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A stochastic programming and simulation based analysis of the structure of production on the arable land

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  • Béla Vizvári
  • Zoltán Lakner
  • Zsolt Csizmadia
  • Gergely Kovács

Abstract

This paper is devoted to the analysis of the effectiveness of the use of arable land. This is an issue, which is important for national-level decision makers. The particular calculations are carried out for Hungary, but similar analysis can be made for each country having several parts with different geographical conditions. In general the structure of the use of arable land has been developed in an evolutionary manner in each country. This paper is devoted to the evaluation of the effectiveness of this structure. Some main crops must be included in the analysis such that the land used for their production is a high percentage in the total arable land of the country. From agricultural point of view the question to be answered is whether or not the same level of supply is achievable with high probability on a smaller area. As the agriculture is affected by stochastic factors via the weather, no supply can be guaranteed up to 100 per cent. Thus each production structure provides the required supply only with a certain probability. One inequality corresponding to each crop must be satisfied at the same time with a prescribed probability. The main theoretical difficulty here is that the inequalities are not independent from one another from stochastic point of view as the yields of the crops are highly correlated. The problem is modeled by a chance constrained stochastic programming model such that the stochastic variables are on the left-hand side of the inequalities, while the right-hand sides are constants. Kataoka was the first in 1963 who solved a similar problem with a single inequality in the probabilistic constraint. The mathematical analysis of the present problem is using the results of Kataoka. This problem is solved numerically via discretization. Numerical results for the optimal structure of the production are presented for the case of Hungary. It is shown that a much higher probability, i.e. a more safe supply, can be achieved on a smaller area than what is provided by the current practice. Copyright Springer Science+Business Media, LLC 2011

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  • Béla Vizvári & Zoltán Lakner & Zsolt Csizmadia & Gergely Kovács, 2011. "A stochastic programming and simulation based analysis of the structure of production on the arable land," Annals of Operations Research, Springer, vol. 190(1), pages 325-337, October.
    • Handle: RePEc:spr:annopr:v:190:y:2011:i:1:p:325-337:10.1007/s10479-009-0635-z
    DOI: 10.1007/s10479-009-0635-z
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    References listed on IDEAS

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    1. Ulrich Hampicke & Dieter Roth, 2000. "Costs of land use for conservation in Central Europe and future agricultural policy," International Journal of Agricultural Resources, Governance and Ecology, Inderscience Enterprises Ltd, vol. 1(1), pages 95-108.
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    4. Csaba Fábián & Zoltán Szőke, 2007. "Solving two-stage stochastic programming problems with level decomposition," Computational Management Science, Springer, vol. 4(4), pages 313-353, October.
    5. Gene M. Grossman & Alan B. Krueger, 1995. "Economic Growth and the Environment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(2), pages 353-377.
    6. Richard Tol, 2002. "Estimates of the Damage Costs of Climate Change. Part 1: Benchmark Estimates," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 21(1), pages 47-73, January.
    7. Ian R. Gordon & Paul C. Cheshire, 1998. "original: Territorial competition: Some lessons for policy," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 32(3), pages 321-346.
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    Cited by:

    1. Béla Vizvári & Zoltán Lakner, 2014. "A stochastic programming based analysis of the field use in a farm," Annals of Operations Research, Springer, vol. 219(1), pages 231-242, August.
    2. Sheng-I Chen & Wei-Fu Chen, 2021. "The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes under Uncertain Demand, Yields and Prices," Sustainability, MDPI, vol. 13(17), pages 1-17, August.
    3. Borodin, Valeria & Bourtembourg, Jean & Hnaien, Faicel & Labadie, Nacima, 2016. "Handling uncertainty in agricultural supply chain management: A state of the art," European Journal of Operational Research, Elsevier, vol. 254(2), pages 348-359.

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