JOURNAL OF ANIMAL SCIENCE AND VETERINARY MEDICINE
Integrity Research Journals
ISSN: 2536-7099
Model: Open Access/Peer Reviewed
DOI: 10.31248/JASVM
Start Year: 2016
Email: jasvm@integrityresjournals.org
Predictive modelling for broiler body weight: Integrating sexual dimorphic variance and correlation heat mapping
https://doi.org/10.31248/JASVM2026.662 | Article Number: CC65BD2C5 | Vol.11 (3) - June 2026
Received Date: 04 May 2026 | Accepted Date: 10 June 2026 | Published Date: 30 June 2026
Authors: Sam, Idorenyin Meme , Udo, Akaninyene Friday* and Oloruntobi, Emmanuel Adesina
Keywords: dimorphism, predictor., allometric, exponential, quadratic
In commercial broilers, sexual dimorphism has a significant impact on body weight (BW) and how it relates to morphometric characteristics. This study used linear body measurements in 200 Ross 308 broiler chicks (100 males and 100 females) to evaluate sex-specific predictive models for live BW. Birds were raised in a deep-litter system for up to eight weeks. They were assigned to two treatments (sex) in a completely randomised design (CRD) with five replications of 20 birds each. The finisher phase yielded morphometric data including Body Weight (BW), Body Length (BL), Chest Girth (CG), Shank Length (SL), Wing Length (WL), Thigh Length (TL), and Neck Length (NL). All data were examined using SPSS version 25. BW was predicted using simple linear, multiple linear, allometric, quadratic, and exponential regression models. The model with the highest coefficient of determination (R²) was chosen. Heat map correlation matrices were used to visualize phenotypic correlations coefficient. In every model, chest circumference was the most reliable predictor. For most traits, including body length and chest girth (e.g., exponential: R²=0.838 and 0.622 for females and males, respectively), females showed significantly higher R² values than males. The best accuracy was obtained using multiple linear regression that combined wing length, thigh length, and chest girth (females R²=0.847; males 0.692). Strong positive correlations between body weight and chest girth/body length were verified by the heat map correlation matrices, with significantly higher correlations in females. The predictive value of shank and thigh lengths was poor. These results demonstrate distinct sexual dimorphism in the relationships between traits and growth allometry. Simple linear, allometric, and quadratic methods were outperformed by multiple linear regression and exponential models, which produced the best accuracy. Across all traits, females consistently displayed better model fits. In every equation, chest circumference continued to be the most accurate predictor.
| Abdel-Lattif, F. H. (2019). The linear association between live body weight and some body measurements in some chicken strains. Plant Archives, 19(1), 595-599. | ||||
| Ajayi, F. O., Ejiofor, O., & Ironkwe, M. (2008). Estimation of body weight from linear body measurements in two commercial meat-type chicken. Nigerian Journal of Animal Production, 35(1), 1-9. https://doi.org/10.4314/gjass.v7i1.2361 |
||||
| Amao, S. R. (2022). Sexual dimorphism on body weight and some conformation traits of Ross 308 broiler chickens using Principal Component Analysis. ADAN Journal of Agriculture, 3, 49-58. https://doi.org/10.36108/adanja/2202.30.0160. https://doi.org/10.36108/adanja/2202.30.0160 |
||||
| Benyi, K., Tshilate, T., Netshipale, A., and Mahlako, K. (2015). Effects of genotype and sex on the growth performance and carcass characteristics of broiler chickens. Tropical Animal Health and Production, 47, 1225-1231. https://doi.org/10.1007/s11250-015-0850-3. https://doi.org/10.1007/s11250-015-0850-3 |
||||
| Bila, L. (2025). Predicting body weight in Ross 308 broiler chickens using a data mining algorithm approach. South African Journal of Animal Science, 55(6), 291-303. https://doi.org/10.17159/sajas.v55i6.03. https://doi.org/10.17159/sajas.v55i6.03 |
