Year Established: 2018 Start Date: 2018-03-01 End Date: 2019-02-28
Total Federal Funds: $34,956 Total Non-Federal Funds: $67,113
Principal Investigators: Allison Reilly, Michelle Bensi
Abstract: Anecdotal evidence suggests that climate change is currently impacting agriculture in the State of Maryland. However, the quantitative impacts of climate change on the agriculture sector are not currently well-characterized. Moreover, there is not an established predictive model for forecasting regional yields for the State of Maryland using climatological and hydrometeorological factors (e.g., precipitation). Given the importance of the agriculture sector to the economy of the State of Maryland, predictive models are needed to support policy-makers in making better and more-informed decisions regarding options for mitigating potential impacts of climate change on agriculture. Thus, the goal of this project is to leverage recent advances in machine learning and parametric and non-parametric statistical methods to create fully-validated predictive models of crop yield under climate forcing, including precipitation, for the State of Maryland.