


Sex-based analysis of linear body measurements and their correlation with body weight in indigenous Sabi sheep
Vol 6, Issue 3, 2025
VIEWS - 3 (Abstract)
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Abstract
Body weight estimation accuracy is key to efficient sheep management and improved animal performance. This study investigated sex-based differences in the correlation between body weight (BWT) and linear body measurements (LBM) in indigenous Sabi sheep. A dataset comprising 173 Sabi sheep (112 ewes, 22 rams, and 39 wethers) from Zimbabwe’s Matopos Research Institute was analyzed, revealing significant positive correlations between body weight and linear measurements, particularly in ewes and rams. Heart girth exhibited the strongest positive correlation with body weight across sexes, with rams demonstrating higher correlation coefficients than ewes. Notably, body length in rams (r = 0.90) had a significantly higher correlation coefficient with body weight compared to ewes (r = 0.79). Conversely, weaker correlations were observed for Thurl width and pin bone width in wethers. The study identified sex-based differences in the relationships between body weight and linear measurements, indicating sexual dimorphism. Heart girth, body length, and chest depth emerged as key predictors of body weight in indigenous Sabi sheep. These findings underscore the importance of considering sex in understanding the relationship between body weight and linear body measurements in this breed, with implications for enhancing breeding programs and management practices for indigenous Sabi sheep. In conclusion, this study emphasizes the necessity of sex-specific data analysis when examining the correlation between body weight and linear body measurements in indigenous Sabi sheep to ensure accurate and reliable results.
Keywords
References
1. Assan N, Musasira M, Mpofu M, Mwareya N. Species-dependent correlation analysis and regression models of body weight on linear body measures in indigenous sheep and goats of Zimbabwe. Advances in Modern Agriculture. 2023; 4(2): 2388. doi: 10.54517/ama.v4i2.2388
2. Assan N, Makuza SM. The effect of non-genetic factors on birth weight and weaning weight in three sheep breeds of Zimbabwe. Asian-Australasian Journal of Animal Science. 2005; 18(2): 151-157. doi:10.5713/ajas.2005.151
3. Matika O, Van Wyk JB, Erasmus GJ, et al. Phenotypic and genetic relationships between lamb and ewe traits for the Sabi sheep of Zimbabwe. South African Journal of Animal Science. 2001; 31(3). doi: 10.4314/sajas.v31i3.3796
4. Ward HK. Some observations on the indigenous ewes. Rhodesia Agricultural Journal. 1959; 56:218–223.
5. Firdaus F, Atmoko BA, Adinata Y, et al. The meta-analysis of sheep body weight prediction with body measurement, breed and sex categories for practical livestock management purposes. Veterinary Integrative Sciences. 2025; 23(3): e2025075-1-10.
6. Málková A, Ptáček M, Chay-Canul A, et al. Statistical models for estimating lamb birth weight using body measurements. Italian Journal of Animal Science. 2021; 20(1): 1063–1068. doi: 10.1080/1828051x.2021.1937720
7. Yağanoğlu AM. A comparative study of the nonlinear methods for estimating body weight based on body measurements in different sample sizes in Morkaraman sheep. Kafkas University Veterinary Faculty Journal. 2022; 28(2): 261–265.
8. Madikadike MK, Tyasi TL. Growth Traits as Predictors of Body Weight in Sheep: A Review. World’s Veterinary Journal. 2024; 14(2): 284–292. doi: 10.54203/scil.2024.wvj35
9. Zhang AL, Wu BP, Wuyun CT, et al. Algorithm of sheep body dimension measurement and its applications based on image analysis. Computers and Electronics in Agriculture. 2018; 153: 33–45. doi: 10.1016/j.compag.2018.07.033
10. Ibrahim A, Budisatria IGS, Baliarti E, et al. Factor and Discriminant Analyses in the Morphostructure of Batur and Wonosobo Sheep Breeds. Indian Journal of Animal Research. 2023; 57(11): 1561–1567.
11. Santos HP, Aiura AL de O, Gonçalves GAM, et al. Phenotypic characterization and weight prediction of crossbred Dorper × Santa Inês ewes. Revista Brasileira de Saúde e Produção Animal. 2020; 21. doi: 10.1590/s1519-99402121332020
12. Diribi BN. Evaluation of morphological differences, structural indices of Arsi-Bale sheep breed and breeding practices of the communities in selected districts of Arsi and Bale Zones, Oromia, Ethiopia [Master’s thesis]. Haramaya University; 2020.
