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Final Fattening Live Weight Prediction in Anatolian Merinos Lambs from Some Body Characteristics at the Initial of Fattening by Using Some Data Mining Algorithms

Year 2023, Volume: 6 Issue: 1, 47 - 53, 01.01.2023
https://doi.org/10.47115/bsagriculture.1181444

Abstract

This study's objective was to compare the performances of Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Bayesian Regularization Neural Network (BRNN) algorithms, which are some data mining algorithms used in final fattening live weight prediction. As the independent variable in the design of the algorithms, some body characteristics taken before fattening of 54 heads of Anatolian Merino lambs, with single birth and male, were withers height (WH), rump height (RH), body length (BL), chest girth (CG), leg girth (LG), and chest depth (CD) was used. The mean±standart errors for the body characteristics of Anatolian Merino lambs were determined to be 63.481±0.538, 63.315±0.501, 78.930±1.140, 60.037±0.549, 47.704±0.543, and 29.926±0.377, respectively. The mean initial live weight (ILW) and the mean final live weight (FLW) were found as 35.89±0.84 and 49.49±0.88 kg, respectively. There was difference of 13.60 kg between ILW and FLW means. The ILW and FLW were shown to positively correlate with body characteristics, and this correlation was statistically significant (P<0.01). While the highest Pearson’s correlation (r=0.95) of FLW was between WH and RH, the lowest Pearson’s correlation (r=0.51) was found between LG and CD. While the largest share of body characteristics in the total variance in the FLW estimation was BL (42.969%) in the XGBoost algorithm, the lowest share was found to be CD (0.00) in the XGBoost algorithm and LG (0.00) in the BRNN algorithm. The model evaluation criterias which were Root mean square error (RMSE), Standard deviation ratio (SDR), Mean absolute percentage error (MAPE), and Adjusted coefficient of determination (R2Adj) performed as 1.492, 0.233, 2.241 and 0.944, in the XGBoost algorithm, as 2.220, 0.347, 3.139 and 0.880 in the BRNN algorithm, as 2.859, 0.446, 4.340 and 0.792 in the RF model, respectively. As a result, it can be said that the data mining algorithms used in prediction FLW taking advantage of body measurements of Anatolian Merino lambs at the beginning of fattening will benefit from their use in fattening due to their high prediction performance.

