Application of the Different Machine Learning Algorithms to Predict Dry Matter Intake in Feedlot Cattle

Year 2025, Volume: 31 Issue: 1, 91 - 99, 14.01.2025

Özgür Koşkan , Malik Ergin , Hayati Köknaroğlu

Abstract

Due to the development of computing technology and different machine learning models, big data sets have gained importance in animal science as well as in many disciplines. The main objective of this study was to compare different machine learning algorithms to predict daily dry matter intake (DMI) in feedlot cattle. The data consisted of 2660 cattle pens placed on feed between January 1988 and December 1997. Machine learning methods were compared in heifers and steers, with 718 in pens of heifers and 1942 in pens of steers. Initial body weight, days on feed, and average proportion of dietary concentrate were used as independent variables to predict DMI in steers and heifers separately. The multivariate linear regression (LR), random forest (RF), gradient boosting regressor (GBR), and light gradient boosting machine (LGBR) algorithms were compared in terms of several performance metrics (MAE, MAPE, MSE, and RMSE). Results showed that the determination coefficient alone is not a good single criterion. It is recommended that the interpretation of model consistency should also consider MAE, MAPE, MSE, and RMSE values. In the current study, all machine learning algorithms yielded similar and lower performance metrics. However, the LGBR and GBR algorithms, were found to perform slightly better than the other algorithms, especially in heifers. Increasing the number of animals and using different independent variables that are related to the DMI can affect the accuracy of DMI prediction.

Keywords

Bigdata, Feedlot cattle, Machine learning algorithms.

