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A dynamic and stochastic analysis of fertilizer use in swidden agriculture

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  • Amitrajeet Batabyal

    (Department of Economics, Rochester Institute of Technology)

  • Gregory DeAngelo

    (Department of Economics, Rochester Institute of Technology)

Abstract

The number of times a crop can be harvested on a cleared parcel of forest land (CPFL) before this land must be fallowed is dependent on the decision to use or not to use fertilizers to enhance soil fertility. As such, we first construct a theoretical model of fertilizer use by a swidden cultivator when this cultivator can choose whether or not to enhance soil fertility by using fertilizers. Second, we analyze two different policies (fertilizer use and no fertilizer use) for overseeing the problem of soil fertility deterioration on the CPFL. Finally, we identify a particular likelihood function and we show that whether the problem of soil fertility impairment is best addressed with a fertilizer use policy or with a no fertilizer use policy depends essentially on this likelihood function.

Suggested Citation

  • Amitrajeet Batabyal & Gregory DeAngelo, 2004. "A dynamic and stochastic analysis of fertilizer use in swidden agriculture," Economics Bulletin, AccessEcon, vol. 17(3), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-04q10001
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    1. Coomes, Oliver T. & Grimard, Franque & Burt, Graeme J., 2000. "Tropical forests and shifting cultivation: secondary forest fallow dynamics among traditional farmers of the Peruvian Amazon," Ecological Economics, Elsevier, vol. 32(1), pages 109-124, January.
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    3. Amitrajeet A. Batabyal & Hamid Beladi, 2008. "Swidden Agriculture In Developing Countries," World Scientific Book Chapters, in: Dynamic And Stochastic Approaches To The Environment And Economic Development, chapter 2, pages 39-56, World Scientific Publishing Co. Pte. Ltd..
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    More about this item

    JEL classification:

    • Q1 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty

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