The impact of credit accessibility and information communication technology on the income of small-scale sugarcane farmers in Ndwedwe Local Municipality, KwaZulu-Natal Province, South Africa
Notice bibliographique
Résumé
Introduction: Access to credit and information and communication technology (ICT) plays a pivotal role in enhancing the practices of small-scale sugarcane farmers, impacting their financial, social, and economic wellbeing. However, many small-scale farmers need help accessing these resources, thereby affecting their ability to generate sustainable income. This study aimed to assess the factors influencing the adoption of ICT and access to credit and their subsequent impact on small-scale farmers' income. Methods: Employing a multistage sampling technique, 300 small-scale farmers were selected as participants in the study. The recursive bivariate probit regression model was used to assess the factors affecting adoption ICT and a selectivity-corrected ordinary least square regression model was utilized to estimate the synergistic effect of ICT adoption and access to credit on the income of small-scale sugarcane farmers. Results and discussion: The findings revealed that approximately 77% of small-scale farmers had access to credit, while more than 80% had adopted ICT. The results derived from the recursive bivariate probit (RBP) regression model indicated that access to credit, education, and extension support positively and significantly influenced the adoption of ICT. Conversely, marital status and non-farm income exhibited a negative and significant influence on the adoption of ICT. Gender and marital status were positively and significantly associated with access to credit, whereas age, education, and non-farm income showed a negative and significant relationship on access to credit. Subsequently, a selectivity-corrected ordinary least square regression model analysis revealed that factors such as gender, marital status, extension, government support, and transportation costs positively and significantly influenced farmer's income. In contrast, education, employment status, and non-farm income exhibited a negative and significant influence on income. Conclusion and recommendations: The study concludes that socio-demographic factors, such as gender, marital status, extension support, government support, and transportation costs, positively contribute to farmers' income. Small-scale sugarcane farmer involvement in other non-farm activities is associated with reduced farm income. This implies that farmers' livelihoods options are reduced as they can only focus on sugarcane development as a source of income. There is a pressing need to educate small-scale farmers on ICT and provide them with access to agricultural credit. Additionally, extension workers should offer advisory support to small-scale farmers requiring assistance in accessing agricultural credit. There is a need to train sugarcane farmers on different agricultural income generating activities to reduce their over-reliance on sugarcane development. By addressing the identified socio-demographic factors and implementing targeted policy interventions, stakeholders can foster an enabling environment for small-scale farmers to thrive, ultimately contributing to the sustainable development of the sugarcane sector and the broader agricultural landscape in South Africa.
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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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,001 | 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 ».