Date of this Version
Applied Engineering in Agriculture Vol. 27:4 (2011): 605‐614
Competition for water is increasing while a growing world population requires more food production. It is critical to develop and implement efficient deficit irrigation strategies and to predict the impacts of deficit irrigation on yield. South Dakota State University (SDSU) Management Software, which simulates evapotranspiration and soil water contents, was originally designed as an on-farm decision support system capable of fully automating center pivot irrigation. A simple yield model was developed for the software in order to extend its use for evaluating deficit irrigation strategies. Yield ratio (i.e., actual yield/potential yield) was predicted based on a normalized transpiration ratio (i.e., seasonal transpiration normalized with daily reference evapotranspiration/normalized potential transpiration), requiring only daily transpiration data. Results from the updated software compared favorably with field data for corn under deficit irrigation, indicating that the yield model accounts for yield reductions due to water stress. SDSU Management Software was used to simulate center pivot irrigation and corn yield at seven locations across the Great Plains with historical weather data. Thirty irrigation strategies were evaluated across three soil water holding capacities and three pumping rates. Strategies with high water use efficiencies performed well across all treatments and locations. The recommended maximum yield strategy is 30–60–30 (strategies were defined by the minimum available soil water (%) for early, middle, and late season), which used 4% to 14% less irrigation water than a traditional strategy with negligible or positive impacts on yield. Recommended limited water supply strategies are 15–50–0, 0–30–0, and 0–15–0 for minimal, moderate, and severe water restrictions, respectively. Annual variation in yield was greatest when water was most limited. Reduced pumping rates substantially limited maximum yields for arid locations.