دانشگاه علوم کشاورزی و منابع طبیعی گرگان

 آمار

تعداد نشریات13
تعداد شماره‌ها665
تعداد مقالات6,950
تعداد مشاهده مقاله10,207,331
تعداد دریافت فایل اصل مقاله9,432,808
Mouffok, C, Semara, L, Ghoualmi, N, Belkasmi, F. (1398). Comparison of Some Nonlinear Functions for Describing Broiler Growth Curves of Cobb500 Strain. , 7(1), 51-61. doi: 10.22069/psj.2019.15965.1386
C Mouffok; L Semara; N Ghoualmi; F Belkasmi. "Comparison of Some Nonlinear Functions for Describing Broiler Growth Curves of Cobb500 Strain". , 7, 1, 1398, 51-61. doi: 10.22069/psj.2019.15965.1386
Mouffok, C, Semara, L, Ghoualmi, N, Belkasmi, F. (1398). 'Comparison of Some Nonlinear Functions for Describing Broiler Growth Curves of Cobb500 Strain', , 7(1), pp. 51-61. doi: 10.22069/psj.2019.15965.1386
Mouffok, C, Semara, L, Ghoualmi, N, Belkasmi, F. Comparison of Some Nonlinear Functions for Describing Broiler Growth Curves of Cobb500 Strain. , 1398; 7(1): 51-61. doi: 10.22069/psj.2019.15965.1386

Comparison of Some Nonlinear Functions for Describing Broiler Growth Curves of Cobb500 Strain

Poultry Science Journal
مقاله 7، دوره 7، شماره 1، مرداد 2019، صفحه 51-61    اصل مقاله (447.27 K)
نوع مقاله: Original Paper
شناسه دیجیتال (DOI): 10.22069/psj.2019.15965.1386
نویسندگان
C Mouffok* 1؛ L Semara1، 2؛ N Ghoualmi1؛ F Belkasmi1، 3
1Department of Agriculture and Animal Production, Setif 1 University, ElbezSetif, Algeria
2Department of Biology, University A. Boussouf, Mila, Algeria3Department of Biology, University of ElbashirIbrahimi, Al-AnasserBordjBouArrerij, Algeria
3Department of Biology, University of Elbashir Ibrahimi, Al-Anasser Bordj Bou Arrerij, Algeria
چکیده
This study was conducted to compare some nonlinear functions to describe the broiler growth curve of the Cobb500 strain. A flock of fifty one-day-old chicks were randomly selected from a henhouse of 2500 chicks. Our goal was to establish a growth curve using weighting data using mathematical solutions of time-dependent differential functions. In total, six equations were subjected to a statistical calibration by a sequential quadratic programming under the non-linear regression procedure of the SPSS program. The results showed that the heterogeneity rate between individuals of the same batch increases with the age of the chicks, from more than 10% an early age to less than 30% at the slaughter age. The goodness of fit for six dynamic models showed that the number of iterations required increases with the number of parameters of the model. However, the three parameter models were the best model for describing growth curve (the greatest efficiencies and the lowest error components). The asymptomatic values ​​(3500g to 7500g) and their estimation errors (2% to 12%) are relatively acceptable for the three-parameter models compared to those of four parameters (more than 8000g and up to 100% error). Finally, the comparison between actual and predicted values by models shows that the Gompertz model was the most suitable till up to the four weeks of age. After 1 month of age, the Gompertz has a lower precision and the logistics, Von Bertalonffy and WLS models accurately described the growth curve.
کلیدواژه‌ها
Model؛ Broiler؛ growth؛ Function؛ Cobb500
مراجع
Abbas AA, Yosif AA, Shukur AM& Ali FH. 2014. Effect of genotypes, storage periods and feed additives in broiler breeder diets on embryonic and hatching indicators and chicks blood parameters. Scientia Agriculturae, 7: 44-48. DOI: 10.15192/PSCP.SA.2014.3.1.4448

Ahmadi H & A. Golian. 2008. Non–linear hyperbolastic growth models for describing growth curve in classical strain of broiler chickens. Research Journal of Biological Sciences, 3: 1300–1304. DOI: RJBSCI.2008.1300.1304.

Al-Samarai FR. 2015. Growth Curve of Commercial Broiler as Predicted by Different Nonlinear Functions. American Journal of Applied Scientific Research, 1: 6-9. DOI: 10.11648/J.AJASR.20150102.11

Atil H, Grossman M &Takma Ç, 2007. Comparison of growth curve models on average and individual body weights in chickens. European Poultry Science, 71: S. 1–5.

Attou S &Homrani A. 2017. New approach for adaptation to high temperatures and its effect on the thermal resistance of broilers reared in western Algeria.Livestock Research for Rural Development 29 (5).

Barbato GF. 1991. Genetic architecture of growth curve parameters in chickens. Theoretical and Applied Genetics, 83: 24-32. DOI: 10.1007/BF00229222

Cangar JO, Aerts JM, Vranken E &Berckmans D. 2006. End-weight prediction in broiler growth. British Poultry Science, 47: 330–335. DOI: 10.1080/00071660600741735

CNIS (Centre National de l’Informatique et des Statistiques), 2011. Importations des intrantsavicoles. Serie statistiques du commerce exterieur, Alger, Algerie.

Cobb500 guide. 2012. Performances et RecommandationsNutritionnelles Cobb500TM. cobb-vantress.com, 10p.

