Abiotic Stress Signaling in Wheat – An Inclusive Overview of Hormonal Interactions During Abiotic Stress Responses in Wheat
Bibliographic record
Abstract
Rapid global warming directly impacts agricultural productivity and poses a major challenge to the present-day agriculture. Recent climate change models predict severe losses in crop production worldwide due to the changing environment and in wheat this can be as large as 42 Mt / °C rise in the temperature. Although wheat occupies the largest total harvested area (38.8%) among the cereals including rice and maize, its total productivity remains the lowest. The major production losses in wheat are caused more by abiotic stresses such as drought, salinity and high temperature than the biotic insults. Thus, understanding the effects of these stresses become indispensable for wheat improvement programs which have largely depended on the genetic variations present in wheat genome through conventional breeding. Notably, recent biotechnological breakthroughs in the understanding of gene functions and access to the whole genome sequences have opened new avenues for crop improvement. Despite the availability of such resources in wheat, progress is still limited to the understanding of the stress signaling mechanisms using model plants such as Arabidopsis, rice (monocot) and Brachypodium (as a model plant for wheat) and not directly using wheat as the model organism. This review presents an inclusive overview of the phenotypical and physiological changes due to various abiotic stresses followed by the current state of knowledge on the identified mechanisms of perception and signal transduction in wheat. Specifically, this review presents an exclusive overview of different hormonal regulations observed during abiotic stress signaling in wheat.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".