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Record W2945248997 · doi:10.3390/su11102814

Sustainable Emerging Country Agro-Food Supply Chains: Fresh Vegetable Price Formation Mechanisms in Rural China

2019· article· en· W2945248997 on OpenAlexaff
Yuliang Cao, Muhammad Mohiuddin

Bibliographic record

VenueSustainability · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsThompson Rivers University
Fundersnot available
KeywordsBusinessSupply chainSubsistence agricultureAgricultural economicsEconomicsCompetition (biology)AgricultureCommerceIndustrial organizationMarketing

Abstract

fetched live from OpenAlex

Price formation mechanisms along the supply chain determine the economic viability of effective agro-food supply chains in emerging countries with small-scale subsistence-based agricultural activities. This study offers an analysis of the price formation mechanism along the Chinese fresh vegetable supply chain. It analyzes the features of market transactions in the upstream and downstream greenhouse cucumber supply chain, and presents an elastic model of pricing in the fresh and raw vegetable market in China. Based on the daily procurement price data of 78 cases in Lingyuan (Liaoning Province, China), and the wholesale price of 78 cases in Xinfadi (Beijing, China), the Augmented Dickey Fuller (ADF) unit root test, co-integration test, and Granger test were applied to reveal the relationship between the prices. Findings indicate that the price of fresh and raw vegetables is formed at the wholesale market, where after it cascades from wholesalers to direct buyers (primary merchants) and farmers, and is passed on to retailers and consumers, where the final market price is formed. Farmers exhibit bounded rationality decision-making, that is, they can only passively accept price fluctuations. Buyers (primary merchants, wholesalers’ agents, and retailers) at each level extract fixed rewards, while making no additional contribution to the price fluctuations along the chain. The wholesalers enjoy an oligopolistic competition market and can better take advantage of the asymmetric information to accommodate market demand.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.238
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.004
GPT teacher head0.179
Teacher spread0.175 · 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.

Study designTheoretical or conceptual
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

Citations34
Published2019
Admission routes1
Has abstractyes

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