Comparison of Nonlinear Functions to Define the Growth in Intensive Feedlot System with XGBoost Algorithm
DOI:
https://doi.org/10.24925/turjaf.v12i8.1408-1416.6562Anahtar Kelimeler:
Brown Swiss- Simmental- büyüme eğrisi- XGBoostÖzet
The aim of this study was to define the growth by using nonlinear functions in intensive feedlot system with XGBoost algorithm. To achieve this aim, five nonlinear functions were implemented. To implementation of the study, Brown Swiss (n=41) and Simental (n=95) breed were used. Each nonlinear functions were examined for each breed. According to the results of the nonlinear functions, logistic model was the best prediction model for defining the growth of each breed. In this study, the parameters in the best prediction model were calculated individually and the relationship of these parameters with body weight was evaluated with the XGBoost algorithm. Model comparison criteria such as standard deviation ratio (SDratio), Pearson’s correlation coefficient (PC), determination of coefficient (R2) and Akaike’s information criteria (AIC) were used to evaluate the XGBoost algorithm. In conclusion, the XGBoost algorithm can be an effective and optional approach that allows breeders to estimate live weight from growth parameters. This algorithm can operate on large data sets with high accuracy and speed, leading to significant improvements in agricultural productivity and animal health management. XGBoost enables more accurate predictions by analyzing the effects of various characteristics (e.g., nutritional level, breed, age). Therefore, this method can be used to determine critical parameters such as body weight in animal breeding practices, serving as a powerful support tool for operational decisions.
Referanslar
Akin, M., Eyduran, E., Niedz, R.P., & Reed, B.M. (2017a). Developing hazelnut tissue culture free of ion confounding. Plant Cell, Tissue and Organ Culture 13(3), 483-494. doi.org/10.1007/s11240-017-1238-z
Akin, M., Eyduran, E., & Reed, B.M. (2017b). Use of RSM and CHAID data mining algorithm for predicting mineral nutrition of hazelnut, Plant Cell, Tissue and Organ Culture 128(2), 303-316. https://doi.org/10.1007/s11240-016-1110-6
Akin, M., Hand, C., Eyduran, E., & Reed, B.M. (2018). Predicting minor nutrient requirements of hazelnut shoot cultures using regression trees. Plant Cell, Tissue and Organ Culture, 132(3), 545-559. https://doi.org/10.1007/s11240-017-1353-x
Akin, M., Eyduran, S.P., Eyduran, E., & Reed, B.M. (2020). Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Plant Cell, Tissue and Organ Culture, 140, 661-670. https://doi.org/10.1007/s11240-019-01763-8
Aksoy, A., Erturk, Y.E., Eyduran, E., & Tariq, M.M. (2019). Utility of MARS Algorithm for Describing Non-Genetic Factors Affecting Pasture Revenue of Morkaraman Breed and Romanov × Morkaraman F1 Crossbred Sheep under Semi Intensive Conditions, Pakistan Journal of Zoology, 51(1), 235-240. https://doi.org/10.17582/journal.pjz/2019.51.1.235.240
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.,& 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.
Alonso, J., Castañón, Á.R.,& Bahamonde, A. (2013). Support Vector Regression to predict carcass weight in beef cattle in advance of the slaughter. Computers and Electronics in Agriculture. 91, 116–120. https://doi.org/10.1016/j.compag.2012.08.009
Arthur, C.K., Temeng, V.A.,& Ziggah, Y.Y. (2020). Multivariate Adaptive Regression Splines (MARS) approach to blast-induced ground vibration prediction, International Journal of Mining, Reclamation and Environment, 34(3), 198-222. https://doi.org/10.1080/17480930.2019.1577940
Aytekin, I., Eyduran, E., Karadas, K., Aksahan, R.,& Keskin, I. (2018). Prediction of Fattening Final Live Weight from some Body Measurements and Fattening Period in Young Bulls of Crossbred and Exotic Breeds using MARS Data Mining Algorithm. Pakistan Journal of Zoology, 50(1), 189-195. https://doi.org/10.17582/journal.pjz/2018.50.1.189.195
Breiman, L. (2001). Random forests. Machine Learning. 45(1), 5–32.
Cam, M.A., Olfaz, M.,& Soydan, E. (2010). Body measurements reflect body weights and carcass yields in Karayaka sheep. Asian Journal of Animal and Veterinary Advances, 5(2), 120-127.
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. Brazilian Journal of Animal Science, 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), Pakistan Journal of Zoology, 50(2), 575-583.