References

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  • Akkol S, Akıllı A, Cemal I. 2017. Comparison of artificial neural network and multiple linear regression for prediction of live weight in hair goats. Yyu J Agric Sci, 27(1): 21-29.
  • Ali M, Eyduran E, Tariq MM, Tırınk C, Abbas F, Bajwa MA, Baloch MH, Nizamani AH, Waheed A, Awan MA, Shah SH, 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 J Zool, 47(6): 1579-1585.
  • Altay Y. 2022. Prediction of the live weight at breeding age from morphological measurements taken at weaning in indigenous Honamli kids using data mining algorithms. Trop Anim Health Prod, 54(3): 1-12.
  • Abbas A, Ullah MA, Waheed A. 2021. Body weight prediction of thalli sheep reared in southern Punjab using different data mining algorithms: body weight prediction of thalli sheep. Proc Pak Acad Sci: A, 58(2): 29-38.
  • Aytekin İ, Karabacak A, Keskin İ. 2015. Akkaraman kuzuların besi performansı kesim ve karkas özellikleri. Selçuk Tar Bil Der, 2(1): 1-9.
  • Aytekin İ, Eyduran E, Karadas K, Aksahan R, Keskin İ. 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 J Zool, 50(1): 189-195.
  • Balta B, Topal M. 2020. Describing factors affecting birth weight and growth traıts in hemsın lambs using decision tree methods. J Anim Plant Sci, 30(3): 560-567.
  • Breiman L. 2001. Random forests. Mach Learn, 45(1): 5-32.
  • Boztepe S, Dağ B, Parlat SS, Yıldız AÖ, Aktaş AH. 1997. Yağlı kuyruklu kimi yerli ırk kuzuların besi performansı ve karkas özellikleri. Selçuk Üniv BAP No: ZF-95/064, Konya, Türkiye.
  • Carmona P, Climent F, Momparler A. 2019. Predicting failure in the US banking sector: an extreme gradient boosting approach. Int Rev Econ Finance, 61: 304-323.
  • Chen T, Guestrin C. 2016. Xgboost: a scalable tree boosting system. 2016. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining’16, August 13–17, San Francisco, CA, USA. 785-794.
  • Coşkun G, Şahin Ö, Özkan İA, Aytekin İ. 2022. Siyah Alaca sığırlarda farklı büyüme ve gelişme dönemlerindeki vücut ölçülerinden canlı ağırlık tahmininde kullanılan veri madenciliği algoritmalarının karşılaştırılması. Ziraat Müh, 375: 37-46.
  • Çelik S, Yılmaz 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 J Zool, 50(2): 575-583
  • Ertuğrul M. 1996. Küçükbaş hayvan yetiştirme uygulamalari. Ankara Üniv. Zir. Fak. Yay. No: 1446, 2. Baskı, Ankara, Türkiye, ss. 426.
  • 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 J Zool, 49(1): 257-265.
  • Eyduran E, Akın M, Eyduran SP. 2019. Application of multivariate adaptive regression splines through R software. Ankara Turkey: Nobel Academic Publishing.
  • Eyduran E. 2020. ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0. URL: https://CRAN.R-project.org/package= ehaGoF. (access date: September 10, 2022).
  • Gertz M, Große-Butenuth K, Junge W, Maassen-Francke B, Renner C, Sparenberg H, Krieter J. 2020. Using the XGBoost algorithm to classify neck and leg activity sensor data using on-farm health recordings for locomotor-associated diseases. Comput Electron Agric, 173: 105404.
  • Hastie T, Tibshirani R, Friedman JH. 2009. The elements of statistical learning: data mining, inference, and prediction. Springer, New York, US, pp. 758.
  • Huma ZE, Iqbal F. 2019. Predicting the body weight of Balochi sheep using a machine learning approach. Turkish J Vet Anim Sci, 43(4): 500-506.
  • Kayri M. 2016. Predictive abilities of bayesian regularization and LevenbergMarquardt algorithms in artificial neural networks: A comparative empirical study on social data. Math Comput Appl, 21(2): 20
