# Monitoring changes in body reserves in gestating dairy heifers with 3D imaging technology: a potential tool to prevent early culling?

Yannick Le Cozler, Laurent Delattre, Thibault Luginbuhl, Maxime Dumesny

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Yannick Le Cozler, Laurent Delattre, Thibault Luginbuhl, Maxime Dumesny. Monitoring changes in body reserves in gestating dairy heifers with 3D imaging technology: a potential tool to prevent early culling?. JDS Communications, In press, 10.3168/jdsc.2025-0830. hal-05301879

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# Monitoring changes in body reserves in gestating dairy heifers with 3D imaging technology: a potential tool to prevent early culling?

Yannick Le Cozler,<fnref n="1" />* and Laurent Delattre,² Thibault Luginbuhl,² and Maxime Dumesny<fnref n="1" />

**Abstract:** The use of 3-dimensional (3D) imaging technology to monitor dairy cows enables the acquisition of morphological, surface, and volume data, which can then be employed to estimate body weight (BW) or body condition score (BCS). Accurate tracking of morphological changes in these animals could lead to better management of the gestation and parturition periods, which are especially critical for primiparous cows. Indeed, BCS values that are too low or too high are associated with reproductive disorder and/or early culling after calving. To assess the potential utility of precise and continuous recordings in gestating animals, we studied BW and morphological changes in 13 gestating Holstein heifers from 3 mo of gestation until the start of lactation. BCS was estimated using both 3D imaging technology (BCS3d) and manual scores determined by trained technicians (BCSm). From mid-April (start of experiment) until the end of August (start of calving season) in 2021, average BW increased from 515 (±standard deviation, 40.5) kg to 604 (±49.4) kg, while average BCS3d decreased slightly, from 2.50 (±0.19) to 2.32 (±0.30). A similar decrease was noted for BCSm, from 2.63 (±0.19) to 2.11 (±0.24). Linear regression between the 2 estimates of BCS indicated that the technicians usually gave a higher BCS value than the imaging device (BCS3d = 0.97 x BCSm; R² = 0.58). Because of the small number of observations and the fact that the experiment ended after the first calving, we are not able to draw any conclusions about the long-term effects of changes in BCS during gestation on the overall longevity of cows. Nevertheless, using our system it was possible to detect small changes in BCS during the first gestation of nulliparous cows. Such information could be employed to adapt the diet of heifers to avoid inadequate, insufficient, or excessive fatness at parturition, thus potentially ameliorating calving difficulties (dystocia) and poor starts to lactation. The precise evolution of BCS in the pregnant heifer is of great interest, and the next step will be to examine larger groups of animals to shed more light on the potential value of this technology.

Body condition score (BCS) and/or variants thereof are used on dairy farms to estimate the body reserves of animals and adapt feeding strategies accordingly (Delaby et al., 2010). It is generally estimated by human evaluators, based on a visual and/or manual (palpation) examination. The attributed value is based on references from over 30 years ago (Bazin, 1984; Edmonson et al., 1989) and although there are some differences between references and published grids, it is generally not difficult to convert one form to another (Bédère et al., 2019). Despite the subjectivity of the method, this non-invasive measurement is considered to be the “gold standard” (Ruegg, 1991) for estimating body reserves of dairy cows and/or similar indicators. Because it is time-consuming to assess, it is generally recorded no more than once a month on farms, which is typically considered to be enough for the management of dairy cows (Hady et al., 1994). However, in certain cases—such as during pregnancy, particularly the first one—more detailed information on changes in BCS over time could be useful for herd management. To our knowledge, only a limited number of studies have specifically examined the potential benefits of evaluating BCS kinetics in pregnant heifers, which can be very different from adult cows in terms of body development and composition. Some studies have used a grid ranging from 1 ( = very thin) to 9 ( = obese), with a step of 1 (e.g., Koenen et al., 2001), while others have employed a rating from 1 to 5, with a step of 0.5

