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Record W2111779753 · doi:10.5539/sar.v1n1p87

Minimising Insecticide Application in the Control of Insect Pests of Cowpea (Vigna Unguiculata (L) WALP) in Delta State, Nigeria

2012· article· en· W2111779753 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
KeywordsVignaInfestationCypermethrinBiologyAgronomyRandomized block designCroppingGrain yieldCropYield (engineering)PesticideAgricultureEcology

Abstract

fetched live from OpenAlex

Many commercial cowpea farmers control insect pests on cowpea with synthetic chemicals and may sometimes spray their farms during the growing season as many as 8 to 10 times. This leads to health hazards and environmental pollution. The present study was conducted to reduce the number of times, cypermethrin (conventional chemical) is applied before harvest and still, produce the expected cowpea grains. The experiments were carried out in two agro-ecological zones - Asaba and Abraka of Delta State during the late cropping season. The experiments consisted of 4 treatments - cowpea plots sprayed 4 times (at 7 days’ intervals), cowpea plots sprayed 5 times (at 10 days’ intervals), cowpea plots whose insect infestation were monitored before chemical application and control plots (without chemical treatment). Each treatment was replicated 3 times. The experiments were organised into a randomised complete block design (RCBD). The results indicated that cypermethrin controlled the major insect pests of cowpea. Second, grain yield was high at both locations; significant differences did not exist (P>0.05) in insect number and grain yield among the treatments. The study provides the evidence that (i) high cowpea grain yield is obtained at reduced number of chemical application of 4 or 5 times during the growing season (ii) Grain yield was significantly (P<0.05) higher at Abraka with1400.60kg ha-1 than Asaba (714.40kg ha-1) during the late cropping season.

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 score0.135
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.035
GPT teacher head0.295
Teacher spread0.260 · 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

Citations6
Published2012
Admission routes1
Has abstractyes

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