Genetic Variation among Robusta Coffee Genotypes for Growth and Yield Traits in Ghana
Bibliographic record
Abstract
Quantifying the level of variation and estimates of genetic parameters are important to make informed decisions regarding the improvement of agronomic traits in Coffea canephora. The objectives of the present study were to assess the growth and yield performance of 54 C. canephora clones derived through ortet selection, based on yield from a previous hybrid trial; estimate genetic parameters of growth traits (stem diameter, height, span, number of laterals, length of laterals and diameter of laterals), and yield; and determine the relationship between yield and the growth traits. The clones were planted in the field in 2009 using a randomized complete-block design with three replications. Significant (p < 0.01) clone effects for all traits and broadsense heritability range of 0.15 (mean yield of last 3 productive years)-0.43 (diameter of laterals) were observed. Stem diameter was moderately and positively correlated with early years’ yield (2012/13 mean yield, r = 0.49; p < 0.001), late years’ yield (2014 to 2016 mean yield, r = 0.44; p < 0.001), and mean yield across five years (r = 0.42; p = 0.001). Relatively high genotypic coefficient of variation and expected genetic advance values were obtained for the evaluated traits, which indicated a high probability of success of selection for these traits.
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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 | 0.001 |
| 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".