A Simple Model of Fuzzy Irrigation Depth Control: An Application of an Intelligent State Dropping (Isd) Mechanism

Irrigation scheduling is still a serious issue for water managers to achieve efficient water utilization. The dynamic nature of rainfall occurrence may lead to deep percolation, runoff and/or crop water stress, when the saturation allowance is not precisely determined for irrigation scheduling. In this paper, it was proposed to eliminate the fixed maximum allowable soil moisture from simulation‐based irrigation scheduling modeling and replace it with dynamic dependent values calculated by a fuzzy inference engine. In this case, the maximum allowable irrigation depth was not controlled by the field capacity level of soil moisture, and the saturation allowance is considered to store rain in the crop root zone. For this purpose, a classical simulation‐based irrigation scheduling model is modified based on an intelligent state dropping (ISD) mechanism. The theoretical basis of the ISD mechanism was previously developed by Ganji and Pouyan (2011). Application of the ISD mechanism considers water balance uncertainty by determining the maximum weekly allowable soil moisture (the level of saturation allowance). The proposed model is used to calculate a real case study of irrigation depth control of winter wheat, and the results are compared with classical irrigation depth control that considers a fixed level of saturation allowance. The results showed that the newly developed model of irrigation control depth effectively improves the results of classical models. As a result of 60 years of simulation, water loss and required irrigation depth were equal to (76.6, 1890) and (155, 2370) for the fuzzy and classical models, respectively. These results show a reduction in water loss of around 49%. It was also shown that although the total irrigation depth has been decreased for the fuzzy irrigation control model, the maximum crop water demand was supplied by the proposed fuzzy model completely.