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Growth rate consists of baseline and systematic deviation components in Thoroughbreds.
Abstract: The objective of this study was to establish a procedure for differentiating a baseline curve from a systematic deviation in weight-age data, and hence to develop a physiological growth model for the Thoroughbred. A total of 2,698 records for 175 foals was obtained during a period of 8 yr (1994 to 2001). Weight-age data were fit with a sigmoid growth equation, W = A(1 + be(-kt))M, where W is BW at age t, A is the asymptotic value of W, b is a scaling parameter that defines the degree of maturity at t = 0, k is a rate constant, and M defines the point of inflection in the sigmoid curve in relation to age. Short-term systematic deviations in the weight-age data were identified by a goodness-of-fit procedure and illustrated in three-dimensional contour plots of the sigmoid equation parameters as they changed upon removal of selected subsets of the data. Based on features of the contour plots, a negative deviation between 210 and 420 d of age was set aside, with the remaining data establishing the baseline data set. The sigmoid growth equation was fit to the baseline data set using a nonlinear mixed model with repeated measures, and indicated a mature weight of 542 +/- 6.2 kg reached at 7 yr. The systematic deviation identified in this weight-age data set is present in other published Thoroughbred growth data and is likely to result in erroneous parameter estimates if not set aside before fitting sigmoid growth equations to the thus-modified weight-age data set. The techniques developed in this study enable identification of short-term systematic deviations in weight-age data and define a realistic baseline growth curve. Differentiation of these two components enables the development of a physiological model of growth that distinguishes between baseline growth and environmental influences, represented respectively, by the baseline curve and the systematic deviation.
Publication Date: 2004-04-15 PubMed ID: 15080321DOI: 10.2527/2004.8241007xGoogle Scholar: Lookup The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Journal Article
- Research Support
- Non-U.S. Gov't
Summary
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This research study aims to establish a method for distinguishing between natural growth patterns and external influences in Thoroughbred horses using weight and age data. The techniques developed help identify temporary growth alterations and form a realistic growth model that separates baseline growth from environmental influences.
Setting up the Study
- The experiment gathered a total of 2,698 records for 175 foals over a span of 8 years, from 1994 to 2001.
- The data on the horses’ weight vis-à-vis their age was matched with a sigmoid growth equation – a function used to represent growth overtime, where a value increases rapidly and then levels off towards an upper limit.
- Considerations within the equation are aspects like the horses’ body weight relative to age, the maximum potential weight, scales of maturity, rate constants, and points of inflection in the sigmoid curve.
Identifying Deviations
- Deviations from the expected growth pattern were determined by examining the goodness-of-fit – a statistical measure to evaluate how well a model’s predictions match the observed data.
- The researchers charted these deviations within three-dimensional plots, which allowed them to see how the parameters of the sigmoid equation changed when selective data subsets were removed.
- This examination led to the identification of a negative deviation between 210 and 420 days of age, which was removed, resulting in a creation of a baseline dataset.
Fitting of Sigmoid Growth Equation
- The researchers proceeded to fit the sigmoid growth equation to the identified baseline dataset.
- A mature weight of 542 +/- 6.2 kg was reached at 7 years. This finding forms the foundation of the physiological model of growth.
- The study points out that ignoring deviations can result in error-prone parameter estimates when fitting the sigmoid growth equation to the weight-age data set.
Conclusions of the Study
- The study developed a method to spot and manage short-term systematic deviations within weight-age data, forming a realistic baseline growth curve for Thoroughbreds.
- Differentiating between deviations and natural growth patterns allows the creation of a physiological growth model that separates inherent growth from environmental factors.
- The experiment gathered a total of 2,698 records for 175 foals over a span of 8 years, from 1994 to 2001.
- The data on the horses’ weight vis-à-vis their age was matched with a sigmoid growth equation – a function used to represent growth overtime, where a value increases rapidly and then levels off towards an upper limit.
- Considerations within the equation are aspects like the horses’ body weight relative to age, the maximum potential weight, scales of maturity, rate constants, and points of inflection in the sigmoid curve.
Identifying Deviations
- Deviations from the expected growth pattern were determined by examining the goodness-of-fit – a statistical measure to evaluate how well a model’s predictions match the observed data.
- The researchers charted these deviations within three-dimensional plots, which allowed them to see how the parameters of the sigmoid equation changed when selective data subsets were removed.
- This examination led to the identification of a negative deviation between 210 and 420 days of age, which was removed, resulting in a creation of a baseline dataset.
Fitting of Sigmoid Growth Equation
- The researchers proceeded to fit the sigmoid growth equation to the identified baseline dataset.
- A mature weight of 542 +/- 6.2 kg was reached at 7 years. This finding forms the foundation of the physiological model of growth.
