Effects of cover crop and tillage management practices on in situ and ex situ water infiltration parameters
Bibliographic record
Abstract
Water infiltration is important for improved crop productivity and environmental sustainability, but the combined effects of cover crops (CCs) and tillage on cumulative infiltration and infiltration parameters are not fully understood. The objectives of this study were to evaluate the influence of CCs and tillage on cumulative water infiltration and infiltration parameters. The field was set up using a randomized complete block design with two levels of CCs (CCs vs no cover crop [NC]) and two levels of tillage (till vs no-till [NT]). The CCs used included winter wheat (Triticum aestivum) and crimson clover (Trifolium incarnatum), and the tillage included disc tillage (to a depth of 10 cm). Results showed that CCs and tillage significantly increased the Parlange and Green-Ampt model estimated sorptivity and saturated hydraulic conductivity parameters during 2022 compared with NC and NT, respectively. Additionally, KGuelph was significantly higher under CC compared with NC during both years, suggesting that CCs can increase groundwater recharge. While CC-Till management had the highest 2-h cumulative infiltration, tillage only significantly increased water infiltration during early times, and CCs increased water infiltration during the infiltration period. Conclusively, CCs can improve the ability of tillage to increase water infiltration.
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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 | 0.000 | 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".