The Department of Environmental Science, Policy and Management at the University of California, Berkeley, is seeking to fill a Postdoctoral Scholar position.

The Postdoctoral Scholar will conduct statistical modeling of impacts of extreme climate events on agriculture in relation to different farming systems. In particular, this postdoc will lead analyses to assess how diversified cropping systems affect crop production during the types of stressful weather that will continue to become more frequent and more severe with climate change. This will include processing existing datasets spanning approximately one million fields across nine states and two decades in the U.S. Corn Belt, including crop yields, remotely sensed data on crop rotation and cover cropping, and environmental variables. The postdoc will then develop statistical models of crop yield risks in a Bayesian causal inference framework. This position would be ideal for either an environmental science PhD with strong statistical training or a statistics PhD with experience or interest in environmental applications.

This position is nested within a broader, multidisciplinary and multisector team with funding from the United States Department of Agriculture and Foundation for Food and Agriculture Research. The goals of the broader team include quantifying the value of farming systems that reduce risk of yield losses for agricultural lenders, insurers, and farmers, via actuarial and economic analyses that will build on the statistical modeling conducted by this postdoc. There is significant potential for real world impact from this research by informing agricultural lending and crop insurance policies in the U.S. Team members span multiple universities and a non profit partner, Land Core.

The postdoc will be based in the Department of Environmental Science, Policy and Management at UC Berkeley, co-advised by Tim Bowles (agroecology) and Perry de Valpine (quantitative ecology). There will be opportunities to engage with the new Eric and Wendy Schmidt Center for Data Science and Environment, which would provide a rich environment for knowledge sharing around data science.

● Develop statistical models linking cropping system diversification (crop rotation and cover cropping) with crop yields during stressful weather. Using a Bayesian causal inference framework is of particular interest.
● Collaborate with multidisciplinary (e.g. agroecologists, agricultural economists, and statisticians) and multi-sector (i.e. academics and NGO) team to contribute to quantify economic risks from different cropping systems and environmental contexts.
● Work collaboratively with and articulate complex statistical concepts to stakeholders without statistical training.
● Lead publication of academic papers and present results at national and international conferences.

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