Circular 112

A yield trial comparing 45 cultivars of potatoes (Solanum tuberosum L.) was conducted during the 1997 growing season at the University of Alaska Fairbanks, Agricultural and Forestry Experiment Station’s (AFES) Palmer Research Center, Matanuska Farm, located six miles west of Palmer, Alaska. Varieties with a history of commercial production in the Matanuska Valley (Alaska 114, Bake-King, Green Mountain, and Superior) were included to serve as a comparative base for newly developed varieties or older named varieties that have not been tested at this location. Russet Burbank, the variety most widely grown in the United States, also was included to broaden the base of comparison although past trials have demonstrated its unsuitability for this area. Varieties that compare favorably with the above listed local standards may warrant consideration by commercial growers. Nonirrigated trials have been conducted annually since 1982 whereas irrigated trials were initiated in 1985. Results of these trials were published in AFES Circulars and are available at AFES offices

Health Benefits of Urban Agriculture

Health professionals increasingly recognize the value of farm-and garden-scale urban agriculture. Growing food and non-food crops in and near cities contributes to healthy communities by engaging residents in work and recreation that improves individual and public well-being. This article outlines the benefits of urban agriculture with regard to nutrition, food security, exercise, mental health, and social and physical urban environments. Potential risks are reviewed. Practical recommendations for health professionals to increase the positive benefits of urban agriculture are provided

Introduction by the Editors

This Essay opens a Symposium honoring the contribution of Mari Matsuda to American legal scholarship. The first Asian American female to gain tenure at a U.S. law school, she helped establish a scholarly movement-critical race theory-that reshaped several academic disciplines. She also was the first to propose a new perspective-looking to the bottom-in which judges and activists would evaluate legal practices from the perspective of the least advantaged members of society. With pathbreaking articles on hate speech, accent discrimination, legal history, affirmative action, feminist legal theory, and the politics of coalition, Matsuda has left her mark on numerous areas of law. Her work has found its way into dozens of casebooks and anthologies. One of her articles, Public Response to Racist Speech: Considering the Victim\u27s Story, is the thirty-third most cited article in the history of American law. It occupies a niche slightly below an article by Kimberlé Williams Crenshaw and just above another by Frank Easterbrook and Daniel Fischel, with the additional distinction of being Michigan Law Review\u27s most-cited article in the journal\u27s history. Each article in the forthcoming series will examine an aspect of anti-oppression thought or practice. Drawing inspiration from Matsuda\u27s foundational essay on accent discrimination, in which she analyzes society\u27s preference for clerks and salespeople who speak unaccented English, an opening article by Richard Delgado discusses resistance to same-sex marriage, identifying a number of core beliefs that underlie that resistance and subjecting them to analysis. Subsequent articles will build on other works by Matsuda to advance our understanding of social problems and issues, including a number that are just now emerging

A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images

A partially unsupervised approach to the classification of multitemporal remote-sensing images is presented. Such an approach allows the automatic classification of a remote-sensing image for which training data are not available, drawing on the information derived from an image acquired in the same area at a previous time. In particular, the proposed technique is based on a cascade classifier approach and on a specific formulation of the expectation-maximization (EM) algorithm used for the unsupervised estimation of the statistical parameters of the image to be classified. The results of experiments carried out on a multitemporal data set confirm the validity of the proposed approach