Irrigation technology choices under hydrologic uncertainty: A case study from Maipo River Basin, Chile

The effect of hydrologic uncertainty on field irrigation technology (FIT) choices is studied within this paper. A two‐stage stochastic programming model (SPM) is developed to incorporate hydrologic uncertainties related to both water requirements and water availability through a probabilistic scenario‐based approach. The SPM can provide a mechanism with which model solutions can be adjusted to account for variations in water availability and water requirements. Moreover, the two‐stage decision process is combined with risk aversion analysis in field irrigation choices, which helps balance the risk of profit loss in dry years and the risk of excess capital cost in wet years. The model can be used to generate decision information to individual farmers or farmer associations, regarding long‐term field irrigation technology choices and crop pattern planning and short‐term water allocation among crops under specific hydrologic scenarios. The model is applied to an irrigation district in the Maipo River Basin, Chile. It is found that higher levels of FIT are more profitable for higher‐valued crops and decisions on FIT choices for low‐valued crops are less sensitive to hydrologic uncertainties. The annualized capital cost per hectare in the district should be within in range of US$105–130, which is a range for tradeoff analysis between maximizing the expected profit and minimizing the risk of profit loss under drought conditions.

[1]  Janet M. Carey,et al.  A Model of Investment Under Uncertainty: Modern Irrigation Technology and Emerging Markets in Water , 2002 .

[2]  Ximing Cai,et al.  Holistic water resources-economic modeling , 2001 .

[3]  Ximing Cai,et al.  Integrated economichydrologic water modeling at the basin scale: the Maipo river basin , 2000 .

[4]  J. A. Frizzone,et al.  Non-uniformity conventional sprinkler irrigation effects on bean yield. , 2000 .

[5]  Leon S. Lasdon,et al.  A scenario-based stochastic programming model for water supplies from the highland lakes , 2000 .

[6]  Guohe Huang,et al.  AN INEXACT TWO-STAGE STOCHASTIC PROGRAMMING MODEL FOR WATER RESOURCES MANAGEMENT UNDER UNCERTAINTY , 2000 .

[7]  Albert J. Clemmens,et al.  Irrigation Performance Measures: Efficiency and Uniformity , 1997 .

[8]  Jay R. Lund,et al.  Shortage Management Modeling for Urban Water Supply Systems , 1997 .

[9]  A. Dinar,et al.  Modeling Economic Management and Policy Issues of Water in Irrigated Agriculture , 1996 .

[10]  Jay R. Lund,et al.  Derived Estimation of Willingness to Pay to Avoid Probabilistic Shortage , 1995 .

[11]  Timothy K. Gates,et al.  Stochastic Design of Hydraulic Structures in Irrigation Canal Networks , 1993 .

[12]  T. Howell,et al.  Management of Farm Irrigation Systems , 1990 .

[13]  D. Brooks,et al.  Determinants of Irrigation Technology Choice , 1990 .

[14]  Muluneh Yitayew,et al.  Preference ranking of alternative irrigation technologies via a multicriterion decision-making procedure , 1990 .

[15]  Timothy K. Gates,et al.  Stochastic Approximation Applied to Optimal Irrigation and Drainage Planning , 1989 .

[16]  David Kendrick,et al.  GAMS, a user's guide , 1988, SGNM.

[17]  G. Cannell,et al.  Surface Irrigation: Theory and Practice , 1988 .

[18]  D. Zilberman,et al.  The Choices of Irrigation Technologies in California , 1985 .

[19]  K. H. Solomon,et al.  Yield related interpretations of irrigation uniformity and efficiency measures , 1984, Irrigation Science.

[20]  Eli Feinerman,et al.  Optimum Crop Water Application as Affected by Uniformity of Water Infiltration1 , 1984 .

[21]  David Zilberman,et al.  Adoption of Agricultural Innovations in Developing Countries: A Survey , 1985, Economic Development and Cultural Change.

[22]  Marvin E. Jensen,et al.  Design and Operation of Farm Irrigation Systems , 1980 .

[23]  L. Lasdon,et al.  Integrated Hydrologic-Agronomic-Economic Model for River Basin Management , 2003 .

[24]  David W. Watkins,et al.  Finding Robust Solutions to Water Resources Problems , 1997 .

[25]  D. Sunding,et al.  Land Allocation, Soil Quality, and the Demand for Irrigation Technology , 1997 .

[26]  D. F. Heermann,et al.  Distribution Functions to Represent Center-pivot Water Distribution , 1992 .

[27]  I. Carruthers.  Economic and social perspectives on new irrigation technology. , 1990 .

[28]  A. Molenaar,et al.  Irrigation by sprinkling. , 1960 .