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Record W2789611435 · doi:10.5539/jas.v10n4p138

Genetic Variation among Robusta Coffee Genotypes for Growth and Yield Traits in Ghana

2018· article· en· W2789611435 on OpenAlexvenueno aff
Abraham Akpertey, E. Anim-Kwapong, Atta Ofori

Bibliographic record

VenueJournal of Agricultural Science · 2018
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsnot available
FundersCocoa Research Institute of Ghana
KeywordsCoffea canephoraHeritabilityRandomized block designYield (engineering)BiologyGenetic gainGenetic variationCoefficient of variationSelection (genetic algorithm)HorticultureMathematicsStatisticsCoffea arabicaGeneticsGene

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.146

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.279
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2018
Admission routes1
Has abstractyes

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