# Adaptation strategies to manage summer forage shortages improve animal performance and better maintain milk and cheese quality in grass- versus corn-based dairy systems

M. Bouchon, B. Martin, C. Bord, A. Ferlay, J.M.G. Bloor, M. Eugène, A. Delacroix-Buchet, C. Cebo, Marie-Caroline Michalski, B. Graulet, et al.

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## ► To cite this version:

M. Bouchon, B. Martin, C. Bord, A. Ferlay, J.M.G. Bloor, et al. Adaptation strategies to manage summer forage shortages improve animal performance and better maintain milk and cheese quality in grass- versus corn-based dairy systems. Journal of Dairy Science, 2025, 108 (5), pp.4796-4817. 10.3168/jds.2024-25730. hal-05071015

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Submitted on 4 Jun 2025

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# Adaptation strategies to manage summer forage shortages improve animal performance and better maintain milk and cheese quality in grass- versus corn-based dairy systems

M. Bouchon,<fnref n="1" />* B. Martin,<fnref n="2" /> C. Bord,<fnref n="3" /> A. Ferlay,<fnref n="2" /> J. M. G. Bloor,<fnref n="4" /> M. Eugène,<fnref n="2" /> A. Delacroix-Buchet,<fnref n="5" />
C. Cebo,<fnref n="5" /> M. C. Michalski,<fnref n="6" /> B. Graulet,<fnref n="2" /> I. Verdier-Metz,<fnref n="3" /> and C. Delbès<fnref n="3" />

## ABSTRACT

In semi-mountainous grass-based dairy systems, summer droughts frequently reduce grass availability, with implications for animal performance as well as milk and cheese quality. The objective of our study was to investigate the effect of a simulated summer forage shortage in a traditional semi-mountainous grass-based system versus a corn-based system with part-time grazing. During a 19-wk experiment, 26 Prim'Holstein and 14 Montbéliarde cows were blocked in 4 balanced groups of 10 cows. During the first 8 wk, 2 groups were fed a grass-based diet (75% of grazed grass, 25% of hay and concentrates) and the remainder were fed a corn-based diet (75% mixed ration, 25% of grazed grass). During the following 11 wk, one group in each feeding system was subjected to a reduction in grazed grass intake and a corresponding increase in indoor feeding, resulting in 50% of grazed grass for grass-fed cows and removal of grazed grass for corn-fed cows. Milk yield and composition were recorded during the whole trial, in addition to indoor individual intake. Grass intake was modeled to calculate feed efficiency and estimate methane emissions. At the end of the experiment, bulk milk from each group was sampled for physicochemical and microbiological analyses and processed into Cantal-type cheeses. After 9 wk of ripening, cheeses underwent physicochemical, microbiological and descriptive sensory analyses. Results showed that the complete removal of grazed grass in corn-based system had no effect on milk production but impaired feed efficiency; grass reduction in the grass-based diet better maintained milk yield, enhanced feed efficiency,

and reduced methane emission intensity. Cheese softness increased with proportion of grass in the diet, and it was positively correlated with primary proteolysis (αS₁-casein and β-casein breakdown) but negatively correlated with the C16:0/C18:1 ratio and the calculated fat melting point. Cheese from cows fed with more grazed grass were the yellowest and had the most pronounced flavors, as well as higher counts of heterofermentative lactobacilli and lactic acid bacteria. Removal of grazed grass from the corn-based diet led to cheese that was less yellow and had less flavor, in line with lower levels of secondary proteolysis. In conclusion, our study suggests that maintaining fresh herbage in corn-based diets is crucial for maintaining cheese quality without impairing animal performance. In grass-based systems, complementation with indoor feeding may have limited effects on product quality but promotes the maintenance of animal performance.

**Key words:** adaptation strategies to climate change, dairy performance, milk and cheese quality, food system experiment

## INTRODUCTION

Dairy cow farming systems face increasing pressures due to ongoing climate change and the increasing frequency of extreme climate events (Gauly and Ammer, 2020). In semi-mountainous areas, such as the French Massif Central, farming systems are regularly confronted by droughts (Deléglise et al., 2023; Allart et al., 2024a), with negative effects on both grass quantity and quality (Liu et al., 2023), particularly in the summer (Hahn et al., 2021). In the short term, farms in these areas often rely on greater levels of indoor feeding of conserved forages and concentrates as a tactical adaptive strategy (Lee et al., 2013). In parallel, the cultivation of complementary

<footnote n="1">¹INRAE, UE Herbipôle, 63122 Saint-Genès-Champanelle, France</footnote>

<footnote n="2">²Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Herbivores, 63122 Saint-Genès-Champanelle, France</footnote>

<footnote n="3">³Université Clermont Auvergne, INRAE, VetAgro Sup, UMR Fromage, 15000 Aurillac, France</footnote>

<footnote n="4">⁴Université Clermont Auvergne, INRAE, VetAgro Sup, UREP, 63000 Clermont-Ferrand, France</footnote>

