Browsing by Author "Yonah, Zaipuna"

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Analysis of Factors for the Uncorrelated Relationship between the Broadband Internet Initiatives against Broadband Adoption for Rural Areas in Low and Middle-Income Countries: A Case of Tanzania
(International Journal of Advances in Scientific Research and Engineering (ijasre), 2022-01) Kalula, Sadiki; Dida, Mussa; Yonah, Zaipuna
As evident as the contribution of broadband internet in developed countries’ economies has been, many developing countries have taken initiatives to exploit the same. However, the efforts are not reflected in the rural areas broadband adoption as reported by global Information and Communication Technologies (ICTs’) ranking bodies. This study aims to analyze the factors towards the uncorrelated relationship between the broadband initiatives against broadband adoption in rural parts of Low Middle-Income Countries (LMICs) looking from the supply side, exemplified here by Tanzania. The initiatives considered are National ICT Broadband Backbone (NICTBB) and Universal Communication Service Access Fund (UCSAF). The study deployed a Qualitative Research Method (QRM) exploiting semi-structured interviews for data collection. 23 interviews were conducted with personnel from the aforementioned entities together with their collaborating agencies. Thematic analysis was opted where vigorous coding and data cleaning resulted in five themes characterizing poor broadband adoption; infrastructure, affordability, digital skills, contents’ relevance and services, and statistical reporting. The majority of the response (84%) agreed that the ranks reflect what is found on the ground hence more work is needed from the supply side on enforcing adoption in rural areas. Nevertheless, 16% of the coded responses maintain a view that improper statistical reporting due to statistical model mismatch and data unavailability from local statistical agencies to some extent causes the poor adoption ranks in the global ranking bodies. This article should provide insights to policymakers towards creating/improving and implementation of broadband plans/policies, and as a result, exploiting broadband contribution to countries’ Gross Domestic Product (GDP).
Application of Multiple Unsupervised Models to Validate Clusters Robustness in Characterizing Smallholder Dairy Farmers
(Hindawi The Scientific World Journal, 2019-01-02) Nyambo, Devotha; Luhanga, Edith; Yonah, Zaipuna; Mujibi, Fidalis
The heterogeneity of smallholder dairy production systems complicates service provision, information sharing, and dissemination of new technologies, especially those needed to maximize productivity and profitability. In order to obtain homogenous groups within which interventions can bemade, it is necessary to define clusters of farmers who undertake similar management activities. This paper explores robustness of production cluster definition using various unsupervised learning algorithms to assess the best approach to define clusters. Data were collected from 8179 smallholder dairy farms in Ethiopia and Tanzania. From a total of 500 variables, selection of the 35 variables used in defining production clusters and household membership to these clusters was determined by Principal Component Analysis and domain expert knowledge. Three clustering algorithms, K-means, fuzzy, and Self-Organizing Maps (SOM), were compared in terms of their grouping consistency and prediction accuracy. The model with the least household reallocation between clusters for training and testing data was deemed the most robust. Prediction accuracy was obtained by fitting a model with fixed effects model including production clusters on milk yield, sales, and choice of breeding method. Results indicated that, for the Ethiopian dataset, clusters derived fromthe fuzzy algorithm had the highest predictive power (77% for milk yield and 48% for milk sales), while for the Tanzania data, clusters derived from Self-Organizing Maps were the best performing.The average cluster membership reallocation was 15%, 12%, and 34% for K-means, SOM, and fuzzy, respectively, for households in Ethiopia. Based on the divergent performance of the various algorithms evaluated, it is evident that, despite similar information being available for the study populations, the uniqueness of the data fromeach country provided an over-riding influence on cluster robustness and prediction accuracy.The results obtained in this study demonstrate the difficulty of generalizing model application and use across countries and production systems, despite seemingly similar information being collected.
