Plant spatial arrangement to maximize spring wheat (Triticum aestivum L.) yield in Manitoba in dry conditions
Bibliographic record
Abstract
Spring wheat (Triticum aestivum L.) currently accounts for the second-largest area of seeded acres in Manitoba. However, information is limited on the optimal plant spatial arrangement to maximize seed yield for modern spring wheat cultivars grown in Manitoba, particularly following a soybean crop. The objectives of this research project were to determine row spacing and seeding density combinations in two modern spring wheat cultivars that maximize seed yield and if the preceding crop (canola or soybean) affects this relationship. Field experiments were conducted at Carman, Howden, and Portage la Prairie, Manitoba, in 2019 and 2020. AAC Brandon and Cardale spring wheat cultivars were subjected to three row spacings (9.5, 19, and 38 cm), four seeding densities (200, 300, 400, and 500 target plants m-2), and two stubble types (canola and soybean). Below-average precipitation was received throughout the growing season at all site-years. AAC Brandon and Cardale seed yield was significantly greater when seeded at the more narrow (9.5 cm or 19 cm) row spacings compared with the wider (38 cm) row spacing. Narrow row spacing treatments resulted in increased and more rapid ground cover, suggesting improved water, sunlight, and nutrient utilization by spring wheat plants. Increasing AAC Brandon and Cardale plant density generally did not influence seed yield. Cardale spring wheat was more responsive to changes in row spacing and seeding density treatments compared with AAC Brandon spring wheat. Collectively, this research indicates that in dry growing conditions, spring wheat growers in Manitoba have the opportunity to increase seed yields by seeding spring wheat at narrower row spacings following a canola or soybean crop. However, more research on producer constraints may lead to increased narrow row adoption by growers. Further research is also necessary to investigate the yield-density relationship during growing seasons that receive more average or above-average precipitation.
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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) | 0.000 | 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 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".