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Scientific irrigation scheduling often relies on calculated ET to estimate daily soil moisture water depletion. Since the resulting estimates of soil water content are uncertain, and become increasingly uncertain as ET estimation errors accumulate over a period of time, it is common practice to measure soil water periodically to correct the soil water content estimates. But soil water content measurements are also subject to substantial error. While both ET-based estimates and field measurements of soil water content provide useful information, neither is sufficiently accurate for purposes of precise irrigation management. An algorithm for minimizing uncertainty of soil water content determinations is presented. Based on Bayesian decision analysis, the algorithm integrates information from ET estimates and soil water content measurements to derive a posterior estimation of soil water status that has the potential to provide a better basis for irrigation decisions.