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Record W4409289405 · doi:10.5539/jas.v17n5p70

Phenological Study and Effect of Two Pollination Techniques on Groundnut Fruiting

2025· article· en· W4409289405 on OpenAlexvenueno aff
Bi Tra Achille Irie, Inza Jésus Fofana, Deless Edmond Fulgence Thiémélé, Yacouba Bakayoko

Bibliographic record

VenueJournal of Agricultural Science · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPeanut Plant Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhenologyPollinationBiologyHorticultureAgronomyBotanyMathematicsPollen

Abstract

fetched live from OpenAlex

Arachis hypogaea Linné is an annual legume and is one of the most widely cultivated oilseeds in West Africa and even worldwide for its high oil content. However, the floral phenology of groundnut accessions and their typology are not fully known. Similarly, the reliable manual pollination technique used to cross-breed elite genotypes of Arachis hypogea is not sufficiently well known. With a view to improving the local yield of this crop, the present study was conducted to assess the morphological diversity of six accessions linked to floral biology, and to evaluate two manual pollination techniques A and B. The results showed morphological variability within accessions. Thus, except for flower length, the other characters (flower width; number of flowers per peduncle; time between flower bloom and pod maturity) significantly differentiated the 6 groundnut accessions. Then, accessions PA1-19, PA1-21, PA8-1, PA12-5, P12-7 and PA17-2 were differentiated by the four characters reflecting floral phenology. Technique B was more effective than technique A, with success rates of 61.35% and 42.04% respectively. This superiority of technique B could be explained by the presence of the carina during pollination. The combination of morphological and phenological floral characteristics showed that accessions PA1-21 and PA1-19 performed better. These accessions could be used in crosses based on the manual pollination technique identified, to create high-performance groundnut hybrids in Côte d’Ivoire.

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)0.002
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 score0.880
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.018
GPT teacher head0.315
Teacher spread0.297 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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