Testing Sensor based behavior - Revision history

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	* How to derive novel traits based on rumination pattern and variability? Studies are still needed.		* How to derive novel traits based on rumination pattern and variability? Studies are still needed.

			=== Informative references ===


	~~'''References'''~~


	L. Brito, Heringstad. B., Klaas, I.C., Schodl, K., Cabrera, V.E., Stygar, A., Iwersen, M., Haskell, M. Stock, K., Gengler, N., JBewley, J., Hostens, M., Vasseur, E., Egger-Danner, C., 2025. Invited Review: Using data from sensors and other precision farming technologies to enhance the sustainability of dairy cattle breeding programs,

	~~Journal of Dairy Science, 2025, ISSN 0022-0302, <nowiki>https://doi.org/10.3168/jds.2025-26554</nowiki>. (<nowiki>https://www.sciencedirect.com/science/article/pii/S0022030225006812</nowiki>)~~


	Byskov, M.V., Fogh, A. and Løvendahl, P., 2017. Genetic parameters of rumination time and feed efficiency traits in primiparous Holstein cows under research and commercial conditions. Journal of Dairy Science, 100(12), pp.9635-9642.


	Egger-Danner, C., I. Klaas, L. Brito, K. Schodl, J.M. Bewley, V. Cabrera, M.J. Haskell, M. Iwersen, B. Heringstad, K. Stock, A. Stygar, R. van der Linde, M. Hostens, N. Charfeddine, N. Gengler, and E. Vasseur. 2024. Improving animal health and welfare by using sensor data in herd management and dairy cattle breeding – a joint initiative of ICAR and IDF. Pages 56_63 in Proc 11th Eur. Conf. Precis. Livest. Farming, Bologna, Italy. Organizing Committee of the 11th European Conference on Precision Livestock Farming (ECPLF), University of Veterinary Medicine, Vienna, Austria		Egger-Danner, C., I. Klaas, L. Brito, K. Schodl, J.M. Bewley, V. Cabrera, M.J. Haskell, M. Iwersen, B. Heringstad, K. Stock, A. Stygar, R. van der Linde, M. Hostens, N. Charfeddine, N. Gengler, and E. Vasseur. 2024. Improving animal health and welfare by using sensor data in herd management and dairy cattle breeding – a joint initiative of ICAR and IDF. Pages 56_63 in Proc 11th Eur. Conf. Precis. Livest. Farming, Bologna, Italy. Organizing Committee of the 11th European Conference on Precision Livestock Farming (ECPLF), University of Veterinary Medicine, Vienna, Austria

	Hogeveeen, H., Klaas, I.C., Dalen, G., Honig, H., Zecconi, A., Kelton, D.F. and Mainar, M.S. 2021. Novel ways to use sensor data to improve mastitis management. Journal of Dairy Science 104, 11317-11332.		Hogeveeen, H., Klaas, I.C., Dalen, G., Honig, H., Zecconi, A., Kelton, D.F. and Mainar, M.S. 2021. Novel ways to use sensor data to improve mastitis management. Journal of Dairy Science 104, 11317-11332.


	Lopes, L.S.F., Schenkel, F.S., Houlahan, K., Rochus, C.M., Oliveira Jr, G.A., Oliveira, H.R., Miglior, F., Alcantara, L.M., Tulpan, D. and Baes, C.F., 2024. Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first lactation Holstein cows. Journal of Dairy Science, 107, 7, 4704-4713.		Lopes, L.S.F., Schenkel, F.S., Houlahan, K., Rochus, C.M., Oliveira Jr, G.A., Oliveira, H.R., Miglior, F., Alcantara, L.M., Tulpan, D. and Baes, C.F., 2024. Estimates of genetic parameters for rumination time, feed efficiency, and methane production traits in first lactation Holstein cows. Journal of Dairy Science, 107, 7, 4704-4713.


