Farmer typology and adoption of improved cassava production technologies in the Eastern Democratic Republic of the Congo

This study aimed to examine the factors driving the adoption of improved cassava seeds and fertilizers among smallholder farmers in South Kivu and Tanganyika provinces, Eastern Democratic Republic of the Congo (DRC), using data from 1317 farming households. Data were analyzed using principal compone...

Descripción completa

Detalles Bibliográficos
Autores principales: Nabahungu, N.L., Mirali, J.C., Simbeko, G., Amato, S., Mirali, G.M., Muhindo, P.M., Kitangala, C., Balangaliza, F.B., Dontsop-Nguezet, P., Kintche, K., Udomkum, P., Mignouna, J., Vanlauwe, B.
Formato: Journal Article
Lenguaje:Inglés
Publicado: CAB International 2025
Materias:
Acceso en línea:https://hdl.handle.net/10568/173811
Descripción
Sumario:This study aimed to examine the factors driving the adoption of improved cassava seeds and fertilizers among smallholder farmers in South Kivu and Tanganyika provinces, Eastern Democratic Republic of the Congo (DRC), using data from 1317 farming households. Data were analyzed using principal component analysis (PCA) and cluster analysis by classifying farmers based on their cassava agricultural practices while capturing their preferences, attitudes, and socioeconomic characteristics. Logistic regression was then applied to evaluate the adoption of improved cassava seeds and fertilizers across different farmer-type groups. Therefore, farmers were classified into five distinct groups in both provinces. In general, for cassava-improved seed, farming experience increases its adoption by 15.1% per year in Cluster 5. At the same time, primary education boosts adoption in Cluster 1 by 17.4%, and university education raises it by 20.1% in Cluster 5. Larger land areas significantly enhance adoption by 46.9% in Cluster 4 and 79.6% in Cluster 1, reflecting the benefits of larger farms. Livestock ownership raises adoption by 26.8% in Cluster 1, highlighting the value of assets in agricultural investment. Agriculture training and income are highly effective, improving adoption by 18.2% in Cluster 5 and 8% overall. Specifically, in South Kivu, gender influences fertilizer adoption by 18.0% (Cluster 1) and 14.9% (Cluster 5) while marital status enhances fertilizer adoption and with being married raising the probability by 0.52% (Cluster 4). These findings emphasize the importance of education, gender, income resource access, and targeted interventions to improve agricultural technology adoption. Furthermore, promoting farmer engagement in agricultural activities is recommended through enhanced extension services and cooperative memberships.