Cold stress effects on morpho-physiological traits in chickpea (Cicer arietinum L.) genotypes at early developmental stages
Bibliographic record
Abstract
Chickpea, often called the "poor man's meat," is a vital, protein-rich crop grown globally. It serves as a low-cost alternative to animal-based protein and enhances soil quality through nitrogen fixation. However, chickpea cultivation faces challenges, particularly cold stress during early growth stages, which can lower yields. This study focused on understanding how two main chickpea types, Desi and Kabuli, respond to cold temperatures during the seedling stage to identify traits that could aid in breeding cold-tolerant varieties.<br/>The research involved testing 22 genotypes at both normal (23°C) and suboptimal (15°C) temperatures. Cold stress significantly reduced growth across all genotypes, especially affecting shoot and root weights. However, Desi types performed better under cold conditions compared to Kabuli types. Six varieties were then selected for further analysis of their root and physiological traits.<br/>Cold temperatures severely inhibited early root growth, particularly the lateral roots. However, early root development did not predict cold tolerance during later growth stages. Detailed root analysis revealed that Desi varieties had beneficial traits, such as a greater number of lateral roots, larger lateral root diameter, and thicker root vessels, which appeared to contribute to their cold tolerance.<br/>Additionally, the physiological responses between one Desi and one Kabuli variety showed that Desi types had higher photosynthesis rates, less chlorophyll damage, and better growth under cold stress. These findings suggest that selecting for specific root and physiological traits could enhance cold tolerance in chickpeas, though further research is needed to develop cold-resistant varieties.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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 teacher head, 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".