<footnote n="5">⁵Université Paris-Saclay, INRAE, AgroParisTech, UMR GABI, 78350 Jouy-en-Josas, France</footnote>

<footnote n="6">⁶Inserm, INRAE, INSA Lyon, Université Claude Bernard Lyon 1, Lyon-Sud Medical School, UMR CarMen, Pierre-Bénite, 69310, France</footnote>
forage crops such as corn silage is increasingly adopted as a medium-term strategy to reduce vulnerability of farming systems to recurrent forage losses (Lee et al., 2013; Allart et al., 2024b). However, the effectiveness and implications of different adaptive strategies on animal performance may depend on existing farming practices which are more or less reliant on grass as animal feed. Improved understanding of these strategies on dairy production and quality is necessary to support the agroecological transition in the dairy sector in a context of climate change.

The quality of products from grassland-based dairy production systems is known to differ from that of products derived from milk drawn from corn-fed cows (Martin et al., 2005; Ferlay et al., 2006). For example, milk and cheese from grass-based production systems may contain a higher proportion of PUFA such as α-linoleic and conjugated linolenic acids, which are of nutritional interest (Ferlay et al., 2006; Cabiddu et al., 2022). Dairy products from grass-based systems are also preferred by many consumers due to their sensory properties, with a more distinctive odor and aroma, as well as a more yellow color (Martin et al., 2005). Previous studies have suggested that the influence of cow diet on milk and cheese microbes may drive differences in the physicochemical properties of dairy products (e.g., Frétin et al., 2017, 2022). These authors reported higher levels of certain microbes, such as heterofermentative lactobacilli, which can come directly from the surface of the grass at pasture (Verdier-Metz et al., 2012) and which contribute to the sensory elaboration of the cheese. The casein profile of milk is also known to affect cheesemaking properties and thus cheese sensory quality (Emmons et al., 2003). However, unlike the large number of studies documenting effects of cow genotypes on milk proteins (Ng-Kwai-Hang et al., 1987; Heck et al., 2009), knowledge on the influence of cow diet on protein profiles from milk to cheese remains scarce.

Where herbage conservation method or grazing intensity affects milk traits (Frétin et al., 2017; Manzocchi et al., 2021, 2022), adaptation strategies to cope with climate change or fodder deficits, which lead to shifts in cow diets, are expected to modify both product quality and the dairy performance of the herd. As yet, no study has comprehensively examined the consequences of short-term adaptation strategies (and associated diet changes) in systems relying on grass only or on grass and corn silage. Here we use a semi-controlled, system-level experiment to evaluate the effects of such strategies on animal performance, environmental impacts, the physicochemical and microbiological properties of milk and cheeses, and the sensory properties of cheeses. Our overarching hypothesis is that the effects of a shift in diet linked to a forage shortage vary depending on the feeding

system and the traits considered (milk production, feed efficiency, methane emission, product quality).

## MATERIALS AND METHODS

### Experimental Design and Animal Management

This study was carried out in 2021 at INRAE's Marcenat facility (45°15'N, 2°55'E, 1,100-1,200 m above sea level; Herbipôle low mountain ruminant experimental facility, Marcenat, France; https://doi.org/10.15454/1.5572318050509348E12). The site has an average annual temperature of 8.4°C and an annual rainfall of 1,105 mm (calculated over the period 2015-2020).

Two systems were implemented with contrasting levels of management intensity (an agroecological system [AE] and an intensive system [IN]) and separate groups of dairy cows (mixed Montbéliarde and Prim'Holstein herds; Figure 1). In May 2021, 26 lactating Prim'Holstein and 14 Montbéliarde cows (54.2 ± 26.7 DIM), producing 27.8 ± 5.66 kg/d of milk (with 33.2 ± 5.70 g/kg of fat and 29.8 ± 3.70 g/kg of proteins) and of an average weight of 651.7 ± 69.41 kg were blocked in 4 balanced groups of 10 cows according to breed, parity, milk yield (MY), and stage of lactation (Table 1). Analysis of milk quantity and fat and protein content before blocking showed no significant breed effects (*P* > 0.05). Two groups were assigned to a low-input diet mainly comprising grazed grass (AE), whereas the remaining 2 groups were assigned to a corn silage-based diet (IN) for an 8-wk period (referred to as "before disturbance"). The AE diet was composed of 75% grazed grass (9 ha, low-intensity rotational grazing, 0.45 acres/livestock unit), 10% hay, and 15% production concentrates, fed manually in the barn after each milking and before return to pasture. For IN, the diet was composed of 20% pasture (1.6 ha, rapid wire grazing), 50% mixed ration (61% corn silage, 25% wheat straw, 14% protein concentrate), and 30% production concentrates. Details on feeding values of the different ingredients used in the study are presented in Table 2. The IN cows had access to the mixed ration and the concentrates fed with an automatic concentrate feeder (Delaval) between the end of the evening milking and until ~1.5 h after the morning milking and their return to the pasture.

