Comparing the Profitability of a Greenhouse to a Vertical Farm in Quebec
Bibliographic record
Abstract
Abstract Rapidly growing demand for year‐round fresh food, regardless of the weather or climate, is driving demand for controlled environment agriculture systems. Sales from greenhouses (GHs) are growing at 8.8%, while sales from vertical farms (VFs) are growing at 30%. It is commonly believed in industry circles that a VF cannot economically compete with a GH, due to the high cost of powering artificial lighting. Nonetheless, researchers have yet to analyze the economics underlying a VF, let alone compare the profitability of a VF to that of a GH. This research gap is particularly relevant to Canada, as it is uniquely positioned to be a leader in the VF market. Below, we report the results of a detailed simulation of the profitability of growing lettuce in a VF and in a GH located near Quebec City. Surprisingly, we find that the costs to both equip and run the two facilities are very similar, while the gross profit is slightly higher for the VF.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".