Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
Shopping Cart: ( empty )
You are here: Home > Journals > AN > AN23395
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.

References

Adrian AM, Norwood SH, Mask PL (2005) Producers’ perceptions and attitudes toward precision agriculture technologies. Computers and Electronics in Agriculture 48, 256-271.
| Crossref | Google Scholar |

Aleksic S, Miščević B, Petrović MM, Pavlovski Z, Josipovic S, Tomasevic D (2002) Investigation of factors affecting the results regarding the dressing percentage value of male young cattle of Domestic Simmental breed and crossbreds of Domestic Simmental and Limousine breed. Biotechnology in Animal Husbandry 18(3–4), 9-14.
| Crossref | Google Scholar |

Bonsma J (2001) ‘Livestock production: man must measure.’ 2nd edn. pp. 100–107. (Agri Books: South Africa)

Bosman DJ (1999) Selecting cattle for functional efficiency. In ‘Beef breeding in South Africa. Commemorating 40 years of beef cattle performance testing 1959-1999’. (Eds MM Scholtz, L Bergh, DJ Bosman) pp. 13–24. (Agricultural Research Council Animal Improvement Institute: Irene, South Africa)

Bruns KW, Pritchard RH, Boggs DL (2004) The relationships among body weight, body composition, and intramuscular fat content in steers. Journal of Animal Science 82, 1315-1322.
| Crossref | Google Scholar | PubMed |

Conroy SB, Drennan MJ, Kenny DA, McGee M (2010) The relationship of various muscular and skeletal scores and ultrasound measurements in the live animal, and carcass classification scores with carcass composition and value of bulls. Livestock Science 127, 11-21.
| Crossref | Google Scholar |

Cooper R, Klopfenstein T, Milton T (2000) Sorting or topping-off pens of feedlot cattle. Nebraska Beef Cattle Reports, 365. University of Nebraska-Lincoln, Animal Science Department

Coyne JM, Evans RD, Berry DP (2019) Dressing percentage and the differential between live weight and carcass weight in cattle are influenced by both genetic and non-genetic factors. Journal of Animal Science 97, 1501-1512.
| Crossref | Google Scholar | PubMed |

Crawford DM, Hales KE, Smock TM, Cole NA, Samuelson KL (2022) Effects of changes in finishing diets and growth technologies on animal growth performance and the carbon footprint of cattle feeding: 1990 to 2020. Applied Animal Science 38-1, 47-61.
| Crossref | Google Scholar |

Fisher J (1989) Economics of beef feedlot production, Ontario, 1987, Economics and Policy Coordination Branch. Report No. 89–10. Ontario Ministry of Agriculture and Food, Toronto, ON, Canada.

Ford D (2002) South African Feedlot industry and economics of beef production. The South African Feedlot Association, Chapter 2. In ‘Feedlot management’. (Ed. KL Leeuw) pp. 12–26. (Agricultural Research Council Animal Production Institute: Irene, Pretoria, South Africa)

Geay Y (1978) Dressing percentage in relation to weight, sex and breed. In ‘Patterns of Growth and Development in Cattle: A Seminar in the EEC Programme of Coordination of Research on Beef Production held at Ghent’, 11–13 October 1977. pp. 35–46 (Springer: Netherlands).

Gilbert RP, Bailey DRC, Shannon NH (1993) Body dimensions and carcass measurements of cattle selected for postweaning gain fed two different diets. Journal of Animal Science 71, 1688-1698.
| Crossref | Google Scholar |

Hentzen AHR, Thompson PN, Holm DE (2020) The effect of preconditioning on production and antibiotic use in a South African beef feedlot. Animal Production Science 60(15), 1822-1829.
| Crossref | Google Scholar |

Jancewicz LJ, Penner GB, Swift ML, Waldner CL, Gibb DJ, McAllister TA (2017) Predictability growth performance in feedlot cattle using fecal near-infrared spectroscopy. Canadian Journal of Soil Science 97, 701-720.
| Google Scholar |

Kamilov FK, Farshatova ER, Enikeev DA (2014) Cellular-molecular mechanisms of bone tissue remodeling and its regulation. Fundamentalnye Issledovaniya 7, 836-842.
| Google Scholar |

Kirton AH, Carter AH, Clarke JN, Duganzich DM (1984) Dressing percentages of lambs. Proceedings of the New Zealand Society of Animal Production 44, 231-233.
| Google Scholar |