On July 12, half of the AE and IN cows were separated according to initial blocking and subjected to a change in diet, simulating a management change in response to fodder shortage (as might arise due to summer drought). This resulted in 4 management groups of 10 cows each (intensive control [INC], intensive disturbed [IND], agroecological control [AEC], and agroecological disturbed [AED]) managed separately for a further 11 wk (second period referred to as "during disturbance"). During this time, the 2 disturbed (Dis) groups (AED and IND) were
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<table><caption>**Figure 1.** Graphical representation of the experimental design, sampling, and analysis carried out during the study and the methods used. References: Slanetz and Bartley (1957); Mossel et al. (1962); Turner et al. (1963); Isolini et al. (1990); Visser et al. (1991); Ardö (1999); Denis et al. (2001); ISO (2004); ISO 5943, ISO (2006); Martin et al. (2009); Ferlay et al. (2010); ISO (2010); ISO (2014); AFNOR (2019); Miranda et al. (2020); Manzocchi et al. (2021, 2022).</caption><thead><tr><th>Analyses</th><th>Individual milk</th><th>Blended milk turned to cheese</th><th>Cheese</th><th>Method</th><th>Reference</th></tr></thead><tbody><tr><td>Fat content</td><td rowspan="2">Twice/wk<br>wk1 to wk19</td><td rowspan="2">3 times<br>wk19</td><td rowspan="2">Core<br>1 cheese /batch</td><td>Infrared<br>spectroscopy</td><td>Manzocchi et al. (2021)</td></tr><tr><td>Protein content</td><td>Manzocchi et al. (2022); Ferlay<br>et al. (2010)</td></tr><tr><td>SCC</td><td>Once wk8<br>and wk19</td><td></td><td></td><td>GC<br>LC/ESI-MI</td><td>Visser et al. (1991); Miranda et<br>al. (2020)</td></tr><tr><td>Fatty acids profile</td><td>Once wk19</td><td></td><td></td><td>Infrared<br>spectroscopy</td><td>Manzocchi et al. (2021)</td></tr><tr><td>Protein profile</td><td></td><td></td><td></td><td>Bactoscan</td><td>Manzocchi et al. (2021)<br>Slanetz and Bartley (1957)<br>Turner et al. (1963)<br>Mossel et al. (1962)<br>Isolini et al. (1990)<br>Denis et al. (2001)</td></tr><tr><td>Urea content</td><td></td><td></td><td></td><td>Spectrometry</td><td>NF EN ISO 13720 (ISO, 2010)<br>Manzocchi et al. (2021)<br>Martin et al. (2009)<br>ISO 5534 (ISO, 2004)</td></tr><tr><td>Lactose content</td><td></td><td></td><td></td><td>Heiss method</td><td>AFNOR NF V 04-287 (AFNOR,<br>2019)</td></tr><tr><td>Casein content</td><td></td><td></td><td></td><td>Potentiometric<br>method</td><td>ISO 5943 (ISO, 2006)</td></tr><tr><td>Total germs</td><td></td><td></td><td></td><td>Kjeldahl<br>method</td><td>ISO 8968 (ISO, 2014); Ardö<br>(1999)</td></tr><tr><td>Microbial numeration</td><td>Enterococci<br>Lactococci<br>Yeasts and Molds<br>Heterofermentative facultative Lactobacillus<br>Gram-positive catalase-positive bacteria<br>Pseudomonas</td><td></td><td></td><td>Core<br>1 cheese /batch</td><td>Manzocchi et al. (2021)</td></tr><tr><td>Color</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>pH</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Dry matter</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fat content</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Chloride content</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Proteolytics indicators</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Sensorial attributes:<br>appearance (2), odor (10), taste (5), flavor (8), texture<br>(6)</td><td></td><td></td><td></td><td>Core/Rind<br>2 cheeses/batch</td><td>Manzocchi et al. (2021)</td></tr></tbody></table>
<table><caption>**Table 1.** Composition and performance of the 4 groups of 10 cows</caption><thead><tr><th rowspan="2">Item</th><th colspan="2">AE</th><th colspan="2">IN</th></tr><tr><th>AEC</th><th>AED</th><th>INC</th><th>IND</th></tr></thead><tbody><tr><td>Parity and breed</td><td></td><td></td><td></td><td></td></tr><tr><td>Parity<fnref n="1" /></td><td>3.4 ± 1.90</td><td>3.5 ± 1.78</td><td>3.5 ± 2.17</td><td>3.7 ± 2.12</td></tr><tr><td>Prim'Holstein (n)</td><td>6</td><td>6</td><td>7</td><td>7</td></tr><tr><td>Primiparous (n)</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td>Montbéliarde (n)</td><td>4</td><td>4</td><td>3</td><td>3</td></tr><tr><td>Primiparous (n)</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Animal performance<fnref n="1" /></td><td></td><td></td><td></td><td></td></tr><tr><td>At blocking (May 17)</td><td></td><td></td><td></td><td></td></tr><tr><td>DIM (d)</td><td>56 ± 28.0</td><td></td><td>52 ± 25.9</td><td></td></tr><tr><td>Milk yield (kg/d)</td><td>28.8 ± 4.88</td><td></td><td>27.8 ± 6.57</td><td></td></tr><tr><td>Fat content (g/kg)</td><td>32.4 ± 5.96</td><td></td><td>33.9 ± 5.24</td><td></td></tr><tr><td>Protein content (g/kg)</td><td>29.2 ± 4.51</td><td></td><td>30.3 ± 2.43</td><td></td></tr><tr><td>BW (kg)</td><td>633 ± 66.9</td><td></td><td>663 ± 65.6</td><td></td></tr><tr><td>At disturbance (July 15)</td><td></td><td></td><td></td><td></td></tr><tr><td>DIM (d)</td><td>113 ± 22.1</td><td>117 ± 34.1</td><td>109 ± 25.1</td><td>114 ± 26.7</td></tr><tr><td>Milk yield (kg/d)</td><td>19.4 ± 5.16</td><td>20.3 ± 3.84</td><td>21.5 ± 4.36</td><td>19.6 ± 4.33</td></tr><tr><td>Fat content (g/kg)</td><td>34.4 ± 6.96</td><td>33.8 ± 2.99</td><td>33.1 ± 5.43</td><td>34.5 ± 4.22</td></tr><tr><td>Protein content (g/kg)</td><td>30.6 ± 3.16</td><td>28.9 ± 1.73</td><td>29.0 ± 2.40</td><td>30.1 ± 2.69</td></tr><tr><td>BW (kg)</td><td>640 ± 65.9</td><td>646 ± 61.7</td><td>683 ± 63.2</td><td>702 ± 58.0</td></tr></tbody></table>

