What is the effect of ley or perennial fallow on the flux of greenhouse gases from arable organic soils?
Bibliographic record
Abstract
Approximately 8.6% of Swedish agricultural soils are classified as organic soils (Berglund et al. 2010). In the early 19th century, the Swedish government drained peatlands to make land suitable for agricultural production (Berglund 2008). When drained, organic soils are a significant source of CO2 because of the breakdown of organic materials (Ballantyne et al. 2014). In order to reach climate national and international climate goals, the agricultural sector has the important task of reducing its climate impact and thus greenhouse gas (GHG) emissions. For this purpose, the European Union and some Nordic countries see potential in changing land use on organic soils to ley production or perennial green fallow as an alternative to rewetting peatlands. However, there is lacking scientific consensus about the effectiveness of reducing GHG emissions using these interventions. In many studies, different sites or years are compared, which limits the comparability between land uses because of the many variables that influence the outcome (Kasimir-Klemedtsson et al. 1997; Maljanen et al. 2001; Lohila et al. 2004; Beetz et al. 2013), and thus the conclusions that can be taken for future policies. This systematic review aims to answer the question of which land use(s) can be suggested as a valid alternative for decreased GHG emissions on organic soils in temperate and boreal climates. The review will be conducted by establishing a detailed review protocol, following the Collaboration for Environmental Evidence (CEE) guidelines (Pullin et al. 2022), including a methodology for literature search, eligibility screening, data extraction, and critical appraisal. After implementation of the protocol, and if enough valid data can be found, data synthesis, interpretation and a scientific publication about the outcomes will follow. Sources: Beetz, S., Liebersbach, H., Glatzel, S., Jurasinski, G., Buczko, U., & Höper, H. (2013). Effects of land use intensity on the full greenhouse gas balance in an Atlantic peat bog. Biogeosciences, 10(2), 1067–1082. https://doi.org/10.5194/bg-10-1067-2013Berglund, K. (2008). Torvmarken, en resurs i jordbruket igår, idag och även i morgon. In Svensk mosskultur - Odling, torvanvändning och landskapets förändring. (Vol. 41, pp. 483–498). Runefelt, Leif.Berglund, Ö., & Berglund, K. (2010). Distribution and cultivation intensity of agricultural peat and gyttja soils in Sweden and estimation of greenhouse gas emissions from cultivated peat soils. Geoderma, 154(3), 173–180. https://doi.org/https://doi.org/10.1016/j.geoderma.2008.11.035Andrew S Pullin, Geoff K Frampton, Barbara Livoreil, & Gillian Petrokofsky. (2022). Guidelines and Standards for Evidence Synthesis in Environmental Management. Guidelines and Standards for Evidence synthesis in Environmental Management. Version 5.1. https://environmentalevidence.org/information-for-authors/ [5-01-23]Kasimir-Klemedtsson, Å., Klemedtsson, L., Berglund, K., Martikainen, P., Silvola, J., & Oenema, O. (1997). Greenhouse gas emissions from farmed organic soils: a review. Soil Use and Management, 13(s4), 245–250. https://doi.org/https://doi.org/10.1111/j.1475-2743.1997.tb00595.xLohila, A., Aurela, M., Tuovinen, J.-P., & Laurila, T. (2004). Annual CO2 exchange of a peat field growing spring barley or perennial forage grass. Journal of Geophysical Research: Atmospheres, 109(D18). https://doi.org/https://doi.org/10.1029/2004JD004715Maljanen, M., Martikainen, P. J., Walden, J., & Silvola, J. (2001). CO2 exchange in an organic field growing barley or grass in eastern Finland. Global Change Biology, 7(6), 679–692. https://doi.org/https://doi.org/10.1111/j.1365-2486.2001.00437.x
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".