Crop Productivity, Land Degradation and Poverty Nexus in Delta North Agricultural Zone of Delta State, Nigeria
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».