TY - JOUR
T1 - Understanding the drivers of smallholder dairy cooperative participation in developing countries
T2 - Evidence from rural Zambia
AU - Cheng, Haotian
AU - Ng'ombe, John N.
AU - Choi, Yejun
AU - Kalinda, Thomson H.
AU - Zheng, Shi
N1 - Publisher Copyright:
© 2025
PY - 2025/3
Y1 - 2025/3
N2 - CONTEXT: Smallholder dairy farmers are among the primary dairy producers in developing countries. In Zambia, they contribute more than 80 % of the country's milk production, which amounts to approximately $80 million annually. Understanding the factors that influence smallholder dairy farmers' decisions to join cooperatives is crucial for enhancing cooperative participation and improving dairy production efficiency in the region. OBJECTIVE: The primary goal of this study is to investigate the determinants of smallholder dairy farmers' decisions to join cooperatives, while also comparing the predictive performance of the random effects logit model and the random forest model in identifying these factors. METHODS: Data were collected from 515 rural smallholder dairy farmers in Zambia. The analysis utilizes a random effects logit model and a random forest model to identify the factors influencing farmers' decisions to join dairy cooperatives. RESULTS AND CONCLUSIONS: Three primary findings were observed. First, the RF model exhibited superior predictive accuracy compared to the random effects logit model, aligning with existing literature on the enhanced predictive capabilities of machine learning techniques. Second, several key factors, including physical proximity to cooperative offices, educational attainment, and dairy farming experience, were identified from the random effects logit model as significantly influencing current farmers' decisions to join dairy cooperatives. Third, the random forest model indicated that demographic and economic characteristics—specifically age of the household head, household size, total cow ownership, dependency ratio, and farming experience—are expected to be the most influential predictors of cooperative membership in future scenarios. SIGNIFICANCE: Findings suggest the need for establishing cooperative offices closer to rural farming communities in developing countries to enhance accessibility and encourage cooperative participation. Policies should focus on improving educational levels and providing accessible knowledge sources through governmental and non-governmental initiatives to foster cooperative membership. Addressing the reluctance of wealthier farmers to join cooperatives requires tailored interventions such as incentives, awareness campaigns, or targeted outreach efforts emphasizing the benefits of cooperative membership across different resource levels.
AB - CONTEXT: Smallholder dairy farmers are among the primary dairy producers in developing countries. In Zambia, they contribute more than 80 % of the country's milk production, which amounts to approximately $80 million annually. Understanding the factors that influence smallholder dairy farmers' decisions to join cooperatives is crucial for enhancing cooperative participation and improving dairy production efficiency in the region. OBJECTIVE: The primary goal of this study is to investigate the determinants of smallholder dairy farmers' decisions to join cooperatives, while also comparing the predictive performance of the random effects logit model and the random forest model in identifying these factors. METHODS: Data were collected from 515 rural smallholder dairy farmers in Zambia. The analysis utilizes a random effects logit model and a random forest model to identify the factors influencing farmers' decisions to join dairy cooperatives. RESULTS AND CONCLUSIONS: Three primary findings were observed. First, the RF model exhibited superior predictive accuracy compared to the random effects logit model, aligning with existing literature on the enhanced predictive capabilities of machine learning techniques. Second, several key factors, including physical proximity to cooperative offices, educational attainment, and dairy farming experience, were identified from the random effects logit model as significantly influencing current farmers' decisions to join dairy cooperatives. Third, the random forest model indicated that demographic and economic characteristics—specifically age of the household head, household size, total cow ownership, dependency ratio, and farming experience—are expected to be the most influential predictors of cooperative membership in future scenarios. SIGNIFICANCE: Findings suggest the need for establishing cooperative offices closer to rural farming communities in developing countries to enhance accessibility and encourage cooperative participation. Policies should focus on improving educational levels and providing accessible knowledge sources through governmental and non-governmental initiatives to foster cooperative membership. Addressing the reluctance of wealthier farmers to join cooperatives requires tailored interventions such as incentives, awareness campaigns, or targeted outreach efforts emphasizing the benefits of cooperative membership across different resource levels.
KW - Dairy cooperatives
KW - Dairy farmers
KW - Developing countries
KW - Random effect logit model
KW - Random forest
KW - Zambia
UR - https://www.scopus.com/pages/publications/85214454711
U2 - 10.1016/j.agsy.2025.104261
DO - 10.1016/j.agsy.2025.104261
M3 - Article
SN - 0308-521X
VL - 224
JO - Agricultural Systems
JF - Agricultural Systems
M1 - 104261
ER -