Algorithmic prediction of internet technology utilization in learning

The conference aimed at supporting and stimulating active productive research set to strengthen the technical foundations of engineers and scientists in the continent, through developing strong technical foundations and skills, leading to new small to medium enterprises within the African sub-continent. It also seeked to encourage the emergence of functionally skilled technocrats within the continent.Internet technology has been revolutionary over the years especially in the educational sector. However, the utility of internet technology in the learning process of a student in a higher learning institution has not been determined over the years. This has been due to the evolution that has taken place in education. This paper aims at helping in the development of an algorithmic model that will be used for the prediction of internet technology utilization in learning. Specifically, the research will focus on modelling the Cobb- Douglas production theorem to predict the learning output of a given student considering the utility of the internet technology, the infrastructural investment made by the institution of higher learning and the effort of the student. The results of this ongoing research will eventually be of great importance in helping institutions of higher learning determine their returns after investing in internet technology. The students will also be informed on how to use the internet technology in a better way in order to get the best out of the resource.Strathmore University; Institute of Electrical and Electronics Engineers (IEEE

Milk quality and hygiene: Knowledge, attitudes and practices of smallholder dairy farmers in central Kenya

Milk production is an important livelihood source for smallholder dairy farmers in low-to-middle-income countries (LMICs) such as Kenya. However, milk quality and safety are a challenge due to unhygienic handling and non-adherence to food safety standards. The objective of this study was to investigate the knowledge, attitudes and adoption of milk quality and food safety practices by smallholder farmers in Kenya.Ten Focus Group Discussions (FGDs), involving 71 smallholder farmers, were held to collect qualitative data on knowledge, attitudes and practices (KAPs) of smallholder dairy farmers in Laikipia, Nakuru, and Nyandarua counties. Additionally, data were collected through a cross-sectional administered to 652 smallholder farming households. The results of the study revealed low knowledge level and negative attitudes towards respecting antibiotics treatment withdrawal periods, milk quality standards and food safety regulations. Farmers stated they had received low levels of training on milk quality and safety standards. The majority of farmers adopted animal health measures and hygienic measures such as hand washing and udder cleaning. However, unhygienic milking environments, the use of plastic containers, the use of untreated water, and lack of teat dipping compromised milk quality and safety. Currently, milk production, handling and consumption could expose actors along the dairy value chain to health risks. The adoption of milk quality and food safety practices was influenced by farmers' knowledge, socioeconomic characteristics, and choice of marketing channel.There is a need to improve farmers' knowledge and attitudes and implement hygienic control, disease control and antibiotic residue control practices in the milk production process to meet required milk quality and food safety standards. Awareness campaigns and training programmes for smallholder dairy farmers could foster behavioural change and lead to an improvement in milk quality in Kenya

The role of power relationships, trust and social networks in shaping milk quality in Kenya

The objective of this study was to examine social networks in dairy value chains (DVCs) in Kenya and understand how DVC actors' power relationships and trust influence their behaviour regarding milk quality. We conducted a stakeholder analysis using the Net-Map tool in Laikipia, Nakuru and Nyandarua counties in Kenya. VisuaLyzer software was used to analyse the social networks. Thematic content analysis of the discussions, recorded during the mapping exercise, was undertaken using ATLAS.ti. Formal DVC had more actors and dense social networks characterised by vertical and horizontal integration, high levels of power asymmetries between actors, limited trust and short-term contractual arrangements. Informal DVC was characterised by fewer actors and less dense social networks, low levels of power asymmetries between actors and a high level of trust due to the existence of reciprocal personal relationships. Milk was perceived to be of higher quality in the formal value chain reflecting top-down enforcement of milk standards, bottom-up collective action, power asymmetries and contractual relationships. Poor milk quality management in the informal DVC underscores the need for powerful actors, e.g. regulatory agencies, and buyers such as processors, to influence other DVC actors' behavioural change. Understanding and leveraging DVC social networks and actors' power and addressing power asymmetries and enhancing trust between actors will increase compliance with milk quality standards. There is an urgent imperative to design policies and interventions which empower DVC actors, by providing economic incentives, enhancing their skills and knowledge and their access to infrastructure which facilitates milk quality improvement

Adoption of dairy technologies in smallholder dairy farms in Ethiopia

The adoption of modern agricultural technologies in Ethiopia’s dairy production system remains underutilized and under-researched yet it is a promising sector to aid in reducing poverty, improving the food security situation and the welfare of rural households, and in ensuring environmental sustainability. This paper uses the Negative Binomial regression model to examine determinants of multiple agricultural technology adoption in the Addis Ababa and Oromia regions of Ethiopia. Data was collected from 159 smallholder dairy farms in Ethiopia's Addis Ababa and Oromia regions exploring 19 technologies used by the farmers during the study period. The findings show that farm location and herd size impact adoption decisions. Increasing herd size is associated with increased uptake of multiple technologies. Further, as farmer education level increases the more likely farmers are to adopt multiple technologies. The increase in the number of female workers is positively associated with the adoption of multiple dairy technologies. In terms of farmers'/workers’ years of experience, those with no years of work experience are less likely to have adopted multiple technologies than those with more than 5 years of experience. However, this could be due to a number of factors where experience stands as a proxy value. Trust in information from government agencies was associated with a higher propensity to adopt multiple dairy technology as was farmer perception of fellow farmers as peers compared to those who perceive them as competitors. This is an important finding as it may help policymakers or institutions explore knowledge exchange and diffusion of innovation strategies tailored to specific farming and community situations. Studies have shown that farmers within a social group learn from each other more fully about the benefits and usage of new technology. These findings are of value in future technology adoption studies, particularly which factors influence the intensity of adoption of multiple technologies by smallscale producers