	Martin, M. J., Dórea, J. R. R., Borchers, M. R., Wallace, R. L., Bertics, S. J., DeNise, S. K., Weigel, K. A., & White, H. M. (2021). Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science, 104(8), 8765–8782. <nowiki>https://doi.org/https://doi.org/10.3168/jds.2020-20051</nowiki>


	Moretti, R., Biffani, S., Tiezzi, F., Maltecca, C., Chessa, S. and Bozzi, R., 2017. Rumination time as a potential predictor of common diseases in high-productive Holstein dairy cows. Journal of Dairy Research, 84(4), 385-390.



	Poppe, M., H.A. Mulder, M.L. van Pelt, E. Mullaart, H. Hogeveen, and R.F. Veerkamp. 2022. Development of resilience indicator traits based on daily step count data for dairy cattle breeding. Genetics Selection Evolution 54:21. doi:10.1186/s12711-022-00713-x



	Schodl, K., Stygar, A., Steininger, F., & Egger-Danner, C., 2024a. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci., 5, p.1444948. <nowiki>https://doi.org/10.3389/fanim.2024.1444948</nowiki>.

	Stygar, A.H., Gómez, Y., Berteselli, G.V., Dalla Costa, E., Canali, E., Niemi, J.K., Llonch, P., Pastell, M. 2021. A systematic review on commercially available and validated sensor technologies for welfare assessment of dairy cattle. Frontiers in Veterinary Science 8, 177.

	=== Authors and contributors to guideline ===		=== Authors and contributors to guideline ===
	These guidelines have been elaborated by the joint ICAR IDF Initiative on “Improving animal health and wellbeing by using sensor data in herd management and dairy cattle breeding” in collaboration of members of the ICAR Working Group on Functional Traits, the IDF Standing Committee of Animal Health and Welfare, international scientists, manufacturer and representatives of other ICAR bodies and stakeholders.		These guidelines have been elaborated by the joint ICAR IDF Initiative on “Improving animal health and wellbeing by using sensor data in herd management and dairy cattle breeding” in collaboration of members of the ICAR Working Group on Functional Traits, the IDF Standing Committee of Animal Health and Welfare, international scientists, manufacturer and representatives of other ICAR bodies and stakeholders.


	C. Egger-Danner<sup>1</sup>, I. Klaas<sup>2</sup>, L. F. Brito<sup>3</sup>, J. M. Bewley<sup>4</sup>, V. E. Cabrera<sup>5</sup>, S. Dagan<sup>6</sup>, R.H. Fourdraine<sup>7</sup>, N. Gengler<sup>8</sup>, M. Haskell<sup>9</sup>, B. Heringstad<sup>10</sup>, J. Heslin<sup>6</sup>, M. Hostens<sup>11</sup>, M. Iwersen<sup>12</sup>, F. Karlsson<sup>2</sup>, G. Katz<sup>13</sup>, M. Moleman<sup>14</sup>, M. Phelan<sup>6</sup>, E. Rossi<sup>6</sup>, K. Schodl<sup>l</sup>, D. Sieben<sup>15</sup>, K. F. Stock<sup>16</sup>, A. Stygar<sup>17</sup>, E. Vasseur<sup>18</sup>, Manufacturer representatives		C. Egger-Danner<sup>1</sup>, I. Klaas<sup>2</sup>, L. F. Brito<sup>3</sup>, J. M. Bewley<sup>4</sup>, V. E. Cabrera<sup>5</sup>, S. Dagan<sup>6</sup>, R.H. Fourdraine<sup>7</sup>, N. Gengler<sup>8</sup>, M. Haskell<sup>9</sup>, B. Heringstad<sup>10</sup>, J. Heslin<sup>6</sup>, M. Hostens<sup>11</sup>, M. Iwersen<sup>12</sup>, F. Karlsson<sup>2</sup>, G. Katz<sup>13</sup>, M. Moleman<sup>14</sup>, M. Phelan<sup>6</sup>, E. Rossi<sup>6</sup>, K. Schodl<sup>l</sup>, D. Sieben<sup>15</sup>, K. F. Stock<sup>16</sup>, A. Stygar<sup>17</sup>, E. Vasseur<sup>18</sup>, Manufacturer representatives

