Assessing Changes in Feed Security of the Québec Dairy Industry in 2050
Bibliographic record
Abstract
Understanding the effects of climate change is central to assessing the resilience of the agricultural sector in Québec. The dairy industry is vulnerable as climate change alters yields for cattle feed grown on-farm. Québec dairy farmers have adopted various strategies to mitigate greenhouse gas emissions on farms, incorporating sustainable agricultural practices such as improved waste and manure management, and altering cow diets to reduce enteric (digestive) methane production. The last of these practices --– altering cow diets that reduce enteric methane emissions --– is valuable, yet it introduces a tradeoff between emission reduction and climate adaptation. Indeed, diets that reduce methane emissions may require crops that are less resilient to future climate conditions, whereas climate-resilient feed crops may not offer the same methane-reduction benefits. In 2050, Québec dairy farmers may not be able to grow all feed crops on their land to support herd health and milk output, both metrics of feed security. Accordingly, this study assesses the regional feed security of the Québec dairy industry by modelling the impact of crop yield change in two climate scenarios and with three diet compositions in 2050. Results show that in 2050, methane-reducing corn-heavy diets will require more cropland than hay- or soy-based diets, presenting an environmental tradeoff between land use and methane emissions. The analysis reveals high projected intraprovincial variability in feed security, with Eastern Québec predicted to be more feed secure than Southwestern Québec. The importance of a sustainable and self-sufficient dairy industry is increasingly important in the face of climate change. More broadly, this research aims to identify potential approaches for farmers to support future successful dairy operations.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".