Analysing the influence of growing conditions on both energy load and crop yield of a controlled environment agriculture space
Bibliographic record
Abstract
Controlled environment agriculture, such as vertical farming, consists of stacking crops in a controlled environment and is transforming agriculture by providing a highly productive solution for year-round production. However, vertical farms are also energy-intensive due to precise control of the growing conditions (temperature, humidity, carbon dioxide, and lighting). While many studies focus on optimising indoor conditions to enhance yield, the impact of those growing conditions on energy is often overlooked. This study aims to provide a comprehensive analysis, using a dynamic model, of the influence of growing conditions typically used to cultivate lettuces on energy and crop yield. Several combinations of air temperatures (20, 24 and 28 °C), vapour pressure deficits (0.54 and 0.85 kPa), lighting intensities (200 to 700 μmol·m−2·s−1) and photoperiods (12 to 24 h) are studied. The dynamic model, developed using a building performance simulation tool, supports the simultaneous assessment of energy load and crop yield. It includes a model of a small-scale vertical farm that integrates a dynamic crop model to estimate heat gains/losses from crops and crop growth rate according to growing conditions. The results indicated that the best compromise between energy load and yield is at an air temperature of 24 °C. Moreover, lowering lighting intensity and extending the photoperiod positively impacted both energy load and yield. Certain growing conditions, such as lowering the vapour pressure deficit, can reduce the need for dehumidification. Additionally, for lighting intensities exceeding 500 μmol∙m−2∙s−1, although the energy load continued to increase linearly with the lighting intensity, the growth rate was limited, resulting in reduced production efficiency. These extensive results and thorough analyses offer valuable insights into the influence of the growing conditions on energy load and yield.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".