Dry riverbeds, also called Iaga, are a complex ecosystem of multispecies interactions between livestock, humans, microorganisms, and their environment. Despite laga’s One Health entanglement of species and environment, few studies have explored the risks of transmission of diseases through direct herd-herd or herd-human contact or indirect contact with fomites surrounding the laga. This study focuses on ethnographic and epidemiological investigations on lagas within Kenya. The study deploys qualitative multimethod-walking interviews, in-depth interviews, key informant interviews, focus group discussions and observations to collect the data from Marsabit and Kajiado Counties in Kenya. Results point to the comingling of infected and healthy herds, cross-livestock species mixing, sharing of watering troughs, and feeding dogs placental and parturition materials at the herd level. The human transmission risks include non-protective parturition assistance, the use of camel urine as an antiseptic substance, humans sharing animal-watering troughs, and consuming non-processed milk. Further, the fomites comprise contaminated excreta, infected placental materials on laga stones, deposition of infected aborted fetuses on the laga body, and bacteria in the sand that end up ingested or inhaled as dust during dry seasons. The study concludes that intensified water insecurity due to climate variability will deepen multispecies interactions at the laga given that it holds a lifeline in drylands for pastoralists, hence, heightening brucellosis transmission risks. The study’s results recommend a reinvention of brucellosis preventive measures that consider the pathogen flux within laga systems and multispecies interactions. Such an approach should consider the multidimensional-clinical, environmental, and cultural co-production of solutions where preventive behaviors are prioritized.
Sustainable development of the poultry industry in Kenya can significantly contribute to economic growth while also improving the livelihoods of millions of people who keep poultry for a living. To achieve this impact, strategies that will advance the industry across all value chain segments are required. In this study, we developed strategies to transform the Kenyan poultry industry through a system dynamic modelling, participatory approach that included Focus Discussion Groups (FDGs) and Key Informant Interviews (KII), with the findings validated through a workshop. These findings are depicted in causal loop diagrams to show how the proposed poultry industry's elements interact in a systematic manner. Using this methodology, it was observed that strengthening socioeconomic factors such as legal framework in associations and cooperatives can significantly improve the industry. The legal framework can provide a clear and stable environment for the operation of these organizations, helping to ensure that they function efficiently and effectively. This, in turn, can improve the competitiveness and sustainability of the poultry industry, as well as support the growth and development of the wider economy. Having a strong legal framework can help to ensure that the rights and interests of producers and other stakeholders are protected, which can increase trust and confidence in the industry. This can lead to greater investment and collaboration and can support the development of new and innovative solutions to the challenges facing poultry value chain actors. Additionally, modern technologies, innovations, and management practices can play a crucial role in improving efficiency along the value chain. These innovations can help to increase productivity, reduce costs, improve the quality and safety of poultry products, and meet the changing needs and preferences of consumers.
COVID-19 is a severe acute respiratory disease that has rapidly spread across the world from the first case that was reported in China in December 2019. The main symptoms of a person infected with corona virus are fever, dry cough, shortness of breath, fatigue, dyspnea and myalgia. One of the main tool employed to control the pandemic is providing the community with correct information about the disease. High knowledge, right attitude towards COVID-19 and are adhering to suggested practices is the most effective approach to control community transmission. It is against this background that this study sought to assess COVID-19 knowledge, attitude and practices (KAP) among Kenyans. An online cross- section survey was used to collect data between July 26-31, 2020. From the findings, 9.1% of the respondents indicated that their relatives, friends or themselves had contracted COVID-19. There is high knowledge of COVID-19 in Kenya, with 83.97% (82.4, 85.54) aware of its symptoms and preventative measures. With regard to containment of the disease, 55% are optimistic than measures instituted by Kenyan government will eventually control its spread. Daily MoH briefing (56%) and mainstream media (55%) are the main and trusted sources of information about COVID-19. Despite, most persons indicating they wore mask and washed hands while in crowded place, 60% indicated other people were not observing the measures. Gender, age group, education level and occupation influence the COVID-19 knowledge level.
Food prices have experienced enormous movements and volatility in the recent past which can be predominantly attributed to climate change. Extreme weather events such as drought, flooding and heat waves have adverse effects on agricultural production in areas where agriculture is weather reliant. Among the extreme weather events experienced in Kenya is a drought in 2008/09 which led to a record increase in food prices. It is against this backdrop that this study sought to investigate the dynamic relationship between maize prices and extreme agro-climatic indicators. The study uses structural vector autoregressive (SVAR) tools; Granger causality, Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD) to examine the dynamic relationship between extreme weather indicators (minimum and maximum temperature and precipitation) and wholesale maize prices. Using different lag length determinant criterion, reduced-form VAR (2) is highlighted as the best model to fit the study data past weather and maize prices information over a data period spanning from January 2000 and December 2016. The study established that there exists granger causality between maize prices and weather variables. Agro-climatic indicators are therefore significant in predicting future maize prices. Principally, this significance can be inferred from the reliance of local agricultural production on phenological patterns. Maize price shocks exhibited inflationary effects on future maize prices, while a shock in weather variables has depreciating effects after three months. With regard to forecast variance, 30-39% of maize price variations resulted from its own shocks. The rest is attributed to precipitation (29-39%); maximum temperature (24-26%); and minimum temperature (7-8%).