	* ''<sup>1</sup> ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str. 89, 1200 Vienna, Austria,''		* ''<sup>1</sup> ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str. 89, 1200 Vienna, Austria,''
	* ''<sup>2</sup> DeLaval International AB, Gustaf de Lavals Väg 15, 14721 Tumba, Sweden,''		* ''<sup>2</sup> DeLaval International AB, Gustaf de Lavals Väg 15, 14721 Tumba, Sweden,''
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	* ''<sup>17</sup> Bioeconomy and Environment, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland,''		* ''<sup>17</sup> Bioeconomy and Environment, Natural Resources Institute Finland (Luke), Latokartanonkaari 9, 00790 Helsinki, Finland,''
	* ''<sup>18</sup> McGill University, Ste Anne de Bellevue, H9X 3V9, QC Canada.''		* ''<sup>18</sup> McGill University, Ste Anne de Bellevue, H9X 3V9, QC Canada.''
			[[File:Section . Figure 3.jpg\|center\|frame\|'''Organisations of the Authors of the Guidelines Section 7.7''']]
	----[CE1]To be added		----[CE1]To be added

Cmosconi: /* Part 1 – General introduction */

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Part 1 – General introduction

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	= Sensor based behavior information for functional traits with focus on rumination =		= Sensor based behavior information for functional traits with focus on rumination =

	== Part 1 – General introduction ==		== Part 1: General introduction ==

	=== Background and aim of the guideline ===		=== Background and aim of the guideline ===

Cmosconi: /* Part 1 – General introduction */

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Part 1 – General introduction

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	The current guideline has the focus on data from sensor systems measuring animal behaviours. These sensor systems can provide information on behavioural measurements like rumination, eating, lying or indexes like activity indexes or alarms like for calving, in heat or health events. Various sensor systems are based on different technologies using different algorithms and provide different information to the farmer.		The current guideline has the focus on data from sensor systems measuring animal behaviours. These sensor systems can provide information on behavioural measurements like rumination, eating, lying or indexes like activity indexes or alarms like for calving, in heat or health events. Various sensor systems are based on different technologies using different algorithms and provide different information to the farmer.

	= Part 2: Terminology =		== Part 2: Terminology ==

	=== Suggested Key Performance Indicators (KPIs) for sensor-based rumination data ===		=== Suggested Key Performance Indicators (KPIs) for sensor-based rumination data ===
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	Welch, J. G. 1982. Rumination, particle size and passage from the rumen. J. Anim. Sci. 54:885–894. https:// doi .org/ 10 .2527/ jas1982.544885x.		Welch, J. G. 1982. Rumination, particle size and passage from the rumen. J. Anim. Sci. 54:885–894. https:// doi .org/ 10 .2527/ jas1982.544885x.

	= Part 3: Sensor data cleaning =		== Part 3: Sensor data cleaning ==

	=== Recommendations for data cleaning ===		=== Recommendations for data cleaning ===
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	More details can be found in Schodl et al. (2024)<ref name="Schodl2024"/>		More details can be found in Schodl et al. (2024)<ref name="Schodl2024"/>
	= Part 4: Use of sensor data (focus on time series data) for genetic improvement =
			== Part 4: Use of sensor data (focus on time series data) for genetic improvement ==

	=== Structure of guidelines related to rumination sensor and use in genetics ===		=== Structure of guidelines related to rumination sensor and use in genetics ===

Cmosconi: /* Sensor based behavior information for functional traits with focus on rumination */

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Sensor based behavior information for functional traits with focus on rumination

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	== Sensor based behavior information for functional traits with focus on rumination ==		= Sensor based behavior information for functional traits with focus on rumination =

	== Part 1 – General introduction ==		== Part 1 – General introduction ==

Cmosconi: /* Sensor based behavior information for functional traits with focus on rumination */