References

• Asadzadeh N, Bitaraf D E, Shams H J, Zare M, Khojestehkey S, Abbaasi S & Shafie N (2021). Body weight prediction of dromedary camels using the machine learning models. Iranian Journal of Applied Animal Science 11(3): 605-614
• Atalay M & Çelik E (2017) Artificial intelligence and machine learning applications in big data analysis. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 9(22): 155-172
• Blake N E, Walker M, Plum S, Hubbart J A, Hatton J, Mata-Padrino D, Holásková I & Wilson M E (2023). Predicting dry matter intake in beef cattle. Journal of Animal Science 101: skad269
• Bovo M, Agrusti M, Benni S, Torreggiani D & Tassinari P (2021). Random forest modelling of milk yield of dairy cows under heat stress conditions. Animals 11(5): 1305
• Breiman L & Cutler A (2015). Random forest. Retrieved June 23, 2015, from https://www.stat.berkeley.edu/~breiman/RandomForests/cc_home.htm
• Breiman L. (2001). Random forests. In: Blockeel H & Leuven K U (Eds.), Machine Learning, Scientific Research Publishing, New York, pp. 5-32
• Celik S & Yılmaz O (2017). Comparison of different data mining algorithms for prediction of body weight from several morphological measurements in dogs. Journal of Animal and Plant Sciences 27(1): 57-64
• Çelik Ş & Yılmaz O (2023). Investigation of the Relationships between Coat Colour, Sex, and Morphological Characteristics in Donkeys Using Data Mining Algorithms. Animals 13(14): 2366. https://doi.org/10.3390/ani13142366
• Chen T, Xu J, Ying H, Chen X, Feng R, Fang X, Gao H & Wu J (2019). Prediction of extubation failure for intensive care unit patients using light gradient boosting machine. Institute of Electrical and Electronics Engineers 7: 960-968
• Cutler A, Cutler D R & Stevens J R (2012). Ensemble Machine Learning: Methods and Applications. In Zhang C. & Ma Y. (Eds.), Random forests (pp. 157–175) Springer
• Defalque G, Santos R, Bungenstab D, Echeverria D, Dias A & Defalque C (2024). Machine learning models for dry matter and biomass estimates on cattle grazing systems. Computers and Electronics in Agriculture 216: 108520
• Di Persio L & Fraccarolo N (2023). Energy consumption forecasts by gradient boosting regression trees. Mathematics 11(5): 1068
• Hastie T, Tibshirani R & Friedman J H (2009). The elements of statistical learning: Data mining, inference, and prediction (2nd ed., pp. 1-758). Springer
• Hicks R B, Owens F N, Gill D R, Oltjen J W & Lake R P (1990). Daily dry matter intake by feedlot cattle: influence of breed and gender. Journal of Animal Science 68(1): 245-253
• Hong W (2015). Wavelet Gradient Boosting Regression Method Study in Short-Term Load Forecasting. Smart Grid 5: 189–196
• Huma Z E & Iqbal F (2019). Predicting the body weight of Balochi sheep using a machine learning approach. Turkish Journal of Veterinary & Animal Sciences 43(4): 500-506
• Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q & Liu T Y (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems 30: 3146–3154
• Koknaroglu H, Demircan V & Yilmaz H (2017). Effect of initial weight on beef cattle performance and profitability. Agronecio 13(1): 26-38
• Koknaroglu H, Loy D D, Wilson D E, Hoffman M P & Lawrence J D (2005). Factors affecting beef cattle performance and profitability. The Professional Animal Scientist 21(4): 286-296
• Koşkan O, Koknaroglu H, Loy D D & Hoffman M P (2014). Predicting dry matter intake of steers and heifers in the feedlot by using categorical and continuous variables. In: American Society of Animal Science Annual Meeting, 20 – 24 July, Kansas City, Missouri, USA, pp. 721-721
• Lahart B, McParland S, Kennedy E, Boland T M, Condon T, Williams M, Galvin N, McCarthy B & Buckley F (2019). Predicting the dry matter intake of grazing dairy cows using infrared reflectance spectroscopy analysis. Journal of dairy science 102(10): 8907-8918
• Mammadova N & Keskin I (2013). Application of the support vector machine to predict subclinical mastitis in dairy cattle. The Scientific World Journal 2013: 897-906
• Mikail N, Keskin I & Altay Y (2014). The use of artificial neural networks and support vector machines methods in milk yield prediction of holstein cows. In: Proceedings of the International Mesopotamia Agriculture Congress, 22 – 25 September, Diyarbakir, 1137 pp
• Müller A C & Guido S (2016). Introduction to Machine Learning with Python: A Guide For Data Scientists. O'Reilly Media, USA. National Academies of Sciences, Engineering, and Medicine (NASEM) (2016). Nutrient Requirements of Beef Cattle, 8th revised edn. Washington, DC: The National Academies Press
• Ogutu J O, Piepho H P & Schulz-Streeck T (2011). A comparison of random forests, boosting and support vector machines for genomic selection. BMC proceedings 5: 1-5
• Otchere D A, Ganat T O A, Ojero J O, Tackie-Otoo B N & Taki M Y (2022). Application of gradient boosting regression model for the evaluation of feature selection techniques in improving reservoir characterisation predictions. Journal of Petroleum Science and Engineering 109: 244-254
• PyCaret (2020). An Open Source, Low-Code Machine Learning Library in Python. Retrieved in August, 23, 2023 from https://pycaret.org/ R Core Team (2024). R: A language and environment for statistical computing.
• R Foundation for Statistical Computing. https://www.R-project.org/ Ray S (2019). A quick review of machine learning algorithms. In: International conference on machine learning, big data, cloud, and parallel computing, 14 – 16 February, Faridabad, India, pp. 35-39
• Refaeilzadeh P, Tang L & Liu H (2016). Cross – Validation. Springer, New York. Salleh S M, Danielsson R & Kronqvist C (2023). Using machine learning methods to predict dry matter intake from milk mid-infrared spectroscopy data on Swedish dairy cattle. Journal of Dairy Research 90(1): 5-8
• Shadpour S, Chud T C, Hailemariam D, Oliveira H R, Plastow G, Stothard P, Lassen J, Baldwin R, Miglior F, Baes C F Tulpan D & Schenkel F S (2022). Predicting dry matter intake in Canadian Holstein dairy cattle using milk mid-infrared reflectance spectroscopy and other commonly available predictors via artificial neural networks. Journal of dairy science 105(10): 8257-8271
• Sibindi R, Mwangi R W & Waititu A G (2023). A boosting ensemble learning based hybrid light gradient boosting machine and extreme gradient boosting model for predicting house prices. Engineering Reports 5(4): e12599.
• Sun X, Liu M & Sima Z (2020). A novel cryptocurrency price trend forecasting model based on LightGBM. Finance Research Letters 32: 101084