Cluster-Based Assessment of Growth Performance in Cameroon Local Chicken

Document Type : Original Paper

Authors

1 Department of Animal Sciences, Faculty of Agronomy and Agricultural Sciences, University of Dschang, Dschang, Cameroon

2 Department of Animal Science, Faculty of Agronomy and Agricultural Sciences, University of Dschang, Dschang, Cameroon

3 Department of Animal Production Technology, College of Technology, University of Bamenda, Bambili, Cameroon

10.22069/psj.2025.23354.2266

Abstract

This study integrates clustering and network analyses to identify performance-based groups and their connections to phenotypes, providing insights to zoptimize local chicken breeding programs in Cameroon. A total of 113 Birds were grouped based on growth patterns between 16 and 22 weeks, assessed using total weight gain (TWG) and leg circumference gain (LCG). Repeated measures and Welch ANOVA were used to test performance differences, while Generalized Least Squares (GLS) ANCOVA identified growth predictors. Although phenotypic diversity was observed, TWG and LCG did not differ significantly across phenotypes (p > 0.05), whereas final body weight (BW) and leg circumference (LC) at 22 weeks did (p<0.01). Cluster analysis identified four distinct performance cluster groups independent of phenotype, with significant divergence in performance. Birds in Clusters 3 and 4 consistently outperformed (p<0.001) their counterparts in the other Clusters 1 and 2, showing superior TWG and LCG while sexual dimorphism was in favour of males (p < 0.05). Cluster 4 exhibited the highest final BW, whereas Cluster 3 had the greatest total weight gain, indicating a distinct tendency for early and late-stage growth, which could be strategically zoptimized for selective crossbreeding to combine their complementary traits. Network analysis indicates historical gene flow and possible heterozygosity within the population, with Normal and Feathered shank phenotypes potentially serving as genetic bridges for performance traits, while the distinct peripheral positioning of Feathered leg and Naked neck, linked only to Clusters 1 and 2, suggests genetic distinctiveness. GLS-ANCOVA confirmed Cluster 3 and 4, as the most significant predictors of TWG (p<0.001), alongside sex (p<0.05) and LCG (p<0.05). Integrating cluster and network analysis can enhance sustainable breeding strategies in low-input systems, balancing growth efficiency with genetic diversity. Breeders and policymakers are encouraged to adopt systematic performance recording practices and promote cross-cluster crossbreeding within local flocks.