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Sensor based behavior information for functional traits with focus on rumination

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	= Sensor based behavior information for functional traits with focus on rumination =		== Sensor based behavior information for functional traits with focus on rumination ==

	== Part 1 – General introduction ==		== Part 1 – General introduction ==

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	=== Five-step framework for cleaning sensor data including ===		=== Five-step framework for cleaning sensor data including ===
	These instructions are proposed by Schodl et al. 2024<ref name="~~Schodl2004~~">Schodl, K., Stygar, A., Steininger, F., & Egger-Danner, C., 2024a. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci., 5, p.1444948. <nowiki>https://doi.org/10.3389/fanim.2024.1444948</nowiki>.</ref>.)		These instructions are proposed by Schodl et al. 2024<ref name="Schodl2024">Schodl, K., Stygar, A., Steininger, F., & Egger-Danner, C., 2024a. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci., 5, p.1444948. <nowiki>https://doi.org/10.3389/fanim.2024.1444948</nowiki>.</ref>.)
	# '''Verification of the data''' preprocessing: Accurate alignment between animal identifiers and sensor data is critical. Errors such as duplicate device assignments to one animal (or vice versa), broken sensors, and time zone mismatches must be identified and corrected, if possible. It is recommended to consult with digital technology companies for information on proper alignment as well as algorithm learning periods.		# '''Verification of the data''' preprocessing: Accurate alignment between animal identifiers and sensor data is critical. Errors such as duplicate device assignments to one animal (or vice versa), broken sensors, and time zone mismatches must be identified and corrected, if possible. It is recommended to consult with digital technology companies for information on proper alignment as well as algorithm learning periods.
	# '''Understanding the data''': This step involves identifying the type of data (e.g., raw sensor data or processed data retrieved from interfaces), its nature including units and whether it is a single shot measurement or an aggregated value, and sampling rates. Proper data visualization is recommended to uncover patterns, distributions, or anomalies.		# '''Understanding the data''': This step involves identifying the type of data (e.g., raw sensor data or processed data retrieved from interfaces), its nature including units and whether it is a single shot measurement or an aggregated value, and sampling rates. Proper data visualization is recommended to uncover patterns, distributions, or anomalies.

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	=== Five-step framework for cleaning sensor data including ===		=== Five-step framework for cleaning sensor data including ===
	These instructions are proposed by Schodl et al. 2024<ref name="Schodl2004">Schodl, K., Stygar, A., Steininger, F., & Egger-Danner, C., 2024a. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci., 5, p.1444948. <nowiki>https://doi.org/10.3389/fanim.2024.1444948</nowiki>.</ref>. ~~Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci. 5:1444948. https://doi.org/10.3389/fanim.2024.1444948. <nowiki></ref></nowiki>~~)		These instructions are proposed by Schodl et al. 2024<ref name="Schodl2004">Schodl, K., Stygar, A., Steininger, F., & Egger-Danner, C., 2024a. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci., 5, p.1444948. <nowiki>https://doi.org/10.3389/fanim.2024.1444948</nowiki>.</ref>.)
	# '''Verification of the data''' preprocessing: Accurate alignment between animal identifiers and sensor data is critical. Errors such as duplicate device assignments to one animal (or vice versa), broken sensors, and time zone mismatches must be identified and corrected, if possible. It is recommended to consult with digital technology companies for information on proper alignment as well as algorithm learning periods.		# '''Verification of the data''' preprocessing: Accurate alignment between animal identifiers and sensor data is critical. Errors such as duplicate device assignments to one animal (or vice versa), broken sensors, and time zone mismatches must be identified and corrected, if possible. It is recommended to consult with digital technology companies for information on proper alignment as well as algorithm learning periods.
	# '''Understanding the data''': This step involves identifying the type of data (e.g., raw sensor data or processed data retrieved from interfaces), its nature including units and whether it is a single shot measurement or an aggregated value, and sampling rates. Proper data visualization is recommended to uncover patterns, distributions, or anomalies.		# '''Understanding the data''': This step involves identifying the type of data (e.g., raw sensor data or processed data retrieved from interfaces), its nature including units and whether it is a single shot measurement or an aggregated value, and sampling rates. Proper data visualization is recommended to uncover patterns, distributions, or anomalies.

