# Indicators for animal health on agroecological dairy farms in mountain areas

Andrea Ceppatelli, Maeva Cremilleux, Audrey A. Michaud, Enrico Sturaro

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Andrea Ceppatelli, Maeva Cremilleux, Audrey A. Michaud, Enrico Sturaro. Indicators for animal health on agroecological dairy farms in mountain areas. Italian Journal of Animal Science, 2025, 24 (1), pp.2013-2028. 10.1080/1828051x.2025.2554933. hal-05288291

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# Indicators for animal health on agroecological dairy farms in mountain areas

Andrea Ceppatelli, Maeva Cremilleux, Audrey Michaud & Enrico Sturaro

**To cite this article:** Andrea Ceppatelli, Maeva Cremilleux, Audrey Michaud & Enrico Sturaro (2025) Indicators for animal health on agroecological dairy farms in mountain areas, Italian Journal of Animal Science, 24:1, 2013-2028, DOI: 10.1080/1828051X.2025.2554933

**To link to this article:** https://doi.org/10.1080/1828051X.2025.2554933

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# Indicators for animal health on agroecological dairy farms in mountain areas

Andrea Ceppatelliª, Maeva Cremilleuxᵇ,ᶜ, Audrey Michaudᵇ and Enrico Sturaroª

ªUniversity of Padova, Dipartimento di Agronomia, Animali, Alimenti, Risorse naturali e Ambiente, Viale dell'Università 16, Legnaro, Italy; ᵇUMR Herbivores, University Clermont Auvergne, INRAE, Saint-Genès-Champanelle, France; ᶜAgroecology and Environment Research Unit, ISARA, Lyon, France

## ABSTRACT

Agroecological grassland-based dairy systems represent a way to improve the sustainability of livestock farming, while meeting the increasing food needs of the world population. Agroecology aims to improve the interactions between plants, animals, humans, environment, enhancing their overall health. However, knowledge about animal health in agroecology is scarce. This research provides a list of indicators to assess animal health, basing on two reference protocols: Herd Medicine of Dairy Farms and Welfare Quality. 11 agroecological grassland-based dairy cow farms in Auvergne-Rhône-Alpes (France) were monitored for two years, conducting four on-site samplings on different domains of animal health: nutrition, housing, individual health, herd health, behaviour, production, reproduction. A Principal Component Analysis was computed on animal health measurements, selecting seven indicators: Body Condition Score, Cleanliness, Integument Alterations, Veterinary Interventions, Qualitative Behaviour Assessment, Somatic Cell Count, Calving Interval. The eleven farms achieved good scores for all the indicators, especially concerning BCS, Integument Alterations, and Cleanliness. Calving Interval and SCC scores were similar to findings from other studies. No previous references were found for Veterinary Interventions and QBA. A second PCA was computed integrating the seven indicators with illustrative variables concerning farm management. It emerged that bigger farms, having higher Utilised Agricultural Area and lactating cows, experienced higher milk production and greater utilisation of concentrate feed. These farms adopted a shorter calving interval, lower veterinary interventions and were characterised by lower scores of correct BCS. Future implications of the study would concern its enlargement to a wider sample of farms, and to different geographical areas.

## HIGHLIGHTS

* Seven indicators were selected to assess animal health within agroecological dairy systems.

* Combining these indicators with farm management variables, allowed to identify practices that would further improve dairy cows' health.

## ARTICLE HISTORY

Received 8 April 2025
Revised 21 July 2025
Accepted 26 August 2025

## KEYWORDS

Agroecology; sustainability;
animal health; Grassland-
Based dairy systems

## Introduction

As the world population is expected to reach an estimate of 9.7 billion in 2050 (United Nations Department of Economic and Social Affairs Population Division 2019), food production will need to increase by almost 50% (FAO 2017). This increase in production must be in line with sustainability, as agriculture is currently responsible for negative environmental consequences, such as massive deforestation, water scarcity, biodiversity loss, soil depletion and high GHGs emissions (FAO 2018). Within the agri-food sector, animal production is the largest

land-use system, relying on vast areas of rangelands, 1/3 of global freshwater, and 1/3 of global cropland as feed (Herrero et al. 2013). Therefore, to reach a higher level of sustainability, husbandry systems need to shift towards more sustainable ways of production (European Union 2020).

Agroecology is an agricultural model that can meet these challenges (Altieri 1989; Wezel et al. 2009; Gliessman 2016). Agroecology originates from the evolution of agronomy and ecology, having been defined as “the application of ecology in agriculture” and “a scientific discipline, movement and set of practices”
(Wezel et al. 2009). Agroecology consists therefore of an integrated approach that simultaneously applies ecological and social concepts and principles to optimise the interactions between plants, animals, humans and the environment (FAO 2018). Agroecological farming systems aim to ensure optimal quantities of healthy production of good quality, being economically beneficial for farmers, socially fair, protecting ecosystems and biodiversity, assuring a high rate of self-sufficiency (Altieri 1989; Wezel and Peeters 2014; FAO 2018).

An integration between agroecology and livestock production could be represented by grassland-based systems (Wezel and Peeters 2014). Grasslands can assure a high rate of self-sufficiency for feed, with less dependence on off-farm inputs (Wezel and Peeters 2014; Brocard et al. 2008), relying on grass and small feed supplementations (Herrero et al. 2013). If properly managed, pastures can sustain animal health, preserve the fertility of the soils, optimise the nutrient cycle, reuse the manure (Wezel and Peeters 2014), contribute to carbon storage and control weeds (Dumont et al. 2013; Bonaudo et al. 2014). On the other hand, the lower level of input, and hosting local animal rustic breeds, could result in lower production levels for the livestock sector (Dumont et al. 2013; Bonaudo et al. 2014; Rodríguez-Bermúdez et al. 2019). This can be compensated by the benefits resulting from the higher biodiversity hosted within these systems, the longer lifetime of the animals that are likely to experience fewer illnesses, the higher added value of production, and the lower input cost (Dumont et al. 2013; Bonaudo et al. 2014).

