Policies Affecting the Efficiency of Beef Production in Alberta: A Supply Chain Analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,004 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».