Improving tomatoes quality in the Sahel through organic cultivation under photovoltaic greenhouse as a climate change adaptation and mitigation strategy
Bibliographic record
Abstract
Climate change negative impacts on food production systems have forced large scale food producers to make available less healthy products. Although available on the markets, tomatoes are no more tasting as they used to be and providing fewer nutrients compared to then. This study investigates and compares the quality and yield of organic tomatoes ( Solanum lycopersicum ) produced in an insect net covered photovoltaic greenhouse against ambient production. Plant’s physical characteristics were measured, yields and nutrient content were found at harvest, and environmental conditions (temperature, relative humidity, solar irradiance and CO 2 ) were recorded. Plants grew as high as 160 cm inside the greenhouse under an average afternoon temperature of 30.71 °C and a vapor pressure deficit (VPD) of 1.88 kPa against outside plant growth of 72 cm height under averages of 36.04 °C and 3.05 kPa. Although, inside greenhouse tomatoes were physically more attractive and firm with two times healthier tomatoes (98%), 52.39% higher content in protein, 13.31% more minerals and 13.19% more dry matter than outside tomatoes, the yield from outside environment was 4.57 times higher than that of inside due to probably the used crop variety adapted to the harsh climate. Using a crop variety optimum for greenhouse, increasing ventilation and using better fertilizers with enough irrigation could help increase productivity while keeping high fruit quality inside the greenhouse, leading to healthier fruits for food security in the Sahel.
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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".