Weed Interference, Pulse Species, and Plant Density Effects on Rotational Benefits
Bibliographic record
Abstract
Pulse crop management can increase pulse yields and N fixation, but the effects of previous pulse crop management on subsequent crop performance is poorly understood. Field studies were conducted at three locations, in the Parkland region of Alberta, Canada, between 2004 and 2006. Tannin-free faba bean, narrowleaf lupin, and field pea were planted at 0.5, 1.0, 1.5, and 2.0 times the recommended pulse planting density (PPD), with or without barley as a model weed. Faba bean produced the highest seed yields in higher precipitation environments, whereas pea produced the highest seed yields in lower precipitation environments. Lupin seed yields were consistently low. In the absence of weed interference, faba bean, pea, and lupin N-fixation yields ranged from 70 to 223, 78 to 147, and 46 to 173 kg N ha−1, respectively. On average, faba bean produced the highest N-fixation yield. The absence of weed interference and a high PPD increased pulse seed and N-fixation yields. Quality wheat crops were grown on pulse stubble without additional N fertilizer in some site–years. Management practices that increased N fixation resulted in only marginal subsequent wheat yield increases. Subsequent wheat seed yield was primarily influenced by pulse species. Pea stubble produced 11% higher wheat yields than lupin stubble but only 2% higher wheat yields than faba bean stubble. Consistently high wheat yields on pea stubble may be attributed to synchronized N release from decomposing pea residues with subsequent crop N demand and superior non-N rotational benefits.
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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) | 0.002 | 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".