How does no-till affect soil-profile distribution of roots?
Bibliographic record
Abstract
No-till (NT) often causes prominent stratification of C and nutrients in the soil profile relative to tilled systems. We hypothesize differences in root distribution within the soil profile between NT and tilled systems could be one factor contributing to stratification. We evaluated how NT affects root length density (RLD), root biomass yield (RBY), and root diameter compared with other tillage systems and factors that may affect root characteristics. We reviewed studies until 23 January 2024 where RLD, RBY, or root diameter were reported under NT and tillage. The data on RLD, RBY, and root diameter were tabulated and the weighted log response ratio ( MLRR) and confidence intervals computed. Our meta-analysis showed NT increased RLD in the 0–10 cm depth, but it reduced RLD at 10–20 cm. It increased RBY and root diameter in the 0–20 cm depth and reduced both characteristics at 20–30 cm. Regardless of root characteristics, NT had mixed effects below 30 cm. However, across the soil profile (minimum 50 cm depth), NT had no effect on RLD and RBY. NT-induced changes in roots can be related to increased compaction at the tillage interface. NT stratified both RLD and RBY compared with high-intensity tillage systems, although there were some conditions where NT stratified only RLD or RBY. NT did not induce stratification of RLD and RBY in dry regions, mild, or hot climates, in medium-textured soils, or compared with intermediate-intensity tillage systems. Overall, NT can result in stratification of both RBY and RLD compared with high-intensity tillage systems.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".