Shading, Defoliation and Light Enrichment Effects on Chickpea in Northern Latitudes
Bibliographic record
Abstract
Abstract Chickpea ( Cicer arietinum L.) has an indeterminate growth nature, and the plant canopy with an improved light environment during critical growth stages may increase biomass (BM) production and improve crop yield. This study examined (i) the effects of shading, light enrichment and defoliation applied at various growth stages on BM and seed yield of chickpea in northern latitudes; and (ii) the difference between cultivars with fern‐ vs. unfoliate‐leaf type in responding to the altered canopy light environments. Field studies were conducted at Saskatoon and Swift Current, Saskatchewan in 2004 and 2005. Different light environments were created by 50 % defoliation at vegetative growth and at first flower, 50 % shading from vegetative growth to first flower, and two light enrichment treatments initiated at the first flower and pod formation stages. The 50 % shade treatment prior to flowering significantly decreased harvest index (HI) and seed yield. Light enrichments increased seed yield only one of three location‐years (the fourth site excluded because of disease damage). Defoliation at vegetative growth or first flower had a marginal effect on seed yield, largely as a result of the regrowth of vegetative tissues compensating for the lost plant tissues. The cultivar CDC Yuma (fern‐leaf type) exhibited consistently greater maximum light interception (LI), cumulative intercepted radiation, HI and seed yield than the cultivar Sanford (unifoliate‐leaf type) across all location‐years. Selective use of chickpea cultivars with improved morphological traits such as fern‐leaf type will likely improve LI and increase crop yield for chickpea in northern latitudes. Moreover, optimized crop management practices should be adopted to ensure that chickpea be grown under conditions with minimum shading before flowering and optimum light environment within the canopy especially during reproductive growth period.
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 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 | 0.000 |
| 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 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".