- The study points out that ignoring deviations can result in error-prone parameter estimates when fitting the sigmoid growth equation to the weight-age data set.
Conclusions of the Study
- The study developed a method to spot and manage short-term systematic deviations within weight-age data, forming a realistic baseline growth curve for Thoroughbreds.
- Differentiating between deviations and natural growth patterns allows the creation of a physiological growth model that separates inherent growth from environmental factors.
- Deviations from the expected growth pattern were determined by examining the goodness-of-fit – a statistical measure to evaluate how well a model’s predictions match the observed data.
- The researchers charted these deviations within three-dimensional plots, which allowed them to see how the parameters of the sigmoid equation changed when selective data subsets were removed.
- This examination led to the identification of a negative deviation between 210 and 420 days of age, which was removed, resulting in a creation of a baseline dataset.
Fitting of Sigmoid Growth Equation
- The researchers proceeded to fit the sigmoid growth equation to the identified baseline dataset.
- A mature weight of 542 +/- 6.2 kg was reached at 7 years. This finding forms the foundation of the physiological model of growth.
- The study points out that ignoring deviations can result in error-prone parameter estimates when fitting the sigmoid growth equation to the weight-age data set.
Conclusions of the Study
- The study developed a method to spot and manage short-term systematic deviations within weight-age data, forming a realistic baseline growth curve for Thoroughbreds.
- Differentiating between deviations and natural growth patterns allows the creation of a physiological growth model that separates inherent growth from environmental factors.
- The researchers proceeded to fit the sigmoid growth equation to the identified baseline dataset.
- A mature weight of 542 +/- 6.2 kg was reached at 7 years. This finding forms the foundation of the physiological model of growth.
- The study points out that ignoring deviations can result in error-prone parameter estimates when fitting the sigmoid growth equation to the weight-age data set.
Conclusions of the Study
- The study developed a method to spot and manage short-term systematic deviations within weight-age data, forming a realistic baseline growth curve for Thoroughbreds.
- Differentiating between deviations and natural growth patterns allows the creation of a physiological growth model that separates inherent growth from environmental factors.
- The study developed a method to spot and manage short-term systematic deviations within weight-age data, forming a realistic baseline growth curve for Thoroughbreds.
- Differentiating between deviations and natural growth patterns allows the creation of a physiological growth model that separates inherent growth from environmental factors.
Cite This Article
APA
Staniar WB, Kronfeld DS, Treiber KH, Splan RK, Harris PA.
(2004).
Growth rate consists of baseline and systematic deviation components in Thoroughbreds.
J Anim Sci, 82(4), 1007-1015.
https://doi.org/10.2527/2004.8241007x Publication
Researcher Affiliations
- Department of Animal and Poultry Sciences, Virginia Polytechnic Institute and State University, Blacksburg 24061-0306, USA. wstaniar@vt.edu
MeSH Terms
- Animals
- Birth Weight / genetics
- Birth Weight / physiology
- Body Weight / genetics
- Body Weight / physiology
- Breeding
- Female
- Horses / genetics
- Horses / growth & development
- Male
- Models, Biological
- Nonlinear Dynamics
- Random Allocation
- Weaning
- Weight Gain / genetics
- Weight Gain / physiology
Citations
This article has been cited 5 times.- Cappai MG, Taras A, Biggio GP, Dimauro C, Gatta D, Cossu I, Cherchi R, Pinna W. NEFA, BHBa, UREA and Liver Enzyme Variation in the Bloodstream of Weaned Foals up to 18 Months of Age. Animals (Basel) 2021 Jun 11;11(6).
- Takahashi Y, Takahashi T. Seasonal fluctuations in body weight during growth of Thoroughbred racehorses during their athletic career. BMC Vet Res 2017 Aug 18;13(1):257.
- Onoda T, Yamamoto R, Sawamura K, Murase H, Nambo Y, Inoue Y, Matsui A, Miyake T, Hirai N. An approach of estimating individual growth curves for young thoroughbred horses based on their birthdays. J Equine Sci 2014;25(2):29-35.
- Onoda T, Yamamoto R, Sawamura K, Murase H, Nambo Y, Inoue Y, Matsui A, Miyake T, Hirai N. Empirical Percentile Growth Curves with Z-scores Considering Seasonal Compensatory Growths for Japanese Thoroughbred Horses. J Equine Sci 2013;24(4):63-9.
- Onoda T, Yamamoto R, Sawamura K, Inoue Y, Matsui A, Miyake T, Hirai N. Empirical Growth Curve Estimation Using Sigmoid Sub-functions that Adjust Seasonal Compensatory Growth for Male Body Weight of Thoroughbred Horses. J Equine Sci 2011;22(2):37-42.
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