Browsing by Author "Mavura, Fatuma"

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Mobile-based peer-to-peer learning prototype for smallholder dairy producers
(NM-AIST, 2023-06) Mavura, Fatuma
About half of Africa's animal production comes from smallholder dairy farmers, who employ various strategies to maximize milk output. Some time-consuming and expensive heuristics are used by smallholder dairy farmers to increase milk yield, trapping them in a cycle of failure and lowering their incentive to continue making agricultural investments. Grouping smallholder dairy producers with comparable characteristics makes information sharing and interventions easier, increasing milk output. This study aimed at developing a mobile-based peer-to-peer learning prototype which considers farmers’ homogeneity with respect to husbandry practices and auto-allocates them to their respective production clusters. The developed prototype's rule-based engine handles the auto-allocation procedure by grouping farmers with similar farming characteristics into the proper production clusters. Smallholder dairy producers exchange knowledge and expertise through these groups to increase milk output. In Tanzania's Arusha Region, 69 smallholder dairy farmers and nine extension workers responded to a questionnaire to provide information, which was then analyzed using R programming. The important findings are; smallholder dairy producers were automatically allocated to their clusters based on their milk output. Cluster position regarding milk yields was determined using cluster performance for overall production attributes. Consequently, high yielding smallholder dairy producers are assigned to the high-yielding cluster, and vice versa, and extension officers provide timely support. This study is unique since smallholder dairy producers may use it to share dairy farming expertise and boost milk output. Mobile-based peer-to-peer should be integrated with the market by engaging enterprises that process milk for other milk products
Rule-Based Engine for Automatic Allocation of Smallholder Dairy Producers in Preidentified Production Clusters
(Hindawi, 2022-06-30) Mavura, Fatuma; Pandhare, Sanket; Mkoba, Elizabeth; Nyambo, Devotha
Smallholder dairy producers account for around half of all African livestock ventures; nevertheless, they face challenges in producing more milk due to an insufficient framework and infrastructure to maximize their output. Smallholder dairy producers in this scenario use a variety of tactics to boost milk output. However, the attempts need multiple heuristics, time, and financial investment. Furthermore, because of a lack of extension officers, smallholder dairy producers become trapped in failure cycles, unsuccessful attempts, and a diminished motivation to continue farming. Therefore, the interventions were more straightforward as smallholder dairy producers with comparable characteristics grouped. This research aimed to create a rule-based engine that automatically assigns smallholder dairy producers to predefined clusters. About 78 stakeholders were interviewed, including 69 smallholder dairy producers and 9 extension officers from Meru-Arusha, Tanzania. The 10 production features and 6 predefined clusters were adopted from the previous study. Therefore, a rule-based engine used the selected 10 production features. As a result, the rule-based engine automatically assigns the smallholder dairy producers to their respective clusters. Therefore, smallholder dairy producers share their farming skills and experience to increase milk output through these clusters. Furthermore, extension officers in the system provide timely assistance to smallholder dairy producers with farming concerns.