Effects of Seeding Rate on Durum Crop Production and Physiological Responses
Bibliographic record
Abstract
Core Ideas Optimum seeding rate on elite durum wheat depends on environment. Seeding rate had a significant positive relationship with grain yield, leaf area index, and carbon isotope discrimination. Seeding rate should be adjusted for environment and genotype for maximum yield. Seeding rate can be manipulated to optimize the ability of the crop to capture available resources and therefore increase yield. Seeding rate may vary between regions according to the climate conditions, soil type, sowing time, and other agronomic practices. Insufficient information is available for optimum seeding rate on durum wheat ( Triticum turgidum L. var durum ) for some production zones, and response to seeding rate is unknown for recently registered durum cultivars in Canada. The objective of this study was to determine the effect of seeding rate (SR) on performance of Canada Western Amber Durum wheat cultivars and study the underlying physiological response to a wide range of SRs. Eight durum wheat cultivars were sown at densities of 163, 217, 272, 326, and 380 seeds m −2 to study the effect of SR on several agronomic and physiological traits. Each experiment was planted as a factorial randomized complete block design with three replications near Swift Current and Regina in 2010 and 2011. High genetic and environmental response to SR was observed between cultivars. The results showed an increase in grain yield as the SR increased. The optimum SR for cultivars grown at Swift Current and Regina was 272 to 326 seeds m −2 and 217 to 272 seeds m −2 . Grain yield showed a positive relationship with carbon isotope discrimination (CID) and leaf area index (LAI). In turn, LAI showed a linear increase with SR. Information generated from this study could enable producers to maximize crop grain profitability by optimizing plant density.
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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 | 0.000 | 0.001 |
| 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 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".