Visualizing regional clusters of Sardinia's EU supported agriculture: A Spatial Fuzzy Partitioning Around Medoids
| Autor: | Pierpaolo D'Urso, Germana Manca, Stefania Girone, Nigel Waters |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Agri-environmental measures
Common Agriculture Policy (CAP) Geographical information science (GIS) Regional development Spatial Fuzzy Partitioning Around Medoids (SFPAM) Geography Planning and Development 0211 other engineering and technologies Distribution (economics) 02 engineering and technology 010501 environmental sciences Management Monitoring Policy and Law 01 natural sciences Regional science media_common.cataloged_instance Dimension (data warehouse) European union 0105 earth and related environmental sciences Nature and Landscape Conservation media_common Sustainable development business.industry 021107 urban & regional planning Forestry Medoid Identification (information) Climate change mitigation Agriculture business |
| Zdroj: | Land Use Policy. 83:571-580 |
| ISSN: | 0264-8377 |
| DOI: | 10.1016/j.landusepol.2019.01.030 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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