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What Can Acreage Allocations Say about Crop Supply Elasticities? A Convex Programming Approach to Supply Response Disaggregation

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  • Cloé Garnache
  • Pierre R. Mérel

Abstract

type="main" xml:id="jage12074-abs-0001"> This article has two goals. The first is to extend previous results regarding calibration of land-constrained programming models of agricultural supply against supply elasticities to the general case of multiple constraints. The second goal is to demonstrate how the resulting calibration conditions can be used as a source of identification to disaggregate crop supply elasticities down to the level at which static information on acreage allocations is available. We propose an information-based disaggregation algorithm to systematically generate regionalised elasticities from a single prior, and provide an empirical illustration.

Suggested Citation

  • Cloé Garnache & Pierre R. Mérel, 2015. "What Can Acreage Allocations Say about Crop Supply Elasticities? A Convex Programming Approach to Supply Response Disaggregation," Journal of Agricultural Economics, Wiley Blackwell, vol. 66(1), pages 236-256, February.
    • Handle: RePEc:bla:jageco:v:66:y:2015:i:1:p:236-256
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    File URL: http://hdl.handle.net/10.1111/jage.2014.66.issue-1
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    Citations

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    Cited by:

    1. Wolfgang Britz & Linda Arata, 2019. "Econometric mathematical programming: an application to the estimation of costs and risk preferences at farm level," Agricultural Economics, International Association of Agricultural Economists, vol. 50(2), pages 191-206, March.
    2. Athanasios Petsakos & Stelios Rozakis, 2022. "Models and muddles: comment on ‘Calibration of agricultural risk programming models using positive mathematical programming’," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(3), pages 713-728, July.
    3. Kamel Louhichi & Daël Merisier, 2024. "Potential impacts of the Common Agricultural Policy's Income Stabilisation Tool on farmers' incomes and crop diversity: A French case study," Journal of Agricultural Economics, Wiley Blackwell, vol. 75(2), pages 716-739, June.
    4. Garnache, Cloé & Mérel, Pierre R. & Lee, Juhwan & Six, Johan, 2017. "The social costs of second-best policies: Evidence from agricultural GHG mitigation," Journal of Environmental Economics and Management, Elsevier, vol. 82(C), pages 39-73.
    5. Mack, Gabriele & Ferjani, Ali & Mohring, Anke & Zimmerman, Albert & Mann, Stefan, 2015. "How did farmers act? An ex-post validation of normative and positive mathematical programming for an agent-based sector model," 2015 Conference, August 9-14, 2015, Milan, Italy 212201, International Association of Agricultural Economists.
    6. Hudak, Michael, 2015. "Three studies on applying Positive Mathematical Programming and Bayesian Analysis to model US crop supply," ISU General Staff Papers 201501010800005844, Iowa State University, Department of Economics.
    7. Fabio Santeramo, 2015. "A cursory review of the identification strategies," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 3(1), pages 1-8, December.
    8. Kamel Louhichi & Daël Merisier, 2023. "Potential impacts of the Income Stabilisation Tool on farmers' income and crop diversity: a French case study [Impacts potentiels de l'outil de stabilisation des revenus sur les revenus des agricul," Post-Print hal-04195630, HAL.
    9. Garnache, Cloe & Merel, Pierre R. & Lee, Juhwan & Six, Johan, 2014. "Markets for Agricultural Greenhouse Gas Offsets: The Role of Policy Design on Abatement Efficiency," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170718, Agricultural and Applied Economics Association.

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