Optimization of irrigation scheduling using crop–water simulation, water pricing, and quantitative weather forecasts

Abstract

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Numerical models of crop response to irrigation and weather forecasts with internet access should be fully utilized in modern irrigation management. In this respect, we developed a new numerical scheme to optimize irrigation depth that maximizes net income over each irrigation interval. The scheme applies volumetric water prices to inspire farmers to save water, and it provides growers with real-time estimates of irrigation depth and net income over the growing season. To evaluate this scheme, we carried out a field experiment for groundnut (Arachis hypogaea L.) grown in a sandy field of the Arid Land Research Center (ALRC), Tottori University, Japan. Two treatments were established to compare the net income of the proposed scheme with that of an automated irrigation system. Results showed that although the proposed scheme gave a larger amount of seasonal irrigation water 28%, it achieved 2.18 times of net income owing to 51% higher yield compared to results of the automated irrigation system. The accuracy of rainfall forecast had little effect on the scheme outputs, where the root mean square error (RMSE) between observed and forecasted rainfall was 4.63 mm. By utilizing numerical simulation information of the soil–plant–atmosphere system into the proposed scheme, it would be a more cost-effective tool for optimizing irrigation depths than automated irrigation systems.

Keywords

• automated irrigation
• soil water content
• drought
• transpiration
• net income
• numerical simulation