<\/p>\n
Burchfield, E. K. & Gilligan, J. (2016). Agricultural adaptation to drought in the Sri Lankan dry zone<\/a>. Applied Geography, 77,<\/em>\u00a092-100.<\/p>\n Burchfield, E. K. & Gilligan, J. M. \u00a0(2016). Dynamics of individual and collective adaptation to water scarcity.\u00a0 In\u00a0Proceedings of the 2016 Winter Simulation Conference<\/em> (pp. 1678-1689). IEEE Press.<\/p>\n Burchfield E,\u00a0Nay, J.J., & Gilligan, J. (2016). Application of Machine Learning to the Prediction of Vegetation Health<\/a>.\u00a0 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B2<\/em>: 465-469, doi:10.5194\/isprs-archives-XLI-B2-465-2016.<\/p>\n Davis, K., Gephart, J. &\u00a0Gunda, T. (2015).Future environmental impacts of Sri Lanka\u2019s policy on self-sufficiency in rice.<\/a> AMBIO, <\/em>DOI:\u00a010.1007\/s13280-015-0720-2.<\/p>\n Gunda, T., Bazuin, J., Nay, J. J. & Yeung, K. (2017).\u00a0Impact of Seasonal Forecast Use on Agricultural Income in a System with Varying Crop Costs and Returns: An Empirically-Grounded Simulation<\/a>. Environmental Research Letters.\u00a0<\/em>DOI:\u00a010.1088\/1748-9326\/aa5ef7<\/p>\n Gunda, T., Benneyworth\u00a0L., &\u00a0Burchfield\u00a0E. (2015). Exploring water indices and associated parameters: A case study approach<\/a>.\u00a0Water Policy\u00a017<\/em>, 98-111.\u00a0DOI:10.2166\/wp.2014.022.<\/p>\n Gunda, T., Hornberger\u00a0G.M., &\u00a0Gilligan\u00a0J.M.\u00a0(2015) Spatiotemporal patterns of meteorological drought in\u00a0Sri\u00a0Lanka\u00a0from 1880-2010<\/a>.\u00a0International Journal of Climatology<\/em>. In press.<\/p>\n Jacobi, J., Perrone, D., Lyons-Duncan, L., & Hornberger, G.M. (2013).\u00a0A Tool for Calculating the Palmer Drought Indices. <\/a>Water Resources Research<\/em>\u00a049,<\/em> 1-4.<\/p>\n Lyons-Duncan, L., Perrone, D., Jacobi, J.H., &\u00a0Hornberger, G.M. (2015). \u00a0Drought: Using High Resolution as Part of the Solution.<\/a> Environ. Sci. Tech<\/em>., In press.<\/p>\n Nay, J. J., & Gilligan, J. M. (2015, December). Data-driven dynamic decision models<\/a>. In\u00a0Proceedings of the 2015 Winter Simulation Conference<\/em> (pp. 2752-2763). IEEE Press.<\/p>\n Perrone, D. &\u00a0Hornberger, G.M. (2013).\u00a0Water, Food, and Energy Security: Scrambling for Resources or Solutions?<\/a>.\u00a0WIRES Water<\/em>\u00a01<\/em>, 1-4.<\/p>\n Perrone, D. &\u00a0Hornberger, G. (2016). Frontiers of the food\u2013energy\u2013water trilemma: Sri Lanka as a microcosm of tradeoffs. <\/a><\/span>Environmental Research Letters<\/em>, <\/span>11<\/em>(1), 14005. <\/span><\/p>\n Stone, E. C., & Hornberger, G. M. (2016). <\/span>Impacts of management alternatives on rice yield and nitrogen losses to the environment: A case study in rural Sri Lanka. <\/a>Science of The Total Environment<\/em>\u00a0<\/span>542<\/em>, 271-276.<\/span><\/p>\n Truelove, H. B., Carrico, A. R., & Thabrew, L. (2015).\u00a0Agricultural Adaptation to Climate Change: A case study of Sri Lankan Farmers. <\/a>Global Environmental Change<\/em>\u00a031,<\/em> 85-97.<\/p>\n Williams, N.E. & Carrico A. (2017). Examining adaptations to water stress among farming households in Sri Lanka’s dry zone<\/a>. AMBIO: A Journal of the Human Environment.<\/em>\u00a0DOI 10.1007\/s13280-017-0904-z<\/p>\n Jacobi, John (2014). A Changing Climate in Sri Lanka: Shifts, Perceptions, and Potential Adaptive Actions<\/a>. Vanderbilt University.<\/p>\n Abstract:\u00a0Adaptation planners and water managers around the world are faced with the increasingly difficult challenge of allocating scarce water resources in a world of climate change and dynamic social pressures. Sri Lanka serves as a microcosm of these challenges and serves as the areas of study for this dissertation. To address the problem of how to distribute scare water resources, an interdisciplinary approach is needed. This dissertation examines the water landscape in Sri Lanka from both physical and social perspectives and integrates the two through the use of agent-based modeling. From the physical perspective, an easy-to-use drought monitoring tool is presented and shifts in spatial and temporal patterns of the Sri Lankan monsoons are examined. Analysis reveals that the timings of the monsoons have shifted over the past thirty years, with the spring monsoon starting later and the winter monsoon starting earlier. Next, Sri Lankan farmers\u2019 perceptions of changes in the climate are compared against measured changes to reveal that perceptions of climate change do not always match the objective reality. Finally, agent-based modeling is used to evaluate the effect of a farmer\u2019s decision model on the effectiveness of a seasonal forecasting program.