Linking Soil Nitrogen Cycling and Plant Biotic Traits to Nitrogen Use Efficiency Parameters Among Diverse Canola (Brassica napus) Genotypes
Bibliographic record
Abstract
Canola (Brassica napus) is a dominant oilseed crop grown globally, second only to soybean. It requires relatively large amounts of nitrogen (N) fertilizer input compared to other oilseeds or cereal crops, but global estimates suggest that less than 50% of applied nitrogen (N) is recovered in canola seed harvest, indicating significant N-use inefficiencies. These inefficiencies contribute to resource overuse and waste, that can negatively impact the environment. There is significant variation in N-use efficiency (NUE) between crop genotypes, across different soil types and regions, and between different crop management systems. These factors make it challenging to implement strategies to improve NUE. Therefore, to create strategies to improve NUE, researchers must examine genotype (G) x environment (E) x crop management practices (M). Three studies employing varying combinations and degrees of G x E x M highlighted below-ground interactions (between soil properties, root phenology, and rhizosphere and root endosphere microbiomes) and above-ground plant traits (N uptake, N utilization, N partitioning, and seed protein) to determine relevant factors that contribute to canola productivity. This dissertation aims to link below-ground soil–plant–bacterial interactions to plant N uptake, remobilization, and partitioning that can improve canola harvest parameters such as yield, partial factor productivity (PFP), and NUE. Sixteen canola genotypes were grown on a Dark Brown Chernozem in Saskatchewan, Canada, with soil and plants sampled five times from 32-81 days after sowing (DAS), and seeds sampled at 81 DAS. Canola genotypes in this thesis exhibited different concentrations of below-ground soil parameters like moisture and NO3- -N; and genotypes with higher NUE and PFP were linked greater absorption of these soil parameters. Root morphology and rhizosphere and root microbiomes did not correlate with NUE or PFP. However, root surface area negatively correlated with soil NO3--N concentrations, suggesting that genotypes with larger absorptive surfaces acquired more soil mineral N. Rhizosphere bacterial diversity and community structure varied with changes in soil NH4+-N and pH, respectively, while root bacterial diversity and community structure varied with changes soil moisture and pH, and soil NO3--N, respectively. Plant-bacterial interactions likely shaped these microbiomes, making them distinct between genotypes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".