Increasing the milk production of small dairy producers is necessary to cover the increase in milk demand in Tanzania. Currently, the population of people in both Tanzania and the world has increased and is predicted to increase more in the year 2050. The use of multilevel association rule mining methods to mine strong patterns among smallholder dairy farmers could help in identifying the best dairy farming practices and increase their milk production by adopting them. This study employed multi-level association rule mining to discover strong rules in three clusters, resulting in three levels of rules in each cluster. These three clusters were high, medium, and low milk producers. Rules were obtained for feeding practices, milk production, and breeding and health practices. These rules represent strong patterns among smallholder dairy farmers that could help them improve their dairy farming practices and have a gradual increase in milk production, from low to medium and from medium to higher milk production. Smallholder dairy producers would be provided with recommendations on their dairy farming practices, using rules based on the cluster to which they belong that could help them achieve higher milk production.

Tanzania's small-scale dairy industry faces similar challenges to those of other developing nations whereby insufficient infrastructure, outdated technology, and low productivity are serious problems for higher milk yield. Tanzania urgently needs to adopt cutting-edge solutions in order to boost dairy performance. With 3500 households' secondary data and 202 households' primary data from 8 villages throughout the Kilimanjaro and Arusha regions, this chapter demonstrates the use of machine learning (ML) techniques to derive homogeneous production clusters and recommendations for more milk yield among dairy farmers. The likelihood for higher milk yield is demonstrated for various clusters with the use of support, confidence, and lift values of association rules analysis. Finally, the production clusters and recommendations are deployed through a mobile application. Recommendations for future improvement are suggested especially on further deployment of learning recommendations and development of a platform-independent mobile solution.