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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.
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.
​​This book provides standards and guidelines for quantifying greenhouse gas emissions and removals in smallholder agricultural systems and comparing options for climate change mitigation based on emission reductions and livelihood trade-offs. Globally, agriculture is directly responsible for about 11% of annual greenhouse gas (GHG) emissions and induces an additional 17% through land use change, mostly in developing countries. Farms in the developing countries of sub-Saharan Africa and Asia are predominately managed by smallholders, with 80% of land holdings smaller than ten hectares. However, little to no information exists on greenhouse gas emissions and mitigation potentials in smallholder agriculture. Greenhouse gas measurements in agriculture are expensive, time consuming, and error prone, challenges only exacerbated by the heterogeneity of smallholder systems and landscapes. Concerns over methodological rigor, measurement costs, and the diversity of approaches, coupled with the demand for robust information suggest it is germane for the scientific community to establish standards of measurements for quantifying GHG emissions from smallholder agriculture. Standard guidelines for use by scientists, development organizations will help generate reliable data on emissions baselines and allow rigorous comparisons of mitigation options. The guidelines described in this book, developed by the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS) and partners, are intended to inform anyone conducting field measurements of agricultural greenhouse gas sources and sinks, especially to develop IPCC Tier 2 emission factors or to compare mitigation options in smallholder systems.
This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.
With reference to India.
The book offers a rich toolkit of relevant, adoptable ecosystem-based practices that can help the world's 500 million smallholder farm families achieve higher productivity, profitability and resource-use efficiency while enhancing natural capital.
The recent food crisis and the ongoing debates on food price volatility, the impact of climate change on agriculture and food security highlight the weaknesses in available agricultural data. The Global Strategy to Improve Agricultural and Rural Statistics is a ground-breaking effort to strengthen agricultural statistics. It provides the framework essential to meeting the current and emerging data requirements and the demands of policy makers and other data users so that they can fill these urgent needs. This report describes the goals and purposes of the Global Action Plan, and provides the proposed global, regional and national governance structures. It examines the linkages of the Plan s technical components including country assessments, the technical assistance plan, the training plan and the research plan, and then outlines the process, assessment of resources, and timeline required for technical assistance, training and research. The report concludes with an overview of the implementation procedures and the monitoring, evaluation, and reporting requirements."
The effective use of data science - the science and technology of extracting value from data - improves, enhances, and strengthens acquisition decision-making and outcomes. Using data science to support decision making is not new to the defense acquisition community; its use by the acquisition workforce has enabled acquisition and thus defense successes for decades. Still, more consistent and expanded application of data science will continue improving acquisition outcomes, and doing so requires coordinated efforts across the defense acquisition system and its related communities and stakeholders. Central to that effort is the development, growth, and sustainment of data science capabilities across the acquisition workforce. At the request of the Under Secretary of Defense for Acquisition and Sustainment, Empowering the Defense Acquisition Workforce to Improve Mission Outcomes Using Data Science assesses how data science can improve acquisition processes and develops a framework for training and educating the defense acquisition workforce to better exploit the application of data science. This report identifies opportunities where data science can improve acquisition processes, the relevant data science skills and capabilities necessary for the acquisition workforce, and relevant models of data science training and education.
Remote Sensing in Precision Agriculture: Transforming Scientific Advancement into Innovation compiles the latest applications of remote sensing in agriculture using spaceborne, airborne and drones' geospatial data. The book presents case studies, new algorithms and the latest methods surrounding crop sown area estimation, determining crop health status, assessment of vegetation dynamics, crop diseases identification, crop yield estimation, soil properties, drone image analysis for crop damage assessment, and other issues in precision agriculture. This book is ideal for those seeking to explore and implement remote sensing in an effective and efficient manner with its compendium of scientifically and technologically sound information. - Presents a well-integrated collection of chapters, with quality, consistency and continuity - Provides the latest RS techniques in Precision Agriculture that are addressed by leading experts - Includes detailed, yet geographically global case studies that can be easily understood, reproduced or implemented - Covers geospatial data, with codes available through shared links