The grazing paradox: how livestock can help or harm grassland carbon storage

A sweeping meta-analysis of Chinese grasslands has determined that livestock grazing is a double-edged sword for carbon storage. The research, led by scientists including Lei Deng and Zhouping Shangguan from the Institute of Soil and Water Conservation, Northwest A&F University, and Yakov Kuzyakov from the University of Göettingen and RUDN University, compiled data from 306 separate studies to create a comprehensive picture of how grazing affects different grassland ecosystems. The findings show that while overgrazing leads to rapid carbon loss, carefully managed grazing can support both productive agriculture and climate change mitigation.

Finding the Right Balance for Each Grassland

The study demonstrates that a one-size-fits-all approach to grazing management is ineffective. The optimal grazing intensity for maximizing ecosystem carbon depends on the specific type of grassland. For drier desert steppes and typical steppes, light grazing was the most beneficial practice. In contrast, wetter meadow steppes and high-altitude alpine steppes benefited most from medium grazing pressure. Under these tailored conditions, plant biomass and soil carbon stocks were either maintained or, in some cases, increased, a finding that supports the ‘intermediate disturbance hypothesis’ in ecology.

The High Cost of Heavy Grazing

The investigation confirms the severe consequences of overgrazing. Across all four grassland types—desert, typical, meadow, and alpine steppes—heavy grazing consistently decreased carbon stocks in plants, litter, and soil. This intensive pressure degrades vegetation, damages soil structure, and accelerates the loss of carbon to the atmosphere. The researchers calculated that heavy grazing resulted in an average ecosystem carbon loss of approximately 1.4 megagrams per hectare each year, a substantial amount that requires a long recovery period.

Timing is Everything: The Role of Grazing Duration

The duration of grazing is another important factor in determining its impact on carbon stocks. The analysis found that in the short term, typically less than three years, grasslands showed a compensatory response where even medium grazing could stimulate plant growth and increase biomass. However, over longer periods, the benefits diminished, and carbon stocks began to decline, especially in the soil. This suggests that the initial stimulation of grass growth, described by the ‘grazing optimization hypothesis’, is a temporary effect that cannot be sustained indefinitely without periods of rest.

Integrating Decades of Ecological Theory

By analyzing this extensive dataset, the research team was able to test and unify several long-standing ecological theories about how ecosystems respond to disturbances like grazing. The results show that different theories apply at different stages and intensities. For instance, the ‘intermediate disturbance hypothesis’ explains the initial carbon gains under light grazing, while the ‘dynamic imbalance hypothesis’ is more relevant for understanding the degradation that occurs under heavy, prolonged grazing. The study synthesizes these ideas into a conceptual model that accounts for grassland type, grazing intensity, and duration.

A Roadmap for Sustainable Grassland Management

Based on their findings, the authors propose several strategies for more sustainable grassland management. The primary recommendation is to set stocking rates according to the specific grassland type, such as fewer than two sheep per hectare for desert steppes and up to eight for alpine steppes. They also suggest that rotational grazing, such as a cycle of three years of grazing followed by three years of rest, could help maintain stable pastures. These informed management practices can increase the resilience of grasslands to climate change while supporting both carbon sequestration and local livelihoods.

Corresponding Author:

Zhouping Shangguan

Original Source:

https://doi.org/10.1007/s44246-023-00051-7

Contributions:

L.D., Z.S. and Y.K. designed the research, L.D., J.L., and J.W. collected the data, L.D. performed the research, K.W., J.L., Z.T., W.Y. and F.Z. contributed new analytical tools, L.D. and X.W. analysed the data, and L.D., Z.S., S.B., A.S., C.H., S.A., X.X. and Y.K. wrote the paper. The authors read and approved the final manuscript.