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Contractualisation et diffusion spatiale des mesures agro-environnementales herbagères

Listed author(s):
  • Gilles Allaire


    (Observatoire des programmes communautaires de développement rural, INRA Toulouse, chemin de Borde-Rouge, BP 52627, 31326 Auzeville)

  • Eric Cahuzac

    (Observatoire des programmes communautaires de développement rural, INRA Toulouse)

  • Michel Simioni

    (Toulouse School of Economics, GREMAQ-INRA, Toulouse)

Cet article propose une analyse de la contractualisation des mesures agro-environnementales herbagères par les éleveurs et de la diffusion géographique du processus de contractualisation. Les données individuelles mobilisées sont géo-référencées et concernent l'ensemble des éleveurs de bovins ou ovins/caprins français bénéficiant d'au moins une mesure PAC du premier pilier et ayant contracté ou non une telle mesure. Une base de données territorialisées de contexte (Observatoire des programmes communautaires de développement rural) est utilisée pour compléter l'information. Dans une première étape, les résultats provenant de l'estimation de modèles probit sur ces données individuelles montrent que l'introduction de variables de contexte en complément des caractéristiques individuelles permet d'obtenir une bonne prédiction des taux de contractualisation à un niveau géographique fin (infra-cantonal). Dans une deuxième étape, la localisation des individus est prise en compte au travers d'un effet de voisinage dans un modèle probit spatial. Les résultats montrent que les effets de proximité spatiale dus à la diversité géographique des systèmes de production et aux réseaux sociaux s'expriment essentiellement au niveau de la commune et de micro-zones (infra-cantonales).

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Article provided by INRA Department of Economics in its journal Review of Agricultural and Environmental Studies.

Volume (Year): 90 (2009)
Issue (Month): 1 ()
Pages: 23-50

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Handle: RePEc:rae:jourae:v:90:y:2009:i:1:p:23-50
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  1. Edi Defrancesco & Paola Gatto & Ford Runge & Samuele Trestini, 2008. "Factors Affecting Farmers' Participation in Agri-environmental Measures: A Northern Italian Perspective," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(1), pages 114-131, February.
  2. N. Turpin & P. Dupraz & C. Thenail & A. Joannon & J. Baudry & S. Herviou & P. Verburg, 2009. "Shaping the landscape: agricultural policies and local biodiversity schemes," Post-Print hal-00453894, HAL.
  3. Pinkse, Joris & Slade, Margaret E., 1998. "Contracting in space: An application of spatial statistics to discrete-choice models," Journal of Econometrics, Elsevier, vol. 85(1), pages 125-154, July.
  4. Holloway, Garth & Shankar, Bhavani & Rahman, Sanzidur, 2002. "Bayesian spatial probit estimation: a primer and an application to HYV rice adoption," Agricultural Economics, Blackwell, vol. 27(3), pages 383-402, November.
  5. Lapar, Ma. Lucila A. & Holloway, Garth J. & Ehui, Simeon K., 2003. "How Big Is Your Neighborhood? Spatial Implications Of Market Participation By Smallholder Livestock Producers," 2003 Annual Meeting, August 16-22, 2003, Durban, South Africa 25860, International Association of Agricultural Economists.
  6. Anselin, Luc, 2007. "Spatial econometrics in RSUE: Retrospect and prospect," Regional Science and Urban Economics, Elsevier, vol. 37(4), pages 450-456, July.
  7. Murdoch, James C. & Sandler, Todd & Vijverberg, Wim P. M., 2003. "The participation decision versus the level of participation in an environmental treaty: a spatial probit analysis," Journal of Public Economics, Elsevier, vol. 87(2), pages 337-362, February.
  8. Case, Anne, 1992. "Neighborhood influence and technological change," Regional Science and Urban Economics, Elsevier, vol. 22(3), pages 491-508, September.
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