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Record W2977007911 · doi:10.5539/sar.v8n4p48

Smallholder Farmers’ Decision to Participate in Vegetable Marketing and the Volume of Sales in West Shewa Zone of Oromia National Regional State, Ethiopia

2019· article· en· W2977007911 on OpenAlexvenueno aff
Aman Rikitu, Bezabih Emana, Jema Haji, Ketema Bekele

Bibliographic record

VenueSustainable Agriculture Research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersAmbo UniversityHaramaya University
KeywordsProbit modelOrdinary least squaresProbitAgricultural economicsMarket accessBusinessIrrigationAffect (linguistics)AgricultureEconomicsAgricultural scienceMarketingSocioeconomicsGeography

Abstract

fetched live from OpenAlex

This study examines vegetable producers’ market participation and sales volume using cross-sectional data obtained from 385 randomly and proportionately sampled households from West Shewa zone, Oromia region of Ethiopia. Heckman two-step procedure was used to analyse the determinants of participation in vegetables markets and volume of sales during the study period. Probit model shows that education level, distance to nearest market, access to irrigation, use of pesticide and participation in any civic organization significantly affect market participation decision. Further, results from ordinary least squares regression show that sex of household head, land size, distance to farmer training centre, access to irrigation, use of pesticide and participation in civic organization significantly affect the level of market participation of the farm households in vegetable markets. The findings imply that support for female households, improving adult based education, participation in civic organization, infrastructure, access to irrigation and improved inputs are a means to increase vegetable production market participation and sales volume in West Shewa, Ethiopia.

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)0.006
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 score0.256
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 science0.0000.000
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 head0.044
GPT teacher head0.322
Teacher spread0.278 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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