# Cow factors to address when performing avoidance distance tests at the feeding rack

Luc Mounier, Isabelle Veissier, Justine Rimbaud, Xavier Boivin, Nancy

Rebout, Alice de Boyer Des Roches

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## To cite this version:

Luc Mounier, Isabelle Veissier, Justine Rimbaud, Xavier Boivin, Nancy Rebout, et al.. Cow factors to address when performing avoidance distance tests at the feeding rack. Animal, 2025, 19 (4), pp.101461. 10.1016/j.animal.2025.101461. hal-05153379

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# Cow factors to address when performing avoidance distance tests at the feeding rack

<figure><img src="image_3.png" /></figure>

L. Mounier<sup>a,b,c</sup>, I. Veissier<sup>a</sup>, J. Rimbaud<sup>c</sup>, X. Boivin<sup>a</sup>, N. Rebout<sup>a</sup>, A. de Boyer des Roches<sup>a,b,c,*</sup>

## ARTICLE INFO

**Article history:**

Received 5 June 2024

Revised 5 February 2025

Accepted 7 February 2025

Available online 12 February 2025

**Keywords:**

Animal welfare

Breed

Dairy cow

Human-animal relationship

Lameness

## ABSTRACT

The human-animal relationship is an important component of farm-animal welfare. It is often assessed using 'avoidance distance', i.e. the distance at which animals avoid humans approaching them. However, this avoidance distance may be influenced by factors other than the human-animal relationship such as genetic background or health status. The objective of this cross-sectional epidemiological study was to assess the size of the effect of animal factors and testing conditions on the results of an avoidance test in dairy cows. Five observers applied a standardised test of avoidance distance at the feeding rack on 2 306 lactating cows from 103 dairy farms. We collected data on the independent variables: breed, parity and lameness, and height at withers, and calculated the height difference between the observer standing on the walking alley and each cow restrained at the feeding table. The avoidance distance of the cows, expressed in cm, was modelled using a linear mixed-effect model, with the above-mentioned independent variables as fixed factors, and the farm nested in observer as a random factor. The avoidance distance expressed in four categories as in the Welfare Quality protocol (2009) was modelled using a mixed effects ordinal regression with the same fixed and random factors. For the avoidance distance expressed in cm, we observed that intra-farm variability was twice as large as inter-farm variability (SD: 33.9 cm intra-farm vs 16.7 cm inter-farm). Avoidance distance increased by 7.0 cm (95%CI [3.8–10.2]) when the cow was multiparous and increased by 4 cm (95%CI [0.2–0.6]) when the height difference between observer and cow increased by 10 cm. Avoidance distance decreased by 9.9 cm (95%CI [−19.6–−0.3]) when the cow was lame and decreased by 6.4 cm (95%CI [−23.4–−0.4]) when the cow breed was Montbéliarde vs Holstein. For the avoidance distance expressed in categories, there was a significant effect of the height difference and the parity. A greater height difference as well as multiparous cows had a slightly increased likelihood of reaching a higher avoidance distance category. We conclude that avoidance-test results are influenced by cow-related factors (parity, height difference to experimenter, lameness status, and breed). Thus, to reliably use the avoidance distance test to assess the human-animal relationship at farm level, we recommend careful sampling of cows for testing based on these factors. The measured avoidance distance can also be corrected with a coefficient based on animal-specific factors and height difference between observer and cows.

© 2025 The Authors. Published by Elsevier B.V. on behalf of The animal Consortium. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

## Implications

Human-animal relationship, often assessed using 'avoidance distance', is an important component of cow welfare. We studied whether avoidance distance may be influenced by factors other than the human-animal relationship. Our results show that the cows' avoidance distance, expressed in cm, is slightly influenced by lameness, parity, breed, and by the height difference between

the observer and the cow. It also varied more within a herd than between herds, highlighting the role of cows' individual factors. Avoidance distance category was influenced by height difference and parity. When sampling cows to be tested, professionals should take these factors into account.

## Introduction

The human-animal relationship is an important component of farm-animal welfare. Fear of humans induces acute and chronic stress, lower milk yields, and reduced milk let-down (Breuer

<footnote><sup>a</sup>Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France</footnote>

<footnote><sup>b</sup>Chaire Bien-Etre Animal, VetAgro Sup, 1 avenue Bourgelat, 69280 Marcy-l'Étoile, France</footnote>

<footnote><sup>c</sup>Université de Lyon, VetAgro Sup, 1 avenue Bourgelat, 69280 Marcy-l'Étoile, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail address: alice.deboyerdesroches@vetagro-sup.fr (A. de Boyer des Roches).</footnote>
et al., 2003; Hemsworth, 2003; Waiblinger et al., 2002; Rushen et al., 1999). A poor human-animal relationship can also result in accidents to both caretakers and their animals (Lindahl et al., 2016; Hemsworth and Boivin, 2011; Kallioniemi et al., 2011). Efforts to improve human-animal relationships hinge on having a reliable assessment of the relationship.

