Predicting Agri-food Quality across Space: A Machine Learning Model for the Acknowledgment of Geographical Indications

Geographical Indications (GIs), as Protected Designation of Origin (PDO) and Protected Geographical Indication (PGI), offer a unique protection scheme to preserve high-quality agri-food productions and support rural development, and they have been recognised as a powerful tool to enhance sustainable development and ecological economic transactions at the territorial level. However, not all the areas with traditional agri-food products are acknowledge with a GI. Examining the Italian wine sector by a geo-referenced and a machine learning framework, we show that municipalities which obtain a GI within the following 10 years (2002-2011) can be predicted using a large set of (lagged) municipality-level data (1981-2001). We find that the Random Forest algorithm is the best model to make out-of-sample predictions of municipalities which obtain GIs. Among the features used, the local wine growing tradition, proximity to capital cities, local employment and education rates emerge as crucial in the prediction of GI certifications. This evidence can support policy makers and stakeholders to target rural development policies and investment allocation, and it offers strong policy implications for the future reforms of this quality scheme.