observation cloud back offices. The openEO interface will consist of three layers of Application Programming Interfaces, namely a core API for finding, accessing, and processing large datasets, a driver APIs to connect to back offices operated by European and worldwide industry, and client APIs for analysing these datasets using R, Python and JavaScript. To demonstrate the capability of the openEO interface, four use cases based chiefly on Sentinel-1 and Sentinel-2 time series will be implemented. openEO will simplify the use of cloud-based processing engines, allow switching between cloud-based back office providers and comparing them, and enable reproducible, open Earth observation science. Thereby, openEO reduces the entry barriers for the adaptation of cloud computing technologies by a broad user community and paves the way for the federation of infrastructure capabilities.

The detection of infectious disease emergence relies on reporting cases, i.e. indicator-based surveillance (IBS). This method lacks sensitivity, due to non or delayed reporting of cases. In a changing environment due to climate change, animal and human mobility, population growth and urbanization, there is an increased risk of emergence of new and exotic pathogens, which may pass undetected with IBS. Hence, the need to detect signals of disease emergence using informal, multiple sources, i.e. event-based surveillance (EBS). The MOOD project aims at harness the data mining and analytical techniques to the big data originating from multiple sources to improve detection, monitoring, and assessment of emerging diseases in Europe. To this end, MOOD will establish a framework and visualisation platform allowing real-time analysis and interpretation of epidemiological and genetic data in combination with environmental and socio-economic covariates in an integrated inter-sectorial, interdisciplinary, One health approach: 1)Data mining methods for collecting and combining heterogeneous Big data, 2)A network of disease experts to define drivers of disease emergence, 3)Data analysis methods applied to the Big data to model disease emergence and spread, 4)Ready-to-use online platform destined to end users, i.e. national and international human and veterinary public health organizations, tailored to their needs, complimented with capacity building and network of disease experts to facilitate risk assessment of detected signals. MOOD output will be designed and developed with end users to assure their routine use during and beyond MOOD. They will be tested and fine-tuned on air-borne, vector-borne, water-borne model diseases, including anti-microbial resistance. Extensive consultations with end users, studies into the barriers to data sharing, dissemination and training activities and studies on the cost-effectiveness of MOOD output will support future sustainable user uptake