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Comparison of tree-based regression tree methods for predicting live body weight from morphological traits in Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breeds raised in South Africa

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Abstract

In animal breeding, more considerable attention is drawn to reveal the relationship between live body weight and morphological traits in identifying breed and species standards. The aim of this study was to predict live body weight from morphological characteristics in the Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breed, native to South African. In the prediction of live body weight, eleven morphological measurements, i.e., wing length, back length, beak length, shank length, shank circumference, chest circumference, wingspan, keel length, body girth, toe length, and body length, were taken. As tree-based regression tree methods, predictive performances of CART, CHAID, and exhaustive CHAID algorithms were measured for body weight prediction. Among those, CART was found to be the best decision tree algorithm that gave the highest predictive accuracy. CART visual results reflected that the heaviest body weight mean (2.000 kg) was obtained from the chickens with 10.250 cm < WL ≤ 10.500 cm. As a result, it could be suggested that the CART decision tree might help to determine breed standards of the Hy-line silver brown commercial layer and, especially, indigenous Potchefstroom Koekoek breeds for breeding programs.

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References

  • Akin, M.; Eyduran, E. and Reed, B. M. 2017. Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut. Plant Cell Tissue and Organ Culture 128:303–316.

    Article  CAS  Google Scholar 

  • Alabi, O. J.; Ng’ambi, J. W. and Norris, D. 2013. Dietary energy level for optimum productivity and carcass characteristics of indigenous Venda chickens raised in closed confinement. South African Journal of Animal Science, 43(5):75–80. https://doi.org/10.4314/sajas.v43i5.14

    Article  Google Scholar 

  • Ali, M.; Eyduran, E.; Tariq, M. M.; Tirink, C.; Abbas, F.; Bajwa, M. A.; Baloch, M. H.; Nizamani, A. H.; Waheed, A.; Awan, M. A.; Shah, S. H.; Ahmad, Z. and Jan, S. 2015. Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai Sheep. Pakistan Journal of Zoology 47:1579–1585.

    CAS  Google Scholar 

  • Biggs, D., De Ville, B., Suen, E. 1991. A Method of Choosing Multiway Partitions for Classification and Decision Trees. Journal of Applied Statistics 18(1):49–62.

  • Breiman, L.; Friedman, J. H.; Olshen, R. A. and Stone, C. J. 1984. Classification and regression trees. Chapman and Hall, Wadsworth Inc., New York, NY, USA.

  • Celik, S., Eyduran, E., Karadas, K., Tariq, M.M. (2017). Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan. Rev. Bras. Zootech. 46(11):863–872.

  • 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). Pak. J. Zool., 50(2):575–583.

    Article  Google Scholar 

  • Dzungwe, J.T., Gwaza, D.S. and Egahi, J.O. (2018). Statistical Modeling of Body Weight and Body Linear Measurements of the French Broiler Guinea Fowl in the Humid Tropics of E Nigeria. Poult Fish Wildl Sci. 6(2):1–4.

    Google Scholar 

  • Egena, S.S.A., Ijaiya, A.T., Ogah, D.M., Aya, V.E. (2014). Principal component analysis of body measurements in a population of indigenous Nigerian chickens raised under extensive management system. Slovak J. Anim. Sci., 47(2):77–82.

    Google Scholar 

  • Eyduran, E. (2019). ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0. https://CRAN.R-project.org/package=ehaGoF.

  • Eyduran, E.; Karakuş, K.; Keskin, S. and Cengiz, F. 2008. Determination of factors influencing birth weight using regression tree (RT) method. Journal of Applied Animal Research 34:109–112.

    Article  Google Scholar 

  • Eyduran, E., Zaborski, D., Waheed, A., Celik, S., Karadas, K. and Grzesiak, W. 2017. Comparison of the Predictive Capabilities of Several Data Mining Algorithms and Multiple Linear Regression in the Prediction of Body Weight by Means of Body Measurements in the Indigenous Beetal Goat of Pakistan. Pak. J. Zool., 49(1):257–265.

    Article  Google Scholar 

  • Eyduran, E., Akin, M., Eyduran, S. P. (2019): Application of Multivariate Adaptive Regression Splines in Agricultural Sciences through R Software. Nobel Bilimsel Eserler Sertifika No: 20779, Ankara. ISBN: 978-605-2149-81-2.

  • FAO, 2012. Phenotypic characterization of animal genetic resources. FAO Animal Production and Health Guidelines, No. 11. Rome.

  • Goodman, L. A. 1979. Simple Models for the Analysis of Association in Cross-Classifications Having Ordered Categories. Journal of the American Statistical Association 74:537–552.

    Article  Google Scholar 

  • Gözüaçık, C., Eyduran, E., Cam, H., Kara, M.K. (2018). Detection of infection preferences of the alfalfa seed chalcid, Bruchophagus roddi Gussakovskiy, 1933 (Hymenoptera: Eurytomidae) in alfalfa (Medicago sativa L.) fields of Igdir, Turkey. Legume Research-An International Journal, 41:150–154.

    Google Scholar 

  • Grzesiak, W. and Zaborski, D. 2012. Examples of the Use of Data Mining Methods in Animal Breeding, Data Mining Applications in Engineering and Medicine, Adem Karahoca, IntechOpen https://doi.org/10.5772/50893. Available from: https://www.intechopen.com/books/data-mining-applications-in-engineering-and-medicine/examples-of-the-use-of-data-mining-methods-in-animal-breeding.

