Effect of rate, frequency and incorporation of feedlot cattle manure on soil nitrogen availability, crop performance and nitrogen use efficiency in east-central Saskatchewan
Bibliographic record
Abstract
A study was initiated in 1996 in the Black Soil zone in east-central Saskatchewan to examine soil and crop response to application of feedlot cattle manure at different application rates, frequencies and incorporation timing in a sandy loam and loam soil. Three rates of feedlot cattle manure (approx. 100, 200 and 400 kg total N ha-1) were applied annually and under reduced frequency application regimes. Canola (Brassica napus, L.), spring wheat (Triticum aestivum, L.), hulless barley (Hordeum vulgare, L.) and canola were seeded in spring of 1997, 1998, 1999 and 2000, respectively. Pre-seeding available N (0–60 cm) increased with application rates. Annual application resulted in a linear increase in grain yield with application rates but had no effect on grain N concentration. Cumulative N use efficiency was low (7–10%) with no significant difference among treatments. Single application showed significant residual fertility benefit in the second year but not in the third year except at the high rate. Incorporation timing of feedlot cattle manure had no impact on soil or crop performance. Low availability of N in feedlot cattle manure over the short-term suggests the need for high application rates or addition of supplemental N fertilizer in order to meet crop N requirements. Key words: Feedlot cattle manure, N availability, manure management, N use efficiency
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".