Ascophyllum nodosum Extract (SealicitTM) Boosts Soybean Yield Through Reduction of Pod Shattering-Related Seed Loss and Enhanced Seed Production
Bibliographic record
Abstract
Soybean is one of the most valuable commercial crops because of its high protein, carbohydrate, and oil content. The land area cultivated with soybean in subtropical regions, such as Brazil, is continuously expanding, in some instances at the expense of carbon storing natural habitats. Strategies to decrease yield/seed losses and increase production efficiency are urgently required to meet global demand for soybean in a sustainable manner. Here, we evaluated the effectiveness of an Ascophyllum nodosum extract (ANE), Sealicit TM , in increasing yields of different soybean varieties, in two geographical regions (Canada and Brazil). In addition, we investigated the potential of Sealicit TM to reduce pod shattering at the trials in Brazil. Three different concentrations of Sealicit TM were applied to pod shatter-susceptible (SS) UFUS 6901 and shatter-resistant (SR) UFUS 7415 varieties to assess their impact on pod firmness. SS variety demonstrated a significant decrease in pod shattering, which coincided with deregulation of GmPDH1.1 and GmSHAT1–5 expression, genes that determine pod dehiscence, and higher seed weight per pod. Sealicit TM application to the SR variety did not significantly alter its inherent pod shatter resistance, but provided higher increases in seed yield at harvest. This yield increase maybe associated with to other yield components stimulated by the biostimulant. This work demonstrates that Sealicit TM , which has previously been shown to improve pod firmness in Arabidopsis and selected commercial oilseed rape varieties through IND gene down-regulation, also has the potential to improve pod resistance and seed productivity in soybean, a member of the legume family sharing a similar strategy for seed dispersal.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".