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Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2

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  • Xiaoping Chen

    (College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China
    State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert Grassland Ecosystem in Xinjiang, Cele 848300, China)

  • Shaoyuan Feng

    (College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China)

  • Zhiming Qi

    (Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC H9X 3V9, Canada)

  • Matthew W. Sima

    (Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA)

  • Fanjiang Zeng

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
    Cele National Station of Observation and Research for Desert Grassland Ecosystem in Xinjiang, Cele 848300, China)

  • Lanhai Li

    (State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China)

  • Haomiao Cheng

    (College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China)

  • Hao Wu

    (College of Hydraulic Science and Engineering, Yangzhou University, Yangzhou 225009, China)

Abstract

Irrigated cotton ( Gossypium hirsutum L.) is produced mainly in Northwest China, where groundwater is heavily used. To alleviate water scarcity and increase regional economic benefits, a four-year (2016–2019) field experiment was conducted in Qira Oasis, Xingjiang Province, to evaluate irrigation water use efficiency (IWUE) in cotton production using the Root Zone Water Quality Model (RZWQM2), that was calibrated and validated using volumetric soil water content ( θ ), soil temperature ( T soil ° ) and plant transpiration ( T ), along with cotton growth and yield data collected from full and deficit irrigation experimental plots managed with a newly developed Decision Support System for Irrigation Scheduling (DSSIS). In the validation phase, RZWQM2 adequately simulated (S) topsoil θ and T soil ° , as well as cotton growth (average index of agreement (IOA) > 0.76). Relative root mean squared error (RRMSE) and percent bias (PBIAS) of cotton seed yield were 8% and 2.5%, respectively, during calibration, and 20% and −10.3% during validation. The cotton crop’s (M) T was well S (−18% < PBIAS < 14% and IOA > 0.95) for both full and deficit irrigation fields. The validated RZWQM2 model was subsequently run with seven irrigation scenarios with 850 to 350 mm water (Irr850, Irr750, Irr700, Irr650, Irr550, Irr450, and Irr350) and long-term (1990–2019) weather data to determine the best IWUE. Simulation results showed that the Irr650 treatment generated the greatest cotton seed yield (4.09 Mg ha −1 ) and net income (US $3165 ha −1 ), while the Irr550 treatment achieved the greatest IWUE (6.53 kg ha −1 mm −1 ) and net water production (0.94 $ m −3 ). These results provided farmers guidelines to adopt deficit irrigation strategies.

Suggested Citation

  • Xiaoping Chen & Shaoyuan Feng & Zhiming Qi & Matthew W. Sima & Fanjiang Zeng & Lanhai Li & Haomiao Cheng & Hao Wu, 2022. "Optimizing Irrigation Strategies to Improve Water Use Efficiency of Cotton in Northwest China Using RZWQM2," Agriculture, MDPI, vol. 12(3), pages 1-15, March.
  • Handle: RePEc:gam:jagris:v:12:y:2022:i:3:p:383-:d:767389
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    References listed on IDEAS

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    1. O’Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D., 2015. "Dynamic prescription maps for site-specific variable rate irrigation of cotton," Agricultural Water Management, Elsevier, vol. 159(C), pages 123-138.
    2. Miller, G.A. & Farahani, H.J. & Hassell, R.L. & Khalilian, A. & Adelberg, J.W. & Wells, C.E., 2014. "Field evaluation and performance of capacitance probes for automated drip irrigation of watermelons," Agricultural Water Management, Elsevier, vol. 131(C), pages 124-134.
    3. Zhao, Jie & Zhang, Xuepeng & Yang, Yadong & Zang, Huadong & Yan, Peng & Meki, Manyowa N. & Doro, Luca & Sui, Peng & Jeong, Jaehak & Zeng, Zhaohai, 2021. "Alternative cropping systems for groundwater irrigation sustainability in the North China Plain," Agricultural Water Management, Elsevier, vol. 250(C).
    4. Chen, Xiaoping & Qi, Zhiming & Gui, Dongwei & Sima, Matthew W. & Zeng, Fanjiang & Li, Lanhai & Li, Xiangyi & Gu, Zhe, 2020. "Evaluation of a new irrigation decision support system in improving cotton yield and water productivity in an arid climate," Agricultural Water Management, Elsevier, vol. 234(C).
    5. Tan, Shuai & Wang, Quanjiu & Zhang, Jihong & Chen, Yong & Shan, Yuyang & Xu, Di, 2018. "Performance of AquaCrop model for cotton growth simulation under film-mulched drip irrigation in southern Xinjiang, China," Agricultural Water Management, Elsevier, vol. 196(C), pages 99-113.
    6. Kothari, Kritika & Ale, Srinivasulu & Bordovsky, James P. & Thorp, Kelly R. & Porter, Dana O. & Munster, Clyde L., 2019. "Simulation of efficient irrigation management strategies for grain sorghum production over different climate variability classes," Agricultural Systems, Elsevier, vol. 170(C), pages 49-62.
    7. Shareef, Muhammad & Gui, Dongwei & Zeng, Fanjiang & Waqas, Muhammad & Zhang, Bo & Iqbal, Hassan, 2018. "Water productivity, growth, and physiological assessment of deficit irrigated cotton on hyperarid desert-oases in northwest China," Agricultural Water Management, Elsevier, vol. 206(C), pages 1-10.
    8. Wang, Ruoshui & Wan, Shuqin & Sun, Jiaxia & Xiao, Huijie, 2018. "Soil salinity, sodicity and cotton yield parameters under different drip irrigation regimes during saline wasteland reclamation," Agricultural Water Management, Elsevier, vol. 209(C), pages 20-31.
    9. Dang, T. & Pedroso, R. & Laux, P. & Kunstmann, H., 2018. "Development of an integrated hydrological-irrigation optimization modeling system for a typical rice irrigation scheme in Central Vietnam," Agricultural Water Management, Elsevier, vol. 208(C), pages 193-203.
    10. Attia, Ahmed & El-Hendawy, Salah & Al-Suhaibani, Nasser & Alotaibi, Majed & Tahir, Muhammad Usman & Kamal, Khaled Y., 2021. "Evaluating deficit irrigation scheduling strategies to improve yield and water productivity of maize in arid environment using simulation," Agricultural Water Management, Elsevier, vol. 249(C).
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    Cited by:

    1. Jiaxin Wang & Xinlin He & Ping Gong & Danqi Zhao & Yao Zhang & Zonglan Wang & Jingrui Zhang, 2022. "Optimization of a Water-Saving and Fertilizer-Saving Model for Enhancing Xinjiang Korla Fragrant Pear Yield, Quality, and Net Profits under Water and Fertilizer Coupling," Sustainability, MDPI, vol. 14(14), pages 1-21, July.
    2. Carlos Chávez & Sebastián Fuentes & Carlos Fuentes & Fernando Brambila-Paz & Josué Trejo-Alonso, 2022. "How Surface Irrigation Contributes to Climate Change Resilience—A Case Study of Practices in Mexico," Sustainability, MDPI, vol. 14(13), pages 1-13, June.

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    Keywords

    RZWQM2; DSSIS; long-term irrigation; cotton seed yield; IWUE;
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