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Despite massive investments in food aid, agricultural extension, and seed/fertilizer subsidies, nearly 1 billion people in the developing world are food insecure and vulnerable to climate variability. Sub-Saharan Africa is most vulnerable, as approximately 25% of its people are undernourished (FAO/FAOSTAT 2013) and 96% of its cropland is rainfed (FAO 2002). The ability of subsistence farmers to respond to changes in water availability involves both inter-and intra-seasonal adaptation. Adaptive capacity diminishes over the season as decisions are made, resources are used, and the set of possible futures becomes restricted. Assessing the intra-seasonal adaptive capacity of smallholders requires integrating physical models of hydrological and agricultural dynamics with farmer decision-making at fine temporal (e.g. weekly) and spatial (e.g. crop field) scales. However, there is an intrinsic challenge to modeling the dynamics of these sociohydrologic systems, because important and uncharacterized spatial and temporal scale mismatches exist between the level at which the water resource is best understood and the level at which human dynamics are more predictable. For example, the skill of current process-based land surface models is primarily confined to short-term (daily to weekly), national- to regional-scale assessments, and reliable agricultural yield estimates and forecasts for small-scale farming systems remain elusive. In contrast, process-based social science modeling has focused on agent-based approaches that generate fine-scale (individual to community) dynamics over rather coarse time scales (yearly to decadal). A major obstacle to addressing this mismatch is the fundamental fact that the highest skill domain of one framework is essentially unpredictable in the other. I present a coupled sociohydrological observation framework designed to addressing this gap, and demonstrate its utility to understand relationships between climate variability, decision making, and crop production for subsistence agriculturalists in Kenya and Zambia.
Professor Caylor is an Associate Professor in the Department of Civil and Environmental Engineering at Princeton University. He received his PhD in Environmental Sciences from the University of Virginia, in 2003. His research seeks to develop improved insight into the way that land use and climate change are interacting to affect the dynamics and resilience of global drylands. His primary research sites are in sub-Saharan Africa, where he is focused on understanding the vulnerability of pastoral and subsistence agricultural communities to current and future changes in hydrological dynamics. Professor Caylor conducts research at a number of spatial and temporal scales; from small-scale experiments during individual rainfall events all the way up to continental-scale analyses of climate trends. A major focus of his current research efforts is the development of new methods for using stable isotopes of water to improve the measurement and prediction of ecosystem water use efficiency under varying pastoral land tenure regimes and subsistence agricultural practices. In addition, he is working on the development and deployment of low-cost cellular-based environmental sensors for improved monitoring of agriculture and ecosystem function in the developing world. Professor Caylor is a recipient of an Early Career Award from the NSF, and he was the inaugural recipient of Early Career Award in Hydrological Sciences given by the American Geophysical Union (AGU).