However, the majority of scientific publications on agroecology are related to crop systems, with only a small focus on animal production within grassland-based systems (Dumont et al. 2013; Wezel and Peeters 2014). Dumont et al. (2013) proposed five key principles to characterise livestock production in agroecology: improving animal and production health, decreasing the inputs needed for production, decreasing pollution, enhancing diversity within animal production systems to strengthen their resilience, preserving biological diversity, and exploit their benefits. These principles can be applied on the field by adopting specific indicators. However, while indicators and tools for evaluating agroecological systems already exist (Botreau et al. 2014; Mottet et al. 2020; Puech and Stark 2023), there is a lack of knowledge about the assessment of animal health within these systems. This could be derived from the different peculiarities of these breeding systems, which make it

difficult to fully adopt the indicators used to evaluate animal health in conventional dairy farming, like lame-ness (Haskell et al. 2006; Olmos et al. 2009), nutritional status and incidence of mastitis (Washburn et al. 2002; Compton et al. 2007). Hence, to assess animal health, these conventional indicators tend to be considered separate from each other, rather than within a comprehensive multi-indicator assessment.

Animal health can be broadly defined as the absence of disease, or the normal functioning of an organism, and normal behaviour, based on the observation of a certain number of individuals that determine the standard and consecutively health (Cerbu et al. 2023). Thus, the level of animal health has a direct effect on breeding efficiency, animal production traits and human health (Marshall and Levy 2011; Nicks and Vandenheede 2014; Santos and Ribeiro 2014). An important contribution to animal health could be represented by welfare, which is related to the capacity of an animal to avoid suffering and sustain fitness, being influenced by the living environment and by the biological role and position of the animal (Carenzi and Verga 2009).

Existing publications on agroecological livestock systems refer to several topics, from food security (Bezner Kerr et al. 2021), to public health (Frison and Clément 2020), and mitigation of external impacts (Bezner Kerr et al. 2023), but there is a lack of knowledge about specific methodologies that can be implemented on the field to evaluate animal health within these systems.

The aim of this study is to propose a set of indicators to evaluate the overall level of animal health within farms adopting agroecological practices, such as grassland-based production systems, considering the region of Auvergne-Rhône-Alps as the study area. Two reference protocols were taken as a reference to select the indicators for animal health assessment. One concerns dairy cows' health assessment at herd level (Herd Medicine of Dairy Farms, Commun et al. 2016). The second one, since welfare also contributes to health, is the one proposed by Welfare Quality Consortium (Welfare Quality 2009), to conduct an integrated assessment specific for agroecological farms.

## Material and methods

### Sampling description

The study has been conducted on farms selected according to the following criteria:

* Geographical area of Massif Central (Auvergne-Rhône-Alps, France),
<figure><img src="image_13.png" /></figure>

* Grassland-based (grassland surface > 70% of the total Utilised Agricultural Area - UAA),

* Claim to be agroecological, due to the management practices adopted according to the principles proposed by Dumont et al. 2013. Specifically, the principles considered mainly concerned the improvement of animal health, the low input use (i.e. concentrate feed) to increase farm self-sufficiency in terms of feeding also through grassland management for on-farm forage production,

* Voluntary participation in the study and adherence to associations of farms involved in agroecological transition.

Overall, eleven dairy farms were selected (Figure 1). Some of the farms only hosted purebred herds, while others had multiple breeds, with a herd size ranging from 13 to 80 lactating cows. Farmers possibly relied on local breeds (Ferrandaise and Montbéliarde), small and/or rustic breeds (Tarine, Abondance, Vosgienne, Brune des Alpes), but also hosted commercial breeds (Prim'Holstein and Simmenthal). Concerning milk production, expressed as litres of Energy Corrected Milk (ECM) per animal, it ranged from 2026 to 6886 l/cow/year. The general characteristics of the farms are reported in Table 1.

All farms were embedded in mid-mountain pasture contexts, from 420 to 1180 m asl, in which strong environmental constraints make farmers a vanguard in the face of climate change. At least half of the UAA was managed as grassland, either permanent or temporary, used for hay production or for direct grazing. The grazing days per year ranged from 188 (for the farms located at the highest altitude) to 330 days. Eight farms were managed collectively, whereas three individually; nine farms were certified as organic. The milk was either sold to dairy companies (five farms) or processed directly on the farm (six farms).

<table><caption>**Table 1.** Principal characteristics for the selected farms. List of abbreviations: UAA (utilized agricultural area); UFA (utilized forage area); LU (livestock units); ECM (Energy Corrected milk).</caption><thead><tr><td>Variables</td><td>Minimum</td><td>Median</td><td>Maximum</td></tr></thead><tbody><tr><td>Altitude (m asl)</td><td>420</td><td>940</td><td>1180</td></tr><tr><td>UAA (ha)</td><td>30</td><td>65</td><td>192</td></tr><tr><td>UFA (ha)</td><td>30</td><td>65</td><td>189</td></tr><tr><td>Stocking Rate (LU/ha)</td><td>0.55</td><td>0.98</td><td>1.60</td></tr><tr><td>Days at pasture (n°)</td><td>188</td><td>225</td><td>330</td></tr><tr><td>Return time on plot (days)</td><td>15</td><td>33</td><td>45</td></tr><tr><td>Lactating cows (n°)</td><td>13</td><td>35</td><td>80</td></tr><tr><td>ECM/cow (L/year)</td><td>2026</td><td>4773</td><td>6886</td></tr><tr><td>Concentrates g/kg milk</td><td>0</td><td>145</td><td>238</td></tr></tbody></table>

<figure><img src="image_14.png" /><figcaption>**Figure 1.** Localisation of the farms involved in the study.</figcaption></figure>
# Data collection

## Periods

Farms were monitored for two years, between 2021 and 2022. Each year was divided in two measurement periods: wintertime (P1 the first year and P3 the second year), when dairy cows were reared indoors, and late spring (P2 the first year and P4 the second year), when dairy cows were reared outdoors at grazing. During each period, specific measurements were conducted to assess dairy cows' health. These assessments only concerned lactating cows, not considering other animal categories like heifers or dry cows.

## Animal-based measures

The animal-based variables to be considered for animal health assessment were selected according to the two different protocols, Herd Medicine of Dairy Farms (Commun et al. 2016), and Welfare Quality (Welfare Quality 2009). These variables refer to the different components of animal health: nutritional status, housing, individual health status, herd health status, behaviour, production, reproduction. Data were collected during on-farm sampling sessions, through direct measurements on the animals, bulk milk analysis, and direct interviews with the farmers. The variables measured during on-field sampling are reported below, divided according to the two protocols, with additional ones collected during direct interviews with the farmers.

* Quantification of dairy cows' health by Herd Medicine protocol:

* Nutritional status: Body Condition Score (BCS), Rumen Filling.

* Housing: Cleanliness.

* Individual Health Status: Legs Rotation, Faeces Consistency.

* Herd Health Status: Parasitism (Paramphistome, Buxtonella sulcata, Eimeria, Strongles, and Toenia).

