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Record W2145188471 · doi:10.5539/sar.v3n2p16

Can Farmers Receive Their Expected Seasonal Tomato Price in Ghana? A Probit Regression Analysis

2014· article· en· W2145188471 on OpenAlexvenueno aff
Caleb Attoh, Edward Martey, Gladys Kwadzo, Prince M. Etwire, Alexander N. Wiredu

Bibliographic record

VenueSustainable Agriculture Research · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsProbitProbit modelProduction (economics)CommitEconomic shortageAgricultural economicsPromotion (chess)EconomicsCensored regression modelAgricultural scienceMultivariate probit modelCropBinomial regressionRegression analysisBusinessAgronomyBiologyMathematicsStatisticsEconometrics

Abstract

fetched live from OpenAlex

Tomato price is an important indicator for farmers to continue producing the crop in Ghana. There are sometimes alleged reports that farmers tend to commit suicide when they are unable to meet their expected tomato price and thereby unable to recover their cost of production. However, data available indicate that the domestic production of fresh tomato is on the decline. The paper therefore, assesses the factors that affect whether or not farmers can receive their expected tomato price. A multistaged sample survey of 215 farmers across three regions was subjected to the binomial probit model. Results indicate that for farmers to receive their expected price, they have to adapt to produce the crop in the drier seasons, where tomato shortage can be observed. Farmer education and experience are also important factors that are likely to help farmers receive their expected tomato price. The studytherefore recommends the promotion of strategies that are cost reducing especially in the dry season and also to improve access to critical production techniques.

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)0.009
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 score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.021
GPT teacher head0.252
Teacher spread0.231 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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