In 2014, the National Agricultural Statistics Service (NASS) engaged the Committee on National Statistics (CNSTAT) to convene a planning committee to organize a public workshop for an expert open discussion of their then-current livestock models. The models had worked well for some time. Unfortunately beginning in 2013, an epidemic that killed baby pigs broke out in the United States. The epidemic was not fully realized until 2014 and spread to many states. The result was a decline in hog inventories and pork production that was not predicted by the models. NASS delayed the workshop until 2019 while it worked to develop models that could help in times both of equilibrium and shock (disease or disaster), as well as alternative approaches to help detect the onset of a shock. The May 15, 2019, workshop was consistent with NASS’s 2014 intention, but with a focus on a model that can help predict hog inventories over time, including during times of shock (see Box 1-1 for the committee Statement of Task).
PLANNING THE WORKSHOP
CNSTAT recruited and gained approval for the planning committee: five individuals with expertise in model-based estimation and measures of uncertainty for small geographic areas using multiple data sources; knowledge of data sources relevant for hogs and pigs; and uses of hog and pig estimates for decision making and analysis. The workshop was organized into an introduction and 10 different sessions: (1) the requirements of the problem; (2) the surveys that collect hog inventory data from
farmers and other auxiliary data sources; (3) the functioning of the Agricultural Statistics Board; (4) the models that have been developed and pursued; (5) current web-scraping efforts; (6) current modeling efforts; (7) discussions on detection and monitoring of disease; (8) discussions of NASS modeling efforts; (9) discussions of extending the models to provide state-level estimates; and (10) concluding thoughts about future directions. The planning committee conducted all of its preparatory work by email and teleconference.
The Workshop on Using Models to Estimate Hog Production took place at the National Academies of Sciences, Engineering, and Medicine in Washington, DC, May 15, 2019. The audience included present and past NASS leadership and technical staff.
THE WORKSHOP AND STRUCTURE OF THIS PROCEEDINGS
The workshop was opened by Eric Slud, chair of the planning committee, who explained the intent of the workshop to present underlying
data sources and the technical basis for models developed by NASS for estimation of national-level hog inventories and to discuss additional data sources, model improvements, and extensions such as responding better to shocks and developing state-level models. The topics, he noted, call in a variety of statistical and agricultural knowledge well represented by the participants.
Brian Harris-Kojetin, CNSTAT director, highlighted Principles and Practices for a Federal Statistical Agency, the flagship CNSTAT publication.1 CNSTAT was pleased to work with NASS on the organization of the workshop, he said, reminding the audience that it was not a consensus study, but a workshop in which individual participants present their ideas to NASS.
Linda Young, NASS director of research and development, said the agency thought it had a good model 5 years ago. However, the model performed well during times of equilibrium, not during shocks such as disease or flooding, which is when the NASS Agricultural Statistics Board could most use the help of a good model. She expressed hope that the workshop would provide NASS with insights to help solve the problem of identifying shocks, monitoring data, and using models to combine data sources to publish high-quality estimates with standard errors, even during times of shock. She said that NASS wants to identify the presence of a shock in real time and determine its impact on the processes NASS is monitoring without having to wait for final definitive data to become available. She said that she was looking forward to the help the committee and invited participants could provide to solve this difficult problem.
After the welcoming remarks and introductions, six sessions provided background to the problem, describing the data, estimation, and modeling that has been done by NASS, with each presentation followed by a question-and-answer period. They were followed by four discussion sessions (see Appendix A for the agenda and a list of participants). The remainder of this proceedings reflects the six NASS presentation sessions and the concluding four discussion sessions. Chapter 2 provides background and challenges for the effort. Chapter 3 describes survey processes and data sources. Chapter 4 describes the Hog Board and its role in setting official estimates. Chapter 5 describes previous and current
1 National Academies of Sciences, Engineering, and Medicine. (2017). Principles and Practices for a Federal Statistical Agency: Sixth Edition. Washington, DC: The National Academies Press. doi: https://doi.org/10.17226/24810.
NASS modeling efforts. Chapter 6 describes NASS web-scraping efforts. Chapter 7 describes NASS modeling innovations that account for swine population dynamics. Chapter 8 turns to the first of the discussion sessions, and it summarizes the discussion of detecting and monitoring outbreaks. Chapter 9 provides a discussion of U.S.-level modeling efforts. Chapter 10 summarizes the discussion of state-level models and data sources. Chapter 11 summarizes the final discussion about visions of future work among steering committee members, invited discussants, and members of the audience.
This proceedings was prepared by a rapporteur as a factual summary of what occurred at the workshop. The steering committee’s role was limited to planning and convening the workshop and serving as expert discussants during the workshop. The views contained in the proceedings are those of individual workshop participants and do not necessarily represent the views of nonparticipants, other workshop participants, the steering committee, or the National Academies of Sciences, Engineering, and Medicine.