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Journal Article

Free weather forecast and open-source crop modeling for scientific irrigation scheduling: proof of concept


Ajaz,  Ali
External Organizations;

Berthold,  T. Allen
External Organizations;

Xue,  Qingwu
External Organizations;

Jain,  Shubham
External Organizations;

Masasi,  Blessing
External Organizations;


Saddique,  Qaisar
Potsdam Institute for Climate Impact Research;

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Ajaz, A., Berthold, T. A., Xue, Q., Jain, S., Masasi, B., Saddique, Q. (2024): Free weather forecast and open-source crop modeling for scientific irrigation scheduling: proof of concept. - Irrigation Science, 42, 179-195.

Cite as: https://publications.pik-potsdam.de/pubman/item/item_28704
Weather forecasts can enhance the utilization of scientific irrigation scheduling tools, crucial for maximizing agricultural water use efficiency. This study employed quantitative weather forecasts of 3-, 7- and 14-day lead times from a weather application programming interface (API) to generate irrigation schedules using the AquaCrop-OSPy model for maize, cotton and sorghum under different regulated deficit irrigation scenarios. The study aimed to determine the suitability of forecast lengths for irrigation scheduling under varying pumping capacities of center pivots (114 m3h−1, 182 m3 h−1 and 250 m3 h−1) in the Texas High Plains and Rio Grande Basin regions, United States. A comparative analysis was carried out to evaluate the irrigation schedules and corresponding crop yields simulated using forecasted and observed weather data. Results indicated that using shorter forecast time allowed the crop model to capture more precise variations in weather patterns, however, shorter lead times also caused over-irrigation in some scenarios. Use of longer lead times tended to be less suitable for scheduling irrigation during water-sensitive growth stages. Center pivots with large pumping capacities and application rates benefited more from longer forecast lengths due to their ability to adapt to weather fluctuations. Unplanned irrigation application occurred in some instances, primarily attributed to uncertainties in weather forecasts and limitations of the crop model. The approach developed and evaluated in this study supports water conservation efforts by promoting scientific irrigation scheduling in weather-data-poor and low adoption regions.