2009 Frank H. Baker Memorial Scholarship awarded to Speidel
Scott Speidel, research associate in breeding and genetics at Colorado State University, Fort Collins, Colo., is a recipient of the 2009 Frank H. Baker Memorial Scholarship Award, presented during the 41st Beef Improvement Federation Research Symposium and Annual Meeting, April 30 to May 3, in Sacramento, Calif.
Speidel accepted the award from Robert Williams, Ph.D., director of breed improvement and foreign marketing for the American-International Charolais Association, Kansas City, Mo.
The late Frank H. Baker played a key leadership role in helping establish the BIF in 1968. Each year since 1994, two deserving graduate students have been recognized for his or her winning essays.
A California native, Speidel holds a bachelor's degree in animal science from California State University, Fresno; a master's degree from the University of Arizona, Tucson, Ariz., and plans to complete his doctorage this fall at Colorado State University.
An abstract from his award-winning essay follows.
"Genetic Analysis of Longitudinal Data in Beef Cattle"
Currently, many different data types are collected by beef cattle breed associations for the purpose of genetic evaluation. These data points are all biological characteristics of individual animals that can be measured multiple times over an animal's lifetime. Some traits can only be measured once on an individual animal; whereas others, such as the body weight of an animal as it grows, can be measured a multitude of times. Data such as growth has been often referred to as "longitudinal" or "infinite-dimensional" since it is theoretically possible to observe the trait an infinite number of times over the life span of a given individual.
The analysis of such data is not without its challenges and, as a result, many different methods have been or are beginning to be implemented in the genetic analysis of beef cattle data--each an improvement over its predecessor. These methods of analysis range from the classic repeated measures to the more contemporary suite of random regressions that use covariance functions or even splines as their basis function.
Each of the approaches has both strengths and weaknesses when it comes to the analysis of longitudinal data. Therefore, the objective of this essay is to summarize past and current genetic evaluation technology for analyzing this type of data and to review some emerging technologies beginning to be implemented in current national cattle evaluation schemes along with their potential implications to the beef industry.