RB30-252 Sex and Age as Factors in Cattle Feeding

Varying economic conditions and changes in the demands of the meat consuming public have been responsible for the turns that have taken place in the beef industry during recent years. Both feeder and producer must recognize and conform to these changes if they are to continue in business. Among the most important of these changes have been the turn toward the marketing of lighter cattle and the gradual disappearance from feed lots of two- and three-year-old animals. Furthermore, the cattle population of the United States is fast reaching stabilization with the resulting effect that more heifers are being marketed, since only one-fourth of the heifer crop is needed to replace worn-out breeding animals. Realizing the increasing importance of the heifer problem from the standpoint of the producer, feeder, and consumer, the Nebraska Experiment Station undertook to compare steers and heifers in a series of trials both in the feedlot and in the beef. It was hoped that these experiments would yield results which would bring out existing differences, if any, between steers and heifers both in quality and quantity of beef produced and thus provide or disprove many of the complaints against heifers. The results of these trials are summarized in this bulletin. Age as well as the sex factor has been considered, since two-year-olds, yearlings, and calves were included in these trials.

Seeking a Second Opinion: Uncertainty in Disease Ecology

Analytical methods accounting for imperfect detection are often used to facilitate reliable inference in population and community ecology. We contend that similar approaches are needed in disease ecology because these complicated systems are inherently difficult to observe without error. For example, wildlife disease studies often designate individuals, populations, or spatial units to states (e.g., susceptible, infected, post-infected), but the uncertainty associated with these state assignments remains largely ignored or unaccounted for. We demonstrate how recent developments incorporating observation error through repeated sampling extend quite naturally to hierarchical spatial models of disease effects, prevalence, and dynamics in natural systems. A highly pathogenic strain of avian influenza virus in migratory waterfowl and a pathogenic fungus recently implicated in the global loss of amphibian biodiversity are used as motivating examples. Both show that relatively simple modifications to study designs can greatly improve our understanding of complex spatio-temporal disease dynamics by rigorously accounting for uncertainty at each level of the hierarchy.