Soaring food prices and the intensified scarcity of water resources put a new emphasis on efficient use of water in irrigation. Numerical models for water flow and crop growth can be used to predict crop water stress and make decisions on irrigation management. To this end, a new irrigation scheme was presented to determine the optimum irrigation depths using WASH_2D, a numerical model of water flow and solute transport in soils and crop growth. By using freely available quantitative weather forecasts and volumetric water price as input data to predict soil water flow and give the recommendation of irrigation depths which maximizes net income during each irrigation interval. Field experiments using potato were conducted for two-seasons in a sandy soil in Japan under three irrigation methods, i.e., using the simulation model named treatment “S” (to distinguish, named S1 in first season and S2 in second season), automatic irrigation method using soil moisture sensors named treatment “A”, and refilling irrigation management supplying 100% consumed water named treatment “R”. To compare S with other two treatments, S1 and A was conducted in the first season, then S2 and R was conducted in the second season. Results showed that S1 improved potato yield by 19%, and reduced water by 28%, resulting in an increased net income by 19% compared with A in the first season. There was no significant difference when compared with R in the second season, which was mainly due to the frequent rainfall during second growing season. In addition, S improved the nitrogen uptake efficiency (NUPE) by 39% and 11% compared with A and R, respectively. The simulated values of water content were in fair agreement with those measured in the root zone. In short, simulated irrigation method was effective in improving yield, saving water and increasing NUPE of potato compared with automatic and refilling irrigation methods in sandy field.
