Optimal Irrigation Strategy Research Articles

Evaluation of Irrigation Planning Decisions

A method is developed to determine optimal irrigation strategies for a single season using crop production functions which incorporate physically based coefficients. The relationship of yield to evapotranspiration is used to develop the yield-irrigation function. The physical parameters used in the production function can be determined from field measurements or various types of computer simulation. Using this approach, the optimal irrigated area and depth of water to apply can be related to prices, costs, and physical parameters. This produces a more general solution than commonly used production functions that depend on limited experimental results. The optimal irrigation depth and irrigated area can be determined for either land or water limiting conditions. The analysis also allows consideration of different irrigated and dryland crops. Three examples are analyzed to illustrate the use of the technique and to develop some general guidelines.

MANAGING ROTATIONAL CANAL WATER SUPPLIES ON THE FARM1

ABSTRACT: A procedure to determine optimal irrigation scheduling strategies under rotational water delivery systems is presented. The methodology involves integration of water delivery amount and frequency, irrigation management strategies, evapotranspiration sequences and crop‐evapotranspiration‐production functions to arrive at an optimal irrigation strategy. Application of the methodology to a farm in the service area of Western Yamuna Canal (India) where a two‐stage system of rotation, one among the irrigation channels and the other among the farmers, is in vogue, reveals that maximum production is obtained with water application in a rotational manner (RI) rather than with irrigation in every or alternate supply periods. Increase in mean water supply which can be effected through improvement in on‐farm conveyance and application systems, has a greater effect on yield than decrease in variance of the supply. Benefit cost analyses indicates that precision land leveling is more cost effective in increasing water supply as compared to water‐course lining.

The Uncertainty of Crop Models in Irrigation Optimization

ABSTRACT THIS research concerns economic optimization of ir-rigation water use. Statistical decision theory is employed in the analyisis, and particular attention is given to questions of uncertainty and utility. The theoretical framework is applied to a case study involving farmers operating with a limited water supply. Uncer-tainty in predictions of crop yield were quantified. This uncertainty is shown to be substantial and pervasive. Decision theory was used to select optimal cropping pat-terns for each of six farmers. The cropping patterns con-sidered include various combinations of two different crops and fallow land. Case study results indicate that optimal irrigation strategies selected for individual farmers in the face of the uncertainty in crop yield models may differ substantially from strategies selected without regard for uncertainty and utility. These optimal strategies will be more consistent with the preferences of the individual farmers.

Optimal Intraseasonal Irrigation Water Allocation

Determining the optimal timing of irrigations over a season in an uncertain environment is a significant problem in operating and planning irrigation schemes. In the absence of suitable experimental results, a plant growth‐soil moisture simulation model is incorporated into a two state variable stochastic dynamic programing model to determine the optimal intraseasonal allocation pattern for irrigation water in a variable environment. Historical traces of climatic data are used, although these data could be synthesized. Given the initial assumptions within the model, the results suggest that an irrigator should maintain available soil moisture in the root zone at a high level, even if it means exhausting the water supply early in the season. However, optimal irrigation strategies are sensitive to changes in the biologic assumptions within the model and thus indicate the importance of good experimental data on which to base these assumptions.