Sources and Measurement of Agricultural Productivity and Efficiency in Canadian Provinces: Crops and Livestock
Bibliographic record
Abstract
This paper measures and assesses the variation in total factor productivity (TFP) growth among Canadian provinces in crops and livestock production over the period 1940–2009. It also determines if agricultural productivity growth in Canada has recently slowed down as indicated by earlier studies. The paper uses the stochastic frontier approach that incorporates inefficiency to decompose TFP growth into technical change (TC), scale effect (SE), and technical efficiency change. The results indicate that productivity changes were mainly driven by TCs for crops, while the productivity changes in livestock was mainly driven by SEs and technical progress. Though change in technical efficiency is mainly positive (except for New Brunswick and Nova Scotia), its contribution to productivity growth was very little for the provinces. We also found that over the entire period, the productivity growth rates for the crop subsector are on average higher for the Prairie provinces than for the Eastern and Atlantic provinces. On the other hand, the productivity growth rates in the livestock subsector are on average higher in the Eastern and Atlantic provinces than in the Prairie region with the exception of Manitoba. Finally, we found that though there is some evidence of a recent decline in productivity growth for the crops subsector, there is no such evidence in the livestock subsector.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".