Several methods have been proposed to assess the human-animal relationship in cattle. These methods are based on measuring the extent to which the animal tolerates a human approach or is motivated to interact with humans (see Waiblinger et al. (2006) for a review). The Welfare Quality® assessment protocol for cattle (Welfare Quality®, 2009) includes the avoidance distance at the feeding rack test. It is expressed in four categories (0: no flight distance to 4: above 100 cm) allowing to record the percentage of cows within each category per farm. Avoidance distance is considered a reliable measure for assessing human-animal relationships because (i) it is closely correlated with other methods used to assess cows' fear response to humans (e.g. a human passing by and touching a lying animal, or walking through the herd and touching animals, animal handling (Bacher et al., 2021; Windschnurer et al., 2008)); (ii) it varies with an animal's previous experience with humans (Lensink et al., 2003; Breuer et al., 2000; Hemsworth et al., 2000); (iii) the results of the test have good intra- and inter-observer reliability in both pasture and indoor conditions (Aubé et al., 2023; Windschnurer et al., 2008). Furthermore, the test is easy to perform on-farm, and the results remain stable when the test is performed by different people and repeatedly over time (Windschnurer et al., 2008; Winckler et al., 2007; Waiblinger et al., 2003). Nevertheless, its precision remains an issue.

Attitudes, especially behavioural attitudes (cognitive and affective attitudes towards interactions with the animals) are one of the most important factors influencing human behaviour towards animals and, in turn, animal behaviour (Hemsworth et al., 2002). Caretakers with a more positive behavioural attitude and less negative one towards animals use more positive and less negative interactions, respectively, and have animals with better human-animal relationship (Andreasen et al., 2020; Hemsworth et al., 2002; Waiblinger et al., 2002; Lensink et al., 2000). Avoidance tests applied at farm level (i.e. observing a sample of animals) are thought to reflect the impacts of the farmer's interactions with his/her animals. In fact, research has found that cows more readily accept being approached when the farmer considers human-animal relationships an important factor for the success of the farm and when they experience positive contacts during milking (de Boyer des Roches et al., 2016; Waiblinger and Menke, 1999). The number of cows per farmer also affects the frequency of farmer interactions with their animals: more cows per person working on the farm means less time spent with them, and so the cows are more difficult to approach (de Boyer des Roches et al., 2016; Waiblinger et al., 2003; Waiblinger and Menke, 1999). Other factors may influence how cattle react to humans: the genetic background of the cows (Haskell et al., 2014), their health status with lame cows accepting to be approached at a closer distance than non-lame cows (Špinka et al., 2005), or the social cohesion of the group – with a shorter avoidance distance when cows often lick each other (Waiblinger et al., 2003). Given that cattle can detect the size of humans (Rybarczyk et al., 2001), they may experience a person taler than the cow as being more threatening than a smaller person. However, avoidance distance tests do not generally consider animal-specific factors and the animal's height difference with the observer. For instance, the Welfare Quality® protocol stipulates how many cows should be observed per farm and that the cows must be selected at random, but it gives no indication of their characteristics (e.g. age, breed, lameness etc.) nor their height compared to the observer (Welfare Quality®, 2009). However, variations

induced by these factors may at least partly mask the effects of the human-animal relationship on cow's avoidance distance.

The objective of this study was to explore the variations in avoidance-test results (expressed in cm or using the Welfare Quality categories) due to animal-specific and test-conditions factors, and to compare these variations with the 'between-farm' variations supposed to result – at least in part – from the human-animal relationships. We used data previously collected in a cross-sectional epidemiological study where we analysed between-farm variability in the proportion of cows that let themselves be touched during an avoidance test at the feeding rack (de Boyer des Roches et al., 2016). This previous study found that farmers' attitudes towards cow health and human-animal relationships, interactions between the farmer and his/her cows around calving, farmer's years of experience, and the ratio between number of workers and number of cows all explained a significant part of the inter-farm variability observed. However, this was without taking into account intra-farm variability. Here, we analyse individual-level variation in avoidance at the feeding rack in order to identify factors–other than the human-animal relationship– that can influence the results of the test (expressed in cm or using the Welfare Quality categories).

