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Record W3084087634 · doi:10.5539/jas.v12n10p116

Assessment of Juvenile Growth and Yield Relationship Among Dwarf Cashew Types in Ghana

2020· article· en· W3084087634 on OpenAlexvenueno aff
Abu Mustapha Dadzie, Paul Kwesi Krah Adu-Gyamfi, Abraham Akpertey, Atta Ofori, Stephen Yaw Opoku, J. Yeboah, E. G. Akoto, Francis Kwame Padi, Ebenezer Obeng‐Bio

Bibliographic record

VenueJournal of Agricultural Science · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsnot available
FundersCocoa Research Institute of Ghana
KeywordsBiologyHeritabilityAnacardiumCropRandomized block designJuvenileYield (engineering)Cash cropAgronomyHorticultureAgricultureEcology

Abstract

fetched live from OpenAlex

Cashew (Anacardium occidentale L.) is an important tropical cash crop cultivated in Ghana. It provides livelihood for about 200,000 people and contributes 6.1% to Ghana’s gross domestic product (GDP). Four Brazilian dwarf accessions were introduced to improve nut yield. Objectives of this study were to (1) assess the agronomic performance of the accessions across two contrasting ecologies, (2) determine environmental influence on juvenile growth, (3) determine the relationship between early vegetative growth and yield and (4) explore heritability and genetic advance for the measured agronomic traits. The experiment was laid out in a randomized complete block design with 3 replications. Results revealed significant (p < 0.05) environmental influence on growth and yield of cashew. Transitional savanna agro-ecology is more suitable for cashew growth and development. Crop year, location and crop year × location interactions also influenced most of the agronomic traits. Early growth characteristics alone were not enough to predict yield. Genotype B2 ranked highest yielding across the agro-ecologies. Moderate to high heritability and genetic advance estimates were observed for nut yield, plant height and girth, an indication of variability among accessions needed for cashew improvement in Ghana.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.205
Threshold uncertainty score0.112

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.028
GPT teacher head0.241
Teacher spread0.212 · 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
Published2020
Admission routes1
Has abstractyes

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