Modeling Deficit Irrigation Water Demand of Maize and Potato in Eastern Germany using ERA5-Land Reanalysis Climate Time Series

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Abstract ERA5-Land reanalysis (ELR) climate time series has proven useful in (hydro)meteorological studies, however, its adoption for local studies is limited due to accuracies constraints. Meanwhile, local agricultural use of ELR could help data-scarce countries by addressing gaps in (hydro)meteorological variables. This study aimed to evaluate the first applicability of the ELR climate time series for modeling maize and potato irrigation water demand (IWD) at field scale and examined the performance of ELR precipitation with bias correction (DBC) and without bias correction (WBC). Yield, actual evapotranspiration (ETa), irrigation, water balance, and crop water productivity (CWP) were evaluated using the deficit irrigation toolbox. The study found that maize (13.98–14.49 ton/ha) and potato (6.84–8.20 tons/ha) had similar mean seasonal yield under different irrigation management strategies (IMS). The Global Evolutionary Technique for OPTimal Irrigation Scheduling (GET-OPTIS_WS) IMS had the highest mean seasonal yields under DBC and WBC, while rainfall and constant IMS had the most crop failures. DBC had a higher mean seasonal ETa than WBC, except for the potato FIT and rainfall IMS. Global Evolutionary Technique for OPTimal Irrigation Scheduling: one common schedule per crop season (GET-OPTIS_OS) and GET-OPTIS_WS IMS outperformed conventional IMS in IWD by 44%. Overall, GET-OPTIS_OS and GET-OPTIS_WS performed best for maize and potato CWP in terms of IWD, scheduling, and timing. Therefore, adoption of ELR climate time series and advanced irrigation optimization strategies such as GET-OPTIS_OS and GET-OPTIS_WS can be beneficial for effective and efficient management of limited water resources, where agricultural water allocation/resource is limited.

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