Ecoregion and farm size differences in feed and manure nitrogen management: 1. Survey methods and results for poultry
Bibliographic record
Abstract
Environmental issues related to agriculture, and especially to animal production, are prominent in the regulatory agenda and are an area where the general public expects improvements. Many of the issues can be mitigated with changes in farm management practices. There is considerable potential for improvement, but before actions are recommended or mandated, it is important to document what are the current management practices and how they vary across the country and with farm size. This is the first of a series of papers that describes a large-scale livestock farm practices survey (LFPS) conducted across livestock farms in Canada, emphasizing manure nitrogen (N) management as it affects ammonia (NH3) emissions to the atmosphere. However, the survey results have much broader applicability. In this paper, the development of the survey and sampling strategy is described along with the results for the three main poultry sectors in Canada: broiler, layer and turkey. Husbandry in each poultry sector is generally uniform, but there were statistically significant regional differences in feeding practices and feed conversion efficiencies, and these imply differences in N excretion rates. Farm size was seldom significant as a covariate, suggesting that both small and large poultry farms have adopted similar husbandry and feeding practices. Key words: Manure, best management practices, emissions, odor
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".