Westward expansion of soybean: adaptability of maturity group 00 genotypes to row spacing and seeding density under irrigation in southern Alberta
Bibliographic record
Abstract
Soybean [Glycine max (L.) Merr.] production has moved rapidly westward on the Canadian prairies, most recently arriving in southern Alberta. Adjusting row spacing (RS) and seeding density (SD) to maximize soybean productivity is well-documented for rainfed conditions but not where irrigation is obligatory. A 3 yr study was conducted at two irrigated locations in southern Alberta using two early-maturity [maturity group 00] soybean genotypes planted at two RSs and three SDs. Soybean reached 95% maturity in 114–132 d and only one of six growing environments experienced a killing frost prior to maturity. Wide rows led to 1 d earlier maturity for one genotype in all six environments and increased grain yield (5%–20%) in four out of six environments compared with narrow rows. Increasing SD from 30 to 80 seeds m−2 generally led to increased pod clearance (from 5.0 to 8.4 cm in one environment) and grain (mean increase of 33%, from 2100 to 2800 kg ha−1) and straw yield, but decreased seeds plant−1 (from 94 to 46). Notwithstanding 9% lower cumulative corn heat units during the study, and an average 5 d longer maturity requirement at Lethbridge, soybean performance was equal to Bow Island in many parameters including grain yield. Our findings will help develop recommendations for new soybean growers in the irrigated region of southern Alberta.
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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) | 0.000 | 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".