Stubble management and microclimate, yield and water use efficiency of canola grown in the semiarid Canadian prairie
Bibliographic record
Abstract
Standing stubble traps snow and creates a favorable microclimate, which increases yields in wheat (Triticum aestivum L.) and pulses [chickpea (Cicer arietinum L.), field pea (Pisum sativum L.) and lentil (Lens culinaris L.)]. Generally, the taller the stubble the greater is the effect on microclimate and yield. A field study using farm-scale seeding and harvesting equipment was conducted over four seasons (1999 to 2002) to assess the effect of stubble management on the microclimate, water use and seed yield of argentine canola (Brassica napus L. ‘Arrow’) in the semiarid prairie surrounding Swift Current. Tall (30 cm), short (15 cm) and cultivated stubble treatments were deployed in fall and in spring. An additional tall stubble treatment with extra fertilizer N (application rate recommended for the Black soil zone in the subhumid prairie) was included to assess the role of fertilizer in canola response to stubble management practices. The differences in wind velocity, soil temperature and solar radiation reaching the soil surface indicated significant modification of the microclimate by tall compared with cultivated stubble. Yields were highest from the tall stubble receiving extra fertilizer. Further research is needed to determine optimum fertilizer rates to maximize canola yields in the semiarid prairie. For treatments receiving equivalent rates of fertilizer, tall stubble increased seed yield of canola by about 24% and water use efficiency (WUE) by about 19% compared with stubble cultivated in the fall. Comparing between stubble treatments deployed on fields that overwintered as tall stubble and which received equivalent rates of fertilizer, tall stubble increased canola yield by about 16% and WUE by about 11% compared with cultivated stubble. Crop water use was not affected by stubble management so the increased grain production was due to increased WUE. Key words: Stubble height, microclimate, canola, yield, water use efficiency
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".