  • Guéye E F, Ndiaye, A., and 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. Volume 10(3): Article #28. Retrieved April 29, 2020, from http://www.lrrd.org/lrrd10/3/sene103.htm.

  • Hu, S., 2007. Akaike Information Criterion. Center for Research in Scientific Computation North Carolina State University Raleigh, NC http://www4.ncsu.edu/~shu3/Presentation/AIC.pdf.

  • Kass, G. V. 1980. An Exploratory Technique for Investigating Large Quantities of Categorical Data. Applied Statistics 20(2):119–127.

    Article  Google Scholar 

  • Liddle, A. R. (2007): Information criteria for astrophysical model selection. - Monthly Notices of the Royal Astronomical Society: Letters, 377:L74–L78.

  • Malomane, D.K., Norris, D., Banga, C.B., Ngambi, J.W. (2014). Use of Factor Scores for Predicting Body Weight From Linear Body Measurements in Three South African Indigenous Chicken Breeds. Trop. Anim. Health Prod. 46(2):331–335. https://doi.org/10.1007/s11250-013-0492-2.

  • Mendes, M., Akkartal, E. (2009). Regression tree analysis for predicting slaughter weight in broilers. Italian Journal of Animal Science, 8(4):615–624. https://doi.org/10.4081/ijas.2009.615.

  • Petrus, N.P., Kangootui, K., Kandiwa, E., Madzingira, O. and Mushonga, B. (2019). Relationship of Age and Live weight to Linear Body Traits in Female Intensively Reared Boschveld Chicken in Namibia. International Journal of Poultry Science, 18:483–491.

    Article  Google Scholar 

  • R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.

  • Ruhil, A. P.; Raja, T. V. and Gandhi, R. S. 2013. Preliminary study on prediction of body weight from morphometric measurements of goats through ANN models. Journal of the Indian Society of Agricultural Statistics 67:51–58.

    Google Scholar 

  • Salawu, E. O.; Abdulraheem, M.; Shoyombo, A.; Adepeju, A.; Davies, S.; Akinsola, O. and Nwagu, B. 2014. Using artificial neural network to predict body weights of rabbits. Open Journal of Animal Sciences 4:182–186.

    Article  Google Scholar 

  • Semakula, J., Lusembo, P. , Kugonza, D. R., Mutetikka, D., Ssennyonjo J., Mwesigwa, M. (2011). Estimation of live body weight using zoometrical measurements for improved marketing of indigenous chicken in the Lake Victoria basin of Uganda. Livestock Research for Rural Development. 23(8):170.

  • Tadele, A. (2019). Statistical Modelling of Indigenous Chicken with Body Weight and Linear Body Measurements in Bench Maji Zone, South Western Ethiopia. Int. J. Environ. Sci. Nat. Res. 22(2):63–67.

    Google Scholar 

  • Tadele, A., Melesse, A., Taye, M. (2018). Phenotypic and morphological characterizations of indigenous chicken populations in Kaffa Zone, South-Western Ethiopia. Anim. Husb. Dairy Vet Sci. 2(1):1–9.

    Google Scholar 

  • Takma. C., Atil. H., Aksakal, V. (2012): Comparison of Multiple Linear Regression and Artificial Neural Network Models Goodness of Fit to Lactation Milk Yields. - Kafkas Üniversitesi Veteriner Fakültesi Dergisi 18:941–944.

  • Tariq, M. M.; Eyduran, E.; Bajwa, M. A.; Waheed, A.; Iqbal, F. and Javed, Y. 2012. Prediction of body weight from testicular and morphological characteristics in indigenous Mengali sheep of Pakistan: Using factor analysis scores in multiple linear regression analysis. International Journal of Agriculture and Biology 14:590–594.

    Google Scholar 

  • Topal, M.; Aksakal, V.; Bayram, B. and Yaganoglu, M. 2010. An analysis of the factor affecting birth weight and actual milk yield in Swedish red cattle using regression tree analysis. The Journal of Animal and Plant Sciences 20:63–69.

    Google Scholar 

  • Willmott, C., Matsuura, K. (2005): Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 30:79–82.

    Article  Google Scholar 

  • Yakubu, A. 2012. Application of regression tree methodology in predicting the body weight of Uda sheep. Animal Science and Biotechnologies 45:484–490.

    Google Scholar 

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Authors and Affiliations

  1. School of Agricultural and Environmental Sciences, Department of Agricultural Economics and Animal Production, University of Limpopo, Private Bag X1106, Sovenga, Limpopo, 0727, South Africa

    Thobela Louis Tyasi

  2. Department of Business Administration, Quantitative Methods, Faculty of Economics and Administrative Sciences, Iğdır University, Iğdır, Turkey

    Ecevit Eyduran

  3. Department of Animal Science, Biometry Genetics Unit, Agricultural Faculty, Bingol University, Bingol, Turkey

    Senol Celik

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  1. Thobela Louis Tyasi
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  2. Ecevit Eyduran
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  3. Senol Celik
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Correspondence to Thobela Louis Tyasi.

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Tyasi, T.L., Eyduran, E. & Celik, S. Comparison of tree-based regression tree methods for predicting live body weight from morphological traits in Hy-line silver brown commercial layer and indigenous Potchefstroom Koekoek breeds raised in South Africa. Trop Anim Health Prod 53, 7 (2021). https://doi.org/10.1007/s11250-020-02443-y

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