Feed and land requirements for livestock production in Canada
Bibliographic record
Abstract
Meeting the future global demand for livestock products is dictated, in part, by a sufficient animal feed supply. Quantifying national feed demand and associated land needs is critical to the assessment of feed security and the impact of livestock on the environment. Despite its importance, the Canadian national aggregate feed demand for livestock production was last estimated in 2001. This study provides methodology to estimate forage, grain, processing co-product and by-product feed needs and land use for animal agriculture (beef, bison, dairy, goats, sheep, horses and ponies, pigs, poultry, and aquaculture) in Canada. Based on 2016 livestock inventory data, the annual feed DM demand estimate was 64.0 Mt, which would require 17.9 Mha of agricultural land, assuming no feed imports. Feed, DM basis, consumed by farm animals was comprised of forages (68.7%), grains (24.9%), and agro-processing co-products and by-products including crop residue and straw used as feed (6.4%). Ruminant livestock (beef, bison, dairy, goats, and sheep) accounted for the greatest portion of feed demand on a DM basis (79.1%), followed by pigs (9.7%) and poultry (6.3%). This methodology can inform assessments of current and future feed and land needs, and potential impacts of shifting land use from feed production to food crops for human consumption or other land uses supporting human activities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".