Regenerative Agriculture—A Literature Review on the Practices and Mechanisms Used to Improve Soil Health
Bibliographic record
Abstract
Conventional farming practices can lead to soil degradation and a decline in productivity. Regenerative agriculture (RA) is purported by advocates as a solution to these issues that focuses on soil health and carbon sequestration. The fundamental principles of RA are to keep the soil covered, minimise soil disturbance, preserve living roots in the soil year round, increase species diversity, integrate livestock, and limit or eliminate the use of synthetic compounds (such as herbicides and fertilisers). The overall objectives are to rejuvenate the soil and land and provide environmental, economic, and social benefits to the wider community. Despite the purported benefits of RA, a vast majority of growers are reluctant to adopt these practices due to a lack of empirical evidence on the claimed benefits and profitability. We examined the reported benefits and mechanisms associated with RA against available scientific data. The literature suggests that agricultural practices such as minimum tillage, residue retention, and cover cropping can improve soil carbon, crop yield, and soil health in certain climatic zones and soil types. Excessive use of synthetic chemicals can lead to biodiversity loss and ecosystem degradation. Combining livestock with cropping and agroforestry in the same landscape can increase soil carbon and provide several co-benefits. However, the benefits of RA practices can vary among different agroecosystems and may not necessarily be applicable across multiple agroecological regions. Our recommendation is to implement rigorous long-term farming system trials to compare conventional and RA practices in order to build knowledge on the benefits and mechanisms associated with RA on regional scales. This will provide growers and policy-makers with an evidence base from which to make informed decisions about adopting RA practices to realise their social and economic benefits and achieve resilience against climate change.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".