Potential of ABA Antagonists in Promoting Germination of Canola, Chickpea and Soybean Seeds under Low Temperature
Bibliographic record
Abstract
Canola (Brassica napus L.), soybean (Glycine max (L.) Merr.) and chickpea (Cicer arietinum L.) are important crops in Canada for their contributions to both the national economy and international markets. However, seed germination of these crops is sensitive to ambient temperature. Cold temperatures in the early spring severely inhibit seed germination, potentially preventing the plant from completing its life cycle within the growing season. One major factor that causes the delay in seed germination is the increased ABA level, which is triggered by cold stress. ABA antagonists, a class of synthetic chemicals, could counteract the effects of ABA and, hence, promote seed germination under low temperature (LT). \nThe main objective of this study was to identify effective ABA antagonists in promoting germination under LT. ABA 1009 was selected for its significant promoting effect on canola seed germination. ABA 1009 was found to be effective across different canola cultivars and it was able to promote radicle growth. The application of ABA 1009 on canola and soybean seeds during germination counteracted the effects of exogenous ABA application. Hormone analysis was done on canola seeds treated with ABA 1009. The increased amount of ABA metabolites in the seeds indicated up-regulation of ABA catabolism caused by the application of ABA 1009. The increased levels of ABA and ABA 1009 concentrations within the seeds over time indicated that overdosage of ABA 1009 caused an increase in ABA biosynthesis. Hormone analysis of similar experiments in soybean and chickpea seeds suggested that the delay in germination was related to the high ABA levels within the seeds. Gene expression analysis on canola seeds treated with ABA 1009 showed that AAO3, AAO4, NCED5, NCED6, and NCED9 genes were involved in ABA biosynthesis, while CYP707A4 was involved in ABA degradation.
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 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".