Pulse Crops for the Northern Great Plains: I. Grain Productivity and Residual Effects on Soil Water and Nitrogen
Bibliographic record
Abstract
Grain producers need to know the comparative productivity of pulse crops, and their effects on soil N and water, to optimize diversified cropping systems. The objective was to compare grain productivity, water use efficiency (WUE), and apparent N margin among dry pea ( Pisum sativum L.), lentil ( Lens culinaris Medik.), and desi chickpea ( Cicer arietinum L.) and their effects on subsequent soil water and N when grown on loam and clay soil textures. This study was conducted in Saskatchewan, Canada, between 1996 and 1999. On the loam soil, pea yield equaled wheat ( Triticum aestivum L.) and was 39 and 34% greater than that of lentil and chickpea, respectively. On the clay soil, pea yields were 26% less than spring wheat yield, equal to chickpea yield, and 29% greater than lentil yield. The apparent N margin for pea averaged 40 and 32 kg ha −1 greater than for lentil and chickpea, respectively, indicating superior N 2 fixation. Postharvest soil water status to a 122‐cm depth was greater for all pulse crops compared with wheat on both soils, ranging from 25 to 49 mm greater under the clay soil and 12 to 31 mm greater under the loam soil. Postharvest soil water differences occurred primarily below 61 cm. However, differences in soil water status to a 122‐cm depth disappeared by spring. Conversely, postharvest differences among crops for soil N increased over winter due primarily to an increase in soil N status above 61 cm. By spring, all three pulse crop stubbles had greater soil NO 3 –N than wheat, averaging 28 and 12 kg ha −1 greater at the clay and loam soil sites, respectively. Pulse crop productivity was less on the clay than the loam soil, but beneficial effects on soil water and N were greater, indicating that pulse crops will be economically valuable on both soil types.
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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".