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Journal of Animal Science Abstract - Animal Production

Hierarchical Bayesian modeling of heterogeneous variances in average daily weight gain of commercial feedlot cattle1


This article in JAS

  1. Vol. 91 No. 6, p. 2910-2919
    Received: June 09, 2012
    Accepted: Feb 27, 2013
    Published: November 25, 2014

    2 Corresponding author(s):

  1. N. Cernicchiaro*,
  2. D. G. Renter*,
  3. S. Xiang,
  4. B. J. White and
  5. N. M. Bello 2
  1. Department of Diagnostic Medicine/Pathobiology, College of Veterinary Medicine, Kansas State University, Manhattan 66506
    Department of Statistics, College of Arts and Sciences, Kansas State University, Manhattan 66506
    Department of Clinical Sciences, Kansas State University, Manhattan 66506


Variability in ADG of feedlot cattle can affect profits, thus making overall returns more unstable. Hence, knowledge of the factors that contribute to heterogeneity of variances in animal performance can help feedlot managers evaluate risks and minimize profit volatility when making managerial and economic decisions in commercial feedlots. The objectives of the present study were to evaluate heteroskedasticity, defined as heterogeneity of variances, in ADG of cohorts of commercial feedlot cattle, and to identify cattle demographic factors at feedlot arrival as potential sources of variance heterogeneity, accounting for cohort- and feedlot-level information in the data structure. An operational dataset compiled from 24,050 cohorts from 25 U. S. commercial feedlots in 2005 and 2006 was used for this study. Inference was based on a hierarchical Bayesian model implemented with Markov chain Monte Carlo, whereby cohorts were modeled at the residual level and feedlot-year clusters were modeled as random effects. Forward model selection based on deviance information criteria was used to screen potentially important explanatory variables for heteroskedasticity at cohort- and feedlot-year levels. The Bayesian modeling framework was preferred as it naturally accommodates the inherently hierarchical structure of feedlot data whereby cohorts are nested within feedlot-year clusters. Evidence for heterogeneity of variance components of ADG was substantial and primarily concentrated at the cohort level. Feedlot-year specific effects were, by far, the greatest contributors to ADG heteroskedasticity among cohorts, with an estimated ∼12-fold change in dispersion between most and least extreme feedlot-year clusters. In addition, identifiable demographic factors associated with greater heterogeneity of cohort-level variance included smaller cohort sizes, fewer days on feed, and greater arrival BW, as well as feedlot arrival during summer months. These results support that heterogeneity of variances in ADG is prevalent in feedlot performance and indicate potential sources of heteroskedasticity. Further investigation of factors associated with heteroskedasticity in feedlot performance is warranted to increase consistency and uniformity in commercial beef cattle production and subsequent profitability.

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