13. Tade B, Melesse A, Betsha S. Characterization of the indigenous goat populations of South Gondar based on their morphometric traits and body indices. Ethiopian Journal of Agricultural Science. 2021; 31(4): 71–87.
14. Udoh JE, David EG, Unah UL. Prediction of body weight from linear body measurement in two breeds of cattle. Open Access Research Journal of Biology and Pharmacy. 2021; 3(1): 041–046. doi: 10.53022/oarjbp.2021.3.1.0052
15. Washaya S, Bvirwa W, Nyamushamba G. Use of Body Linear Measurements to Estimate Live Weight in Communal Beef Cattle. Journal of Environmental and Agricultural Studies. 2021; 2(2): 11–20. doi: 10.32996/jeas.2021.2.2.2
16. Mathapo MC, Tyasi TL. Prediction of Body Weight of Yearling Boer Goats from Morphological Traits Using Classification and Regression Tree. American Journal of Animal and Veterinary Sciences. 2021; 16(2): 130-135.
17. Ilham F, Ciptadi G, Susilorini TE, et al. Morphology and morphometric diversity of three local goats in Gorontalo, Indonesia. Biodiversitas Journal of Biological Diversity. 2023; 24(3). doi: 10.13057/biodiv/d240305
18. Depison D, Putra WPB, Gushairiyanto G, et al. Morphometric characterization of Kacang goats raised in lowland and highland areas of Jambi Province, Indonesia. Journal of Advanced Veterinary and Animal Research. 2020; 7(4): 734–743. doi: 10.5455/javar. 2020.g475
19. Castillo PE, Macedo RJ, Arredondo V, et al. Morphological Description and Live Weight Prediction from Body Measurements of Socorro Island Merino Lambs. Animals. 2023; 13(12): 1978. doi: 10.3390/ani13121978
20. Selala LJ, Tyasi TL. Using Morphological Traits to Predict Body Weight of Dorper Sheep Lambs. World's Veterinary Journal. 2022; 12(1): 66-73. doi: 10.54203/scil.2022.wvj9
21. Contreras JP, Cordero AG, Rojas YC, et al. Prediction models for live body weight and body compactness of Criollo sheep in Huancavelica Region, Peru. The Indian Journal of Animal Sciences. 2024; 94(7): 637–641. doi: 10.56093/ijans.v94i7.148186
22. Kusminanto RY, Alawiansyah A, Pramono A, et al. Body Weight and Body Measurement Characteristics of Seven Goat Breeds in Indonesia. IOP Conference Series: Earth and Environmental Science. 2020; 478(1): 012039. doi: 10.1088/1755/1315/478/1/012039
23. Kebede K, Asaminew M, Megersa AG. Predicting the Body Weight of Indigenous Sheep from Linear Body Measurement Traits Using Classification and Regression Tree Data Mining Algorithm. Biomedical Journal of Scientific & Technical Research. 2024; 56(4). doi: 10.26717/bjstr.2024.56.008875
24. Birteeb P, Al-Rauf M, Husein SMA, et al. Morphological Variations and Path Coefficient Analysis of Zoometric Traits of Local Chickens in Tolon District of Northern Ghana. Ghana Journal of Science, Technology and Development. 2024; 9(2): 28–42. doi: 10.47881/384.967x
25. Ormachea VE, Calsin BC, Aguilar ES, et al. Principal Component Analysis of Morphological Characteristics in Creole Sheep (Ovis aries). Advances in Animal and Veterinary Sciences. 2023; 11(6). doi: 10.17582/journal.aavs/2023/11.6.903.909
26. Tirink C, Tosun R, Saftan M, et al. Prediction of birth weight from body measurements with the CART algorithm in Morkaraman Lambs. Large Animal Review. 2022; 28: 187–192.
