Benchmarking technical, financial analysis and economic efficiency in Maritime Canadian agriculture
Bibliographic record
Abstract
Purpose This study examines the technical and economic efficiency of beef farming in Maritime Canada through benchmarking. Design/methodology/approach Drawing data from the Canadian Cow-Calf Cost of Production Network and 12 focus group sessions conducted in 2021, this evaluation assesses key parameters, including feed costs, income diversification and productivity indicators. Findings Results highlight that feed costs, particularly for grains and hay, are the most significant expenditure in beef farming. Benchmarked farms in Maritime Canada show notable variations in economic and technical performance compared to those in other regions, influenced by factors such as feed usage, income sources and family labor contributions. The study emphasizes the significance of strategic resource utilization, including alternative feed options and family labor, in improving productivity and profitability. Practical implications Practical recommendations include educating farmers on ration balancing programs, adopting alternative feeds like corn silage to mitigate high grain prices and fostering knowledge-sharing networks. Social implications The findings aim to support stakeholders, including local governments and industry councils, in developing evidence-based policies and training programs to strengthen the region’s beef industry. Originality/value By leveraging the resource-based view (RBV) theory, this research contributes to understanding performance metrics and strategic resource management in the agricultural sector.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".