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Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency

Author

Listed:
  • Shi, Jianchu
  • Wu, Xun
  • Zhang, Mo
  • Wang, Xiaoyu
  • Zuo, Qiang
  • Wu, Xiaoguang
  • Zhang, Hongfei
  • Ben-Gal, Alon

Abstract

Knowledge-driven "smart" irrigation proposes to achieve explicitly targeted crop yield and/or irrigation water use efficiency (WUE). A coupled crop growth and soil water transport model was established and applied to schedule irrigation for drip-irrigated and film-mulched maize through numerical simulation. By designing various scenarios with either a constant or variable threshold of plant water deficit index (PWDI) to initiate irrigation, the quantitative relationship between PWDI threshold and the corresponding yield and WUE was investigated with acceptable errors between the measured and simulated values (R2 > 0.85). The model allowed determination of PWDI thresholds designed to reach specific combinations of yield and WUE to consider actual conditions such as availability and cost of water resources. Regulated deficit irrigation with a variable threshold, considering variability of physiological response to water stress, was superior to a constant PWDI threshold in improving WUE. A constant PWDI threshold of 0.54 and 45 threshold combinations among various growth stages were suggested to obtain same relative values of yield and WUE. Numerical simulation has the potential to provide reliable dynamic information regarding soil water and crop growth, necessary for smart irrigation scheduling, due to its ability in integrating the effects of environmental conditions and economic considerations and, as such, should be further studied to enhance simulation accuracy and subsequently to optimize irrigation scheduling under complex situations.

Suggested Citation

  • Shi, Jianchu & Wu, Xun & Zhang, Mo & Wang, Xiaoyu & Zuo, Qiang & Wu, Xiaoguang & Zhang, Hongfei & Ben-Gal, Alon, 2021. "Numerically scheduling plant water deficit index-based smart irrigation to optimize crop yield and water use efficiency," Agricultural Water Management, Elsevier, vol. 248(C).
    • Handle: RePEc:eee:agiwat:v:248:y:2021:i:c:s0378377421000391
    DOI: 10.1016/j.agwat.2021.106774
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    References listed on IDEAS

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    Cited by:

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    2. Meng, Wenjie & Xing, Jinliang & Niu, Mu & Zuo, Qiang & Wu, Xun & Shi, Jianchu & Sheng, Jiandong & Jiang, Pingan & Chen, Quanjia & Ben-Gal, Alon, 2023. "Optimizing fertigation schemes based on root distribution," Agricultural Water Management, Elsevier, vol. 275(C).
    3. Liu, Lining & Zuo, Qiang & Shi, Jianchu & Wu, Xun & Wei, Congmin & Sheng, Jiandong & Jiang, Pingan & Chen, Quanjia & Ben-Gal, Alon, 2023. "Balancing economic benefits and environmental repercussions based on smart irrigation by regulating root zone water and salinity dynamics," Agricultural Water Management, Elsevier, vol. 285(C).
    4. Liu, Lining & Wang, Tianshu & Wang, Lichun & Wu, Xun & Zuo, Qiang & Shi, Jianchu & Sheng, Jiandong & Jiang, Pingan & Chen, Quanjia & Ben-Gal, Alon, 2022. "Plant water deficit index-based irrigation under conditions of salinity," Agricultural Water Management, Elsevier, vol. 269(C).

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