Value chain mapping to assess Governance challenges, food safety risks, and escherichia coli genetic diversity along the Dairy food system in Nairobi Kenya

Abstract

Food systems present complicated networks where production and distribution of food products can either be achieved through simple or complex value chains. Such system complexities present avenues for introduction, transmission and maintenance of food-borne pathogens and other food hazards. It follows that management of food safety require a holistic analysis of these networks, particularly to understand their structures and practices that influence the system’s sustainability and resilience. The overarching objective of this study was to investigate the structure, governance and food safety challenges in the Nairobi dairy value chain (NDVC) with a hypothesis that rapid urbanization exerts pressure on interconnected complex food systems within rapidly growing urban areas, consequently impairing the quality of livestock sourced foods supplied to consumers. From January 2014 to January 2015, various data were gathered through conducting of 23 key informant interviews, 22 focus group discussions and collection of 290 milk samples from 144 nodes of the NDVC. Data collection methods and analysis included value chain mapping, investigation of governance structure and its influence on food safety, bacteriological sampling of cow milk for analysis of bacterial quality and genetic diversity of Escherichia coli. Content thematic analysis was utilized for qualitative data to identify the emerging themes that described patterns of operations, interactions of people, flow of commodities, governance themes, key challenges and their implication on food safety. Bacterial quality of milk was done by determination of total bacteria (TBC) and total coliform (TCC) counts which were described in the context of average values for colony forming units per milliliter (cfu/ml) of milk with reference to the East Africa Standards (EAS). The acceptability of milk samples based on the EAS was analyzed by performing logistic regression analysis while the genetic diversity of E. coli was determined by GTG5 fingerprinting method and dendrograms were generated to show relatedness of E. coli based on banding patterns from different bacterial isolates. Results on mapping indicated that NDVC were vast and complex, comprising of seven chain profiles which were principally dominated by independent, yet highly interconnected small-scale operators. Each profile linked to other chain profiles hence forming the overall complex NDVC. Therefore, interventions on improvement need to consider these numerous inter-linked chain profiles to achieve the desired impact. Furthermore, interaction of actors was shown to be diverse through fragmented governance structures characterized with noncompliance to existing regulatory requirements. Hence, understanding of value chain governance would help decision-makers on potential areas that could improve efficiency and food safety along the dairy value chain. The mean values of cfu/ml for milk obtained at farm and collection centres was within acceptable EAS limits for TBC; However, TCC values at farm level were 3-times higher than lowest limits of EAS. When compared to the farm, acceptability of milk reduced downstream with milk from retail being less acceptable on TBC and TCC. Several practices with influence on bacterial quality of milk were identified: very muddy cowsheds, unconventional sources for animal feeds, use of spoilt milk for value added products, adulteration of milk, acceptance of low quality milk for processing and lack of cold chain. E. coli genetic diversity revealed a similarity matrix of between 50–70 % amongst isolates from the same region, signifying independent bacterial evolution or distinct milk contamination sources and not clonal spread of certain strains. In conclusion, the findings reflect diversity of people involved in the NDVC, their relationships and varied food safety practices which create opportunities for milk contamination. Therefore, enhancing system's efficiency requires a holistic approach to policy interventions. A risk benefit analysis along the value chain nodes will be important to identify which particular nodes generates more benefits to value chain actors and level of risk around such nodes with regard to food safety.