Response of Chickpea Yield to High Temperature Stress during Reproductive Development
Bibliographic record
Abstract
Minimizing the exposure of an annual crop to abiotic stresses may increase seed yield. A study was conducted to determine the effect of high temperature stress during reproductive development on pod fertility, seed set, and seed yield of chickpea ( Cicer arietinum L). ‘Myles’ desi and ‘Xena’ kabuli chickpea were grown in a controlled environment under 20/16°C day/night air temperatures (control). High (35/16°C) and moderate (28/16°C) temperature stresses were imposed for 10 d during early flowering and pod development. Compared to the control, the early flower high temperature stress decreased ( P < 0.01) pod production by 34% for Myles and 22% for Xena, whereas high temperature stress during pod development decreased ( P < 0.05) seeds per plant by 33% for Myles and 39% for Xena. Consequently, the high temperature stress during pod development decreased ( P < 0.01) seed yield by 59% for Myles and 53% for Xena. Yield reduction was greater due to the stress during pod development compared to the stress during early flowering. Plants recovered to a greater degree from the early flower stress compared to the pod development stress. The Myles desi produced 40 seeds per plant and the Xena kabuli produced 15 seeds per plant, whereas the Myles had smaller individual seed size than the Xena. Consequently, the Myles desi produced 26% greater seed yield than the Xena kabuli under the same conditions. Minimizing the exposure of chickpea to high temperature stress during pod development will increase pod fertility, seed set, and seed yield of the crop.
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 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".