Leeuw KJ (2002) Introduction to nutrition. Agricultural Research Council Animal Nutrition and Animal Products Institute, Irene. Chapter 4. In ‘Feedlot management’. (Ed. K-J Leeuw) pp. 54–62. (Agricultural Research Council Animal Production Institute: Irene, Pretoria, South Africa)

Litherland A, Dynes R, Moss R (2010) Factors affecting dressing-out percentage of lambs. Proceedings of the New Zealand Society of Animal Production 70, 121-126.
| Google Scholar |

Massman CP (2015) ‘Visual evaluation of Simmental Fleckvieh cattle.’ (Bayern-Genetik GmbH: Munich, Germany)

Maslov LB (2013) Mathematical model of structural adjustment of bone tissue. Russian Journal of Biomechanics 17, 39-63.
| Google Scholar |

McCabe ED, King ME, Fike KE, Hill KL, Rogers GM, Odde KG (2019) Breed composition affects the sale price of beef steer and heifer calves sold through video auctions from 2010 through 2016. Applied Animal Science 35, 221-226.
| Crossref | Google Scholar |

Parham JT, Tanner AE, Wahlberg ML, Grandin T, Lewis RM (2019) Subjective methods to quantify temperament in beef cattle are insensitive to the number and biases of observers. Applied Animal Behaviour Science 212, 30-35.
| Crossref | Google Scholar |

Perry D, Yeates AP, McKiernan WA (1993) Meat yield and subjective muscle scores in medium weight steers. Australian Journal of Experimental Agriculture 33, 825-831.
| Crossref | Google Scholar |

Purchas RW, Fisher AV, Price MA, Berg RT (2002) Relationships between beef carcass shape and muscle to bone ratio. Meat Science 61, 329-337.
| Crossref | Google Scholar | PubMed |

Reinhardt CD, Busby WD, Corah LR (2009) Relationship of various incoming cattle traits with feedlot performance and carcass traits. Journal of Animal Science 87, 3030-3042.
| Crossref | Google Scholar |

Reyneke J (1976) Comparative Beef production from bulls, steers and heifers under intensive feeding conditions. South African Journal of Animal Science 6, 53-58.
| Google Scholar |

Schipper C, Church T, Harris B (1989) A review of the Alberta certified preconditioned feeder program (1980–1987). Canadian Veterinary Journal 30, 736-741.
| Google Scholar |

Schutz JS, Wagner JJ, Neuhold KL, Archibeque SL, Engle TE (2011) Effect of feed bunk management on feedlot steer intake. The Professional Animal Scientist 27, 395-401.
| Crossref | Google Scholar |

Silvestre AM, Cruz GD, Owens FN, Pereira MCS, Hicks RB, Millen DD (2019) Predicting feedlot cattle performance from intake of dry matter and Neg early in the feeding period. Livestock Science 223, 108-115.
| Crossref | Google Scholar |

Smith MT, Oltjen JW, Dolezal HG, Gill DR, Behrens BD (1992) Evaluation of ultrasound for prediction of carcass fat thickness and longissimus muscle area in feedlot steers. Journal of Animal Science 70(1), 29-37.
| Crossref | Google Scholar |

Tatum JD, Platter WJ, Bargen JL, Endsley RA (2012) Carcass-based measures of cattle performance and feeding profitability. The Professional Animal Scientist 28, 173-183.
| Crossref | Google Scholar |

Thrift FA, Kratzer DD, Kemp JD, Bradley NW, Garrigus WP (1969) Effect of sire, sex and sire x sex interactions on beef cattle performance and carcass traits. Journal of Animal Science 30, 182.
| Crossref | Google Scholar |

Torres-Vázquez JA, van der Werf JHJ, Clark SA (2018) Genetic and phenotypic associations of feed efficiency with growth and carcass traits in Australian Angus cattle. Journal of Animal Science 96, 4521-4531.
| Crossref | Google Scholar |

Trenkle A (2002) Effects of sorting steer calves on feedlot performance and carcass value. Iowa State University Animal Industry Report 1(1).

Webb EC (2015) Description of carcass classification goals and the current situation in South Africa. South African Journal of Animal Science 45, 229-233.
| Crossref | Google Scholar |

Wells S (2020) Prediction of the growth performance of feedlot cattle using phenotypic and anthropometric measures. MSc thesis, University of Pretoria, South Africa.

Yan M, Schmit TM, Baker MJ, Le Roux MN, Gómez MI (2022) Sell now or later? A decision model for feeder cattle selling. Agricultural and Resource Economics Review 51(2), 343-360.
| Crossref | Google Scholar |