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A pro-poor approach to upgrade agri-food value chains in Tanintharyi region of Myanmar : A thesis submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy at Lincoln University

Berends, Jared
Fields of Research
ANZSRC::300207 Agricultural systems analysis and modelling
Following decades of isolation, Myanmar’s continuing transition to democracy, peace-building, and economic liberalisation has resulted in sustained economic growth and subsequent reductions in poverty. Although Myanmar’s economy is underpinned by its agricultural sector, agricultural productivity and profitability are among the lowest in Asia, leading to higher rates of poverty in rural areas. With almost 70% of Myanmar’s population engaged in agriculture, small-scale farms are critical leverage points for improving the livelihoods of farm and non-farm households. In 2017, the New Zealand government approved a five-year project to upgrade agri-food value chains to strengthen rural livelihoods in the Tanintharyi Region of Myanmar. Embedded within this livelihoods project, this research incorporated action research methods to select upgrading interventions that target small-scale farmers in the pork value chain. Small-scale farmers in the project’s target villages face multiple production, processing, and marketing constraints and system shocks that result in poor quality products that are not viable for higher value markets. Traditional pro-poor upgrading approaches primarily rely on qualitative and descriptive data collected through a one-time “snapshot” of the value chain. However, the search for interventions to upgrade smallholder agri-food value chains needs tools that consider the dynamic and complex nature of the chain while allowing for trade-off analysis to strengthen pro-poor decision-making. This research used participatory spatial group model building tools to engage a diverse group of stakeholders to identify and describe the dynamic processes in the pork value chain system. A quantitative system dynamics model of the pork value chain was constructed to account for critical feedback loops, structures, and relationships in the system. The model integrated modules of animal production, marketing, investment, finance, knowledge, credit, and collective action. The latter two modules represent new innovations in agri-food systems modeling. Once validated, the model was used to conduct a comprehensive ex-ante impact evaluation of potential pro-poor upgrading interventions, including trade-off analysis across diverse performance indicators, value chain actors, and temporal horizons. Results showed that technical upgrading activities implemented along with novel producer group arrangements brought sustained financial benefits to target communities and outperformed the short-term gains generated by these activities in the absence of collective action. A distinct rank order of individual technical interventions emerged: (1) establishing animal health workers, (2) microcredit, (3) technical training, and (4) artificial insemination. The model showed that a well-sequenced, multipronged approach with these technical activities enabled a larger number of poor households to benefit from pig livelihoods while also reducing risks from environmental and economic shocks. The model’s results determined the upgrading strategy of the project: establishing producer groups whose members are empowered to produce hybrid pig breeds for the burgeoning domestic premium pork market. The institutional arrangements underpinning the producer groups must be investor-friendly to encourage investment in value-adding assets and continued patronage by members. The study demonstrated how a systems dynamics model can engage the complexity within agri-food value chains using spatial group model building tools to identify critical problems and relationships in the system. Moreover, it demonstrated the merit of integrating such models into rural development projects that require ex-ante information about value chain interventions that best sustain growth in smallholder incomes.
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Attribution-NonCommercial 4.0 International
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