points (Elanco, 2004). Most often, the grids used for heifers were the same as those for dairy cows, and the results revealed only weak changes in BCS over time, with maximum variations ranging from 0.5 to 1 point. This probably explains why this method is seldom used on growing animals, especially gestating heifers, in which BCS changes can be very hard to detect. Because of this, there is much that we do not understand regarding the possible negative consequences of inadequate feeding management during the first pregnancy for a cow's subsequent health and longevity. From the existing literature, it is clear that BCS values that are too high or low at calving are associated with negative effects on performance (Laven, 2015). Moreover, the number of cows culled in the first lactation is high in France, and represents around 25% of all culled animals, compared with 17% in Ireland for example (Van Arendonk, 1985; Reproscope, 2019). Reasons for culling are diverse, but problems with reproduction and milk insufficiency are commonly noted (Van Arendonk, 1985). It has been hypothesized that calving difficulties (dystocia) and poor starts to lactation in nulliparous heifers may be the result of inadequate, insufficient, or excessive fattening during gestation, potentially caused or exacerbated by poor feed management (Troccon et al., 1989). In this context, data on the precise evolution of BCS during gestation in the pregnant heifer may prove to be of interest for efforts to reduce problems that lead to early culling.

<footnote n="1">¹PEGASE, INRAE, Institut Agro Rennes-Angers, 35590, Saint Gilles, France, ²3D Ouest, 5 Rue de Broglie, 22300 Lannion, France. *Corresponding author: yannick.lecozler@institut-agro.fr. ©, The Authors. Published by Elsevier Inc. on behalf of the American Dairy Science Association*. This is an open access article under the CC BY license (https://creativecommons.org/licenses/by/4.0/). Received June 30, 2025. Accepted September 22, 2025.</footnote>
To overcome the difficulties associated with BCS monitoring— namely, that it is time-consuming and subjective—tools based on imaging technologies have been developed for both experimental (Fischer et al., 2015) and commercial purposes (Delaval, 2025). These technologies are based either on 2-dimensional (2D) or 3-dimensional (3D) imaging approaches. The methods are non- intrusive; they acquire the external envelope (visible to the naked eye) of a shape and export it in several formats, such as depth maps or point clouds (Le Cozler et al., 2022a). To date, numerous studies have used 3D imaging to monitor BCS in dairy cows (Negretti et al., 2008; Bewley et al., 2008; Fischer et al., 2015; Kuzuhara et al., 2015; Hansen, 2018) but very few, if any, have examined heifers. One reason is because, like weighing equipment, a BCS camera is usually installed on the cow walkway at the entrance or exit of the milking parlor or barn. As a result, these devices are sel- dom used for heifers. We hypothesized that, despite differences in morphology, it would be possible to monitor the evolution of BCS in pregnant dairy heifers using tools initially developed for cows. The main objective of present study was then to confirm that the method presented by Fischer et al. (2015) on cows was validated and easy to use on heifers, using small adjustments of technology (one-shot, automation).

The experiment was conducted as part of the ANR Deffilait project, with procedures approved by the local ethics committee and the French Ministry of Higher Education, Research, and Innovation (reference number APAFIS 3122-2015112718172611). The study was performed from April to December 2021 at the Méjusseaume experimental station of INRAE, Dairy Nutrition and Physiology unit (IE PL, 35560 Le Rheu, France (https://doi.org/10.15454/yk9q-pf68), under agreement for animal housing no. C-35-275-23). This facility has a herd of approximately 180 Holstein cows and their offspring (e.g., replacement heifers), produc- ing an average of 8,500 kg of milk per cow per year. All animals are reared and managed under routine farm procedures consistent with French animal-welfare regulations.

Three-dimensional images of the body shape of animals were acquired and processed using the Deffilait3D device, installed at the end of 2020 (Le Cozler et al., 2024). This prototype consists of a fixed metal frame to which are attached 15 RGB-D sensors

(Intel RealSense D415). The RealSense D415 is a depth camera comprising a pair of depth sensors, an RGB sensor, and an infrared spotlight. This technology is a stereo system that triangulates the depth (distance) for each pixel between the 2 sensors. The infrared projector helps the algorithm to find the correspondence between the 2 images. To reconstruct the image of an animal using a pas- sive stereo system, 2 RealSense D415 cameras, whose relative position and orientation are known, are used to capture the same scene. The 2 images are searched for corresponding pixels, and for each correspondence found, the depth is estimated using camera parameters and triangulation. Finally, all images are combined to reconstruct a 3D image of the entire animal. The full system and its validation are described in Le Cozler et al. (2024). In the present trial, we used only one camera—number 15, the one at the top-back of the device—to assess BCS changes during gestation. Changes in morphological traits (e.g., withers height, chest depth) during the experiment are not discussed here, but the main mor- phological traits measured at the start of experiment by the device are presented in Table 1.

