Interrelationships among yield components of chickpea in semiarid environments
Bibliographic record
Abstract
Chickpea (Cicer arietinum L.) seed yield can be increased by identifying and managing the key yield components. A field study was conducted in southwestern Saskatchewan in 1999 and 2000 to determine the direct and indirect effects of various yield components on chickpea seed yield . Both desi- and kabuli-chickpea were planted at the target plant populations of 20, 30, 40, and 50 plants m-2 on conventional summerfallow (CS) and no-till wheat stubble (NT). Path coefficient analyses revealed that seed yield for both chickpea classes largely depended upon pods m-2 and seed weight, with the kabuli crop having higher coefficient values than the desi. These relationships were stronger when the pulses were grown on CS than on NT. Seeds pod-1 had a negative effect on seed yield for the kabuli crop, but this negative effect was counterbalanced by a strong, positive effect of seed weight on seed yield. The total pod production of the desi crop depended on plants m-2 more than on pods plant-1, whereas the pod production of the kabuli crop relied equally on plants m-2 and pods plant-1. For both chickpea classes, mean seed weight decreased with prolonged vegetative growth period regardless of tillage environment. Seed weight was positively related to the length of reproductive growth and seeds pod-1 only when the pulses were grown on CS. Seed yield potential of desi chickpea would be increased by increasing plant population to produce more pods per unit area, whereas the seed yield potential of kabuli chickpea would be increased by shortening the period of vegetative growth, promoting the number of pods per plant, and increasing mean seed weight. Key words: Cicer arietinum, yield components, plant density, seed weight, canopy, path analysis
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".