Applying Theory of Planned Behavior to Examine Users' Intention to Adopt Broadband Internet in Lower-Middle Income Countries' Rural Areas: A Case of Tanzania
(Korea Institute of Science and Technology Information, 2024-03-30) Kalula, Sadiki; Dida, Mussa; Yonah, Zaipuna
Broadband Internet has proven to be vital for economic growth in developed countries. Developing countries have implemented several initiatives to increase their broadband access. However, its full potential can only be realized through adoption and use. With lower-middle-income countries accounting for the majority of the world’s unconnected population, this study employs the theory of planned behavior (TPB) to investigate users’ intentions to adopt broadband. Rural Tanzania was chosen as a case study. A cross-sectional study was conducted over three weeks, using 155 people from seven villages with the lowest broadband adoption rates. Non-probability voluntary response sampling was used to recruit the participants. Using the TPB constructs: attitude toward behavior (ATB), subjective norms (SN), and perceived behavioral control (PBC), ordinal regression analysis was employed to predict intention. Descriptive statistical analysis yielded mean scores (standard deviation) as 3.59 (0.46) for ATB, 3.34 (0.40) for SN, 3.75 (0.29) for PBC, and 4.12 (0.66) for intention. The model adequately described the data based on a comparison of the model with predictors and the null model, which revealed a substantial improvement in fit ( p<0.05). Moreover, the predictors accounted for 50.3% of the variation in the intention to use broadband Internet, demonstrating the predictive power of the TPB constructs. Furthermore, the TPB constructs were all significant positive predictors of intention: ATB (β=1.938, p<0.05), SN (β=2.144, p<0.05), and PBC (β=1.437, p=0.013). The findings of this study provide insight into how behavioral factors influence the likelihood of individuals adopting broadband Internet and could guide interventions through policies meant to promote broadband adoption.
An Approach for Systematically Analyzing and Specifying Security Requirements for the Converged Web-Mobile Applications
(Scientific Publishing Center, 2014-09-01) Nyambo, Devotha; Yonah, Zaipuna; Tarimo, Charles
As the use of web and mobile applications is becoming pervasive for service delivery and user mobility support, enterprises are now increasingly fighting against a huge number of emerging security threats which interfere with the process of service delivery. As an attempt to help the enterprises in dealing with the emerging security threats in the converged service delivery architecture, this paper presents a methodology for security threat analysis and security requirements specification in web/mobile applications development. The presented methodology is based on a case study Livestock Data Center (LDC) system, which is being developed and it allows both web and mobile interfaces as service delivery channels. Hence the system serves as a representative of other similar setups of service delivery. In addition to the processes of analysis and security specification, the methodology involves threat modeling as well. There are several threat models in the literature. The STRIDE threats model is one among the existing threats models that is used to identify security threats that needs to be addressed in systems such as the LDC system. The STRIDE threats model has been used to identify the likely security threats to our case study. On applying the STRIDE threats model the following threats were identified as prominent: sensitive data exposure, weak server side controls, client side injection, and weak authentication and authorization. The identified security threats were compared to existing threats in traditional web and mobile applications separately in order to figure out the changes when the two computing platforms come together. The findings from our case study have shown that the proposed methodology for security threat analysis and security design can be useful in security requirements specifications in the converged web-mobile applications during development, and can be generally used to assist developers of other similar systems.
External Services and their Integration as a Requirement in Developing a Mobile Framework to Support Farming as a Business via Benchmarking: The Case of NM-AIST
(TRANSACTIONS ON MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE, 2020-10-30) Kyaruzi, John; Yonah, Zaipuna; Swai, Hulda; Nyambo, Devotha
Research on agricultural and rural development (ARD) systems in general, and farming as a business (FAAB) in particular, face the limitation of availability of credible and reliable benchmarking data, both for on-farm support for farm management decision making and off-farm support for research, investment and policy decision making. One of the main part of this limitation is to obtain reliable benchmarking data for decision making, both for current conditions and under scenarios of changed bio-physical and socioeconomic conditions. This paper presents a framework for mobile application development to support farming as a business via benchmarking (FAABB). This is done with a model that distinguishes between internal and external sources of data and between codified and computed information. Also, the paper demonstrates and emphasizes how integration should be considered as a requirement when developing a typical mobile application for ARD. The paper ends with a description of an ongoing research project at Nelson Mandela African Institution of Technology (NM-AIST) in Tanzania that aims to develop a new framework to facilitate development of mobile applications for FAABB.
