Modeling inequality of access to agricultural productive resources in coastal and non-coastal rural communities in Central Region of Ghana: Implication for food security and women empowerment
Notice bibliographique
Résumé
Abstract Background Women in rural communities remain the most vulnerable population in accessing agricultural productive resources with dire implications for food security, malnutrition, and household wealth. The study quantified the level of inequality and employed rigorous statistical models to understand the complex interrelationships of gender, women empowerment, geographic location, and their relative effect on women's access to agricultural productive resources in rural coastal and non-coastal communities in the Central region, a Coastal Savannah Agro-ecological zone of Ghana. Methods This was a community-based cross-sectional study using a multi-stage stratified cluster random sampling design to generate a representative sample of men and women who live in coastal and non-coastal communities in the Central region of Ghana. The Gini inequality index was used to determine the level of inequality in access to agricultural productive resources. The multivariable modified Poisson and Negative binomial regression models were used to quantify the linkages between geographic location, gender, and women empowerment in agricultural production decision-making and access to agricultural productive resources. Results The estimates from the Gini index showed that inequality in the access to agricultural productive resources was marginally higher among women than in men; higher in the coastal areas than in the non-coastal areas, and higher among women with low empowerment in agricultural production decision-making. Access to agricultural productive resources was higher by approximately 21% among women living in the non-coastal communities compared to those living in the coastal communities [adjusted prevalence ratio, aPR = 1.21, 95% CI: 1.04–1.42]; also, was higher by 46% among women who were adequately empowered to make decisions in agricultural productive services compared to women who were not adequately empowered ([aPR = 1.46, 95% CI: 1.18–1.82 ]). The prevalence of women being empowered in agricultural decision-making if the woman lives in a non-coastal area was higher by 10% compared to those who live in coastal areas [aPR = 1.10, 95% CI: 1.04–1.16]. Women's empowerment in agricultural decision-making was found to increase with age, as older women were more empowered to make decisions in agriculture. The prevalence of being empowered in agricultural decision-making was 33% higher among women aged 50 years and above compared to those aged 18–24 years [aPR = 1.33, 95% CI: 1.15–1.55]. Conclusion Men and women have differential access to agricultural productive resources in the Central region of Ghana linked to empowerment, location, and age. To bridge the existing gap, interventions must prioritize addressing barriers that hinder access to agricultural productive resources, especially among younger women who live in coastal rural communities and who are not empowered to participate in decision-making. Policies geared towards improving women's access must consider the gender-specific constraints, legal framework, socio-cultural factors, employment, and decision-making power that remain the core drivers of inequality and hinder access to agricultural productive resources among women.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 source (Gemma direct ou Codex distillé), 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 ».