* Production: Fat, Protein, Somatic Cell Count (SCC), Individual Production.

* Quantification of dairy cows' health by Welfare Quality tool:

* Housing: Time needed to lie down, Animals colliding with housing equipment during lying down, Udder Cleanliness, Hindquarter Cleanliness, Lower Hindleg Cleanliness.

* Individual Health Status: Nasal Discharge, Integument Alterations, Lameness, Ocular

* Discharge, Vulvar Discharge, Hampered Respiration, Coughing.

* Behaviour: Qualitative Behaviour Assessment (QBA), Avoidance Distance, Agonistic Behaviour (head butt, displacement, fighting, chasing).

* Reproduction: Dystocia, Downer cows.

* Quantification of dairy cows' health by direct interviews with farmers:

* Herd Health Status: Veterinary Interventions, Incidence of Mastitis, Treatments (preventive, vermifuges, curative, and antibiotic), Vaccines.

* Reproduction: Culling Rate, Calving Interval, and Calves Mortality Rate.

According to the "Herd Medicine Protocol", concerning the nutritional status, Body Condition Score was measured based on the scoring grid developed by Bazin et al. 1984, by assigning to each lactating cow a score between 0 (emaciated) and 5 (excessive fat), with increments of 0.25. BCS was then associated with the lactation stage of the cow, divided into four phases, and assigning each a reference BCS value (Table 2). Then, BCS was classified around three classes, by comparing the recorded value with the reference, considering a tolerance value of 0.5:1 for excessively lean, 0 for proper status, 2 for excessively fat. Rumen Filling was assessed during individual observation, after the morning feeding, observing the left side of the cow to assign it a score from 1 (empty rumen) to 5 (full).

Housing assessment concerned the evaluation of animals' Cleanliness, by assigning each lactating cow a score from 1 (very clean) to 5 (very dirty).

Concerning the individual health status, Legs Rotation was evaluated during individual observation, assigning each cow a score of 1 (parallel), 2 (slight rotation), 3 (severe rotation). Faeces Consistency was evaluated by visually assigning to each animal a score from 1 (excessively liquid excreta) to 5 (excessively solid excreta).

Concerning the herd health status, Parasitism analysis was conducted to assess the intestinal

<table><caption>**Table 2.** Classification of lactation phase according to the number of months from calving, and reference values of body condition score (BCS) for each phase according to Commun et al. (2016).</caption><thead><tr><td>Months from calving</td><td>Lactation phase</td><td>Reference BCS value</td></tr></thead><tbody><tr><td>&lt;1</td><td>Beginning</td><td>3</td></tr><tr><td>1-4</td><td>First half</td><td>2.5</td></tr><tr><td>4-6</td><td>Second half</td><td>2.75</td></tr><tr><td>&gt;6</td><td>Final</td><td>3.25</td></tr></tbody></table>
colonisation of lactating cows by parasites during the grazing period. The sampling activity concerned the collection of fresh faeces samples from at least 20% of the herd, with a minimum of 5 cows. The sample was conserved in sterile tubes at 4°C until the laboratory analysis (Terana, France), conducted by sedimentation and flotation with zinc sulphate.

Milk Composition and SCC were calculated as the average value from the analyses of six milk samples collected per farm: four during the four sampling periods, and two additional ones during spring and autumn of the first year. Each time, 30 mL of bulk milk were collected using sterile tubes, preserved with 2-bromo-2-nitropropane-1,3-diol (Bronopol; D&F Inc., Dublin, CA) at 4°C. Then, Fat and Protein content were determined by medium infra-red spectroscopy (Agrolab's, Aurillac, France) according to ISO 9622:2013, while SCC was determined by epifluorescence (Agrolab's) according to ISO 13366-2:2006. Individual Milk Production of each cow was accessed from official milk controls reports, then averaged for the lactating cows in the farm on a yearly basis.

According to the Welfare Quality protocol, housing concerned five measurements: Time Needed to Lie Down, Animals Colliding with Housing Equipment During Lying Down, Cleanliness of Udder, Cleanliness of Hindquarters, Cleanliness of Lower Hindleg. The first two variables were measured during the behavioural observation of the herd. The three indicators around cleanliness were measured during the individual observation of the cows, and classified using 2 scores: 0 if clean, 1 if dirty.

Concerning the individual health status, each lactating cow was observed to assess Nasal Discharge (score 0 if absent, and 1 if present), Integument Alterations (absent, presence of hairless patches and lesions), Lameness (score 0 if absent, 1 moderate, and 2 severe), Ocular Discharge (score 0 if absent and 1 if present), Vulvar Discharge (score 0 if absent, 1 if present), Hampered Respiration (score 0 if absent and 1 if present). Coughing was measured during the behavioural observation, by counting the times it occurred.

Animal behaviour concerned three categories. Qualitative Behaviour Assessment was recorded to measure the emotional state of dairy cows. After having waited five minutes to allow the cows to adapt to the presence of the observer, animals were observed for 20 min, indoors during winter and at grazing during spring, to classify their behaviour and emotional state around 15 indicators. During the grazing season, the herd was divided into groups of 20-25 animals, observed each for 20 min. Then, a unique score was

calculated for each farm, according to the formula proposed by the Welfare Quality protocol and then computed on a scale from 0 to 100. Avoidance Distance was evaluated by approaching the cows from the front, starting from 1.5 m, and gradually moving towards them, recording when the animal moved away: > 1 m; 0.5 m < > 1 m; < 0.5 m; T possible to touch. During wintertime, the evaluation was carried out at the feeding track, while during summertime, the approach concerned the cows at grazing, adopting the same methodology. Agonistic Behaviour was assessed through visual observation of the herd during indoor periods, divided in homogeneous groups of about 15 animals each according to the herd size, repeating the observation twice for each group. The observation concerned 4 types of behaviours: Head Butt, Displacement, Fighting, and Chasing, all of them against another cow, by noting an even number of times each behaviour occurred.

Reproduction concerned Dystocia and Downer Cows, by asking the farmer the average number of difficult calvings and downer cows during the year.

Direct interviews with farmers were conducted to investigate herd health management practices and reproduction. Herd health practices concerned Veterinary Interventions (number per cow per year), Mastitis (number per cow per year), Treatments (number of preventives, curative, antibiotic, and vermifuge treatments per cow per year), Vaccines (number per cow per year).

Reproduction concerned Culling Rate (percentage of culled cows per year), Calving Interval (number of days between two subsequent calvings), Calves Mortality Rate (percentage of dead calves aged less than one month per year).