How Information Communication Technology Can Enhance Evidence-Based Decisions and Farm-to-Fork Animal Traceability for Livestock Farmers
(Hindawi, 2020-12-17) Mwanga, Gladness; Mbega, Ernest; Yonah, Zaipuna; Chagunda, Gift
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
Machine learning models for predicting the use of different animal breeding services in smallholder dairy farms in Sub-Saharan Africa.
(Springer Nature Switzerland AG., 2020-05-01) Mwanga, Gladness; Lockwood, Sarah; Mujibi, D F N; Yonah, Zaipuna; Chagunda, M G G
This 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.
Mobile application development framework to support farming as a business via benchmarking: the case of Tanzania
(International Journal of Advanced Computer Research (IJACR), 2019-11) Kyaruzi, John; Yonah, Zaipuna; Swai, Hulda
Contributions from various researchers and scholars have made major advances relevant to a wide range of mobile applications at various scales. Although current agricultural and rural development (ARD) systems have features that are needed for farming as a business (FAAB). It is established that all of them have limitations in realising benchmarking as their basic principle. Common limitations across all systems, include 1) scarcity of data for modelling, evaluating, and applying benchmarking and 2) inadequate knowledge systems that effectively communicate benchmarking results to farmers. These two limitations are greater obstacles to developing useful mobile applications than gaps in conceptual theory or available methods for using “Farming as a Business via Benchmarking (FAABB)”. This paper presents reviews of the current state of mobile application development frameworks, focusing on their capabilities and limitations to support FAABB. The paper presents a new framework to support FAABB in the Tanzanian context, which is implemented through a FAABB cyber studio hosted at the Nelson Mandela –African Institution of Science and Technology (NM-AIST) in Tanzania. The framework promises to address not only the knowledge codification problem, but also the need for a cultural change among agricultural researchers to ensure that data for addressing the range of use-cases are available for future mobile application development. The FAABB framework has been tested in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) and its initial results provides a useful starting point for developing m-apps for addressing ARD challenges in developing countries.
On the Identification of Required Security Controls Suitable for Converged Web and Mobile Applications
(International Journal of Computing and Digital Systems, 2016-01-01) Nyambo, Devotha; Yonah, Zaipuna; Tarimo, Charles
Contemporary development of information systems for service delivery is at the present a matter of bringing together use of web and mobile applications. However, this advancement in the field of computing is happening at the expense of increased security risks to the system users and owners. This is due to the fact that the advancement in systems security controls is not taking place at the same pace. In the converged web and mobile applications, developers lack formal development standards for security design and verification. As a result, applications are built with ad hoc implementations of security controls depending on context of usage. In view of the above, this paper attempts to put forward a possible set of security controls considered to be suitable for addressing the security demands in converged web and mobile applications environments. To achieve this objective, use is made of a Livestock Data Center (LDC) system as a case study for analysis and reasoning. By design, the system can be accessed through web and mobile applications. The overall process involved here had the following phases: the first phase involved reviewing existing security controls and assessment of their usage in the converged web and mobile applications. The output from this stage was a review of security controls assessment report. The second phase involved devising and proposing a possible, security assessment model for the converged web and mobile applications. The last phase of this process, involved employing the proposed security controls assessment model and the case study to identify the possible security controls suitable for the converged web and mobile applications. The approach used for security controls assessment involved a combination of white box and black box techniques. Whereas the platforms used for Web and mobile applications development were PHP and Java, respectively. This last item has been done to practically assess the security controls at an application level, and consequently to come up with suitable controls for the same.
