Morpho-Physiological and Biochemical Responses of Tomato, Lettuce, Mexican Mint, and Industrial Hemp to Different Temperature Regimes
Bibliographic record
Abstract
Temperature stress is a major abiotic factor significantly impacting crop growth, development and productivity. In this study, we investigated the effects of different growth temperatures (10°C, 22°C, and 35°C) on the morphological, physiological and biochemical responses of five important crop species: green lettuce, hemp, mint, red lettuce and tomato. The results revealed that plant height and stem girth of tomato plants were not significantly (p > 0.05) altered under 22°C and 35°C, but were reduced by ca. 61.9% and 65.7% respectively under 10°C compared to 22°C. Green lettuce plants grown under 22°C had significantly (p < 0.001) higher total chlorophyll content of ca. 35.9% than those under 10°C. Mint plants exposed to extreme temperatures of 35°C and 10°C showed significant (p < 0.001) reductions of up to 19.7%, 60.2%, and 45.8% in chlorophyll fluorescence indices Fv/Fm, Fv/Fo, and total chlorophyll content, respectively, compared to those grown under 22°C. Malonaldehyde content (MDA) in mint and tomato plants increased by ca. 686.7% and 476.4%, respectively, under 10°C compared to 22°C, while in red lettuce, MDA content was reduced by ca. 136% under 10°C compared to 35°C. Notably, flavonoid accumulation was significantly higher in hemp plants compared to other species. The results suggest that crop species exhibit diverse responses to temperature stress, extending beyond morphological, physiological and biochemical adaptations. This diversity shows the differential adaptive strategies employed by various plant species in response to temperature stress conditions. The findings of this study can be explored by various crop improvement programs to enhance the tolerance of these crops to temperature extremes, thereby contributing to food security amidst the threats posed by climate change.
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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".