<\/p>\n Perrone, Debra (2014). Characterizing Water, Food, and Energy Interrelationships<\/a>. Vanderbilt University.<\/span><\/p>\n Abstract:\u00a0Recent increases in competition for water, food, and energy are exposing the complex network threading these resources together. My dissertation explores how the interactions among these resources affect decisions about the delivery of adequate water, food, and energy to communities. I include technical elements that are ingredients in traditional engineering computations but also explore how societal norms and values enter into resource allocation decisions. My analyses show that for Tucson, AZ conveying water from long distances is 20 times more energy intensive than pumping groundwater and that coal-based electricity has eight times the water intensity of natural gas resources. Additional analyses illuminate tradeoffs made in using water for food (irrigation) versus using water for energy (electricity generation) and show that decisions are made partly on the basis of stakeholder preferences and perceptions and not just on the basis of a maximization of an overall benefit-cost ratio. I conclude with my views on approaches to mix, and properly balance, technological and non-technological water-food-energy considerations in sustainability planning.<\/p>\n Provenzano, Andrew (2015). Adapting to Water Scarcity: Effects of Irrigation Management and Psychological Factors on Crop Productivity<\/a><\/span>. <\/em>University of North Florida.<\/span><\/p>\n Abstract:\u00a0In developing countries, farmers are dealing with climatic changes by adapting their agricultural practices. Little work has investigated the direct impact of structural variables (e.g., central vs. local management of irrigation water, location of village), psychological variables (e.g., risk perceptions, self-efficacy), and adaptation on crop yield. \u00a0We tested a psychology-based model that focused on risk perceptions and efficacy beliefs by longitudinally surveying 278 Sri Lankan rice farmers. We assessed risk perceptions and efficacy beliefs before the major paddy-growing season and measured whether farmers performed adaptations as well as their paddy yield\/acre after the season. The model significantly predicted more than 25% of the variance in crop yield, with increased yields associated with centrally managed irrigation resources and with farmers low in perceived climate risk at the start of the growing season. Findings support the notion that while psychological factors are important, structural variables are the most important predictors of farm productivity in times of uncertain water supply.<\/span><\/p>\n Stone, E. C. (2015). Water and Nutrient Management in a Changing Climate: A Case Study from Rural Sri Lanka.<\/a> Vanderbilt University.<\/p>\n Abstract:\u00a0Efficient management of freshwater resources is critical as threats to water security increase due to changes in climate, population, and land use. The water and agricultural sectors of Asian countries have been identified as the most at risk for climate change impacts, and small-scale farmers are particularly vulnerable to changes in water availability and water quality. This research explored the tradeoffs between maximization of yield and minimization of environmental impact in rice production in Sri Lanka. The study used the DeNitrification-DeComposition (DNDC) model to examine how variations in climate, soil, and paddy management affect outputs of yield, greenhouse gas (GHG) emissions, and nitrogen (N) leaching in paddy systems in Sri Lanka from 1991 to 2010. Reducing fertilizer had little effect on yield, and N leaching and N2O emissions declined as a result. Reliable water inputs in irrigated systems increased yields over rain-fed schemes; alternate flooding techniques to mitigate water stress did not reduce yield. Alternate flooding increased N leaching as did rain-fed cultivation. A sensitivity analysis of soil parameters determined that high clay content reduced N leaching, low soil organic carbon increased yield, and the more basic the pH of the soil, the greater the reduction in GHG emissions. The results inform best practices for Sri Lankan farmers and decision makers on the supervision of water resources and agricultural inputs. This research demonstrates how cultivation in rice-growing regions in south Asia affects the environment and the nitrogen cycle on a global scale, in turn how informed management of these systems can adapt to a changing environment.