fed with either a lower proportion of grass (i.e., 50% grazed grass, 20% hay, 30% production concentrates for AED) or no grazed grass (0% grazed grass, 60% mixed ration, and 40% concentrates for IND), whereas the 2 control (Con) groups (INC and AEC) remained on the same diets as before. During disturbance, AED cows were kept in the barn during the night to allow access to hay and concentrates. The IND cows had ad libitum access to the mixed ration and the concentrates even during the day and had access to an outdoor exercise area of ~500m² adjacent to their pen. For all groups with pasture access, the nutritional values of grazed grass evolved over the season (Table 2).

## Characteristics of Grazed Pastures

Access to pasture during the trial involved 2 separate sets of fields for the IN and AE groups. The soil across the site is a clay loam (28.8% clay, 45.4% silt, and 25.9%

sand) and has a pH<sub>H2O</sub> of 5.8 measured according to ISO 10390 (ISO, 2021). The 2 sets of fields differ in their management history, soil properties, and dominant plant species. The soil organic content, nitrogen content, and cation exchange capacity values are 8.83, 5.1, and 28.3 g/kg, respectively, for the AE fields, and 17.2, 10.3, and 34.7 g/kg, respectively, for the IN fields. The plant community in all fields was dominated by grasses. Abundant species in the AE field were *Alopecurus pratensis*, *Cyanosorus cristatus*, and *Lolium perenne*. Other species include forbs (*Achillea millefolium*, *Trifolium repens*). The plant community in the IN field was dominated by *Lolium perenne*, *Poa trivialis*, and *Alopecurus pratensis*, with a lower presence of forbs (*Trifolium repens*, *Ranunculus acris*). Before the start of this experiment in 2021, the IN field had been subject to high-intensity cattle grazing for many years, whereas the AE field had been subjected to low-intensity continuous cattle grazing. On the March 26, 2021, both fields were fertilized with 16 m³/ha of

<table><caption>**Table 2.** Feed values of the different ingredients used to feed the cows during the experiment; values are expressed in g/kg DM, except for gross energy (GE) and NEL expressed in kcal/kg DM</caption><thead><tr><th>Item</th><th>DM</th><th>CP</th><th>NDF</th><th>ADF</th><th>GE</th><th>NEL</th><th>Starch</th><th>Ash</th></tr></thead><tbody><tr><td>Grazed grass</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Before disturbance AE</td><td>143</td><td>240</td><td>402</td><td>253</td><td>4,349</td><td>1,832</td><td>—</td><td>130</td></tr><tr><td>Before disturbance IN</td><td>139</td><td>201</td><td>406</td><td>241</td><td>4,394</td><td>1,883</td><td>—</td><td>105</td></tr><tr><td>During disturbance AE</td><td>227</td><td>175</td><td>521</td><td>295</td><td>4,367</td><td>1,657</td><td>—</td><td>101</td></tr><tr><td>During disturbance IN</td><td>225</td><td>87</td><td>598</td><td>356</td><td>4,309</td><td>1,374</td><td>—</td><td>80</td></tr><tr><td>Hay</td><td>887</td><td>96</td><td>559</td><td>312</td><td>4,498</td><td>1,441</td><td>—</td><td>61</td></tr><tr><td>Corn silage</td><td>320</td><td>89</td><td>376</td><td>204</td><td>4,474</td><td>1,719</td><td>322</td><td>48</td></tr><tr><td>Straw</td><td>882</td><td>38</td><td>798</td><td>504</td><td>4,301</td><td>782</td><td>—</td><td>80</td></tr><tr><td>Production concentrate</td><td>869</td><td>156</td><td>201</td><td>94</td><td>4,431</td><td>1,819</td><td>368</td><td>46</td></tr><tr><td>Protein concentrate</td><td>880</td><td>436</td><td>215</td><td>138</td><td>4,695</td><td>1,777</td><td>42</td><td>70</td></tr></tbody></table>

