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Analysing impacts of changing price variability with estimated farm risk-programming models

Author

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  • Jansson, Torbjörn
  • Heckelei, Thomas
  • Gocht, Alexander
  • Basnet, Shyam Kumar
  • Zhang, Yinan
  • Neuenfeldt, Sebastian

Abstract

We formulate and estimate a farm level simulation model of agricultural crop production, and apply it to a scenario with increasing yield variability. The objective function is of the mean-variance utility type with a positive mathematical programming (PMP) cost function, and it is estimated using the optimality conditions and a large panel data set obtained from the FADN. Special attention is given to the problem of separating the effect of the covariance matrix from that of the quadratic PMP terms. The model is applied in a partial analysis of impacts of climate change in Germany by exogenously changing yield patterns.

Suggested Citation

  • Jansson, Torbjörn & Heckelei, Thomas & Gocht, Alexander & Basnet, Shyam Kumar & Zhang, Yinan & Neuenfeldt, Sebastian, 2014. "Analysing impacts of changing price variability with estimated farm risk-programming models," 2014 International Congress, August 26-29, 2014, Ljubljana, Slovenia 182665, European Association of Agricultural Economists.
    • Handle: RePEc:ags:eaae14:182665
    DOI: 10.22004/ag.econ.182665
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    References listed on IDEAS

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    1. Gocht, Alexander & Britz, Wolfgang, 2011. "EU-wide farm type supply models in CAPRI--How to consistently disaggregate sector models into farm type models," Journal of Policy Modeling, Elsevier, vol. 33(1), pages 146-167, January.
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    4. Christopher Gilbert & Wyn Morgan, 2010. "Has food price volatility risen?," Department of Economics Working Papers 1002, Department of Economics, University of Trento, Italia.
    5. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 30(1), pages 27-50, March.
    6. Heckelei, Thomas & Britz, Wolfgang & Zhang, Yinan, . "Positive Mathematical Programming Approaches – Recent Developments in Literature and Applied Modelling," Bio-based and Applied Economics Journal, Italian Association of Agricultural and Applied Economics (AIEAA), vol. 1(01), pages 1-16.
    7. Severini, Simone & Cortignani, Raffaele, "undated". "Modeling farmer participation to a revenue insurance scheme by means of Positive Mathematical Programming," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 116001, European Association of Agricultural Economists.
    8. Petsakos, Athanasios & Rozakis, Stelios, 2011. "Integrating risk and uncertainty in PMP models," 2011 International Congress, August 30-September 2, 2011, Zurich, Switzerland 114762, European Association of Agricultural Economists.
    9. Richard E. Howitt, 1995. "Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 77(2), pages 329-342.
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    2. Carpentier, Alain & Gohin, Alexandre & Sckokai, Paolo & Thomas, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 131-165, March.
    3. Kamel Elouhichi & Maria Espinosa Goded & Pavel Ciaian & Angel Perni Llorente & Bouda Vosough Ahmadi & Liesbeth Colen & Sergio Gomez Y Paloma, 2018. "The EU-Wide Individual Farm Model for Common Agricultural Policy Analysis (IFM-CAP v.1): Economic Impacts of CAP Greening," JRC Research Reports JRC108693, Joint Research Centre.

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