Combined effects of plant growth‐promoting rhizobacteria and genistein on nitrogen fixation in soybean at suboptimal root zone temperatures
Bibliographic record
Abstract
Application of plant growth‐promoting rhizobacteria (PGPR) or the plant to bacteria signal molecule genistein has been shown to increase nodulation and nitrogen (N) fixation by soybean [Glycine max (L.) Merr.] over a range of root zone temperatures (RZTs) and, specifically, off‐sets at least some of the ill‐effects of low RZTs. Two sets of controlled‐environment experiments, one on a growth bench and the other in a greenhouse, were conducted to examine the combined ability of both PGPR and genistein to reduce the negative effects of low RZT on soybean nodulation and N fixation. Each of two the PGPR strains, Serratia proteamaculans 1–102 and Serratia liquefaciens 2–68 were co‐inoculated with Bradyrhizobium japonicum USDA 110 preincubated with 17.5 (somewhat inhibitory), and 15°C (very inhibitory). At RZTs of 25 and 17.5°C PGPR strains and genistein in combination increased the number of nodules and the amount of Nn fixed. The most stimulatory effect was observed at 17.5°C for the combination: S. proteamaculans 1–102 plus B. japonicum USDA 110 pre‐incubated in 15 μM genistein under greenhouse conditions. For most treatment combinations the stimulatory effects of PGPR and genistein were additive at RZTs of 17.5 and 25°C. Surprisingly, the combination of these two factors resulted in antagonism at the very inhibitory RZT of 15°C. The results suggest that the negative effects of certain low RZTs could be more effectively off‐set by combined treatments of PGPR plus geneistin pre‐incubation of rhizobial cultures than by their individual treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".