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	The items in this summary checklist correspond to and summarise the five-step framework described below and are intended as a quick user guide to the more detailed explanations.		The items in this summary checklist correspond to and summarise the five-step framework described below and are intended as a quick user guide to the more detailed explanations.

	=== Five-step framework for cleaning sensor data including (proposed by Schodl et al. 2024<ref name="Schodl2004">Schodl, K., Stygar, A., Steininger, F., & Egger-Danner, C., 2024a. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci., 5, p.1444948. <nowiki>https://doi.org/10.3389/fanim.2024.1444948</nowiki>.</ref>. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci. 5:1444948. https://doi.org/10.3389/fanim.2024.1444948. </ref>) ~~===~~		=== Five-step framework for cleaning sensor data including ===
			These instructions are proposed by Schodl et al. 2024<ref name="Schodl2004">Schodl, K., Stygar, A., Steininger, F., & Egger-Danner, C., 2024a. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci., 5, p.1444948. <nowiki>https://doi.org/10.3389/fanim.2024.1444948</nowiki>.</ref>. Sensor data cleaning for applications in dairy herd management and breeding. Front. Anim. Sci. 5:1444948. https://doi.org/10.3389/fanim.2024.1444948. <nowiki></ref></nowiki>)
	# '''Verification of the data''' preprocessing: Accurate alignment between animal identifiers and sensor data is critical. Errors such as duplicate device assignments to one animal (or vice versa), broken sensors, and time zone mismatches must be identified and corrected, if possible. It is recommended to consult with digital technology companies for information on proper alignment as well as algorithm learning periods.		# '''Verification of the data''' preprocessing: Accurate alignment between animal identifiers and sensor data is critical. Errors such as duplicate device assignments to one animal (or vice versa), broken sensors, and time zone mismatches must be identified and corrected, if possible. It is recommended to consult with digital technology companies for information on proper alignment as well as algorithm learning periods.
	# '''Understanding the data''': This step involves identifying the type of data (e.g., raw sensor data or processed data retrieved from interfaces), its nature including units and whether it is a single shot measurement or an aggregated value, and sampling rates. Proper data visualization is recommended to uncover patterns, distributions, or anomalies.		# '''Understanding the data''': This step involves identifying the type of data (e.g., raw sensor data or processed data retrieved from interfaces), its nature including units and whether it is a single shot measurement or an aggregated value, and sampling rates. Proper data visualization is recommended to uncover patterns, distributions, or anomalies.

Cmosconi at 17:43, 11 March 2026

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	More details can be found in Schodl et al. (2024)<ref name="Schodl2024"/>		More details can be found in Schodl et al. (2024)<ref name="Schodl2024"/>


	~~'''Sensor based behavior information for functional traits with focus on rumination'''~~

	= Part 4: Use of sensor data (focus on time series data) for genetic improvement =		= Part 4: Use of sensor data (focus on time series data) for genetic improvement =

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	These guidelines are intended to provide stakeholders using data from sensors in dairy farms with recommendations for recording, processing, integration, and standardization across sensors; deriving novel traits for management and breeding purposes; and genetically evaluating derived functional traits. By adhering to these recommendations, stakeholders can ensure consistent and reliable data collection, leading to improved management and breeding decisions. This specific guideline focuses on rumination sensors, which monitor cows' chewing activity to assess their health and productivity, and it is part of a series of guidelines related to the use of sensor data for dairy cattle management and breeding purposes.		These guidelines are intended to provide stakeholders using data from sensors in dairy farms with recommendations for recording, processing, integration, and standardization across sensors; deriving novel traits for management and breeding purposes; and genetically evaluating derived functional traits. By adhering to these recommendations, stakeholders can ensure consistent and reliable data collection, leading to improved management and breeding decisions. This specific guideline focuses on rumination sensors, which monitor cows' chewing activity to assess their health and productivity, and it is part of a series of guidelines related to the use of sensor data for dairy cattle management and breeding purposes.