<\/p>\n Nay, John J. (2017). A Machine Learning Approach to Modeling Dynamic Decision-Making in Strategic Interactions and Prediction Markets<\/a>.\u00a0Vanderbilt University.<\/p>\n Abstract:\u00a0<\/strong>My overarching modeling goal for my dissertation is to maximize generalization \u2013 some function of data and knowledge \u2013 from one sample, with its observations drawn independently from the distribution D, to another sample drawn from D, while also obtaining interpretable insights from the models. The processes of collecting relevant data and generating features from the raw data impart substantive knowledge into predictive models (and the model representation and optimization algorithms applied to those features contain methodological knowledge). I combine this knowledge with the data to train predictive models to deliver generalizability, and then investigate the implications of those models with simulations systematically exploring parameter spaces. The exploration of parameter space provides insights about the relationships between key variables.<\/p>\n Burchfield, Emily (2017) Agricultural adaptation to drought.<\/a>\u00a0<\/em>Vanderbilt University.<\/p>\n Abstract:\u00a0This research integrates geospatial and social datasets to understand the adaptive strategies employed by individuals, communities, and institutions during periods of drought. I apply dimension reduction techniques to remotely sensed data to identify agricultural communities in which cultivation occurred during an extreme drought. Two dominant adaptive strategies emerged from this research: crop diversification and a complex land reallocation process known locally as bethma. I supplemented this qualitative research with Bayesian analysis of project survey data to identify the multi-scalar factors driving participation in these adaptive behaviors. Results suggest that farmers with more assets (agrowell, land, higher socio-economic status) are more likely to engage in adaptive behaviors. To better understand the role of farmers\u2019 risk perception in influencing adaptive behaviors, I constructed an agent-based model to assess the extent to which farmer decision heuristics affect participation in crop diversification and bethma. Simulation results suggest that though environmental changes may produce sudden disruptions in agricultural outcomes, the variations in these outcomes are strongly influenced by the mental models farmers use to make agricultural decisions. I also collaborated with a data scientist to apply machine learning techniques to large remotely sensed datasets to create an open-source prediction software to forecast vegetation health. This is particularly important in tropical countries where cloud cover significantly reduces data availability. The latest version of the software is global in coverage and performs extremely well across agroecological contexts, time, and levels of data availability. The results of my research in Sri Lanka will increase the capacity of decision makers to monitor agricultural systems, identify and promote successful adaptive strategies, and increase agricultural adaptation to changing climate.<\/p>\n \n \n Bazuin, J.T. (2013). \u201cClimate change and the political economy and political ecology of land in post-conflict areas.\u201d Annual <\/span>Meeting of the Peace and Justice Studies Association. Waterloo, Canada.<\/span><\/p>\n Bazuin, J.T. & Fraser, J.C. (2014). \u201cNot food but debt: Drought vulnerability in Sri Lanka\u2019s Dry Zone.\u201d Annual Meeting of the <\/span>Association of American Geographers. Tampa, FL.<\/span><\/p>\n Berry, S., Greene, A., and Truelove, H.B. (February 2014). \u201cLost in translation: The meaning of climate change to rural farmers in Sri Lanka.\u201d Society for Personality and Social Psychology. Austin, TX.<\/span><\/p>\n Burchfield, E.K. (February 2013) \u201cResettlement and coloniality in the Mahaweli Ganga Watershed.\u201d Annual Dimensions of Political Ecology Conference on Nature\/Society, Lexington, KY.