Bradyrhizobium japonicum strains and mutants allow improved soybean nodulation, nitrogen fixation and yield in a short season (cool spring) area
Bibliographic record
Abstract
In the soybean nitrogen fixing symbiosis, suboptimal root zone temperatures (RZTs) inhibit the inception and development of nodules, leading to reduced nitrogen fixation and soybean yield. The purpose of this thesis was to evaluate the effects of selected with potential low temperature tolerant strains, originating from the northern areas of the USA, and mutants made from Bradyrhizobium japonicum USDA 110, on soybean nodulation, nitrogen fixation and yield in a short season area with cool spring conditions. Among the 40 B. japonicum strains evaluated, only USDA 30, USDA 31, 532 C and USDA 110 grew well at 15°C. USDA 30 and USDA 31 grew better than 532 C and USDA 110 at 15°C. Mutants Bj 30050--Bj 30059 could not produce lipo-chito-oligosaccharide (LCO) at measurable levels in the absence of genistein. All mutants produced more LCOs than 532 C and USDA 110 at the same temperature and genistein concentration. Temperature and genistein concentration did not affect LCO production dynamics for the following: mutant Bj 30055, 532 C and USDA 110. Both mutant production and identification of low temperature tolerant strains achieved the general objective of improved soybean nitrogen fixation in a cool climate. Inoculation with low temperature tolerant strains (USDA 30, USDA31), or mutants (Bj 30055 and Bj 30058) improved soybean development (increases in leaf area and shoot nitrogen content), nodulation (increases in nodule number and nodule weight), nitrogen fixation and yield relative to inoculation with B. japonicum strain 532 C, the strain currently included in most Canadian soybean inocula.
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) | 0.001 | 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) | 0.001 | 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".