  • Liaw A, Wiener M. 2022. Classification and regression by random forest. R News, 2(3): 18-22.
  • Louis-Tyasi T, Tshegofatso-Mkhonto A, Cyril-Mathapo M, Madikadike-Molabe K. 2021. Regression tree analysis to predict body weight of South African non-descript goats raised at Syferkuil farm, Capricorn district of South Africa. Biotechnol Anim Husb, 37(4), 293-304.
  • Ma X, Sha J, Wang D, Yu Y, Yang Q, Niu X. 2018. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electron Commer Res Appl, 31: 24–39.
  • Mathapo MC, Tyasi TL. 2021. Prediction of body weight of yearling boer goats from morphometric traits using classification and regression tree. Am. J. Anim. Vet. Sci, 16(2): 130-135.
  • Mathapo MC, Mugwabana TJ, Tyasi TL. 2022. Prediction of body weight from morphological traits of South African non-descript indigenous goats of Lepelle-Nkumbi Local Municipality using different data mining algorithm. Trop Anim Health Prod, 54(2): 1-9.
  • Pesmen G, Yardimci M. 2008. Estimating the live weight using some body measurements in Saanen goats. Arch zootech, 11(4): 30-40.
  • Pérez-Rodríguez P, Gianola D, Weigel KA, Rosa GJM, Crossa J. 2013. Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding. J Anim Sci, 91(8): 35223531.
  • R Core Team 2020. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL: https://www.R-project.org/ (access date: September 10, 2022).
  • Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, 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). Sci Total Environ, 476-477: 189-206.
  • Salawu EO, Abdulraheem M, Shoyombo A, Adepeju A, Davies S, Akinsola O, Nwagu B. 2014. Using artificial neural network to predict body weights of rabbits. Open J Anim Sci, 4: 182-186.
  • Şahin EH, Akmaz A. 2002. Farklı kesim ağırlıklarında Akkaraman kuzuların besi performansı, kesim ve karkas özellikleri. Vet Bil Derg, 18(3): 29-36.
  • Şahin Ö, Boztepe S. 2010. Anadolu Merinosu erkek kuzularında besi başı canlı ağırlığının besi performansı ve karkas karakterlerine etkisi I. besi performansı. Selcuk J Agr Food Sci, 24(4): 25-29.
  • Tırınk C. 2022. Comparison of Bayesian Regularized Neural Network, Random Forest Regression, Support Vector Regression and Multivariate Adaptive Regression Splines Algorithms to Predict Body Weight from Biometrical Measurements in Thalli Sheep. Kafkas Univ Vet Fak Derg, 28(3): 411-419.
  • Usman SM, Singh NP, Dutt T, Tiwari R, Kumar A. 2020. Comparative study of artificial neural network algorithms performance for prediction of FL305DMY in crossbred cattle. J Entomol Zool Stud, 8(5): 516-520.
  • 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. J Crop Prod., 4(3): 212-219.
  • Yakubu A. 2012. Application of regression tree methodology in predicting the body weight of Uda sheep. J Anim Sci Biotechnol, 45(2): 484-490.
  • Zaborski D, Ali M, Eyduran E, Grzesiak W, Tariq MM, Abbas F, Tırınk C. 2019. Prediction of selected reproductive traits of indigenous Harnai sheep under the farm management system via various data mining algorithms, Pakistan J Zool, 51(2): 421-431.
  • Zhang W, Goh ATC. 2016. Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front, 7(1): 45–52.
  • Zhong J, Sun Y, Peng W, Xie M, Yang J, Tang X. 2018. XGBFEMF: an XGBoost-based framework for essential protein prediction. IEEE Trans Nanobiosci 17(3): 243-250.
  • Zülkadir U, Şahin Ö, Aytekin İ, Boztepe S. 2008. Malya kuzularda canlı ağırlık ve bazı vücut ölçülerinin tekrarlanma dereceleri. Selçuk Üniv Zir Fak Derg, 22(45): 89-93.
Year 2023, Volume: 6 Issue: 1, 47 - 53, 01.01.2023
https://doi.org/10.47115/bsagriculture.1181444

