| Titre : | Analyzing transition to organic farming in Italy through a dynamic mathematical programming model: impacts on agricultural area and budget allocations (2026) |
| Auteurs : | R. Buttinelli ; R. Ercolini ; R. Cortignani |
| Type de document : | Article |
| Dans : | Sustainability (vol. 18, n. 9, May 2026) |
| Article en page(s) : | p. 4581 |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.4 - Production Agricole. Système de ProductionThésaurus IAMM AGRICULTURE BIOLOGIQUE ; SYSTEME DE PRODUCTION ; MODELE MATHEMATIQUE ; MODELE ; ITALIE |
| Résumé : | The European Union aims to achieve the target of 25% of land under organic farming by 2030. Italy reached the share of 18.7% in 2022, although significant regional differences persist. This study analyzes farms' conversion response in the Lazio region (Italy) using a dynamic version of the AGRITALIM agro-economic supply model on a sample of 578 FADN farms. Addressing the limitations of static modeling frameworks that assume full conversion, this study aims to simulate individual farm conversion choices over time, by accounting for conversion and maintenance phases costs and price premiums. This framework tests the hypotheses that a dynamic modeling approach can highlight nuanced responses and that increases in Common Agricultural Policy (CAP) payments are able to increase organic conversion rates. Results show limited effects of increased economic support: the 2023-2027 CAP reform, characterized by higher support, leads to a 5.1% increase in the area under organic farming, while a 40% increase in financial support generates an expansion of 12%. Farm responses are highly heterogeneous: rural provinces, larger and arable farms are more responsive, while smaller farms and livestock are less likely to convert. These findings highlight the need for integrated policy strategies combining financial support, reduced costs, technical assistance, and improved market access. The methodological approach adopted in this study provides a useful tool for supporting the design of targeted and effective policy interventions. |
| Cote : | En ligne |
| URL / DOI : | https://doi.org/10.3390/su18094581 |
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