Cocoa Production and Related Social-Economic and Climate Factors: A Case Study of Ayedire Local Government Area of Osun State, Nigeria
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cocoa has been a major source of income for many Nigerians and a major source of foreign exchange earnings for the country. However its production has been experiencing a declining trend in recent times. Many factors have been implicated. One major factor is changes in climate variables. This study therefore investigates into the socio-economic effects of some climate variables on cocoa production and aims at guiding policy makers in drawing policies that will mitigate the effect of these variables. The study was carried out in Ayedire Local Government Area (LGA) of Osun State. Data were collected with the aid of structured questionnaire employing interview schedule. One hundred cocoa farmers registered with the state’s Cocoa Growers Association (CGA) were randomly selected from four major cocoa growing areas of the L.G.A. The data set was then analyzed using descriptive statistics and regression techniques. The study found that major climate variables affecting cocoa production were rainfall, sunshine and temperature. Other factors observed was ageing cocoa tree and the prevalence of pest infestation and disease emergence occurring as the result of climate variation thereby causing yield reduction as well as loss of income. In the short run, enactment and implementation of policies that can mitigate the adverse impact of climate variations can help to improve the yield of cocoa, thereby increasing the producers’ income and consequently boost their living standard. In the long run, conscientious efforts should be made to educate and train the minds of all towards safety and best practices for the prevention of climatic adversities.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it