Effects of converting cropland to grassland on greenhouse gas emissions from peat and organic-rich soils in temperate and boreal climates: a systematic review
Bibliographic record
Abstract
Abstract Background To align with climate goals, greenhouse gas (GHG) emissions from agriculture must be reduced significantly. Cultivated peatlands are an important source of such emissions. One proposed measure is to convert arable fields on peatlands to grassland, as the Intergovernmental Panel on Climate Change (IPCC) default emission factors (EF) for organic soils are lower from grasslands. Yet, these EFs are based on limited data with high variability and comparisons are difficult due to differences in climate, soil properties, and crop management. This systematic review synthesizes available evidence on the effects of converting cropland to grassland on GHG emissions from peat and organic-rich soils in temperate and boreal climates using data from comparable fields. Methods Literature was searched using five bibliographic databases, four archives or search engines for grey literature, and Google Scholar. Eligibility screening was performed in two steps on (1) title/abstract, with consistency among reviewers assessed by double-screening 896 articles, and (2) full text screened by two reviewers. Eligible articles were critically appraised independently by at least two reviewers. Disagreements were reconciled through discussions. Data and key metadata are presented in narrative synthesis tables, including risk of bias assessments. Meta-analyses comparing grasslands with croplands were performed using raw mean difference as the effect size. Review findings A total of 10,352 unique articles were retrieved through the literature searches, and 18 articles including 29 studies were considered relevant to answer the review question. After critical appraisal, it was concluded that two articles reported the same data, so a total of 28 studies, comprising 34 comparisons were included in the systematic review. Most of the included studies were conducted in the Nordic countries and Germany, one in Belarus and one in Canada. A meta-analysis was conducted on 24 studies pairing cropland and grassland sites. No significant differences in carbon dioxide (CO2) or methane (CH4) emissions were found. Emissions of nitrous oxide (N2O) from grasslands were found to be 7.55 kg ha−1 y−1 lower than from cropland, however the sensitivity analysis showed that the difference was not robust, making it uncertain whether conversion from cropland to grassland has a significant effect on N2O emissions from organic soils. The difference was also smaller when root crops were excluded from the comparator group. Further, net ecosystem exchange (NEE) of CO2 and net ecosystem carbon balance (NECB) were higher in grasslands compared to croplands in cases where the grasslands were fertilized. Conclusions This systematic review underlines the ambiguity of GHG emissions from peatlands and their relationship to land use. Our understanding of the factors influencing emissions from these soils remains incomplete, and the specific impact of land use on emissions is still unclear. CO2 emissions represent a major part of the climate impact of cultivated peat soils, so the data analyzed allow to draw the conclusion that a conversion from arable to grassland would not lead to large benefits in terms of GHG emissions, especially if root crops are not part of the arable crop rotation, or the grassland is fertilized. Graphical abstract
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".