A study on modeling growth model of Adana pigeons
A study on modeling growth model of Adana pigeons
The study aims to determine a mathematical model that can be used to describe the growth of the Adana pigeon. Since pigeons have only one breeding season, just one or two pairs of baby pigeons are raised per year. Hatchlings sometimes die before reaching adulthood. For this reason, measurements can be taken for 10, 15 and 60 days periods. Related with this issue, only 43-days measurements of 68 pigeons are used over a 6-year period. The study is modelled by taking the day-to-day average of the data (43 days) of 68 pigeons. The study was conducted on 68 Adana pigeons in the interval between the age of 1 and 43 days. The growth of pigeon cub was measured by daily live weight until 1 to 43 days. The estimation is carried out by writing the specific Matlab codes. Classical growth functions used in animals are in nonlinear form. Various numerical methods have been developed to estimate parameters in nonlinear functions. Special program routines have been developed to implement these methods. In these nonlinear models, there are more than one parameter to be estimated. Therefore, the number of mathematical operations in estimating the parameters is large. The most used models in the literature are Brody, Bertalanffy, Logistic, Generalized Logistic, Gompertz, Richards, Negative Exponential, Stevens, and Tanaka. However, as far as is known, there is no published article for Adana pigeons that uses all of these models and compares which one is better. These models are Brody, Bertalanffy, Logistic, Generalized Logistic, Gompertz, Richards, Negative Exponential, Stevens, and Tanaka. The best analysis was done by the Richards model in terms of both the Mean Squared Error (MSE), mean absolute percentage error (MAPE) and (Coefficient of Determination) R2 .
Keywords:
Growth function, Adana pigeon Gompertz model,
___
- Secord, J.A., Nature's Fancy: Charles Darwin and the breeding of pigeons, Isis, 72 (2) (1981), 162-186.
- Shapiro, M., Genomic diversity and evolution of the head crest in the rock pigeon, Science, 339 (6123) (2013), 1063-1067, https://doi.org/10.1126/science.1230422.
- Yılmaz, O., Ertuğrul, M., Importance of pigeon husbandry in history, J.Agric. Fac. HR.U., 16 (2) (2012), 1-7.
- Yılmaz, O., Savaş, S., Ertuğrul, M., Pigeon and pigeon rearing in the Turkish culture, Nevşehir Üniversitesi Fen Bilimleri Enstitü Dergisi, 2 (2012), 79-86.
- https://guvercinadana.tr.gg/, Accessed: 08.08.2020.
- Gompertz B., On the nature of the function expressive of the law of human mortality, and on a new mode of determining the value of life contingencies, Philos. Trans. R. Soc., 115 (1825), 513-583.
- Bertalanffy, L., Problems of organic growth, Nature, 163 (1949) 156-158, https://doi.org/10.1038/163156a0.
- Richards, F.A., Flexible growth function for empirical use, J. Exp. Bot., 10 (1959), 290- 301, https://doi.org/10.1093/jxb/10.2.290.
- Zwietering, M., Jongenburger, I., Rombouts, F., Van’t, R.K., Modeling of the bacterial growth curve, Appl. Environ. Microbiol., 56 (6) (1990), 1875-1881, https://doi.org/10.1128/aem.56.6.1875-1881.1990.
- Gerlee, P., The model muddle: in search of tumor growth laws, Cancer Res., 73 (8) (2013), 2407-2411, https://doi.org/10.1158/0008-5472.CAN-12-4355.
- Kathleen, M.C., Tjørve, E., The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the unified-Richards family, PLOS ONE, 12 (6) (2017), 1-17, https://doi.org/10.1371/journal.pone.0178691.
- Topal, M., Ozdemir, M., Aksakal, V., Yildiz, N., Dogru, U., Determination of the best non-linear function in order to estimate growth in Morkaraman and Awassi lambs, Small Rumin. Res., 55 (2004), 229-232, https://doi.org/10.1016/j.smallrumres.2004.01.007.
- Şengul, T., Kiraz, S., Non-linear models for growth curves in large white turkeys, Turk. J. Vet. Anim. Sci., 29 (2005) 331-337.
- Sezer, M., Tarhan, S., Model parameters of growth curves of three meat-type lines of Japanese quail, Czech J. Anim. Sci., 50 (2005), 22-30, https://doi.org/10.17221/3991- CJAS.
- Kizilkaya, K., Balcioglu, M., Yolcu H., Karabag, K., Genc I., Growth curve analysis using nonlinear mixed model in divergently selected Japanese quails, EPS, 70 (2006), 181-186.