	For genetic purposes, rumination time has been evaluated as a proxy of feed efficiency (Byskov et al., 2017<ref>Martin, M. J., Dórea, J. R. R., Borchers, M. R., Wallace, R. L., Bertics, S. J., DeNise, S. K., Weigel, K. A., & White, H. M. (2021). Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science, 104(8), 8765–8782. https://doi.org/https://doi.org/10.3168/jds.2020-20051</ref>; Martin et al., 2021<ref>Martin, M. J., Dórea, J. R. R., Borchers, M. R., Wallace, R. L., Bertics, S. J., DeNise, S. K., Weigel, K. A., & White, H. M. (2021). Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science, 104(8), 8765–8782. <nowiki>https://doi.org/https://doi.org/10.3168/jds.2020-20051</nowiki></ref>) and functional traits such as metabolic diseases and claw health (Moretti et al., 2017). However, there is limited information in the literature highlighting the value of rumination time as an auxiliary trait. In addition to average rumination time over specific periods, there is a growing interest in using longitudinal measurements of rumination time to define overall resilience (defined as the ability of an animal to be minimally affected by environmental disturbances and also to quickly return to its normal state).		For genetic purposes, rumination time has been evaluated as a proxy of feed efficiency (Byskov et al., 2017<ref>Martin, M. J., Dórea, J. R. R., Borchers, M. R., Wallace, R. L., Bertics, S. J., DeNise, S. K., Weigel, K. A., & White, H. M. (2021). Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science, 104(8), 8765–8782. https://doi.org/https://doi.org/10.3168/jds.2020-20051</ref>; Martin et al., 2021<ref>Martin, M. J., Dórea, J. R. R., Borchers, M. R., Wallace, R. L., Bertics, S. J., DeNise, S. K., Weigel, K. A., & White, H. M. (2021). Comparison of methods to predict feed intake and residual feed intake using behavioral and metabolite data in addition to classical performance variables. Journal of Dairy Science, 104(8), 8765–8782. <nowiki>https://doi.org/https://doi.org/10.3168/jds.2020-20051</nowiki></ref>) and functional traits such as metabolic diseases and claw health (Moretti et al., 2017<ref>Moretti, R., Biffani, S., Tiezzi, F., Maltecca, C., Chessa, S. and Bozzi, R., 2017. Rumination time as a potential predictor of common diseases in high-productive Holstein dairy cows. Journal of Dairy Research, 84(4), 385-390.</ref>). However, there is limited information in the literature highlighting the value of rumination time as an auxiliary trait. In addition to average rumination time over specific periods, there is a growing interest in using longitudinal measurements of rumination time to define overall resilience (defined as the ability of an animal to be minimally affected by environmental disturbances and also to quickly return to its normal state).

	Therefore, although we recognize the potential limitations of rumination variables for direct genetic evaluations, standardizing recording and data editing could facilitate the comparison of future research results (e.g., identification of novel traits for breeding purposes). Furthermore, rumination variables might be more useful for breeding and management purposes when combined with other datasets such as sensor-based activity measures (e.g., lying, standing, eating, drinking). It has to be stated, that sensor derived traits are proxies and are not comparable with veterinarian diagnoses.		Therefore, although we recognize the potential limitations of rumination variables for direct genetic evaluations, standardizing recording and data editing could facilitate the comparison of future research results (e.g., identification of novel traits for breeding purposes). Furthermore, rumination variables might be more useful for breeding and management purposes when combined with other datasets such as sensor-based activity measures (e.g., lying, standing, eating, drinking). It has to be stated, that sensor derived traits are proxies and are not comparable with veterinarian diagnoses.