As this study was designed to investigate the potential utility of this methodology for this particular application, we focused on a limited number of animals, sufficient for revealing the strengths and weaknesses of this approach while avoiding an unnecessarily large workload and the collection of a large amount of data that might not be useful in the future. A total of 13 pregnant Holstein heifers, chosen to be heterogeneous in terms of body weight (BW), morphological development, and body reserve content (estimated through BCSm; see below), were selected on average one month before the start of the experiment (2021/04/15) and followed until calving (Table 1). The experiment started at least after 3 mo after the start of gestation to avoid possible spontaneous abortion due to excessive handling and stress caused by the experiment.

Heifers were turned out to pasture in March and received no feed except for grass, along with supplemental vitamins and miner- als. Three weeks before the expected date of calving, heifers were group-housed with multiparous cows. They received a total of 11.5 kg dry matter (DM) per day of a total mixed ration (8 kg corn silage, 1 kg soybean meal, 1.5 kg straw, 400 g minerals + vitamins, 100 g urea). This ration was complemented with 1 kg of a com-

<table><caption>Table 1. Main characteristics of selected heifers at the start of the experiment</caption><thead><tr><th>ID</th><th>Age (months)</th><th>Gestation stage (months)</th><th>BW (kg)</th><th>BCS</th><th>Volume (m³)</th><th>HW (mm)</th><th>WH (mm)</th><th>CD (mm)</th><th>HG (mm)</th><th>WB (mm)</th><th>Calving date</th></tr></thead><tbody><tr><td>9209</td><td>18.9</td><td>2.97</td><td>518</td><td>2.50</td><td>0.582</td><td>517</td><td>1421</td><td>735</td><td>2012</td><td>526</td><td>23/09/21</td></tr><tr><td>9210</td><td>18.9</td><td>3.20</td><td>518</td><td>2.75</td><td>0.618</td><td>530</td><td>1366</td><td>783</td><td>2179</td><td>515</td><td>14/09/21</td></tr><tr><td>9212</td><td>18.8</td><td>3.14</td><td>474</td><td>2.5</td><td>0.533</td><td>470</td><td>1356</td><td>725</td><td>2004</td><td>479</td><td>16/09/21</td></tr><tr><td>9213</td><td>18.8</td><td>2.54</td><td>556</td><td>3.00</td><td>0.617</td><td>522</td><td>1359</td><td>748</td><td>2185</td><td>529</td><td>11/10/21</td></tr><tr><td>9215</td><td>18.8</td><td>2.74</td><td>554</td><td>2.75</td><td>0.611</td><td>552</td><td>1431</td><td>806</td><td>2111</td><td>520</td><td>02/10/21</td></tr><tr><td>9221</td><td>18.63</td><td>2.74</td><td>532</td><td>2.50</td><td>0.622</td><td>523</td><td>1413</td><td>763</td><td>2222</td><td>473</td><td>09/10/21</td></tr><tr><td>9224</td><td>18.6</td><td>3.07</td><td>475</td><td>2.25</td><td>0.534</td><td>507</td><td>1448</td><td>761</td><td>1992</td><td>509</td><td>16/10/21</td></tr><tr><td>9230</td><td>18.5</td><td>2.97</td><td>504</td><td>2.25</td><td>0.567</td><td>482</td><td>1410</td><td>783</td><td>2107</td><td>489</td><td>29/09/21</td></tr><tr><td>9238</td><td>18.4</td><td>3.14</td><td>564</td><td>3.00</td><td>0.653</td><td>516</td><td>1396</td><td>794</td><td>2260</td><td>532</td><td>18/09/21</td></tr><tr><td>9239</td><td>18.4</td><td>2.84</td><td>490</td><td>2.50</td><td>0.556</td><td>496</td><td>1297</td><td>738</td><td>2058</td><td>521</td><td>02/10/21</td></tr><tr><td>09244</td><td>18.2</td><td>3.00</td><td>516</td><td>2.75</td><td>0.613</td><td>509</td><td>1326</td><td>750</td><td>2142</td><td>490</td><td>27/10/21</td></tr><tr><td>9252</td><td>18.0</td><td>3.10</td><td>418</td><td>2.25</td><td>0.514</td><td>508</td><td>1340</td><td>701</td><td>2020</td><td>521</td><td>24/09/21</td></tr><tr><td>9270</td><td>17.3</td><td>2.61</td><td>448</td><td>2.50</td><td>0.536</td><td>461</td><td>1324</td><td>731</td><td>1954</td><td>481</td><td>10/10/21</td></tr><tr><td>Average</td><td>18.5</td><td>2.93</td><td>505</td><td>2.58</td><td>0.581</td><td>507</td><td>1376</td><td>755</td><td>2096</td><td>506</td><td>—</td></tr><tr><td>Standard deviation</td><td>0.46</td><td>0.21</td><td>43</td><td>0.26</td><td>0.581</td><td>25</td><td>47</td><td>30</td><td>97</td><td>21</td><td>—</td></tr></tbody></table>

