Artificial insemination (AI) and selective bull mating are considered as robust methods for dairy cattle breeding. Globally, these methods have been used to enhance productivity and realize rapid genetic gains. However, these technologies have had low adoption rates in sub-Saharan Africa (SSA). Even though available evidence suggests that this is due to various infrastructural and technical challenges. There is limited information about what drives this low uptake of AI from a farmer's perspective. Therefore, the main objective of this study was to determine and characterize factors that influence the choice by smallholder farmers between bull service and AI for dairy cow breeding. Further, the relationships between the breeding choices and the bio-physical elements of dairy farming, mainly, farmer characteristics, household income levels, farm management practices, and institutional support structures, were investigated. Data were collected through face-to-face interviews from a total of 16,308 small-scale dairy farmers in Ethiopia (n = 4679), Kenya (n = 5278), Tanzania (n = 3500), and Uganda (n = 2851). The questionnaire was coded in an electronic form using Open Data Kit (ODK) platform to allow for real-time data entry and management. Descriptive statistics, chi-square test, and a t-test were used to evaluate the independent and dependent variables, while logistic regression and factor analysis were used to identify factors that influenced farmers' breeding decisions. Results showed that there was a significant difference in animal husbandry practices between farmers who used artificial insemination (AI) and those who practiced bull mating. The majority of farmers who used AI kept records, purchased more animal feeds, had more labor by hiring workers whose average wages were higher than those of bull service farmers. However, farmers who used AI pay more for services such as water access and breeding while their service providers had to cover long distances compared to farmers who used bulls. This indicates limited access to services and service providers for AI farmers. The ratio of AI to bull service users was even for Ethiopia and Kenya, while in Uganda and Tanzania, more farmers preferred bull service to AI. It was established that the factors that influence farmers' breeding decision were not the same across the region. Factors such as farmer's experience in dairy farming, influence of the neighbor, farmer's ability to keep records, and management practices such as water provision and availability of feeds had a significant association (p < 0.001) with AI adoption among dairy farmers. In contrast, large herd and large land size negatively influenced AI adoption. Institutional settings including cost of AI service and the distance covered by the service provider negatively affected (p < 0.001) the choice of AI as a breeding option. There was a high probability of continued use of a specific breeding method when there was a previous conception success with that same method. Based on the results obtained, we...

Due to changes in the livestock sector and the rise of consumer demand for comprehensive and integrated food security and safety, there has been a concern on the use of farm data in enhancing animal traceability and decision-making by farmers and other decision-makers in the livestock sector. To ensure high production through effective decision-making and auditable standards, producers are required to have better traceability and record systems. Therefore, this study aimed at (1) reviewing the current recording/data management and animal traceability systems used by small-scale farmers in developing countries and (2) analyzing how data management systems should be designed to enhance efficient decision-making and animal traceability from farm to fork. This study found that, still, a majority of small-scale farmers do not keep records leading to poor decision-making on the farm and policymaking. We also found that those who keep records do not store their data in electronic format, which again poses another challenge in data analysis. Moreover, this study found that the majority of traceability tools used by farmers in developing countries do not meet international standards based on tools they use for tracing animals; farmers were reported to use tools like branding and ear tagging, which provide very little information about the animal. Such tools lack the capability to keep track of useful information about an animal, e.g., information about feeding and animal health. In conclusion, this study recommended a better electronic system to be used at the farm level to facilitate data analysis, hence promoting informed decision-making and adherence to the international animal traceability standards. Otherwise, there is a need for researchers to conduct more studies in developing different analytical models for exploring on-farm data in order to improve the decision-making process by farmers and other stakeholders.

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Background: The National Blood Transfusion Service (NBTS) in Tanzania uses the Copper Sulphate (CuSO4) gravimetric method to estimate hemoglobin (Hb) in blood donors. However, this and other point-of-care methods, including HemoCue, may provide false results. Therefore, this study aimed to evaluate the performance of CuSO4 and HemoCue methods for Hb estimation compared with automated haematology analyzer (AHA). Methods: The cross-sectional study was conducted among (N=204) blood donors in Dar es Salaam. Capillary blood samples were obtained for Hb estimation by CuSO4 and HemoCue methods, 3 mls of venous blood were also collected for Hb quantification by AHA (gold standard), HemoCue and CuSO4 gravimetric method. Data were analyzed by Epi info 7.2.2.6, statistical significance was defined at a P value of <0.05, and kappa agreement was calculated. Results: The median age of the study participants was 30 years (IQR: 20-39). The proportion of false eligible donors was 19.6%, and false deferral donors were 2.9% by the CuSO4 gravimetric method. The specificity, sensitivity, positive and negative predictive values, and Kappa agreement for CuSO4 were 28.6%, 95.9%, 78.0%, 72.7%, and 0.1, respectively. In contrast, the specificity, sensitivity, positive and negative predictive values, and Kappa agreement for HemoCue were 62.5%, 98.6%, 87.4%, 94.6%, and 0.63, respectively. Conclusion: Our study revealed that the performance of the CuSO4 gravimetric method in Tanzania is relatively poor, with a high proportion of false eligible donors than the HemoCue method. These findings warrant further studies to evaluate the quality control measures for CuSO4 gravimetric method and explore alternative point-of-care methods for Hb estimation among blood donors in similar resource limited-settings.

In dairy, lack of decision support tools for identifying farmers' needs and demands have caused many programs, strategies, and projects to fail. This has led to the inefficient and fragmented allocation of scarce development resources. This study demonstrated how machine learning (ML) can be used as a tool to bridge this gap; by developing ML models to be used in identifying factors that can influence farmers decisions, predicting decision to be made by a farmer and forecast on farmers demands regarding to their specific need or service. Four countries: Ethiopia, Kenya, Tanzania and Uganda were selected for this study. In the course of the study four models were developed one for each country with regard to the usage of animal supplements, keeping of exotic animals, use of Artificial insemination (AI) as breeding methods and animal milk productivity. Data was collected through face to face interviews, from a total of 16 308 small scale dairy farmers in Ethiopia (n = 4679), Kenya (n = 5278), Tanzania (3500) and Uganda (n = 2851). The decision tree algorithm was used to model categorical problems (use of supplement and breeding decision), which attained the accuracy of 78%-90%. Moreover, K-nearest neighbor was employed for numeric problems (keeping of exotic animals and animal milk productivity) with an accuracy of 0.78-0.96 Adjusted R The use of ML techniques assisted in classifying farmers based on their characteristics and it was possible to identify the key factors that can be taken then prioritized to improve the dairy sector among countries. Also, the results of this study offer a number of practical implications for the dairy industry where the proposed ML models can enable decision-makers in developing the National Dairy Master Plan and design policies that promote the growth of smallholder dairy farming. Moreover, these models shade light to potential service providers and investors who want to invest in dairy to identify potential areas or groups of farmers to focus with. 2 value.