A total of 34 health variables were measured on each farm and reported in Table 3. Particularly, they were 2 for Nutritional Status, 6 for Housing, 9 for Individual Health Status, 5 for Herd Health Status, 3 for Behaviour, 4 for Production, 5 for Reproduction. Among them, 15 were considered for the subsequent statistical data analysis, reported in Table 4, and explained as follows.

One variable was retained for Nutritional Status: Body Condition Score. Rumen Filling was not considered due to differences in feeding time and subsequent recording procedures between the eleven farms.

One variable was retained for Housing: Cleanliness. Time needed to lie down and Animals Colliding with housing equipment during lying down were excluded since they were only recorded during the indoor
<table><caption>**Table 3.** Animal-based variables recorded during on farm samplings, selected according to herd health protocol (Commun et al. 2016), Welfare Quality (2009), and collected during interviews.</caption><thead><tr><th>Category</th><th>Variable</th><th>Classification</th><th>Reference protocol</th></tr></thead><tbody><tr><td rowspan="2">Nutritional status</td><td>Body Condition Score (BCS)</td><td>0: normal<br>1: excessively lean<br>2: excessively fat</td><td>Commun et al. (2016)</td></tr><tr><td>Rumen Filling</td><td>1: triangle clearly visible<br>2: triangle shape appearance<br>3: slight triangle shape<br>4: triangle almost invisible<br>5: triangle invisible</td><td>Commun et al. (2016)</td></tr><tr><td rowspan="7">Housing</td><td>Cleanliness</td><td>1: clean<br>2: slightly clean<br>3: moderate dirty<br>4: dirty<br>5: excessively dirty</td><td>Commun et al. (2016)</td></tr><tr><td>Udder Cleanliness</td><td>0: clean<br>1: dirty</td><td>Welfare Quality (2009)</td></tr><tr><td>Hindquarter Cleanliness</td><td>0: clean<br>1: dirty</td><td>Welfare Quality (2009)</td></tr><tr><td>Lower Hindleg Cleanliness</td><td>0: clean<br>1: dirty</td><td>Welfare Quality (2009)</td></tr><tr><td>Time needed to lie down</td><td>Seconds each time</td><td>Welfare Quality (2009)</td></tr><tr><td>Animals colliding with housing equipment during lying down</td><td>Number of times</td><td>Welfare Quality (2009)</td></tr><tr><td>Legs Rotation</td><td>1: parallel<br>2: slightly rotated<br>3: severely rotated</td><td>Commun et al. (2016)</td></tr><tr><td rowspan="10">Individual Health Status</td><td>Faeces Consistency</td><td>1: water-like<br>2: creamy<br>3: thick<br>4: hard texture<br>5: pieces</td><td>Commun et al. (2016)</td></tr><tr><td>Nasal Discharge</td><td>0: absent<br>1: present</td><td>Welfare Quality (2009)</td></tr><tr><td>Integument Alterations</td><td>0: absence</td><td>Welfare Quality (2009)</td></tr><tr><td>Lameness</td><td>Hairless patches + lesions<br>0: absence<br>1: moderate<br>2: severe</td><td>Welfare Quality (2009)</td></tr><tr><td>Ocular Discharge</td><td>0: absent<br>1: present</td><td>Welfare Quality (2009)</td></tr><tr><td>Vulvar Discharge</td><td>0: absent<br>1: present</td><td>Welfare Quality (2009)</td></tr><tr><td>Hampered Respiration</td><td>0: absent<br>1: present</td><td>Welfare Quality (2009)</td></tr><tr><td>Coughing</td><td>Number of times</td><td>Welfare Quality (2009)</td></tr><tr><td>Parasitism</td><td>Average 20% herd</td><td>Commun et al. (2016)</td></tr><tr><td rowspan="6">Herd Health Status</td><td>Veterinary Interventions</td><td>N°/cow/year</td><td>–</td></tr><tr><td>Mastitis</td><td>N°/cow/year</td><td>–</td></tr><tr><td>Treatments<br>(preventives + curative + antibiotic + vermifuge)</td><td>N°/cow/year</td><td>–</td></tr><tr><td>Vaccines</td><td>N°/cow/year</td><td>–</td></tr><tr><td>Qualitative Behaviour Assessment</td><td>0 (low) – 100 (high)</td><td>Welfare Quality (2009)</td></tr><tr><td>Avoidance Distance</td><td>T: possible to touch<br>&lt; 0.5 m<br>0.5 m &lt; &gt; 1 m<br>&gt; 1 m</td><td>Welfare Quality (2009)</td></tr><tr><td rowspan="2">Agonistic Behaviour (Head Butt – Displacement – Fighting – Chasing)</td><td>Number of times</td><td>Welfare Quality (2009)</td></tr><tr><td>Milk fat</td><td>Milk analysis, %</td><td>Commun et al. (2016)</td></tr><tr><td rowspan="10">Production</td><td>Milk protein</td><td>Milk analysis, %</td><td>Commun et al. (2016)</td></tr><tr><td>Somatic Cell Count</td><td>Milk analysis, n°</td><td>Commun et al. (2016)</td></tr><tr><td>Individual milk production</td><td>Milk controls</td><td>–</td></tr><tr><td>Dystocia</td><td>N°/Year</td><td>Welfare Quality (2009)</td></tr><tr><td>Downer Cows</td><td>N°/Year</td><td>Welfare Quality (2009)</td></tr><tr><td>Culling Rate</td><td>Percentage</td><td>–</td></tr><tr><td>Calving Interval</td><td>Days</td><td>–</td></tr><tr><td>Calves Mortality Rate</td><td>Percentage</td><td>–</td></tr></tbody></table>

