4.6 Article

Feed Conversion Ratio (FCR) and Performance Group Estimation Based on Predicted Feed Intake for the Optimisation of Beef Production

Journal

SENSORS
Volume 23, Issue 10, Pages -

Publisher

MDPI
DOI: 10.3390/s23104621

Keywords

feed intake estimation; Feed Conversion Ratio; beef production; precision livestock farming; Machine Learning

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This study predicts the Feed Conversion Ratio (FCR) for individual animals by using estimates of individual animal feed intake made through time spent feeding measurements. Data of eating time for 80 beef animals over a 56-day period were collected to predict feed intake. A Support Vector Regression (SVR) model was trained to predict feed intake and animals were categorized into three groups based on estimated FCR values. The results demonstrate the feasibility of using "time spent eating" data to estimate feed intake and provide insights for optimizing production costs.
This paper reports on the use of estimates of individual animal feed intake (made using time spent feeding measurements) to predict the Feed Conversion Ratio (FCR), a measure of the amount of feed consumed to produce 1 kg of body mass, for an individual animal. Reported research to date has evaluated the ability of statistical methods to predict daily feed intake based on measurements of time spent feeding measured using electronic feeding systems. The study collated data of the time spent eating for 80 beef animals over a 56-day period as the basis for the prediction of feed intake. A Support Vector Regression (SVR) model was trained to predict feed intake and the performance of the approach was quantified. Here, feed intake predictions are used to estimate individual FCR and use this information to categorise animals into three groups based on the estimated Feed Conversion Ratio value. Results provide evidence of the feasibility of utilising the 'time spent eating' data to estimate feed intake and in turn Feed Conversion Ratio (FCR), the latter providing insights that guide farmer decisions on the optimisation of production costs.

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