Systematic Review of Greenhouse Gas Emissions from Peat and Organic-Rich Soils under Grassland and Cropland Management
Bibliographic record
Abstract
Peat and organic-rich soils are critical carbon stores but are also major sources of greenhouse gas (GHG) emissions when drained for agriculture. In temperate and boreal regions, the conversion of cropland to grassland has been proposed as a strategy to mitigate emissions of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O). However, the effectiveness of this approach remains uncertain due to the complex interactions between soil properties, land management practices, and environmental conditions. This systematic review synthesises evidence from peer-reviewed field studies to assess the impact of grassland conversion on GHG emissions from peat and organic-rich soils.Out of 10,352 records initially screened, 28 studies comprising 34 comparisons met the inclusion criteria, focusing on GHG fluxes under comparable field conditions. The analysis revealed that grassland systems do not universally reduce CO₂ or CH₄ emissions compared to croplands, with no statistically significant differences observed. In contrast, N₂O emissions showed a mean reduction of 7.55 kg N₂O ha⁻¹ yr⁻¹ in grasslands. However, this reduction was not robust across all scenarios and was influenced by key factors such as crop type, fertilisation, and drainage management. For example, excluding root crops from the cropland comparator significantly narrowed the observed differences in N₂O emissions, highlighting the critical role of cropping systems.Grasslands with fertilisation often exhibited higher net ecosystem exchange (NEE) and net ecosystem carbon balance (NECB), indicating increased carbon losses that could counteract the benefits of reduced N₂O emissions. Furthermore, the findings challenge the Intergovernmental Panel on Climate Change (IPCC) guidelines, which assume consistently lower emissions from grasslands on organic soils. These results emphasise the need to reassess emission factors and refine policy recommendations for managing peatlands.The review underscores the complexity of GHG fluxes in managed organic soils and highlights the limitations of grassland conversion as a standalone mitigation strategy. The variability in outcomes demonstrates the importance of considering site-specific factors, such as soil properties, hydrology, and climate, alongside management practices. Strategies like optimised water table management, reduced fertiliser inputs, and mixed cropping systems could complement grassland conversion to enhance its effectiveness in reducing GHG emissions.Future research should prioritise long-term field experiments incorporating detailed soil and environmental characterisation, consistent methodologies, and comprehensive management data. Emphasis on cross-regional studies would also help address gaps in understanding how local conditions influence GHG dynamics. These efforts are essential for developing tailored, evidence-based strategies for mitigating emissions from peat and organic-rich soils.This review provides valuable insights into the trade-offs and opportunities associated with grassland conversion, offering guidance for policymakers and land managers aiming to balance agricultural productivity with climate goals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".