## Material and methods

The data used in the present study come from our earlier database of 118 commercial dairy farms where the Welfare Quality® protocol had been applied (de Boyer des Roches et al., 2016). Here, we were only able to use data from 103 of the farms due to missing data. Farm herd size ranged from 15 to 120 dairy cows, and the cows were either Montbéliarde (a French dual-purpose breed) or Holstein breed: 48 farms had only Holstein cows, 39 farms had only Montbéliarde cows and 16 farms had cows of both breeds (for a detailed description of the farms, see de Boyer des Roches et al., 2016). Each farm was visited once by one of five observers who had been pretrained by an associate from the Welfare Quality® project. Training had taken place on four farms. Training ensured that observers performed the test in accordance with Welfare Quality (2009) specifications and that their estimated avoidance distance was close to that of the trainer.

### Avoidance testing at the feeding rack

We applied the Welfare Quality® protocol for sampling cows on each farm and performing the avoidance test (Welfare Quality®, 2009). On each farm, the number of cows to be tested was defined according to the Welfare Quality® (2009) protocol: if there were 30 or fewer lactating cows in the herd, then all animals were tested; otherwise, 75-45% of cows were randomly chosen, the proportion decreasing with number of cows in the herd (e.g. 30 cows out of 44, 54 cows out of 120). The cows were tested at the feeding rack, when all the cows from the herd were back in their home pen after morning milking, and blocked at the feeding rack. The observer (wearing a dark coat and dark boots) stood at a distance of 2 m in front of the cow to be tested, making sure that the cow was paying attention (not foraging), and noted the individual cow's number (ear tag number). The observer then approached the cow at a speed of 60 cm/s, looking at the cow's muzzle, and with an arm held at an angle of 45° from the body. The observer stopped walking when the cow showed a sign of withdrawal (moving back, turning its head to the side, or pulling its head back) or when she/he could touch the cow's muzzle. The observer noted the distance, by visual estimation, between her/his hand and the cow's muzzle at the moment of withdrawal, to the nearest 10 cm. In addition, the avoidance distance was expressed in four categories
as in the Welfare Quality Protocol for Dairy Cattle (2009): 0 cm (the animal was touched by the observer), 0 cm - 50 cm], 50 cm - 100 cm], and above 100 cm.

## Factors studied

We considered three animal-related factors (parity, lameness, breed), and one test-related factor (the height difference between observer and cow at the time of the test) (Table 1). Animal characteristics were collected by direct observation (lameness, animal height) or taken from milking records (breed, parity). Factors related to test conditions (observer height, feeding barrier characteristics) were collected by direct observation during the visit. In detail, cow height at the withers was taken using a measuring rod when the cow was blocked at the feeding rack (de Boyer des Roches et al., 2019). Then, when the cow was released, she was scored for lameness following the Welfare Quality® protocol (Welfare Quality®, 2009). A cow was considered “lame” as soon as we observed any imperfect temporal rhythm in stride creating a limp. The breed and parity of each cow were extracted from milk records. Regarding the parity, we considered two classes: primi-

parous or multiparous. At each farm, after the behavioural observations, once all cows were released, we measured the difference in height between the feeding floor and the walking alley floor, taking the walking alley floor as the reference (Lardy et al., 2020; de Boyer des Roches et al., 2019) (Fig. 1). Finally, we used cow height at withers, each observer's height, and the height difference between the alley floor and feeding floor to calculate the height difference between observer and cow at the time of the test for each individual cow, with a resolution of 1 cm.

## Statistical analyses

The statistical unit was the cow. Statistical analyses were performed using R® software build 4.2.2 (R Development Core Team, 2022). First, cow avoidance distance was modelled using a linear mixed-effect model. The model was written with 'avoidance distance' as a dependent variable and 'height difference' + 'lameness' ('not lame' vs 'lame') + 'breed' ('Holstein' vs 'Montbeliarde') + 'parity' ('primiparous' vs 'multiparous') as independent variables. To account for repeated observations, we included a random effect for the farm, nested within a random effect for the observer. We

