Evaluation of the Effect of SoyaSignal Technology on Soybean Yield [<i>Glycine max</i> (L.) Merr.] under Field Conditions Over 6 Years in Eastern Canada and the Northern United States
Bibliographic record
Abstract
Previous studies showed that inoculation of soybean [Glycine max (L.) Merr] with Bradyrhizobium japonicum preactivated with plant‐to‐bacteria signal molecules increased nodule number, particularly at low root zone temperatures, thereby improving plant seasonal nitrogen fixation and final grain and protein yield under cool spring conditions. Two products carrying this technology, SoyaSignalTM and Affix+TM, were designed and tested at 127 locations in Canada and the United States from 1994 to 1999. A summary of the field test results shows that preincubation of B. japonicum with genistein and daidzein, as well as directly increasing the genistein and daidzein concentration in the soybean root rhizosphere, gave an average final grain yield increase of 7 %. The success of SoyaSignal technology was temperature dependent. The plants responded better to the SoyaSignal products when grown under cool soil conditions. Application of SoyaSignal to early planted soybean (before the soil temperature rose above 17.5 °C) increased yields by an average of 10 %. The responses declined with delayed planting dates. Soybean genotypes with high yield potential had greater yield increases than those with low yield potential. As the ratio of return to cost for SoyaSignal technology was 5.3 : 1 over the 127 site‐years, SoyaSignal technology can be used as a tool to improve soybean yield in production areas with cool springs.
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 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 | 0.000 | 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 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".