Monthly Price Analysis of Cassava Derivatives in Rural and Urban Markets in Akwa Ibom State, Southern Nigeria
Bibliographic record
Abstract
The study examined the price transmission and extent of market integration of yellow Garri and Fufu (fermented cassava tubers) in the rural and urban markets of Akwa Ibom State in Southern region of Nigeria. Average monthly prices (measured in naira per kilogram) of Garri and Fufu in the rural and urban markets were used in the analysis. The data was obtained from the quarterly publications of the Akwa Ibom State Agricultural Development Programme [AKADEP] (2013). The data covered January 2005 to June 2013. The trend analysis showed that, prices of Garri and Fufu in the rural and urban markets have exponential growth rates less than unity, which suggests possible co-movement of these prices in the study area. Also, the Pearson correlation coefficient generated for the pair of rural and urban prices of Garri and Fufu revealed significant linear symmetric relationships. The Granger causality test further revealed bi-directional relationships between the rural and urban price of Garri and Fufu in Akwa Ibom State, Nigeria. The results of the co-integration test revealed the presence of co-integration between the rural and urban prices of Garri. The theory of one price was tested for; in the Fufu markets and the result implies weak Fufu market integration in the study area. The results of the error correction model (ECM) confirm the existence of short run market integration between rural and urban prices of Garri in the study area. In addition, the result shows that, the price of Garri in urban market adjusted faster than that of the rural market once there is exogenous shock in the marketing system in the State. The estimated index of market connection (IMC) supported the high short run market integration between prices in rural and urban markets for Garri. Based on the findings, it is recommended that, the Akwa Ibom State government should continue to provide marketing infrastructures and reduced externality costs in order to improve the symmetric nature of information among participants in Garri and Fufu marketing in the state. Also, individuals and government should established market information units or centers and awareness programmes on mass media to facilitate efficient communication of market information in the state.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".