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Estimation of carcass weight of Hanwoo (Korean native cattle) as a function of body measurements using statistical models and a neural network

  • Lee, Dae-Hyun (Department of Biosystems Machinery Engineering, Collage of Agricultural and Life Science, Chungnam National University) ;
  • Lee, Seung-Hyun (Department of Biosystems Machinery Engineering, Collage of Agricultural and Life Science, Chungnam National University) ;
  • Cho, Byoung-Kwan (Department of Biosystems Machinery Engineering, Collage of Agricultural and Life Science, Chungnam National University) ;
  • Wakholi, Collins (Department of Biosystems Machinery Engineering, Collage of Agricultural and Life Science, Chungnam National University) ;
  • Seo, Young-Wook (National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Cho, Soo-Hyun (Animal Products Utilization Division, National Institute of Animal Science, Rural Development Administration) ;
  • Kang, Tae-Hwan (Major in Bio-Industry Mechanical Engineering, Kongju National University) ;
  • Lee, Wang-Hee (Department of Biosystems Machinery Engineering, Collage of Agricultural and Life Science, Chungnam National University)
  • Received : 2019.09.26
  • Accepted : 2019.12.03
  • Published : 2020.10.01

Abstract

Objective: The objective of this study was to develop a model for estimating the carcass weight of Hanwoo cattle as a function of body measurements using three different modeling approaches: i) multiple regression analysis, ii) partial least square regression analysis, and iii) a neural network. Methods: Data from a total of 134 Hanwoo cattle were obtained from the National Institute of Animal Science in South Korea. Among the 372 variables in the raw data, 20 variables related to carcass weight and body measurements were extracted to use in multiple regression, partial least square regression, and an artificial neural network to estimate the cold carcass weight of Hanwoo cattle by any of seven body measurements significantly related to carcass weight or by all 19 body measurement variables. For developing and training the model, 100 data points were used, whereas the 34 remaining data points were used to test the model estimation. Results: The R2 values from testing the developed models by multiple regression, partial least square regression, and an artificial neural network with seven significant variables were 0.91, 0.91, and 0.92, respectively, whereas all the methods exhibited similar R2 values of approximately 0.93 with all 19 body measurement variables. In addition, relative errors were within 4%, suggesting that the developed model was reliable in estimating Hanwoo cattle carcass weight. The neural network exhibited the highest accuracy. Conclusion: The developed model was applicable for estimating Hanwoo cattle carcass weight using body measurements. Because the procedure and required variables could differ according to the type of model, it was necessary to select the best model suitable for the system with which to calculate the model.

