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Abstract Demand for dairy products in sub-Saharan Africa, is expected to triple by 2050, while limited increase in supply is predicted. This poses significant food security risk to low income households. Understanding how the dairy food system operates is essential to identify mitigation measures to food insecurity impact. This study aims to determine the structure and functionality of Nairobi's dairy system using a value chain mapping approach. Primary data were gathered through focus group discussions and key informant interviews with dairy value chain stakeholders in Nairobi to obtain qualitative information on people and products in the chains while describing their interactions and flows. Qualitative thematic analysis combined with flowcharts created by participants enabled identification of key food system segments and the development of chain profiles (or flow-diagrams) which together form Nairobi's dairy system. Seven chain profiles forming Nairobi's dairy value chain were identified. These were found to be dominated by small-scale individuals who operate largely independently. Our profiles for the urban and peri-urban farming systems were structurally similar in their downstream networks, obtaining inputs from similar sources. Upstream, the urban systems were shorter, supplying mostly to immediate neighbours or based on own consumption, while the peri urban systems supplied to a wider network and showed some affiliations to producers' associations. Two distinct profiles characterize the milk flow from traders belonging either to a Dairy Traders Association (DTA) or those not belonging to this association (non-DTA). DTA traders sell mainly to fixed retailers and non-DTA traders to mobile retailers (hawkers or roadside vendors). Profiles associated with medium and large cooperatives were driven by networks of collection centres, but with medium-sized cooperatives selling half of their production to large processing companies, and large cooperatives only to fixed retailers. Large processing companies' profiles indicated distribution of high volumes and value addition processing. They reported strategic milk collection arrangements with suppliers on long, medium - or short - term contracts and with well-established product distribution channels. We have identified numerous inter-linkages across dairy chain profiles in Nairobi's complex system, demonstrating significant interdependency among the stakeholders. Therefore, enhancing the system's efficiency requires a holistic, system-wide approach and any policy interventions should consider every segment of the value chain. This study provides a methodological approach for organizations and policy makers to understand and address structural and functional vulnerabilities within food systems more broadly. The insights from this study are relevant to other rapidly growing cities in the region. Highlights • The structure of Nairobi's complex dairy value chain is provided. • Inter-dependency and inter-linkages of the stakeholders involved in the dairy chain is described • To enhance the system's efficiency, interventions require a holistic approach targeting every segment of the value chain [ABSTRACT FROM AUTHOR]

Achieving inclusive value chain development is a challenging task due to the complex and dynamic nature of interconnected value chains and their social, economic, and ecological dimensions. While many policies and intervention options exist to upgrade value chains, there are fewer methods that can be used to understand and quantify the multidimensional impacts that value chain policies and interventions may have throughout the value chain. This paper addresses this methodological gap by employing a system dynamics (SD) modeling approach. SD models allow us to model and quantify the processes and relationships inherent in the value chain through simulations, serving as a policy laboratory for the empirical assessment of intervention options. An SD model of the Matiguás dairy value chain in Nicaragua was developed and tested through a participatory modeling process. Our research tested and evaluated the short-, medium-, and long-term impacts of specific interventions and policies in the Matiguás dairy value chain with the goal of strengthening the competitiveness and inclusion of small- and medium-scale producers. These interventions centered on improving the feeding system, which was identified by stakeholders as the critical constraint to competitiveness. The policy analysis reveals that both improved pastures and increased use of concentrates raise producer milk productivity by 5% and 11%, respectively in the long run, but are also expensive strategies for smallholder producers, leading to a reduction in profits relative to the baseline by 1% and 3%, respectively. Consequently, policymakers should identify strategies that help to reduce concentrate costs and support producers with investments in improved pasture, while also promoting training in pasture management skills. Indeed, in the long-run, model results reveal that investment and training in pasture management results in a 30% and 35% increase in milk production during the wet and dry season, respectively. Simulation results further highlighted that intensifying the feeding system to improve cow milk yields is mainly profitable in the long term, and thus requires a longer-term perspective by policymakers. The model provides a deeper understanding of the complex and dynamic nature of the Matiguás dairy value chain and the interactions between markets, coordination aspects, biophysical phenomena, and income. The system dynamics approach to value chain analysis further addresses a major analytical shortcoming in value chain analysis and provides decision makers with an improved platform for planning and policy formulation. [ABSTRACT FROM AUTHOR]

Value chain analysis provides a rational and systematic framework for describing and evaluating the roles and relationships of people and organisations that exist in complex and dynamic systems and networks. This includes understanding the flows of materials and commodities and value-adding activities between the different parts of value chains. Value chain analysis also has people as a primary focus, thereby enabling an understanding of their roles, motivations and behaviours in context with cultural, socio-economic and other drivers. This study explores the hypothesis, proposed here for the first time, that the utility of value chain analysis can be further extended to enable evaluation of health surveillance systems. The main characteristics of a poultry food system value chain were evaluated first to provide a conceptual framework for the description of a surveillance system value chain. The scanning surveillance system for the detection and early warning of diseases and health threats in poultry in Great Britain was then used as an example, to which a value chain analysis was applied. The key surveillance value chain commodity was identified as livestock health data and intelligence, which describes and contextualises the health status of the given ‘at risk’ population. Furthermore, some fundamental asymmetries in the surveillance system value chain have been identified relating to poultry demographics (as the denominator livestock population) and the people, organisations, resource flows, power and institutional environment. In addition, other insights have been gained enabling the identification of factors that help to understand the behaviours of people, which in turn may influence their decision-making, and may also result in changes to the activities and risk profile and management in the surveillance system value chain, specifically: (i) information-sharing and collaboration; (ii) social capital and trust; (iii) prioritisation and deployment of resources; (iv) the basis for people to engage and participate; (v) cooperation between the public and private sectors, including sharing of responsibility and costs. This study has shown that value chain analysis provides a robust, systems-based approach that can be used as a systematic framework to evaluate a livestock health surveillance system and the context in which disease and associated surveillance information is placed in the surveillance system value chain by the people, businesses and organisations involved. In addition, this information can be used for decision-support, to manage risk, generate value and ensure that people in the system bear the costs of surveillance and disease management appropriately. [ABSTRACT FROM AUTHOR]

Value chain analysis has been widely used as a diagnostic approach to identify and address complex issues across value chains in an agricultural development context. While comprehensive, the method is primarily qualitative and static in nature, it has limited capability in capturing the dynamic interactions between components from production to consumption and their linkages within a complex system. The application of system dynamics in value chain analysis has the potential to address the limitations. We conducted a systematic literature review to synthesise the underlying reasons behind the application of system dynamics in value chain analysis and understand how it has been operationalised. Results show that the primary reasons why system dynamics is used in value chain analysis were improved ability in capturing the feedbacks, dynamic behaviours and time delays, modelling the dynamics of governance and roles of support systems over time, incorporating multiple causal factors, and measuring performance and policy interventions over time. This can be broadly attributed to the need to improve the analytical rigour of value chain analysis. The applications of system dynamics in value chain analysis varied according to the agricultural commodities analysed, the modelling process followed, dynamic complexity modelled, and the scope of the value chain components analysed. We discuss the contributions, limitations, and opportunities for future improvements. A preliminary framework is proposed to improve the application of system dynamics in value chain analysis. Unlabelled Image • System dynamics enhances value chain analysis in capturing feedback interactions. • It enables quantitative assessment of value chain performance and interventions. • Large variation in the application of system dynamics (SD) in value chain analysis • There is no comprehensive framework currently available to guide the integration. • The proposed framework can potentially improve the effectiveness of the application. [ABSTRACT FROM AUTHOR]