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Record W3123299410 · doi:10.5539/sar.v10n2p1

Market Participation and Farm Profitability: The Case of Northern Ghana

2021· article· en· W3123299410 on OpenAlexvenueno aff
Agness Mzyece

Bibliographic record

VenueSustainable Agriculture Research · 2021
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Innovations and Practices
Canadian institutionsnot available
FundersUnited States Agency for International Development
KeywordsProfitability indexEndogeneityGross marginAgricultural economicsTransaction costProfit maximizationAgribusinessProfit (economics)EconomicsProduction (economics)Profit marginBusinessSample (material)Agricultural scienceEconometricsAgricultureMicroeconomicsMarketingGeography

Abstract

fetched live from OpenAlex

This study examines the effect of quantity sold (sales volume) on profitability of market participating smallholder farmers in northern Ghana. Market participation has been shown to be important for increasing incomes and improving production efficiency for farm households but still remains low in SSA. While agribusiness and development experts generally advocate for more intensive market participation, it is not clear if selling more results in more profits for smallholder farmers in remote markets that are prone to exorbitant transaction costs. The data used in this study is from the APS survey conducted in 2013 and 2014 in Northern Ghana which had a sample size of 527. The study is based on the theory of profit maximization, in which separability is inferred from observed market participation. OLS regression is used for empirical estimation after rejecting the hypothesis of endogeneity in the model. Mean gross margin/ kg across four groups of farmers ranked by quantity sold is also statistically examined. The results confirm the existence of economies of scale and also show that different crops have different effects on profitability. The results also show that although unambiguously positive, the relationship between quantity sold and profitability may not be linear.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
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 head0.052
GPT teacher head0.340
Teacher spread0.289 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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