Note: The table reports the category of animal health of which each variable refers to, the name of each variable measured during on-farm sampling, the score achievable for each variable (classification), and the related reference protocol.
<table><caption>**Table 4.** Variables retained for data analysis, and their reference scores.</caption><thead><tr><th>Category</th><th>Variable</th><th>Classification</th><th>Reference protocol</th></tr></thead><tbody><tr><td>Nutritional Status</td><td>Body Condition Score</td><td>0: normal</td><td>Commun et al. (2016)</td></tr><tr><td>Housing</td><td>Cleanliness</td><td>1: clean</td><td>Commun et al. (2016)</td></tr><tr><td rowspan="3">Individual Health Status</td><td>Nasal Discharge</td><td>0: absent</td><td>Welfare Quality (2009)</td></tr><tr><td>Integument Alterations</td><td>Hairless patches + lesions</td><td>Welfare Quality (2009)</td></tr><tr><td>Lameness</td><td>0: absence</td><td>Welfare Quality (2009)</td></tr><tr><td rowspan="3">Herd Health Status</td><td>Veterinary Interventions</td><td>N°/cow/year</td><td>–</td></tr><tr><td>Mastitis</td><td>N°/cow/year</td><td>–</td></tr><tr><td>Treatments (preventives + curatives + antibiotics + vermifuges)</td><td>N°/cow/year</td><td>–</td></tr><tr><td rowspan="3">Behaviour</td><td>Vaccines</td><td>N°/cow/year</td><td>–</td></tr><tr><td>Qualitative Behaviour Assessment</td><td>0 (low) – 100 (high)</td><td>Welfare Quality (2009)</td></tr><tr><td>Avoidance Distance</td><td>Average T (winter); < 0.5 m (summer)</td><td>Welfare Quality (2009)</td></tr><tr><td>Production</td><td>Somatic Cell Count</td><td>Number</td><td>Commun et al. (2016)</td></tr><tr><td rowspan="3">Reproduction</td><td>Culling Rate</td><td>Percentage</td><td>–</td></tr><tr><td>Calving Interval</td><td>Days</td><td>–</td></tr><tr><td>Calves Mortality Rate</td><td>Percentage</td><td>–</td></tr></tbody></table>

Note: The table reports the category of animal health of which each variable refers to, the name of each variable, the score achievable for each variable (classification), and the related reference protocol.

period. Udder Cleanliness, Hindquarter Cleanliness, and Lower Hindleg Cleanliness were excluded as well, since Cleanliness from Commun et al. (2016) was considered more reliable and moreover it included the first three variables.

Three variables were retained for Individual Health Status: Nasal Discharge, Integument Alterations, Lameness. Ocular Discharge, Vulvar Discharge, and Hampered Respiration were excluded, since almost all the farms reported animals with a very low or absent occurrence of them. Legs Rotation was excluded due to its similarity with Lameness, with the latter considered as more reliable for legs and locomotion problems. Coughing was excluded due to the high differences between indoors and outdoors, since at grazing cows moved on wider surfaces, making it difficult to visually monitor separate groups and thus complicating data collection. Faeces Consistency had a low reliability, due to the difficulties in the direct sampling collection at grazing.

Four variables were retained for Herd Health: Veterinary Interventions, Mastitis, Treatments, and Vaccines. Parasitism was excluded since it was only measured at grazing.

Two variables were retained about Behaviour: Avoidance Distance and QBA. Agonistic Behaviour was not considered for the same reasons as about Coughing.

One variable was retained for Production: SCC.

Three variables were retained for Reproduction: Culling Rate, Calving Interval, and Calves Mortality Rate. Dystocia and Downer Cows were not considered, due to lack of data for some farms.

Concerning the retained variables, data referring to BCS, Cleanliness, SCC, QBA, Avoidance Distance, Nasal Discharge, Integument Alterations, and Lameness,

were averaged for the number of lactating cows in each farm, to obtain a unique value for each farm. Then, the four values of each farm referring to the four sampling periods were averaged together to obtain a unique value for indoors and outdoors. The measures conducted on the eleven farms concerned 805 dairy cows in 2021 (406 for P1 and 399 for P2), and 736 in 2022 (358 for P3 and 378 for P4). Data referring to Veterinary Interventions, Mastitis, Treatments, Vaccines, Culling Rate, Calving Interval, and Calves Mortality Rate were considered as referring to the whole year.

In order to perform data analysis, for the variables classified around multiple scores (BCS, Cleanliness, Avoidance Distance, Nasal Discharge, Lameness) only the positive ones were considered. Specifically: score 1 for Cleanliness, score 0 for BCS, Lameness, and Nasal Discharge. Avoidance Distance was considered as average between T for indoors and sum of T + < 0.5 m for outdoors, to counterbalance the higher difficulties in approaching the cows at grazing. Integument Alterations was considered as the sum between hairless patches and lesions, since all the farms had few animals experiencing no alterations at all. All the other variables were considered as collected during sampling activity, with Table 4 reporting the variables and their scores considered for data analysis.

Production data were used to compute the average annual Energy Corrected Milk per cow. The calculation was based according to INRA (2018), as following:

$$ (MY * (0.42 + 0.0053 * (FC - 40) + 0.0033 * (PC - 31))/0.42) $$

* MY (daily milk yield), from dairy control data

* FC (fat content), from milk analyses
<table><caption>**Table 5.** Farm management variables. Abbreviations: Livestock units (LU), meters above the sea level (m asl), hectares (ha).</caption><thead><tr><th>Category</th><th>Variable</th><th>Classification</th></tr></thead><tbody><tr><td rowspan="7">Farm management</td><td>Altitude</td><td>M asl</td></tr><tr><td>Stocking Rate</td><td>LU/ha</td></tr><tr><td>Lactating Cows</td><td>N°</td></tr><tr><td>Utilised Forage Area</td><td>Ha</td></tr><tr><td>Grazing Period</td><td>Days</td></tr><tr><td>Plot Return Time</td><td>Days</td></tr><tr><td>Concentrate Feed</td><td>g/kg ECM</td></tr><tr><td>Production</td><td>Energy Corrected Milk</td><td>Kg/cow/year</td></tr><tr><td>Grassland diversity</td><td>Floral Diversity</td><td>Score</td></tr></tbody></table>

* PC (protein content), from milk analyses

## Farm management measures

In addition to the animal-based variables, measurements also concerned farm and grasslands management (Table 5). Direct interviews with farmers were conducted at the beginning of the study to collect data about the altitude of the farm, total livestock units, number of lactating cows, utilised forage area, length of grazing period, cows' return time on the same grazing plot, and concentrate feed. Direct samplings on grasslands were conducted to assess the typology of floral diversity. The variables that concerned direct interviews with the farmer were collected once a year, as they did not vary throughout the year. The annual Energy Corrected Milk, computed as above, was also included.

## Data analysis

In order to select the most suitable indicators to assess dairy cows' health on agroecological grassland-based dairy farms, a Principal Component Analysis (PCA – R Studio Software version 2024.09.0 + 375) was conducted on 15 out of the 34 animal health variables (Table 4), with ECM set as an illustrative variable to investigate its influence on the other variables. The outcomes of the analysis were used to retain the indicators according to their values of contribution (higher than 0.5 or lower than -0.5) and cos2. Then, expertise was adopted to retain at least one indicator for each category, considering the relevance for farm management and animal health. The expertise work was performed by the authors and consisted of analysing the results of the PCA from a strictly zootechnical point of view, to decide which indicators should be retained or excluded from the final list.

