Geographical Indications (GIs) offer a unique protection scheme to preserve high-quality agri-food productions and support sustainable rural development 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 database and a machine learning framework, this paper shows 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). Results point out that local wine growing tradition, participation and education rates as well as the engagement in other GI quality schemes (food and spirits) are determinant in the prediction of GI certifications. This evidence can support policy makers and stakeholders to target rural development policies and investment allocation, offering strong highlights for the future reforms of GIs quality scheme.

Resce, G., Vaquero-Pineiro, C. (2022). Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications.

Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications

Resce Giuliano;Vaquero-Pineiro Cristina
2022

Abstract

Geographical Indications (GIs) offer a unique protection scheme to preserve high-quality agri-food productions and support sustainable rural development 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 database and a machine learning framework, this paper shows 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). Results point out that local wine growing tradition, participation and education rates as well as the engagement in other GI quality schemes (food and spirits) are determinant in the prediction of GI certifications. This evidence can support policy makers and stakeholders to target rural development policies and investment allocation, offering strong highlights for the future reforms of GIs quality scheme.
Resce, G., Vaquero-Pineiro, C. (2022). Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/403053
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