The practical detection and correction for the test-day milk yields records of dairy cows

Jian Yang; Xiuxin Zhao; Xiao Wang; Lingling Wang; Guanghui Xue; Yan Liu; Zhaowei Yuan; Fen Pei; Xiaoman Li; Xueyan Lin; Yundong Gao; Jianbin Li; Jian Yang; Xiuxin Zhao; Xiao Wang; Lingling Wang; Guanghui Xue; Yan Liu; Zhaowei Yuan; Fen Pei; Xiaoman Li; Xueyan Lin; Yundong Gao; Jianbin Li

doi:10.3934/aas.2025005

AIMS Animal Science

2025, Volume 1, Issue 1: 51-64. doi: 10.3934/aas.2025005

Previous Article Next Article

Research article

The practical detection and correction for the test-day milk yields records of dairy cows

1.
Institute of Animal Science and Veterinary Medicine, Shandong Academy of Agricultural Sciences, Jinan, China
2.
College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Tai'an, China
3.
Shandong OX Livestock Breeding Industry Co., Ltd, Jinan, China

Received: 13 May 2025 Revised: 28 August 2025 Accepted: 01 September 2025 Published: 08 September 2025

In this study, we applied a detection and correction approach to identify and adjust abnormal test-day milk yield records of Holstein dairy cows from the Dairy Herd Improvement (DHI) database in Shandong Province, China. By dividing the lactation period into ascending and descending phases, we used adjacent test-day data to predict expected yields and correct deviations through multiple regression analysis. The correction improved lactation curve fitting and 305-day milk yield calculations, which was crucial for generating accurate and consistent data. These improvements enhanced the reliability of genetic evaluations and herd management decisions by providing a clearer representation of milk production patterns. Following correction, data variability was reduced, as reflected by a decreased coefficient of variation. Additionally, the lactation curves more accurately captured the natural progression of milk yield, from gradual increase to peak and subsequent decline. The study highlights the importance of precise data correction for maintaining high-quality DHI records, ultimately supporting better genetic and production assessments in dairy cattle.
- DHI,
- Abnormal milk yield,
- evaluation
Citation: Jian Yang, Xiuxin Zhao, Xiao Wang, Lingling Wang, Guanghui Xue, Yan Liu, Zhaowei Yuan, Fen Pei, Xiaoman Li, Xueyan Lin, Yundong Gao, Jianbin Li. The practical detection and correction for the test-day milk yields records of dairy cows[J]. AIMS Animal Science, 2025, 1(1): 51-64. doi: 10.3934/aas.2025005

Related Papers:

Abstract

In this study, we applied a detection and correction approach to identify and adjust abnormal test-day milk yield records of Holstein dairy cows from the Dairy Herd Improvement (DHI) database in Shandong Province, China. By dividing the lactation period into ascending and descending phases, we used adjacent test-day data to predict expected yields and correct deviations through multiple regression analysis. The correction improved lactation curve fitting and 305-day milk yield calculations, which was crucial for generating accurate and consistent data. These improvements enhanced the reliability of genetic evaluations and herd management decisions by providing a clearer representation of milk production patterns. Following correction, data variability was reduced, as reflected by a decreased coefficient of variation. Additionally, the lactation curves more accurately captured the natural progression of milk yield, from gradual increase to peak and subsequent decline. The study highlights the importance of precise data correction for maintaining high-quality DHI records, ultimately supporting better genetic and production assessments in dairy cattle.

References

[1]	Hudson CD, Green MJ (2018) Associations between routinely collected Dairy Herd Improvement data and insemination outcome in UK dairy herds. J Dairy Sci 101: 11262–11274. https://doi.org/10.3168/jds.2017-13962 doi: 10.3168/jds.2017-13962
[2]	Koorn D (1998) The introduction of a milk recording system based on milk yields of ICAR approved electronic milk meters and AT4 sampling for fat, protein and SCC. Public-Europ Assoc Anim Product 91: 147–150. https://doi.org/10.1163/9789004684010_026 doi: 10.1163/9789004684010_026
[3]	Bünger A, Kuwan K, Reinhardt F, Brahmstaedt H-U, Reents R (2005) Performance Recording of Animals-State of the Art, 2004. Wageningen Academic, 229–235. https://doi.org/10.3920/9789086865369_038
[4]	Gao H, Madsen P, Pösö J, Aamand G, Lidauer M, Jensen J (2018) Multivariate outlier detection for routine Nordic dairy cattle genetic evaluation in the Nordic Holstein and Red population. J. Dairy Sci. 101: 11159–11164. https://doi.org/10.3168/jds.2018-15123
[5]	Madsen P, Pösö J, Pedersen J, Lidauer M, Jensen J (2012) Screening for outliers in multiple trait genetic evaluation. Interbull Bulletin 46.
[6]	Lamanna M, Bovo M, Cavallini D (2025) Wearable collar technologies for dairy cows: A systematized review of the current applications and future innovations in precision livestock farming. Animals 15: 458. https://doi.org/10.3390/ani15030458 doi: 10.3390/ani15030458
[7]	Cavallini D, Giammarco M, Buonaiuto G, Vignola G, De Matos Vettori J, Lamanna M, et al., (2025) Two years of precision livestock management: Harnessing ear tag device behavioral data for pregnancy detection in free-range dairy cattle on silage/hay-mix ration. Front. Animal Sci. 6: 1547395. https://doi.org/10.3389/fanim.2025.1547395
[8]	Wiggans G, Vanraden P, Philpot J (2003) Detection and adjustment of abnormal test-day yields. J Dairy Sci 86: 2721–2724. https://doi.org/10.3389/fanim.2025.1547395 doi: 10.3389/fanim.2025.1547395
[9]	Group IDCMRW (2017) Section 2-Guidelines for Dairy Cattle Milk Recording.
[10]	Wood P (1967) Algebraic model of the lactation curve in cattle. Nature 216: 164–165. https://doi.org/10.1038/216164a0 doi: 10.1038/216164a0
[11]	Gillon A, Abras S, Mayeres P, Bertozzi C, Gengler N (2010) Adding value to test-day data by using modified best prediction method. ICAR Technical Series 14.
[12]	Liseune A, Salamone M, Van Den Poel D, Van Ranst B, Hostens M (2021) Predicting the milk yield curve of dairy cows in the subsequent lactation period using deep learning. Comput Electron Agri 180:105904. https://doi.org/10.1016/j.compag.2020.105904 doi: 10.1016/j.compag.2020.105904
[13]	Wiggans G, Vanraden P, Bormann J, Philpot J, Druet T, Gengler N (2002) Deriving lactation yields from test-day yields adjusted for lactation stage, age, pregnancy, and herd test date. J Dairy Sci 85: e261–264. https://doi.org/10.3168/jds.S0022-0302(02)74077-0 doi: 10.3168/jds.S0022-0302(02)74077-0
[14]	Cao Z, Zhao H, Xu J, Zhang G, Li Y, Su Y, et al. (2022) Using empirical modal decomposition to improve the daily milk yield prediction of cows. Wireless Commun Mobile Comput 1: 1685841. https://doi.org/10.1155/2022/1685841 doi: 10.1155/2022/1685841

Reader Comments

Your name:*

Email:*
© 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)