| dc.description.abstract | Traditional clustering algorithms have often been used to categorize farmers but tend to overlook
the underlying reasons for these groupings. Typically, clusters are formed based on common
metrics such as dispersal and centrality, which provide limited insights into the relationships
among key attributes. This study introduces an innovative approach using pattern and association
rules analysis to better understand the characteristics of dairy production clusters. Focusing on
Tanzanian smallholder farmers, the research moves beyond identifying clusters to uncovering the
hidden relationships within them. Through pattern analysis, the study logically examines the
behavioral mechanisms that define these clusters, highlighting service gaps that, if addressed,
could enhance smallholder dairy farmers’ productivity. Frequent patterns with support ranging
from 57 % to 93 % and confidence levels between 85 % and 100 % were identified, revealing
critical challenges faced by these farmers. For instance, farmers using Artificial Insemi-
nation—typically younger or new entrants—face constraints related to farm size, land holdings,
fodder production, lack of farmer groups, and insufficient formal training in dairy care. Mean-
while, seasoned farmers deal more with institutional barriers such as limited access to market-
places, extension services, and distant water sources. The study highlights the diverse challenges
faced by different farmer groups and provides strategic recommendations for improving dairy
productivity. Enhancing access to formal training, improving fodder production, supporting the
formation of farmer groups, and addressing institutional barriers are key actions that could help
Tanzanian smallholder dairy farmers increase milk yield and overall productivity. | en_US |
