72 Estimating the supply and movement of feed for beef production in Alberta, Canada
Bibliographic record
Abstract
Abstract This study estimated movement of feed between regions of Alberta to meet the needs of the beef sector in 2001, 2006, 2011 and 2016, and included barley and wheat grain, grass hay and grass-legume hay, which can be cost-effectively transported. Feed demands of beef and other livestock (i.e., dairy cattle, chickens, pigs, sheep, bison, horses, turkeys and other poultry) were estimated using county-level inventories from Alberta Agriculture and Rural Development and Statistics Canada, and aggregated to 7 land-use regions: Lower Athabasca, Upper Athabasca, Lower Peace, Upper Peace, North Saskatchewan, Red Deer and South Saskatchewan. Feed requirements were sourced from literature and expert opinion pertinent to Canadian context, and feed demand estimated using annualized feeding days and dry matter (DM) intake. Feed available for beef production was estimated from crop yields minus other livestock feed use. Average feed demand for beef production across Alberta was (tons of DM) 2.71 M, 0.68 M, 2.21 M, and 1.95 M for barley and wheat grain, grass hay, and grass-legume hay, respectively. North Saskatchewan, Red Deer and South Saskatchewan accounted for 90% of the total demand for barley and wheat grain and 81% of grass hay and grass-legume hay. South Saskatchewan had the largest feed deficit and required feed movement in all years. Across years, barley grain deficits ranged from -94% to -31%, grass hay deficit from -142% to a surplus of 1.6% and grass-legume hay deficits from -167% to -15%. Unmet deficits at provincial level occurred for barley grain (2006), grass hay (2001) and grass-legume hay (2001, 2006). Wheat was surplus in all years, ranging from 17% to 90%. Feed movement is critical to support beef production in Alberta, and is highly variable depending on crop yield in different regions. This analysis enables the beef industry to assess provincial feed sourcing and environmental footprints.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".