Influence of crop residues and nitrogen fertilizer on soil water repellency and soil hydrophobicity under long-term no-till
Bibliographic record
Abstract
Crop residues and N fertilizer under no-till may increase soil water repellency (SWR) and soil hydrophobicity, but few studies have examined these two treatment factors and their interaction. A laboratory study was conducted using a long-term (since 1999) field experiment on a clay loam soil to determine the effect of three crop residues and two N fertilizer levels on SWR and soil hydrophobicity under no-till within the Dark Brown soil zone of the semi-arid Canadian prairies. The three residue treatments were residues removed from soil (Rx0), residues returned to soil (Rx1), and residues supplemented to soil (Rx2). The two fertilizer N treatments were 0 (N0) and 45 kg N ha−1 (N1). Surface (0–10 cm) soil samples were taken in the spring of 2017 after 17 yr. Laboratory measurements were conducted on air-dried and sieved (<2 mm) soil to determine SWR using the repellency index method (RI), soil organic C, hydrophobic CH and hydrophilic CO functional groups, and soil hydrophobicity (CH/CO ratio). Mean RI values ranged from 2.19 to 2.75, indicating subcritical (RI > 1.95) SWR. Similar (P > 0.05) RI values were found for the three residue and two N fertilizer treatments, but the trend was for greater RI with increased residue addition (by 12%–26%) and N fertilizer (by 8%). Soil hydrophobicity was significantly greater by 47%–82% for straw returned or supplemented than straw removed treatments, and by 33% for fertilized than unfertilized treatments. Overall, greater residues and N fertilizer had no effect on SWR, but significantly increased soil hydrophobicity.
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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 | 0.001 |
| 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) | 0.001 | 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".