Abstract

References

  • Akıllı A, Hülya A. 2020. Evaluation of normalization techniques on neural networks for the prediction of 305-day milk yield. Turk J Agr Eng Res, 1(2): 354-367.
  • Akkol S, Akıllı A, Cemal I. 2017. Comparison of artificial neural network and multiple linear regression for prediction of live weight in hair goats. Yyu J Agric Sci, 27(1): 21-29.
  • Ali M, Eyduran E, Tariq MM, Tırınk C, Abbas F, Bajwa MA, Baloch MH, Nizamani AH, Waheed A, Awan MA, Shah SH, 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 J Zool, 47(6): 1579-1585.
  • Altay Y. 2022. Prediction of the live weight at breeding age from morphological measurements taken at weaning in indigenous Honamli kids using data mining algorithms. Trop Anim Health Prod, 54(3): 1-12.
  • Abbas A, Ullah MA, Waheed A. 2021. Body weight prediction of thalli sheep reared in southern Punjab using different data mining algorithms: body weight prediction of thalli sheep. Proc Pak Acad Sci: A, 58(2): 29-38.
  • Aytekin İ, Karabacak A, Keskin İ. 2015. Akkaraman kuzuların besi performansı kesim ve karkas özellikleri. Selçuk Tar Bil Der, 2(1): 1-9.
  • Aytekin İ, Eyduran E, Karadas K, Aksahan R, Keskin İ. 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 J Zool, 50(1): 189-195.
  • Balta B, Topal M. 2020. Describing factors affecting birth weight and growth traıts in hemsın lambs using decision tree methods. J Anim Plant Sci, 30(3): 560-567.
  • Breiman L. 2001. Random forests. Mach Learn, 45(1): 5-32.
  • Boztepe S, Dağ B, Parlat SS, Yıldız AÖ, Aktaş AH. 1997. Yağlı kuyruklu kimi yerli ırk kuzuların besi performansı ve karkas özellikleri. Selçuk Üniv BAP No: ZF-95/064, Konya, Türkiye.
  • Carmona P, Climent F, Momparler A. 2019. Predicting failure in the US banking sector: an extreme gradient boosting approach. Int Rev Econ Finance, 61: 304-323.
  • Chen T, Guestrin C. 2016. Xgboost: a scalable tree boosting system. 2016. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining’16, August 13–17, San Francisco, CA, USA. 785-794.
  • Coşkun G, Şahin Ö, Özkan İA, Aytekin İ. 2022. Siyah Alaca sığırlarda farklı büyüme ve gelişme dönemlerindeki vücut ölçülerinden canlı ağırlık tahmininde kullanılan veri madenciliği algoritmalarının karşılaştırılması. Ziraat Müh, 375: 37-46.
  • Çelik S, Yılmaz 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 J Zool, 50(2): 575-583
  • Ertuğrul M. 1996. Küçükbaş hayvan yetiştirme uygulamalari. Ankara Üniv. Zir. Fak. Yay. No: 1446, 2. Baskı, Ankara, Türkiye, ss. 426.
  • 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 J Zool, 49(1): 257-265.
  • Eyduran E, Akın M, Eyduran SP. 2019. Application of multivariate adaptive regression splines through R software. Ankara Turkey: Nobel Academic Publishing.
  • Eyduran E. 2020. ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0. URL: https://CRAN.R-project.org/package= ehaGoF. (access date: September 10, 2022).
  • Gertz M, Große-Butenuth K, Junge W, Maassen-Francke B, Renner C, Sparenberg H, Krieter J. 2020. Using the XGBoost algorithm to classify neck and leg activity sensor data using on-farm health recordings for locomotor-associated diseases. Comput Electron Agric, 173: 105404.
  • Hastie T, Tibshirani R, Friedman JH. 2009. The elements of statistical learning: data mining, inference, and prediction. Springer, New York, US, pp. 758.
  • Huma ZE, Iqbal F. 2019. Predicting the body weight of Balochi sheep using a machine learning approach. Turkish J Vet Anim Sci, 43(4): 500-506.
  • Kayri M. 2016. Predictive abilities of bayesian regularization and LevenbergMarquardt algorithms in artificial neural networks: A comparative empirical study on social data. Math Comput Appl, 21(2): 20
  • Liaw A, Wiener M. 2022. Classification and regression by random forest. R News, 2(3): 18-22.
  • Louis-Tyasi T, Tshegofatso-Mkhonto A, Cyril-Mathapo M, Madikadike-Molabe K. 2021. Regression tree analysis to predict body weight of South African non-descript goats raised at Syferkuil farm, Capricorn district of South Africa. Biotechnol Anim Husb, 37(4), 293-304.
