Distribution of livestock sectors in Canada: Implications for manureshed management
Bibliographic record
Abstract
Abstract Canada's livestock production and human populations are concentrated in southern regions. Understanding spatial and temporal distributions of animals and excreted nutrients is key to optimizing manure resources and minimizing impact of livestock. Here, we identify manureshed concerns and opportunities by reconciling nitrogen supply and demand on a regional and national scale. Data based on national statistics and farm surveys were allocated to homogeneous soil polygons (Soil Landscapes of Canada [SLC]) to quantify changes in nutrient distribution and ammonia (NH 3 ) emissions across Canada (1981–2018). Livestock sectors tied to domestic consumption, dairy and poultry, were stable over time and well dispersed. Export driven beef production has moved west since 1981, whereas pig production was prominent in Manitoba, Quebec, and Ontario. Per ha manure N excretion across livestock sectors in 2018 was generally low with 58% and 6% of the SLCs averaging <25 and >100 kg N ha −1 , respectively. Although only 3% of SLCs had average NH 3 emissions reaching 16–200 kg ha −1 , most of these were located near cities and emissions spiked in spring when more people might be exposed. The greatest concentrations of nutrients and livestock occurred around the three largest metropolitan areas: Toronto, Montreal–Quebec City, and Vancouver, posing challenges for nutrient recycling and public health. This study shows that as Canadian cities and livestock agriculture grow in southern Canada, so will challenges around food production, human health, and managing nutrients. Livestock and land use strategies are needed to reconcile changing animal sectors and growing populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".