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Record W4400898325 · doi:10.55016/ojs/sppp.v17i1.78295

Policies Affecting the Efficiency of Beef Production in Alberta: A Supply Chain Analysis

2024· article· en· W4400898325 on OpenAlexaboutno aff
Derek G. Brewin

Bibliographic record

VenueThe School of Public Policy Publications · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessAgricultural economicsProduction (economics)Supply chainLivestockAgricultural scienceAsset (computer security)Investment (military)EconomicsGeographyMarketing

Abstract

fetched live from OpenAlex

Shoppers face high beef prices at the supermarket, but those prices are not a reflection of what Canadian farmers and ranchers earn from their cow-calf herds. In the past 30 years, the average beef producer’s operating margin has never reached $50,000, despite the fact that the average beef farm’s asset base stands at more than $2 million. Better access to export markets, including the U.S., South Asia and North Africa, would help to remedy the producers poor returns. Export prices would need to cover production costs, the largest of which is feed for the producers’ cattle herds, accounting for 77 per cent of the average ranch’s cash costs. As of July 2023, Alberta’s herd consisted of 1.77 million beef and dairy cows. With demand for livestock-derived food expected to jump by 38 per cent in the next 30 years, Canadian cattle ranchers need to take advantage of this global increase through freer trade. Canadian beef can remain competitive globally if the supply chain accesses world markets beyond the U.S., especially in developing countries where consumer incomes are increasing. The industry also needs investments in research, farm extension and supply chain co-ordination from national and provincial self-funded producer groups. Producers must look outward to global trade but be ready to capture new innovations at home. The dominant economies of scale are available to beef processors and finding savings is difficult for farmers and ranchers. However, there is potential for the supply chain to see savings from new technology, which is why investment in continuing support for ranch-level production research is necessary. Producers also need to focus on national co-ordination aimed at protecting trade access and responding to trends in consumer demand for beef. Any new industry policies must also consider key factors that currently affect market demand and expansion including: changing consumer preferences globally, the welfare of animals raised for slaughter and the effects of greenhouse gas emissions on supply chain sustainability. As some of the output and byproducts of the grain production sector provide feed for cattle, policies meant to support the grain sector may be indirectly influencing the beef sector significantly — for good and bad. Infrastructure required for worker safety, animal welfare improvements or improved food safety also adds to the cost of the beef supply. Protectionist trends and increased tariffs pose a threat to the supply chain because they too can create new costs. This is why access to foreign markets is crucial for producers, along with continued investment in research, sector-wide co-ordination to support market access and reviewing crop support to ensure livestock producers are compensated if grain policy changes harm them. Although live animals and much processed Canadian beef are exported to the U.S., fostering good trade relations in Asia and Africa is vital, given the growth in incomes and consumer demand for beef that is predicted for those regions. Free trade is the basis of good agriculture policy and any move towards protectionist policies and higher tariffs is the biggest threat for new costs in the supply chain. Canada’s beef sector requires low-cost access to foreign markets, making free trade policy the single most important policy focus for the sector.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.319

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0020.001
Scholarly communication0.0040.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.000

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.

Opus teacher head0.012
GPT teacher head0.270
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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