Standardised framework for analysis of greenhouse performance using key performance indicators
Bibliographic record
Abstract
CONTEXT There is a growing interest in sustainable and year-round food production through protected agriculture. Greenhouses play a key role in this transition, but their performance varies significantly with climate conditions and operational strategies. OBJECTIVE As a result, this study proposes a standardised and practical framework for evaluating greenhouse performance, grounded in a systematic analysis of key performance indicators (KPI). METHODS A total of 16 key performance indicators (KPI) were identified from the literature and classified into three main categories: thermal, daylighting, and energy. From these, a refined set of 10 KPI was selected based on their applicability, non-redundancy, and relevance for both passive and active greenhouses. These KPI were applied to a case study involving a naturally ventilated, free-standing Gothic arch greenhouse, modelled using the TRNSYS dynamic simulation software. The model was validated using measured data and used to assess greenhouse performance under three distinct Canadian climates: cold (Montréal), very cold (Baie-Comeau), and subarctic (Kuujjuaq). RESULTS AND CONCLUSION The analysis revealed that while some KPI, such as the average indoor air temperature ( T ¯ a i , ND ) and daily light integral ( DLI ), are essential for assessing crop survival, others provided insights into growing potential, operational climate control or the environmental and economic viability of the system. This study introduced two refined indicators for greenhouse cultivation in cold climates: TGSL limit , which excludes lethal short-term cold events, and OGSL , which combines temperature and daylight to define realistic growing conditions. These demonstrated that combining different classes of KPI enabled more meaningful, comparative assessments of greenhouse suitability, offering practical guidance for optimising crop production and energy use under diverse climates. SIGNIFICANCE This work contributes to a standardised and practical framework for evaluating greenhouse performance, paving the way for more informed decision-making in controlled environment agriculture.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".