Celik, S. (2019). Comparing Predictive Performances of Tree Based Data Mining Algorithms and MARS Algorithm in the Prediction of Live Body Weight from Body Traits in Pakistan Goats. Pakistan Journal of Zoology, 51(4), 1447.
Dolezel, P., Skrabanek, P., & Gago, L. (2016). Weight initialization possibilities for feedforward neural network with linear saturated activation functions. IFAC-PapersOnLine, 49(25), 49-54.
Eyduran, E., Karakus, K., Karakus, S., & Cengiz, F. (2009). Usage of factor scores for determining relationships among body weight and some body measurements. Bulgarian Journal of Agricultural Science. 15, 373-377.
Eyduran, E., Zaborski, D., Waheed, A., Celik, S., Karadas, K.,& 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. Pakistan Journal of Zoology, 49(1), 257-265. https://doi.org/10.17582/journal.pjz/2017.49.1.257.265
Eyduran, E., Akin, M.,& Eyduran, S.P. (2019). Application of Multivariate Adaptive Regression Splines through R Software, Nobel Academic Publishing, Ankara.
Eyduran, E. (2020). ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.1. https://CRAN.R-project.org/package=ehaGoF.
FAO, (2019). FAOSTAT. Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat/en/#data/QL. Access date: 11.03.2021.
Faraz, A., Tirink, C., Eyduran, E., Waheed, A., Tauqir, N. A., Nabeel, M. S.,& Tariq, M. M (2021). Prediction of live body weight based on body measurements in Thalli sheep under tropical conditions of Pakistan using CART and MARS. Tropical Animal Health and Production. 53(2), 1-12. https://doi.org/10.1007/s11250-021-02748-6
Fatih, A., Celik, S., Eyduran, E., Tirink, C., Tariq, M. M., Sheikh, I. S., Faraz, A.,& Waheed, A. (2021). Use of MARS algorithm for predicting mature weight of different camel (Camelus dromedarius) breeds reared in Pakistan and morphological characterization via cluster analysis. Tropical Animal Health and Production. 53(1), 1-14. https://doi.org/10.1007/s11250-021-02633-2
Foresee, F. D.,& Hagan, M. T. (1997). Gauss-Newton approximation to Bayesian learning. International Conference on Neural Networks. 3, 1930–1935.
Friedman, J., 1991. Multivariate adaptive regression splines, Annals of Statistics. 19(1), 1–67.
Ghotbaldini, H., Mohammadabadi, M., Nezamabadi-pour, H., Babenko, O. I., Bushtruk, M. V., & Tkachenko, S. V. (2019). Predicting breeding value of body weight at 6-month age using Artificial Neural Networks in Kermani sheep breed. Acta Scientiarum. Animal Sciences. 41. https://doi.org/10.4025/actascianimsci.v41i1.45282
Grzesiak, W., & Zaborski, D. (2012). In Karahoca A (Eds). Examples of the use of data mining methods in animal breeding: Data mining applications in engineering and medicine (pp.303–324). InTech, Rijeka, Croatia, in IntechOpen.
Huma, Z. E., Iqbal, F. (2019). Predicting the body weight of Balochi sheep using a machine learning approach. Turkish Journal of Veterinary and Animal Sciences. 43(4), 500-506. https://doi.org/10.3906/vet-1812-23
Hussain, A., Akhtar, P., Ali, S., Younas, M.,& Shafiq, M. (2006a). Effect of inbreeding on pre-weaning growth traits in Thalli sheep. Pakistan Veterinary Journal. 26(3), 138-140.
Hussain, A., Akhtar, P., Ali, S., Younas, M.,& Javed, K.(2006b). Inbreeding effects on post-weaning growth traits of Thalli sheep in Pakistan. Pakistan Journal of Agricultural Sciences. 43, 89-92.
Hussain, A., Akhtar, P., Ali, S., Younas, M., Yaqoob, M., Babar, M.E., Kaved, K., Shakoor, A., 2013. Factors influencing body weights at different ages in Thalli sheep. Journal of Animal and Plant Sciences. 23(1), 1-6.
Hussain, A., Akhtar, P., Ali, S., Javed, K., Younas, M., Shakoor, A., & Waheed, U. (2014). Genetic analysis of post-weaning growth traits of Thalli sheep under tropical conditions. Tropical Animal Health and Production. 46, 1527–1531.
Kavaklioglu, K. (2011). Modeling and prediction of Turkey’s electricity consumption using Support Vector Regression. Applied Energy. 88, 368–375
Kayri, M. (2016). Predictive abilities of bayesian regularization and Levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data. Mathematical and Computational Applications. 21(2), 20.
