Comparative analysis of households' socioeconomic and demographic characteristics and food security status in urban and rural areas of Kwara and Kogi States of north-central Nigeria
Notice bibliographique
Résumé
Food security is a critical issue in Nigeria today as the country struggles with high rates of food prices and poverty. This study analysed the socioeconomic and demographic characteristics of Household Heads (HHH) and classified them according to food security status. Household level data from the cross-sectional survey was employed in November 2006 to February 2007through a well-structured questionnaire to 396 HHH with a multi-stage sampling procedure. Data were analysed through a descriptive statistics and Rasch model. Average age of the HHH was 42.45years with Standard Deviation (SD) of 9.57 years in Rural Areas (RA) against 43.29 years and SD of 9.83 years in Urban Areas (UA). The HHH level of education was much higher in UA compared to RA. The Household Size (HSZ) was 5.88 with SD of 2.29 in RA against 5.91 and SD of 2.17 in UA, and monthly income of N9, 244.86 with SD of N11, 071.77 in RA against N10, 194.15 and SD of N14, 936.30 in UA. The results from Rasch Model for classifying households according to food security status show that differences exist between households’ food security status in rural and urban areas of Kwara and Kogi States. While 15.6% HHH were food secure (FS) in RA of Kogi State, only 11.1% were FS in the RA of Kwara State. On the other hand, 20.7% HHH were FS in UA of Kogi State compared to 17.1% in UA of Kwara State. Disaggregating food security status of adults and children in households separately revealed that, 25.8% adults in RA of Kogi State were FS compared to 19.2% in Kwara, while 24.4% urban adults were FS in Kogi against 23.2% in Kwara. In addition, 40.6% children in RA of Kogi State were FS against 32.3% in Kwara, while only 29.9% Kogi urban children were FS against 46.3% in Kwara. In general, households were more FS in Kogi State compared to Kwara and more FS in UA compared to RA. The rural children in Kogi State were also more FS compared to the urban, while urban children in Kwara were more FS when compared to rural children. In order to improve households’ food security status in both rural and urban areas, there is the need to take into account some significant variables such as reduction in household size through birth control, and increase in household heads’ participation in agricultural activities especially those residing in urban areas through urban agriculture.
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Comment cette classification a été obtenuedéplier
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| 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 ».