Evaluation of growth curves in three local quail lines using four diverse non-linear models
Abstract
Aim of study: To examine growth patterns in three local quail lines using various flexible growth functions and to identify the most suitable non-linear model for describing their growth performance.
Area of study: The research was conducted and validated at Salahaddin University-Erbil’s Quail Research Hall, Grdarasha Station, Department of Animal production and health, and College of Agricultural Engineering Sciences.
Material and methods: A total of 540 quail chicks from three local lines (Desert, Brown, and White) were studied to evaluate growth patterns using four non-linear models (Gompertz-Laird, Logistic, Richards, and Von Bertalanffy). Model comparisons were based on coefficient of determination (R²), adjusted coefficient of determination (R²adj), akaike’s Information Criterion (AIC), and Bayesian Information Criterion (BIC) to determine the best fit. Growth parameters, including asymptotic weight (β0), maturity index (β0), inflection point weight (IPW), and inflection point time (IPT), were estimated.
Main results: Both genetic line and sex significantly influenced growth parameters, with Desert quails showing superior growth performance and higher body weights than Brown and White lines. The Gompertz model provided the best fit, based on higher R² and R²adj values and lower AIC and BIC values. Desert quails had the highest asymptotic weights (β0), while White quails exhibited the highest maturity index (β0), suggesting faster growth rates, and Desert quails had higher IPW and IPT values, with females maturing later than males.
Research highlights: The findings give beneficial insights for improving quail breeding programs that can enhance their growth efficiency and meat yield.
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References
Abbas A, Basheer A, Kallon MN, Sharif N, Ghiasi H, Mehmood T, Azam B, Khalique A, Dawood M, Zahoor I, 2024. Development of growth curve and estimation of genetic parameters of Nili-Ravi buffalo calves from birth to one year of age through non-linear models. Trop Anim Health Prod 56(9):390. https://doi.org/10.1007/s11250-024-04231-4
Abou Khadiga G, Mahmoud BYF, El-Full EA, 2018. Modelling of growth alteration in Japanese quail after a selection experiment for body weight at 4 weeks of age. J Agric Sci 156(9): 1153-1159. https://doi.org/10.1017/S0021859619000029
Adedeji OA, Omolara AM, Osamede OH, Emmanuel SA, 2017. Analysis of growth models of Japanese quails (Coturnix coturnix japonica) in Nigeria. Thai J Agric Sci 50(3-4): 155-165.
Afonso VAC, Quirino CR, Costa RD, Silva RD, 2009. Application of nonlinear models in the growth adjustment curves of sheep raised in the North Fluminense area. Boletim de Indústria Anim 66(2): 115-120. https://doi.org/10.1590/0103-8478cr20190990
Aggrey SE, 2002. Comparison of three nonlinear and spline regression models for describing chicken growth curves. Poultry Sci 81(12): 1782-1788. https://doi.org/10.1093/ps/81.12.1782
Ahmed L, 2021. Repeatability estimates of egg number and egg weight under various production periods in three lines of local quail. Adv Anim Vet Sci 9(12): 2216-2220. http://dx.doi.org/10.17582/journal.aavs/2021/9.12.2216.2220
Ahmed LS, Ahmed DM, Yaseen KK, Sabir PS, 2024. GH and IGF1 gene variation and carcass yield characteristics in three Kurdish quail lines. J Wild Biodivers 9(1): 134-152. https://www.wildlife-biodiversity.com/index.php/jwb/article/view/774
Ahmed LS, 2020. DNA markers of three genes and their associations with the meat and egg production traits in local varieties of quail in Kurdistan Region. Doctoral thesis. Salahaddin University-Erbil, Iraq.
Ahmed LS, 2022. Impact of egg shell and spots colour on the quality of hatching eggs derived from three lines of local quail. Iraqi J Agric Sci 53(6): 1256-1269. https://doi.org/10.36103/ijas.v53i6.1640
Ahmed LS, Al-Barzinji YMS, 2019. Genetic diversity among local quail using RAPD-DNA marker. Rev Científica-Facultad de Ciencias Veterinarias 29(3): 178-185.
Ahmed LS, Al-Barzinji YMS, 2020. Three candidate genes and their association with quantitative variation of egg production traits of local quail by using PCR-RFLP. Iraqi J Agric Sci 51: 124-131. https://doi.org/10.36103/ijas.v51iSpecial.889
Ahmed LS, Al-Barzinji YMS, 2022. Detection of quantitative loci correlation with growth traits in local quail using PCR-RFLP technique. Iraqi J Agric Sci 53(1): 16-26. https://doi.org/10.36103/ijas.v53i1.1501
Akaike H, 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6): 716-723.
