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CornSoyWater, a web application for irrigation decision support on corn and soybean

Author

Listed:
  • Han, Chengchou
  • Yang, Haishun
  • Payyala, Dharmic
  • Xia, Tiyuan
  • Liu, Jiani
  • Zhang, Yongfu
  • Yin, Lixin
  • Ren, Zhen
  • Chen, Ye
  • Su, Yuan
  • Mo, Liling
  • Zhang, Yuyu
  • Zhong, Yu
  • Bi, Xiaoxu
  • Li, Jianbin
  • Chen, Yong

Abstract

Irrigation is crucial for agricultural production. With the rise in data availability related to agriculture, we developed an irrigation decision support application, CornSoyWater (http://cornsoywater.unl.edu). The application uses real-time weather data and forecasts along with field-specific soil and crop management details to run multiple crop simulations. The outputs include current crop stage, soil water balance, and irrigation recommendations. The application visualizes the user's fields on Google Maps, indicating irrigation needs with color-coded icons. As of now, CornSoyWater operates across ten western U.S. Corn Belt states, and has been validated by field data. It has been highly promoted by Nebraska agricultural educators and has more than a thousand registered users. This crop irrigation scheduling tool enables irrigators to better utilize water resources, increase productivity, and reduce production costs across the western U.S. Corn Belt.

Suggested Citation

  • Han, Chengchou & Yang, Haishun & Payyala, Dharmic & Xia, Tiyuan & Liu, Jiani & Zhang, Yongfu & Yin, Lixin & Ren, Zhen & Chen, Ye & Su, Yuan & Mo, Liling & Zhang, Yuyu & Zhong, Yu & Bi, Xiaoxu & Li, Ji, 2025. "CornSoyWater, a web application for irrigation decision support on corn and soybean," Agricultural Water Management, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:agiwat:v:313:y:2025:i:c:s0378377425001684
    DOI: 10.1016/j.agwat.2025.109454
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    References listed on IDEAS

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    1. Mun, S. & Sassenrath, G.F. & Schmidt, A.M. & Lee, N. & Wadsworth, M.C. & Rice, B. & Corbitt, J.Q. & Schneider, J.M. & Tagert, M.L. & Pote, J. & Prabhu, R., 2015. "Uncertainty analysis of an irrigation scheduling model for water management in crop production," Agricultural Water Management, Elsevier, vol. 155(C), pages 100-112.
    2. Umutoni, Lisa & Samadi, Vidya, 2024. "Application of machine learning approaches in supporting irrigation decision making: A review," Agricultural Water Management, Elsevier, vol. 294(C).
    3. Sandhu, Rupinder & Irmak, Suat, 2019. "Performance of AquaCrop model in simulating maize growth, yield, and evapotranspiration under rainfed, limited and full irrigation," Agricultural Water Management, Elsevier, vol. 223(C), pages 1-1.
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    5. Sandhu, Rupinder & Irmak, Suat, 2020. "Performance assessment of Hybrid-Maize model for rainfed, limited and full irrigation conditions," Agricultural Water Management, Elsevier, vol. 242(C).
    6. Pereira, L.S. & Paredes, P. & Hunsaker, D.J. & López-Urrea, R. & Mohammadi Shad, Z., 2021. "Standard single and basal crop coefficients for field crops. Updates and advances to the FAO56 crop water requirements method," Agricultural Water Management, Elsevier, vol. 243(C).
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