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dc.contributor.authorWinkler, Jordanen_US
dc.date.accessioned2016-04-11T17:43:50Z
dc.date.available2016-04-11T17:43:50Z
dc.date.issued2015
dc.identifier.urihttps://hdl.handle.net/2144/15665
dc.description.abstractClimate change presents a clear threat to the future of global food security. Changes in the patterns of temperature and precipitation have the potential to greatly decrease agri- cultural production. Developing successful adaptation strategies is dependent on under- standing both the potential changes in yield of a given crop, as well as the likelihood those changes occurring. This requires an understanding of the uncertainty in the geographic patterns of future climate change, as well as the response of a crop to those changes. In this dissertation I explore a framework for generating rapid estimates of the risk of climate change to agricultural yields. Using data from multiple climate models I use a regression based pattern scaling ap- proach in conjunction with a multi-resolution Gaussian spatial process model to emulate the output of a multi-model ensemble of global climate models. The approach is flexible across climate scenarios, allowing it to be easily used in conjunction with other impact models. Using this model I am able to rapidly emulate thousands of runs of a climate model on a laptop computer. The resulting synthetic distributions retain the spatial variability of the initial emulated models and provide a tool for generating probabilistic forecasts of regional climate change. Next I use a generalized additive model approach to estimate the stable manifold yield response surface of a set of irrigated and rained crops in China. This approach highlights the nonlinear relationship between changes in temperature and precipitation and yield. Results suggest that irrigation alone cannot prevent losses from climate change. Predictions of future temperature and precipitation show a trend towards temperatures above the critical threshold for many crops, indicating the potential for large losses. In the final chapter I combine the previously described methods to assess the impact of climate change on the spatial patterns of crop yield change in China. Result indicate overall losses to crop yield in the majority of cropped regions for both irrigated and non irrigated crops. These results represent a new methodology for rapidly assessing the risk of climate change to crop yield, and provide a new tool for prioritizing adaptation measures.en_US
dc.language.isoen_US
dc.subjectGeographyen_US
dc.subjectProbabilityen_US
dc.subjectRisken_US
dc.subjectClimate changeen_US
dc.subjectCrop yielden_US
dc.subjectSpatial statisticsen_US
dc.titleClimate change impact on crop yield: towards a probabilistic modeling frameworken_US
dc.typeThesis/Dissertationen_US
dc.date.updated2016-04-08T20:08:39Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineEarth & Environmenten_US
etd.degree.grantorBoston Universityen_US


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