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TR-75

The Effect of Changing Input and Product Prices on the Demand for Irrigation Water in Texas

R. D. Lacewell, G. D. Condra

Agriculture is a major income-producing sector in the Texas economy and a large part of this economic activity originates in irrigated crop production. For example, in 1973, 50% of all grain sorghum and 46% of all cotton in Texas were produced on irrigated acreage [Texas Crop and Livestock Reporting Service]. These two crops alone produced 26% of the cash receipts from the sale of Texas farm commodities in 1973 [Texas Crop and Livestock Reporting Service]. There are several other crops in Texas including vegetables which generate significant levels of income and rely heavily on irrigation. Further there are several associated industries which rely on production from irrigated agriculture, such as the cattle feeding industry in the Texas Panhandle. It is evident from this rather cursory examination of statistics that irrigation plays a large role in Texas agriculture.

Both producers and policy-makers have found themselves faced in the past two years with many uncertainties. The U.S., plagued in the past with surplus production and supply control problems, now finds itself in a world shortage of food products. The long range signals seem to call for increased production, yet the policy-maker faces decisions concerning not only how to increase production, but more basically, how to maintain current levels of production. Groundwater resources in many areas are being diminished and annual irrigation water supplies fully committed in other areas. Long run planning for Texas agriculture requires that interbasin transfers of water be evaluated. Texas holds a position of prominence in the production of U.S. food and fiber products, and the evaluation of these alternatives has implications not only for Texas, but for the U.S. and possibly the world. To objectively evaluate water transfer proposals, it is necessary that the value of irrigation water in different regions of Texas be established.

The producer faces the same call for maintaining or increasing production as the policy-maker, but he does so with many uncertainties which often have not disturbed the policy-maker in evaluating alternatives. Product prices have risen and fallen at an unprecedented rate while input prices have steadily risen at rates which preclude realistic budgeting. For example, during the recent energy crisis, the prices of fuel and fertilizer have more than doubled. These variable input and product prices weigh heavily upon production decisions by the producer, and likewise must receive serious consideration in evaluation of resource allocation alternatives by policy-makers. The demand for irrigation water is derived from the production of crops and any change in production patterns, input prices or availability, and product prices directly affects this demand.

Current and future water resources planning requires an estimate of the various quantities of water which will be used for irrigation under differing assumptions concerning price of water, other input prices, and product prices. Of particular importance are shifts in cropping patterns, changes in level of agricultural production and net effect on producers income. Since many policy decisions are made in relatively short periods of time, there is an urgent need for a capability to evaluate alternative policies and change input or product prices in a timely fashion.

Objectives

The general purpose of this study was to satisfy the need to have rapid evaluation capability as to effects on irrigated agriculture of selected policies or changes in prices. Specific objectives of the study were as follows:

  1. Develop a model of the Southern High Plains of Texas with the capability of estimating cropping pattern changes, changes in agricultural output, adjustments in net producer income and quantity of inputs used (i.e., water, natural gas, diesel, fertilizer, etc.) given alternative policies and input and product prices.
  2. Develop an analogous model for a second region in Texas.
  3. Link the two models together and test validity of results produced by the models.
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