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Improving Crop Estimates by Integrating Multiple Data Sources (2017)

Chapter: Appendix D: Biographical Sketches of Panel Members and Staff

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Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
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Appendix D

Biographical Sketches of Panel Members and Staff

Mary Ellen Bock (Chair) is professor emerita of statistics at Purdue University. Her current research interests are in massive data sets and data mining and bioinformatics. She is a fellow of the American Association for the Advancement of Science, the Institute of Mathematical Statistics, and the American Statistical Association. She is a past president of the American Statistical Association and a member of the Committee on National Statistics of the National Academy of Sciences. She holds a Ph.D. in mathematics from the University of Illinois.

Julie Gershunskaya is a mathematical statistician with the Statistical Methods Staff of the Office of Employment and Unemployment Statistics at the U.S. Bureau of Labor Statistics. Her research has involved small-area estimation and treatment of influential observations, with special application to the U.S. Current Employment Statistics Program. She holds an M.S. in mathematics from Moscow State University and a Ph.D. in survey methodology from the University of Maryland.

Malay Ghosh is distinguished professor of statistics at the University of Florida. He has been a leader in research on and applications of small-area estimation methods. In addition to small-area estimation, he is interested in Bayesian inference, decision theory, and likelihoods. His current research includes, in particular, Bayesian analysis of case-control data, multiple hypothesis testing, Bayesian variable selection, empirical likelihood, and composite likelihood. He is a fellow of the American Statistical Association

Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×

and the Institute of Mathematical Statistics. He holds a Ph.D. in statistics from the University of North Carolina at Chapel Hill.

Michael F. Goodchild (NAS) is emeritus professor of geography at the University of California, Santa Barbara. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and he is a foreign fellow of the Royal Society of Canada. Goodchild’s research achievements center on the measurement, description, and analysis of phenomena on the surface of the earth. He has explored using digital information gathered by remote sensing satellites to create spatial and environmental models of the planet, construct maps, and create digital libraries of geographic information that can be widely accessed electronically. He has also developed mathematical models to help quantify the difference between these geographic measurements and the real world. Goodchild holds a Ph.D. in geography from McMaster University.

Chad Hart is associate professor of economics, crops markets specialist, and extension economist at Iowa State University. Prior to that, he was a U.S. policy and insurance analyst with the Food and Agricultural Policy Research Institute (FAPRI) and a scientist with the Center for Agricultural and Rural Development at Iowa State University. For FAPRI, he was responsible for directing econometric and modeling efforts for the crop insurance component of the FAPRI modeling system, and he has engaged in research examining the interaction between the agricultural commitments within the World Trade Organization (WTO) and the agricultural policies and programs of WTO members. Hart received his Ph.D. in economics and statistics from Iowa State University.

Olga Isengildina Massa has been associate professor in the Department of Agricultural and Applied Economics at Virginia Polytechnic Institute and State University since fall 2015. She worked as associate clinical professor in the Economics Department of the College of Business at the University of Texas at Arlington, and previously worked at the University of Texas, Clemson University, and the Office for Futures and Options Research at the University of Illinois. Her research focuses on forecast analysis and evaluation, price risk management, value of information, agricultural marketing, and agribusiness. She holds a Ph.D. in agricultural economics from Mississippi State University.

Susan E. Offutt was chief economist for the U.S. Government Accountability Office (GAO) until her retirement in May 2015. Before joining GAO, she served as administrator of the Economic Research Service at the U.S. Department of Agriculture for 10 years; and prior to that, she was

Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×

executive director of the National Research Council’s Board on Agriculture and Natural Resources, which conducts studies on a range of topics in agricultural science. She was chief of the Agriculture Branch at the Office of Management and Budget (OMB). During her tenure at OMB, she coordinated budget and policy analyses of the farm bill and trade negotiations. She began her career on the faculty at the University of Illinois Urbana–Champaign, where she taught econometrics and public policy in the agricultural economics department. She received an M.S. and a Ph.D. from Cornell.

