Effect of fall-applied manure practices on runoff, sediment, and nutrient surface transport from silage corn in south coastal British Columbia
Bibliographic record
Abstract
Runoff from manured cropland during the wet fall and winter season, when 70% of the annual rainfall occurs, is a surface water quality concern in the Lower Fraser Valley of British Columbia. This study compares different fall-manure application strategies on runoff and contaminant transport from silage corn (Zea mays) land. The treatments were (i) a control, which did not receive manure in the fall; (ii) manure broadcast in the fall on corn stubble; and (iii) manure broadcast in the fall on corn stubble with an established relay crop. Runoff, solids, and nutrients loads from natural precipitation were measured on replicated experimental plots (0.0125 ha) from 1996 to 1998. Fall-applied manure on 3–5% sloping silage cornland without a relay crop resulted in a high risk to surface water quality, due to high suspended solid loads of between 7 and 14 Mg ha-1 yr-1 and high nutrient transport with mean annual total Kjeldahl N (TKN) P, and K loads of 98, 21, and 63 kg ha-1, respectively. Compared with no relay crop, intercropping silage corn with a relay crop of Italian ryegrass (Lolium multiflorum) reduced the mean annual runoff and suspended solid load by 53 and 74%, respectively, TKN load by 56%, P load by 42%, K load by 31%, and Cu load by 57%. Even though total nutrient loads were lower with the relay crop treatment, all fall manure treatments including the relay crop resulted in nutrient loads above guidelines for the first three runoff events immediately following application. Key words: Runoff, suspended solids, nutrient loads, relay crop, cover crop, silage corn, manure
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".