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Estimation of Irrigation Water Demand on a Regional Scale: Combining Positive Mathematical Programming and Cluster Analysis in Model Calibration
Abstract
Mathematical programming tools are widely used to simulate agriculture water use thanks to their ability to provide a detailed technical and economic representation of farm choices. However, they also require a significant amount of basic information and appropriate methods for the organization of such information. The objective of the paper is to test a methodology for the estimation of irrigation water demand using a combination of Positive Mathematical Programming (PMP) at farm level, and a cluster analysis. The methodology is applied in an area of Northern Italy. The main outcome of our empirical application is the variety and complexity of reactions of different farms. The scenarios considered highlight the potential importance of the effects of price and cost variables, while the changes in the (area-based) tariff system appear less significant. The change in water cost/pricing appears somehow relevant, but does not motivate major changes in present water management policy, at least in the range of scenarios considered.
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