Keywords

References

  1. Anderson RV, Rasby RJ, Klopfenstein TJ, Clark RT. An evaluation of production and economic efficiency of two beef systems from calving to slaughter. J Anim Sci 2005;83:694-704. https://doi.org/10.2527/2005.833694x
  2. Selk GE, Wettemann RP, Lusby KS, et al. Relationships among weight change, body condition and reproductive performance of range beef cows. J Anim Sci 1998;66;12:3153-9. https://doi.org/10.2527/jas1988.66123153x
  3. Kause A, Mikkola L, Stranden I, Sirkko K. Genetic parameters for carcass weight, conformation and fat in five beef cattle breeds. Animal 2015;9;1:35-42. https://doi.org/10.1017/S1751731114001992
  4. Yan T, Mayne CS, Patterson DC, Agnew RE. Prediction of body weight and empty body composition using body size measurements in lactating dairy cows. Livest Sci 2009;124:233-41. https://doi.org/10.1016/j.livsci.2009.02.003
  5. Ozkaya S, Bozkurt Y. The relationship of parameters of body measures and body weight by using digital image analysis in pre-slaughter cattle. Arch Anim Breed 2008;51:120-8. https://doi.org/10.5194/aab-51-120-2008
  6. Alberti P, Panea B, Sanudo C, et al. Live weight, body size and carcass characteristics of young bulls of fifteen European breeds. Livest Sci 2008;114:19-30. https://doi.org/10.1016/j.livsci.2007.04.010
  7. Koenen EPC, Groen AF. Genetic evaluation of body weight of lactating Holstein heifers using body measurements and conformation traits. J Dairy Sci 1998;81:1709-13. https://doi.org/10.3168/jds.S0022-0302(98)75738-8
  8. Heinrichs AJ, Rogers GW, Cooper JB. Predicting body weight and wither height in Holstein heifers using body measurements. J Dairy Sci 1992;75:3576-81. https://doi.org/10.3168/jds.S0022-0302(92)78134-X
  9. Ozkaya S, Bozkurt Y. The accuracy of prediction of body weight from body measurements in beef cattle. Archiv Anim Breed 2009;52:371-7. https://doi.org/10.5194/aab-52-371-2009
  10. Tasdemir S, Urkmez A, Inal S. Determination of body measurements on the Holstein cows using digital image analysis and estimation of live weight with regression analysis. Comput Electron Agric 2011;76:189-97. https://doi.org/10.1016/j.compag.2011.02.001
  11. Haryoko I, Suparman P. Evaluation of carcass production of PO cattle based on heart girth measurement, body condition score and slaughter weight. Anim Prod 2009;11:28-33.
  12. Shim JY, Kim HY, Cho BK, et al. Multivariate analysis of deboning data for classifying Hanwoo (Korean native cattle) by gender. Curr Sci 2018;114:1075-82. https://doi.org/10.18520/cs/v114/i05/1075-1082
  13. Huh D. A study on result appraisement and effects estimation of Korean cattle improvement policy. J Rural Dev 2003;26:63-79. https://doi.org/10.22004/ag.econ.288195
  14. Lee JJ, Choi SD, Dang CG, Kang SN, Kim NS. The effect of carcass traits on economic values in Hanwoo. Food Sci Anim Resour 2011;31:603-8. https://doi.org/10.5851/kosfa.2011.31.4.603
  15. Lee JG, Lee SS, Cho KH, et al. Estimation of primal cuts yields by using body size traits in Hanwoo steer. J Anim Sci Technol 2013;55:373-80. https://doi.org/10.5187/JAST.2013.55.5.373
  16. Choy YH, Choi SB, Jeon GJ, et al. Prediction of retail beef yield using parameters based on Korean beef carcass grading standards. Food Sci Anim Resour 2010;30:905-9. https://doi.org/10.5851/kosfa.2010.30.6.905
  17. Lee JG, Lee SS, Cho KH, et al. Correlation analyses on body size traits, carcass traits and primal cuts in Hanwoo steers. J Anim Sci Technol 2013;55:351-8. https://doi.org/10.5187/JAST.2013.55.5.351
  18. Ha DW, Kim HC, Kim BW, et al. A study on the body type of Hanwoo (Korean cattle) steer by using principal components analysis. J Anim Sci Technol 2002;44:643-52. https://doi.org/10.5187/JAST.2002.44.6.643
  19. Sun DW, Kim BW, Park JW, et al. The effect of body measurements type on carcass traits in Hanwoo. J Anim Sci Technol 2008;50:763-74. https://doi.org/10.5187/JAST.2008.50.6.763
  20. Kim JH, Ba HV, Seong PN, et al. Carcass characteristics and primal cuts yields by live weight of Hanwoo steers in Gyeongbuk province. J Agric Life Sci 2018;52:151-67. https://doi.org/10.14397/jals.2018.52.2.151
  21. Montgomery DC, Runger GC. Applied statistics and probability for engineers. 7th ed. Hoboken, NJ, USA: Wiley; 2018.
  22. Wold S, Ruhe A, Wold H, Dunn III WJ. The collinearity problem in linear regression. The partial least squares (PLS) approach to generalized inverses. SIAM J Sci Stat Comput 1984;5:735-43. https://doi.org/10.1137/0905052
  23. Xu Y, Hu W, Yang Z, Xu C. A multivariate partial least squares approach to joint association analysis for multiple correlated traits. Crop J 2016;4:21-9. https://doi.org/10.1016/j.cj.2015.11.001
  24. Ahmed RM, Yasmin J, Lee WH, Mo CY, Cho BK. Imaging technologies for nondestructive measurement of internal properties of agricultural products: a review. J Biosyst Eng 2017;42:199-216. https://doi.org/10.5307/JBE.2017.42.3.199
  25. Zupan J. Introduction to artificial neural network (ANN) methods: what they are and how to use them. Acta Chim Slov 1994;41:327-52.
  26. Lee KY, Kim KH, Kang JJ, et al. Comparison and analysis of linear regression & artificial neural network. Int J Appl Eng Res 2017;12:9820-5.
  27. Bartoldson BR, Morcos AS, Barbu A, Erlebacher G. The generalization-stability trade off in neural network pruning. arXiv Prepr 2020;arXiv:1906.03728.
  28. Afifi A, May S, Clark VA. Practical multivariate analysis. 5th ed. Boca Raton, FL, USA: CRC Press; 2011.
  29. Chen S, Billings SA, Grant PM. Non-linear system identification using neural networks. Int J Control 1990;51:1191-214. https://doi.org/10.1080/00207179008934126
  30. Felfoldi J, Baranyai L, Firtha F, Friedrich L, Balla Cs. Image processing based method for characterization of the fat/meat ratio and fat distribution of pork and beef samples. Prog Agric Eng Sci 2013;9:27-53. https://doi.org/10.1556/progress.9. 2013.2
  31. Puzio N, Purwin C, Nogalski Z, Bialobrzewski I, Tomczyk L, Michalski JP. The effects of age and gender (bull vs steer) on the feeding behavior of young beef cattle fed grass silage. Asian-Australas J Anim Sci 2019;32:1211-8. https://doi.org/10.5713/ajas.18.0698

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