title: A review of population data utilization in beef cattle research.
abstract: Controlled experimentation has been the most common source of research data in most biological sciences. However, many research questions lend themselves to the use of population data, or combinations of population data and data resulting from controlled experimentation. Studies of important economic outcomes, such as efficiency, profits, and costs, lend themselves particularly well to this type of analysis. Analytical methods that have been most commonly applied to population data in studies related to livestock production and management include statistical regression and mathematical programming. In social sciences, such as applied economics, it has become common to utilize more than one method in the same study to provide answers to the various questions at hand. Of course, care must be taken to ensure that the methods of analysis are appropriately applied; however, a wide variety of beef industry research questions are being addressed using population data. Issues related to data sources, aggregation levels, and consistency of collection often surface when using population data. These issues are addressed by careful consideration of the questions being addressed and the costs of data collection. Previous research across a variety of cattle production and marketing issues provides a broad foundation upon which to build future research. There is tremendous opportunity for increased use of population data and increased collaboration across disciplines to address issues of importance to the cattle industry.
authors.affiliation: NW Area Extension Office, Oklahoma State University, Enid, OK 73701, USA. rodney.jones@okstate.edu
authors.forename: R
authors.initials: R
authors.lastname: Jones
authors.forename: M
authors.initials: M
authors.lastname: Langemeier
mesh_terms: D000822:Animal Husbandry / Q000379:methods / Q000706:statistics & numerical data*; D000818:Animals; D002417:Cattle / Q000254:growth & development* / Q000502:physiology; D003627:Data Interpretation, Statistical; D012106:Research / Q000592:standards; D012107:Research Design; D013223:Statistics as Topic