<table><caption>**Table 1**
Factors studied: categories (Observer, Cow), and levels.</caption><thead><tr><th rowspan="2">Item</th><th>Qualitative factors:</th><th colspan="2">Quantitative factors:</th></tr><tr><th>number of cows per category</th><th>median<br>[1st-3rd quartiles]</th><th>missing data</th></tr></thead><tbody><tr><td><strong>Observer</strong></td><td></td><td></td><td></td></tr><tr><td>1 (female, 160 cm in height) Alice</td><td>682 (29 farms)</td><td></td><td></td></tr><tr><td>2 (male, 185 cm) Christophe</td><td>287 (15 farms)</td><td></td><td></td></tr><tr><td>3 (male, 180 cm) Eric</td><td>234 (11 farms)</td><td></td><td></td></tr><tr><td>4 (female, 165 cm) Maud</td><td>616 (28 farms)</td><td></td><td></td></tr><tr><td>5 (male, 191 cm) Remi</td><td>487 (20 farms)</td><td></td><td></td></tr><tr><td><strong>Cow characteristics</strong></td><td></td><td></td><td></td></tr><tr><td>Lameness</td><td></td><td></td><td></td></tr><tr><td>Not Lame</td><td>2 195</td><td></td><td></td></tr><tr><td>Lame</td><td>62</td><td></td><td></td></tr><tr><td>Missing data</td><td>49</td><td></td><td></td></tr><tr><td>Breed</td><td></td><td></td><td></td></tr><tr><td>Holstein</td><td>1 242</td><td></td><td></td></tr><tr><td>Montbéliarde</td><td>1 051</td><td></td><td></td></tr><tr><td>Missing data</td><td>13</td><td></td><td></td></tr><tr><td>Parity</td><td></td><td></td><td></td></tr><tr><td>Primiparous</td><td>798</td><td></td><td></td></tr><tr><td>Multiparous</td><td>1 508</td><td></td><td></td></tr><tr><td>Height at withers</td><td>-</td><td>144.0 [140.0-176.0]</td><td>157</td></tr><tr><td>Height difference between the observer and the cow</td><td>-</td><td>32.0 [21.0-43.0]</td><td>157</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Measures performed on the observer, the cow, and the self-locking barrier. Data used in the model are highlighted in bold.</figcaption></figure>
used the *lmer* function bundled with the *lme4* package for the linear mixed-effect models (Bates et al., 2015). A fixed-effect parameter was considered significant when its 95% confidence interval did not contain 0 (Bates et al., 2015). Normality of residuals and random effect distributions were visually verified using plots of residuals and quantile-quantile plots of residuals and random effect. We also checked for homoscedasticity and independence of residuals using plots of residuals. Second, we ran a mixed-effects ordinal regression model using the ordinal package (Christensen, 2023); with avoidance distance as the response variable expressed in categories. Fixed effects included the height difference between the observer and the cow, the cow's lameness, breed, and parity. A random effect for the experimental unit, nested within a random effect for the observer was also applied here. We calculated the odds ratio by exponentiating the estimated coefficient. The odds ratio represents the percentage change in the likelihood of being in a higher avoidance distance category for each unit increase in the factor. For instance, an odds ratio of 1.021 indicates a 2.1% increase ((1.021 - 1) * 100) in the odds of reaching a higher avoidance category for each unit increase in the factor.

## Results

The final dataset included 2 306 lactating cows from the 103 farms. Median number of cows per farm was 51 (min: 15; max: 120). Median number of cows tested per farm was 36.5 (min: 15; max: 60). Median avoidance distance of the cows was 40.0 cm

(min: 0.0 cm; 25% quartile: 20.0 cm; 75% quartile: 80.0 cm; max: 200.0 cm; Fig. 2). Among the 2 306 lactating cows, 265 were touched during the test, 1 080 had an avoidance distance of ] 0 cm – 50 cm], 788 had an avoidance distance of ] 50 cm – 100 cm], and 173 had an avoidance distance above 100 cm. Among the 2 306 cows in the sample dataset, 95.2% were not lame, 53.9% were Holstein, 65.4% were multiparous, and height at withers (expressed as median [1st–3rd quartile]) was 144.0 [140–176] cm (Table 1). The five observers were 160 cm, 165 cm, 180 cm, 185 cm, and 191 cm tall. The median [1st–3rd quartile] of the height difference was 32.0 cm [21.0–43.0] (Table 1).

