Nitrogen and phosphorus uptake and nitrogen fixation estimation of faba bean in western Canada
Bibliographic record
Abstract
Abstract Faba bean (Vicia faba L.) has very efficient dinitrogen (N2) fixation, the greatest among grain legumes. The main aims of this study were to investigate the effect of nitrogen (N) and phosphorus (P) fertilizers on yield, N and P uptake, and biological nitrogen fixation (BNF) potential of faba bean. Four faba bean cultivars (‘CDC Snowdrop’, ‘219‐16’, ‘Snowbird’, and ‘Tabasco’) were grown in a 2‐yr field experiment at four site‐locations in Saskatchewan, Canada, during 2016 and 2017. In addition, a controlled‐environment study was also conducted to address how P fertilization influences growth and proportion of nitrogen derived from the atmosphere (%Ndfa) through BNF in 2017. Yield was not significantly affected by cultivar, fertilization or site‐location, however, N and P uptake were significantly affected to varying degrees by all three effects. The average faba bean grain yield was 5,280 kg ha–1 in field experiments. Average grain N uptake (117–300 kg N ha–1) and P uptake (15–35 kg P ha–1 and 34–80 kg P2O5 ha–1) were significantly greater than straw N and P uptake in the field. An estimated 88% Ndfa was obtained in the controlled‐environment study and 230 kg N ha–1 of N uptake was observed in aboveground biomass (grain and straw) in the field experiments. Results support the efficiency of N2 fixation in this species in western Canada.
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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 | 0.000 | 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 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".