Pulse Crops for the Northern Great Plains
Bibliographic record
Abstract
To optimize cropping system benefits from pulse crops, it is important to understand their effects on subsequent crops. The objective of this study was to compare the effects of chickpea (Cicer arietinum L.), lentil (Lens culinaris Medik.), and pea (Pisum sativum L.) stubbles on yield and quality of wheat (Triticum aestivum L.), mustard (Brassica juncea L.) or canola (B. napus L.), and lentil or pea when grown on soils with clay and loam textures. This study was conducted between 1996 and 1999 in southwestern Saskatchewan. Rotational benefits of pulse crops (chickpea, lentil, and pea) to wheat appeared more consistent on the clay than the silt loam soil. Adjusting fertilizer N rates to account for estimated total N contribution from the previous pulse crop effectively neutralized the benefits on wheat yield and protein compared with the effects following mustard. Canola or mustard productivity was occasionally greater when grown on pea or lentil stubbles compared with mustard and wheat stubbles. The yield increase was attributed to increased available water. Under drier‐than‐normal conditions, pea yields were highest when grown on wheat stubble. Wheat productivity was least when grown on its own stubble. Pea and lentil provided rotational benefits to wheat, mustard, and canola and benefitted most from being grown in wheat stubble, indicating a strong fit for diversified cropping systems on the semiarid northern Great Plains.
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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) | 0.017 | 0.002 |
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".