The linear mixed effect model estimated a mean avoidance distance of 34.7 cm (95%CI [27.0–42.4]; Table 2). “Parity”, “height difference”, “lameness” and “breed” contributed to 54.2, 27.7, 9.3, and 8.8%, respectively, of the variability explained by the model (i.e. the sum of squares between classes). Avoidance distance increased by 7.0 cm (95%CI [3.8–10.2]) when the cow was multiparous and increased by 3.9 cm (95%CI [1.9–5.8]) when the height difference between observer and cow increased by 10 cm. Avoidance distance decreased by 9.9 cm (95%CI [−19.6 – −0.2]) when the cow was lame and decreased by 6.4 cm (95%CI [−12.4 – −0.4]) when the cow breed was Montbéliarde vs Holstein. Regarding the random effects, intra-farm variability (SD between cows of the same farm, 33.9 cm) was twice as large as the inter-farm variability (16.7 cm).

For the ordinal regression model using the four categories for avoidance distance from the Welfare Quality® Protocol (2009) (Table 3), there was a significant effect of the height difference

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Distribution of avoidance distance (in cm) in the 2 306 cows tested on 103 commercial dairy farms.</figcaption></figure>

**Table 2**

Coefficients of the full multivariate linear mixed model between farm/cow/observer variables and avoidance distance at feeding rack (t-value are stated for Wald tests of differences between ‘intercept’* and other modalities).

<table><thead><tr><th rowspan="2">Item</th><th rowspan="2">Estimate</th><th colspan="2">Confidence interval</th><th rowspan="2">t value</th></tr><tr><th>2.50%</th><th>97.50%</th></tr></thead><tbody><tr><td colspan="5"><strong>Fixed effects (coefficients of the model)</strong></td></tr><tr><td>Intercept*</td><td>35</td><td>27</td><td>42</td><td>8.78</td></tr><tr><td>Height difference (Observer – Cow)</td><td>4</td><td>2</td><td>6</td><td>3.81</td></tr><tr><td>Lameness: Lame</td><td>-10</td><td>-20</td><td>-0</td><td>-2.01</td></tr><tr><td>Breed: Montbéliarde</td><td>-6</td><td>-12</td><td>-0</td><td>-2.10</td></tr><tr><td>Parity: multiparous</td><td>7</td><td>4</td><td>10</td><td>4.29</td></tr><tr><td colspan="5"><strong>Random effects (SD)</strong></td></tr><tr><td>Farm / Observer</td><td>16.7</td><td>13.8</td><td>19.4</td><td>-</td></tr><tr><td>Observer</td><td>0.00</td><td>0.00</td><td>6.0</td><td>-</td></tr><tr><td>Residuals</td><td>33.9</td><td>32.9</td><td>35.0</td><td>-</td></tr></tbody></table>

<footnote>* Intercept: non-lame cow, Holstein, primiparous.</footnote>
<caption>**Table 3**</caption>

Coefficients of the full multivariate ordinal regression model (Cumulative Link Mixed Model) between explanatory variables and the four categories of dairy cow avoidance distance at feeding rack (Touched; [0–50] cm; [50–100] cm; above 100 cm).

<table><thead><tr><th>Item</th><th>Estimate</th><th>SE</th><th>Odds Ratio</th><th>95% Confidence interval</th><th>Z value</th><th>P-value</th></tr></thead><tbody><tr><td>Fixed effects (coefficients of the model)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Height difference (Observer – Cow)</td><td>0.02</td><td>0.01</td><td>1.02</td><td>1.01 – 1.03</td><td>3.72</td><td>&lt; 0.001</td></tr><tr><td>Locomotion: Not Lame</td><td>Reference</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Locomotion: Lame</td><td>–0.50</td><td>0.28</td><td>0.61</td><td>0.35 – 1.04</td><td>–1.81</td><td>0.0706</td></tr><tr><td>Breed: Holstein</td><td>Reference</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Breed: Montbéliarde</td><td>–0.27</td><td>0.17</td><td>0.76</td><td>0.55 – 1.06</td><td>–1.60</td><td>0.1101</td></tr><tr><td>Parity: primiparous</td><td>Reference</td><td>–</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Parity: multiparous</td><td>0.40</td><td>0.09</td><td>1.49</td><td>1.24 – 1.79</td><td>4.26</td><td>&lt; 0.001</td></tr><tr><td>Random effects (SD)</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Farm / Observer</td><td>0.7525</td><td>0.8675</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Observer</td><td>0.00</td><td>0.00</td><td>–</td><td>–</td><td>–</td><td>–</td></tr><tr><td>Threshold coefficients</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Touched | [0–50]</td><td>–1.54</td><td>0.22</td><td>–</td><td>–1.96 – –1.10</td><td>–6.99</td><td>–</td></tr><tr><td>[0–50] | [50–100]</td><td>1.19</td><td>0.22</td><td>–</td><td>0.76 – 1.61</td><td>5.46</td><td>–</td></tr><tr><td>[50–100] | Above 100 cm</td><td>3.74</td><td>0.24</td><td>–</td><td>3.27 – 4.20</td><td>15.7</td><td>–</td></tr></tbody></table>

