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Visualizing regional clusters of Sardinia's EU supported agriculture: A Spatial Fuzzy Partitioning Around Medoids

Author

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  • D’Urso, Pierpaolo
  • Manca, Germana
  • Waters, Nigel
  • Girone, Stefania

Abstract

The recurrent question about the effectiveness of agri-environmental measure (AEM) in Sardinia (Italy) is whether European Union (EU) funds allocate resources to where they are most needed. To answer this question, a spatial approach is suggested, namely an approach that considers geography as a factor in measuring the success of such policy. A geographical approach can be used to pinpoint “hotspots” in order to determine an appropriate distribution of funds. To implement such an approach to the distribution of EU funding, a Spatial Fuzzy Partitioning Around Medoids (SFPAM) analysis is advocated. The contribution of this research is that it combines a temporal dimension within an explicitly spatial approach. It achieves this by using a dataset that includes both geographical and economic factors such as farm sizes, their management, the number of organic farms involved, the agriculture area invested by the AEM and the size of the workforce involved. Its strategy is the identification of medoids which are represented by a specific municipality. This allows the identification of aggregated neighborhoods for the visualization of AEM outcomes based on a fuzzy partitioning method. The results provide useful policy implications to determine where and when financial efforts should be renewed, where to negotiate sustainable development strategies, and how to expand spatially the benefits of financial funding to other agricultural measures, such as technological innovations in agriculture, reforestation programs, marketing strategies, climate change mitigation, and rural development.

Suggested Citation

  • D’Urso, Pierpaolo & Manca, Germana & Waters, Nigel & Girone, Stefania, 2019. "Visualizing regional clusters of Sardinia's EU supported agriculture: A Spatial Fuzzy Partitioning Around Medoids," Land Use Policy, Elsevier, vol. 83(C), pages 571-580.
  • Handle: RePEc:eee:lauspo:v:83:y:2019:i:c:p:571-580
    DOI: 10.1016/j.landusepol.2019.01.030
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    References listed on IDEAS

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    1. de Montis, Andrea & Manca, Germana, 1999. "Economic Forecast and fuzzy spatial analysis: integrated tools for assessing the development tendency of an Italian region," ERSA conference papers ersa99pa184, European Regional Science Association.
    2. Willem Heiser & Patrick Groenen, 1997. "Cluster differences scaling with a within-clusters loss component and a fuzzy successive approximation strategy to avoid local minima," Psychometrika, Springer;The Psychometric Society, vol. 62(1), pages 63-83, March.
    3. Gordon, A. D., 1996. "A survey of constrained classification," Computational Statistics & Data Analysis, Elsevier, vol. 21(1), pages 17-29, January.
    4. Germana Manca, 2015. "Modeling European agri-environmental measure of spatial impact in the region of Sardinia, Italy, through fuzzy clustering means," Economia agro-alimentare, FrancoAngeli Editore, vol. 17(1), pages 13-27.
    5. Lobianco, Antonello & Esposti, Roberto, 2010. "The Regional Multi-Agent Simulator (RegMAS): an open-source spatially explicit model to assess the impact of agricultural policies," MPRA Paper 25817, University Library of Munich, Germany.
    6. Renato Coppi & Pierpaolo D’Urso & Paolo Giordani, 2010. "A Fuzzy Clustering Model for Multivariate Spatial Time Series," Journal of Classification, Springer;The Classification Society, vol. 27(1), pages 54-88, March.
    7. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2015. "Trimmed fuzzy clustering for interval-valued data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(1), pages 21-40, March.
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