Global meta-analysis reveals different grazing management strategies change greenhouse gas emissions and global warming potential in grasslands

Abstract

Grazing management significantly influences greenhouse gas (GHG) emissions and the global warming potential (GWP) in grasslands. Yet, a limited understanding of the impact of grazing and grazing exclusion on GHG emissions and GWP in grasslands hinders progress towards grassland ecosystem sustainability and GHG mitigation. We conducted a global meta-analysis of 75 published studies to investigate the effects of grazing and grazing exclusion on methane (CH₄), carbon dioxide (CO₂), nitrous oxide (N₂O), and GWP. Our results revealed that grazing and grazing exclusion significantly increased the CO₂ and CH₄ emissions, respectively. The responses of GHG emissions and GWP to grazing were regulated by grazing intensity and elevation. We also found that light grazing significantly decreased GWP but heavy grazing increased GWP. Reducing grazing intensity was a simple and effective method through stocking rate adjustment, which promised a large GHG mitigation potential. Our results demonstrated that GHG emissions increased with elevation under grassland grazing, implying that irrational grazing in high-elevation grasslands promoted GHG emissions. In comparison with grazing, only long-term grazing exclusion reduced the GWP, and CH₄ emissions enhanced with grazing exclusion duration. However, long-term grazing exclusion may shift economic demand and grazing burden to other areas. Overall, we suggested that regulating the grazing intensity, rather than grazing exclusion, was an effective way to reduce GHG emissions. Our study contributed to the enhancement of sustainable grazing management practices for GHG balance and GWP in global grasslands, and offered a global picture for understanding the changes in GHG emissions and GWP under different grazing management regimes.

Keywords

Grazing management / Global grasslands / Greenhouse gases (GHG) emissions / Global warming potential (GWP) / Meta-analysis

CRediT authorship contribution statement

Lingfan Wan: Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. Guohua Liu: Writing – review & editing, Visualization, Validation, Supervision. Xukun Su: Writing – review & editing, Visualization, Supervision.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

We would like to thank all the people who helped us to complete the study. This study was supported by National Natural Science Foundation of China (Grant No. 72394401).

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2024.09.012.

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