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Feed Efficiency: Difference between revisions

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BIF recommends that a 42-day test length is sufficient for collecting accurate [[Feed Intake | feed intake]] data.  However, the BIF recommends a longer period for measuring [[Gain | gain]], and discusses alternative strategies in the [[Gain | Gain article]]. Recommendations for feeding trial design and procedures are provided in the [[Feed Intake | Feed Intake article]].
BIF recommends that a 42-day test length is sufficient for collecting accurate [[Feed Intake | feed intake]] data.  However, the BIF recommends a longer period for measuring [[Gain | gain]], and discusses alternative strategies in the [[Gain | Gain article]]. Recommendations for feeding trial design and procedures are provided in the [[Feed Intake | Feed Intake article]].
== Dry matter intake vs residual feed intake ==
Organizations producing genetic predictions for feed consumption and partial efficiency differ in the expression of the EPD.  While some EPD are expressed as measured daily dry matter intake (DMI), others are published in index form to quantify partial efficiency such as residual feed intake (RFI) and residual average daily gain (RADG).
<blockquote>
===RFI and RADG===
Phenotypic-based RFI attempts to adjust observed intake for phenotypically correlated sources of variation, so RFI is not correlated with indicator traits. Most commonly these include gain and metabolic mid-weight, although measures of body composition have also been used. This process creates a restricted selection index based on phenotypes, whereby selection for RFI will reduce intake without changing gain.  Alternately, RADG is a restricted index that allows change in gain whilst holding feed intake constant.  To generate EPD for RFI, two alternative methods have been proposed.  A phenotypic-based RFI can be calculated using estimated relationships between maintenance requirements, and anticipated requirements for growth and fat deposition, and this phenotype becomes the dependent variable in the genetic evaluation. More commonly, the DMI phenotype is a dependent variable in a model that includes correlated factors as covariables - e.g., weight, gain, fat thickness, etc. The resulting genetic prediction of RFI is intended to be genetically independent of the covariates included in the model.  Alternately, RFI can be obtained using an index that includes DMI EPD and the EPD of the RFI covariables. The same issues and approaches exist for producing genetic predictions of RADG.
===DMI===
EPDs produced for DMI are produced simply by fitting an analytical model that does not adjust for genetically correlated covariables.  Instead, these other traits may serve as indicators of DMI in a multiple-trait model.  This is analogous to how all other traits in the genetic evaluations are considered. 
</blockquote>
Kennedy et al. (1993)<ref>Kennedy, B. W., J. H. J. van der Werf, and T. H. E. Meuwissen. 1993. Genetic and statistical properties of residual feed intake. J.Anim. Sci. 71:3239–3250. https://doi.org/10.2527/1993.71123239x</ref> showed the equivalence of [[Selection Index | selection indexes]] that incorporated intake or RFI when the economic weights were calculated correctly.  Of course, this assumes the production of the RFI phenotype is performed sensibly when this method is used.  By definition, RFI is not an [[Economically Relevant Traits | economically relevant trait]] given it only accounts for a portion of feed consumed and thus cannot be a sensible trait in an economically rational [[Breeding Objectives | breeding objective]].  Moreover, RFI could have a dramatically different definition depending on the class of cattle used to develop it and thus apply it to whereas DMI could be applied to a wide-range of animals. It has been argued that RFI should be published because not all producers use selection index methods.  However, this logic promotes sub-optimal selection practices including single-trait selection or at best two-trait selection methods. Given the straight-forward definition of DMI, the fact that it is an ERT, and the relative ease at which an economic value can be assigned to it, ''BIF recommends that if an EPD for growing animal intake and/or partial efficiency be published that DMI EPD be made available and not RFI and RADG EPD. Moreover, BIF recommends that economic selection indexes be made available to select for feed efficiency in an economic context with other appropriate economically relevant traits related to more comprehensive breeding objectives.''
</blockquote>


==Note on feed efficiency ratio==
==Note on feed efficiency ratio==

Revision as of 16:16, 25 September 2022

THIS PAGE IS UNDER CONSTRUCTION

Historically, measures of feed utilization incorporated both feed consumption and measures of body weight gain (e.g., ADG or gain ratios)[1] [2] [3]; however, when expressed in a linear form (e.g. selection index) application of values for costs and returns results in outcomes more closely associated with net return (revenue – cost) and therefore a selection index considering both cow/calf performance, postweaning growth and carcass merit measures is likely optimum for the beef industry [4]. Application of either approach could result in genetic change in feed utilization. Regardless, for genetic improvement programs selection should be based on EPD (or EBV) resulting from multiple-trait genetic evaluation of feed intake [5][6].

Data collection for efficiency

Accurate feed utilization testing in beef cattle is dependent on collecting reliable and sufficiently precise measures of daily feed intake and an appropriate measure of body weight gain. Measurement of both phenotypes is subject to some degree of error. Therefore, much care should be given to the development and implementation of testing procedures that systematically minimize the errors associated with measuring these two components. Costs of feed data collection using automated systems are typically higher than most other traits and can be a barrier to obtaining large quantities of data. Therefore, prediction accuracy relies more on the precision of the instrumentation and the design of the feeding test.

BIF recommends that a 42-day test length is sufficient for collecting accurate feed intake data. However, the BIF recommends a longer period for measuring gain, and discusses alternative strategies in the Gain article. Recommendations for feeding trial design and procedures are provided in the Feed Intake article.

Note on feed efficiency ratio

...

Attribution

The contents of this article are a result of an ad hoc BIF committee with additions, from various authors including Snelling, Spangler, and Golden. This article is derivative of the original Guidlines wiki page Intake and Feed Efficiency.

References

  1. ↑ Koch, R. M., L. A. Swiger, D. Chambers, and K. E. Gregory. 1963. Efficiency of feed use in beef cattle. J. Anim. Sci. 22:486-494. doi:10.2527/jas1963.222486x.
  2. ↑ Dickerson, G. E., N. Kunzi, L. V. Cundiff, R. M. Koch, V. H. Arthaud, and K. E. Gregory. 1974. Selection criteria for efficient beef production. J. Anim. Sci. 39:659-673. doi:10.2527/jas1974.394659x.
  3. ↑ Berry, D. P., and J. J. Crowley. 2012. Residual intake and body weight gain: A new measure of efficiency in growing cattle. J. Anim. Sci. 90:109–115. doi:10.2527/jas.2011-4245.
  4. ↑ Nielsen, M. K., M. D. MacNeil, J. C. M. Dekkers, D. H. Crews Jr., T. A. Rathje, R. M. Enns, and R. L. Weaber. 2013. Review: Life-cycle, total industry genetic improvement of feed efficiency in beef cattle: Blueprint for the Beef Improvement Federation. Prof. Anim. Sci. 29:559–565.
  5. ↑ MacNeil, M. D., N. Lopez-Villalobos, and S. L. Northcutt. 2011. A prototype national cattle evaluation for feed intake and efficiency of Angus cattle. J. Anim. Sci. 89:3917-3923. doi:10.2527/jas.2011-4124.
  6. ↑ Thallman, R. M., L A Kuehn, W M Snelling, K J Retallick, J M Bormann, H C Freetly, K E Hales, Gary L Bennett, R L Weaber, D W Moser, and M D MacNeil 2018. Reducing the period of data collection for intake and gain to improve response to selection for feed efficiency in beef cattle, Journal of Animal Science, Volume 96, Issue 3, March 2018, Pages 854–866, https://doi.org/10.1093/jas/skx077
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