Characterization of soil microbiome associated with soybean and soybean cyst nematode «Heterodera glycines» in Eastern Canada
Bibliographic record
Abstract
Soybean is already well established in Canada and acreages are still increasing rapidly in many provinces, including Quebec. This crop offers many advantages to growers, obviously economically, but also because of the ecological services it renders such as nitrogen fixation. However, this expansion in cultivated area has also favored the spread of the soybean cyst nematode (SCN), Heterodera glycines Ichinohe (1952). This plant-parasitic nematode is present in Ontario since 1987 and has been discovered for the first time in Quebec in 2013. Currently, the only viable way to manage SCN is the use of resistant soybean cultivars and rotation with non-host crops. This method has proven to be effective to reduce SCN populations to a low level but is in no way able to eradicate these nematodes. However, since the resistance is only partial, SCN populations quickly adapt and overcome the mechanism involved. Therefore, there is a need to find new ways to fight this pest before the current pest management approach become completely useless. One of these possibilities is based on studies that have demonstrated the nematicidal properties of soils where soybean was cultivated in monoculture for a long period of time. These so-called suppressive soils were shown to contain microorganisms, including bacteria and fungi that demonstrated antagonistic effects on SCN.Using a metagenomic approach, the present study characterized for the first time the soil microbiome associated with soybean in Eastern Canada. Additionally, the internal microbiome of SCN cysts (cystobiomes) was studied. Results seem to indicate that soybean strongly influence the soil microbiome on a short period of time and that the cystobiome is different from the soil microbiome. The data analysis made to fulfill the two primary objectives of this study also revealed possible new ways to manage SCN infestations. Amongst them, high aluminium concentrations in the soil and simultaneous presence of specific bacterial and fungal species seem to be negatively correlated to the viability of SCN
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".