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Market Supply Response of Cassava Farmers in Ile-Ife, Osun State

2012· article· en· W1911341263 on OpenAlexvenueno aff
O. F. Adesiyan, Adewumi Titus Adesiyan, R. O. Oluitan

Bibliographic record

VenueCanadian social science · 2012
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCassava research and cyanide
Canadian institutionsnot available
Fundersnot available
KeywordsDescriptive statisticsHectareAgricultureAgricultural scienceRegression analysisAgricultural economicsEconomicsSocioeconomicsGeographyMathematicsBiologyStatistics

Abstract

fetched live from OpenAlex

This study examined the market supply response of cassava farmers in Ile-Ife, Osun State. Data were collected from 80 cassava farmers from four cassava producing Local Government Areas (LGAs) namely; Ife-East, Ife-Central, Ife-North and Ife-South. These were analysed using descriptive statistics and regression technique. The results of the descriptive analysis showed that method of cassava farming was mainly traditional and cassava was mostlly cultivated with maize. Majority of the cassava farmers were married, literate and of about 35 years meaning that more young people were into cassava cultivation in Ile-Ife. Also, the farmers had an average of 8 members per household. The results of the regression analysis revealed that 97% of the variations in the marketed surplus of cassava were explained by the variables in the model. The result also revealed that the quantity of cassava output in kg and the family size had positive and significant effects on the marketed surplus while losses, quantity of cassava consumed in kg, payments in kind in kg, size of land in hectares had negative effects on the marketed surplus. The elasticity of marketed surplus was 1.6 meaning that the supply response was elastic indicating that the higher the price of cassava output in kg the more the quantity of cassava that will be supplied. Key words: Cassava Farmers; Market supply response; Nigeria; Osun State

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.471
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.023
GPT teacher head0.260
Teacher spread0.238 · 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 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

Citations8
Published2012
Admission routes1
Has abstractyes

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