Khan, B.B., Iqbal, A.,& Mustufa, M.I. (2003). Sheep and goat production. Department of Livestock Management, University of Agriculture, Faisalabad.
Khan, M.S., Khan, M.A., Ahmad, S., & Mahmood, S. (2007). Genetic resources and diversity in Pakistani sheep. International Journal of Agriculture & Biology. 9, 941-944.
Khan, M.A., Tariq, M.M., Eyduran, E., Tatliyer, A., Rafeeq, M., Abbas, F., Rashid, N., Awan, M.A., & Javed, K. (2014). Estimating body weight from several body measurements in Harnai sheep without multicollinearity problem, The Journal of Animal & Plant Sciences. 24(1), 120-126. https://doi.org/10.17582/journal.pjz/2017.49.5.1731.1738
Khorshidi-Jalali, M., Mohammadabadi, M., Koshkooieh, A. E., Barazandeh, A.,& Babenko, O. (2019). Comparison of artificial neural network and regression models for prediction of body weight in Raini Cashmere goat. Iranian Journal of Applied Animal Science. 9(3), 453-461. https://doi.org/10.1007/s11250-013-0393-4 .
Kovalchuk, I.Y., Mukhitdinova, Z., Turdiyev, T., Madiyeva, G., Akin, M., Eyduran, E.,& Reed, B.M., (2017). Modeling some mineral nutrient requirements for micropropagated wild apricot shoot cultures. Plant Cell, Tissue Organ Culture. 129, 325–335. https://doi.org/10.1007/s11240-017-1180-0
Kovalchuk, I.Y., Mukhitdinova, Z., Turdiyev, T., Madiyeva, G., Akin, M., Eyduran, E., & Reed, B.M. (2018). Nitrogen ions and nitrogen ion proportions impact the growth of apricot (Prunus armeniaca) shoot cultures. Plant Cell, Tissue Organ Culture. 133, 263–273. https://doi.org/10.1007/s11240-018-1379-8
Kuhn, M., Johnson, K. (2013). Applied Predictive Modeling. New York: Springer.
Kuhn, M., (2020). caret: Classification and Regression Training. R package version 6.0-86. https://CRAN.R-project.org/package=caret.
Liaw, A., & Wiener, M. (2002). Classification and Regression by randomForest. R News 2(3), 18--22.
Linan, M. N., Gerardo, B., & Medina, R. (2019). Modified weight initialization in the self-organizing map using Nguyen-Widrow initialization algorithm. In Journal of Physics: Conference Series (Vol. 1235, No. 1, p. 012055). IOP Publishing.
MacKay, D.J.C.(1992). A practical Bayesian framework for backpropagation networks. Neural Computation. 4, 448–472.
Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., & Leisch, F. (2020). e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-4. https://CRAN.R-project.org/package=e1071
Nguyen, D., Widrow, B. (1990). Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. 21–26.
Nguyen, Q.T., Fouchereau, R., Frénod, E., & Gerard, C. (2020). Comparison of forecast models of production of dairy cows combining animal and diet parameters. Computers and Electronics in Agriculture. 170, 105258. https://doi.org/10.1016/j.compag.2020.105258
Olfaz, M., Tirink, C., & Onder, H. (2019). Use of CART and CHAID algorithms in Karayaka sheep breeding. Journal of the Journal of the Faculty of aculty of Veterinary Medicine, Kafkas Uni erinary Medicine, Kafkas University, 25(1), 105-110. https://doi.org/10.9775/kvfd.2018.20388
Patel, A.K., Chatterjee, S., & qGorai, A.K. (2019). Development of a machine vision system using the support vector machine regression (SVR) algorithm for the online prediction of iron ore grades. Earth Science Informatics.12, 197–210. https://doi.org/10.1007/s12145-018-0370-6
Pérez-Rodríguez, P., Gianola, D., Weigel, K. A., Rosa, G. J. M., & Crossa, J. (2013). An R package for fitting Bayesian regularized neural networks with applications in animal breeding. Journal of Animal Science, 91(8), 3522-3531. https://doi.org/10.2527/jas.2012-6162
R Core Team (2020). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Revelle, W. (2020). psych: Procedures for Personality and Psychological Research, Northwestern University, Evanston, Illinois, USA, https://CRAN.R-project.org/package=psych Version = 2.0.12.
