A Study of Agrivoltaics Peanut Production With Single-Axis Tracking Systems
Bibliographic record
Abstract
This study investigates the application of single-axis solar tracking systems in peanut production, a crucial crop for global food security and economic sustainability. The experiment was conducted at the FIT Solar Laboratory in Sorocaba, Brazil, from May to September 2023. Peanut plants were cultivated under two conditions: an Agrivoltaic system with bifacial solar modules with a single axis tracking system, and in a conventional agricultural setup. Key findings indicate that peanut biomass yield under Agrivoltaic systems was 17.02% lower than in traditional agricultural systems, primarily due to shading effects inherent to the dual-use configuration. Despite this reduction, the morpho-physiological parameters of peanut plants, including plant height (31.2 ± 1.5 cm), stem diameter (5.3 ± 0.3 mm), stomatal conductivity (0.24 ± 0.02 mol/m²·s), and chlorophyll index (34.6 ± 2.1 SPAD units), remained stable under the Agrivoltaic system, demonstrating the adaptability of peanut plants to lower light conditions. Photosynthetically active radiation (PAR) measurements varied significantly depending on the position under the trackers, with values ranging from 150 to 1,200 µmol/m²·s, reflecting the heterogeneity of light distribution. The Agrivoltaic system achieved an energy production of 2,354.82 kWh/kWp, reflecting a minimal deviation of 4.36% compared to the PV-only control system, which recorded 2,462.26 kWh/kWp. The Energy Performance Index (EPI) for the Agrivoltaic system was calculated at 0.96, demonstrating its efficiency in converting available solar energy under real-world conditions. This performance aligns with industry standards, underscoring the efficiency of single axis tracking systems in balancing agricultural and energy outputs.
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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) | 0.001 | 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".