Gender-Differentiated Contribution of Goat Farming to Household Income and Food Security in Semi-arid Areas of Msinga, South Africa
Notice bibliographique
Résumé
Small-scale goat farming can significantly contribute to livelihoods, particularly in semi-arid areas where rainfall is erratic and crop farming is too risky. The study investigated the gendered-differentiated contribution of goat farming to household food income and food security in the semi-arid area of Msinga in South Africa using focus group discussions, key informant interviews and a questionnaire survey of 241 households. Using the Household Food Insecurity and Access Scale to measure the household food security of goat farming households, descriptive statistics and the Chi-square statistics, results showed a significant relationship between food security and the household socio-economic parameters such as the education level of the household head (p < 0.05), the gender of the household head (p < 0.05) and the total household income (p < 0.01). The Tobit regression model showed that the main factors determining food security at the household level were education levels, gender and the total household income. Female-headed households were less food secure than male-headed households because they did not have reliable employment to provide adequate and nutritious food for their households. Therefore, empowering women is crucial to ensuring food security because unstable employment opportunities lead to households’ failure to cope with food insecurity adequately. Goat farming did not contribute to household food security because it generated little income as goat sales were generally low, with a mean of 2.1 for male headed-households and 1.0 for female headed-households in 12 months (p < 0.05). Farmers obtained little income from goat farming because goat flock sizes for most households did not increase due to poor nutrition, diseases, predation, and theft. With the household food basket cost reported to be ZAR3 400/US$188, a household would need to sell up to four goats each month to survive solely on goat farming. However, where goat flock size was small, households limited goat sales to maintain the potential to increase their flock size. Empowering women by promoting rural education may increase their chances of being exposed to better management options, acquiring a better understanding of goat management practices, and making informed decisions, thereby contributing to the improvement of food security. Enhancing goat production is essential to increase flock sizes, as this enables farmers to make more sales, thereby improving food security. Therefore, extension workers need to help farmers better manage and utilize goat farming to their full potential. Finally, rural households need to reduce their autonomy and dependency on supermarket goods and become more agri-oriented.
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| 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 | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».