Latest Progress on the Effects of Drought, Salinity, and Temperature Stress on Sweet Potatoes and Their Resistance Mechanisms
Bibliographic record
Abstract
Abiotic stresses such as drought, salinity, extreme temperatures, and heavy metal toxicity pose significant challenges to global agriculture, impacting crop yields and food security. Sweet potato ( Ipomoea batatas ), an essential staple crop, is particularly affected by these stresses, necessitating enhanced tolerance mechanisms to maintain productivity. This study examines the physiological, molecular, and genetic mechanisms that support the abiotic stress tolerance of sweet potatoes, with a focus on key traits such as water use efficiency, osmotic regulation, and antioxidant defense. At the same time, specific genes and transcription factors involved in stress response pathways, including ABA and ROS signaling, as well as the role of epigenetic modifications in adapting to environmental stress, were also analyzed. Additionally, breeding strategies and biotechnological interventions such as CRISPR and marker-assisted selection are discussed, emphasizing their role in developing stress-resilient varieties. Case studies on drought and salinity-resistant sweet potato varieties highlight practical outcomes of current breeding programs. This study summarizes the limitations of existing methods and proposes directions for future research. Enhancing abiotic stress tolerance in sweet potato remains a crucial goal, with promising potential through integrated breeding and biotechnological approaches to support sustainable agriculture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".