||||
| Bila, L., & Tyasi, T. L. (2022). Multivariate principal component analysis of morphological traits in Ross 308 broiler chicken breed. Asian Journal of Agricultural Biology, 2022(3), Article 202103132. https://doi.org/10.35495/ajab.2021.03.132 https://doi.org/10.35495/ajab.2021.03.132 |
||||
| Bila, L., Tyasi, T. L., Tongwane, T. W. N., & Mulaudzi, A. P. (2021). Correlation and path analysis of body weight and biometric traits of Ross 308 breed of broiler chickens. Journal of World's Poultry Research, 11(3), 344-351. https://doi.org/10.36380/jwpr.2021.41. https://doi.org/10.36380/jwpr.2021.41 |
||||
| Celik, S., & Yilmaz, O. (2018). Prediction of body weight of Turkish tazi dogs using data mining Techniques: Classification and Regression Tree (CART) and multivariate adaptive regression splines (MARS). Pakistan Journal of Zoology, 50(2), 575-583. https://doi.org/10.17582/journal.pjz/2018.50.2.575.583. https://doi.org/10.17582/journal.pjz/2018.50.2.575.583 |
||||
| Chiekezie, N. R., Nwankwo, E. C., and Ozor, M. U. (2022). Analysis of small scale broiler poultry production in south East Nigeria, West Africa. International Journal of Animal and Livestock Production Research, 6(1), 1-16. https://doi.org/10.37745/ijahlpr.15/vol6n1116 |
||||
| Ebong, U. N., Sam, I. M., Essien, C. A., and Okon, L. S. (2023). Estimation of carcass yield from morphometric traits of ROSS 308 strain of broiler chicken raised in humid zone of Nigeria. AKSU Journal of Agriculture and Food Science, 7(2), Article 009. https://doi.org/10.61090/aksuja.2023.009. https://doi.org/10.61090/aksuja.2023.009 |
||||
| Egena, S. S. A., Ijaiya, A. T., & Kolawole, R. (2014). An assessment of the relationship between body weight and body measurements of indigenous Nigeria chickens (Gallus gallus domesticus) using path coefficient analysis. Livestock Research for Rural Development, 26(3). Retrieved from https://www.lrrd.org/lrrd26/3/egen26051.htm. | ||||
| Freitas, R. C., Calderano, A. A., Oliveira, C. H., Neto, M. G., & Genova, J. L. (2025). Combined analysis of multiple linear regression and principal components for predicting performance indicators in broiler chickens under commercial conditions. Poultry Science, 105728. https://doi.org/10.1016/j.psj.2025.105728 |
||||
| Guèye, E. F., Ndiaye, A., & Branckaert, R. D. S. (1998). Prediction of body weight on the basis of body measurements in mature indigenous chickens in Senegal. Livestock Research for Rural Development, 10(3). http://www.lrrd.org/lrrd10/3/ sene103.htm. | ||||
| Hikawczuk, T., Wróblewska, M., Szuba-Trznadel, A, Rusiecka, A., Zinchuk, A., & Laszki-Szcząchor, K. (2025). A multiple regression model analysing additional sources of dietary fibre as a factor affecting the development of the gastrointestinal tract in broiler chickens. Applied Sciences, 15(9), Article 4994. https://doi.org/10.3390/app15094994. https://doi.org/10.3390/app15094994 |
||||
| Isaac, U. C., Okafor, N. J., Nwachukwu, B. C., Albert, J. C., Aniemena, C. F., & Igbokwe, C. A. (2024). Stepwise canonical discriminant analysis for morphometric characterisation of three strains of broiler chicken. Genetika, 56(1), 43-54. https://doi.org/10.2298/GENSR2401043I |
||||
| Iwujia, T., Iheanachoa, G., Ogambaa, M., & Odunfab, O. (2022). Relationship between live weight, internal organs, and body part weights of broiler chickens. Malaysian Animal Husbandry Journal, 2(1), 64-66. https://doi.org/10.26480/mahj.02.2022.64.66 |
||||
| Kadurumba, O. E., Ahamba, I., Udealor, C., and Ikele, C. (2024). Pattern of growth of Ross 308 strain of broilers in Owerri, Imo State. Nigerian Journal of Animal Production. Nigerian Society for Animal Production (NSAP) 46th Annual Conference - Dutsin-Ma 2021 Book of Proceedings. Pp. 309-313. | ||||
