In this paper a systematic methodology for making real‐time irrigation decisions is presented. A physically based representation of the dynamics of the soil‐crop‐atmosphere system is used. The variables characterizing the crop and soil status are concurrently simulated with an integrated state space model. Soil moisture and salinity conditions, which synergistically control the plant water uptake, are obtained by using lumped parameter mass balance models for the root zone. Crop yield is predicted by explicitly modeling the plant growth processes, such as assimilation, respiration, and transpiration, which are driven by the climatic inputs. The control model is an analytical optimization method for multistage multidimensional sequential decision‐making problems. It is suitable for systems with nonlinear dynamics and objective functions. The method is based on local iterative approximations of the nonlinear problem with a linear quadratic problem. This approach is evaluated in a series of case studies, where optimal irrigation schedules are obtained on an hourly basis over the growing
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