ID: identity number, BW: body weight, BCS: body condition score (according to Bazin, 1984); HW: hip width; WH: withers height; CD: chest depth; HG: heart girth; WB: buttocks width.
mercial concentrate (87.9% DM; 118 g/kg of protein digestible in the small intestine; 90 g/kg of protein digestible in the small intestine, 0.96 feed units / kg DM for lactation; based on INRA 2010 approaches and recommendations. In the INRA system, feed units (UF) is based on the value of barley as a reference: 1 kg of barley = 1760 kcal of net energy = 1 UF.

Each week, 5 3D images were recorded per heifer using the Defilait3D device, with the goal of obtaining at least one of very good quality [score 3+ or 4+; the "+" meaning the entire animal with the head; see Le Cozler et al. (2024) for more information]. In the end, from the 5 images recorded per heifer, at least 3 good-quality images were obtained. The images from camera 15 were then used to estimate the average body condition score (BCS3d), according to the method developed by Fischer et al. (2015). This method can be assumed as follows: (i) normalization of the 3D surface from 4 anatomical landmarks (top of the left/right ischiums, basis of the left/right sacrum), extraction of a region of interest (hips-tips region) sampled on a 150 × 150 pixels grid; (ii) shape analysis by Principal Component Analysis (PCA) and then multiple linear regression of the PCA coordinates to predict the BCS. This method has been validated on cows with a root mean square error of prediction of 0.31 to 0.32 BCS points and a significantly better repeatability/reproducibility than manual BCS. The hypothesis was that the statistical model obtained on cows could be generalized to heifers without "recalibrating" the database, even if input data type changed, using a single depth image as input instead of a reconstruction using the Kinect fusion algorithm. The detection of the 4 points was also automatized so that the measurement could be obtained directly from the depth images without manual pointing.

Meanwhile, manual measurements of BCS (BCSm) were carried out once a month (until the end of December), according to the methodology developed by Bazin (1984). In the IEPL facility, specially trained and dedicated technicians routinely estimate body condition score (BCSm) once a month on all cows. As manual measurement is still considered to be the reference method ("gold standard"), we used these data to compare the BCS values from the 2 methods. At the same time, i.e., every month, body weight (BW) was recorded using a weighing scale, but during lactation, measurements were taken 3 times a week on average. Weighing usually occurred between 10:00 and 12:00 to maintain consistency in the conditions. The number of BW available varied then from 26 to 49 and in total, 29 BCS3D (once a week) and 8 to 10 BCSm were recorded per animal. Indeed, some BCSm and BW values were considered to be abnormal (more than 0.5 BCS point difference or 20% changes in BW from previous and subsequent animal's value) and were not kept.

As explained above, for this pilot study we deliberately restricted the number of observations and only descriptive analyses were performed. Data were visualized using the ggplot2 package (v. 3.2.1; Wickham et al., 2019) of R software (R Core Team, 2016). In addition to studying BCS changes, we also tested a data-smoothing method for taking into account missing and/or aberrant data, which are common in commercial herds. Using this method, it was possible to assign a value close to reality. For this, we used LOESS regression ("LoCally Estimated Scatterplot Smoothing"), a non-parametric regression method (i.e., it is not associated with an equation, such as a classic linear or polynomial regression) that makes it possible to produce smoothed curves fitted to a point cloud (blue curves on Figures 1 and 2; Cleveland, 1979).