27. Fonseca J dos S, Pimenta JLL de A, Moura LS de, et al. Correlations between body measures with live weight in young male goats. Acta Scientiarum Animal Sciences. 2021; 43: e52881. doi: 10.4025/actascianimsci.v43i1.52881
28. Rather MA, Bashir I, Hamdani A, et al. Prediction of Body Weight From Linear Body Measurements in Kashmir Merino Sheep. Advances in Animal and Veterinary Sciences. 2020; 9(2). doi: 10.17582/journal.aavs/2021/9.2.189.193
29. Canul-Solís JR, Portillo-Salgado R., García-Herrera RA, et al. Comparison of mathematical models to estimate live weight through heart girth in growing Pelibuey sheep. Revista Colombiana de Ciencias Pecuarias. 2022; 36(2): 89–97. doi: 10.17533/udea.rccp.v36n2a4
30. Firdaus F, Atmoko B, Baliarti E, et al. The meta-analysis of beef cattle body weight prediction using body measurement approach with breed, sex, and age categories. Journal of Advanced Veterinary and Animal Research. 2024; (0): 1. doi: 10.5455/javar.2023.j718
31. Firdaus F, Atmoko BA, Panjono PP, et al. The development of a body weight prediction method for Ongole Crossbred cattle using a meta-analysis and field experiment approach. Veterinary Integrative Sciences. 2025; 23(3): 1–11.
32. Simone SK, Yeheyis L. Prediction of Live Weight and Carcass Characteristics from Linear Body Measurements of Yearling Male Local Sheep. Turkish Journal of Agriculture - Food Science and Technology. 2024; 12(4): 625–630. doi: 10.24925/turjaf.v12i4.625-630.6676
33. Sun M, Hossain M, Islam T, et al. Different Body Measurement and Body Weight Prediction of Jamuna Basin Sheep in Bangladesh. SAARC Journal of Agriculture. 2020; 18(1): 183–196. doi: 10.3329/sja.v18i1.48392
34. Hagreveas SK, Bruce D, Beffa LM. Disaster mitigation options for livestock production in communal farming systems in Zimbabwe. ICRISAT and FAO; 2004.
35. Homann S, Van Rooyen AF, Moyo T, Nengomahsa Z. Goat production and marketing: Baseline information for semi-arid Zimbabwe. International Crops Research Institute for the Semi-Arid Tropics; 2007.
36. Ward HK, Richardson V, Denny RP, Dye PJ. Matopos Research Station: A perspective: Rhodesia Agriculture Journal. 1979; 76(1): 5–18
37. Dube A, Assan N, Mwareya N, Musasira M. Sex-specific path coefficient and path analysis of body weight and linear body measurements in indigenous Sabi sheep of Zimbabwe. GSAR Journal of Agriculture and Veterinary Sciences. 2025; 2(4): 10–17.
38. Assan N, Musasira M, Mwareya N, et al. The effect of age on the prediction of body weight from body linear measurements of female indigenous Matebele goats in Zimbabwe. Journal of Animal Production and Health. 2025; 13(2): 265-274. doi: 10.17582/journal.jahp/2025/13.2.265.274
39. FAO. Phenotypic characterization of animal genetic resources. FAO Animal Production and Health Guidelines No. 11. Rome: FAO; 2012.
40. Yilmaz O, Cemal I, Karaca O. Estimation of mature live weight using some body measurements in Karya sheep. Tropical Animal Health and Production. 2012; 45(2): 397–403. doi: 10.1007/s11250-012-0229-7
41. Djaout A, El-bouyahiaoui R, Belkheir B, et al. Prediction of the body weight of Algerian Tazegzawt sheep breed from body measurements. Iraqi Journal of Agricultural Sciences. 2022; 53(5): 1138–1144. doi: 10.36103/ijas.v53i5.1627
42. Gamasaee VA, Hafezian SH, Ahmadi A, et al. Estimation of genetic parameters for body weight at different ages in Mehraban sheep. African Journal of Biotechnology. 2010; 9: 5218–5223.
43. Maria SF, Castelli L, Bogani D, Panella F. The measurement of chest girth as an alternative to weight determination in the performance recording of meat sheep. Italian Journal of Animal Science. 2003; 2: 123-129.
