Nutritional and environmental impacts of livestock production systems in Canada: a food systems perspective
Bibliographic record
Abstract
Meeting the challenge of providing a consistent supply of nutritious food for a growing global population is a significant issue facing humanity in the 21st century. Animal-sourced foods (ASF) play a vital role in global food security and nutrition, but their production is often criticized for its high resource demand and greenhouse gas (GHG) emissions. This study examined the relationship between animal production and the environmental and nutritional impacts of land use and dietary choices in Canada. Data regarding animal feed demand and land base requirements, nutrient composition, prices and GHG emissions of crop and animal-based products, human nutritional requirements, and socio-demographic factors affecting food choices were collected from various sources, including Statistics Canada, USDA, industry reports, and published literature. The research employed a combination of mixed research methods, such as multilevel mixed-effects probit regression, inverse probability weighted with regression adjustment, mathematical diet optimization techniques, and spreadsheet models for data analysis. The analysis demonstrated that Canada's total annual dry matter (DM) feed demand in 2016 was approximately 63.9 million t, requiring approximately 17.9 million ha of land. Diet optimization indicated that nutrient intake requirements of Canadian population could generally be met from the domestic food supply, except for certain fatty acids and vitamins. Omnivore, lacto-ovo, and lacto-vegetarian diets required more food to meet Recommended Daily Allowance (RDA) requirements and produced more GHG emissions than vegan diets. However, completely removing animals from Canadian farming systems and transitioning to vegan diets led to increased diet costs. Based on our analysis, the exclusion of red meat from diets resulted in statistically significant differences in the intake of 14 -17 nutrients, depending on the analytical approach used. Further, the risk of calcium, energy, potassium, and vitamin D inadequacy was higher for people who did not consume red meat, while potential inadequacy for magnesium, fiber, and vitamin A was lower for those that did. Sex, education, family status, and cultural background are important determinants of dietary choice among Canadians. These findings can help scientists, policymakers, farmers, and other stakeholders make informed decisions about how to achieve food security and sustainability in a changing world.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".