Supplemental LED lighting improves plant growth without affecting biological control in a tri-trophic greenhouse system
Bibliographic record
Abstract
Abstract Artificial lighting, including light-emitting diode (LED) illumination, is increasingly being optimized in protected agricultural systems to maximize plant yield and quality. However, it may also cause other top-down and bottom-up effects in these relatively simple ecological communities that also include insect pests and their natural enemies. While some effects of LED lighting on insects have been demonstrated to date, it is not known how they influence biological control of insect pests in practice. To examine potential top-down and bottom-up impacts of LED illumination on greenhouse biological control with parasitoids, we studied the effects of artificially lengthened days on a tri-trophic system in cages and in a greenhouse. We grew plants under a 12-hour photoperiod of white-supplemented light with 6 hours of additional 1) white light or 2) red and blue light, or 3) with no additional light. We exposed the plants to the pest aphid Myzus persicae (Hemiptera : Aphididae) with or without its parasitoid wasp Aphidius matricariae (Hymenoptera : Braconidae), or to no insects. The 18-hour light treatments increased mean plant dry mass by 127% compared with the 12-hour control without affecting the aphid’s population density or the parasitoid’s biological control efficacy under relatively low light conditions. This suggests that insect communities in protected agriculture can be resilient to even drastic changes in their light environment, and that adjusting crop lighting in a manner that affects plant growth does not necessarily compromise biological control’s effectiveness.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".