- Topal, M., Bolukbasi, Ş., Comparison of nonlinear growth curve models in broiler chickens, J. App. Anim. Res., 34 (2008) 149-152, https://doi.org/10.1080/09712119.2008.9706960.
- Gbangboche, A.B., Glele, K.R., Salifou, S., Albuquerque, L.G., Comparison of nonlinear growth models to describe the growth curve in West African Dwarf sheep, Animal, 2 (2008) 1003-1012, https://doi.org/10.1017/S1751731108002206.
- Narinc, D., Karaman, E., Firat, M.Z., Aksoy, T., Comparison of nonlinear growth models to describe the growth in Japanese quail, J. Anim. Vet. Adv., 9 (2010) 1961-1966.
- Özçelik, R., Yavuz, H., Karatepe, Y., Gürlevik, N., Kırış, R., Development of ecoregionbased height–diameter models for 3 economically important tree species of southern Turkey, Turk. J. Vet. Anim. Sci., 38 (2014), 399-412.
- Ghaderi, Z.M., Rafeie, F., Bahreini, B.M.R., Simple hierarchical and general nonlinear growth modeling in sheep”, Turk. J. Vet. Anim. Sci., 42 (2018) 326-334.
- Faraji, A.H, Rokouei, M., Ghazaghi, M., Comparative study of growth patterns in seven strains of Japanese quail using nonlinear regression modeling, Turk. J. Vet. Anim. Sci., 42 (2018) 441-451.
- Sariyel, V., Aygun, A., Keskin, I., Comparison of growth curve models in partridge, Poult. Sci., 96 (2017), 1635-1640, https://doi.org/10.3382/ps/pew472.
- Ramos, S. B., Caetano, S., Savegnago, R.P., Nunes, B.N., Ramos, A.A., Munari. D.P., Growth curves for ostriches (struthio camelus) in a Brazilian population, Poult. Sci., 92 (2013) 277-282, https://doi.org/10.3382/ps.2012-02380.
- Gon, A.¸ Gotuzzo, C., Piles, M., Pillon, R., Genetics and genomics, Bayesian hierarchical model for comparison of different nonlinear function and genetic parameter estimates of meat quails, Poult. Sci., 98 (2019) 1601-1609, https://doi.org/10.3382/ps/pey548.
- Aggrey, S.E., Comparison of three nonlinear and spline regression models for describing chicken growth curves, Poult. Sci., 81 (2002) 1782-1788, https://doi.org/10.1093/ps/81.12.1782.
- Kuhleitner, M., Brunner, N., Nowak, W.G., Best-fitting growth curves of the von Bertalanffy type, Poult. Sci., 98 (2019) 3587–3592, https://doi.org/10.3382/ps/pez122.
- Gao, C.Q., Yang, J.X., Chen, M.X., Yan, H.C., Wang, X.Q., Growth curves and agerelated changes in carcass characteristics, organs, serum parameters, and intestinal transporter gene expression in domestic pigeon (Columba livia), Poult. Sci., 95 (2016) 867-877, https://doi.org/10.3382/ps/pev443.
- Vincek, D., Kralik, G., Kušec, G., Sabo, K., Scitovski, R., Application of growth functions in the prediction of live weight of domestic animals, CEJOR, 20 (2012) 719- 733, https://doi.org/10.1007/s10100-011-0199-2.
- Özbek, L., A Study on a new estimating growth model of Adana pigeons using discrete-time stochastic Gompertz model and adaptive Kalman filter, Gazi Univ. J. Sci., in press, (2022).
- Reddingius, J., Gambling for existence: A discussion of some theoretical problems in animal population ecology, E.J. Brill, Leiden, 1971.
- Pollard, E., Lakhani, K. H., Rothery, P., The detection of density-dependence from a series of annual censuses, Ecology, 68 (1987) 2046-2055, https://doi.org/10.2307/1939895.
- Dennis, B., Taper, M. L., Density dependence in time series observations of natural populations: estimation and testing, Ecol. Monogr., 64 (1994) 205-224.
- ISSN: 1303-6009
- Yayın Aralığı: Yılda 2 Sayı
- Başlangıç: 2019
- Yayıncı: Ankara Üniversitesi
Sayıdaki Diğer Makaleler
Evaluation of the impact of I/Q imbalance compensation on communication performance
Gizem Eda SAĞIR, Selçuk TAŞCIOĞLU
A method for analyzing suspect-filler similarity using convolutional neural networks
Design and implementation of a microcontroller based split air conditioner control system
A study on modeling growth model of Adana pigeons
Determination of the surface topography in rill erosion by imaging techniques
Utilization of deep learning architectures for MIMO detection