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

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Abstract

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.

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Data available on request from the authors.

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Acknowledgements

Funding for this project is provided by USDA to Project No. 2017–68007-26318, through the National Institute of Food and Agriculture’s Agriculture and Food Research Initiative. The authors are thankful to Kirk E. Jessup from Texas A&M AgriLife Research, Amarillo, TX, for sharing the experimental data for model calibration.

Funding

This study was supported by National Institute of Food and Agriculture, 2017-68007-26318, 2017-68007-26318.

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Authors and Affiliations

  1. Texas A&M AgriLife, Texas Water Resources Institute, College Station, TX, 77843, USA

    Ali Ajaz & T. Allen Berthold

  2. Texas A&M University, Texas Water Resources Institute, College Station, TX, 77843, USA

    Shubham Jain

  3. Texas A&M AgriLife Research and Extension Center, Amarillo, TX, 79106, USA

    Qingwu Xue

  4. Department of Natural Resources and Environmental Design, North Carolina Agricultural and Technical State University, Greensboro, NC, 27411, USA

    Blessing Masasi

  5. Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, Telegrafenberg, 14473, Potsdam, Germany

    Qaisar Saddique

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  1. Ali Ajaz
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Contributions

AA: manuscript text, major analysis, programming script; AB: manuscript text, funding; QX: data, review; SJ: review, programming script, BM: calibrated models; QS: review, analysis.

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Correspondence to Ali Ajaz.

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271_2023_881_MOESM1_ESM.docx

Long term (1991–2020) monthly averages of precipitation (PPT), reference evapotranspiration (ETo), and mean temperature (TEMP). Supplementary file1 (DOCX 18 KB)

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Ajaz, A., Berthold, T.A., Xue, Q. et al. Free weather forecast and open-source crop modeling for scientific irrigation scheduling: proof of concept. Irrig Sci 42, 179–195 (2024). https://doi.org/10.1007/s00271-023-00881-8

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