Review of Agricultural and Rural Development System Models and Frameworks to Support Farming as a Business via Benchmarking: The Case of Tanzania
(International Journal of Computing and Digital Systems, 2019-11) Kyaruzi, John; Yonah, Zaipuna; Swai, Hulda
This paper presents a review of the current state of agricultural and rural development (ARD) system frameworks, focusing on their capabilities and limitations to support farming-as-a-business via benchmarking (FAABB). Presented and discussed include the state of system models in relation to five modelling views of the ARD systems, namely: (i) defining factors for agricultural echo systems, (ii) farm characterization and management practices, (iii) simulation systems for predictable farm data, (iv) limiting factors for agricultural optimization, and (v) performance estimation through benchmarking. Also, the paper proposes a new framework to support FAABB in Tanzania that is being tested through various use-cases in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) with a FAABB Cyber Studio hosted at the Nelson Mandela – African Institution of Science and Technology (NM-AIST) also in Tanzania. The FAABB setup at NM-AIST promises to address not only the agricultural knowledge codification problem, but also the need for cultural change among agricultural researchers to ensure that data for addressing a range of use-cases is available for future mobile application development. The proposed FAABB framework provides a useful starting point for addressing limitations of existing frameworks and considering a ubiquitous m-app development framework for targeted ARD research in developing countries.
Review of Agricultural and Rural Development System Models and Frameworks to Support Farming as a Business via Benchmarking: The Case of Tanzania
(International Journal of Computing and Digital Systems, 2019-11-01) Kyaruzi, John; Yonah, Zaipuna; Swai, Hulda
This paper presents a review of the current state of agricultural and rural development (ARD) system frameworks, focusing on their capabilities and limitations to support farming-as-a-business via benchmarking (FAABB). Presented and discussed include the state of system models in relation to five modelling views of the ARD systems, namely: (i) defining factors for agricultural echo systems, (ii) farm characterization and management practices, (iii) simulation systems for predictable farm data, (iv) limiting factors for agricultural optimization, and (v) performance estimation through benchmarking. Also, the paper proposes a new framework to support FAABB in Tanzania that is being tested through various use-cases in the Southern Agricultural Growth Corridor of Tanzania (SAGCOT) with a FAABB Cyber Studio hosted at the Nelson Mandela – African Institution of Science and Technology (NM-AIST) also in Tanzania. The FAABB setup at NM-AIST promises to address not only the agricultural knowledge codification problem, but also the need for cultural change among agricultural researchers to ensure that data for addressing a range of use-cases is available for future mobile application development. The proposed FAABB framework provides a useful starting point for addressing limitations of existing frameworks and considering a ubiquitous m-app development framework for targeted ARD research in developing countries.
A Review of Characterization Approaches for Smallholder Farmers: Towards Predictive Farm Typologies.
(Hindawi, 2019-05-22) Nyambo, Devotha; Luhanga, Edith; Yonah, Zaipuna
Characterization of smallholder farmers has been conducted in various researches by using machine learning algorithms, participatory and expert-based methods. All approaches used end up with the development of some subgroups known as farm typologies. The main purpose of this paper is to highlight the main approaches used to characterize smallholder farmers, presenting the pros and cons of the approaches. By understanding the nature and key advantages of the reviewed approaches, the paper recommends a hybrid approach towards having predictive farm typologies. Search of relevant research articles published between 2007 and 2018 was done on ScienceDirect and Google Scholar. By using a generated search query, 20 research articles related to characterization of smallholder farmers were retained. Cluster-based algorithms appeared to be the mostly used in characterizing smallholder farmers. However, being highly unpredictable and inconsistent, use of clustering methods calls in for a discussion on how well the developed farm typologies can be used to predict future trends of the farmers. A thorough discussion is presented and recommends use of supervised models to validate unsupervised models. In order to achieve predictive farm typologies, three stages in characterization are recommended as tested in smallholder dairy farmers datasets: (a) develop farm types from a comparative analysis of more than two unsupervised learning algorithms by using training models, (b) assess the training models' robustness in predicting farm types for a testing dataset, and (c) assess the predictive power of the developed farm types from each algorithm by predicting the trend of several response variables.