The effects of three pulse crops on a second subsequent crop
Bibliographic record
Abstract
Ross, S. M., King, J. R., Williams, C. M., Strydhorst, S. M., Olson, M. A., Hoy, C. F. and Lopetinsky, K. J. 2015. The effects of three pulse crops on a second subsequent crop. Can. J. Plant Sci. 95: 779-786. Pulse crops can provide benefits to cropping systems, but few studies follow the effects beyond one subsequent crop. This study investigated the effects of three pulses on 2 yr of subsequent crops at Barrhead and St. Albert in central Alberta. In year 1 (YR1), field pea (Pisum sativum L.), faba bean (Vicia faba L.), lupin (Lupinus angustifolius L.), barley (Hordeum vulgare L.), and canola (Brassica napus L.) were grown without added N. The design included plus N controls, eight different crops in YR2, and barley in YR3. YR1 effects on YR3 barley varied between sites and years, and drought conditions in 2009 affected results. Effects of YR1 faba bean were greater than pea or lupin. Increases in YR3 barley grain yields averaged 11% (0.33 Mg ha-1) and increases in seed N yields averaged 11% (7.2 kg N ha-1) after YR1 faba bean, compared with after YR1 canola or barley without added N (BCO). Increases in YR3 barley grain yields and seed N yields averaged 3 to 5% after YR1 pea or lupin, compared with BCO.YR1 crops had few effects on YR3 barley P uptake. Results indicated that pulse crops can improve the yield and quality of a second subsequent crop.
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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 | 0.001 |
| 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".