Regenerative agriculture: increasing plant diversity and soil carbon sequestration on agricultural landscapes
Bibliographic record
Abstract
Soil carbon sequestration is a proposed method of mitigating climate change by removing atmospheric carbon dioxide and depositing it in soil. Sustainable agricultural systems such as regenerative agriculture are capitalizing on the opportunity for soil carbon sequestration to be a positive outcome of new land management practices. Regenerative agriculture is a soil-focused approach to farming with the goal of supporting soil health by increasing biodiversity and soil-focused practices. This article explores the possibility of regenerative agriculture’s land management practices to impact soil carbon sequestration via the promotion of plant biodiversity restoration. Examining studies testing the effects of three sustainable practices on soil carbon storage: agroforestry, crop diversification, and crop rotation, as well as native restoration efforts, a positive relationship is found between plant diversity and carbon sequestration. Agroforestry benefits carbon sequestration through stable deep-rooting systems and carbon storage in biomass. Crop diversification and rotation practices encourage nutrients to cycle into the soil and diversify soil microorganisms. The overall effectiveness of these practices in different environments, upper limits to carbon sequestration, and increased nitrogen requirements are possible limitations to these practices. However, the opportunity for plant diversity to restore soil health and carbon storage is critical. With Canada’s commitments to its 2030 emissions reduction targets, increasing sustainable agricultural action may present an opportunity to reduce carbon dioxide emissions substantially while remedying issues of biodiversity and food insecurity.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".