Influence of pea cultivar and inoculation on the nitrogen budget of a pea-wheat rotation in northwestern Canada
Bibliographic record
Abstract
Three field experimentswere conducted in northwestern Alberta to assess the influence of pea (Pisum sativum L.) cultivars on the N economy and the performance of the sequent wheat (Triticum aestivum L.) crop. Three pea varieties and two levels of Rhizobium inoculant (none and 5 kg ha -1 ), in factorial combination, were tested at each site; overall, four pea cultivars were tested with barley (Hordeum vulgare L.) as the check. All plots were seeded to wheat in the second year. All but one experimental year had below-average growing season rainfall. Dinitrogen fixation decreased in the following order among pea cultivars: Grande > Carerra ≥ Eiffel ≥ Swing, the same order as net productivity: the correlation between fixed N and shoot dry matter at harvest was highly significant (R 2 = 0.982; P < 0.001). Only Grande pea resulted in balanced soil N (exported N = fixed N); the deficit in the N balance, in kg N ha -1 , was 7–38 for Carrera, 20–37 for Swing and 18–37 for Eiffel. However, even where the soil N balance was negative, wheat follow ng pea mostly had higher seed protein content and yield than wheat following barley due to a high correlation between the yield of the sequent wheat and pea-fixed N. Rhizobium inoculation increased nodule formation and N 2 fixation in only one of the experiments; however, it did not affect the yield of the sequent wheat as compared with uninoculated soil. We conclude that selection of a high net productivity pea cultivar should typically result in greater N and yield benefits to the sequent cereal crop than a low net productivity cultivar. Key words: N budget, Rhizobium inoculation, wheat, pea varieties, N2 fixation
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 teacher head, 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".