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Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield

Author

Listed:
  • Anzhen Qin

    (Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural Affairs, Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

  • Dongfeng Ning

    (Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural Affairs, Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

  • Zhandong Liu

    (Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural Affairs, Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

  • Sen Li

    (Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural Affairs, Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

  • Ben Zhao

    (Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural Affairs, Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

  • Aiwang Duan

    (Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture and Rural Affairs, Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang 453002, China)

Abstract

The temperature-based crop water stress index (CWSI) can accurately reflect the extent of crop water deficit. As an ideal carrier of onboard thermometers to monitor canopy temperature (T c ), center pivot irrigation systems (CPIS) have been widely used in precision irrigation. However, the determination of reliable CWSI thresholds for initiating the CPIS is still a challenge for a winter wheat–summer maize cropping system in the North China Plain (NCP). To address this problem, field experiments were carried out to investigate the effects of CWSI thresholds on grain yield (GY) and water use efficiency (WUE) of winter wheat and summer maize in the NCP. The results show that positive linear functions were fitted to the relationships between CWSI and canopy minus air temperature (T c − T a ) ( r 2 > 0.695), and between crop evapotranspiration (ET c ) and T c ( r 2 > 0.548) for both crops. To make analysis comparable, GY and WUE data were normalized to a range of 0.0 to 1.0, corresponding the range of CWSI. With the increase in CWSI, a positive linear relationship was observed for WUE ( r 2 = 0.873), while a significant inverse relationship was found for the GY ( r 2 = 0.915) of winter wheat. Quadratic functions were fitted for both the GY ( r 2 = 0.856) and WUE ( r 2 = 0.629) of summer maize. By solving the cross values of the two GY and WUE functions for each crop, CWSI thresholds were proposed as being 0.322 for winter wheat, and 0.299 for summer maize, corresponding to a T c − T a threshold value of 0.925 and 0.498 °C, respectively. We conclude that farmers can achieve the dual goals of high GY and high WUE using the optimal thresholds proposed for a winter wheat–summer maize cropping system in the NCP.

Suggested Citation

  • Anzhen Qin & Dongfeng Ning & Zhandong Liu & Sen Li & Ben Zhao & Aiwang Duan, 2021. "Determining Threshold Values for a Crop Water Stress Index-Based Center Pivot Irrigation with Optimum Grain Yield," Agriculture, MDPI, vol. 11(10), pages 1-16, October.
    • Handle: RePEc:gam:jagris:v:11:y:2021:i:10:p:958-:d:649172
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    References listed on IDEAS

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    1. Gontia, N.K. & Tiwari, K.N., 2008. "Development of crop water stress index of wheat crop for scheduling irrigation using infrared thermometry," Agricultural Water Management, Elsevier, vol. 95(10), pages 1144-1152, October.
    2. 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.
    3. Wu, Xun & Zhang, Wenjing & Liu, Wen & Zuo, Qiang & Shi, Jianchu & Yan, Xudong & Zhang, Hongfei & Xue, Xuzhang & Wang, Lichun & Zhang, Mo & Ben-Gal, Alon, 2017. "Root-weighted soil water status for plant water deficit index based irrigation scheduling," Agricultural Water Management, Elsevier, vol. 189(C), pages 137-147.
    4. Wang, Feng & Xiao, Junfu & Ming, Bo & Xie, Ruizhi & Wang, Keru & Hou, Peng & Liu, Guangzhou & Zhang, Guoqiang & Chen, Jianglu & Liu, Wanmao & Yang, Yunshan & Qin, Anzhen & Li, Shaokun, 2021. "Grain yields and evapotranspiration dynamics of drip-irrigated maize under high plant density across arid to semi-humid climates," Agricultural Water Management, Elsevier, vol. 247(C).
    5. Sepaskhah, A. R. & Kashefipour, S. M., 1994. "Relationships between leaf water potential, CWSI, yield and fruit quality of sweet lime under drip irrigation," Agricultural Water Management, Elsevier, vol. 25(1), pages 13-21, February.
    6. Li, Xiumei & Zhao, Weixia & Li, Jiusheng & Li, Yanfeng, 2019. "Maximizing water productivity of winter wheat by managing zones of variable rate irrigation at different deficit levels," Agricultural Water Management, Elsevier, vol. 216(C), pages 153-163.
    7. Alessandro Matese & Salvatore Filippo Di Gennaro, 2018. "Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture," Agriculture, MDPI, vol. 8(7), pages 1-13, July.
    8. Krista C. Shellie & Bradley A. King, 2020. "Application of a Daily Crop Water Stress Index to Deficit Irrigate Malbec Grapevine under Semi-Arid Conditions," Agriculture, MDPI, vol. 10(11), pages 1-17, October.
    9. Abdul Latief A. Al-Ghzawi & Yahya Bani Khalaf & Zakaria I. Al-Ajlouni & Nisreen A. AL-Quraan & Iyad Musallam & Nabeel Bani Hani, 2018. "The Effect of Supplemental Irrigation on Canopy Temperature Depression, Chlorophyll Content, and Water Use Efficiency in Three Wheat ( Triticum aestivum L. and T. durum Desf.) Varieties Grown in Dry R," Agriculture, MDPI, vol. 8(5), pages 1-23, May.
    10. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
    11. O'Shaughnessy, Susan A. & Evett, Steven R. & Colaizzi, Paul D. & Howell, Terry A., 2012. "A crop water stress index and time threshold for automatic irrigation scheduling of grain sorghum," Agricultural Water Management, Elsevier, vol. 107(C), pages 122-132.
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    1. Anzhen Qin & Dongfeng Ning & Zhandong Liu & Sen Li & Ben Zhao & Aiwang Duan, 2022. "Impacts of Irrigation Time and Well Depths on Farmers’ Costs and Benefits in Maize Production," Agriculture, MDPI, vol. 12(4), pages 1-15, March.
    2. 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).
    3. Haoteng Zhao & Liping Di & Liying Guo & Chen Zhang & Li Lin, 2023. "An Automated Data-Driven Irrigation Scheduling Approach Using Model Simulated Soil Moisture and Evapotranspiration," Sustainability, MDPI, vol. 15(17), pages 1-17, August.

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