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Improvement and Application of Key Pasture Theory for the Evaluation of Forage–Livestock Balance in the Seasonal Grazing Regions of China’s Alpine Desert Grasslands

Author

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  • Hui Liu

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

  • Xiaoyu Song

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

  • Lin Qin

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

  • Wang Wen

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

  • Xiaodi Liu

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

  • Zhiqiang Hu

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

  • Yu Liu

    (State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi’an University of Technology, Xi’an 710048, China)

Abstract

The calculation of theoretical carrying capacity (TCC) is one of the most fundamental tasks for the evaluation of the forage–livestock balance on grassland pastures. At present, the main methods for calculating TCC are the traditional theory (TT) and key pasture theory (KPT), but they both have obvious limitations in practical applications for the seasonal grazing regions in the alpine desert grasslands of China. In this study, the pastures in Wulan County (PWC) were selected as the research area. The unique features of the research area as well as the faulty applications of TT and KPT were fully analyzed, and then a new method named dynamic key pasture theory (DKPT) was established for calculating TCC by improving KPT with the introduction of the two dynamic factors of the livestock slaughter rate ( α ) and coefficient of grassland productivity ( β ). TT, KPT and DKPT were respectively used to calculate the TCC of the PWC under different precipitation scenarios. The forage–livestock balance in the PWC determined using DKPT was assessed by the forage–livestock balance index (FLBI). The results showed that the natural processes of grassland supply and livestock demand were significantly imbalanced in time and space and formed a dynamic cycle with four subprocesses, which was the supporting basis of DKPT; DKPT effectively improved the rationality of TCC and offered greater guidance for the evaluation of the forage–livestock balance in the seasonal grazing regions of China’s alpine desert grasslands. In the PWC, the TCCs of different pastures calculated by DKPT were clearly different from those calculated by TT and KPT; the areas of the pastures divided were extremely imbalanced, with a huge surplus of more than 50% in cool-season pastures; in the representative year of 2016, the pastures in the Xisai Basin were underloaded (FLBI = −35.19%) on the whole, while the pastures in the Chaka Basin were overloaded (FLBI = 24.34%).

Suggested Citation

  • Hui Liu & Xiaoyu Song & Lin Qin & Wang Wen & Xiaodi Liu & Zhiqiang Hu & Yu Liu, 2020. "Improvement and Application of Key Pasture Theory for the Evaluation of Forage–Livestock Balance in the Seasonal Grazing Regions of China’s Alpine Desert Grasslands," Sustainability, MDPI, vol. 12(17), pages 1-12, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6794-:d:402209
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    References listed on IDEAS

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    1. Qingqing Ma & Linrong Chai & Fujiang Hou & Shenghua Chang & Yushou Ma & Atsushi Tsunekawa & Yunxiang Cheng, 2019. "Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau," Sustainability, MDPI, vol. 11(2), pages 1-14, January.
    2. Fan Yang & Quanqin Shao & Xingjian Guo & Yuzhi Tang & Yuzhe Li & Dongliang Wang & Yangchun Wang & Jiangwen Fan, 2018. "Effect of Large Wild Herbivore Populations on the Forage-Livestock Balance in the Source Region of the Yellow River," Sustainability, MDPI, vol. 10(2), pages 1-18, January.
    3. E. N. J. Brookshire & T. Weaver, 2015. "Long-term decline in grassland productivity driven by increasing dryness," Nature Communications, Nature, vol. 6(1), pages 1-7, November.
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    Cited by:

    1. Zefu Gao & Qinyu Zhu & Haicheng Tao & Yiwen Jiao, 2023. "Grassland Health in Xilin Gol League from the Perspective of Machine Learning—Analysis of Grazing Intensity on Grassland Sustainability," Sustainability, MDPI, vol. 15(4), pages 1-31, February.

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