During the experiment, changes in BCS (both BCSm and BCS3d) of the 13 heifers were limited, as expected. Comparison of BCS3d (black dots in Figure 1) and BCSm (red dots in Figure 1) showed that the 2 sets of values were very close.

From April (start of experiment) until the end of August (start of calving season), average BCS3d decreased slightly, from 2.50 (±standard deviation 0.19) to 2.32 (±0.30), whereas BW increased from 515 (±40.5) kg to 604 (±49.4) kg (Figure 2). After calving and until the end of experiment (December 31st), BCS still decreased in most cows. On the contrary, most of them gained BW.

The decrease in BCS value was also noted in BCSm estimations (from 2.63 (±0.19) to 2.11 (±0.24). The higher frequency of BCS estimations from the Deffilait3D device (weekly vs. monthly) made it possible to highlight slight variations in the fattening state of heifers that would be invisible to the naked eye. Linear regression between the 2 estimates of BCS indicated that the technicians usually gave a higher BCS value than the one calculated from the device (BCS3d = 0.97 x BCSm; R² = 0.58). The correlation is however not very high, with important variations.

Our analyses revealed that, even as the BW of the 13 Holstein heifers increased during the course of gestation, their fattening state (BCS) decreased slightly. The gain in BW during gestation was in the range of what was expected and has been described in the literature. The BCS values at calving appeared slightly lower than those expected and/or recommended in the literature for primiparous Holstein cows, but they were still in line with what we expected from this experimental herd. Moreover, although absolute BCS scores are useful indicators for breeding, for many stakeholders it is the evolution of the fattening state that is typically more relevant. This kind of information has already demonstrated its usefulness in adult cows (Delaby et al., 2009), but these data are not commonly recorded in heifers, especially during gestation. Indeed, although previous research has examined the evolution of body weight in heifers (Trocccon and Petit, 1989), information on the fine-scale evolution of BCS values, assessed manually or via a 3D imaging device, is still not widely available. With our approach, in which BCS was estimated once a week using a 3D imaging device, we were able to detect fine-scale variations (increments of 0.1 to 0.15 points) that were not visible in the monthly scores assigned by the technicians (increments of 0.25 or 0.5 points).

Changes in BCS in gestating heifers are generally low, with a maximum gain of 0.5 to 1 point and sometimes no change at all. Here, the gains we observed were close to zero or even negative. In a previous experiment (Le Cozler et al., 2019c), we noted BCS gains in 2 groups of late first-calving animals (36 mo of age on average) of 0.1 and 1 point, in Holstein heifers who respectively reached either 479 g/d or 892 g/d of growth from 4 mo of gestation until 3 weeks before calving. In the present study, average daily gain was 665 g/d, without any visible effect on BCS gain. The likely explanation for this is that young gestating heifers (calving at 24 mo) still require substantial resources for growth, both their own and of the calf *in utero*; if their feed allowance is not adequate or sufficient, animals may need to mobilize their own body reserves to compensate. With detailed, real-time information on changes in body reserves, it becomes possible to adapt the feeding recommendations for pregnant heifers to limit the risks at calving, ensure the optimal development of the fetus, and prepare the mother for a good first lactation. Previous studies were mainly performed on lactating cows (Hallén Sandgren and Emanuelson,
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Changes in body condition score (BCS) over time in 13 gestating heifers</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Changes in body weight (BW) over time in 13 gestating heifers</figcaption></figure>
2016) but the relationships between BCS changes during the last 2/3 of gestation in heifers and subsequent performances, in primiparous cows especially, is not well documented. Combined with the measurements of other changes such as weight or morphological traits, the Deffilait3D device is then opening interesting for further investigations.

The results of the present study indicate that the Deffilait3D device can be used to estimate BCS in young animals, in addition to generating data on morphology and BW. For this last trait for example, the equation published by Le Cozler et al. (2019) allowed us to estimate the initial BW of heifers in the present study, with values varying from 507 to 523 kg (depending on the parameters included in the equations), which is close to the mean value of 515 kg measured by the weighing scale. We also previously estimated body weight and morphological changes from birth to first calving in 5 Holstein heifers with another device (Le Cozler et al., 2022b), and it will be interesting to test if we can estimate body reserves changes with this approach during the entire rearing period. With a comparable Deffilait3D system on-farm, Lebreton et al. (2023) confirmed that it is possible to obtain information on morphological traits quickly, frequently, and from a large number of growing beef animals. Xiong et al. (2023) also demonstrated the interest of depth images to estimate BW and BCS on mature beef cows and Xavier et al. (2024) used 3D images to estimate the body composition of growing bulls. All of these results highlight the promise of 3D imaging technologies for the future of dairy and beef production. In addition, it is likely that these technologies can be successfully applied in other production systems such as dairy goats and ewes.

