A Perspective Emphasizing Circadian Rhythm Entrainment to Ensure Sustainable Crop Production in Controlled Environment Agriculture: Dynamic Use of LED Cues
Bibliographic record
Abstract
World-wide, sustainable crop production is increasingly dependent on the protection of crops from adverse local climate conditions by using controlled environment agriculture (CEA) facilities. Today's greenhouses and plant factories are becoming very technologically advanced. Important breakthroughs in our understanding of the deployment of affordable artificial lighting systems that can supplement and even replace solar radiation is the subject of this perspective article. The key to improving sustainable CEA is to synchronize those environmental cues that best entrain the natural circadian rhythm of the crop. Patterns of circadian rhythms reflect the balance of daily metabolic cycles and phenological stages of development that integrate and anticipate environmental changes for all complex organisms. Within the last decade, our understanding of the use of light-emitting diodes (LEDs) as spectrally tunable tools for stimulating plant responses has expanded rapidly. This perspective proposes that extending the photoperiod in CEA is an economically sustainable goal to for year-round productivity of tomato, using dynamic LED shifts that entrain the circadian rhythm. When the photoperiod is extended too far, tomato experiences injury. To avoid yield reduction, we look to nature for clues, and how circadian rhythms evolved in general to long-photoperiods during the summer in high-latitudes. It follows that circadian rhythm traits are good targets for breeders to select new tomato cultivars suitable for CEA. Circadian rhythm entrainment, using dynamic LED cues, can be tailored to any latitude-of-origin crop, and thus expands the strategies ensuring sustainable food security including healthy diets locally in any region of the world.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".