Light interception and radiation use efficiency of fern- and unifoliate-leaf chickpea cultivars
Bibliographic record
Abstract
A chickpea (Cicer arietinum L.) crop with rapid leaf development, high solar radiation interception, and efficient use of radiation can maximize the yield potential in a short-season typical of the Northern Great Plains. This study determined the effects of cultivars varying in leaf architecture on light interception (LI) and radiation use efficiency (RUE) in chickpea. Six kabuli chickpea cultivars with fern and unifoliate-leaf traits were grown under low (45 plants m-2) and high (85 plants m-2) population density at Saskatoon and Swift Current, Saskatchewan, in 2003 and 2004. Fern-leaf cultivars achieved consistently higher maximum LI, and greater cumulative intercepted radiation than cultivars with the unifoliate-leaf. Estimated RUE varied largely with growing season, but did not differ among cultivars or between plant populations. Compared with low plant population, high plant population resulted in greater maximum LI in only 1 out of 4 location-years, but higher cumulative intercepted radiation in 3 out of 4 location-years. Our results indicated that future high-yielding kabuli chickpea cultivars for short seasons will benefit from increased canopy LI and seasonal cumulative intercepted radiation via the fern-leaf trait, although the fern-leaf does not further increase RUE. Use of fern-leaf cultivars, coupled with adoption of strategies that promote a rapid canopy development and improved radiation interception are keys to maximizing chickpea yield potential in the short-seasons experienced in the Northern Great Plains. Key words: Cicer arietinum, pinnate fern-leaf, unifoliate, plant population, canopy, radiation interception
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".