The Prediction of Internet Technology Utilization within Learning Environments

Internet technology has been revolutionary over the years especially in the educational sector. However, the utility of internet technology in the learning process of a student in a higher learning institution has not been determined over the years. This has been due to the evolution that has taken place in education. This paper aims at helping in the development of an algorithmic model that will be used for the prediction of internet technology utilization in learning. Specifically, the research will focus on modelling the Cobb- Douglas production theorem to predict the learning output of a given student considering the utility of the internet technology, the infrastructural investment made by the institution of higher learning and the effort of the student. The results of this ongoing research will eventually be of great importance in helping institutions of higher learning determine their returns after investing in internet technology. The students will also be informed on how to use the internet technology in a better way in order to get the best out of the resource.</jats:p

Informal value chain actors' knowledge and perceptions about zoonotic diseases and biosecurity in Kenya and the importance for food safety and public health

Zoonotic diseases, transmitted from animals to humans, are a public health challenge in developing countries. Livestock value chain actors have an important role to play as the first line of defence in safeguarding public health. However, although the livelihood and economic impacts of zoonoses are widely known, adoption of biosecurity measures aimed at preventing zoonoses is low, particularly among actors in informal livestock value chains in low and middle-income countries. The main objective of this study was to investigate knowledge of zoonoses and adoption of biosecurity measures by livestock and milk value chain actors in Bura, Tana River County, in Kenya, where cattle, camels, sheep and goats are the main livestock kept. The study utilised a mixed methods approach, with a questionnaire survey administered to 154 value chain actors. Additional information was elicited through key informant interviews and participatory methods with relevant stakeholders outside the value chain. Our results found low levels of knowledge of zoonoses and low levels of adherence to food safety standards, with only 37% of milk traders knowing about brucellosis, in spite of a sero-prevalence of 9% in the small ruminants tested in this study, and no slaughterhouse worker knew about Q fever. Actors had little formal education (between 0 and 10%) and lacked training in food safety and biosecurity measures. Adoption of biosecurity measures by value chain actors was very low or non-existent, with only 11% of butchers wearing gloves. There was a gendered dimension, evidenced by markedly different participation in value chains and lower adoption rates and knowledge levels among female actors. Finally, cultural and religious practices were shown to play an important role in exposure and transmission of diseases, influencing perceptions and attitudes to risks and adoption of biosecurity measures

Milk quality along dairy farming systems and associated value chains in Kenya : An analysis of composition, contamination and adulteration

Poor milk safety constitutes a persistent public health risk in Kenya. Poor milk composition, microbial contamination and adulteration is a constraint to dairy sector development. We hypothesise that variation in milk quality and safety depends on variation between farming systems. We argue that this variation between farming systems is associated with spatial location which affects the agro-ecological conditions and the avail-ability of labour and land. We used a spatial framework based on the distance to urban markets to distinguish the following farming systems: relatively intensive dairy systems in urban locations (UL), semi-intensive dairy systems in mid-rural locations (MRL) and extensive dairy systems in extreme rural locations (ERL). We aimed to investigate the variation in the quality of raw milk in these dairy farming systems and associated value chains in central Kenya. For this reason, we combined several methods such as participatory rural appraisal, participant observation, and milk physicochemical and microbiological analyses to collect data. Milk samples were collected at the informal and informal value chain nodes farms, informal collection centres, informal retailing centres including milk vending machines, and formal bulking centres where milk changes hands between value chain actors. Milk quality was compared to standards recommended by the Kenya Bureau of Standards (KeBS). There were no differences in the quality of raw milk between locations or between nodes. The overall milk physicochemical composition means (standard error) of the milk were within KeBS standards: fat 3.61 (0.05), protein 3.46 (0.06), solid-not fats 9.18 (0.04), density 1.031 (0.0002) and freezing point-0.597 (0.019). The protein percentage was below KeBS standards at all value chain nodes, except at the formal bulking node. There was significant contamination of milk samples: 16.7% of samples had added water, 8.8% had somatic cell count SCC above 300,000, 42.4% had E. coli, 47.9% had Pseudomonas spp., 3.3% had Staphylococcus spp. and 2.9% tested positive for brucellosis antibodies. Unsanitary milk handling practices were observed at farms and all value chains nodes. Milk physicochemical composition except for protein content meets the KeBS Standard. High levels of microbial contamination of milk pose a public health risk to consumers and show that urgent action is needed to improve milk quality

The Kenyan dairy sector: stakeholder roles and relationships, and their impact on milk quality

The dairy sector in Kenya is an economically important sector providing employment and a source of income value chain actors. Although demand for milk and dairy products is high and increasing, sector growth is constrained by milk quality issues stemming from physical-chemical composition, microbial contamination and adulteration which pose a risk to human health.The objectives of this research were to identify which stakeholders in the Kenyan dairy sector play a role in determining milk quality, and to explore whether roles are affected by power relationships between stakeholders. The study used Social Network Analysis (SNA), and employed process Netmap, to examine the roles of, and relationships between dairy sector stakeholders, and the impact of actual and perceived power on the quality of milk and dairy products traded in formal and informal dairy value chains in Nakuru county Kenya.Results show that the dairy sector in Nakuru county is a multi-layered network of stakeholders, encompassing stakeholders from both the formal and informal dairy value chains. Farmers, cooperatives and processors play a key role in determining the quality of milk and dairy products, while cooperatives, processors, government agencies exert influence over milk quality as the most powerful stakeholders in the network. Stakeholder relationships in the formal value chain are more conducive to the enforcement of regulation and standards, and thus the production of high quality milk and dairy products, than those in the informal value chain