  • Ma X, Sha J, Wang D, Yu Y, Yang Q, Niu X. 2018. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electron Commer Res Appl, 31: 24–39.
  • Mathapo MC, Tyasi TL. 2021. Prediction of body weight of yearling boer goats from morphometric traits using classification and regression tree. Am. J. Anim. Vet. Sci, 16(2): 130-135.
  • Mathapo MC, Mugwabana TJ, Tyasi TL. 2022. Prediction of body weight from morphological traits of South African non-descript indigenous goats of Lepelle-Nkumbi Local Municipality using different data mining algorithm. Trop Anim Health Prod, 54(2): 1-9.
  • Pesmen G, Yardimci M. 2008. Estimating the live weight using some body measurements in Saanen goats. Arch zootech, 11(4): 30-40.
  • Pérez-Rodríguez P, Gianola D, Weigel KA, Rosa GJM, Crossa J. 2013. Technical Note: An R package for fitting Bayesian regularized neural networks with applications in animal breeding. J Anim Sci, 91(8): 35223531.
  • R Core Team 2020. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. URL: https://www.R-project.org/ (access date: September 10, 2022).
  • Rodriguez-Galiano V, Mendes MP, Garcia-Soldado MJ, 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). Sci Total Environ, 476-477: 189-206.
  • Salawu EO, Abdulraheem M, Shoyombo A, Adepeju A, Davies S, Akinsola O, Nwagu B. 2014. Using artificial neural network to predict body weights of rabbits. Open J Anim Sci, 4: 182-186.
  • Şahin EH, Akmaz A. 2002. Farklı kesim ağırlıklarında Akkaraman kuzuların besi performansı, kesim ve karkas özellikleri. Vet Bil Derg, 18(3): 29-36.
  • Şahin Ö, Boztepe S. 2010. Anadolu Merinosu erkek kuzularında besi başı canlı ağırlığının besi performansı ve karkas karakterlerine etkisi I. besi performansı. Selcuk J Agr Food Sci, 24(4): 25-29.
  • Tırınk C. 2022. Comparison of Bayesian Regularized Neural Network, Random Forest Regression, Support Vector Regression and Multivariate Adaptive Regression Splines Algorithms to Predict Body Weight from Biometrical Measurements in Thalli Sheep. Kafkas Univ Vet Fak Derg, 28(3): 411-419.
  • Usman SM, Singh NP, Dutt T, Tiwari R, Kumar A. 2020. Comparative study of artificial neural network algorithms performance for prediction of FL305DMY in crossbred cattle. J Entomol Zool Stud, 8(5): 516-520.
  • 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. J Crop Prod., 4(3): 212-219.
  • Yakubu A. 2012. Application of regression tree methodology in predicting the body weight of Uda sheep. J Anim Sci Biotechnol, 45(2): 484-490.
  • Zaborski D, Ali M, Eyduran E, Grzesiak W, Tariq MM, Abbas F, Tırınk C. 2019. Prediction of selected reproductive traits of indigenous Harnai sheep under the farm management system via various data mining algorithms, Pakistan J Zool, 51(2): 421-431.
  • Zhang W, Goh ATC. 2016. Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geosci Front, 7(1): 45–52.
  • Zhong J, Sun Y, Peng W, Xie M, Yang J, Tang X. 2018. XGBFEMF: an XGBoost-based framework for essential protein prediction. IEEE Trans Nanobiosci 17(3): 243-250.
  • Zülkadir U, Şahin Ö, Aytekin İ, Boztepe S. 2008. Malya kuzularda canlı ağırlık ve bazı vücut ölçülerinin tekrarlanma dereceleri. Selçuk Üniv Zir Fak Derg, 22(45): 89-93.
There are 42 citations in total.

Details

Primary Language English
Subjects Agricultural Engineering
Journal Section Research Articles
Authors

Gizem Coşkun 0000-0003-2519-7885

Özcan Şahin 0000-0003-2170-2055

Yasin Altay 0000-0003-4049-8301

İbrahim Aytekin 0000-0001-7769-0685

Publication Date January 1, 2023
Submission Date September 28, 2022
Acceptance Date December 6, 2022
Published in Issue Year 2023 Volume: 6 Issue: 1

Cite

APA Coşkun, G., Şahin, Ö., Altay, Y., Aytekin, İ. (2023). Final Fattening Live Weight Prediction in Anatolian Merinos Lambs from Some Body Characteristics at the Initial of Fattening by Using Some Data Mining Algorithms. Black Sea Journal of Agriculture, 6(1), 47-53. https://doi.org/10.47115/bsagriculture.1181444

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