Development of the BeefSpecs fat calculator: A tool designed to assist decision making to increase on-farm and feedlot profitability

Abstract

The BeefSpecs fat calculator combines data from beef cattle growth-path studies with the extensive body of knowledge within animal growth and body composition models to predict body composition of cattle. It uses easy to obtain on-farm measurements to assist beef producers make management decisions to better achieve domestic and international market specifications. To facilitate uptake, it makes explicit use of practical end-user knowledge of cattle and production systems that can be translated for incorporation into the underpinning research models and output is returned in producer language. The simple interface of BeefSpecs has been refined and developed to be used in a structured manner. • Its primary use is as an educational tool to demonstrate the relationship(s) between management actions and the performance of groups of animals, •The next level of use is to facilitate refined animal management on-farm by assisting drafting decisions to create sub-groups of animals according to expected performance, and in its final form, •It will be used to optimise feeding and marketing decisions to increase profitability in both feedlots and pasture finishing systems. BeefSpecs has evolved. Initially BeefSpecs (BeefSpecs1) was developed using an animal growth and body composition model called the Davis Growth Model (DGM) which requires an estimate of feed intake to operate. However accurate feed intake information is not a realistic input in commercial production systems. To overcome this, a wide variety of alternative scenarios were simulated using the DGM, and a multiple regression interpolation of the simulation results was used to predict P8 rump fat depth. The agreement between observed and predicted P8 fat depths using this approach in commercial cattle was relatively high. However, there were some circumstances where the multiple linear regression method produced poor agreement with observed P8 fat depths, especially when growth rates were below 0.5 kg/day. To resolve this, phase two of BeefSpecs (BeefSpecs2) was implemented to directly use an alternative animal growth model (Meat Animal Research Centre model, MARC), that does not require feed intake as an input. A simplified version of the MARC model is used to predict the composition of empty body weight from animal growth rate (kg/day) given a description of animal type. Agreement between observed and predicted P8 fat depths in BeefSpecs2 was considerably better than for BeefSpecs1. Where BeefSpecs1 had problems predicting P8 fat depth at low growth rates, BeefSpecs2 has increased prediction robustness. Refinements to the modeling systems underlying BeefSpecs have allowed development of increasingly sophisticated applications. A tool for on-farm drafting has been developed to assist producers explore the effects drafting animals into sub-groups to manage independently has on their capacity to meet market specifications. This tool is designed to work in association with a national mechanism that provides carcass feedback data following slaughter allowing the impacts of alternative management strategies on carcass traits to be to explored. Additional tools have been developed that target refinement of animal allocations to either pens or paddocks in feedlots and pasture finishing systems, with the purpose of reducing days to slaughter to increase overall production system profit. Work is also progressing to extend BeefSpec's capabilities to predicting retail meat yield and intramuscular fat content using on-farm measurements.