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Section 02 – Cattle Milk Recording

Cmosconi — Wed, 22 Jul 2026 18:12:04 GMT

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	= Procedures =		= Procedures =
	== Procedure 1: Computing 24-hour Yields ==		== Procedure 1: Computing 24-hour Yields ==

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	=== Methods to calculate 24-hour yield for milk yield and fat percentage from a single milking ===		=== Methods to calculate 24-hour yield for milk yield and fat percentage from a single milking ===

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	* Jenko, J., Perpar, T., Logar, B., Sadar, M., Ivanovič, B., Jeretina, J., Verbič, J., Podgoršek, P. 2008. Comparison of different models for estimating daily yields from a.m./p.m. milkings in Slovenian dairy scheme. Presented at the 36th ICAR Session, Niagara Falls, New York, United States, June 16-20, 2008.		* Jenko, J., Perpar, T., Logar, B., Sadar, M., Ivanovič, B., Jeretina, J., Verbič, J., Podgoršek, P. 2008. Comparison of different models for estimating daily yields from a.m./p.m. milkings in Slovenian dairy scheme. Presented at the 36th ICAR Session, Niagara Falls, New York, United States, June 16-20, 2008.
	* Jenko, J., Perpar, T., Gorjanc G., Babnik, D. 2010. Evaluation of different approaches for the estimation of daily yield from single milk testing scheme in cattle, J. Dairy Res., 77 (2010), pp. 137-143; DOI: 10.1017/S0022029909990586		* Jenko, J., Perpar, T., Gorjanc G., Babnik, D. 2010. Evaluation of different approaches for the estimation of daily yield from single milk testing scheme in cattle, J. Dairy Res., 77 (2010), pp. 137-143; DOI: 10.1017/S0022029909990586

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	== Procedure 2 – Computing of Accumulated Lactation Yield ==		== Procedure 2 – Computing of Accumulated Lactation Yield ==
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	=== Conclusions ===		=== Conclusions ===
	The regression formula to estimate the 24-hour fat % based on one milk sample was improved. Improvements were first of all considering non-linearity of the variables by using polynomials for protein % of the milk sample (3<sup>rd</sup> order), interval before sampling (3<sup>rd</sup> order), milk amount of sample (3<sup>rd</sup> order), milk amount of previous milking (3<sup>rd</sup> order) and interval before the previous milking (2<sup>nd</sup> order). Secondly, adding the effects of time of sampling (class variable), lactation stage (3<sup>rd</sup> order polynomial), parity (class variable) and month of sampling (class variable) gave a further reduction of the difference between true and estimated 24-hour fat %. The total reduction in standard deviation of the difference between true and estimated 24-hour fat % is 2.4% (0.2856 to 0.2788). The correlation between the two fat %s increased from 0.898 to 0.903, the b-factor of the linear regression between the two fat %s increased from 0.807 to 0.817.		The regression formula to estimate the 24-hour fat % based on one milk sample was improved. Improvements were first of all considering non-linearity of the variables by using polynomials for protein % of the milk sample (3<sup>rd</sup> order), interval before sampling (3<sup>rd</sup> order), milk amount of sample (3<sup>rd</sup> order), milk amount of previous milking (3<sup>rd</sup> order) and interval before the previous milking (2<sup>nd</sup> order). Secondly, adding the effects of time of sampling (class variable), lactation stage (3<sup>rd</sup> order polynomial), parity (class variable) and month of sampling (class variable) gave a further reduction of the difference between true and estimated 24-hour fat %. The total reduction in standard deviation of the difference between true and estimated 24-hour fat % is 2.4% (0.2856 to 0.2788). The correlation between the two fat %s increased from 0.898 to 0.903, the b-factor of the linear regression between the two fat %s increased from 0.807 to 0.817.
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			== Appendix 3 - A unified Python implementation of standardized 305 day yield calculation methods ==
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			The ICAR guideline is translated into an open-source Python package that can serve as a reference implementation for 305-day yield calculation. In addition to implementing the methods described in the original guideline (with the exception of the multi-trait method, which will be added in future work), the package incorporates 14 lactation-curve models, including traditional parametric models, Bayesian fitting approaches, and an AI-based model. The package also provides tools to derive biologically relevant lactation characteristics such as time to peak, peak yield, cumulative yield, and persistency. The package is publicly available through PyPI and can be installed directly using pip install lactationcurve (van Leerdam et al., 2026). Extensive documentation was developed alongside the package to improve transparency and reproducibility [https://bovi-analytics.github.io/bovi/lactationcurve.html https://bovi-analytics.github.io/bovi/lactationcurve.html.]

			Through a companioning website (https://tools.bovi-analytics.org<nowiki/>/), users can upload milk-recording data in CSV format, fit and visualize the implemented lactation-curve models, and compare different cumulative milk-yield methodologies on both test-day and fully daily-recorded lactations using metrics such as RMSE, Pearson correlation, MAPE, and MAE. Reference datasets are provided to allow organizations to benchmark their own calculations against alternative methodologies. In addition, downloadable PDF reports summarize the results through detailed statistics and scatterplots, both overall and stratified by parity.
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Section 20: Activities

Cvangemert — Wed, 22 Jul 2026 10:08:27 GMT

Added WG presentations for beef and microbiome, first set is now complete

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	27 November 2025: Presentation materials [[:File:20251127 GMG Microbiome WG presentation.pdf\|here.]]		27 November 2025: Presentation materials [[:File:20251127 GMG Microbiome WG presentation.pdf\|here.]]

			09 June 2026: Presentation materials [[:File:2026 06 09 Presentation GMG Microbiome working group.pdf\|here.]]

	==== Latin America ====		==== Latin America ====
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	11 November 2025: Presentation materials [[:File:20251106 GMG Working group Beef meeting.pdf\|here.]]		11 November 2025: Presentation materials [[:File:20251106 GMG Working group Beef meeting.pdf\|here.]]

			18 May 2026: Presentation materials [[:File:2026 05 18 Presentation GMG Beef Working Group.pdf\|here.]]

	==== Asia ====		==== Asia ====