Enhancing Canada’s Beef Exports and Competitiveness in the Global Market
Bibliographic record
Abstract
Alberta reports the highest revenues among all provinces in beef cattle ranching and farming,but there is still considerable potential to maximize profits by expanding international trade.In 2022, Canada exported 0.78 million head of cattle and 430,000 metric tonnes of beef, for atotal trade value of C$5.2 billion. That same year, Canada ranked eighth globally in the volumeof beef exports, sixth in the value of beef exports and fourth in the aggregate value of live cattleand beef exports.This study aims to provide a policy brief on Canada’s beef and cattle production and exports,and international market access and conditions. Trade barriers are identified and evaluated, andthe implications of non-tariff measures (NTMs) are examined. These include, for example, the banimposed by the European Union and the United Kingdom on the importation of peroxyacetic acid(PAA)-treated and hormone-treated beef, and the United States’ political/legislative attemptsto invoke the mandatory country-of-origin labelling (CoOL) standard for Canada’s beef exports.This study also discusses the benefits and limitations of preferential trade agreements (PTAs) forCanada’s beef exports, such as the Canada-U.S.-Mexico Agreement (CUSMA); the Canada-EUComprehensive Economic and Trade Agreement (CETA); and the Comprehensive and ProgressiveAgreement for Trans-Pacific Partnership (CPTPP). The study concludes by exploring theopportunities to increase Canada’s beef exports and competitiveness in international markets.The analysis indicates that the Canadian cattle/beef sector would benefit from pursuing generalstrategies to increase its competitiveness in foreign markets by promoting its beef, raisingproduction efficiency and reducing production costs, tackling labour shortages, alleviatingregulatory constraints and integrating novel technologies in beef production and processing.The Canadian cattle/beef sector must increase its competitiveness in international markets anddiversify export destinations while maintaining access to the U.S. market. Canada is a major playerglobally, but it needs to increase its competitiveness in, for example, China, Japan, South Korea,Saudi Arabia, Vietnam, Taiwan and the United Arab Emirates as these markets currently relyheavily on beef imports from Australia, Brazil and the U.S., among other countries.It would be critical to encompass strategies tailored to the market characteristics/conditions ofdestination countries, raise the effectiveness of PTAs and stimulate bilateral business/marketingnetworks. This study emphasizes the importance of sustaining market access to the U.S.,enhancing market access to other foreign countries and pursuing pronounced diversificationstrategies in beef exports to mitigate risks and uncertainties in international markets. It alsounderlines the basic requirement of raising competitiveness vis-à-vis other beef-exportingcountries in various international markets. In this context, trade preferences may not necessarilylead to significant increases in Canada’s beef exports to foreign markets when preferential accessis more pronounced for beef exported from other countries. Also, cost reductions should berelatively significant to effectively compete with other beef-exporting countries and realizeincreases in Canada’s beef exports to international markets.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".