Exploring the Effects of Climate Change on Net Revenue of Farmers: An Econometric Investigation Using Farm-Level Data in Cross River State, Nigeria
Bibliographic record
Abstract
This paper addressed the effects of climate on the net revenue of farmers in Cross River State. The specific objectives of this paper are; to examine the level of yam production in Cross River State and, to determine the factors that affect farmers' net revenue. Data was collected from 209 farmers using a well-structured questionnaire. The analysis was done using the independent sample t-test, the Chi-square test and Ordinary Least Square (OLS) regression. The findings revealed that the mean value of the respondents on the level of profit was N88,192.13, while the maximum and minimum amount were N110,000 and N50,000, respectively. The independent sample t-test showed that education produced a statistically significant difference in means. The Chi-square test showed that educational level (p-value =0.047), age (p-value=0.034), farming experience (p-value=0.061) and access to credit (p-value=0.088) have a relationship with the net revenue of farmers. The result from the OLS regression revealed that the variables that affect the net revenue of farmers are farming experience, household size, access to the weather forecast and tenure status. This research recommends that policymakers should emphasise how to improve these factors to enhance farmers' net income to increase self-sufficiency in food crop production.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".