Low Temperature–Tolerant <i>Bradyrhizobium japonicum</i> Strains Allowing Improved Soybean Yield in Short‐Season Areas
Bibliographic record
Abstract
In short‐season soybean [Glycine max (L.) Merr.] production areas, low soil temperature is potentially an important factor limiting soybean growth and yield. Some strains originating from cooler areas can cause more nodulation and nitrogenase activity under low‐temperature conditions. We have attempted to find Bradyrhizobium japonicum strains that can fix more N than strain 532 C under low‐temperature conditions. We selected 40 B. japonicum strains from the USDA collection based on their isolation from soils of northern locations. These 40 strains were tested for their ability to grow at a low (15°C) temperature, and the best two (USDA 30 and USDA 31) were selected for evaluation under field conditions. Inoculation with USDA 30 and USDA 31 resulted in greater soybean yields (an 8% increase, averaged over the 2 yr) than inoculation with 532 C. The increased yield was due to the formation of more pods per plant, and more seeds per plant, but not due to an increase in 100‐seed weight. This indicated that the benefit caused by the superior strains occurred early in plant development, probably due to increased N fixation early in the growing season. This possibility was supported by the observations that leaf areas, grain protein production, and total protein levels for plants inoculated with USDA 30 and USDA 31 were greater than those inoculated with 532 C. These findings clearly demonstrate that inoculant strains likely to perform best in a given geographical area are those selected for the conditions prevalent in the area.
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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) | 0.000 | 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 | 0.001 |
| 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".