Secondarily, to investigate possible relations between animal health indicators and farm management practices, a second PCA was performed on the indicators retained from the first PCA, combined with illustrative data concerning farm management

reported in Table 5. The outcomes of this PCA were used to check for correlations between animal health indicators and farm management practices, to identify practices that could further improve animal health on agroecological grassland-based farms. Descriptive variables, together with management practices, were considered as illustrative in the analysis, to exclude their influence on animal health indicators.

## Results

### Dairy cows' health indicators for agroecological farms

The outcomes of the first PCA conducted on animal health variables are reported in Figure 2, representing the projection of the variables for the first two principal components. Together, they explain 46.2% of the total variance, specifically 26.2% for the first principal component and 20% for the second one. The first dimension is positively associated with Veterinary Interventions, Avoidance Distance, Absence of Lameness, and Calving Interval. It is negatively associated with Culling Rate and Vaccines. The second principal component is positively associated with SCC, Integument Alterations, and Calves Mortality Rate. It is negatively associated with Cleanliness and QBA.

According to their rate of contribution, and to expertise, seven indicators were retained to assess dairy cows' health on agroecological grassland-based dairy farms (Table 6). They were one for each category of animal health: Correct Body Condition Score, Cleanliness, Integument Alterations, Veterinary Interventions, Qualitative Behaviour Assessment, Somatic Cell Count, Calving Interval.

Expertise was adopted to interpret PCA results according to the outcomes of the sampling activity. Consequently, some variables were excluded since data collection was performed only partially over the sampling period (i.e. parasitism) or considered as not representative in agroecology for the very low incidence (i.e. lameness).

### Link between health indicators and farm management

In order to investigate relations between animal health indicators and farm management practices, a second PCA was performed (Figure 3). Here, the seven selected animal health indicators were combined with variables concerning management practices and farm characteristics, set as illustrative. The first two dimensions explain together 65.5% of total variance.
<figure><img src="image_15.png" /><figcaption>**Figure 2.** Biplot of Principal Component analysis for health indicators. Active variables are reported in black in the plot, while the illustrative one is reported in blue and with a dotted line.</figcaption></figure>

<table><caption>**Table 6.** Indicators for the assessment of animal health, retained from the first Principal Component analysis, and their minimum, median and maximum values for the eleven dairy farms analysed during the study.</caption><thead><tr><th>Indicator</th><th>Classification</th><th>Minimum</th><th>Median</th><th>Maximum</th></tr></thead><tbody><tr><td>BCS_0</td><td>% per farm</td><td>44.74</td><td>74.63</td><td>88.30</td></tr><tr><td>Cleanliness_1</td><td>% per farm</td><td>24.32</td><td>43.44</td><td>87.62</td></tr><tr><td>(Cleanliness 1 + 2)</td><td>% per farm</td><td>65.14</td><td>92.50</td><td>100</td></tr><tr><td>Integument Alterations</td><td>% per farm</td><td>11.75</td><td>22.88</td><td>40.12</td></tr><tr><td>Veterinary Interventions</td><td>n/cow/year</td><td>0.06</td><td>0.11</td><td>0.28</td></tr><tr><td>Qualitative Behaviour Assessment</td><td>Score (0–100)</td><td>22.41</td><td>67.51</td><td>80.58</td></tr><tr><td>Somatic Cell Count</td><td>number</td><td>75333</td><td>1,83,000</td><td>4,96,600</td></tr><tr><td>Calving Interval</td><td>days</td><td>320</td><td>380</td><td>440</td></tr></tbody></table>

Particularly, the first one explains 36% of total variance, being positively associated with SCC and Integument Alterations, and negatively with Cleanliness. The second dimension explains 29.5% of total variance, being positively associated with Veterinary Interventions, Calving Interval, Cleanliness,

and QBA. Concerning the variables referring to farm and grassland management, a negative correlation is shown for Veterinary Interventions, Calving Interval, Correct BCS, and Plot Return Time with Energy Corrected Milk, Stocking Rate, Lactating Cows, UFA, and Concentrate Feed. QBA and Cleanliness are
<figure><img src="image_16.png" /><figcaption>**Figure 3.** Biplot of Principal Component analysis for the first two principal components, referring to animal health indicators retained from the first step (in black as active variables), with variables of farm and grassland management as illustrative (in blue with dotted line as illustrative variables).</figcaption></figure>

positively correlated with Altitude and Floral Diversity, which are all negatively correlated with Integument Alterations. A positive correlation is also shown between the duration of the Grazing Period and SCC.

## Discussion

The sample on which the study is based consists of eleven farms, hosting different numbers of dairy cows and adopting a variety of practices in terms of farm and grassland management. These farms are located in the mountain area of the Massif Central, characterised by a large availability of grasslands, which represent a source of local feed at lower cost. However, grazing could be associated with specific health challenges for dairy cows, in terms of limited grass

availability, daily ingestion, and reduced yields (Wilkinson et al. 2020) but it could also be beneficial for animal welfare and milk quality (Hennessy et al. 2020). Taking into account the peculiarities of this production system, the aim of this study is to provide a list of indicators for a comprehensive assessment of dairy cows' health on agroecological dairy farms, contributing to addressing the lack of knowledge on this aspect. Hence, the analysis of animal health indicators, combined with farm and grassland management variables, allows investigation of possible relationships within them, contributing to further improve animal health. However, since our study is based on a relatively small sample size (11 farms), further research would be needed to confirm whether these findings are adaptable to agroecological dairy systems in a
broader way, as well as to farms located in other regions, with different climatic and management conditions.

## Dairy cows' health assessment of the selected farms

According to the seven selected indicators, the eleven farms achieve overall good scores. Among them, BCS, Cleanliness, QBA, SCC, and Calving Interval show a high variability among the farms, even if their average scores are positive or within the average reference.

About BCS, the median number of cows with a normal score in our study (74.63%) is similar to the one reported by Trachsel et al. (2000), a study conducted on organic Swiss dairy farms, with 69% of cows with BCS between 3 and 3.75. Coignard et al. (2013) reported that the herd is in a good nutritional status when less than 35% of the cows present an abnormal BCS that, on average, our farms met. A study conducted in Northern Italy in free and tie stalls reported cows with normal BCS as 57.4% (Katzenberger et al. 2020), while another study in the same area reported 86.3% of lactating cows showing normal BCS (Mattiello et al. 2009).

