Effects of Spectral Ranges on Growth and Yield in Vertical Hydroponic–Aeroponic Hybrid Grow Systems for Radishes and Turnips
Bibliographic record
Abstract
As climate change destabilizes food crop production, there is a growing interest in controlled environment agriculture (CEA). Although light-emitting diodes (LED) have made CEA economically viable for some high-value crops when coupled to agrivoltaics (solar photovoltaics + agriculture), it has generally not been used for root vegetables. This is the first study to demonstrate that radishes and turnips could be grown in a reasonable period of eight weeks in an agrivoltaic agrotunnel using both lighting and grow walls optimized for lettuce growth. As reduction in LED energy use is important to minimize capital costs for solar energy, this study investigated three lighting treatments (red, white, and full-spectrum as control). The normalized yields (adjusted for total energy provided by each treatment) showed that both cultivars preferred red light, and harvested green leaves provided higher masses than the roots, although turnips appeared to be far more adaptable to vertical growth than radishes (>450% for roots and >50% for leaves per pot compared to radishes for the control treatment). The results show promise for providing true net-zero carbon emission root vegetables year-round with similar agrivoltaics-powered CEAs. Future work is needed with light intensity trials to optimize light recipes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".