Fresh, Stockpiled, and Composted Beef Cattle Feedlot Manure
Bibliographic record
Abstract
The fate of manure nutrients in beef cattle (Bos taurus) feedlots is influenced by handling treatment, yet few data are available in western Canada comparing traditional practices (fresh handling, stockpiling) with newer ones (composting). This study examined the influence of handling treatment (fresh, stockpiled, or composted) on nutrient levels and mass balance estimates of feedlot manure at Lethbridge, Alberta, and Brandon, Manitoba. Total carbon (TC) concentration of compost (161 kg Mg(-1)) was lower (P < 0.001) than stockpiled (248 kg Mg(-1)), which was in turn lower (P < 0.001) than fresh manure (314 kg Mg(-1)). Total nitrogen (TN) concentration was not affected by handling treatment while total phosphorus (TP) concentration increased with composting at Lethbridge. The percent inorganic nitrogen (PIN) was lower (P < 0.01) for compost (5.1%) than both fresh (24.7%) and stockpiled (28.9%) manure. Composting led to higher (P < 0.05) dry matter (DM) losses (39.8%) compared to stockpiling (22.5%) and higher (P < 0.05) total mass (water + DM) losses (65.6 vs. 35.2%). Carbon (C) losses were higher (P < 0.01) with composting (66.9% of initial) than with stockpiling (37.5%), as were nitrogen (N) losses (46.3 vs. 22.5%, P < 0.05). Composting allowed transport of two times as much P as fresh manure and 1.4 times as much P as stockpiled manure (P < 0.001) on an "as is" basis. Our study looked at one aspect of manure management (i.e., handling treatment effects on nutrient concentrations and mass balance estimates) and, as such, should be viewed as one component in the larger context of a life cycle assessment.
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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 | 0.001 | 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".