Breeding Kiwifruit for Enhanced Stress Tolerance: Advances and Challenges
Bibliographic record
Abstract
Kiwifruit ( Actinidia spp.) is a valuable fruit crop that faces significant challenges due to various environmental stresses, including drought, salinity, heat, cold, and waterlogging. Recent advances in molecular breeding and functional genomics have identified several key genes and regulatory mechanisms that enhance stress tolerance in kiwifruit. For instance, the R1R2R3-MYB transcription factor AcMYB3R has been shown to improve drought and salinity tolerance in transgenic Arabidopsis plants by upregulating stress-responsive genes. Similarly, the heat shock transcription factor (Hsf) gene family has been implicated in high-temperature tolerance, with specific Hsf genes like AcHsfA2a playing crucial roles. Salt stress tolerance has been linked to various physiological and biochemical adaptations, including increased proline content and enhanced antioxidant enzyme activities. Waterlogging tolerance mechanisms involve complex metabolic and transcriptional responses, as demonstrated by the superior performance of certain kiwifruit genotypes and rootstocks under waterlogged conditions. Additionally, melatonin application has been found to mitigate heat stress by promoting antioxidant pathways. These findings provide a comprehensive understanding of the genetic and molecular bases of stress tolerance in kiwifruit, offering valuable insights for breeding programs aimed at developing more resilient cultivars.
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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) | 0.000 | 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) | 0.000 | 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 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".