Bibliographic record
Abstract
No-till is an agricultural practice widely promoted by governments, development agencies, and agricultural organisations worldwide. However, the costs and benefits to farmers adopting no-till are hotly debated 1–4 . Using a meta-analysis of unprecedented study size, Pittelkow et al. 5 reported that adopting no-till results in average yield losses of -5.7%, but that these losses can be limited with the added implementation of two additional conservation agriculture practices - crop rotation and crop residue retention, and in dry environments. They claimed that, as a result, resource limited smallholder farmers, that are unable to implement the whole suite of conservation agriculture practices are likely to experience yield losses under no-till. In a re-evaluation of their analysis, we found that they overly biased their results toward showing that no-till negatively impacts yields, and overlooked the practical significance of their findings. Strikingly, we find that all of the variables they used in their analysis (e.g. crop residue management, rotation, site aridity and study duration) are not much better than random for explaining the effect of no-till on crop yields. Our results suggest that their meta-analysis cannot be used as the basis for evidence-based decision-making in the agricultural community.
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 | 0.041 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".