This study showed that it was possible to closely monitor changes in the fattening status of heifers. This will make it possible to track any links between this status and calving difficulties or unsatisfactory early lactation. This may result in early culling of animals, and while it was obviously beyond the scope of this study to establish a link between changes in BCS during this first gestation and the longevity of the animals, it nevertheless opens up new perspectives. We had only a limited number of observations, ended the experiment after first calving and, moreover, none of the 13 heifers had any calving problems. Our main aim was to demonstrate that it was possible to detect such changes during the first gestation of nulliparous cows. We did not modify any aspect of animal management during this period; all animals were fed and housed together. The fact that changes in BCS were detected, even small ones, suggests that in the future it may be feasible to use this type of monitoring to adapt cows' feeding regimens during gestation, thus avoiding inadequate, insufficient, or excessive weight gain. It has long been known that, inadequate fattening can lead to calving difficulties (dystocia) and poor starts to lactation (Troccon et al., 1989). A tool that can help illuminate the precise evolution of BCS during gestation in the pregnant heifer may thus help to preserve animal health and prevent early culling.

This study demonstrates that it is possible to use 3D imaging technology to detect and track fine-scale variations in BCS in pregnant heifers. Changes in BCS are considered to be more important for management of animals than absolute value of BCS itself, since it can be operator dependent. Such changes are generally invisible with the traditional manual method, and are not easy to manage given the rapid weight gain that occurs in this period (up to 100 kg of BW in the second and third trimester of gestation). The present

study was carried out on a limited number of heifers, and it will be necessary to conduct additional research, with a larger number of heifers and over a longer period, to support these preliminary results. Such studies will open opportunities for studying the effect of early-life management not only on early culling, but on the longevity of dairy cows more generally.

## References

- Bazin, S. 1984. Grille de notation de l'état d'engraissement des vaches piénoires. ITEB-RNED, Paris (France), 31.

- Bédère, N., E. Cutullic, L. Delaby, F. Garcia-Launay, and C. Disenhaus. 2018. Meta-analysis of the relationships between reproduction, milk yield and body condition score in dairy cows. Livest. Sci. 210:73–74. https://doi.org/10.1016/j.livsci.2018.01.017.

- Bewley, J. M., A. M. Peacock, O. Lewis, R. E. Boyce, D. J. Roberts, M. P. Coffey, S. J. Kenyon, and M. M. Schutz. 2008. Potential for Estimation of Body Condition Scores in Dairy Cattle from Digital Images. J. Dairy Sci. 91:3439–3453. https://doi.org/10.3168/jds.2007-0836.

- Cleveland, W. S. 1979. Robust Locally Weighted Regression and Smoothing Scatterplots. J. Am. Stat. Assoc. 74:829–836. https://doi.org/10.1080/01621459.1979.10481038.

- Delaby, L., P. Faverdin, G. Michel, C. Disenhaus, and J. L. Peyraud. 2009. Effect of different feeding strategies on lactation performance of Holstein and Normande dairy cows. Animal 3:891–905. https://doi.org/10.1017/S1751731109004212.

- Delaby, L., S. Leurent, Y. Gallard, and T. Schmitt. 2010. Effet de la race, de la parité, du potentiel laitier et de l'état au vêlage sur l'évolution de l'état corporel des vaches laitières au cours de la lactation. Renc. Rech. Ruminants 17:1.

- Delaval, 2025. DeLaval Body condition scoring BCS. Available at https://www.delaval.com/en-gb/discover-our-farm-solutions/farm-management/delaval-biosensors/delaval-body-condition-scoring-bcs/ (retrieved 2025/04/15).

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

- Elanco, 2004. Body Condition Scoring for dairy replacement heifers. Elanco Animal Health, available at http://www.elanco.com (retrieved 2021-02-25).

