Impact of Climate Change Adaptation Strategies on Food Security of Farm Households in Rural Dire Dawa Administration, Ethiopia
Notice bibliographique
Résumé
Background: The impact of climate change on smallholder farmers in underdeveloped countries—specifically, Ethiopia is widely recognized. Farm households employ a range of geographically and temporally varying adaptation strategies to cope with the adverse consequences of climate change. Therefore, it is critical to examine the few empirical studies that examine how rural Ethiopian farm households confronting drought have responded to climate change to ensure food security. Primary data were collected from 385 randomly selected farm households using a semi-structured survey form. Data analysis was performed using endogenous switching regression models and descriptive statistics. Result: Results show that the majority of the sample households (76.7%) adopted climate change adaptation strategies (livelihood diversification, soil and water conservation, and chemical fertilizers separately or in combination) while the remaining 23.3% are non-adopters. Climate change knowledge is validated as an instrumental variable. Model results revealed that adopter farmers would have significantly lower (11.6%) daily calorie intake if they had not adopted them, and non-adopter farmers would have gained significantly higher (12.8%) daily calorie intake if they had adopted them. Sex, marital status, land fragmentation, education, family size, farm size, credit access, extension contacts, and livestock ownership are significantly associated with the likelihood of adoption. Results also show systematic differences where the sex of the head variable is inversely related to the food security of adopters and vice versa for non-adopters. Conclusion: The majority of farm households in the study area know the implications of climate change (73.5%) and suffer from food insecurity (59%). Farmers' knowledge about climate change and variability varies, affecting social, economic, biophysical, and institutional issues. This was shown using descriptive statistics and OLM data. Farm households led by young, male farmers who are married, frequently interact with extension agents, have access to loans and information about climate change, and have non-fragmented plots possess greater knowledge about climate change than other households. Adaptation interventions should consider the above factors and heterogeneities to increase adoption and improve the food security of farm households in the study area.
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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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,003 |
| 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 | 0,001 |
| 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 ».