Transparency effects in agrivoltaics lettuce cultivation using uniform/non-uniform semitransparent photovoltaic modules in controlled environments
Bibliographic record
Abstract
Agrivoltaic systems offer a dual-use solution to land constraints by integrating food and energy production, yet the influence of light distribution characteristics—particularly uniformity—on crop performance remains poorly understood. This study investigates the physiological and yield responses of lettuce under two contrasting agrivoltaic lighting strategies while maintaining comparable transparency: (1) uniform illumination from cadmium telluride (CdTe) thin-film photovoltaic (PV) modules and (2) non-uniform illumination from bifacial crystalline silicon (c-Si) PV modules with alternating cell and glass regions. Experiments were conducted using CdTe modules at 40 %, 50 %, and 70 % transparency and bifacial c-Si modules at 44 % and 69 % transparency. Key parameters measured include photosynthetically active radiation (PAR), gas exchange metrics (photosynthesis rate, stomatal conductance, intercellular CO 2 , transpiration), and morphological traits (plant height, leaf count, fresh weight). Results reveal that 69 % transparent c-Si modules not only preserved lettuce yield relative to open-field controls but achieved a 3.6 % enhancement. Conversely, CdTe modules of similar transparency caused a 6 % yield reduction, underscoring the importance of light non-uniformity in optimizing plant response. These findings demonstrate that spatial light heterogeneity – characteristic of c-Si systems – can enhance crop performance, presenting an opportunity for sustainable intensification. Scaling this technology across Canada’s lettuce-growing regions could generate 1,200 MW of solar power while boosting agricultural revenue by CAD $20 million annually, with an additional CAD $30 million from solar land leasing. This study supports the integration of non-uniform, semi-transparent PV modules as a viable pathway toward synergistic energy-agriculture systems for a sustainable future.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".