Concerning Cleanliness, the protocol of Commun et al. (2016) reported a normal situation with at least 60% of the cows with score 1-2. The eleven farms of our study had a higher Cleanliness score, with a median value of 92.5%, of which 43.44% for score 1.

Integument Alterations, considered as the sum of hairless patches and lesions, achieved a median score of 22.88% in our study. This value is lower compared to the 47.3% found in a combination of grazing and zero grazing dairy farms in Denmark (Burow, Thomsen, et al. 2013), and in line with a study conducted on dairy cows' farms before, during, and after grazing, which reported a value of 18.6% for at least hairless patches (Corazzin et al. 2010). Hence, the value of 22.88% in our study was lower than the one found in a health assessment study of dairy cows' farms throughout France, which reported 15.9% for hairless patches only and 33.3% for at least one lesion (Coignard et al. 2013). Another study conducted in South Tyrol (Alrhmoun et al. 2023) revealed an average of 43.1% of moderate and severe skin alterations in dairy cows.

Calving Interval had a high variation between the farms, ranging from 320 to 440 days. The average of 380 days found by our study is within the range of 365-390 days indicated by the reference protocol (Commun et al. 2016), but slightly lower than the

408 days reported for the Dutch dairy sector (Van Arendonk and Liinamo 2003). This value is also very similar to 378 days, reported for pasture-based Brown Swiss dairy cows in Switzerland, with higher values (405 days) reported by the same study for indoor breeding (Hofstetter et al. 2014).

Concerning SCC, Commun et al. (2016) reported a normal situation with at least 85% of the cows with SCC values lower than 3,00,000/mL. The median value of 1,83,000 achieved by the farms involved in our study is well below this threshold but more than double compared to 78,000 for grazing Brown Swiss cows in Southern Italy (Di Grigoli et al. 2019), and slightly higher than 1,32,000 from a study conducted on French dairy farms (Barnouin et al. 2005).

To our knowledge, no references were found concerning the median number of Veterinary Interventions and QBA for grassland-based dairy farms.

## Indicators to assess animal health: relationships and possible trends between the variables

The outcomes from the PCA related to animal health (Figure 2) highlighted several correlations between the variables that, together with contributions and expertise, concurred to select the indicators for dairy cows' health assessment.

Good Cleanliness level and Qualitative Behaviour Assessment were positively correlated. Animals experiencing positive behaviour could be able to choose optimal places where to lie down, also dedicating more time to grooming and self-cleaning (Lundmark Hedman et al. 2021). This could be the case on our farms where, during the grazing period, most of the herds used to lie down on grasslands especially during the afternoon. In addition, calm and relaxed cows, under a good dietary balance, defaecate less or with a denser consistency, resulting in higher levels of skin cleanliness (Ruud et al. 2010). In fact, diarrhoea could be also derived from an unbalanced diet, decreasing cleanliness levels (De Vries et al. 2015; Calderón-Amor and Gallo 2020).

A negative correlation appeared between Veterinary Interventions and Culling Rate, suggesting a possible lower need to replace cows if farmers are well supported from a health and veterinary perspective, with a lower incidence of severe illnesses that would finally lead to culling (Fetrow et al. 2006). Hence, Veterinary Interventions were also negatively correlated with Vaccines, possibly due to a preventive role of vaccinations in limiting the possible outbreaks
of diseases, reducing veterinary interventions needed to treat sick animals (Richens et al. 2016). Moreover, especially in the case of organic agriculture, veterinary interventions and the related number of treatments on dairy cows are reported to be, on average, 3.5 times lower than in conventional agriculture (Moussel 2011).

A strong positive correlation was observed between absence of Lameness and positive scores of Avoidance Distance, suggesting that non-lame cows are more prone to human contact. However, limited scientific evidence exists, to our knowledge. A stressful management of dairy cows could result in a higher number of hyperalgesic cows, which could have higher possibilities to become less fit, losing body condition and mobility, resulting in a behavioural desynchronisation from the herd, increasing their isolation and making a lame cow feel more vulnerable, also avoiding human contact (Whay and Shearer 2017).

A slight negative correlation appeared between Energy Corrected Milk and Somatic Cell Count. An explanation could be that SCC tends to increase with decreasing daily milk production (Frondelius et al. 2020), because of the dilution effect of somatic cell concentration with high milk yields (Green et al. 2006).

A positive correlation emerged between Integument Alterations, SCC and correct BCS, which were slightly negatively correlated with Cleanliness. Some studies found a positive correlation between low cleanliness levels of hocks, flanks, and udder with SCC in milk (Reneau et al. 2005; Mondini et al. 2023), especially for multiparous cows, highlighting the importance of high cleanliness levels to prevent high SCC in milk. However, no specific references were found concerning the correlation between SCC and Integument Alterations. Another study (Andreasen and Forkman 2012) found that cows housed on sand stall surfaces experienced fewer integument alterations, better cleanliness scores and higher milk yield. Therefore, the positive correlation between Integument Alterations and SCC could also be due to the choice of the stall surface material, with positive effects in reducing both Integument Alterations and SCC. Concerning BCS, its positive correlations could suggest an increase in SCC in case of correct BCS, although another study found a positive association between BCS losses from calving until mid-lactation with the decrease in SCS (Berry et al. 2007). The positive correlation between normal BCS and Integument Alterations suggests a possible increase in skin alteration associated with normal BCS. This is in line with a study conducted in South Tyrol by Alrhmoun et al.

(2023), who found normal BCS as a risk factor for knee skin alterations, but only BCS lower than 3 as a risk factor for hock skin alterations.

A positive correlation emerged between ECM, absence of Nasal Discharge, and Treatments, suggesting possible higher milk yields with a lower incidence of nasal discharge and higher number of treatments, but to our knowledge no specific references were found to support this finding.

ECM was also negatively correlated with Calving Interval, suggesting that a longer calving interval would lead to a lower milk production. However, a study comparing two different calving interval lengths found no differences in terms of milk yield (Österman and Bertilsson 2003). In our case, this could be associated with the generally shorter calving intervals adopted by larger farms, which were also characterised by higher milk yields.

