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EU Fruit & Vegetable Regional Vulnerability Index: Assessment & Analysis

This project focused on assessing the vulnerability of EU fruit and vegetable sectors on a regional level by analyzing supply chains and economic factors. A Synthetic Regional Vulnerability Index (SRVI) was developed, incorporating various scores and weightings to rank regions based on factors such as regional wealth indicator, fresh and processed fruit and vegetable sectors, and marketing channels. The methodology included surveys, multi-criteria analysis, forecast scenarios, and result benchmarking. Discussion points covered industry performance, database limitations, heterogeneous sector characteristics, and asymmetries among chain actors. Proposals included linking different work packages and estimating sector impacts by sub-regions. The project aimed to provide insights and strategies to address vulnerabilities in the EU fruit and vegetable industry.

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EU Fruit & Vegetable Regional Vulnerability Index: Assessment & Analysis

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  1. EuMed Agpol Project Annual meeting Montpellier, 17th - 19th, May, 2006 The EU Fruit & Vegetable Synthetic Regional Vulnerability Index (SRVI) Jean-Louis Rastoin, Nassima Ayadi, Jean-Claude Montigaud Agro.Montpellier, Inra, UMR Moisa

  2. Theoretical Background • Vulnerability depends on capabilities level (Sen, 1985) • Capabilities are constituted by actors resources & competencies (RBV, Wernerfelt, 1984) and macro-economic & institutional framework (North, 1990) • To estimate regional vulnerability, we have to characterize the “filière” (supply chain, marketing channels), and its embeddedness (economic & institutional environment)

  3. Methodology • Survey at regional level (EU nuts 2) • Construction of a multi-criteria Index for each region based on “supply to consumer chain” analysis with SSP (Scherer, 1973) and GVC (Gereffi, 1994) models • Identification of forecast scenarios • Benchmarking (ranking) of the Regions sample on a synthetic index basis

  4. The Synthetic Regional Vulnerability Index (SRVI) • Linear function of 4 “scores” IVR = 1/ (FF&V) x  + (PF&V) x  + (MCE) x  + (RWI) x  • , , , , weighting coefficients • The 4 scores (“strategic levels” of SCC) • FF&V : Fresh fruit & vegetable sector • PF&V : Processed Fruit & vegetable • MCE : Marketing channels enterprises • RWI : Regional Wealth Indicator • Weighting coefficient are estimated by expertise and simulations

  5. The SRVI Components

  6. The SRVI Components

  7. The SRVI Components

  8. The SRVI Components

  9. The RVI calculation principle • For each indicator i : Value for region j / Average of all regions • Algebraic sum of i values = score value (FF&V, PF&V, MCE, RWI)

  10. The sample

  11. The 4 scenarios

  12. Main results (scenario 4)

  13. Discussion • Results are globally conform to empiric observations (i.e. F&V SCC is a global value chain driven by retailers) • SRVI reflects industry performances, but also economic environment situation • Limits of databases : RICA, Amadeus representativeness and errors • Strong heterogeneity of F&V sector between regions and products • Asymmetries between chain actors • Corporate governance : localization of firms headquarters (where is the decision-making ?)

  14. Conclusion : Some proposals to link together WPs • Assumptions • - EU Import increase from SEMC (CAPRI & Delphi results) => • - Market loss for EU med countries => • - F&V production decrease (Yd) => • - F&V producers revenue loss • Yd regional estimation (for each EU med country): • Yd for region j = (RVIj / Sum RVIj) x (National import increase or loss in national agricultural income)

  15. Proposal to link up WPs: possible weighting coefficient for loss calculation by region

  16. Proposal to link together WPs • Possibility to estimate impact by sub-sector: • Fresh Fruit (23 EU SEMC regions) • Fresh vegetable (24 regions) • Fresh F&V (34 regions) • Processed F&V (63 regions) (Biggest EU SEMC production regions)

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