<footnote n="1">¹Values are expressed as mean ± SD.</footnote>
cattle slurry, corresponding to ≈57 kg N per hectare. The available biomass offered to the cows remained stable around 1.80 t DM/ha during the study for the IN field, whereas it varied from 0.62 t DM/ha to 1.99 t DM/ha for AE, with the peak in the middle of the experiment (mid-August).

## Measurement of Animal Performance

Individual DMI of feed distributed inside the barn was recorded by manual weighing of the individual quantity offered (assuming no refusals) for AE, AEC, and AED. In the case of IN, INC, and IND, DMI was recorded automatically with weighing troughs (CRFI, Biocontrol) and automatic concentrate feeders. Additional herbage intake for cows at pasture was estimated for each cow following INRAE equations (INRAE, 2018). This model also allowed the estimation of individual methane (CH₄) emissions expressed in grams per day. Briefly, the INRAE model takes into account DMI (expressed as % of BW) and the digestible organic matter and NDF content of the diet to estimate CH₄ production. Methane intensity was calculated as the ratio between CH₄ emissions expressed in grams per day and MY expressed in kilograms per day.

Cows were milked twice a day at 0630 h and 1630 h in a herringbone milking parlor (DeLaval) equipped with flowmeters and infrared analyzers (MM27BC, DeLaval), which record individual MY at each milking. Fat- and protein-corrected milk (FPCM) was calculated according to Reincke et al. (2018). All cows were weighed after each milking session using automatic weighing scales (AWS100, DeLaval) at the exit of the milking parlor.

## Milk Analyses

Twice a week during the experimental period, milk samples were taken from each cow during morning and evening milking sessions and preserved with bronopol at 4°C before analysis for fat and protein content (PC) using infrared spectroscopy methods (Milkoscan 4000, Foss System). Somatic cell counts were automatically counted (Fossomatic 5000, Foss System; Agrolabs, Autillac, France; Figure 1).

At the end of the “before disturbance” and “during disturbance” periods, 3 mL of individual milk was taken from each cow during morning and evening milkings. In addition, milk samples were taken from the blended milk processed into cheese at the end of the “during disturbance” period; all milk samples were stored without preservative at -20°C until lyophilization and fatty acid (FA) GC analysis (Figure 1). Lyophilized samples were methylated following Manzocchi et al. (2022) and FA composition was analyzed by GC following Ferlay et al.

(2010). In brief, the FA methyl esters were injected by autosampler into a Trace-GC 2000 series gas chromatograph equipped with a flame ionization detector (Thermo Finnigan). Methyl esters were then separated on a fused-silica capillary column (CP-Sil 88, Chrompack) and identified as described by Ferlay et al. (2010).

The FA concentrations (g/100 g total FA) were calculated based on individual MY and fat content of morning and evening milking. Given that FA profiles have an important effect on product texture via fat melting properties, FA data were used to calculate the melting point of the fat as described by Toral et al. (2013). We calculated the ratio between FA that melt above or below 37°C (which is the temperature of the mouth), and the ratio between FA that melt over 45°C (which we considered as hardening FA) and FA that melt under 15°C, (softening FA). The FA melting points were obtained from LipidBank (http://www.lipidbank.jp; Japanese Conference on the Biochemistry of Lipids). Melting points of FA that were not reported in the database were evaluated from neighboring isomers according to Toral et al. (2013). Sums of SFA, MUFA, PUFA, n-3 FA, n-6 FA, and spreadability index (ratio C16:0/C18:1; Couvreur et al., 2006) were also computed.

Detailed milk protein profiles were determined by liquid chromatography/electrospray ionization-MS (LC/ESI-MS) analysis on fresh morning milk samples (1 mL stored at -20°C until analysis) from individual cows and from blended milks processed into cheese at the end of the “during disturbance” period. Before LC/ESI-MS analysis, samples were skimmed and clarified, and caseins and whey proteins were separated by reverse phase high-performance liquid chromatography (Ultimate LC 3000, Thermo Fisher Scientific). The output was interfaced with an electrospray ionization time of flight mass spectrometer (micrOTOF II focus, Bruker Daltonics), and results were compared with a theoretical protein mass database. More details on this analysis can be found in Miranda et al. (2020) and in Visser et al. (1991).

