Crop Productivity, Land Degradation and Poverty Nexus in Delta North Agricultural Zone of Delta State, Nigeria
Bibliographic record
Abstract
This paper examined the nexus among crop productivity, land degradation and poverty in Delta North Agricultural Zone of Delta State, Nigeria. The hypothesis was that there is no significant relationship among crop productivity, land degradation and poverty in the study area A Multistage sampling technique was used to collect data from 150 respondents. Data were analyzed using percentages and Logit regression. In the regression analysis of Determinants of Crop Productivity, the adjusted R-square showed that about 46 percent of the variability in crop productivity was due to the explanatory variables. The F-stat of 21.41 was significant P = 0.01. All significant variables were positively related to the farmers’ crop productivity. The weighted measure of poverty was employed to determine the poverty line as N5, 383.98. The logit model estimated the determinants of poverty in the study area. The model was well fitted with the log-likelihood function (-54.39) and the Chi-square X2(98.74) significant at 1% level and different variables being significant in the model. The estimated household size variable has a positive coefficient of 0.84 at 1 % significance level. The dependency ratio (X4) coefficient of -0.52 was significant p = 0.05 %. The value of elasticity showed that if dependency ratio decreases by one percent, the probability of being poor will increase by 0.13 percent. Household farm income (X5) coefficient was found to be significant at 1% and negatively related to poverty status. Also the marginal analysis revealed that if farm income increases by 1 percent, the poverty status will remain unchanged. Land ownership (X13) variable has a positive coefficient of 1.07 at 10 % significant level. Agricultural information (X14) was also found to be statistically significant at 5 % level but with negative coefficient of 1.56. We recommend that Policy on land management practices and natural resource exploitation should be reviewed or put in place where not existing and adhered to strictly by all relevant bodies and individuals as it will go a long way to conserving the natural resources and promoting crop yields with resultant increased farm income, all things being equal. Secondly, that family planning policy/programme of a maximum of four children to a family be revisited with a view to implementing it rigorously if the problems of large family size and unemployment are to be effectively addressed in the medium to long term.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".