Keywords

References

Adeleke MA, Peters SO, Ozoje MO, Ikeobi CON, Bamgbose AM & Adebambo OA. 2010. Growth performance of Nigerian local chickens in crosses involving an exotic broiler breeder. Tropical Animal Health and Production, 43(3): 643–650. DOI: 10.1007/s11250-010-9747-3.
Bembidé C, Hako Touko BA, Christian KT, Osama S, Oguho M, Cho CY, Skilton RA & Appolinaire D. 2024. Assessment of genetic diversity of local chicken (Gallus gallus domesticus) population in Central African Republic. Journal of the Cameroon Academy of Sciences, 20(2): 153–165. DOI: 10.4314/jcas.v20i2.5
Bembidé C, Hako Touko BA, Manjeli Y & Keambou Tiambo C. 2013. Caractérisation morphobiométrique de la poule locale en Centrafrique. Animal Genetic Resources, 53: 33–44. DOI: 10.1017/s2078633612000525.
Bendahman N, & Lotfi D. 2024. Unveiling Influence in Networks: A Novel centrality metric and comparative analysis through graph-based models. Entropy, 26(6), 486. DOI: 10.3390/e26060486
Besbes B. 2009. Genotype evaluation and breeding of poultry for performance under sub-optimal village conditions. World’s Poultry Science Journal, 65(2): 260-271. DOI: 10.1017/s0043933909000221
Birhanu MY, Osei-Amponsah R, Obese FY, & Dessie T. 2023. Smallholder poultry production in the context of increasing global food prices: Roles in poverty reduction and food security. Animal Frontiers, 13(1): 17-25. DOI: 10.1093/af/vfac069
Castellini C, Berri C, Le Bihan-Duval E & Martino G. 2008. Qualitative attributes and consumer perception of organic and free-range poultry meat. World’s Poultry Science Journal, 64(4): 500–512. DOI: 10.1017/s0043933908000172
Celik N. 2020. Welch’s ANOVA: Heteroskedastic skew-t error terms. Communications in Statistics - Theory and Methods, 51(9): 3065-3076. DOI: 10.1080/03610926.2020.1788084
Dahloum L, Moula N, Halbouche M, & Mignon-Grasteau S. 2016. Phenotypic characterization of the indigenous chickens (Gallus gallus) in the northwest of Algeria. Archives Animal Breeding, 59(1): 79-90. DOI: 10.5194/aab-59-79-2016
Delacre M, Lakens D, & Leys C. 2022. Correction: Why psychologists should by default use Welch’s t-test instead of student’s t-test. International Review of Social Psychology, 35(1). DOI: 10.5334/irsp.661
Ezugwu AE, Ikotun AM, Oyelade OO, Abualigah L, Agushaka JO, Eke CI, & Akinyelu AA. 2022. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Engineering Applications of Artificial Intelligence, 110: 104743. DOI: 10.1016/j.engappai.2022.104743
Faostat. 2022. Food and Agriculture Organization of the United Nations Statistical Database. Retrieved February 6, 2025, from https://www.fao.org/faostat/en/
Fotsa JC, Kamdem DP, Bordas A, Tixier-Boichard M, & Rognon X. 2011. Assessment of the genetic diversity of Cameroon indigenous chickens by the use of microsatellites. Livestock Research for Rural Development, 23(5): 16p. Retrieved February 6, 2025, from http://www.lrrd.org/lrrd23/5/fots23118.htm
Fotsa JC, Rognon X, Tixier-Boichard M, Coquerelle G, Poné-Kamdem D, Ngou-Ngoupayou JD, Manjeli Y, & Bordas A. 2010. Caractérisation phénotypique des populations de poules locales (Gallus gallus) de la zone forestière dense humide à pluviométrie bimodale du Cameroun. Animal Genetic Resources, 46: 46-59. DOI: 10.1017/S207863361000069X
Galdino LSM & da Silva JD. 2024. Hybrid clustering: Combining K-Means and interval-valued data-type hierarchical clustering. Acta Polytechnica Hungarica, 21(9): 175–186. DOI: 10.12700/aph.21.9.2024.9.12.
Games PA, & Howell JF. 1976. Pairwise Multiple Comparison Procedures with Unequal N’s and/or Variances: A Monte Carlo Study. Journal of Educational Statistics, 1(2): 113. DOI: 10.2307/1164979
GIZ. 2018. Poultry production in Cameroon: How the import restriction affects the Cameroonian poultry sector. Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH. Retrieved February 5, 2025, from https://www.giz.de/de/downloads/GIZSVAAAPolicy-Brief-Cameroon-Chicken_EN.pdf