The raw blended milks from the 4 cow groups were used to assess fat, protein, casein, urea, lactose, and somatic cells by infrared spectroscopy (as described for individual milk), as well as total germ content (Bactoscan, Foss System; Manzocchi et al., 2021). Milk pH and color (CM-2600d, Minolta) were evaluated. Microbial cultures were used to determine presence and abundance of enterococci (Slanetz and Bartley, 1957), lactococci (Turner et al., 1963), yeasts and molds (Mossel et al., 1962), heterofermentative facultative lactobacilli (Isolini et al., 1990), gram-positive catalase-positive bacteria (Denis et al., 2001), and *Pseudomonas* (NF EN ISO 13720, ISO, 2010; Figure 1).
Cheese Production and Analyses

For 3 d at the end of the “during disturbance” period (wk 19), the raw tank milks from the 4 management treatments (AEC, AED, INC, IND) were collected separately at the morning milking and transported to the cheesemaking experimental facility (INRAE UMR Fromage, Aurillac, France) in airtight cans filled to the brim. For each treatment, 35 L of raw blended milk was simultaneously processed into cheese in separate vats. The cheese model applied was Cantal-type cheese, an uncooked, ground, and pressed cheese, produced with nonstandardized milk. The Cantal-type small-sized cheeses (~500 g) were produced as described by Manzocchi et al. (2021). Briefly, milk was heated at 33°C for inoculation with lyophilized mesophilic starter culture (Flora Danica Direct, Chr. Hansen) after its preparation in sterile skim milk, ripening starters (Penbac, Laboratoire Interprofessionnel de Production) and rennet. After 45 min, the curd was cut for 1.5 min to reach corn kernel size, blended for 12 min, and left to rest for 5 min. After the whey was drained, the curd was pressed into a tray, cut into 5-cm cubes, and turned every 15 min for 90 min. After pressing, the curd was left to drain for 24 h at 24.5°C, pounded to 20-mm grains, salted (2 g of NaCl/100 g of curd), and rested for 6 h at 18°C (90% relative humidity). The resulting curd was then put into 6 molds (90 mm in diameter) per vat. Overall, 72 cheeses were made from raw milk (4 groups × 6 cheeses per group × 3 cheesemaking dates) and were ripened for 2 mo at 10°C and ≥97% relative humidity; cheeses were turned twice a week during this period.

At the end of the ripening period, 12 ripened Cantal-type cheeses (4 groups × 1 cheese per group × 3 cheesemaking dates) were analyzed for chemical composition (pH, dry matter, fat, protein, and chloride contents). Additional measurements were carried out using rheological, color and proteolysis indicators (Figure 1). The FA profile and calculation on melting points, and the microbial numerations were also evaluated for each cheese. Dry matter was determined according to reference method ISO 5534 (ISO, 2004), fat content was determined using the Heiss method (AFNOR NF V 04-287; AFNOR, 2019), chloride contents by the potentiometric method (ISO 5943, ISO, 2006), and total nitrogen (TN) by the Kjeldahl method (ISO 8968; ISO, 2014). Water-soluble nitrogen (WSN) and phosphotungstic acid-soluble nitrogen (PTSN) were determined according to Ardo (1999). Color measurements were done by spectrometry (CM-2600d, Minolta) and rheological measurements according to Martin et al. (2009) by measuring the strengths exerted to deform the sample by 20%, 40%, 60%, as well as Young's modulus using a uniaxial compression Lloyd testing machine (Ametek S.A.S., Lloyd Instruments). Ten trained panel-

ists assessed 24 ripened Cantal-type cheeses (4 groups × 2 cheeses per group × 3 cheesemaking dates) using the sensory profile method for 28 attributes (1 for color, 10 for odor, 4 for taste, 8 for aroma, 5 for texture) as described in Manzocchi et al. (2021).

Statistical Analyses

The dataset was split in 2 periods, namely “before disturbance” and “during disturbance,” according to the date when the diet change was applied. Data of one IND and 2 INC cows were removed because of cow health issues. For the “during disturbance” dataset, wk 17 was considered as an outlier due to a change in corn silage quality impairing intake and milk quality. Animal performance was averaged per 4-wk period (M1 = wk 1–4; M2 = wk 5–8; M3 = wk 12–15: M4 = wk 16, 18, and 19; Figure 1) before sampling at the end of each period.

Data were analyzed with general linear mixed models. Effects of the feeding system on milk parameters before disturbance were tested with a model including breed, month (M1, M2) and system (AE vs. IN) and the interaction term month × system as fixed effects, and cow as a random factor. The model used to study the effect of the feeding system and the simulated forage shortage during disturbance on milk parameters included breed, month (M3, M4), system, and disturbance (Con vs. Dis) and the interaction terms month × system, disturbance × system, and disturbance × system × month as fixed effects, and cow as a random factor.

