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Meeting the United Nations Sustainable Development Goals (SDGs) will require adapting or redirecting a variety of very complex global and local human systems. It is essential that development scholars and practitioners have tools to understand the dynamics of these systems and the key drivers of their behavior, such as barriers to progress and leverage points for driving sustainable change. System dynamics tools are well suited to address this challenge, but they must first be adapted for the data‐poor and fragmented environment of development work. Our key contribution is to extend the causal loop diagram (CLD) with a data layer that describes the status of and change in each variable based on available data. By testing dynamic hypotheses against the system's actual behavior, it enables analysis of a system's dynamics and behavioral drivers without simulation. The data‐layered CLD was developed through a 4‐year engagement with USAID/Uganda. Its contributions are illustrated through an application to agricultural financing in Uganda. Our analysis identified a lack of demand for agricultural loans as a major barrier to broadening agricultural financing, partially refuting an existing hypothesis that access to credit was the main constraint. Our work extends system dynamics theory to meet the challenges of this practice environment, enabling analysis of the complex dynamics that are crucial to achieving the SDGs. [ABSTRACT FROM AUTHOR]

Designing international development projects is challenging because the complexity of the systems on which they act makes it difficult to identify the best leverage points for intervention. This paper seeks to identify the best combinations of interventions to increase the availability of and demand for quality seeds in Uganda and similar markets. A system dynamics model simulates the current dynamics in Ugandan seed markets based on data gathered by ongoing development projects. The findings show that one intervention is critical to enabling growth—investing in a system for verifying the quality of seeds—and that a combination of quality verification with education-oriented interventions is more powerful than quality verification alone. The results have implications for systems approaches to development: they suggest that a combination of interventions in different parts of the value chain enables larger changes than any single intervention alone. [ABSTRACT FROM AUTHOR]