Impact of irrigation scheduling methods on corn yield under climate change

To meet rising global food demands, existing agricultural management strategies will need to be transformed to mitigate the negative impacts of climate change on crop yields. Climate change which includes elevated CO₂, temperature increase, and change in precipitation variability give rise to uncertainties for predicting crop yields. We used a multilayer canopy-root-soil model (MLCan) to (i) explore the adverse impacts of climate change on corn yields, and (ii) investigate three irrigation scheduling methods to improve the yield. To estimate crop yields, we implemented crop growth processes in MLCan. This model was applied to an experimental farm located in Urbana, Illinois, USA and was validated using aboveground carbon and leaf area index measurements. A weather generator was used to develop forcings corresponding to future climate scenarios. Climate change scenarios were considered with ambient and elevated CO₂ concentration, 1^oC to 3^oC temperature increases, and precipitation changes. The 2^oC and 3^oC temperature change reduces the crop yields up to ~38%. The simulation results showed that a 30% decrease in precipitation could reduce the mean yields of up to ~10%. The three irrigation scheduling methods were applied on dry years as adaptation strategies, which were decided based on water balance and two plant attributes of canopy temperature-based crop water stress index and leaf water potential. The water balance approach was designed to reflect an existing irrigation scheduling method, which was found to be not efficient and required more irrigation to improve crop yields under future climate conditions. We found that the leaf water potential method was more effective and efficient to improve crop yields under climate change among the three irrigation methods considered in this study.