{"title":"Phenotypic characterization of indigenous Xhosa goat ecotype in three agro-ecological zones in the eastern cape province, South Africa","authors":"Sibulele Praise Ntonga , Oluwakamisi Festus Akinmoladun , Ziyanda Mpetile","doi":"10.1016/j.vas.2025.100512","DOIUrl":null,"url":null,"abstract":"<div><div>This study characterized the phenotypic attributes of indigenous Xhosa goats across three agro-ecological zones and developed predictive models for estimating body weight. A total of 450 Xhosa goats were sampled using a stratified random approach based on sex, age, and agro-ecological region. Morphometric traits, including body weight (BW), heart girth (HG), body length (BL), wither height (WH), and body depth (RD), were recorded. Data were analysed using the General Linear Model (GLM) of SPSS (v.20) to assess the effects of zones, sex, and age on body traits, while Pearson’s correlation and stepwise regression identified the best predictors of body weight. Results showed significant (p < 0.05) variations in body measurements across zones, with Savanna goats exhibiting superior traits. Males had significantly (p < 0.05) higher BW and body dimensions than females and castrates. Correlation analysis revealed HG (r=0.80), BL (r=0.84), and RD (r=0.82) as the strongest predictors of BW in males, while BL (r=0.66), HG (r=0.65), WH (r=0.62), and RD (r=0.61) were best for females. Stepwise regression identified HG and BL as the best predictors for males, and HG, BL, and SH for females. The predictive models (R² = 0.74–0.85) provide a practical tool for estimating body weight in field conditions. These findings provide practical tools for field-based weight estimation and highlight the importance of conserving the phenotypic diversity of Xhosa goats for sustainable breeding and genetic improvement programs in South Africa.</div></div>","PeriodicalId":37152,"journal":{"name":"Veterinary and Animal Science","volume":"30 ","pages":"Article 100512"},"PeriodicalIF":1.9000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Veterinary and Animal Science","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2451943X25000845","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AGRICULTURE, DAIRY & ANIMAL SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
This study characterized the phenotypic attributes of indigenous Xhosa goats across three agro-ecological zones and developed predictive models for estimating body weight. A total of 450 Xhosa goats were sampled using a stratified random approach based on sex, age, and agro-ecological region. Morphometric traits, including body weight (BW), heart girth (HG), body length (BL), wither height (WH), and body depth (RD), were recorded. Data were analysed using the General Linear Model (GLM) of SPSS (v.20) to assess the effects of zones, sex, and age on body traits, while Pearson’s correlation and stepwise regression identified the best predictors of body weight. Results showed significant (p < 0.05) variations in body measurements across zones, with Savanna goats exhibiting superior traits. Males had significantly (p < 0.05) higher BW and body dimensions than females and castrates. Correlation analysis revealed HG (r=0.80), BL (r=0.84), and RD (r=0.82) as the strongest predictors of BW in males, while BL (r=0.66), HG (r=0.65), WH (r=0.62), and RD (r=0.61) were best for females. Stepwise regression identified HG and BL as the best predictors for males, and HG, BL, and SH for females. The predictive models (R² = 0.74–0.85) provide a practical tool for estimating body weight in field conditions. These findings provide practical tools for field-based weight estimation and highlight the importance of conserving the phenotypic diversity of Xhosa goats for sustainable breeding and genetic improvement programs in South Africa.