Adoption of BMPs and technical inefficiency in Canadian canola production
Bibliographic record
Abstract
This study examines the BMP adoption and technical efficiency for canola producers in the Canadian Prairie Provinces. A Just-Pope stochastic frontier production function is estimated using data from a survey of canola producers conducted in early 2012. Yield is modeled as a function of nutrients and precipitation. A linear inefficiency function includes farm specific variables and a set of binary variables representing BMP adoption. These include use of environmental farm plans and soil testing, precision farming techniques or nutrient management practices. Model results indicate that BMP variables for soil tests, nutrient management planning and precision farming are positively related to technical efficiency while other BMP indicators are not significant. Producer characteristics such as experience and off-farm income tend to have the expected relationship with technical efficiency. Model results appear to be significantly influenced by moisture problems that occurred through the Prairie region during the 2011 cropping year. The results in this paper suggest that for Western Canadian canola producers, there is potential complementarity for some BMPs in terms of improving technical efficiency while simultaneously advancing environmental stewardship. This study extends the limited literature that combines stochastic production frontier analysis with flexible risk specifications to incorporate environmental stewardship practices in the inefficiency model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".