Testing 7.7

ICAR IDF Guidelines for Body Condition Scoring (BCS)

Introduction

Body Condition Scoring (BCS) is a crucial method for assessing the health and metabolic status of dairy cows by estimating their body fat reserves. Regular monitoring of BCS is essential for developing strategies for maintaining optimal body condition, health, welfare and productivity in dairy herds. This document provides standardized guidelines for BCS recording and use, emphasizing its applications in herd management, genetic evaluation, and welfare assessment.

Defining Body Condition Score (BCS)

BCS is an indicator of the proportion of body fat in cows, providing a reliable measure of body reserves. It is assessed through visual or tactile appraisal and is rationalized into various numerical systems using different scales. The primary purpose of body conditions scoring is to evaluate the energy reserves in dairy cows, which are critical for their health, fertility, longevity, and productivity.

BCS as an Indicator of Fat Reserve

Before the 1970s, there were no simple measures of a cow’s energy reserves or body condition. Body weight alone is not a reliable measure due to variations in frame size and gut fill. BCS provides a more accurate assessment by focusing on body fat reserves, which are crucial for buffering cows against negative energy balance during early lactation.

BCS Scoring Systems and Their Diversity

A variety of BCS scales inside different systems are used globally, each tailored to specific purposes such as conformation scoring for genetic evaluation, herd management, welfare assessment, and others. The variability in scales can cause confusion when comparing targets and results across farms and breeding programs. Moreover, the precision of BCS scales must be considered as defined by the number of used classes and not the range of the scales. Commonly scales used are:

• 1-3 scale: Used for welfare assessment (Welfare Quality®: Assessment protocol for cattle (2009)).
• 0-5 scale: Used in the UK and Ireland, developed by Jefferies (1961) for ewes and adapted for beef cattle by Lowman et al. (1973) .
• 1-10 scale: Used in New Zealand, developed by Roche et al. (2004) .
• 1-8 scale: Used in Australia, developed by Earle et al. (1977) Used in the UK and Ireland, developed by Jefferies (1961) for ewes..
• 1-5 scale: Used in the US and European countries, with variants proposed by Wildman et al. (1982)and Ferguson et al. (1994) . The Ferguson et al. (1994) scale with 0.25 increments is widely used by veterinarians in health assessment, as it captures the dynamics in body fat during and across lactations.
• 1-9 scale: Used of conformation scoring programs to determine genetic differences among animals.

Examples for BCS Systems Across Countries

Different countries use various BCS scales and associated systems based on local practices and requirements for specific purposes. Table 1 gives details on some of the most commonly used systems:

Country	Scale	Interval (classes)	Method	References
United Kingdom	0 to 5	0.5 (11)	Palpation	Mulvany (1977)
New Zealand	1 to 10	0.5 (19)	Palpation	Roche et al. (2004)
Australia	1 to 8	0.5 (15)	Visual	Earle et al. (1977)
United States	1 to 5	1 (5)	Palpation/Visual	Wildman et al. (1982)
United States	1 to 5	0.25 (17)	Palpation/Visual	Ferguson et al. (1994)
Multiple	1 to 9	1 (9)	Visual	ICAR confirmation classification system

Using Body Condition Score (BCS)

Manual Assessment

Manual assessment of BCS involves palpating key body regions (e.g., ribs, spine, hips) to estimate fat and muscle reserves. This method remains reliable but is subject to assessor variability. Consistency in training assessors is crucial to reduce this variability. As differences between scorers, despite efforts to harmonize, can be expected, coded identification of assessors needs to be retained.

Example for BCS Based on a 1-5 Scoring Scale

Detailed information describing the 1-5 scoring scale with 0.25 intervals (17 classes) were given by Edmonson et al. (1989). In Figure 1, the major elements for assigning the 5 major steps are given as an example.

Figure 1. Example of an 1-5 BCS scale chart (Modified from Edmonson et al., 1989).

Digital Tools

Three main levels of digital tools exist:

1. Use of digital tools to facilitate on-farm recording and documentation: Facilitates the use of standards when scoring, the documentation and the recording of still visual assessments.
2. Technology-assisted assessments: Human assessors still doing the scoring but using devices to support manual assessment, replacing the human eye.
3. Technology-driven assessments with vision-based sensor systems: Purely automatic sensor-based assessments that also allow daily on-farm BCS assessments.