The models used to study feeding related variables were performed only on M2 and M4 (before and during disturbance, respectively), as nutritional values of the feed ingredients were only available at the end of each period. The models used to study FA, protein profile and sensory properties of blended milk processed into cheeses and cheeses were the same as for feeding related variables, with the cheesemaking repetition as a random factor. Normality and homoscedasticity of all residuals were visually inspected; SCC and total germ numeration were log-transformed to achieve normalization. Finally, a multiple factor analysis was carried out to explore the link between physicochemical and microbial characteristics of the cheeses and their sensory properties. Sixty-six variables from the whole dataset were kept based on significance and relevance according to expert opinion and literature; they were split into 9 blocks based on the nature of the variables (i.e. odor, aroma, texture, taste, FA, microbiological, physicochemical, protein, and proteolysis). All statistical analyses were performed with R software (V4.3.0; R Core Team, 2023). General linear mixed models were performed using the lmer method from the lme4 package (V1.1-35.1; Bates et al., 2015). Adjusted estimated means were calculated with the
<figure><img src="image_4.png" /><figcaption>**Figure 2.** Animal dairy performance (milk yield [MY]; fat content [FC]; protein content [PC]; fat- and protein-corrected milk [FPCM]) illustrated per group (AE vs. IN ± Con vs. Dis) and per period (before and during disturbance), each being divided in subperiods of 4 wk (M1–M4) before each sampling period. Light blue-green = AE and AEC; dark blue-green = AED; light brown = IN and INC; dark brown = IND. The table presents the *P*-value of the effect tested in the model for each period. Br = breed (Prim'Holstein vs. Montbéliarde); Sys = system (AE vs. IN); Mth = month (M1–M4); Dist = disturbance (Con vs. Dis). n = number of animals taken into account into the analysis for each period. Upper whisker: maximum; lower whisker: minimum; points (●): outliers; cross (×): mean; middle line: median; upper end of the box: 75th percentile; lower end of the box: 25th percentile.</figcaption></figure>

emmeans method from the emmeans package (V1.9.0; Lenth, 2023). The multiple factor analysis was perfomed using the MFA method from the FactoMineR package (V2.9; Lê et al., 2008).

# RESULTS

## Animal Performance

Before disturbance, MY, PC, and FPCM decreased from M1 to M2 but did not differ significantly between AE and IN cows (Figure 2). Observed decreases in MY were higher in AE than in IN (−6.1 vs. −4.7 kg/d, *P* = 0.040). Milk fat content, body weight, and BCS were similar in AE and IN cows during this period (Supplementary Table S1, see Notes).

During disturbance (i.e., dietary change), treatment differences on MY were only apparent in M4. During this

period, MY decreased in all groups, but the decrease was the highest for AEC (Figure 2). Milk yield was, on average, 28% higher in IN groups compared with AE groups, and the disturbance increased MY by 11% in AED cows compared with AEC. Milk fat content increased significantly between M3 and M4 for AE cows (+2.01 g/kg, *P* = 0.030), whereas it remained stable for IN cows. The disturbance resulted in an increase of milk PC in IN cows, whereas IND cows tended to have a higher PC than the other groups (+1.4 g/kg, *P* = 0.084). The disturbance also led to an increase of PC between M3 and M4 for AED cows (Figure 2). This resulted in a greater decrease in FPCM for AEC cows compared with AED and IND cows (−2.9 vs. −1.3 kg FPCM, *P* = 0.001; Figure 2). Fat- and protein-corrected milk remained stable during disturbance for INC. In IN systems, animals were heavier than in AE at the end of the experiment (+62 kg, *P* = 0.03); the disturbance was associated with a decrease in
BCS for AE cows but an increase in BCS for IN cows (Supplemental Table S1).

## Ingestion, Feed Efficiency, and Estimated CH₄ Emissions

Before disturbance, the total DMI of IN and AE cows did not differ significantly (Figure 3) despite differences in the composition of the feeding ration. Total NEL and CP were higher in the feed given to AE cows, whereas starch was lower (Figure 3). The disturbance resulted in a decrease in total DMI for AED cows (–1.09 kg DM compared with AEC, *P* < 0.001) and an increase in total DMI for IND cows (+4.13 kg DM, *P* < 0.001; Figure 3). Compared with AEC, the higher intake of hay and energy concentrate in the AED diet did not compensate for the lower estimated grazed grass intake, whereas in IND cows, the higher intake of corn silage, straw, and concentrates advantageously replaced the grazed grass. Higher NEL intake was observed for IND (+21%, *P* < 0.001) compared with AEC, AED, and INC, and IND also showed a higher CP intake compared with INC (+37%, *P* < 0.001; Figure 3). For AE cows, the disturbance resulted in a lower CP intake (–12%, *P* < 0.001) and NDF intake (–23%, *P* = 0.007; Figure 3). Starch intake was generally higher for IN versus AE cows; the disturbance doubled starch intake for AE cows and generated an 80% increase for IN cows (Figure 3).