Rodriguez-Galiano, V., Mendes, M.P., Garcia-Soldado, M.J., Chica-Olmo, M., & Riberio, L.(2014). Predictive modeling of groundwater nitrate pollution using Random Forest and multisource variables related to intrinsic and specific vulnerability: A case study in an agricultural setting (Southern Spain). Science of The Total Environment. 467-477, 189-206. https://doi.org/10.1016/j.scitotenv.2014.01.001
Sabbioni, A., Beretti, V., Superchi, P., & Ablondi, M., (2020). Body weight estimation from body measures in Cornigliese sheep breed, Italian Journal of Animal Science, 19:1, 25-30. https://doi.org/10.1080/1828051x.2019.1689189
Salawu, E.O., Abdulraheem, M., Shoyombo, A., Adepeju, A., Davies, S., Akinsola, O. & Nwagu, B. (2014) Using Artificial Neural Network to Predict Body Weights of Rabbits. Open Journal of Animal Sciences, 4, 182-186. https://doi.org/10.4236/ojas.2014.44023
Smola, A.J., Schölkopf, B. (2004). A tutorial on support vector regression. Statistics and Computing. 14, 199–222. https://doi.org/10.1023/b:stco.0000035301.49549.88
Tariq, M.M., Bajwa, M.A., Jawasreh, K., Awan, M.A., Abbas, F., Waheed, A., Rafeeq, M., Wadood, A., Khan, K.U., Rashid, N., Atique, M.A., & Bukhari, F.A. (2012). Characterization of four indigenous sheep breeds of Balochistan, Pakistan by random amplified polymorphic DNAs. African Journal of Biotechnology, 11, 2581-2586. https://doi.org/10.5897/ajb11.3196
Tatliyer, A. (2020). The Effects of Raising Type on Performances of Some Data Mining Algorithms in Lambs. KSU Journal of Agriculture and Nature. 23(3), 772-780. https://doi.org/10.18016/ksutarimdoga.vi.651232
Taye, M., Bimerow, T., Yiyayew, A., Mekuriaw, S., & Mekuriaw, G. (2012) Estimation of live body weight from linear body measurements for Farta Sheep. Online Journal of Animal and Feed Research, 2(1), 98-103. https://doi.org/10.5897/ajar2016.11826
Topal, M.,& Macit, M. (2004). Prediction of body weight from body measurements in morkaraman sheep, Journal of Applied Animal Research, 25(2), 97-100. https://doi.org/10.1080/09712119.2004.9706484
Wang, L., Zhou, X., Zhu, X, Dong, Z., Guo, W. (2016). Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal. 4, 212-219. https://doi.org/10.1016/j.cj.2016.01.008
Weber, V. A. M., de Lima Weber, F., da Silva Oliveira, A., Astolfi, G., Menezes, G. V., de Andrade Porto, J. V., Rezende, F.P.C., de Moraes, P.H., Matsubara, E.T., Mateus, R.G., de Araújo, T.L.A.C., da Silva, L.O.C., de Queiroz, E.Q.A., de Abreu, U.G.P., da Costa Gomes, R., & Pistori, H. (2020). Cattle weight estimation using active contour models and regression trees Bagging. Computers and Electronics in Agriculture, 179, 105804. https://doi.org/10.1016/j.compag.2020.105804
Wei, T., & Simko, V., (2017). R package “corrplot”: Visualization of a Correlation Matrix (Version 0.84). Available from https://github.com/taiyun/corrplot.
Yakubu, A. (2012). Application of regression tree methodology in predicting the body weight of Uda sheep. Animal Science and Biotechnologies, 45:484-490. https://doi.org/10.4081/ijas.2009.615
Yavuz, E., Önem, A. B., Kaya, F., Çanga Boğa, D., & Şahin, M. (2019). Modeling of Individual Growth Curves in Japanese Quails. Black Sea Journal of Engineering and Science, 2(1), 11–15.
Zaborski, D., Ali, M., Eyduran, E., Grzesiak, W., Tariq, M.M., Abbas, F., Waheed, A., & Tirink, C., (2019). Prediction of selected reproductive traits of indigenous Harnai sheep under the farm management system via various data mining algorithms. Pakistan Journal of Zoology, 51, 421-431. https://doi.org/10.17582/journal.pjz/2019.51.2.421.431
Zhang, A.L., Wu, B.P., Wuyun, C.T., Jiang, D.X., Xuan, E. C., & Ma, F.Y., (2018). Algorithm of sheep body dimension measurement and its applications based on image analysis. Computers and Electronics in Agriculture, 153, 33-45. https://doi.org/10.1016/j.compag.2018.07.033
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