For tools of types 2 and 3, reference populations need to include sufficiently extreme animals in order to develop prediction models covering the full range of possible BCS variability in animals to be scored.

Automated BCS recordings using digital technologies, such as 3D imaging systems (i.e., tools of type 3) offer a more objective and consistent assessment of BCS, typically multiple daily scoring when cows exit the milking system. The frequent and consistent measurements enable detailed analysis for each cow within and across lactations including short term individual and group level management. While minimizing human error and variation, the performance of automated BCS sensor system depends, among other factors, on the training and validation of the models. Human observers should be well trained showing high inter-observer and intra-observer agreement to generate a suitable reference standard. However, technological limitations due to on-farm conditions still make it challenging to achieve full accuracy, particularly when compared with manual palpation. Recent advances in AI models will be crucial to improve even more accuracy (e.g., detection of outliers).

Recommendations for Use of BCS Scales

Conversion Between BCS Scales

Conversions between different scales should be used with caution. Simple mathematical conversions may not be accurate due to non-linear use of scales. Conversion methods ranked from least to most reliable ones are:

• Mathematical Conversion of Scales: Develop purely mathematical conversions, to be used with extreme caution.
• Distribution-Based Conversion: Map attributed scores to a common scale using z-scores (Snell, 1965) based on the comparison of uses of scales, can be used under the assumption that the underlying populations have similar distributions of body condition.
• Aligning Calibrated BCS scales: An objective way to calibrate any BCS scale is to quantify the change in body weight (kg) associated with a one-unit change in BCS. If such relationships are available for different BCS scales, a direct and biologically meaningful conversion can be established between them.
• Simultaneous Scoring: Develop conversion equations based on simultaneous scoring of large groups of cows, covering the full range of variability in body condition.

Conversion methods should always work sufficiently also for extreme animals covering the full range of possible BCS variability in animals to be scored.

Recommendations for Herd Management

Body condition scoring plays a vital role in managing dairy herds, allowing farmers to adjust feeding strategies and monitor metabolic health. Frequent BCS assessments help identify cows that are either losing or gaining condition too quickly, which may indicate underlying health or nutritional issues. Table 2 outlines various BCS scales proposed for specific purposes.

Table 2. Purpose of example BCS Scale

Purpose	BCS Scale Interval (classes)	Frequency	Remarks
Feeding advice	1 to 5 1 (5)	Frequent and longitudinal	Identification of cows with BCS change, indicating potential health problems and allowing optimization of feeding
Detection of metabolic disturbance	1 to 5 0.25 (17à	Before and after calving and at least 2 times before peak of lactation (~50 DIM)	Enables detection of BCS changes within cow during different stages of lactation in the herd
Welfare assessment	1 to 3 1 (3)	Detect general status of cows (thin-normal-fat)	Focus on identification of proportion of cows with unacceptable BCS that is indicator of and risk factor for diseases and disorders

Table 3 outlines the recommended frequency for BCS assessment based on the key stages in the cow’s lactation cycle. For metabolic risk assessment and nutritional management, the within cow differences in BCS between measurement moments should be calculated.

Table 3. Recommendations for the frequency of BCS assessments

Moment	Recommendation
Pre-calving	Approximately 3 weeks before calving to ensure optimal condition
Early lactation	Close monitoring at calving/fresh cow
Peak lactation	Detection of nadir in BCS
Dry off period	Assess 7-8 weeks before calving to adjust feeding as needed

An optimal recording scheme could include dry off, pre-calving, calving, early lactation/pre-service, 1^st service, pregnancy check, and late lactation. A representative random stratified sample of cows representing all lactations should be measured at key stages to ensure effective assessment.

Recommendations for Individual Cow Management

For individual cow management, BCS can be used as a trouble-shooting tool to recognize that an adjustment of the feeding program is required or to identify health concerns. For example, cows that drop below a certain BCS threshold or show a drop respectively increase in BCS across key stages of lactation may require increased respectively reduced energy intake, while those with higher-than-recommended scores might benefit from a restricted diet. These measures are essential for improving not only productivity but also fertility, feed efficiency, longevity, and overall well-being in dairy herds.

Detecting the dynamics of BCS during and between lactations is required for individual cow management, therefore recording with sufficient granularity (i.e., more than five classes) and repeated recordings to enable detection of body condition changes are recommended. Developing optimal BCS lactation curves based on breeds and management systems can help farmers monitor changes over the lactation for individual cows. Automated on-farm BCS allows for earlier detection of deviating BCS from target values and short-term operational decisions.

