Abstract
Designing research for beef cattle production in rangeland environments is an ongoing challenge for researchers worldwide. Specifically, creating study designs that mirror actual production environments yet have enough observations for statistical inference is a challenge that often hinders researchers in efforts to publish their observations. Numerous journals will accept “case study” or observational results that lack valid statistical inference. However, these journals are limited in number and often lack impact. Approaches are available to gain statistical inference by creating multiple observations within a common group of animals. Approaches to increasing statistical observations will be discussed in this presentation. Modeling animal behavior and performance on extensive rangeland landscapes is commonly practiced in wildlife ecology and, more recently, has been published in Animal Science journals. Additionally, new technology has made it possible to apply treatments (e.g., supplementation studies) to individual animals on extensive environments where large, diverse herds/flocks of cattle/sheep are managed as a single group. Use of individual animal identification (EID) and feed intake technology has opened a wide range of research possibilities for beef cattle production systems research in rangeland environments. Likewise, global positioning system (GPS) collars and activity monitors have created the opportunity to evaluate animal grazing behavior in remote and extensive landscapes. The use of multiple regression models to evaluate resource use in extensive environments will, in turn, help managers optimize beef cattle production and the sustainable use of forage/rangeland resources. Embracing new technologies such as GPS, activity monitors, EID tags, and feed intake monitors combined with multiple regression modeling tools will aid in designing and publishing beef cattle production research in extensive rangeland environments.
Desacuerdo entre pares epistémicos en el debate naturaleza-cultura