## Comparison with indicators adopted in conventional dairy farms

The indicators selected in this study aim to assess dairy cows' health specifically within agroecological and grassland-based dairy farms. Grassland-based dairy farming is generally less exposed to specific health problems of indoor dairy farming, such as lameness, hoof pathologies, and hock lesions, and is generally characterised by higher cleanliness levels of the cows (Arnott et al. 2017). However, grassland-based dairy systems could be more subject to non-optimal nutritional status of the cows, resulting in possible losses of body weight and lower milk production (Fontaneli et al. 2005; Hernandez-Mendo et al. 2007). The comparison with indicators from other studies revealed some differences, mainly concerning the scale of the assessment. The approach adopted in our study covers all the categories of animal health, while the majority of publications focus on specific issues, or single health domains. Therefore, to our knowledge, there are no references for the assessment of animal health specifically designed for agroecological grassland-based dairy farming, with the existing studies covering only conventional dairy farming.

Mulligan et al. (2006) proposed a set of indicators specifically aimed at monitoring and preventing production diseases in dairy cattle, focusing on BCS, Negative Energy Balance, Hypocalcaemia, Rumen Health, and Trace Elements.

Coignard et al. (2013) conducted a survey on 130 dairy farms representative of the French production context, assessing the overall herd health status and
adopting the indicators proposed by the Welfare Quality protocol. Likewise, Burow, Rousing, et al. (2013) evaluated Danish dairy cows' welfare by adopting only some of the indicators from the Welfare Quality protocol, similarly to Katzenberger et al. (2020) in Northern Italy.

To our knowledge, only one study adopted similar indicators to our work, mainly derived from the Welfare Quality protocol, and was also based on a similar number of farms (14), either adopting or not summer grazing (Mattiello et al. 2009), but these farms did not claim to be agroecological.

Other studies focused on assessing dairy cows' health by considering single indicators, like gait (Flower and Weary 2009) or treatments (Houe et al. 2011).

## Combination with animal health indicators and farm management

Concerning the association between the selected indicators for the assessment of dairy cows' health with farm management and descriptive variables, relationships and trends emerged. The second PCA revealed a positive association between Veterinary Interventions, Calving Interval, correct BCS, and Plot Return Time, which were negatively associated with ECM, Stocking Rate, Lactating Cows, UFA, and Concentrate Feed. This could be due to the fact that bigger farms, which have bigger herds with more cows, higher stocking rate and milk production, seem to adopt shorter calving interval, as well as a lower recurrence to veterinary interventions. Hence, concerning pasture management, their higher stocking rate seems to result in a shorter return time on the same grazed plot. The positive association between Stocking Rate, Lactating Cows, and Energy Corrected Milk could suggest that bigger and more intensive farms are characterised by higher milk yields per cow. However, Macdonald et al. (2008) reported that, overall, research so far is unable to account for the complexity of the farming system regarding this topic. Lower values of stocking rate seem to be beneficial to individual milk yield, probably due to the higher availability of grass (McCarthy et al. 2013), even if the lowest class of stocking rate adopted by McCarthy et al. (2013) is much higher than the median value of the eleven farms involved in our study. Hence, most of our eleven farms used to integrate the cows' diet with concentrate feed.

Altitude and Floral Diversity of grasslands were positively correlated, revealing a possible higher species richness in highland grasslands. Hence, these two

variables were also positively associated with good levels of Cleanliness and Behaviour, but no specific references were found to support this tendency. These four variables together were negatively associated with Integument Alterations.

About production levels, the median ECM production for the eleven farms (15.65 l/day) was slightly lower compared to the value of 18 l/day reported by Brocard et al. (2015) for grassland-based French dairy farms located in mountain areas, and with the one reported by Trachsel et al. (2000).

## Conclusions

This study aims to provide knowledge about the assessment of dairy cows' health on agroecological grassland-based dairy farms, through the selection of specific indicators since, to our knowledge, references on this topic are scarce.

Indicators were selected by combining the outcomes of a Principal Component Analysis, conducted on animal-based measurements, with expertise. The selected indicators were one for each of the seven categories of dairy cows' health: Correct BCS according to lactation stage (nutritional status), Cleanliness (housing), Integument Alterations (individual health status), Veterinary Interventions (herd health status), Qualitative Behaviour Assessment (behaviour), Somatic Cell Count (production), and Calving Interval (reproduction).

The eleven farms involved in the study achieved overall good scores for all the indicators, with more variation in terms of BCS, Cleanliness, QBA, SCC, and Calving Interval. However, the median values for BCS and Calving Interval were similar to other studies, whereas the median values for Cleanliness and SCC were well below the thresholds set by the reference protocol of Commun et al. (2016). About QBA, no specific references were found to make a comparison.

The outcomes of the second PCA, combining the seven selected indicators with farm management variables, suggested that bigger farms, with a wider Utilised Forage Area, hosting bigger herds with a higher number of lactating cows, are characterised by a higher milk production, possibly obtained with higher levels of concentrate feed. These farms seem to adopt a shorter calving interval, a lower recurrence to veterinary interventions and seem to be characterised by a lower presence of cows with a correct BCS. Moreover, the return time on the same grazing plot seems to be shorter.
The high health scores achieved by the farms and the related better conditions of the cows represent a positive model within the dairy sector, encouraging other farmers to take part in the agroecological transition. Hence, this list of indicators provides knowledge to develop strategies and guidelines aimed at supporting animal health in agroecology. However, since the study is based on a small number of farms located in the same region (Auvergne-Rhône-Alpes), future implications for research in agroecology could include the enlargement of the sample of farms, and the application of the selected indicators for the assessment of dairy cows' health on farms located in different geographical contexts.

Animal health represents a key aspect in agroecology. Therefore, a comprehensive assessment based on a multi-indicator protocol, and the positive scores achievable, provide knowledge to further promote the agroecological transition of dairy production, increasing sustainability within livestock farming systems.

## Acknowledgements

The authors acknowledge the farmers for their participation in the study, Mr Denis Grancher for his expertise and contribution in developing the survey protocol, and Dr. Nicolò Amalfitano for the support in data analysis.

## Ethical approval

The present study respected the principles of the Declaration of Helsinki, in terms of ethical approval.

## Disclosure statement

No potential conflict of interest was reported by the authors.

## Funding

The study was funded by the project "SAARA – réseau régional thématique de recherche pour la Santé et le bien-être Animal en Auvergne Rhône-Alpes." The PhD grant of Andrea Ceppatelli was funded by the European Union, Next Generation EU, and the Italian Ministry of University and Research, Ministerial Decree 351/2022, PNRR, Missione 4, Componente 1, Investimento 3.4 "Programmi Dedicati alle Transizioni Digitali e Ambientali", project id C96E22000240007.

## Data availability statement

The data of this study will be freely available from the corresponding author upon request. The data are not publicly available since private entities are involved (farmers).

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