<\/span><\/p>\n Burchfield, E.K. (March 2013) \u201cWater security and rural livelihood: Water management regimes and livelihood outcomes in the Dry Zone of Sri Lanka.\u201d Community Research and Action Departmental Conference, Nashville, TN.<\/span><\/p>\n Burchfield, E. (June 2014) \u201cPatterns of agricultural drought in Sri Lankan paddy fields.\u201d Poster presented at Borlaug Summer Institute on Global Food Security, Purdue University.<\/span><\/p>\n Carrico, A.R. (January 2013). \u201cClimate, Drought & Agricultural Adaptations: Vulnerabilities and Responses to Water Stress Among Paddy Farmers in Sri Lanka.\u201d 2013 NSF Water Sustainability and Climate Annual Meeting, Washington DC.<\/span><\/p>\n Greene, A., Berry, S. and Truelove, H.B. (February 2014). \u201cBenevolence trust as a predictor of perceptions of socially dependent <\/span>agricultural systems.\u201d Society for Personality and Social Psychology. Austin, TX.<\/span><\/p>\n Gunda, T. and G.M. Hornberger (November 2014). \u201cEffects of Climate Change on Irrigation Demand from Rice Fields in the Dry Zone of Sri Lanka.\u201d Talk presented at American Water Resources Association Annual Meeting, Tysons Corner, VA.<\/span><\/p>\n Gunda, T. and E.K. Burchfield (August 2014). “Patterns of meteorological and agricultural drought in Sri Lankan agricultural areas.” Poster presented at Gordon Research Seminar and Conference on Science & Technology Policy: Systems Approaches to Research and Practice, Waterville Valley Resort, NH.<\/span><\/p>\n Gunda, T. and G.M. Hornberger (December 2013). \u201cComparative Assessment of Irrigation Water Quality in Sri Lanka\u2019s Tank-Cascade and Mahaweli Irrigation Schemes.\u201d Poster presented at American Geophysical Union Fall Meeting, San Francisco, CA.<\/span><\/p>\n Hornberger, G.M. and Carrico, A.R. (December 2012). \u201cDetermining paths by which farmers can adapt effectively to scarce freshwater resources.\u201d Invited talk at American Geophysical Union Annual Meeting, San Francisco, CA.<\/span><\/p>\n Hornberger G.M. and A. R. Carrico (June 2014). \u201cInterdisciplinary Challenges in Studying Social-Ecological Systems: Partnerships across the Natural and Social Sciences.\u201d Gordon Research Conference \u2013 Water, Holderness New Hampshire.<\/span><\/p>\n Jacobi, J., Carrico, A.R., Gilligan, J., and Hornberger, G. (December 2012). \u201cDiffusion of a sustainable farming technique in Sri Lanka: An agent-based modeling approach.\u201d Poster presented at American Geophysical Union Annual Meeting, San Francisco, CA.<\/span><\/p>\n Jacobi, J., J. Nay, and J. Gilligan (December 2013). \u201cThe Effect of Seasonal Forecasts on Farmer Behavior.\u201d American Geophysical Union Fall Meeting. San Francisco, CA.<\/span><\/p>\n Perrone, D., J. Jacobi, and G. Hornberger (December 2013). Adaptation Planning in Sri Lanka under Shifting Rainfall Patterns. <\/span>Poster presented at American Geophysical Union Fall Meeting. San Francisco, CA.<\/span><\/p>\n Provenzano, A.C. (April 2014) \u201cI Love to Take Risks…Will that Make Me an Effective Farmer?\u201d Annual Meeting of the Georgia Psychological Society, Brunswick, GA.<\/span><\/p>\n Provenzano, A.C. and Truelove, H.B. (April 2014) \u201cRisk aversion and farming success: An investigation of climate change adaptation among Sri Lankan farmers.\u201d Showcase of Osprey Advancements in Research and Scholarship Jacksonville, FL.<\/span><\/p>\n Truelove, H. B. (February 2014). \u201cThe social psychology of climate change adaptation: A study of Sri Lankan farmers\u201d. Invited presentation at Society for Personality and Social Psychology Sustainability Psychology Pre-Conference. Austin, TX.<\/span><\/p>\n Truelove, H. B. (April 2014). \u201cCrossing national and disciplinary borders: A multi-disciplinary investigation of climate change adaptation among Sri Lankan farmers.\u201d Scholars Transforming Academic Research Conference. Jacksonville, FL.<\/span><\/p>\n Truelove, H.B., Carrico, A., Thabrew, L., Jacobi, J. and Hornberger, G. (August 2012). \u201cAgricultural adaptation in Sri Lanka: Psychological and Environmental Influences.\u201d\u00a0 American Psychological Association Annual Meeting, Orlando, FL.<\/span><\/p>\n Nay, J., Gilligan, J. & Burchfield, E. (2016).\u00a0Forecasting Vegetation Health<\/a> [computer software].<\/span><\/p>\nTheses\u00a0and Dissertations<\/h2>\n
Conference Presentations<\/h2>\n
Software<\/h2>\n