MétaCan
Menu
Back to cohort
Record W4296130883 · doi:10.1108/ijse-03-2022-0165

Determinants of smallholder farmers' membership in co-operative societies: evidence from rural Kenya

2022· article· en· W4296130883 on OpenAlexfundno aff
Obadia Okinda Miroro, Douglas N. Anyona, Isaac K. Nyamongo, Salome A. Bukachi, Judith K. Chemuliti, Kennedy Munyua Waweru, Lucy Maina Kiganane

Bibliographic record

VenueInternational Journal of Social Economics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersInternational Development Research CentreGlobal Affairs CanadaBill and Melinda Gates Foundation
KeywordsLivelihoodAgriculturePsychological interventionBusinessProbit modelModerationSocioeconomicsAgricultural scienceEconomicsGeography

Abstract

fetched live from OpenAlex

Purpose Despite the potential for co-operatives to improve smallholder farmers' livelihoods, membership in the co-operatives is low. This study examines factors that influence smallholder farmers' decisions to join agricultural co-operatives. Design/methodology/approach This study involved a survey of 1,274 smallholder chicken farmers. The data were analysed through a two-sample t -test of association, Pearson's Chi-square test and binary probit regression model. Findings The results suggest that farming as the main source of income, owning a chicken house, education attainment, attending training or accessing information, vaccination of goats and keeping a larger herd of goats are the key factors which significantly influence co-operative membership. However, gender, age, household size, distance to the nearest agrovet, vaccinating chicken and the number of chickens kept do not influence co-operative membership. Research limitations/implications The survey did not capture data on some variables which have been shown to influence co-operative membership. Nevertheless, the results show key explanatory variables which influence membership in co-operatives. Practical implications These findings have implications for development agencies that seek to use co-operatives for agricultural development and improvement of smallholder farmers' livelihoods. The agencies can use the results to initiate interventions relevant for different types of smallholder farmers through co-operatives. Originality/value This study highlights the influence of smallholder farmers' financial investments in farming and the extent of commercialisation on co-operative membership. Due to low membership in co-operatives, recognising the heterogeneity of smallholder farmers is the key in agricultural development interventions through co-operative membership. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2022-0165 .

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.056
GPT teacher head0.318
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 source (direct Gemma or distilled Codex), 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

Citations18
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Social EconomicsSame topicAgricultural Innovations and PracticesFrench-language works237,207