S. Lynne Stokes is a professor in the Department of Statistical Science at Southern Methodist University. Her current research interests include sampling and nonsampling error modeling, psychometrics, and capture–recapture methodology. She is an elected fellow of the American Statistical Association (ASA) and a recipient of ASA’s Founder’s Award. For the past 15 years, she has been a member of the Technical Advisory Committee for the U.S. Department of Education’s National Assessment of Educational Progress. She received her Ph.D. in mathematical statistics from the University of North Carolina at Chapel Hill.

Jonathan Wakefield is a professor in the Department of Statistics and the Department of Biostatistics at the University of Washington. His research interests include spatial epidemiology, ecological inference, genetic epidemiology, genome-wide association studies, the analysis of next-generation RNA sequence data, space–time models for infectious disease data, small-area estimates, hierarchical models for survey data, and the links between Bayes and frequentist procedures. He is a fellow of the American Statistical Association and a recipient of the Guy Medal in Bronze from the Royal Statistical Society. He holds a Ph.D. in mathematics from the University of Nottingham.

Robert E. Young is chief economist and deputy executive director of public policy at the American Farm Bureau Federation. He directs the organization’s Economic Analysis Team, which conducts and coordinates economic research to support Farm Bureau public policy positions on such topics as farm policy, agricultural trade, regulatory costs, labor, and taxation. Young previously served as co-director for the Food and Agricultural Policy Research Institute at the University of Missouri; as a research associate professor in agricultural economics at the University of Missouri; and as chief economist for the U.S. Senate Committee on Agriculture, Nutrition, and Forestry. He holds a Ph.D. in agricultural economics from the University of Missouri–Columbia.

Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×

Nancy J. Kirkendall (Study Director) is a senior program officer for the Committee on National Statistics. Previously, she served as director of the Statistics and Methods Group of the Energy Information Administration (EIA) and as a member of EIA’s senior staff. She also served as senior mathematical statistician in the Statistical Policy Branch of the Office of Information and Regulatory Affairs of the U.S. Office of Management and Budget, serving as the desk officer for the U.S. Census Bureau and chair of the Federal Committee on Statistical Methodology. She is a fellow and past vice president of the American Statistical Association and a past president of the Washington Statistical Society. She is a recipient of the American Statistical Association’s Roger Herriot Award for Innovation in Federal Statistics and its Founder’s Award. She holds a Ph.D. in mathematical statistics from George Washington University.

Glenn D. White, Jr. (Senior Program Officer) is a senior program officer of the Committee on National Statistics. Previously, he was a senior manager at Ernst & Young’s Quantitative Economic and Statistics Practice, where he established and directed a quantitative survey practice. His primary responsibilities as practice leader included general statistical consulting, with an emphasis on survey and web data collection. Previously, he was a senior mathematical statistician at the Internal Revenue Service and a supervisory mathematical statistician at the U.S. Census Bureau. He holds a B.A. from the University of San Diego and an M.S. from the University of Vermont.

Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
Page 131
Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
Page 132
Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
Page 133
Suggested Citation:"Appendix D: Biographical Sketches of Panel Members and Staff." National Academies of Sciences, Engineering, and Medicine. 2017. Improving Crop Estimates by Integrating Multiple Data Sources. Washington, DC: The National Academies Press. doi: 10.17226/24892.
×
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The National Agricultural Statistics Service (NASS) is the primary statistical data collection agency within the U.S. Department of Agriculture (USDA). NASS conducts hundreds of surveys each year and prepares reports covering virtually every aspect of U.S. agriculture. Among the small-area estimates produced by NASS are county-level estimates for crops (planted acres, harvested acres, production, and yield by commodity) and for cash rental rates for irrigated cropland, nonirrigated cropland, and permanent pastureland. Key users of these county-level estimates include USDA’s Farm Services Agency (FSA) and Risk Management Agency (RMA), which use the estimates as part of their processes for distributing farm subsidies and providing farm insurance, respectively.

Improving Crop Estimates by Integrating Multiple Data Sources assesses county-level crop and cash rents estimates, and offers recommendations on methods for integrating data sources to provide more precise county-level estimates of acreage and yield for major crops and of cash rents by land use. This report considers technical issues involved in using the available data sources, such as methods for integrating the data, the assumptions underpinning the use of each source, the robustness of the resulting estimates, and the properties of desirable estimates of uncertainty.

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