between the observer and the cow (P < 0.001). The positive coefficient of the estimate (0.021 ± 0.006) means that a greater height difference between the observer and the cow favoured a higher avoidance distance. The odds ratio of 1.021 indicates that for each unit increase (one cm increase) in height difference, the odds of being in a higher avoidance distance category increased by 2.1% ((1.021 - 1) * 100). There was also a trend towards an effect of lameness (P = 0.071). The negative coefficient (–0.501 ± 0.277) indicates that lame cows tended to have lower chances of being in a higher avoidance distance category. The odds ratio of 0.606 indicates that the odds of being in a higher avoidance distance category are approximately 39.4% ((1 - 0.606) * 100) lower for lame animals than for non-lame ones. The results did not show a significant effect of breed. The results showed a significant effect of parity (P < 0.001). The positive coefficient (0.399 ± 0.094) indicates that multiparous cows had an increased likelihood of reaching a higher avoidance distance category compared to primiparous ones. The odds ratio of 1.49 indicates more precisely that multiparous cows had a 49% ((1.49 - 1) * 100) higher chance of being in a higher avoidance distance category than primiparous cows.

## Discussion

This study, based on a cross-sectional epidemiological approach, found that the individual avoidance distance of cows (expressed in cm) approached by an observer increased with increasing cow parity and increasing height difference between observer and cow, and decreased when cows were lame or Montbéliarde breed (compared to Holstein). When considering avoidance distance as a categorical variable as in the Welfare Quality® protocol, only two factors had an influence on the outcome: height difference between observer and cow, and parity. Higher height difference between observer and cow and multiparous cow had an increased likelihood of the cow reaching a higher avoidance distance category. Variations between animals within farms were twice as much as variations between farms. These findings show associations between several factors and avoidance distance, which may not be cause-effect associations but do point to hypotheses to be tested in controlled studies.

The differences in avoidance distance reached 7 [4–10] cm between multiparous and primiparous cows, 10 cm [0–20] cm between non-lame and lame cows, and 6 [0–12] cm between Holstein and Montbéliarde cows. This may seem too small to reach biological significance, especially since the observers estimated avoidance distance by sight with an estimated precision of

10 cm. Nevertheless, when considering avoidance distance as a categorical variable (0, [0 – 50], [50 – 100], and above 100 cm) as in the Welfare Quality protocol, height difference and parity led to a change of categories for some cows and may thus affect the results of comparisons between farms.

## Animal factors and test conditions

Avoidance distance increased when the cow was multiparous and decreased when the cow was lame or Montbéliarde breed (compared to Holstein). In our study, multiparous cows were less ready to accept being approached by a human than primiparous cows, both in avoidance distance measured in cm or expressed as a categorical variable as in the Welfare Quality® (2009) protocol. This suggests that multiparous cows are more wary of humans than primiparous cows. Multiparous cows will likely have experienced more encounters with unfamiliar persons in aversive contexts, such as hoof trimming, veterinary examination, or artificial insemination. As cows are able to discriminate between different persons (Rybarczyk et al., 2001; Munksgaard et al., 1997), and therefore between the farmer and other persons, multiparous cows may have become more suspicious of unfamiliar persons, such as the unfamiliar observers in this study. However, there seems to be no uniform association between cow avoidance distance and cow age. A study by Waiblinger et al. (2003) found no difference in avoidance distance at the feeding rack between cows of different ages in 24 farms out of the 27 farms, but the old cows were less approachable than young cows on one farm and young cows were less approachable than old cows on two farms. The human–animal relationship results from the balance between positive and negative interactions (Hemsworth and Boivin, 2011; Waiblinger et al., 2006), and it is likely that this balance leans towards negative interactions in some farms but towards positive interactions in others. Given that all cows in commercial farms may experience negative human interactions at some points (health care, hoof trimming, ...) and that negative experiences are usually more powerful than positive experiences (Broom, 2023), the overall trend across farms, as found in our sample here, would be that older cows may be slightly more afraid of humans than young cows.