González Ariza A, Arando Arbulu A, Navas González FJ, Nogales Baena S, Delgado Bermejo JV, & Camacho Vallejo ME. 2021. The Study of Growth and Performance in Local Chicken Breeds and Varieties: A Review of Methods and Scientific Transference. Animals, 11(9): 2492. DOI: 10.3390/ani11092492
Hako TBA, & Yoniwo SN. 2023a. Effect of the “Naked neck” gene (Na) on the growth of indigenous chicken fed with suboptimal feed rations in Cameroon. Bio-Research, 21(1): 1870-1880. DOI: 10.4314/br.v21i1.10
Hako TBA, & Yoniwo SN. 2023b. The “Naked neck” gene and the adaptability of the local chicken to heat stress on station in Cameroon. Bio-Research, 21(1): 1881-1895. DOI: 10.4314/br.v21i1.11
Hako Touko BA, Keambou CT, Han J-M, Bembidé C, Skilton RA, Ogugo M, Manjeli Y, Osama S, Cho C-Y, & Djikeng A. 2015. Molecular typing of the major histocompatibility complex B microsatellite haplotypes in Cameroon chicken. Animal Genetic Resources, 56: 47–54. DOI: 10.1017/s2078633614000538
Hako Touko BA, Kong Mbiydzenyuy AT, Tumasang TT, & Awah-Ndukum J. 2021. Heritability Estimate for Antibody Response to Vaccination and Survival to a Newcastle Disease Infection of Native chicken in a Low-Input Production System. Frontiers in Genetics, 12. DOI: 10.3389/fgene.2021.666947
Halima H, Neser FWC, van Marle-Koster E, & de Kock A. 2007. Phenotypic variation of local chicken populations in northwest Ethiopia. Tropical Animal Health and Production, 39(7): 507-513. DOI: 10.1007/s11250-007-9032-2
Humaira H & Rasyidah R. 2020. Determining the appropriate cluster number using Elbow Method for K-Means algorithm. Proceedings of the 2nd Workshop on Multidisciplinary and Applications (WMA) 2018, 24–25 January 2018, Padang, Indonesia. DOI: 10.4108/eai.24-1-2018.2292388.
Ikotun AM, Ezugwu AE, Abualigah L, Abuhaija B & Heming J. 2023. K-means clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data. Information Sciences, 622: 178–210. DOI: 10.1016/j.ins.2022.11.139.
JASP Team. 2024. JASP (Version 0.19.3). Computer Software. University of Amsterdam. URL https://jasp-stats.org/download/
Keambou TC, Hako BA, Bembide C, Ngono PJ & Manjeli Y. 2013. Effect of genetic type and sex on reproductive, growth, survival performance and thermal tolerance index of the local chicken (Gallus gallus) of the Western Highlands of Cameroon. International Journal of Poultry Science, 12(2): 80–89. DOI: 10.3923/ijps.2013.80.89.
Keambou TC, Hako BA, Ommeh S, Bembide C, Ngono EP, Manjeli Y, Wamonje FN, Wanjala B, Wamalwa M, Cho CY, Skilton RA & Djikeng A. 2014. Genetic diversity of the Cameroon indigenous chicken ecotypes. International Journal of Poultry Science, 13(5): 279–291. DOI: 10.3923/ijps.2014.279.291.
Keambou TC, Mboumba S, Hako Touko BA, Bemide C, Mezui Mezui T, Tedongmo AMY & Manjeli Y. 2015. Growth performances, carcass and egg characteristics of the local chicken and its first-generation reciprocal crossbreds with an exotic strain in Cameroon. Advances in Animal and Veterinary Sciences, 3(10): 507–513. DOI: 10.14737/journal.aavs/2015/3.10.507.513.
Kindie B & Tamiru C. 2021. Review on phenotypic characterization of indigenous chicken and farmer breeding trait preference ecotypes in Sekela Woreda, Northern Ethiopia. American Journal of Bioscience and Bioengineering, 9(2): 49. DOI: 10.11648/j.bio.20210902.13.
Kochish II, Titov VY, Nikonov IN, Brazhnik EA, Vorobyov NI, Korenyuga MV, Myasnikova OV, Dolgorukova AM, Griffin DK & Romanov MN. 2023. Unraveling signatures of chicken genetic diversity and divergent selection in breed-specific patterns of early myogenesis, nitric oxide metabolism, and post-hatch growth. Frontiers in Genetics, 13. DOI: 10.3389/fgene.2022.1092242.
Kpomasse CC, Kouame YAE, N’nanle O, Houndonougbo FM, Tona K, & Oke OE. 2023. The productivity and resilience of the indigenous chickens in the tropical environments: improvement and future perspectives. Journal of Applied Animal Research, 51(1): 456-469. DOI: 10.1080/09712119.2023.2228374
