Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters

Area of Science:

• Agricultural Science
• Data Science
• Livestock Management

Background:

• Smallholder dairy producers are vital to African agriculture but face challenges in milk production due to inadequate infrastructure and support.
• Limited access to extension services and the complexity of improving yields lead to farmer disengagement and cycles of failure.

Purpose of the Study:

• To develop a rule-based engine for automatically assigning smallholder dairy producers to predefined clusters based on production characteristics.
• To facilitate knowledge sharing and targeted support among farmers with similar profiles to enhance milk output.

Main Methods:

• Interviewed 78 stakeholders (69 smallholder dairy producers, 9 extension officers) in Meru-Arusha, Tanzania.
• Utilized 10 production features and 6 predefined clusters from a previous study.
• Developed and applied a rule-based engine for automated farmer clustering.

Main Results:

• The rule-based engine successfully assigned smallholder dairy producers to their respective clusters.
• Automated clustering enables efficient grouping of farmers for targeted interventions and peer-to-peer learning.
• Facilitates timely assistance from extension officers to farmers within their identified clusters.

Conclusions:

• The developed rule-based engine offers a scalable solution for supporting smallholder dairy producers.
• Clustering enhances knowledge exchange and optimizes the delivery of extension services, ultimately aiming to increase milk production.
• This approach empowers smallholder farmers by connecting them with relevant resources and peers for improved agricultural outcomes.