Food insecurity, coping strategies, and resilience of agricultural cooperative members during COVID-19 in West Africa
Notice bibliographique
Résumé
Abstract Background Lockdown measures of COVID-19 have had different repercussions on the well-being of households in West Africa depending on their resilience capacity. This study compares the dynamic of households’ food insecurity during COVID-19 pandemic according to their membership in different types of agricultural cooperatives in four West African countries, namely Ghana, Mali, Ivory Coast, and Senegal. Methodology We used data collected from 1270 members of agricultural cooperatives and regression analyses, to understand the link between the nature of their cooperatives and the food insecurity dynamic of their household, while controlling for other sociodemographic characteristics. Cooperative were categorized either “active” or “poorly/not active” depending on their capacity to conduct initiatives that address the needs of their members, to maintain communication between leaders and members, the participation of members to decisions, and their possession of a good understanding of business management. Food insecurity is measured using the Food Insecurity Experience Scale (FIES) and the Coping Strategy Index (CSI). Respondents were asked to answer questions related to their food security status for the period before and during the pandemic. Results The COVID-19 pandemic has adversely affected respondents' food security status. These effects varied according to the severity of sanitary measures implemented and to the dynamism of cooperatives. Households of poorly or not active cooperatives have experienced more food insecurity in Ivory Coast and Senegal than those who were members of active cooperatives; in Ghana the effects were significant but similar in both types of cooperatives. Members of both cooperatives in Mali appear to have been less affected than members in other countries. Furthermore, households of poorly/not active cooperatives have used more severe coping strategies in Ivory Coast, Ghana, and Senegal during the pandemic. Conclusions Strong collaboration and support provided by cooperatives can contribute to increase the resilience capacity of their members to shocks such as the COVID-19 pandemic.
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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) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| 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 ».