Responses of soil greenhouse gas fluxes to land management in forests and grasslands: A global meta-analysis

Land management practices significantly influence soil greenhouse gas (GHG) emissions. Despite individual measurements of the impacts of forest and grassland ecosystem management practices (FGEM) on GHG emissions, a comprehensive global-scale synthesis and comparison remain absent. In this study, a global meta-analysis was conducted to analyze the responses of three key soil GHGs, including carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O), to various FGEM, including forest burning (FB) and thinning (FT), grassland grazing (GG), fencing (GF), and mowing (GM) based on 1643 observations from 317 individual studies. Moderator factors and the underlying mechanisms driving these responses were also explored. Results revealed that in managed forests, FB significantly reduced soil CO₂ and N₂O emissions, while FT decreased soil CH₄ uptake capacity without affecting CO₂ and N₂O emissions. In managed grasslands, GG reduced soil CO₂ emission, while GF increased it; both had neutral impacts on soil CH₄ and N₂O fluxes. GM did not affect GHG fluxes. Overall, forest management decreased soil CO₂ emission and CH₄ uptake capacity, whereas grassland management had a neutral effect on soil GHG fluxes. Temporal analysis revealed diminishing effects of FGEM on CO₂ emissions over the long term. Soil CH₄ uptake exhibited divergent responses over time, and soil N₂O emissions remained relatively constant. Compared to managed grassland, soil GHG fluxes in managed forests were more sensitive to aridity conditions, with forest management generally restraining soil CO₂ and N₂O emissions and CH₄ uptake in humid regions. Meta-regression analysis highlighted carbon content, soil temperature, and soil moisture as primary drives of changes in soil CO₂ and CH₄ fluxes, while soil N₂O fluxes were more susceptible to soil organic carbon and microbial biomass nitrogen. The dependence of soil GHG fluxes on climate zones and management duration should be integrated into Earth system models for more accurate predictions of the impact of human interference.