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