Ranking of Greenhouse Vegetable Suppliers across Three Canadian Provinces Using Data Envelopment Analysis with Multiple Inputs and Outputs
Bibliographic record
Abstract
This study explores the application of Data Envelopment Analysis (DEA) as a tool for evaluating the operational efficiency of agricultural operations in Canada under varying conditions. While traditional DEA models are designed for precise input-output data, they may not adequately address uncertainties present in real-world scenarios. This research extends the conventional DEA framework to accommodate multiple scenarios, specifically assessing greenhouse, sod, and nursery operations in British Columbia, Ontario, and Quebec from 2019 to 2023. Utilizing a modified DEA model that remains linear and computationally efficient, this study evaluates efficiency based on various input and output metrics, including operational expenses and product value. Findings indicate that Quebec achieved full operational efficiency consistently, whereas Ontario and British Columbia showed improvement over time but did not match Quebec’s performance. Introducing a multi-scenario approach enhances the robustness of efficiency analysis in agricultural contexts. However, the study notes certain limitations, such as the static nature of the analysis and the exclusion of qualitative factors.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".