Related Experiment Video
Updated: Oct 17, 2026

Protocol for Assessing the Relative Effects of Environment and Genetics on Antler and Body Growth for a Long-lived Cervid
Published on: August 8, 2017
Relationship Between Live Body Weight and Linear Body Measurements in Goats: A Meta-Analysis Using a Multilevel
Thobela Louis Tyasi1,2, Gaetan Kabera1
1Department of Statistics, University of South Africa, Pretoria 0002, South Africa.
Abstract:
This study determined the relationship between live body weight (BW) and linear body measurements, including heart girth (HG), body length (BL) and withers height (WH) using a meta-analytic method. This meta-analysis synthesized the evidence from studies conducted in goats to examine the relationship between BW and HG, BL, and WH. Five electronic databases (Web of Science, Google Scholar, PubMed, OpenAlex, and Scopus) were systematically searched for studies published between 2005 and 2025. The Joanna Briggs Institute (JBI) tool was used to assess the risk of bias, and a multilevel random-effects model was used for analysis due to many studies containing multiple effect sizes. A total of 45 studies were included, with the sample size ranging from 30 to 1072 goats. The multilevel random-effects model revealed a pooled correlation coefficients of 0.83 between BW and HG (95% CI: 0.79-0.87), 0.79 between BW and BL (95% CI: 0.74-0.83), and 0.72 between BW and WH (95% CI: 0.64-0.78). The results also revealed a pooled regression coefficients of 0.65 (95% CI: 0.55-0.74), 0.40 (95% CI: 0.04-0.76), and 0.06 (95% CI: -0.02-0.14) for HG, BL and WH, respectively. This meta-analysis provides evidence of strong positive significant associations between body weight and heart girth, body length, and withers height in goats. The findings highlight the potential of these linear body measurements as practical indicators of body weight, particularly in production systems where weighing equipment is not available.
Related Concept Videos
Regression Toward the Mean
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
