ABSTRACT

Despite significant attention from governments, donor agencies, and NGOs, food security remains an unresolved challenge in the context of global human welfare. Both technical and conceptual limits have prevented the collection and analysis of rich empirical datasets with high temporal frequency over large spatial extents necessary to investigate how changes to seasonal precipitation patterns are affecting food security. This research project will transform both methodological and conceptual frameworks for assessing the sustainability of dryland agricultural systems. The research will bring new understanding of how dryland farmers adapt to within-season variability in climate and how those adaptations affect their current and future resilience to climate variability and climate change. Project findings will improve forecast models used to monitor and predict the sustainability of water-dependent agricultural systems. By marrying the simple idea of cell phone adoption with state-of-art research in data science, crop prediction, and environmental/social monitoring, the project will advance and accelerate scientific understanding of an important global sustainability problem.

This project will focus on characterizing the nature and impact of intra-seasonal smallholder decision making on adaptation to climate variability in semi-arid agricultural systems. Specifically, the research addresses three critical research questions: (1) How do intra-seasonal dynamics of both the environment and social systems shape farmer adaptive capacity? (2) To what extent does intra-seasonal decision making enable farmers to adapt to climate uncertainty? and (3) How can intra-seasonal data improve the ability to model, predict, and improve adaptation to climate variability in ways that enhance food security? The research team will integrate physical models of hydrological and agricultural dynamics with real-time environmental data and weekly farmer decision making in individual fields. These real-time data are obtained from previously-developed novel cellular-based environmental sensing pods coupled to real-time reports of farmer decision making submitted via cell phones. The team will use a combination of environmental and social data to develop a suite of modeling tools for understanding how climate variability impacts the sustainability of agricultural systems in the study regions. The research team also will develop modeling tools for improved forecasts of food security capable of producing new understandings of the intra-seasonal dynamics of both social and environmental processes. Although the test bed for this research is the Southern Province of Zambia and portions of the Rift Valley and Central Provinces of Kenya centered around the Laikipia District, the results may well be broadly applicable to other semi-arid and arid regions of the world.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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