Growth, Development and Yield Response of Soybean Maturity Groups 000 and 00
Bibliographic record
Abstract
With the growing interest of soybean production and its expansion into Western Canada, this research aimed at characterizing soybean adaptation to Saskatchewan (SK), which is considered a new frontier for soybeans in Canada. This region has a cool temperate climate with limited precipitation and growing seasons are typically less than 130 days. Soybean growth and development were measured over four summer seasons (2019-2022) across 12 locations in SK. Data were divided into three environments: cool (year 2019), mid (year 2020), and hot (years 2021 and 2022). Field trials at each of the 12 locations was composed of 24 commercial cultivars that ranged from two Maturity Groups (MG 000 and 00). The cultivars were further subdivided into three subgroups: Super Ultra Early (SUE; all MG 000), Ultra Early (UE; MG 00.1 to 00.4), and Early (E; MG 00.5 to 00.9). Soybean required 135 days (d) to complete its lifecycle. Cultivars in the SUE (MG 000) and UE (early MG 00) subgroups demonstrated better adaptation to Saskatchewan conditions when compared to the E (late MG 00) subgroup. Additionally, a genotyping experiment was conducted in 2022 using competitive allele-specific PCR (KASP) assays on a subset of 26 cultivars, all of which were adapted to Western Canada. Allelic variations for the six loci E1, E2, E3, E4, E9, and E10, were associated with the time of flowering and maturity. Most of the cultivars possessed the E1 dominant allele, which delays flowering, while all cultivars had the E9 and e2-ns alleles promoting early time of flowering. The most common genotype background was E1/e2/e3/E4/E9. Over all data, MG 000 (1201 kg. ha-1) performed better than MG 00 (1120 kg. ha-1), exhibiting a 6.7% higher seed yield. Soybean seed composition oil, protein, and residual concentration were also determined by near infra-red spectroscopy for 11 locations. Soybean produced protein concentration of 370 mg. g-1 on a dry weight of meal basis (37%), an oil concentration of 235 mg. g-1 (23.5%), and a residual concentration of 395 mg. g-1 (39.5%).
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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) | 0.001 | 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".