In the early twentieth century, many Americans were troubled by the way agriculture was becoming increasingly industrial and corporate. Mainline Protestant churches and cooperative organizations began to come together to promote agrarianism: the belief that the health of the nation depended on small rural communities and family farms. In Baptized with the Soil, Kevin M. Lowe offers for the first time a comprehensive history of the Protestant commitment to rural America. Christian agrarians believed that farming was the most moral way of life and a means for people to serve God by taking care of the earth that God created. When the Great Depression hit, Christian agrarians worked harder to keep small farmers on the land. They formed alliances with state universities, cooperative extension services, and each other's denominations. They experimented with ways of revitalizing rural church life--including new worship services like Rural Life Sunday, and new strategies for raising financial support like the Lord's Acre. Because they believed that the earth was holy, Christian agrarians also became leaders in promoting soil conservation. Decades before the environmental movement, they inspired an ethic of environmental stewardship in their congregations. They may not have been able to prevent the spread of industrial agribusiness, but their ideas have helped define significant and long-lasting currents in American culture.
This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: - Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects - Methods and theories for medical image recognition, segmentation and parsing of multiple objects - Efficient and effective machine learning solutions based on big datasets - Selected applications of medical image parsing using proven algorithms - Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects - Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets - Includes algorithms for recognizing and parsing of known anatomies for practical applications
Three leading scholars in the field explain why place and provenance are assuming more importance in the food chain to producers, consumers, and regulators. They examine how these concerns influence debates on the future of food and farming, exploring the implications for three very different regions: California, Tuscany, and Wales.
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