Al-Kafajy FR, Al-Shuhaib MBS, Al-Jashami GS, Al-Thuwaini TM, 2018. Comparison of three lines of Japanese quail revealed a remarkable role of plumage color in the productivity performance determination. J World’s Poult Res 8(4): 111-119. http://jwpr.science-line.com
Alkan S, Mendes M, Karabag K, Balcioglu MS, 2009. Effect of short-term divergent selection for 5-week body weight on growth characteristics of Japanese quail. Arch Geflügelk 73: 124-131.
Bashiru HA, Oseni SO, Omadime LA, 2019. Evaluation of four classical non-linear models to describe the growth curve of FUNAAB-Alpha chickens. Bull Anim Health Prod Afr 67(4): 323-332. https://www.researchgate.net/publication/350089790
Beiki H, Pakdel A, MoradiShahrbabak M, Mehrban H, 2013. Evaluation of growth functions on Japanese quail lines. J Poult Sci 50(1): 20-27. https://doi.org/10.2141/jpsa.0110142
Carneiro PLS, Malhado CHM, Affonso PDM, Pereira DG, Suzart JCC, Ribeiro Júnior M, Rocha JL, 2009. Growth curve in Mambrina goats raised in caatinga. Rev Bras Saúde Prod Anim 10(3): 536-545.
Caron N, Minvielle F, Desmarais M, Poste LM, 1990. Mass selection for 45-day body weight in Japanese quail: selection response, carcass composition, cooking properties, and sensory characteristics. Poultry Sci 69(7): 1037-1045. https://doi.org/10.3382/ps.0691037
da Silveira FG, Silva FFE, Carneiro PLS, Malhado CHM, Muniz JA, 2011. Cluster analysis applied to nonlinear regression models selection for growth curves of crossed lambs. Ciência Rural 41(4): 692-699. https://doi.org/10.1590/S0103-84782011000400024
Duncan DB, 1955. Multiple ranges and multiple F test. Biometrics 11: 1-42. https://doi.org/10.2307/3001478
Emam AM, 2021. Growth curves models in two lines of Japanese quail selected for high body weight. Egypt Poult Sci J 40(4): 915-928. https://doi.org/10.21608/epsj.2021.135682
Ersoy IE, Mendeş M, Aktan S, 2006. Growth curve establishment for American Bronze turkeys. Arch Anim Breed 49(3): 293-299. https://doi.org/10.5194/aab-49-293-2006
GraphPad Software, 2018. GraphPad Prism, Version 8.0.2 for Windows. GraphPad Software, San Diego, CA, USA. Available at: https://www.graphpad.com
Güler S, Arslan E, Sarı MM, Cetin O, 2022. Comparison of growth curves with non-linear models in Japanese quails of different plumage color. Eurasian J Vet Sci 38(3): 173-179. https://doi.org/10.15312/EurasianJVetSci.2022.380
Gurcan EK, Cobanoglu O, Genc S, 2012. Determination of body weight-age relationship by non-linear models in Japanese quail. J Anim Vet Adv 11(3): 314-317. http://docsdrive.com/pdfs/medwelljournals/javaa/2012/314-317.pdf
Gürcan EK, Kaplan S, 2017. Flexible alternatives to models widely used for describing growth in Japanese quail. J Anim Plant Sci 27(1): 48-56.
Huss D, Poynter G, Lansford R, 2008. Japanese quail (Coturnix japonica) as a laboratory animal model. Lab Anim 37(11): 513-519. https://doi.org/10.1258/002367708786547007
Hussain J, Akram M, Javed K, Ahmad HA, Mahmud A, Mehmood S, Hussnain F, 2016. Quail breeder’s production performance in response to selection for higher three weeks body weight. J Anim Plant Sci 26(3): 588-593.
Hussain J, Mehmood S, Ullah I, Mahmud A, Ghayas A, Usman M, Rehman A, Ahmad S, 2019. Post-hatch performance of meat-type Japanese quail influenced by time of offering feed and ambient environment. J Anim Plant Sci 29(2): 365-369. https://www.researchgate.net/publication/331832090
Hussen SH, Saleh JH, 2019. Comparison among three quail (Coturnix coturnix spp.) lines in their productive performance. Syrian J Agric Res 6(4): 516-527.
Kaplan S, Gürcan EK, 2018. Comparison of growth curves using non-linear regression functions in Japanese quail. J Appl Anim Res 46(1): 112-117. https://doi.org/10.1080/09712119.2016.1268965
Kebreab E, Strathe AB, Yitbarek A, Nyachoti CM, Dijkstra J, López S, France J, 2011. Modeling the efficiency of phosphorus utilization in growing pigs. J Anim Sci 89(9): 2774-2781. https://doi.org/10.2527/jas.2009-2550
Kızılkaya K, Balcıoğlu MS, Yolcu HI, Karabağ K, 2005. The application of exponential method in the analysis of growth curve for Japanese quail. Arch Geflügelk 69(5): 193-198. https://doi.org/10.1016/S0003-9098(25)00595-8
Kucuk M, Eyduran E, 2009. The determination of the best growth model for Akkaraman and German Blackheaded Mutton × Akkaraman B1 cross bred lambs. Bulgarian J Agric Sci 15(1): 90-92.
