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A Linear Dynamic Programming Approach to Irrigation System Management with Depleting Groundwater

Author

Listed:
  • A. L. Stoecker

    (Department of Agricultural Economics, Texas Tech University, Lubbock, Texas 79409)

  • A. Seidmann

    (School of Engineering, Tel Aviv University, Tel Aviv, Israel)

  • G. S. Lloyd

    (High Plains Federal Land Bank Association, P.O. Box 2639, Pampa, Texas 79066-2639)

Abstract

A model for measuring the economic benefits of irrigation system development over a depleting aquifer is presented, along with related methodology for detailed long-range farm planning. The paper considers management issues, such as distribution system configuration, drilling policy, area developed for irrigation, and crop production. A Linear Dynamic Programming (LDP) method is developed and applied to derive optimal temporal investments in the use of stock resources and long-term cropping plans. First, parametric linear programming (PLP) is used to maximize periodic profits subject to specified values of state variables related to annual water use and irrigation system capacity. The PLP results are then used in a dynamic programming model to determine the optimal allocation of water and irrigation resources over time. The impact of aquifer depletion on the profitability of furrow and pivot irrigation systems is illustrated for a typical farm situation in the Texas High Plains. Results indicate that the economic benefits of modern water and energy efficient irrigation systems may come from the expansion of current irrigation intensity rather than from an extended period of irrigation when water is initially scarce relative to land. Several conditions are identified where economic depletion may occur before the point of physical exhaustion is reached.

Suggested Citation

  • A. L. Stoecker & A. Seidmann & G. S. Lloyd, 1985. "A Linear Dynamic Programming Approach to Irrigation System Management with Depleting Groundwater," Management Science, INFORMS, vol. 31(4), pages 422-434, April.
    • Handle: RePEc:inm:ormnsc:v:31:y:1985:i:4:p:422-434
    DOI: 10.1287/mnsc.31.4.422
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    Cited by:

    1. Timothy J. Lowe & Paul V. Preckel, 2004. "Decision Technologies for Agribusiness Problems: A Brief Review of Selected Literature and a Call for Research," Manufacturing & Service Operations Management, INFORMS, vol. 6(3), pages 201-208.
    2. Miguel Ángel Gutiérrez Andrade & Francisco Venegas Martínez & Hector Manuel Bravo Pérez, 2005. "Política fiscal en el manejo de los recursos hidráulicos: Un modelo de equilibrio general computable," Estudios Económicos, El Colegio de México, Centro de Estudios Económicos, vol. 20(2), pages 219-261.
    3. Kizer, Michael A. & Stoecker, Arthur L. & Carreira, Rita I. & Hattey, Jeffory A., 2004. "Management Strategies To Mitigate Environmental Impact Of Weather Uncertainty In Effluent Application To Irrigated Corn In Semiarid Regions," 2004 Annual meeting, August 1-4, Denver, CO 20110, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    4. Robert, Marion & Thomas, Alban & Bergez, Jacques Eric, 2016. "Processes of adpatation in farm decision-making models. A review," TSE Working Papers 16-731, Toulouse School of Economics (TSE).
    5. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    6. Kumari, M. & Singh, O.P. & Meena, D.C., 2017. "Optimising Cropping Pattern in Eastern Uttar Pradesh Using Sen’s Multi Objective Programming Approach," Agricultural Economics Research Review, Agricultural Economics Research Association (India), vol. 30(2).
    7. Ahumada, Omar & Villalobos, J. Rene, 2009. "Application of planning models in the agri-food supply chain: A review," European Journal of Operational Research, Elsevier, vol. 196(1), pages 1-20, July.

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