Simulating the impact of climate change on energy consumption and yield in Canadian greenhouse horticulture
Bibliographic record
Abstract
• Energy use in greenhouses declines by 11 % in Canada by 2080 due to climate change. • By 2080, the increase in canopy temperatures could reduce crop yield by 17 %. • Cooling units restore yield, but at the expense of a higher energy use. • Modifications to energy use and yield are highly dependent on the location. • Changes to greenhouse design and operation are required to face climate change. Greenhouse horticulture is a very energy-intensive industry in cold regions such as Canada due to heating and lighting needs. It is still largely unknown how climate change will impact the energy profile and productivity of this vital industry. In this work, a greenhouse producing tomatoes has been simulated in eight Canadian cities under current and 2080 climates based on climatic trajectory RCP8.5, in order to determine how the energy consumption and tomato yield would be affected. Results show that, on average, energy consumption decreases by 11 % due to a reduction of the heating needs, whereas yield decreases by 17 % due to a higher canopy temperature. The use of light-emitting diodes (LED) resulted in a lower energy consumption than that of high-pressure sodium (HPS) lighting in both current and future weather conditions. This work suggests that the greenhouse industry is likely to require some adaptations to climate change and that reaching a balance between energy consumption and productivity will be a challenge. As an example, the addition of a mechanical cooling and dehumidification system was simulated and allowed to increase the yield compared to the current situation, even in the 2080 climate change scenario, at the expense of higher energy consumption. However, a more in-depth analysis is required to identify the best adaptative strategies for mitigating the impacts of climate change on greenhouse production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".