Response of growth, yield, and quality of edible-podded snow peas to supplemental LED lighting during winter greenhouse production
Bibliographic record
Abstract
The occurrence of low natural light levels during winter months is a major limiting factor for greenhouse plant production in northern regions. To determine the effects of supplemental lighting (SL) on winter greenhouse production of pea pods, plant growth, pod yield, and quality were investigated under SL at a photosynthetic photon flux density (PPFD) of 50, 80, 110, and 140 μmol m −2 s −1 , plus a no-SL control treatment, inside a Canadian greenhouse from January to March. Light-emitting diodes with a red-to-blue PPFD ratio of 4:1 and a 16 h photoperiod were used for the lighting treatments. During the trial period, the average natural daily light integral (DLI) inside the greenhouse was 6.6 mol m −2 d −1 and the average daily temperature was around 13 °C. Compared with the control, SL treatments increased pod yield and promoted plant growth, as demonstrated by faster main stem extension and greater aerial biomass. Also, total pod yield (g plant −1 or no. plant −1 ) and some growth traits (e.g., stem diameter, branch number, leaf thickness, and leaf chlorophyll content) were proportional to supplemental PPFD within the range of 0–140 μmol m −2 s −1 . However, SL levels of 50–80 μmol m −2 s −1 , corresponding to a total (natural + supplemental) DLI of 9.4–11.1 mol m −2 d −1 , resulted in the best pod quality based on evaluations of individual fresh mass, length, soluble solids content, succulence, and firmness. Therefore, a total DLI ranging between 9.4 and 11.1 mol m −2 d −1 can be recommended as a target light level for greenhouse production of pea pods using SL under winter environment conditions.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".