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Record W4312132087 · doi:10.1016/j.heliyon.2022.e12591

The need for alternative pest management methods to mitigate risks among cocoa farmers in the Volta region, Ghana

2022· article· en· W4312132087 on OpenAlexfundno aff
Michael Miyittah, Richard K. Kosivi, Samuel Kofi Tulashie, Maxwell N. Addi, Josephine Y. Tawiah

Bibliographic record

VenueHeliyon · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCocoa and Sweet Potato Agronomy
Canadian institutionsnot available
FundersOntario Ministry of Food and Agriculture
KeywordsIntegrated pest managementPEST analysisAgroforestryAgricultural scienceCrop managementPest controlBusinessAgricultureAgronomyBiologyMarketingEcology

Abstract

fetched live from OpenAlex

= 39.275, p < 0.000) and farmers' knowledge on alternative pest control methods. Farmers who relied on agrochemical shop services for pest control methods were 87% less likely to be knowledgeable on alternative pest control methods while those who considered degree of pest infestation in pest management were (OR = 1.150, p <0.008) more likely to be knowledgeable on alternative pest control methods. For the socio-cultural factors, Leklebi Kame (OR = 9.53-e 08, p < 0.000), Bla (OR = 0.280, p < 0.027) and Gbledi Chebi (OR = 0.287, p < 0.053) were less likely to be knowledgeable on alternative method of pest control compared to Kpedze. Fellow farmers and extension agents were the major sources of information on alternative pest control methods in the study area. Economic, technical, unavailability of labour, and farm implements were factors hampering adoption of alternative pest control methods in the study area. The most pesticide toxicological symptom reported was skin irritation and was recorded among majority of the farmers in Hohoe and Afadjato South districts where low knowledge and patronage of alternative pests control methods were identified. Awareness creation and capacity building programs should be organized through fellow farmers and extension agents on the need to reduce the use of chemical pesticide in pest management.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.543

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.051
GPT teacher head0.308
Teacher spread0.257 · 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 designOther design
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

Citations13
Published2022
Admission routes1
Has abstractyes

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