Modeling the Grazing Effect on Dry Grassland Carbon Cycling with Biome-BGC Model

Identifying the factors that determine the carbon source/sink of ecosystems is important for reducing uncertainty in the global carbon cycle. Arid grassland ecosystems are a widely distributed biome type in Xinjiang, covering approximately one-fourth the country’s land surface. For decades, the intensity of livestock grazing in most of these arid ecosystems has become 2–4 times higher than is recommended. Prolonged heavy grazing by livestock has become a major disturbance, which in conjunction with climate change, has resulted in wide-spread vegetation and soil degradation. Therefore, clarifying the connection between herbivory and carbon stocks is an important pre-requisite for understanding the regional carbon cycle.

The study, by employing daily air temperature, precipitation, shortwave radiation, vapor pressure, day length and soil property data and using a process-based terrestrial carbon cycle model, modeled carbon dynamics in Xinjiang, Northwest China for grasslands that varied in grazing intensity. On the basis of investigating long-term terrestrial carbon dynamics under grazing over 29 years, the study determined how carbon variables might change as a result of the cessation of grazing and what relationships exist between carbon flux/stocks and grazing intensity in Xinjiang.

The results show that there were significant effects of grazing on carbon dynamics, and grazing resulted in a net carbon source of 23.45 g C m^-2 yr^-1, which equaled 0.37 Pg in Xinjiang in the last 29 years. The soil C increased significantly (17%) with long-term grazing, while the soil C stock exhibited a steady trend without grazing.

These findings have implications for grassland ecosystem management as it relates to carbon sequestration and climate change mitigation, e.g., removal of grazing should be considered in strategies that aim to increase terrestrial carbon sequestrations at local and regional scales. The study was published in Ecological Complexity in April 2014.