Machine learning models for predicting decisions to be made by small scale dairy farmers in Eastern Africa

A Dissertation Submitted in Partial Fulfilment of the Requirements for the Degree of Doctor of Philosophy in Information and Communication Science and Engineering of the Nelson Mandela African Institution of Science and TechnologyIn 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

Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.

This research article published by Springer Nature Switzerland AG., 2020This study is concerned with developing predictive models using machine learning techniques to be used in identifying factors that influence farmers' decisions, predict farmers' decisions, and forecast farmers' demands relating to breeding service. The data used to develop the models comes from a survey of small-scale dairy farmers from Tanzania (n = 3500 farmers), Kenya (n = 6190 farmers), Ethiopia (n = 4920 farmers), and Uganda (n = 5390 farmers) and more than 120 variables were identified to influence breeding decisions. Feature engineering process was used to reduce the number of variables to a practical level and to identify the most influential ones. Three algorithms were used for feature selection, namely: logistic regression, random forest, and Boruta. Subsequently, six predictive models, using features selected by feature selection method, were tested for each country-neural network, logistic regression, K-nearest neighbor, decision tree, random forest, and Gaussian mixture model. A combination of decision tree and random forest algorithms was used to develop the final models. Each country model showed high predictive power (up to 93%) and are ready for practical use. The use of ML techniques assisted in identifying the key factors that influence the adoption of breeding method that can be taken and prioritized to improve the dairy sector among countries. Moreover, it provided various alternatives for policymakers to compare the consequences of different courses of action which can assist in determining which alternative at any particular choice point had a high probability to succeed, given the information and alternatives pertinent to the breeding decision. Also, through the use of ML, results to the identification of different clusters of farmers, who were classified based on their farm, and farmers' characteristics, i.e., farm location, feeding system, animal husbandry practices, etc. This information had significant value to decision-makers in finding the appropriate intervention for a particular cluster of farmers. In the future, such predictive models will assist decision-makers in planning and managing resources by allocating breeding services and capabilities where they would be most in demand

Abstracts of Tanzania Health Summit 2020

This book contains the abstracts of the papers/posters presented at the Tanzania Health Summit 2020 (THS-2020) Organized by the Ministry of Health Community Development, Gender, Elderly and Children (MoHCDGEC); President Office Regional Administration and Local Government (PORALG); Ministry of Health, Social Welfare, Elderly, Gender, and Children Zanzibar; Association of Private Health Facilities in Tanzania (APHFTA); National Muslim Council of Tanzania (BAKWATA); Christian Social Services Commission (CSSC); & Tindwa Medical and Health Services (TMHS) held on 25–26 November 2020. The Tanzania Health Summit is the annual largest healthcare platform in Tanzania that attracts more than 1000 participants, national and international experts, from policymakers, health researchers, public health professionals, health insurers, medical doctors, nurses, pharmacists, private health investors, supply chain experts, and the civil society. During the three-day summit, stakeholders and decision-makers from every field in healthcare work together to find solutions to the country’s and regional health challenges and set the agenda for a healthier future. Summit Title: Tanzania Health SummitSummit Acronym: THS-2020Summit Date: 25–26 November 2020Summit Location: St. Gasper Hotel and Conference Centre in Dodoma, TanzaniaSummit Organizers: Ministry of Health Community Development, Gender, Elderly and Children (MoHCDGEC); President Office Regional Administration and Local Government (PORALG); Ministry of Health, Social Welfare, Elderly, Gender and Children Zanzibar; Association of Private Health Facilities in Tanzania (APHFTA); National Muslim Council of Tanzania (BAKWATA); Christian Social Services Commission (CSSC); & Tindwa Medical and Health Services (TMHS)