Methodological framework for integration of multidisciplinary data into coffee agroforestry systems: Example of the CAFNET project

Globally coffee is cultivated on 10 billion hectares in 80 countries. The coffee industry generates livelihoods for about 125 million people worldwide. The environmental impacts of these coffee plantations are large, yet coffee is seen as an eco-friendly crop as it is generally grown under shade. To better understand the benefits of coffee agroforestry systems (CAFS), an integrated approach is required. To this end, stakeholders of coffee, researchers, local authorities and government agencies are involved in a participatory research and development project called CAFNET (Connecting, enhancing and sustaining environmental services and market values of coffee agroforestry). In India, coffee is estimated to cover around 350 000 hectares with a total annual production of 288 000 MT. About 85 percent of the area lies in the Western Ghats, one the hotspots of biological diversity. As part of the CAFNET project, researchers from different disciplines have come together to work on CAFS in Kodagu district, India. As a result of the magnitude and multidisciplinary nature of the research involved, one of the most important tasks was to understand and integrate data collected by researchers with backgrounds in ecology, economics, policy, agronomy, biophysics, social sciences and geo-informatics. In order to seamlessly integrate the different data, a landscape approach for collection of data was followed, as envisaged by the CAFNET framework. The idea was to come up with broadly applicable scientific outputs including ecosystem services that will benefit the stakeholders of CAFS. We present the framework used to integrate data collected from various disciplines. These data drawn from plot level were up-scaled to the landscape level using approaches drawn from geographical information systems and database management. Companion modelling was used to build scenarios that can be proposed to stakeholders to help them in their decision-making processes. (Texte intégral)