Soil Nitrogen Cycling Processes Under Diverse Canola Genotypes
Bibliographic record
Abstract
Canola is one of the most important oil crops globally behind soybean and oil palm. Nitrogen (N) is one of the limiting resources for canola production, yet canola is often blamed for its low nitrogen use efficiency (NUE). This project aims to identify canola lines with consistently high NUE for further exploitation in canola breeding programs. Specifically, I sought to evaluate genotypic differences in soil N cycling processes in a field study established at two sites located on Black (Melfort) and Dark Brown (Llewelyn) Chernozems in Saskatchewan, Canada. The experiment consisted of eight canola genotypes grown under two levels of N fertility and arranged as a split-plot randomized complete block design. The abundance of microbial N cycling genes (bacterial amoA and archaeal amoA) was assessed in the rhizosphere and extracellular enzyme activities (leucine aminopeptidase; LAP and N-acetyl glucosaminidase; NAG) were determined in soils collected from the root-zone and between rows at the 6-leaf and flowering stages. Overall, the rhizosphere supported higher extracellular enzyme activities in comparison to between-row soils. Development stage was also an important factor for both LAP and NAG activities. However, for the effect of canola cultivar and N fertilization, although NAG varied significantly in soils collected in 2019, the effect of N fertilization rates and cultivar on enzyme activities was inconsistent among site-years and was not the same between LAP and NAG. For bacterial and archaeal amoA gene abundance, the effect of development stage was significant for all site-years, with the exception of the bacterial amoA gene abundance at Melfort in 2019. However, N application rate and cultivar did not have any significant influences on the gene abundances. There was positive correlation between extracellular enzyme activity and gene abundance, which indicated that the increase in extracellular enzyme activity was accompanied by the proliferation of the amoA communities. However, the effect was dependent on the site and year. Overall, extracellular enzyme activities and amoA gene abundances appear to be most strongly related to sampling locations within the plot (rhizosphere versus between row). In contrast, cultivars and N treatments did not have significant effects. Therefore, the identification of canola lines with high NUE enhance the overall economic and environmental values for canola production.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".