Selection for seed size and its impact on grain yield and quality in kabuli chickpea
Bibliographic record
Abstract
Seed cost is a major input expense for the production of kabuli chickpea (Cicer arietinum L.) due to its large seed size. Use of small seeds could reduce production costs because a lower volume of seeds would be needed per unit area. This study determined the effects of seed size, and selective use of small seeds, year after year, on the field performance of kabuli chickpea in Swift Current, Saskatchewan from 2000 to 2003. Separated large (9.1–11.0 mm diameter) and small (8.1–9.0 mm) seeds of certified CDC Xena were compared with the original, unseparated seeds during the 4-yr study period. Also, small seeds separated from small-seeded previous crops and large seeds separated from large-seeded previous crops were compared with the respective generations of unseparated seeds. Year significantly affected plant biomass, seed yield, and the proportion of ≥ 9-mm-diameter seeds (9DSeeds) in the harvested seed lot, and crops grown from large and small seeds separated from the original seed lot (i.e., 1st year of separation) did not differ in a given year. The 2nd year of selection for small seeds affected seed yield and 9DSeeds, although the effect was minimal. With 3 consecutive years of selection, small seeds lowered seed yield by 23% and decreased 9DSeeds by 10% compared with the unseparated seeds. Selective use of large seeds improved biomass, but did not increase seed yield or 9DSeeds compared with the unseparated seeds. Small seeds of a certified kabuli cultivar can be selectively used for up to 2 consecutive years before incurring a yield penalty, but the use of small seeds will decrease the percentage of large seeds in the harvested seed lot. Key words: Seed weight, selection pressure, Cicer arietinum L.
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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".