44. Costa Júnior G da S, Campelo JEG, Azevêdo DMMR, et al. Morphometric characterization of Santa Inês sheep raised in the micro-regions of Teresina and Campo Maior, Piauí (Portuguese). Revista Brasileira de Zootecnia. 2006; 35(6): 2260–2267. doi: 10.1590/s1516-35982006000800009
45. Baneh H, Ghaderi-Zefrehei M, Pouryaei R, et al. Genetic analysis of sexual size dimorphism in Markhoz goat. Tropical Animal Health and Production. 2021; 53(1). doi: 10.1007/s11250-020-02528-8
46. van der Heide EMM, Lourenco DAL, Chen CY, et al. Sexual dimorphism in livestock species selected for economically important traits1. Journal of Animal Science. 2016; 94(9): 3684–3692. doi: 10.2527/jas.2016-0393
47. Lopez JA, Bowdridge EC, McCosh RB, et al. Morphological and functional evidence for sexual dimorphism in neurokinin B signalling in the retrochiasmatic area of sheep. Journal of Neuroendocrinology. 2020; 32(7). doi: 10.1111/jne.12877
48. Norris D, Brown D, Moela AK, et al. Path coefficient and path analysis of body weight and biometric traits in indigenous goats. Indian Journal of Animal Research. 2015; 49(5).
49. Melesse A, Banerjee S, Lakew A, et al. Variations in linear body measurements and establishing prediction equations for live weight of indigenous sheep populations of southern Ethiopia. Scientific Journal of Animal Science. 2013; 2(1): 15–25.
50. Musa AM, Idam NZ, Elamin KM. Heart girth reflect live body weight in Sudanese Shogur sheep under field conditions. World’s Veterinary Journal. 2012; 2(4): 54–56.
51. Mohammad MT, Rafeeq M, Bajwa MA, et al. Prediction of body weight from body measurements using regression tree (RT) method for indigenous sheep breeds in Balochistan, Pakistan. Journal of Animal and Plant Sciences. 2012; 22(1): 20–24.
52. Sam I, Ekpo J, Ukpanah U, Eyoh G, Warrie M. Relationship between linear body measurement and live body weight in West African Dwarf goats in Obio Akpa. Journal of Biology and Agricultural Healthcare. 2016; 6(16): 118–124.
53. Kumar S, Dahiya SP, Dahiya ZS, et al. Prediction of body weight from linear body measurements in sheep. Indian Journal of Animal Research. 2017; 52(9): 1263–1266. doi: 10.18805/ijar.B-3360.
54. Cam MA, Olfaz M, Soydan E. Body Measurements Reflect Body Weights and Carcass Yields in Karayaka Sheep. Asian Journal of Animal and Veterinary Advances. 2010; 5(2): 120–127. doi: 10.3923/ajava.2010.120.127
55. Iyiola-Tunji AO, Olugbemi TS, Ali AO, Ojo OA. Inter-relationship between body measurements and price of sheep in an open market in Kano State. Animal Production. 2011; 13(1): 64–68.
56. Petrović MP, Caro Petrović V, Ružić Muslić D, et al. Genetic and phenotypic aspects of the body measured traits in the Merino landscape breed of sheep. Biotechnology and Animal Husbandry. 2012; 28: 733 741.
57. Jafari S, Hashemi A. Estimation of genetic parameters for body measurements and their association with yearling liveweight in the Makuie sheep breed. South African Journal of Animal Science. 2014; 44(2): 140. doi: 10.4314/sajas.v44i2.6
58. Atac FE, Altincekic SO. The relationship between live weight and body measurements of Chios lambs at different periods. South African Journal of Animal Science. 2024; 53(5): 696–705. doi: 10.4314/sajas.v53i5.09
59. Avalos-Castro R, Segura-Correa JC, Palacios-Espinoza A, et al. Prediction of live weight by morphometric measurements in females and males of criollo sheep of the Oaxacan Mixteca, MEXICO. Tropical and Subtropical Agroecosystems. 2023; 26(2). doi: 10.56369/tsaes.4771
60. Bekele D, Tadesse T. Prediction of Body Weight from Linear Body Measurements for Horro Sheep Breeds in Oromia, Ethiopia. International Journal of Genetics and Genomics. 2021; 9(3): 56. doi: 10.11648/j.ijgg.20210903.13
61. Canaza-Cayo AW, Mota RR, Amarilho-Silveira F, et al. Principal Component Analysis for Body Weight Prediction of Corriedale Ewes from Southern Peru. Journal of Animal Health and Production. 2021; 9(4). doi: 10.17582/journal.jahp/2021/9.4.417.424
62. Lakew M, Tesema Z, Zegeye A. Body weight prediction from linear body measurements in Awassi Crossbred sheep of North Eastern Ethiopia. Journal of Applied Agriculture and Biotechnology. 2017; 2(2): 28–36.
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