- Fischer, A., T. Luginbühl, L. Delattre, J. M. Delouard, and P. Faverdin. 2015. Rear shape in three dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows. J. Dairy Sci. 98:4465–4476. https://doi.org/10.3168/jds.2014-8969.

- Hady, P. J., J. J. Domecq, and J. B. Kaneene. 1994. Frequency and precision of body condition scoring. J. Dairy Sci. 77:1543–1547. https://doi.org/10.3168/jds.S0022-0302(94)77095-8.

- Halachmi, I., P. Polak, D. J. Roberts, and M. Klopcic. 2008. Cow Body Shape and Automation of Condition Scoring. J. Dairy Sci. 91:4444–4451. https://doi.org/10.3168/jds.2007-0785.

- Hallén Sandgren, C., and U. Emmanuelson. 2016. Consistency of measurements from an automatic body condition scoring camera. In: Precision Dairy Farming 2016, Kamphuis & Steenvemld eds, pages 285–290. DOI: https://doi.org/10.3920/9789086868292_048

- Hansen, M. F., M. L. Smith, L. N. Smith, K. Abdul Jabbar, and D. Forbes. 2018. Automated monitoring of dairy cow body condition, mobility and weight using a single 3D video capture device. Comput. Ind. 98:14–22. https://doi.org/10.1016/j.compind.2018.02.011.

- INRA. 2010. Alimentation des bovins, ovins et caprins. Besoins des animaux - Valeurs des aliments: Tables Inra 2010. Édition remaniée. Quae éditions., Paris France. 311 pages.

- Koenen, E. P. C., R. F. Veerkamp, P. Dobbelaar, and G. De Jong. 2001. Genetic Analysis of Body Condition Score of Lactating Dutch Holstein and Red-and-White Heifers. J. Dairy Sci. 84:1265–1270. https://doi.org/10.3168/jds.S0022-0302(01)74588-2.

- Kuzuhara, Y., K. Kawamura, R. Yoshitoshi, T. Tamaki, S. Sugai, M. Ikegami, Y. Kurokawa, T. Obitsu, M. Okita, T. Sugino, and T. Yasuda. 2015. A preliminarily study for predicting body weight and milk properties in lactating Holstein cows using a three-dimensional camera system. Comput. Electron. Agric. 111:186–193. https://doi.org/10.1016/j.compag.2014.12.020.
Laven, R. 2015. Disease A-Z. Fertility in dairy herds – advanced. Part 9: cow body condition score BCS and fertility performance in dairy herds. NADIS Animal Health skills, available at https://www.nadis.org.uk/disease-a-z/cattle/fertility-in-dairy-herds-advanced/part-9-cow-body-condition-score-bcs-and-fertility-performance-in-dairy-herds/ (accessed on 24 October 2024).

Le Cozler, Y., A. Allain, C. Xavier, L. Depuille, A. Caillot, J. M. Delouard, L. Delattre, T. Luginbuhl, and P. Faverdin. 2019a. Volume and surface area of Holstein dairy cows calculated from complete 3D shapes acquired using a high-precision scanning system: Interest for body weight estimation. Comput. Elect. Agric. DOI https://doi.org/10.1016/j.compag.2019.104977.

Le Cozler, Y., C. Allain, A. Caillot, J. M. Delouard, L. Delattre, T. Luginbuhl, and P. Faverdin. 2019b. High-precision scanning system for complete 3D cow body shape imaging and analysis of morphological traits. Comput. Electron. Agric. 157:447–453. https://doi.org/10.1016/j.compag.2019.01.019.

Le Cozler, Y., C. Allain, A. Fischer, A. Caillot, L. Depuille, C. Xavier, J. M. Delouard, L. Delattre, T. Luginbuhl, A. Lebreton, and P. Faverdin. 2022a. Chapter 18. The use of 3D imaging technology in animal management, with a special emphasis on ruminant production. In Practical Precision Livestock Farming (PLF): Hands-on experiences with PLF technologies in commercial and R&D settings', Banhazi et al edts, Wageningen Academic Press.

Le Cozler Y., Brachet E., Bourguignon L., Delattre L., Luginbuhl T., Faverdin P. 2022b. Three-dimensional (3D) imaging technology to monitor growth and development of Holstein heifers and estimate body weight, a preliminary study. Sensors, 4635 doi: https://doi.org/10.3390/s22124635.

