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An integrated PMP model to assess the development of agro-energy crops and the effect on water requirements

  • Donati, Michele
  • Bodini, Diego
  • Arfini, Filippo
  • Zezza, Annalisa
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    This paper presents an integrated model for the economic and environmental assessment of the use of natural resources when new activities (i.e. biomass crops for energy production) are introduced into the farm production plan. The methodology is based on the integration of positive mathematical programming (PMP) with the AquaCrop model developed by FAO. PMP represents farmer decision processes and evaluates how farms react to the biomass-sorghum activity option at different price levels. AquaCrop evaluates the relationship between water needs and biomass production and assesses the effect of the land allocation on water requirements at regional level. The integration of these two models assists global policy evaluation at regional level as it makes it possible to identify the economic threshold for biomass crops, the change in land allocation and total water requirement. The model can help policy makers to evaluate the impacts of variations in crop profitability and market innovations on farm profitability, land use and water consumption and the sustainability of the market scenario.

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    File URL: http://purl.umn.edu/162076
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    Article provided by Italian Association of Agricultural and Applied Economics (AIEAA) in its journal Bio-based and Applied Economics Journal.

    Volume (Year): (Bio-Economy)
    Issue (Month): 3 ()
    Pages:

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    Handle: RePEc:ags:aieabj:162076
    Contact details of provider: Web page: http://www.aieaa.org/

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    1. Thomas Heckelei & Hendrik Wolff, 2003. "Estimation of constrained optimisation models for agricultural supply analysis based on generalised maximum entropy," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 30(1), pages 27-50, March.
    2. Quirino Paris & Richard E. Howitt, 1998. "An Analysis of Ill-Posed Production Problems Using Maximum Entropy," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 124-138.
    3. Rosegrant, Mark W. & Ringler, Claudia & McKinney, Daene C. & Cai, Ximing & Keller, Andrew & Donoso, G., 2000. "Integrated economic-hydrologic water modeling at the basin scale: the Maipo river basin," EPTD discussion papers 63, International Food Policy Research Institute (IFPRI).
    4. Ottmar R�hm & Stephan Dabbert, 2003. "Integrating Agri-Environmental Programs into Regional Production Models: An Extension of Positive Mathematical Programming," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 254-265.
    5. Blanco, Maria & Cortignani, Raffaele & Severini, Simone, 2008. "Evaluating Changes in Cropping Patterns due to the 2003 CAP Reform. An Ex-post Analysis of Different PMP Approaches Considering New Activities," 107th Seminar, January 30-February 1, 2008, Sevilla, Spain 6674, European Association of Agricultural Economists.
    6. Cortignani, Raffaele & Severini, Simone, 2009. "Modeling farm-level adoption of deficit irrigation using Positive Mathematical Programming," Agricultural Water Management, Elsevier, vol. 96(12), pages 1785-1791, December.
    7. Heckelei, Thomas & Britz, Wolfgang & Zhang, Yinan, 2012. "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), issue 1, April.
    8. Jan-Erik Petersen, 2008. "Energy production with agricultural biomass: environmental implications and analytical challenges-super- †," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 35(3), pages 385-408, September.
    9. Bartolini, F. & Bazzani, G.M. & Gallerani, V. & Raggi, M. & Viaggi, D., 2007. "The impact of water and agriculture policy scenarios on irrigated farming systems in Italy: An analysis based on farm level multi-attribute linear programming models," Agricultural Systems, Elsevier, vol. 93(1-3), pages 90-114, March.
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