Pea green manure management affects organic winter wheat yield and quality in semiarid Montana
Bibliographic record
Abstract
Miller, P. R., Lighthiser, E. J., Jones, C. A., Holmes, J. A., Rick, T. L. and Wraith, J. M. 2011. Pea green manure management affects organic winter wheat yield and quality in semiarid Montana. Can. J. Plant Sci. 91: 497–508. Organic farmers in semiarid Montana desire green manures that supply sufficient soil nitrate-N (NO3-N) to subsequent crops with minimal soil water depletion. Spring and winter pea (Pisum sativum L.) green manures were compared at the bloom and pod stages for soil NO3-N contribution and water use, and subsequent winter wheat (Triticum aestivum L.) grain yield and quality in a long-term organic farm in northern Montana. Winter wheat was managed with three additional variables (cultivar, row spacing, and seeding rate). Winter pea had 15–33 kg ha−1 greater shoot N content (at pod stage only), contributed 14–20 kg ha−1 greater soil NO3-N, used 26–31 mm less soil water, and increased winter wheat grain yield by 13–39% and protein by 1.5 percentage units (2007 only), compared with spring pea. Pea green manure type was of primary importance, pea manure termination timing and wheat cultivar generally were of secondary importance, and row spacing and seeding rate were relatively unimportant to wheat yield and quality. Although wheat yield and quality were superior following winter pea green manure in this study, grain protein concentrations were inadequate to meet organic milling industry standards following both green manure types. This suggests that a long-term organic farmer in semiarid northern Montana may not solely rely upon annual legume green manures to sufficiently condition soil NO3-N for milling wheat 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 | 0.000 | 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) | 0.001 | 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".