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Record W2018727108 · doi:10.5539/sar.v1n2p275

Effect of Native Soap on Insect Pests and Grain Yield of Cowpea (Vigna unguiculata (L) Walp) in Asaba and Abraka during the Late Cropping Season in Delta State, Nigeria

2012· article· en· W2018727108 on OpenAlexvenueno aff
E. O. Egho, E. C. Enujeke

Bibliographic record

VenueSustainable Agriculture Research · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural pest management studies
Canadian institutionsnot available
Fundersnot available
KeywordsAphis craccivoraVignaBiologyAphidThripsCropHorticultureAgronomyPoint of deliveryLegumePopulationPEST analysisHomopteraAphididae

Abstract

fetched live from OpenAlex

<p>Studies were conducted to test the effectiveness of native soap against cowpea insect pests during the late cowpea cropping season in two agro-ecological zones-Asaba and Abraka, Delta State. Four major insect pests, namely the cowpea aphid, <em>Aphis craccivora</em> Koch, the legume flower bud thrips, <em>Megalurothrips sjostedti </em>Tryb, the legume pod borer, <em>Maruca vitrata </em>Fab and pod sucking bugs were studied. The experiment was made up of five treatments-1, 2 and 3 percent concentrations of native soap, cypermethrin (as conventional chemical and check) and a control. Each treatment was replicated three times. The experiment was arranged into a randomised complete block design (RCBD). The results showed that all the major insect pests occurred in the study areas but were more at Asaba compared to Abraka. Native soap was effective against <em>A. craccivora </em>and flower bud thrips population at Asaba. <em>Maruca vitrata </em>was not affected by soap application. Grain yield was high at Abraka and significantly (P<0.05) higher than Asaba. The use of native soap as non-conventional insecticide in cowpea insect pests management appears promising, more so as it is not expensive and safe to handle. Farmers may prefer it to synthetic chemical pesticides with their associated dangers.</p>

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.021
GPT teacher head0.282
Teacher spread0.262 · 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

Citations4
Published2012
Admission routes1
Has abstractyes

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