Le Cozler Y., Dumesny M., Delouard J.M., Delattre L., Luginbuhl T., Faverdin P. 2024. One-shot technology for 3D imaging of large animals: perspective for ruminant management. Trans. Anim. Sci., txa018, doi: https://doi.org/10.1093/tas/txae018.

Le Cozler, Y., J. L. Troccon, B. Marquis, and P. Faverdin. 2019c. Early lactation performance in Holstein heifers first calving at 36 months managed on pasture and fed a controlled total mixed ration during mid- and late gestation. J. Dairy Res. 86:272–278. https://doi.org/10.1017/S002202991900044X.

Lebreton, A., C. Allain, L. Delattre, J. Loof, Y. Do, and M. Bruyas. 2023. Validation of high-throughput movable 3D device for phenotyping the whole cattle body. Presented at the 74th EAAP Congress, Lyon, France - August 26th / September 1st.

Negretti, P., G. Bianconi, S. Bartocci, S. Terramoccia, and M. Verna. 2008. Determination of live weight and body condition score in lactating Mediterranean buffalo by Visual Image Analysis. Livest. Sci. 113:1–7. https://doi.org/10.1016/j.livsci.2007.05.018.

R Core Team. R: A Language and Environment for Statistical Computing; Version 3.2.4.; R Foundation for Statistical Computing, Vienna, Austria, 2019; Available online: https://www.r-project.org/ (accessed on 1 April 2023).

Reproscope. 2019. Observatoire de la reproduction des bovins. Available at http://www.reproscope.fr/ (retrieved 2021/07/08).

Ruegg, P. L. 1991. Body condition scoring in dairy cows: Relationships with production, reproduction, nutrition, and health. The Compendium North America Edition 13:1309–1313.

Troccon, J. L., and M. Petit. 1989. Croissance des génisses de renouvellement et performances ultérieures. INRA Productions Animales, Paris: INRA, 2 (1), 55–64. hal-00895854.

Xavier, C., Y. Le Cozler, L. Depuille, A. Caillot, A. Lebreton, C. Allain, J. M. Delouard, L. Delattre, T. Luginbuhl, P. Faverdin, and A. Fischer. 2022. The use of 3-dimensional imaging of Holstein cows to estimate body weight and monitor the composition of body weight change throughout lactation. J. Dairy Sci. 105:4508–4519. https://doi.org/10.3168/jds.2021-21337 https://dois.org/10.3168/jds.2021-21337.

Xavier, C., I. Morel, R. Siegenthaler, F. Dohme-Meier, P. Bressy, T. Luginbühl, Y. Le Cozler, and S. Lerch. 2024. Three-dimensional (3D) imaging to estimate in vivo body and carcass chemical composition of growing crossbred bulls. Animal 18:1–13. https://doi.org/10.1016/j.animal.2024.101174.

Xiong, Y., I. Condotta, J. Musgarve, T. Brown-Brandl, and T. Mullinks. 2003. Estimating body weight and body condition score of mature beef cows using depth images. Transl. Anim. Sci. 7:txad085. https://doi.org/10.1093/tas/txad085.

Van Arendonk, J. A. M. 1985. Studies on the replacement policies in dairy cattle. II. Optimum policy and influence of changes in production and prices. Livest. Prod. Sci. •••:101–121.

Waltner, S. S., J. P. McNamara, and J. K. Hillers. 1993. Relationships of body condition score to production variables in high producing Holstein dairy cattle. J. Dairy Sci. 76:3410–3419.

Wickham, H., W. Chang, L. Henry, T. L. Pedersen, K. Takahashi, C. Wilke, K. Woo, and H. Yutani. 2019. RStudio Ggplot 2 (3.2.1). Available at: https://cran.r-project.org/web/packages/ggplot2/index.html (accessed on 15 September 2021).

## Notes

Yannick Le Cozler, https://orcid.org/0000-0001-9644-317X

Laurent Delattre, https://orcid.org/0000-0001-5720-3982

Thibault Luginbuhl, https://orcid.org/0000-0002-9622-920X

The authors are grateful to the national Deffilait project (Agence Nationale de la Recherche, Paris, France, ANR-15-CE20-0014), APIS-GENE (Paris, France) for funding the Deffilait3D project and #DigitAg for financial support during the internship of Maxime Dumesny. The authors have no conflicts of interest to report.