Recommendations for Genetic Evaluation

BCS is recognized as an intermediate optimum trait in genetic selection. Incorporating BCS data into genetic evaluations can enhance breeding programs, particularly for selecting cows with a more favorable balance between milk production and metabolic health (see Table 3). The use of BCS as an auxiliary trait is common in many genetic evaluation systems (e.g., for fertility). Regular and accurate BCS data collection allows for better selection within herd and ultimately contributes to long-term herd sustainability.

Current practice involves recording BCS once in a lifetime during the first lactation using the same 1-9 scale as for linear scores as explained by the ICAR Guidelines for Conformation Traits. For genetic evaluation of BCS changes, it is recommended that BCS is recorded on all cows frequently throughout their lives. Even if extending the existing scale to additional recording, simpler scales, but with at least a 5-class scale could suffice. Repeated records of BCS can also be useful for deriving and analyzing novel traits such as resilience and resource allocation. BCS changes based on BCS recorded before calving and after calving or twice in early lactation can be used as an auxiliary trait for the metabolic status of the cow.

Recommendations for Welfare Monitoring

Current practice in welfare monitoring BCS systems involves using a 3-class scale, which is considered sufficient for detecting the general status of cows (thin, normal, or fat) on a farm or group of farms. The boundaries of these broad categories should be defined with caution, as the expected BCS can vary substantially with breed, stage of lactation, and other cow-specific characteristics. Because assessment is only conducted periodically (e.g., once per year) and individual scores expressed as a summary for the herd (e.g., % of fat or thin cows) it is crucial to sample a representative group of animals, including recording relevant elements such as parity and lactation stage.

To maximize synergies with herd and individual cow management and breeding, it is more beneficial for welfare to assess all animals. This allows the detection of individuals with specific welfare issues.

Additional Important Considerations

Additional Data to be Recorded

In addition to the recorded BCS, the following information is recommended to be recorded: unique Animal ID, Herd ID, breed, date of recording, assessor-ID, BCS Scoring System (linked to a comprehensive description of the system), most recent calving date, and parity number.

Training of Assessors

An important element is the training of assessors. They need to have a clear understanding of and training on the respective BCS Scoring System. Standard Operating Procedures (SOP) along with the scoring chart and ensured comprehensive and regular training on utilizing these resources effectively need to be established. Regular and relatively frequent harmonization between assessors is needed. Best practice is that different assessors score the same farm(s) and grouping of data across different farms is done. Frequent evaluation of inter- and intra-assessors’ repeatability is important, especially for use in research studies or genetic evaluations.

Benchmarking and Use for Herd Management

For herd management, information on individual cows could be of less importance. However, to effectively benchmark, manage herds, and genetically evaluate animals, it is crucial to centralize the collected information into a central database. Benchmarking enables comparisons among farms and the identification of areas for improvement. For meaningful comparisons between herds, also for management purposes, factors such as assessment systems used, assessment conditions such as frequency, assessor identity, but also a summary of information from individual records such as lactation stages, parity numbers, etc. must be recorded.

Additional information

For further details, please refer to Gengler et al. (2024) and to the workshop “Recording and evaluation of BCS and its relationship with health and welfare” held in Montreal on the 31st of May 2022, organised by the “ICAR–IDF Joint Expert Advisory Group on BCS Guidelines”.

Informative Readings

Andrew, S. M., Waldo, D. R., & Erdman, R. A. (1994). Direct analysis of body composition of dairy cows at three physiological stages. J. Dairy Sci. 77(10): 3022–3033. https://doi.org/10.3168/jds.S0022-0302(94)77244-1

Bewley, J. M., & Schutz, M. M. (2008). An interdisciplinary review of body condition scoring for dairy cattle. Prof. Anim. Sci. 24(6): 507-529. https://doi.org/10.15232/S1080-7446(15)30901-3

Domecq, J. J., Skidmore, A. L., Lloyd, J. W., & Kaneene, J. B. (1997). Relationship between body condition scores and milk yield in a large dairy herd of high yielding Holstein cows. J. Dairy Sci. 80(1): 101–112. https://doi.org/10.3168/jds.S0022-0302(97)75917-4

