Journal
GLOBAL CHANGE BIOLOGY
Volume 23, Issue 3, Pages 1167-1179Publisher
WILEY
DOI: 10.1111/gcb.13431
Keywords
carbon sequestration; CO2 emission; heavy grazing; mineralization; soil microbial biomass
Funding
- National Natural Science Foundation of China [31370489]
- Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning
- 'Thousand Young Talents' Program in China
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Livestock grazing activities potentially alter ecosystem carbon (C) and nitrogen (N) cycles in grassland ecosystems. Despite the fact that numerous individual studies and a few meta-analyses had been conducted, how grazing, especially its intensity, affects belowground C and N cycling in grasslands remains unclear. In this study, we performed a comprehensive meta-analysis of 115 published studies to examine the responses of 19 variables associated with belowground C and N cycling to livestock grazing in global grasslands. Our results showed that, on average, grazing significantly decreased belowground C and N pools in grassland ecosystems, with the largest decreases in microbial biomass C and N (21.62% and 24.40%, respectively). In contrast, belowground fluxes, including soil respiration, soil net N mineralization and soil N nitrification increased by 4.25%, 34.67% and 25.87%, respectively, in grazed grasslands compared to ungrazed ones. More importantly, grazing intensity significantly affected the magnitude (even direction) of changes in the majority of the assessed belowground C and N pools and fluxes, and C : N ratio as well as soil moisture. Specifically, light grazing contributed to soil C and N sequestration whereas moderate and heavy grazing significantly increased C and N losses. In addition, soil depth, livestock type and climatic conditions influenced the responses of selected variables to livestock grazing to some degree. Our findings highlight the importance of the effects of grazing intensity on belowground C and N cycling, which may need to be incorporated into regional and global models for predicting effects of human disturbance on global grasslands and assessing the climate-biosphere feedbacks.
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