Heat stress in grain legumes during reproductive and grain-filling phases
Bibliographic record
Abstract
Thermal stress during reproductive development and grain-filling phases is a serious threat to the quality and productivity of grain legumes. The optimum temperature range for grain legume crops is 10-36°C, above which severe losses in grain yield can occur. Various climatic models have simulated that the temperature near the earth’s surface will increase (by up to 4°C) by the end of this century, which will intensify the chances of heat stress in crop plants. The magnitude of damage or injury posed by a high-temperature stress mainly depends on the defence response of the crop and the specific growth stage of the crop at the time of exposure to the high temperature. Heat stress affects grain development in grain legumes because it disintegrates the tapetum layer, which reduces nutrient supply to microspores leading to premature anther dehiscence; hampers the synthesis and distribution of carbohydrates to grain, curtailing the grain-filling duration leading to low grain weight; induces poor pod development and fractured embryos; all of which ultimately reduce grain yield. The most prominent effects of heat stress include a substantial reduction in net photosynthetic rate, disintegration of photosynthetic apparatus and increased leaf senescence. To curb the catastrophic effect of heat stress, it is important to improve heat tolerance in grain legumes through improved breeding and genetic engineering tools and crop management strategies. In this review, we discuss the impact of heat stress on leaf senescence, photosynthetic machinery, assimilate translocation, water relations, grain quality and development processes. Furthermore, innovative breeding, genetic, molecular and management strategies are discussed to improve the tolerance against heat stress in grain legumes.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".