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.

The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals.

In 2014, the National Agricultural Statistics Service (NASS) engaged the National Academies of Sciences, Engineering, and Medicine 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. This publication summarizes the presentations and discussions from the workshop.

accuracy than LR models, and the models were able to explain up to 83% within-field yield variability. Model based yield maps demonstrated within-field yield differences better than yield monitor data. Spatial variability of crop yield was generally lower than temporal variability. Topographic properties were found to play significant role in within-field yield differences. Areas with lower slope were found to have higher yield suggesting the need to consider topographic variabilities in implementation of agricultural practices. Improved understanding on processes underlying spatial and temporal variability of crop yield can help develop management practices for optimal productivity with improved environmental quality.

"The Food and Agriculture Organization of the United Nations (FAO) is mandated to provide technical assistance to countries to build their capacities to produce timely and reliable information at the country level for mitigating food insecurity risks and for planning related government interventions and programmes. Estimates and forecasts of crop area and yield are of critical importance to policy makers for the planning of agricultural production and monitoring of food supply. The possible links between poverty and crop yields, which depend upon a variety of factors such as cultivation practices, availability of irrigation, access to resources to buy agricultural inputs for adoption of new technology, cannot be fully understood without reliable estimates of crop area and yields. In the absence of reliable information on crop productivity the reasons behind food insecurity of agricultural households cannot be precisely identified. The research agenda of the Global Strategy to Improve Agricultural and Rural Statistics foresees the potential of alternative methods and opportunities such as advances in satellite-based technology, for improving crop estimation and monitoring. Many institutions in Asia and Pacific region are using remotely sensed data in conjunction with conventional statistical methodologies to estimate the crop area and to forecast yield. These methods have seen a diverse degree of success, depending upon the nature of agriculture and/or access to advanced satellite imagery. A comparative study of these methods is needed to formulate technical recommendations to the countries who want to adopt these new technologies as an integral part of their statistical programme. The Expert Meeting on Crop Monitoring for Improved Food Security, organized as a side event of the 25th Session of the Asia and Pacific Commission on Agricultural Statistics (APCAS) held in Vientiane, Lao PDR, provided an occasion for over 50 experts from Asia and other regions to deliberate on best practices and methodological issues, and to identify challenges for future research work. The partnership with the Asian Development Bank (ADB) in the organization of the meeting enriched the technical content of the meeting. This publication summarizes the outcomes of the deliberations in the meeting and puts together a series of technical papers presented in the meeting and some reference papers. We hope this document will be a useful reference document for those interested in improving the current agricultural statistics using modern technologies. FAO remains committed to working with all stakeholders in its endeavour to make a desired contribution towards the sustainable development of agricultural and rural statistics systems of the countries in this region and elsewhere."--Foreword, page v.