Yield and water use efficiency of pulses seeded directly into standing stubble in the semiarid Canadian Prairie
Bibliographic record
Abstract
In semiarid climates, appropriate management of the previous crop stubble in combination with seeding method is important to improve growing conditions for the subsequent crop. To determine the effects of standing stubble of various heights on the microclimate and on the growth and yield of pulse crops, we seeded desi chickpea (Cicer arietinum L. “Cheston”), field pea (Pisum sativum L. “Grande”), and lentil (Lens culinaris L. “Laird”) directly into cultivated, short (15 to 18 cm), and tall (25 to 36 cm) spring wheat (Triticum aestivum L.) stubble. Standing stubble changed the microclimate near the soil surface by reducing soil temperatures, solar radiation, wind speed, and potential evapotranspiration throughout the life cycle of these crops. Microclimate effects were much more pronounced for tall versus short stubble. The three pulses responded similarly to increasing stubble height. Vine length increased as stubble height increased, but the plants did not stand more erect. However, there was a tendency for plant height to increase as stubble height increased. Tall and short stubble increased the overall average grain yield by 13 and 4% compared to cultivated stubble. Crop water use was not affected by stubble height so the increased grain production was due to increased water use efficiency. Tall and short stubble increased the overall average water use efficiency by 16 and 8% compared to cultivated stubble. Key words: Stubble height, pulse, microclimate, evapotranspiration, yield
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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 | 0.001 | 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".