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Record W3121124776 · doi:10.5539/jas.v13n2p66

Enabling Environment for Inclusive Horticultural Value Chain for Smallholders in Gauteng Province, South Africa

2021· article· en· W3121124776 on OpenAlexvenueno aff
Portia Ndou, Bridget Taruvinga, C.P. Du Plooy, T. D. Ramusandiwa, Michael Mokwala

Bibliographic record

VenueJournal of Agricultural Science · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessUpstream (networking)Downstream (manufacturing)Market accessGovernment (linguistics)ProductivityAgricultureLeafy vegetablesOrder (exchange)Access to financeAgricultural economicsStakeholderMarketingEconomic growthEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the enabling environment within which smallholder farmers operate amidst the uneven playing field in the agricultural sector and the stringent demands of the consumer driven market. Most of the smallholder farmers utilise informal vegetable markets and these offer higher prices for the leafy vegetables. The study is based on data collected from 56 smallholder vegetable producers in Gauteng Province of South Africa. The study unveiled that the business environment has many challenges for the smallholder to competitively function in formal marketing channels, including poor upstream and downstream linkages and access to finance and technology. Access to inputs is a limiting factor to productivity with almost 41.7% of the farmers depending of government input handouts. The results of the logistic regression analysis shows a positive relationship between the choice of most utilised market and age, level of education, established arrangement with certain markets and sources of information on markets. This study concludes that there is need for multi-stakeholder engagements including organisations already working with smallholder farmers in order to ensure that there is no overlap of support services and hence indirectly ensuring wider coverage of farmer support. Both upstream and downstream linkages need to be promoted and this needs the intervention of the government through the support of organisations such as the national Department of Agriculture.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.301

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 studies0.0000.000
Scholarly communication0.0000.001
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 head0.017
GPT teacher head0.214
Teacher spread0.197 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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