Climate‐Resilient Crop Production Under Agrivoltaics: Experimental Evaluation of Amaranth Production With Semi‐Transparent Photovoltaic Modules in Canada Under Changing Climates
Bibliographic record
Abstract
ABSTRACT Climate change threatens global food security, requiring climate‐smart systems that enhance both crop resilience and sustainable energy production. While agrivoltaics is recognized for combining solar power generation with agriculture, its effects on emerging stress‐tolerant crops such as amaranth remain largely unexplored, particularly under future climatic scenarios. This study evaluates the growth of amaranth, a highly nutritious and stress‐tolerant crop (heat, drought and shade), under 10 photovoltaic (PV) module types with varying transparencies, using controlled biomes simulating present (2025) and projected (2050) climates. Amaranth yields improved by more than 115% under several PV configurations (50%–80% transparent thin‐film, 25% wavelength selective PV, and 44% crystalline silicon (c‐Si)) in 2050 conditions, with only 7% of this increase attributable to climate change alone. Certain modules (69% c‐Si and 80% thin‐film) even outperformed unshaded controls. These findings highlight the potential of agrivoltaic‐amaranth systems to enhance food production while advancing clean energy and climate adaptation goals.
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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 | 0.001 | 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) | 0.001 | 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".