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RESEARCH ARTICLE
Contents Vol 64(3)

A novel production profile classification system for incoming calves that predicts feedlot growth performance

Andreas H. R. Hentzen A and Dietmar E. Holm https://orcid.org/0000-0002-9340-6573 A *
+ Author Affiliations
- Author Affiliations

A Department of Production Animal Studies, Faculty of Veterinary Science, University of Pretoria, Private Bag X04, Onderstepoort 0110, South Africa.

* Correspondence to: dietmar.holm@up.ac.za

Handling Editor: Robin Jacob

Animal Production Science 64, AN23395 https://doi.org/10.1071/AN23395
Submitted: 12 August 2023  Accepted: 11 January 2024  Published: 5 February 2024

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing

Abstract

Context

Mitigating financial risk in the feedlot environment is an ongoing occurrence, and good production is a key risk mitigator. However, production protocols are based on historic averages because of the inability to predict growth potential of incoming calves. Production profiling of individual incoming feeder calves could address these limitations.

Aims

The aim of this study was to establish criteria for optimal sorting of incoming feeder calves into various cattle groups in a feedlot that maximises feedlot profit.

Methods

South African feeder calves (n = 436) were classified into four production-profile (PP) categories according to a predetermined set of phenotypic traits: PP 3 (n = 72) representing feeder calves with the poorest feedlot growth potential, PP 2− (n = 191) with below-average potential, PP 2+ (n = 139) with above-average potential and PP 1 (n = 34) with above-average feedlot growth potential. After combining the data of PP 2− and PP 2+ into PP 2, mixed modelling of economically important feedlot growth traits (average daily gain (ADG), carcass ADG, and carcass exit weight) was performed to evaluate the effect of PP classification (PP 1 and PP 3), while adjusting for potential confounding effects such as starting weight (entry weight) and gender.

Key results

Carcass weights for calves with a PP classification of 3 and 1 were 15.54 kg less (P < 0.000), and 11.34 kg more (P = 0.007) respectively, than those with a PP classification of 2 (261.27 kg, 95% CI 257.94–264.57), after adjusting for entry weight, calf gender and the random effect of the feeding pen. Similar to carcass weight, calves with a PP 3 classification were outperformed by other classifications in all the measured traits (P < 0.05).

Conclusions

This is the first report demonstrating the ability of subjective production-profile classification to predict growth performance of individual feeder calves.

Implications

The opportunity of the PP classification system lies in value-based procurement of incoming feeder calves based on their growth potential at the start of the feeding period, and then to use technology to improve and finalise the current subjective PP classification system.

Keywords: animal functional traits, animal production, cattle feedlot, phenotype, precision farming, production profiling.

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