Characterization of first cut alfalfa grass silage management practice on Canadian dairy farms and their impact on silage quality
Bibliographic record
Abstract
This study was conducted to compare silage management practices on Canadian dairy farms and evaluate their impact on the quality of first cut alfalfa grass silages. In manuscript 1, n=288 responses were collected in the British Columbia (n=21), Prairie (n=32), Central (n=218), and Atlantic (n=17) regions from an online survey distributed in 2020. Forage type (p<0.0001), silo type (p<0.0001), and wilting method (p<0.0001) were the most significantly different among regions. Alfalfa-grass was the most common forage type reported in the Central (93.0%), Atlantic (88.2%), and Prairie (68.8%) regions whereas grass was more common in British Columbia (66.7%). Use of all silo types were reported in the Prairies, Central, and Atlantic regions while only bunkers (52.4%) and baleage (28.6%) were reported in British Columbia. The Central region had the highest response rate for towers (37.2%). Majority of respondents in the Central (78.9%), Prairie (75.9%), Atlantic (71.0%), and British Columbia (55.0%) regions reported wilting forages in windrows. However British Columbia had a sufficient response rate for the use of a tedder (45.0%), which corresponds with the regions high response rate for grass. In manuscript 2, (n=186) first cut alfalfa, alfalfa-grass, and grass silage samples were collected from British Columbia (n=15), Prairies (n=55), Central (n=34), and Atlantic (n=82) regions. Relative Feed Value (RFV) and Relative Forage Quality (RFQ) were developed to have similar index scales where the minimum value of 125 is recommended for lactating dairy cows. When forages were evaluated with RFV only alfalfa (136.34) met the minimum recommendations. Evaluating forages with RFQ saw all forages meet the minimum recommendation with alfalfa averaging (163.92), alfalfa-grass (158.53), and grass (146.42). The average IVDMD values for alfalfa (66.68 %DM), alfalfa-grass (62.95 %DM), and grass (57.19 %DM) were in line with previous studies, however VolGAS48 averages could not be compared to other studies. This study concluded that (i) there is a large variation in silage quality across Canada, (ii) region and forage type influence quality among other silage management factors (iii) measured estimates such IVDMD and VolGAS48 need further research to standardize the protocol for industry use.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 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".