Frequency of field pea in rotations impacts biological nitrogen fixation
Bibliographic record
Abstract
Knight, J. D. 2012. Frequency of field pea in rotations impacts biological nitrogen fixation. Can. J. Plant Sci. 92: 1005-1011. Economic, environmental and energy concerns about the use of nitrogen (N) fertilizers in crop production have prompted the examination of increasing the frequency of pulses in crop rotations to capitalize on biological nitrogen fixation (BNF). Plots from a field experiment established in 1998 at the Agriculture and Agri-Food Canada Research Farm at Scott, SK, were sampled in 2008, 2009 and 2010. Rotations that included pea every year (continuous pea), every second year (pea-wheat), every third year (pea-canola-wheat) and every fourth year (canola-wheat-pea-wheat) were evaluated for BNF using the enriched 15N isotope dilution technique. Nitrogen from BNF in the seed and straw, total above-ground N, seed and straw yield and soil available N and P were evaluated. In 2 of 3 yr, the highest BNF occurred in the two most diverse rotations. Continuous cropping of pea resulted in drastically low BNF in 2008 and 2009. Nitrogen derived from atmosphere in the continuous pea was 15% compared with an average of approximately 55% across all other rotations in these 2 yr. The reduction in BNF was not due to lower productivity in the continuous pea rotation, nor from higher initial soil inorganic N levels inhibiting BNF. In the third year of the study (2010), the more than double the normal precipitation received during the growing season stimulated BNF in pea in the continuous pea rotation. Determining whether the rotation effects on BNF are due to N mineralization of the previous years' crop residues requires further investigation.
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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) | 0.002 | 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".