Effects of Plant Growth Regulators (PGRs) on Endogenous Hormone Contents and Activities of Protective Enzymes in Soybean Leaves
Bibliographic record
Abstract
The leaf senescence is one of the main problems at filling stage in soybean grain, affecting leaf photosynthetic physi-ology. Previous researches have showed that chemical growth regulators have been used to delay leaf senescence, therefore, to raise grain yield. In the present study, a soybean (Glycine max) cultivar, ‘Kennong4’ with three treatments by spraying SOD simulation material (SODM), Choline chloride (Cc) and Diethyl aminoethyl hexanoate (DTA-6) were employed to compare dif-ferences of several endogenous hormones and protective enzyme activities in soybean leaves in a field experiment. The results show that, compared with CK, 7–15% of yield increases were obtained with SODM and DTA-6 treatments. IAA, GA, and CTK contents significantly increased with SODM treatment from the 5th to the 30th day after spraying. DTA-6 improved the contents of IAA and CTK from the 15th to the 30th day after spraying, however, the contents of IAA and CTK reduced with Cc treatment in varying degrees. On the other hand, with the time elongation after spraying PGRs, three regulators increased SOD and POD ac-tivities in soybean leaves. SOD activity in leaves with DTA-6 spraying was higher than that with SODM spraying, although POD activity in leaves with DTA-6 spraying was lower than that with SODM spraying. In addition, SODM and DTA-6 also enhanced CAT activity in soybean leaves and slowed the increase of MDA. But the effect of Cc was not obvious. The results above indi-cated that it is effective to increase seed yield or delay leaf senescence, and regulate the level of endogenous hormones and physiological function of protective enzymes by spraying SODM and DTA-6 on soybean leaves.
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".