My research last summer is part of the Cross-Sector Research in Residence Program established by the National Institute of Statistical Sciences (NISS) in partnership with the National Agricultural Statistics Service (NASS), the survey and estimation arm of the U.S. Department of Agriculture. This new collaborative venture by NISS and the USDA is the first project of a NISS initiative to host academic-government research teams focused on specific federal agency objectives. Three teams, each composed of five people (a faculty researcher in statistics, a NASS researcher, a NISS mentor, a postdoctoral fellow and a graduate student) were formed to work intensively together at NISS during the summers of 2009 and 2010 to solve research questions posed by NASS. The research problem assigned to my team is entitled: “Statistical Multi-Source Predictive Models and Error Estimates: Major USDA Crop Protection Forecasts and Estimates.”
The main goal of our team’s research project is to improve the process of producing multiple forecasts of crop protection throughout the growing season and estimates production at the end-of-season or after harvest by building a statistical model using information collected from multiple sources (USDA surveys and administrative/auxiliary information, including weather and remotely sensed data). At present, these information are synthesized by a panel of experts in USDA�s Agricultural Statistics Board (ASB) to come up with the official forecasts/estimates that are published. Our aim is to obtain a more objective process of obtaining official forecasts using data modeling.