The Agricultural Resource Management Survey (ARMS) is the federal government's primary source of information on the financial condition, production practices, and resource use on farms, as well as the economic well-being of America's farm households. ARMS data are important to the U.S. Department of Agriculture (USDA) and to congressional, administration, and industry decision makers when they must weigh alternative policies and programs that touch the farm sector or affect farm families. ARMS is unique in several respects. As a multiple-purpose survey with an agricultural focus, ARMS is the only representative national source of observations of farm-level production practices, the economics of the farm businesses operating the field (or dairy herd, greenhouse, nursery, poultry house, etc.), and the characteristics of the American farm household (age, education, occupation, farm and off-farm work, types of employment, family living expenses, etc.). No other data source is able to match the range and depth of ARMS in these areas. American agriculture is changing, and the science of statistical measurement is changing as well. As with every major governmental data collection with such far-reaching and important uses, it is critical to periodically ensure that the survey is grounded in relevant concepts, applying the most up-to-date statistical methodology, and invested with the necessary design, estimation, and analytical techniques to ensure a quality product. ARMS is a complex undertaking. From its start as a melding of data collected from the field, the farm, and the household in a multiphase, multiframe, and multiple mode survey design, it has increased in complexity over the decade of its existence as more sophisticated demands for its outputs have been made. Today, the survey faces difficult choices and challenges, including a need for a thorough review of its methods, practices, and procedures. Understanding American Agriculture : Challenges for the Agricultural Resource Management Survey summarizes the recommendations of the committee who wrote the survey.

Provides a solid foundation for understanding American agricultural history and offers new directions for research A Companion to American Agricultural History addresses the key aspects of America’s complex agricultural past from 8,000 BCE to the first decades of the twenty-first century. Bringing together more than thirty original essays by both established and emerging scholars, this innovative volume presents a succinct and accessible overview of American agricultural history while delivering a state-of-the-art assessment of modern scholarship on a diversity of subjects, themes, and issues. The essays provide readers with starting points for their exploration of American agricultural history—whether in general or in regards to a specific topic—and highlights the many ways the agricultural history of America is of integral importance to the wider American experience. Individual essays trace the origin and development of agricultural politics and policies, examine changes in science, technology, and government regulations, offer analytical suggestions for new research areas, discuss matters of ethnicity and gender in American agriculture, and more. This Companion: Introduces readers to a uniquely wide range of topics within the study of American agricultural history Provides a narrative summary and a critical examination of field-defining works Introduces specific topics within American agricultural history such as agrarian reform, agribusiness, and agricultural power and production Discusses the impacts of American agriculture on different groups including Native Americans, African Americans, and European, Asian, and Latinx immigrants Views the agricultural history of America through new interdisciplinary lenses of race, class, and the environment Explores depictions of American agriculture in film, popular music, literature, and art A Companion to American Agricultural History is an essential resource for introductory students and general readers seeking a concise overview of the subject, and for graduate students and scholars wanting to learn about a particular aspect of American agricultural history.

In this introductory volume, readers will learn about the vital role that the various Critical Infrastructure (CI) sectors play in America, in the context of homeland security. The protection, maintenance, and monitoring of these interdependent CI assets is a shared responsibility of governments, private sector owner/operators, first responders, and all those involved in homeland security and emergency management. As this foundational learning resource demonstrates, rapidly advancing technologies combined with exponential growth in demand on the aging infrastructure of America’s power grid is setting the stage for a potentially catastrophic collapse that would paralyze each and every facet of civilian life and military operations. This meticulously researched primer will guide readers through the known world of power failures and cyber-attacks to the emerging threat from a High-altitude Electromagnetic Pulse (HEMP). A HEMP would cause cascading failures in the power grid, communications, water treatment facilities, oil refineries, pipelines, banking, supply chain management, food production, air traffic control, and all forms of transportation. Each chapter in America’s Greatest Existential Threat (Vol. 1) begins with learning objectives and ends with a series of review questions to assess take-up of the chapter material. Similarly, subsequent volumes will explore HEMP and emerging issues in closer detail with current research and analysis now in development.

Despite the fact that every year it produces a larger surplus of agricultural products than any other country in the world, the U.S. still must contend with a number of important but often unaddressed issues related to food security, including problems of soil erosion, water supply, energy availability, nutrition; farm worker health and safety, and product distribution. This book; containing contributions from authorities in both the natural and social sciences, expands the range of issues pertinent to the security of the U.S. food system, taking into account the adequacy and sustainability of the food supply, equity in access to food by the entire population, the nutritional quality of food, and the costs and benefits (social, economic, and health) of the food system as it is presently organized. Each of the authors considers an aspect of U.S. food security from the point of view of a specific discipline, as well as in terms of broader policy implications.

Handbook of Agricultural Economics, Volume Five highlights new advances in the field, with this new release exploring comprehensive chapters written by an international board of authors who discuss topics such as The Economics of Agricultural Innovation, Climate, food and agriculture, Agricultural Labor Markets: Immigration Policy, Minimum Wages, Etc., Risk Management in Agricultural Production, Animal Health and Livestock Disease, Behavioral and Experimental Economics to Inform Agri-Environmental Programs and Policies, Big Data, Machine Learning Methods for Agricultural and Applied Economists, Agricultural data collection to minimize measurement error and maximize coverage, Gender, agriculture and nutrition, Social Networks Analysis In Agricultural Economics, and more. - Presents the latest release in the Handbook of Agricultural Economics - Written and contributed by leaders in the field - Covers topics such as The Economics of Agricultural Innovation, Climate, Food and Agriculture, Agricultural Labor Markets, and more

This volume constitutes reviewed and selected papers from the 11th International Advanced Computing Conference, IACC 2021, held in December 2021. The 47 full papers and 4 short papers presented in the volume were thorougly reviewed and selected from 246 submissions. The papers are organized in the following topical sections: application of artificial intelligence and machine learning in healthcare; application of AI for emotion and behaviour prediction; problem solving using reinforcement learning and analysis of data; advance uses of RNN and regression techniques; special intervention of AI.