Rhizosphere interactions under diverse canola genotypes: a key component in seed nitrogen use efficiency
Bibliographic record
Abstract
Belowground dynamics between soil-plant-microbial interactions impact crop nitrogen use efficiency (NUE), but current research focuses on aboveground crop management and breeding, leaving belowground dynamics scarcely studied. Canola (Brassica napus L.) is an agriculturally relevant crop in Canada, with relatively high N requirement and largely unknown soil-plant-microbial impact. We explored canola genotypic and temporal changes in soil N concentrations, root morphology, and rhizosphere and root microbiomes to identify belowground traits important to canola NUE. Sixteen canola genotypes were grown on a Dark Brown Chernozem in Saskatchewan, Canada, in a randomized complete block design with three blocks. Soil and plant were sampled five times over the growing season from 32- 81 days after sowing (DAS), and seeds sampled at 81 DAS. Soil moisture and NO3--N were negatively correlated with canola NUE, with genotype specific changes possibly related to genetically inherited traits that controlled soil moisture and N cycling. Root length and surface area were highest under seed ripening (67 DAS) and root surface area was negatively correlated with soil NO3--N concentrations. This suggests that root morphology changed to facilitate greater soil mineral N absorption, with genetically driven changing controlled by plant inherited traits. Rhizosphere microbial diversity and community structure were largely predicted by soil NH4+-N and pH respectively; and root microbial diversity and community structure predicted by soil moisture and pH, and soil NO3--N respectively. We determined canola genotypes had a significant influence on rhizosphere and root microbial assemblages, and plant-microbial interactions likely shaped these microbiomes, making them distinct between genotypes. This research helps us understand belowground predictors on canola seed NUE that can benefit agronomic practices for greater canola productivity and NUE metrics.
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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 | 0.001 | 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 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".