Leroy G, Kayang BB, Youssao IA, Yapi-Gnaoré CV, Osei-Amponsah R, Loukou NE, Fotsa J-C, Benabdeljelil K, Bed’hom B, Tixier-Boichard M, & Rognon X. 2012. Gene diversity, agroecological structure and introgression patterns among village chicken populations across North, West and Central Africa. BMC Genetics, 13(1). DOI: 10.1186/1471-2156-13-34
Li J, Lee M, Davis BW, Lamichhaney S, Dorshorst BJ, Siegel PB, & Andersson L. 2020b. Mutations upstream of the TBX5 and PITX1 transcription factor genes are associated with feathered legs in the domestic chicken. Molecular Biology and Evolution, 37(9): 2477-2486. DOI: 10.1093/molbev/msaa093
Li W, Liu R, Zheng M, Feng F, Liu D, Guo Y, Zhao G, & Wen J. 2020a. New insights into the associations among feed efficiency, metabolizable efficiency traits and related QTL regions in broiler chickens. Journal of Animal Science and Biotechnology, 11(1). DOI: 10.1186/s40104-020-00469-8
Liswaniso S, Haachilala BH, Chibinga O, Simbaya J, Tyasi LT, Sun X, Qin N & Xu R. 2024. Prediction of growth rate and body weight of Sasso chickens from linear body measurements using path analysis. Pakistan Journal of Zoology. DOI: 10.17582/journal.pjz/20230127010128
Malomane DK, Weigend S, Schmitt AO, Weigend A, Reimer C & Simianer H. 2021. Genetic diversity in global chicken breeds in relation to their genetic distances to wild populations. Genetics Selection Evolution, 53(1). DOI: 10.1186/s12711-021-00628-z.
Mapiye C, Chikwanha OC, Chimonyo M, & Dzama K. 2019. Strategies for sustainable use of indigenous cattle genetic resources in Southern Africa. Diversity, 11(11): 214. DOI: 10.3390/d11110214
Miyumo SA, Wasike CB, Ilatsia ED, Bennewitz J, & Chagunda MGG. 2023. Genetic and phenotypic correlations among feed efficiency, immune and production traits in indigenous chicken of Kenya. Frontiers in Genetics, 13. DOI: 10.3389/fgene.2022.1070304
Murtagh, F., & Legendre, P. (2014). Ward’s hierarchical agglomerative clustering method: Which algorithms implement Ward’s criterion? Journal of Classification, 31(3), 274-295. DOI: 10.1007/s00357-014-9161-z1
Mustofa F, Fathoni A, Sari APZNL, Sasongko H, & Maharani D. 2021. Body weight and body size measurement of five Indonesian local chicken. IOP Conference Series: Earth and Environmental Science, 788(1): 012016. DOI: 10.1088/1755-1315/788/1/012016
Nguyen Van D, Moula N, Moyse E, Do Duc L, Vu Dinh T & Farnir F. 2020. Productive performance and egg and meat quality of two indigenous poultry breeds in Vietnam, Ho and Dong Tao, fed on commercial feed. Animals, 10(3): 408. DOI: 10.3390/ani10030408.
Nigussie D, Tadelle D, Liesbeth H, van der Waaij LD, & van Arendonk JAM. 2010. Morphological features of indigenous chicken populations in Ethiopia. Animal Genetic Resources, 46: 11-23. DOI: 10.1017/S2078633610000652
Nosike RJ, Nwakpu OF, Okocha CN, Inyang EC, Ezike JC, & Onunkwo DN. 2020. Phenotypic correlations between biologic markers and growth traits in Arbor acres broiler chicken strain. Nigerian Journal of Animal Production, 45(2): 21-28. DOI: 10.51791/njap.v45i2.481
Osei-Amponsah R, Kayang BB, & Naazie A. 2011. Age, genotype and sex effects on growth performance of local chickens kept under improved management in Ghana. Tropical Animal Health and Production, 44(1): 29-34. DOI: 10.1007/s11250-011-0010-3
Palarea‐Albaladejo J & McKendrick I. 2020. Best practice for the design and statistical analysis of animal studies. Veterinary Record, 186(2): 59–64. DOI: 10.1136/vr.m117
Pinheiro JC, & Bates DM. 2000. Mixed-Effects Models in Sand S-PLUS. In Statistics and Computing. Springer New York. DOI: 10.1007/978-1-4419-0318-1
Portet S. 2020. A primer on model selection using the Akaike Information Criterion. Infectious Disease Modelling, 5: 111-128. DOI: 10.1016/j.idm.2019.12.010