Lame cows were easier to approach: the difference in avoidance distance between lame and non-lame cows reached 10 [0.21–19.56] cm. A similar trend was observed when the avoidance distance was expressed as a categorical variable. This is consistent with the higher percentage of touch contact by humans in lame than non-lame cows in Czech farms (Špinka et al., 2005). However,
an Austrian study found that avoidance distance in the barn did not correlate with lameness (Mülleder et al., 2003). Lameness is associated with pain (Whay et al., 1998). The avoidance distance test was performed at the feeding rack, after milking, which means the cows had already been standing for a long time by the time the test was performed. We therefore hypothesise that lame cows were more motivated to not move to avoid pain by bearing their weight on their feet rather than recoiling from the experimenter. Cows in pain are also more apathetic (Ginger et al., 2023; Ledoux et al., 2023; de Boyer des Roches et al., 2017; Prunier et al., 2013), which may further explain why they react less to environmental stimuli such as an approaching observer. Based on these arguments, we propose to exclude lame animals from the avoidance test protocol if they are already known or include lameness as a corrective factor when measuring avoidance at the feeding rack in cows.

The effects of genetics on cow reactivity have been documented for some breeds and cattle temperament clearly appears heritable with several traits including avoidance distance (Pinto et al., 2024; Haskell et al., 2014). Santos et al. (2017) calculated a heritability of 0.27 within the Holstein population in this response to the human approach at the feeding rack (Santos et al., 2017). However, here, we provide the first evidence of differences in the avoidance distance measured in cm between Holstein and Montbéliarde cows in a standardised avoidance test: Montbéliarde cows were easier to approach during the test. However, the magnitude of the effect was not strong enough to change the avoidance distance category of cows. The result on avoidance distance expressed in cm does not fit with Waiblinger et al. (2003) who reported no particular differences between dairy breeds. However, their study design included 35 farms and eight breeds, with some farms having up to four breeds, making it difficult to precisely assess the 'breed' effect. On the other hand, one study on dairy breeds reported a lower percentage of cows showing nervousness during milking in Jersey cows compared with Holstein and Ayrshire (Sewalem et al., 2010). Holstein and Montbéliarde are both dairy breeds, and so they come into contact with humans twice daily for milking. Besides production-level traits, their breed selection history may therefore have included behavioural traits such as temperament to ensure efficient animal handling and human safety (Haskell et al., 2014). Another explanation would be that technical management practices (e.g. human-calf contact, calves raised in groups or not, environmental enrichment, social mixing, handling techniques, etc.) – not explored here – varied between farmers, which would make breed somehow a confounding factor since farms with cows of only one breed being predominant in our study (87 farms over 103). Further studies are needed to examine responses to the avoidance distance test at the feeding rack in cows of different breeds but raised in the same environment in order to provide evidence of genetics as a factor influencing avoidance distance in dairy breeds.

The greater the difference in height between observer and cow, the earlier the cows moved backwards. This was confirmed when the avoidance distance was expressed as a categorical variable. Here, we measured the height difference between observer and cow using data on each cow's height at withers, the observer's height, and the height difference between the alley floor and the feeding floor. The fact that a physically higher stimulus. i.e. a stimulus approaching the cow from a greater height, led to earlier avoidance by moving backwards, may be explained by the visual system of cattle which, like in sheep and horses, is designed for horizontally scanning the horizon to detect movement while they graze (Grandin and Deesing, 2022). Cattle could therefore not be used to being confronted with a vertical form that moves towards them from a height. This hypothesis should be checked by testing how the same cow responds and reacts to different height stimuli, e.g. humans wearing similar clothes and approaching at similar

pace but from different heights. If this hypothesis is confirmed, then results from the avoidance test to assess the human-animal relationship in farms may be interpreted taking into account the "height difference" factor, for instance by applying a corrective factor, as the results may be linked to the relationship *per se* but also to the height of the observer, the height of the cows, and the height difference between the alley floor and the feeding floor.

## Intra- and inter-farm variability

In this study, intra-farm variability in avoidance distance was 33.88 cm and inter-farm variability was 16.66 cm. This means that the reactions of individual cows on a given farm varied twice as much as the average of cows from different farms. Variations in avoidance distance between cows from the same farm have already been reported in the literature (Waiblinger et al., 2003), but our study is the first to compare inter-farm variation with intra-farm variation. The large variability in avoidance distance between the animals within each farm can be explained, at least in part, by local conditions when performing the test such as distraction sources, by individual cow-specific factors studied here, but probably also more by each cow's previous individual experiences with humans, rather than solely by the farmer's general attitudes and behaviours (which will be essentially the same across all the cows of a farm) or the organisation of on-farm work (de Boyer des Roches et al., 2016). The accumulation of such experiences will modulate the cow's reaction to a standardised human approach. In addition, each cow may have its own temperament that would further influence its reactions during testing. Further studies including animals of identical parity and breed and raised in identical conditions would be helpful to assess the 'individual cow' factor of reactivity. Finally, each farm was visited once by one person, and so we cannot consider the "farm" factor and the "observer" factor as independent. Further studies should be performed with more than one observer assessing the avoidance distance of cows in a given farm in order to estimate independently the "farm" and the "observer" effects. In such studies, care should be taken not to do repeat tests on the same animal to avoid habituation which would affect the results.

