No tillage and leguminous cover crop improve soil quality in a typical rainfed Mediterranean system
Bibliographic record
Abstract
Soil and crop management influence soil organic carbon (SOC), chemical composition, and overall soil quality. In a Mediterranean region, a study initiated in 1994 examined the long-term effects of conventional tillage (CT) versus no-tillage (NT) practices. Initially focusing on continuous durum wheat cultivation until 2009, the experiment later introduced a 2-year rotation of durum wheat and Vicia faba L. cover crops in half of the CT and NT fields. SOC was monitored from 2008 to 2018, while microbial biomass (as dsDNA), soluble nitrogen (N), and enzyme activities (EAs) were monitored from 2011 to 2014 to evaluate the rotation’s impact. Between 2009 and 2018, CT yields were on average 15% higher than NT, especially during high rainfall years. NT significantly increased SOC content in the 0–30 cm soil layer, along with higher levels of soluble N, dsDNA, and EAs at 0–10 cm depth. NT led to a 23% and 10% increase in SOC stock and SOC stock per equivalent soil mass compared to CT. EAs increased by over 50% under NT, indicating enhanced biological activity. The SOC increase due to NT was limited to the top 10 cm, with a decrease at deeper depths (up to 50 cm). Introducing cover crops over 4 years did not yield significant impacts, suggesting the need for a longer period to observe noticeable effects. Overall, adopting NT practices resulted in higher SOC concentration, enhanced soil biological activity, and improved biogeochemical cycles, emphasizing the positive impact of NT on soil health and sustainability.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".