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New management grading for pig farms: management grading system using pig carcass weight, back fat thickness and k-means algorithm

  • Youngho Lim (Department of Animal Science, Chungbuk National University) ;
  • Jaeyoung Kim (Department of Animal Science, Chungbuk National University) ;
  • Gwantae Kim (Department of Animal Science, Chungbuk National University) ;
  • Jungseok Choi (Department of Animal Science, Chungbuk National University)
  • Received : 2024.05.24
  • Accepted : 2024.07.26
  • Published : 2025.02.01

Abstract

Objective: This study categorized farm management levels to improve the productivity and uniformity of pork from pigs shipped from farms. Methods: A total of 48,298 pigs were grouped (A, B, C, D group) using the k-means algorithm, carcass weight and backfat thickness. The results of the grouping were used to classify Farm Management Grades (A, B, C, D grade). Results: The proportion of primal cuts in pigs, according to the new classification method, increased from group A to group D for shoulder blade, shoulder picnic, and ham, but decreased for loin and belly. In the regression analysis of the five primal cuts (shoulder blade, shoulder picnic, loin, belly, and ham) production (kg) for each group, all regression equations showed low errors (MAE<0.7), indicating that the model can predict the production of primal cuts by group. As the Farm Management Grade decreased, the proportion of pigs in the group with large differences from the mean of carcass weight and backfat thickness of the whole pig increased. Conclusion: The results of this study confirmed the differences in primal cut traits by pig grouping and created a method to classify farms who ship non-uniform pigs. This is expected to provide indicators for improvement and supplementation to farms that ship uneven pigs, helping to enhance the production of standardized pigs at the farm level.

Keywords

Acknowledgement

This work was supported by the Bugyeong Pig Farmers Cooperative. This work was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE)(2021RIS-001).