| Kadurumba, O., Ahamba, I., Udealor, C., & Ikele, C. (2022). Determination of the growth pattern of Ross 308 broiler strain reared in a humid tropical environment. Black Sea Journal of Agriculture, 5(3), 208-211. https://doi.org/10.47115/bsagriculture.1035050 |
||||
| Kareem, O. L., Zubair, J. I., Useni, S. S., & Zanna, A. (2016). Effects of sexual dimorphism on two strains of broiler birds (Anak and Shaver). Gashua Journal of Irrigation and Desertification Studies, 2(1), 149-157 https://doi.org/10.67390/gjids.2016.7zlf31g3 |
||||
| Lyu, P., Min, J., & Song, J. (2023). Application of machine learning algorithms for on-farm monitoring and prediction of broilers' live weight: A quantitative study based on body weight data. Agriculture, 13(12), 2193. https://doi.org/10.3390/agriculture13122193 |
||||
| Mendeş, M. (2009). Multiple linear regression models based on principal component scores to predict slaughter weight of broiler. European Poultry Science, 73(2), 139-144. https://doi.org/10.1016/S0003-9098(25)00852-5 |
||||
| Mottet, A., & Tempio, G. (2017). Global poultry production: current state and future outlook and challenges. World's Poultry Science Journal, 73, 245-256. https://doi.org/10.1017/s0043933917000071. https://doi.org/10.1017/S0043933917000071 |
||||
| Müsse, J., Louton, H., Spindler, B., & Stracke, J. (2022). Sexual dimorphism in bone quality and performance of conventional broilers at different growth phases. Agriculture, 12(8), 1109. https://doi.org/10.3390/agriculture12081109. https://doi.org/10.3390/agriculture12081109 |
||||
| Sam, I. M., & Essien, C. A. (2024). Evaluation of growth performance and morphometric traits of two strains of pullets raised in Obio Akpa, Akwa Ibom State. Animal Research International, 21(1), 5389-5395. | ||||
| Sam, I. M., Akpa, G. N., Alphonsus, C. G., Iyeghe-Erakpotobor, I., & Agubosi, O. C. P. (2010). Effect of sex separation on growth performance and carcass characteristics of broilers raised to maturity. Continental Journal of Animal and Veterinary Research, 2(1), 35-40. | ||||
| Sam, I. M., Essien, C. A., Ukpanah, U. A., & Ekpo, J. S. (2019). Influence of sex on relationship between morphometric trait measurement and carcass traits in broiler chicken raised in humid tropic. Journal of Animal and Veterinary Advances, 18(11), 309-314. https://doi.org/10.36478/javaa.2019.309.314 |
||||
| Shafiq, M., Khan, M. T., Rehman, M. S., Raziq, F., Bughio, E., Farooq, Z., Gondal, M. A., Rauf, M., Liaqat, S., Sarwar, F., Azad, A., Asad, T., Arslan, M., Azhar, M., Kamal, R. M. A., & Shakir, M. (2022). Assessing growth performance, morphometric traits, meat chemical composition and cholesterol content in four phenotypes of naked neck chicken. Poultry Science, 101(3), 101667. https://doi.org/10.1016/j.psj.2021.101667 |
||||
| Tixier-Boichard, M., & Duclos, M. J. M. (2022). 26th World's Poultry Congress, abstracts selected in 2020, Vol. 2. | ||||
| Tompić, T., Dobša, J., Legen, S., Tompić, N., & Medić, H. (2011). Modeling the growth pattern of in-season and off-season Ross 308 broiler breeder flocks. Poultry Science, 90(12), 2879-2887. https://doi.org/10.3382/ps.2010-01301. https://doi.org/10.3382/ps.2010-01301 |
||||
| Udo, A. F., Sam, I. M., & Udo, M. D. (2025). Evaluation and correlation of egg quality traits between Isa Brown and Nera Black commercial layer strains. Animal Research International, 22(3), 6472-6482. | ||||
| Udom, I., Nta, S., Usoh, G., Kamai, M., & Ugwuishiwu, B. (2024). Rainfall-groundwater table fluctuations Impact on Root zone soil water simulated in Upflow for crop production planning in Obio Akpa watershed, Nigeria. Journal of Digital Food, Energy & Water Systems, 5(1), 88-103. https://doi.org/10.36615/995gzf79 |
||||
| Yakubu, A., Idahor, K. O., & Agade, Y. U. I. (2009). Using factor scores in multiple linear regression model for predicting the carcass weight of broiler chickens using body measurements. Revista Cientifica UDO Agricola, 9(4), 963-967. | ||||
Copyright © 2026 Integrity Research Journals. All rights reserved.