Edmonson, A. J., Lean, I. J., Weaver, L. D., Farver, T., & Webster, G. (1989). A body condition scoring chart for Holstein dairy cows. J. Dairy Sci. 72(1): 68–78. https://doi.org/10.3168/jds.S0022-0302(89)79081-0

Garnsworthy, P. C. (2006). Body condition score in dairy cows: Targets for production and fertility. Recent Adv. Anim. Nutr. 40: 61–86. https://doi.org/10.5661/recadv-06-61

Gibb, M. J., Ivings, W. E., Dhanoa, M. S., & Sutton, J. D. (1992). Changes in body components of autumn-calving Holstein-Friesian cows over the first 29 weeks of lactation. Anim. Sci. 55(3): 339-360. https://doi.org/10.1017/S0003356100021036

Gyawali, A., Egger-Danner, C., & Gengler, N. (2024). Body Conditions Scoring – first proposal for recommendations for recording and use for herd management, genetic improvement and welfare assessment. ICAR Conference 2024, Bled, Slovenia. https://www.icar.org/wp-content/uploads/documents/ICAR-Technical-Series-28-Bled-2024-Proceedings.pdf. Assessed 19th of November, 2025.

Huang, X., Hu, Z., Wang, X., Yang, X., Zhang, J., & Shi, D. (2019). An improved single shot multibox detector method applied in body condition score for dairy cows. Animals 9(7): 470. https://doi.org/10.3390/ani9070470

Snell, E. J. 1964. A scaling procedure for ordered categorical data. Biometrics 20(3):592-607. https://doi.org/10.2307/2528498

Stockdale, C. R. (2001). Body condition at calving and the performance of dairy cows in early lactation under Australian conditions: a review. Aust. J. Exp. Agric. 41(6): 823-839.

Authors and contributors to guideline

These guidelines have been elaborated by a “Joint Expert Advisory Group on BCS Guidelines” which was composed out of members of the ICAR Functional Traits Working Group and the IDF Standing Committee of Health and Welfare as well as members of other ICAR Groups and international experts. We would like to thank also the participants can contributors to the ICAR-IDF webinar in Montreal 2022 for their valuable contribution. The corresponding author and leader of elaboration of these guidelines is Nicolas Gengler <nicolas.gengler@uliege.be> .

Gengler, N.^1, Gyawali, A.², Brito, L.F.³, Bewley, J. M.⁴, Cole, J.⁵, de Jong, G.⁶, Fourdraine, R.H.⁷, Friggens, N.⁸, Haskell, M.⁹, Heringstad, B.¹⁰, Kelton, D.¹¹, Pryce, J.¹², Sievert, S.¹³, Stock, K. F.¹⁴, Stephen, M.¹⁵, Vasseur, E.¹⁶, Klaas, I.¹⁷, Egger-Danner, C^.18. 2025. ICAR Guidelines for Body Condition Scoring (BCS).

• ¹TERRA Teaching and Research Center, Gembloux Agro-Bio Tech, University of Liège, 5030 Gembloux, Belgium
• ²Aashish Gywali, LMU, Germany
• ³Department of Animal Sciences, Purdue University, West Lafayette, IN, 47907, USA
• ⁴Holstein Association USA, 1 Holstein Place, PO Box 808, VT 05302-0808 Brattleboro, United States
• ⁵CDCB, USA
• ⁶ CRV, Netherlands
• ⁷Dairy Records Management Systems, NC State University, 313 Chapanoke Road, Suite 100, Raleigh NC 27603, USA
• ⁸INRAE, France
• ⁹SRUC (Scotland’s Rural College), West Mains Road, Edinburgh EH9 3JG, United Kingdom
• ¹⁰Norwegian University of Life Sciences, Ås, Norway
• ¹¹University of Guelph, Canada
• ¹²Agriculture Victoria Research, Australia
• ¹³National DHIA & DHIA Services, USA
• ¹⁴IT Solutions for Animal Production (vit), Heinrich-Schroeder-Weg 1, 27283 Verden, Germany
• ¹⁵Dairy New Zealand, New Zealand
• ¹⁶McGill University, Ste Anne de Bellevue, H9X 3V9, QC Canada
• ¹⁷DeLaval International AB, Gustaf de Lavals Väg 15, 14721 Tumba, Sweden
• ¹⁸ ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str. 89, 1200 Vienna, Austria.