Frequency of soybean in rotation and persistence of Rhizobia in Manitoba soils
Bibliographic record
Abstract
Soybean (Glycine max), along with canola and wheat, are some of Canada’s top agricultural exports. It is also one of the few crops that does not require industrial nitrogen fertilizer. Soybean gets nitrogen from forming a symbiotic relationship with a soil bacterium called Bradyrhizobium japonicum. B. japonicum is not native in Manitoba soils and must be inoculated into soybean fields. The objectives of this thesis are to examine the persistence of the B. japonicum inoculant in the soil, as well as to observe whether varying the crop rotation will affect its abundance. The overall bacterial communities will also be examined for compositional changes between crop rotation treatments (continuous soybean, canola–soybean, corn–soybean, diversified), timepoints (before planting, emergence, pod fill, and full maturity), and sites (Carman, Kelburn, Melita). The bacterial communities were analyzed using 16S rRNA sequencing, and B. japonicum quantification in the soil was measured using qPCR. B. japonicum was shown to persist in the soil years after the initial inoculation. It was also observed that there are native species of Bradyrhizobium present in Manitoba soils that cannot nodulate soybean. The crop rotation effect on B. japonicum, as well as the bacterial community, was minimal. The principal reason for observed differences seem to be the site locations and their soil properties, such as soil type and pH. Carman had significantly higher bacterial diversity, as measured by the Shannon index, than Kelburn and Melita. However, in all three locations, the majority of the bacteria were from three phyla: Proteobacteria, Actinobacteria, and Acidobacteria. It is the subdivisions within the phyla that varied greatly depending on the location. Observations made in this study can lead to a better understanding of the complex plant–microbe and microbe–microbe relationships for future research and applications.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".