Cold stress in plants: Strategies to improve cold tolerance in forage species
Bibliographic record
Abstract
Cold stress (CS) affects the survivability, geographical distribution, and yield stability of crops. Suitable management and agronomic practices can minimize the crop losses associated with cooler environments. However, agronomic practices alone can't support plants adequately to withstand the harsh cold. Therefore, exploring plants cold stress-responsive factors such as genetic, epigenetic, physiological, and cellular is crucial. This report discusses on cold stress effect, signal perception, signal transduction, gene expression, and associated molecular phenomena in plants. Three cold acclimation response pathways: Ca2+ mediated ICE1- CBF/ DREB1, hormonal, and reactive oxygen species (ROS), are elucidated. Also, this report summarizes the latest research work on genetics and genomics of forage species from the perspectives of cold tolerance improvement. In several instances, our hypotheses have been supported by a recent research output from our genetic analysis experiment on alfalfa (Medicago sativa L.) cold tolerance. We further review the importance of high-throughput genomics and phenomics for cold tolerance improvement in forage species and recommended implementing widely recognized techniques such as genomic selection (GS) and genome-wide association studies (GWAS) to develop climate-resilient cultivars. The transgenics and genome-edited cold-tolerant forage cultivars with low or no yield penalty must be the goals of future research.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".