NRC, 1994. Nutrient Requirements of Poultry (9th ed). National Academy Press, Washington, D.C., USA.
Magotra A, Bangar YC, Yadav AS, 2023. Modelling the growth curve and estimation of associated genetic parameters in Munjal sheep. Trop Anim Health Prod 55(3): 205. https://doi.org/10.1007/s11250-023-03624-1
Mahdi LS, Ahmed LS, Ibrahim PW, Radha GH, 2023. Efficiency of different levels of L-carnitine supplementation on growth performance and blood parameters in three lines of Kurdish quails (Coturnix coturnix). Iraqi J Agric Sci 54(6): 1509-1519. https://doi.org/10.36103/ijas.v54i6.1850
Malhado CHM, Carneiro PLS, Santos PF, Azevedo DMMR, Souza JD, Affonso PRM, 2008. Growth curve in crossbred Santa Inês × Texel ovines raised in the southwestern region of Bahia state. Rev Bras Saúde Reprod Anim 9(2): 210-218.
Mokhtari MS, Borzi NK, Fozi MA, Behzadi MB, 2019. Evaluation of non-linear models for genetic parameters estimation of growth curve traits in Kermani sheep. Trop Anim Health Prod 51: 2203-2212. https://doi.org/10.1007/s11250-019-01927-w
Narinç D, Narinç NÖ, Aygün A, 2017. Growth curve analyses in poultry science. World’s Poult Sci J 73(2): 395-408. https://doi.org/10.1017/S0043933916001082
Nelder JA, 1961. The fitting of a generalization of the logistic curve. Biometrics 17(1): 89-110. https://doi.org/10.2307/2527416
Raji AO, Mbap ST, Aliyu J, 2014. Comparison of different models to describe growth of the Japanese quail (Coturnix japonica). Trakia J Sci 12(2): 182-188.
Rezvannejad E, Pakdel A, Miraee Ashtianee SR, Mehrabani Yeganeh H, Yaghoobi MM, 2013. Analysis of growth characteristics in short-term divergently selected Japanese quail lines and their cross. J Appl Poult Res 22(4): 663-670. https://doi.org/10.3382/japr.2011-00332
Richards FJ, 1959. A flexible growth function for empirical use. J Exp Bot 10(2): 290-301. https://doi.org/10.1093/jxb/10.2.290
Roush WB, Dozier WA, Branton SL, 2006. Comparison of Gompertz and neural network models of broiler growth. Poult Sci 85(4): 794-797. https://doi.org/10.1093/ps/85.4.794
Salako AE, 2014. Asymptotic nonlinear regression models for the growth of White Fulani and N’dama cattle in Nigeria. Livest Res Rural Dev 26(5). http://www.lrrd.org/lrrd26/5/sala26091.html
Sarmento JLR, Rezazzi AJ, Souza WH, Torres RA, Breda FC, Menezes GRO, 2006. Study of the growth curve of Santa Inês sheep. Rev Bras Zootec 35(2): 435-442. https://doi.org/10.1590/S1516-35982006000200014
SAS, Statistical Analyses System, 2004. User’s guide: statistics 8.6. 6th ed. SAS Institute Inc., Cary, North Carolina, USA.
Schwarz G, 1978. Estimating the dimension of a model. Ann Stat 6(2): 461-464. https://doi.org/10.1214/aos/1176344136
Silveira FG, Silva FFE, Carneiro PLS, Malhado CHM, Muniz JA, 2011. Cluster analysis applied to nonlinear regression models selection to growth curves of crossed lambs. Ciência Rural 41(4): 692-699. https://doi.org/10.1590/S0103-84782011000400024
Taskin A, Karadavut U, Tunca RI, Genc S, Cayan H, 2017. Effect of selection for body weight in Japanese quails (Coturnix coturnix japonica) on some production traits. Indian J Anim Res 51(2): 358-364. https://dx.doi.org/10.18805/ijar.11466
Vali N, 2009. Growth, feed consumption and carcass composition of Coturnix japonica, Coturnix ypsilophorus and their reciprocal crosses. Asian J Poult Sci 3(4): 132-137. https://doi.org/10.3923/ajpsaj.2009.132.137
Von Bertalanffy L, 1957. Quantitative laws in metabolism and growth. Q Rev Biol 32(3): 217-231. https://doi.org/10.1086/401873
Winsor CP, 1932. The Gompertz curve as a growth curve. Proc Natl Acad Sci 18(1): 1-8. https://doi.org/10.1073/pnas.18.1.1
Yavuz E, Önem AB, Kaya F, Çanga D, Şahin M, 2019. Modeling of individual growth curves in Japanese quails. Black Sea J Eng Sci 2(1): 11-15. https://doi.org/10.31590/bsjes.545125
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