Irrigation impacts on improving crop yield for corn and soybean in Central U.S.

Irrigation has been conducted to improve crop yield by relieving soil water stress under drought condition. On the other hand, irrigation practice has also altered the surface moisture and energy balance as well as the biogeochemical cycle in the agricultural ecosystem. Given its significance to the environment and food security, there is an emerging trend to incorporate dynamic crop growth and irrigation into land surface models (LSMs) and it is desirable to utilize the model to provide reasonable crop yield estimate and irrigation amount. Previous LSMs with dynamic crop and irrigation module are developed at field-scale and many site-specific parameters are calibrated locally. In this work, we attempted to propagate these field-scale efforts to model the crop yield and irrigation amount on regional scale and assess the impacts of irrigation on improving crop yield in Central U.S, using Noah-MP LSM. The site-specific parameters are constrained by best-available spatial datasets, including cropping calendar, crop and irrigation fractions. The model results are evaluated against the county-level crop yield and irrigation water withdrawal report. The results found that, in the rainfed region, the root-mean-square-error (RMSE) for crop yield are 22% and 27% for corn and soybean, respectively. However, in the irrigated region, the irrigation has significantly reduced the RMSE for corn, demonstrating the critical impacts of irrigation to improve crop yield. On the other hand, the irrigation impacts for soybean are not as strong as for corn; the RMSE are 27% and 25% with and without irrigation. A strong positive correlation between crop yield and total water use is identified and the irrigation could increase the crop yield around 35 ~ 60%. Several uncertainties stemmed from model parameterizations are identified, including the plant photosynthesis and phenology as well as the coupling between carbon and moisture cycle. These findings have great implications to future crop model development as well as reliable crop yield and irrigation water estimates under climate change.