Da Silva Marinho KN, de Freitas AR, da Silva Falcão AJ & Ferreira Dias FE., 2013. Nonlinear models for fitting growth curves of Nellore cows reared in the Amazon Biome. RevistaBrasileira de Zootecnia, 42: 645-650. DOI: 10.1590/S1516-35982013000900006

Eleroglu H, Yıldırım A, SekerogluA, ÇoksöylerFN &Duman M. 2014. Comparison of growth curves by growth models in slow–growing chicken genotypes raised the organic system. International Journal of Agriculture and Biology, 16: 529–535.

Fatten AM. 2015. Comparison of Three Nonlinear functions for Describing Chicken Growth Curves. Scientia Agriculturae, 9: 120-123. DOI: 10.15192/PSCP.SA.2015.9.3.120123

Fernandes JIM, Bortoluzzi C, Triques GE, Neto AFG & Peiter DC. 2013. Effect of strain, sex and age on carcass parameters of broilers. Acta Scientiarum. Animal Sciences, 35: 99-105.DOI: 10.4025/ACTASCIANIMSCI.V35I1.13354.

IBM Corp. 2015. IBM SPSS Statistics for Windows, Version 23.0. IBM Corp., Armonk, NY.

Kaci A & Cheriet F. 2013. Analyse de la compétitivité de la filière de viande de volailleenAlgérie: tentativesd’explicationd’unedéstructurationchronique. New Medit, 12: 11-21.

Knitztova H, Hyanek J, Hyankova L &Belicek P. 1995. Comparative study of growth curve in poultry. Genetics, Selection, Evolution, 27: 365-375.DOI: 10.1186/1297-9686-27-4-365

Mahmoudi N, Yakhlef H &Thewis A. 2015. Caractérisationtechnico-socio-professionnelle des exploitations avicolesen zone steppique (wilaya de M'sila, Algérie). Cahiers Agricultures, 24: 161-169.DOI: 10.1684/AGR.2015.0752

Marcato SM, Sakomura NK, Munari DP, Fernandes JBK, Kawauchi ÍM &Bonato M A. 2008. Growth and Body Nutrient Deposition of Two Broiler Commercial Genetic Lines. Brazilian Journal of Poultry Science, 10: 117-123.DOI: 10.1590/S1516-635X2008000200007

Mohammed FA. 2015. Comparison of three nonlinear functions for describing chicken growth curves. Scientia Agriculturae, 9: 120-123. DOI: 10.15192/PSCP.SA.2015.9.3.120123

Moharrery A& Mirzaei M. 2014. Growth characteristics of commercial broiler and native chickens as predicted by different growth functions. Journal of Animal and Feed Sciences, 23: 82–89.DOI: 10.22358/JAFS/65720/2014

Moula N, Antoine Moussiaux N, Farnier F & Leroy P. 2009. Réhabilitationsocioéconomiqued’unepoule locale envoied’extinction: la poulekabyle (thayazitlekvayel). Annales de MedecineVeterinaire, 153, 178-186.

Narinç D, ÖksüzNarinç N &Aygün A. 2017. Growth curve analyses in poultry science. World's Poultry Science Journal, 73: 395-408.DOI: 10.1017/S0043933916001082

Nešetřilová H. 2005. Multiphasic growth models for cattle. Czech Journal of Animal Science, 50, 347–354.

Prince SH. 2002. Modeling the broiler performance under small-scales and semi commercial management condition. Dissertation, Port Elizabeth Technician, George Campus.

Raji  AO, Alade NK &Duwa H. 2014.  Estimation of model parameters of the Japanese quail growth curve using gompertz model. Archivos de Zootecnia, 63: 429-435. DOI: 10.4321/s0004-05922014000300004

Rizzi C, Contiero B &Cassandro M. 2013. Growth patterns of Italian local chicken population. Poultry Science, 92: 2226-2235.DOI: 10.3382/PS.2012-02825

Roush WB, Dozier WA &Branton SL. 2006. Comparison of Gompertz and Neural Network Models of Broiler Growth. Poultry Science, 85:794–797.DOI: 10.1093/PS/85.4.794

Şekeroğlu A, Tahtali Y, Sarica M, Gülay M& Abaci SH &Duman M. 2013. Comparison of Growth Curves of Broiler under Different Stocking Densities by Gompertz Model.  KafkasUniversitesiVeterinerFakultesiDergisi, 19: 669-672. DOI: 10.9775/KVFD.2013.8635

Sengül T &Kiraz S. 2005. Non-linear models for growth curves in Large White turkeys. Turkish Journal of Veterinary and Animal Sciences, 29: 331–337.

SimizE, Mic F, Mtef L, Mhiş C, Julean C&Drinceanu D. 2013. Growth modeling using nutritional interventions during growth of the Tetra H hybrid avian youth in organic system. Romanian Biotechnological Letters, 18: 8481-8488.

Topal M&Bolukbasi SC. 2008.Comparison of Nonlinear Growth Curve Models in Broiler Chickens. Journal of Applied Animal Research, 34: 149-152. DOI: 10.1080/09712119.2008.9706960

Yang Y, Mekki DM, Lv SJ, Wang LY& Wang JY. 2006. Analysis of fitting growth models in Jinghai mixed-sex yellow chicken. International Journal of Poultry Science, 5: 517-521. DOI: 10.3923/ijps.2006.517.521

Zhao ZH, Li SF, Huang HY, Li CM, Wang QB&Xue LG. 2015. Comparative Study on Growth and Developmental Model of Indigenous Chicken Breeds in China. Open Journal of Animal Sciences, 5: 219-223.DOI: 10.4236/OJAS.2015.52024.

آمار
تعداد مشاهده مقاله: 983
تعداد دریافت فایل اصل مقاله: 895


سامانه مدیریت نشریات علمی. طراحی و پیاده سازی از سیناوب