R Core Team. 2022. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Ren X, Guan Z, Li H, Wen J, Zhao X, Wang G, Zhang X, Wang H, Zhang L, Yu F, & Qu L. 2023. Extensive intra- and inter-genetic admixture of Chinese gamecock and other indigenous chicken breeds revealed by genomic data. Poultry Science, 102(7): 102766. DOI: 10.1016/j.psj.2023.102766
Rosario MF, Silva MAN, Coelho AAD, & Savino VJM. 2008. Selection of traits in poultry breeding using cluster analysis. International Journal of Poultry Science, 7(4): 374-378. DOI: 10.3923/ijps.2008.374.378
Rosenberg NA, Burke T, Elo K, Feldman MW, Freidlin PJ, Groenen MAM, Hillel J, Mäki-Tanila A, Tixier-Boichard M, Vignal A, Wimmers K & Weigend S. 2001. Empirical evaluation of genetic clustering methods using multilocus genotypes from 20 chicken breeds. Genetics, 159(2): 699–713. DOI: 10.1093/genetics/159.2.699
Rother B, Sosa S, Mitra P, Kim D, Pierre G, Fayad D, Debbich M, Thevenot C, Kohler L, Kato N, Castrovillari C, Sharifzoda K, Van Heuvelen E, & Machado F. 2022. Tackling the Global Food Crisis. IMF Notes, 2022(004): 1. DOI: 10.5089/9798400221972.068
Shapiro SS & Wilk MB. 1965. An analysis of variance test for normality (complete samples). Biometrika, 52(3/4): 591. DOI: 10.2307/2333709.
Sutherland C, Hare D, Johnson PJ, Linden DW, Montgomery RA & Droge E. 2023. Practical advice on variable selection and reporting using Akaike information criterion. Proceedings of the Royal Society B: Biological Sciences, 290(2007). DOI: 10.1098/rspb.2023.1261.
Syakur MA, Khotimah BK, Rochman EMS, & Satoto BD. 2018. Integration K-Means clustering method and elbow method for identification of the best customer profile cluster. IOP Conference Series: Materials Science and Engineering, 336: 012017. DOI: 10.1088/1757-899x/336/1/012017
Tixier-Boichard M. 2002. From phenotype to genotype: major genes in chickens. World’s Poultry Science Journal, 58(1), 65–75. DOI: 10.1079/wps20020008 
Vakhrameev AB, Narushin VG, Larkina TA, Barkova OY, Peglivanyan GK, Dysin AP, Dementieva NV, Makarova AV, Shcherbakov YS, Pozovnikova MV, Bondarenko YV, Griffin DK & Romanov MN. 2023. Disentangling clustering configuration intricacies for divergently selected chicken breeds. Scientific Reports, 13(1). DOI: 10.1038/s41598-023-28651-8.
Venturini T, Jacomy M, & Jensen P. 2021. What do we see when we look at networks: Visual network analysis, relational ambiguity, and force-directed layouts. Big Data & Society, 8(1). DOI: 10.1177/20539517211018488
Yang S, Shi Z, Ou X & Liu G. 2019. Whole-genome resequencing reveals genetic indels of feathered-leg traits in domestic chickens. Journal of Genetics, 98(2). DOI: 10.1007/s12041-019-1083-4.
Yi Z, Li X, Luo W, Xu Z, Ji C, Zhang Y, Nie Q, Zhang D & Zhang X. 2018. Feed conversion ratio, residual feed intake, and cholecystokinin type A receptor gene polymorphisms are associated with feed intake and average daily gain in a Chinese local chicken population. Journal of Animal Science and Biotechnology, 9(1). DOI: 10.1186/s40104-018-0261-1.
Zhang Y, Phillips CA, Rogers GL, Baker EJ, Chesler EJ, & Langston MA. 2014. On finding bicliques in bipartite graphs: a novel algorithm and its application to the integration of diverse biological data types. BMC Bioinformatics, 15(1). DOI: 10.1186/1471-2105-15-110
Zhang Z, Xu Z, & Li H. 2021. Identification of QTL regions and candidate genes for growth and feed efficiency in broilers using a genome-wide association study. Genetics Selection Evolution, 53(1): 1-12. DOI: 10.1186/s12711-021-00608-3
Zhao S, Sun J, Shimizu K, & Kadota K. 2018. Silhouette Scores for Arbitrary Defined Groups in Gene Expression Data and Insights into Differential Expression Results. Biological Procedures Online, 20(1): 5. DOI: 10.1186/s12575-018-0067-8
Poultry Science Journal
Volume 14, Issue 1
January 2026

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