Scalable, low-cost livestock bodyweight estimation for Sub-Saharan African production systems using robust segmented heart-girth equations - Hawkins, James; Broonsvort, Barend M.deC; Odadi, W.O.; Gurmu, Endale Balcha; Garcia, Edward; Assouma, M.H; Dossa, L.H.; Leitner, Sonja; Arndt, Claudia (2025)
Accurate prediction of bodyweight (BW) from measurable animal characteristics serves many practical purposes for livestock farmers in sub-Saharan Africa, where weighing scales are often impractical or unaffordable. A volume of literature is devoted to BW estimation using heart girth (HG) (circumference of chest diameter behind front legs) as a main predictor, yet most published equations are derived from narrowly defined animal samples (e.g., single breeds, age classes, or production systems), limiting their applicability across Africa’s diverse livestock populations.. Based on a large and geographically diverse repeated measurement sample of African bovines (n = 6,434; 13,790 total measurements) and caprines (n = 2,113; 6,049 total measurements) we developed prediction equations specific to ruminant species (bovines, sheep, goats), breed (indigenous caprines or Bos indicus bovines vs. crosses thereof with exotics or B. taurus), and production system (extensive vs. semi-intensive) by segmenting regression formulae according to tertiles (equal thirds) of HG. Segmentation by HG improved model fit at upper extremities of HG where residual errors are typically highest. Among four regression formulae: linear, square root, quadratic, Box-Cox -- Box-Cox consistently performed best across sub-samples of species, breed, and system, with the exception of indigenous African sheep breeds (best fit using a quadratic formula). Lines-of-best fit were characterized as convex, with Box-Cox lambda parameters ranging from 0.141-0.788, a finding in general agreement with other studies performing non-linear regression. At the extremes of HG segmented regression had superior predictive accuracy, in particular, with as much as a 25% reduction in NRMSE relative to non-segmented regression at the upper HG tertile. The contributions of this study are thus: (i) provision of the most comprehensive, genetically and geographically diverse set of BW prediction equations for African domestic ruminants to-date, and (ii) for bovines, using segmented regression, BW prediction robust to the extremities of BW/HG, yielding substantially improved prediction accuracy (NRMSE < 5%) over existing published formulae. As Box-Cox formulae are most accurate but structurally complex, authors propose formula transformations embedded directly on weigh-tapes or charts to maximize ease-of-use and to enable scalable, low-cost, and accurate BW estimation for African production systems.