Influence of long-term manure application on mineral composition of irrigated barley silage
Bibliographic record
Abstract
Miller, J. J., Beasley, B. W., Drury, C. F., Larney, F. and Hao, X. 2015. Influence of long-term manure application on mineral composition of irrigated barley silage. Can. J. Plant Sci. 95: 759–770. The long-term effect of land application of manure type (composted vs. stockpiled manure), bedding type (wood-chips vs. straw), and application rate on feed quality of barley silage as feed for beef cattle is unknown. We measured selected minerals [P, Ca, Ca:P ratio, Mg, K, K:(Ca+Mg) ratio, Na, Fe, Mn, Zn, Cu] of irrigated barley silage (Hordeum vulgare L.) on a clay loam soil after 4 (2002), 7 (2005) and 11 (2009) years of annual applications of composted (CM) or stockpiled (SM) feedlot manure with wood-chips (WD) or straw (ST) bedding at three application rates (13, 39, 77Mg ha−1 dry wt.). The treatments also included an unamended control and inorganic fertilizer treatment. Manure type generally had inconsistent or no significant (P≤0.05) effect on the concentrations of these minerals in barley silage. Most crop minerals were generally greater under ST than WD. The findings for P, K, Na, and K:(Ca+Mg) ratio generally supported our hypothesis of greater crop concentrations with greater application rate, but Ca and Mg decreased at higher rates. Overall, our findings suggest that bedding and application rate have more potential than manure type for managing the feed quality of barley silage.
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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) | 0.001 | 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".