The primary purpose of this interdisciplinary research was to develop an irrigated agricultural decision-making model–including the development of a probabilistic precipitation prediction model, water production functions, and an economic decision strategy dynamic programming model (DPM).

The primary findings were: The weather simulation model, which was a good estimator of yearly rainfall, tended to overestimate solar radiation but was satisfactory for other climate parameters. The irrigation scheduling model was generally consistant with the measured seasonal evapotranspiration (E) for corn but overpredicted seasonal evapotranspiration for wheat.

Comparison of the DPM with physically based irrigation models indicated that the DPM increased net returns per acre from $7 to $25 for corn with flood irrigation and $2 to $106 per acre for sprinkler systems. For wheat, the DPM increased net returns from $1 to $15 for flood irrigation and $20 to $38 for sprinkler systems. The DPM also estimated water demand functions for corn, sorghum, and wheat. These results indicated that the demand for water was inelastic for corn but elastic for wheat and sorghum. The DPM can be used by farmers with an on-farm microcomputer.

The results should lead to improved ground water management in the declining Ogallala aquifer and should help farmers and public agencies improve irrigation water management decisions.