## Conclusion

Parity, lameness, breed, and the height difference between observer and cow all can influence the cow's avoidance distance (expressed in cm) at the feeding rack. In our study, each factor affected the avoidance distance by less than 10 cm, which is a small effect size. These effects were reduced when the avoidance distance was expressed in 50 cm – categories but were still present, at least for parity and height of the observer. In addition, there was more variability in cow avoidance distance within a farm than between farms. Therefore, to guarantee the result of the human-animal relationship test is representative of the farm, we recommend careful sampling of cows for testing. If avoidance distance is expressed in cm, then the sample should either include a large number of cows per farm with a variety of animal types (parity, breed, lameness) to be tested, or be restricted to a smaller but more homogenous panel of cows, for instance by excluding severely lame cows. If avoidance distance is expressed in categories, only parity should be taken into account in the selection of the cows to sample. Alternatively, a corrective factor could be applied on the avoidance distance measured, based on the characteristics of the animals (see above) and – whatever the way the avoidance distance is expressed (cm or category) – the height of the observers relative to the height of the cows, thus leading to a new way to score the human-animal relationship.
## Ethics approval

At the time of the study, a review of ethical standards per European Directive 2010/63/EU was not required because the protocols did not affect the farms' routines: we did not apply any experimental treatments, and the animals were observed remotely (without invasive measures). However, when we re-analysed the data, nevertheless, we submitted the protocol to the VetAgro-Sup institutional animal care and use ethics committee, which issued approval (agreement 'CEEA18') on 5 March 2019.

## Data and model availability statement

The data that support the findings of this study are available from the corresponding authors Alice de Boyer des Roches upon request. None of the data were deposited in an official repository.

## Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) did not use any AI and AI-assisted technologies.

## Author ORCIDs

L. Mounier: https://orcid.org/0000-0003-1083-8563.

I. Veissier: https://orcid.org/0000-0002-8497-5395.

J. Rimbaud: no orcid.

X. Boivin: https://orcid.org/0000-0002-4518-5454.

N. Rebout: https://orcid.org/0000-0002-7071-0011.

A. de Boyer des Roches: https://orcid.org/0000-0002-6903-7456.

## CRediT authorship contribution statement

L. Mounier: Writing - review & editing, Writing - original draft, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualisation. **I. Veissier:** Writing - review & editing, Validation, Supervision, Project administration, Methodology, Funding acquisition, Conceptualisation. **J. Rimbaud:** Writing - review & editing, Validation, Formal analysis, Data curation. **X. Boivin:** Writing - review & editing, Validation, Methodology. **N. Rebout:** Writing - review & editing, Writing - original draft, Visualisation, Formal analysis. **A. de Boyer des Roches:** Writing - review & editing, Writing - original draft, Visualisation, Methodology, Investigation, Formal analysis, Data curation, Conceptualisation.

## Declaration of interest

The authors declare that they have no financial or non-financial conflicts of interest.

## Acknowledgements

We thank the Groupements de Défense Sanitaire (GDS) for providing lists of eligible farms, all the farmers who freely cooperated in this survey, and Danone Research for financial support. We are also particularly grateful to Maud Coignard (ONIRIS), Eric Delval (INRAE), Christophe Mallet (INRAE), and Remi Debauchez (ISARA) for their help with data collection, and Jean-Yves Audiart (ONIRIS) and Didier Billon (ONIRIS) for their help with data entry. We thank Marie-Laure Delignette-Muller (VetAgro Sup) for her advices in statistical analyses. We also thank Metaform-Langues for providing valuable English-language proofing support.

## Financial support statement

This work received financial support from Danone Research (Paris, France) and from the GIS Pôle d'Enseignement Sciences Technologiques et d'Innovation dans les domaines du Vivant et de l'Environnement - ESTIVE (INRA Centre de Clermont-Theix, Cemagref centre de Clermont/Lyon/Grenoble, ENITAC, ENVL, ENSVL, ISARA) GRANT 'bourse de thèse' 2009.

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