References

  1. USDA. United States standards for grades of slaughter cattle. Washington, DC, USA: USDA;1996. 
  2. Liu J, Chriki S, Ellies-Oury MP, et al. European conformation and fat scores of bovine carcasses are not good indicators of marbling. Meat Sci 2020;170:108233. https://doi.org/10.1016/j.meatsci.2020.108233 
  3. Čandek-Potokar M, Lebret B, Gispert M, Font-i-Furnols M. Challenges and future perspectives for the European grading of pig carcasses–a quality view. Meat Sci 2024;208:109390. https://doi.org/10.1016/j.meatsci.2023.109390 
  4. Wang L, Li D. — Invited Review — Current status, challenges and prospects for pig production in Asia. Anim Biosci 2024; 37:742-54. https://doi.org/10.5713/ab.23.0303 
  5. KAPE. 2022 Livestock products distribution information survey report. Sejong, Korea: Institute for Animal Products Quality Evaluation; 2023. No 11-B552679-000003-10. 
  6. MAFRA. Statistical yearbook of agriculture, food and rural affairs. Sejong, Korea: Ministry of Agriculture, Food and Rural Affairs; 2023. No. 11-1543000-000261-10.
  7. MAFRA. Statistical yearbook of agriculture, food and rural affairs. Sejong, Korea: Ministry of Agriculture, Food and Rural Affairs; 2017. No. 11-1543000-000261-10. 
  8. KAPE. 2014 Livestock product distribution status. Gunpo, Korea: Institute for Animal Products Quality Evaluation; 2014. No. 11-B552679-000003-10. 
  9. Yongcheol K. Fundamental measures must be taken to address the problem of excessive pork belly intramuscular fat. Meat J 2023;374:54-7. 
  10. Byeongmoo H. [Special Feature 1] Development and challenges of domestic pig brands 2020. Seoul, Korea: Farminsight; c2020 [cited 2024 May 3]. Available from: https://www.farminsight.net/news/ articleView.html?idxno=6902 
  11. Jeong BG. Direction of government support for farmers producing standard pigs. Korea Swine J 1998;20:118-20. 
  12. Ministry of Agriculture, Food and Rural Affairs (MAFRA). Detailed criteria for livestock product grading, Amendment No. 2023-102. Sejong, Korea: MAFRA; 2023. 
  13. Oh SH, See M. Pork preference for consumers in China, Japan and South Korea. Asian-Australas J Anim Sci 2012;25:143-50. https://doi.org/10.5713/ajas.2011.11368 
  14. Haneul P. Korean pork industry spurs premiumization strategy... Pork grading system supplementation 'starts'2023. Seoul, Korea: Nongmin News Corp.; c2023 [cited 2024 May 3]. Available from: https://www.nongmin.com/article/20230202500238 
  15. Dohyeon K, Junheon H. 20 years since the pork grading system was implemented... Consumers ask, "What is that?" Is it effective? Daegu, Korea: Maeil; c2023 [cited 2024 May 3]. Available from: https://www.imaeil.com/page/view/2023112814402150734 
  16. Kim GT, Kang SJ, Yoon YG, Kim HS, Lee WY, Yoon SH. Introduction of automatic grading and classification machine and operation status in Korea. Korean Soc Food Sci Anim Resour 2017;6:34-45. 
  17. Park Y, Kim K, Kim J, Seo J, Choi J. Verification of reproducibility of VCS2000 equipment for mechanical measurement of Korean Landrace× Yorkshire (F1), F1× Duroc (LYD) pig carcasses. Food Sci Anim Resour 2023;43:553-62. https://doi.org/10.5851/kosfa.2023.e17 
  18. Kim J, Han HD, Lee WY, et al. Economic analysis of the use of vcs2000 for pork carcass meat yield grading in Korea. Animals 2021;11:1297. https://doi.org/10.3390/ani11051297 
  19. Lohumi S, Wakholi C, Baek JH, et al. Nondestructive estimation of lean meat yield of South Korean pig carcasses using machine vision technique. Korean J Food Sci Anim Resour 2018;38:1109-19. https://doi.org/10.5851/kosfa.2018.e44 
  20. Tang J, Wang D, Zhang Z, He L, Xin J, Xu Y. Weed identification based on k-means feature learning combined with convolutional neural network. Comput Electron Agric 2017; 135:63-70. https://doi.org/10.1016/j.compag.2017.01.001 
  21. Bansal A, Sharma M, Goel S. Improved k-mean clustering algorithm for prediction analysis using classification technique in data mining. Int J Comput Appl 2017;157:35-40. https://doi.org/10.5120/ijca2017912719 
  22. Géron A. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow. Sebastopol, CA, USA: O'Reilly Media, Inc.; 2022. 
  23. McKinney W. Data structures for statistical computing in Python. In: SciPy 2010: Proceedings of the 9th Python in Science Conference; 2010 Jun 28 - Jul 3. Austin, TX, USA. pp. 56-61. https://doi.org/10.25080/Majora-92bf1922-00a 
  24. Harris CR, Millman KJ, Van Der Walt SJ, et al. Array programming with NumPy. Nature 2020;585:357-62. https://doi.org/10.1038/s41586-020-2649-2 
  25. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011;12:2825-30. 
  26. Virtanen P, Gommers R, Oliphant TE, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods 2020;17:261-72. https://doi.org/10.1038/s41592-019-0686-2 
  27. Seabold S, Perktold J. Statsmodels: econometric and statistical modeling with python. In: SciPy 2010: Proceedings of the 9th Python in Science Conference; 2010 Jun 28 - Jul 3. Austin, TX, USA. pp. 92-6. https://doi.org/10.25080/Majora-92bf1922-011 
  28. KAPE. 2021 Animal products grading statistical yearbook. Sejong, Korea: Institute for Animal Products Quality Evaluation; 2022. No. 11-B552679-000006-10 
  29. Yeonbok J. Understanding pork carcass grading standards and suggesting high-quality pork production plans. Seoul, Korea: Pig Pork Korean Pork c2020 [cited 2024 May 3]. pp. 392-7. Available from: https://www.pignpork.com/news/articleView.html?idxno=1543 
  30. Hutagalung J, Ginantra NLWSR, Bhawika GW, Parwita WGS, Wanto A, Panjaitan PD. Covid-19 cases and deaths in southeast Asia clustering using k-means algorithm. J Phys Conf Ser 2021;1783:012027. https://doi.org/10.1088/1742-6596/1783/1/012027 
  31. Hananto AL, Assiroj P, Priyatna B, et al. Analysis of drug data mining with clustering technique using k-means algorithm. J Phys Conf Ser 2021;1908:012024. https://doi.org/10.1088/1742-6596/1908/1/012024 
  32. Irawan Y. Implementation of data mining for determining majors using k-means algorithm in students of sma negeri 1 pangkalan kerinci. J Appl Eng Technol Sci 2019;1:17-29. https://doi.org/10.37385/jaets.v1i1.18 
  33. Javadi S, Hashemy SM, Mohammadi K, Howard KWF, Neshat A. Classification of aquifer vulnerability using k-means cluster analysis. J Hydrol 2017;549:27-37. https://doi.org/10.1016/j.jhydrol.2017.03.060 
  34. KAPE. 2022 Animal Products Grading Statistical Yearbook. Sejong, Korea: Institute for Animal Products Quality Evaluation; 2023. No. 11-B552679-000006-10 
  35. Park Y, Ko E, Park K, et al. Correlation between the Korean pork grade system and the amount of pork primal cut estimated with AutoFom III. J Anim Sci Technol 2022;64:135-42. https://doi.org/10.5187/jast.2021.e135 
  36. Renaud O, Victoria-Feser MP. A robust coefficient of determination for regression. J Stat Plan Inference 2010;140:1852-62. https://doi.org/10.1016/j.jspi.2010.01.008 
  37. Willmott CJ, Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim Res 2005;30: 79-82. https://doi.org/10.3354/cr030079