Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approach
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 attri...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Scientific African |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S246822762400334X |
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| author | Devotha G. Nyambo |
| author_facet | Devotha G. Nyambo |
| author_sort | Devotha G. Nyambo |
| collection | DOAJ |
| description | 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 Insemination—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. Meanwhile, seasoned farmers deal more with institutional barriers such as limited access to marketplaces, 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. |
| format | Article |
| id | doaj-art-cbe97bf21762417895dbd466e9912c6f |
| institution | Kabale University |
| issn | 2468-2276 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj-art-cbe97bf21762417895dbd466e9912c6f2024-12-21T04:29:03ZengElsevierScientific African2468-22762024-12-0126e02392Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approachDevotha G. Nyambo0Nelson Mandela African Institution of Science and Technology, P. O. Box 447, Tengeru, Arusha, TanzaniaTraditional 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 Insemination—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. Meanwhile, seasoned farmers deal more with institutional barriers such as limited access to marketplaces, 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.http://www.sciencedirect.com/science/article/pii/S246822762400334XAssociation rulesFrequent patternsSmallholder farmersMilk productionOn-farm decisions |
| spellingShingle | Devotha G. Nyambo Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approach Scientific African Association rules Frequent patterns Smallholder farmers Milk production On-farm decisions |
| title | Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approach |
| title_full | Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approach |
| title_fullStr | Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approach |
| title_full_unstemmed | Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approach |
| title_short | Uncovering service gaps and patterns in smallholder dairy production systems: A data mining approach |
| title_sort | uncovering service gaps and patterns in smallholder dairy production systems a data mining approach |
| topic | Association rules Frequent patterns Smallholder farmers Milk production On-farm decisions |
| url | http://www.sciencedirect.com/science/article/pii/S246822762400334X |
| work_keys_str_mv | AT devothagnyambo uncoveringservicegapsandpatternsinsmallholderdairyproductionsystemsadataminingapproach |