The AE cows were as efficient as IN cows on both a MY and a FPCM basis before disturbance. During disturbance, IN cows were more efficient than AE cows. The disturbance resulted in a higher FE for AE cows (+0.14 kg FPCM/kg DMI, *P* < 0.001), whereas it impaired FE in IN cows (–0.13 kg FPCM/kg DMI, *P* < 0.001; Table 3).

Raw estimated CH₄ emissions were higher for AE cows than IN cows before disturbance (412 vs. 387 g/d, *P* = 0.033) but methane emission intensity, expressed on a MY basis, was similar for both groups (Table 3). At the end of the trial, raw CH₄ emissions were generally higher for the IN cows compared with the AE system, whereas CH₄ emission intensity was lower for IN cows compared with AE cows. Disturbance increased raw CH₄ emissions in the IN system (+10.3% for IND compared with INC, *P* = 0.003), but decreased CH₄ emission intensity in AE cows, resulting in the highest CH₄ emission intensity for AEC cows (+34% compared with AED, INC, and IND, *P* = 0.004; Table 3).

## Individual Milk Composition

**Milk Fatty Acid Composition.** During disturbance, AE cows produced milk lower in SFA (–7.9%, *P* < 0.001) and higher in MUFA (+15.2%, *P* < 0.001) compared with

IN cows. Polyunsaturated FA were higher (+28.7%, *P* < 0.001) in AE compared with IN milk. The AE milk also had double the amount of n-3 FA but lower levels of n-6 FA (–31.6%) compared with IN milks. Disturbance increased n-6 FA by 28.6% in both systems (*P* < 0.001) but decreased n-3 FA by 14.2% in AE and 44.1% in IN (*P* = 0.037) and decreased PUFA by 12.7% in AE alone. The spreadability index was 30.7% higher in IN versus AE milk (indicating a more rigid fat fraction) and was unaffected by disturbance. Calculated fat melting point was generally lower in AE than IN (36.3°C in AE vs. 37.2°C in IN, *P* = 0.021), whereas the ratios between FA with melting point ≥37°C and <37°C or between FA with melting point ≥45°C and <15°C were higher in IN than in AE milk (*P* = 0.006 and *P* < 0.001, respectively; Figure 4). Disturbance had no effect on FA and FA ratios.

**Milk Protein Composition.** The greatest difference between systems was observed on αS2-casein proportion (+13% in IN compared with AE, *P* = 0.002; Figure 5). The αS1-casein proportion was also lower in IN milk (–8%, *P* = 0.013), whereas the proportion of α-LA was higher in IN compared with AE milk (+15%, *P* < 0.001). Disturbance had no effect on milk protein composition, and limited system × disturbance interactions were observed. The only exception was for the proportion of β-casein, which was 6% higher in IND compared with INC, but 8% lower in AED compared with AEC (*P* = 0.040; Figure 5).

## Blended Milk Processed into Cheese

Blended milk of the 4 groups presented similar fat contents, but IND milk protein and casein contents were 8% higher compared with milk of the 3 other groups (*P* = 0.003 and *P* = 0.003, respectively). Milk urea content was 19% higher in AE versus IN milk (*P* = 0.032). Disturbance was associated with an increase in milk lactose (+4%; *P* = 0.007) and a decrease in urea content (–20%; *P* = 0.032) and SCC (–58%; *P* < 0.001) compared with milk from control groups (Figure 3).

The FA profiles and calculated melting points of blended milks differed depending on system and disturbance; overall trends mirrored patterns observed with individual milk, but disturbance increased SFA and spreadability index and decreased MUFA in both systems (+5.9%, +21.3%, and –14.8%, respectively, *P* = 0.005; Figure 4; Supplemental Table S2, see Notes).

The protein profile of IN blended milks had a higher proportion of total β-casein (*P* = 0.003) and α-LA (*P* = 0.001; Figure 5). The IN blended milk also tended to contain more αS1-casein (*P* = 0.090; Figure 5). In contrast, AE milks had a higher proportion of β-LG (*P* = 0.001). Disturbance reduced the proportion of total αS2-
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Description of the intake (DMI, NEL, CP, NDF, starch) illustrated per group (AE vs. IN ± Con vs. Dis) and per period (before and during disturbance). Graphics show the DMI per ingredients (in kg/d); CP, NDF, and starch intakes (in g/d); and NEL intake (in Mcal/d). The total is also reported at the top of each bar for each parameter. The table presents the *P*-value of the effect tested in the model for each period for total intakes of each parameters. Br = breed (Prim'Holstein vs. Montbéliarde); Sys = system (AE vs. IN); Dist = disturbance (Con vs. Dis).</figcaption></figure>

casein, β-casein plasmin peptide, and β-LG in blended milk samples (Figure 5; Supplemental Table S3, see Notes).

Milk yellowness (b*) was generally higher in AE versus IN milk (*P* < 0.001). Disturbance was associated with a decrease in milk yellowness irrespective of system,
<table><caption>**Table 3.** Effect of feeding system (Sys) and simulated forage shortage (i.e. disturbance [Dist]) on feed efficiency expressed according to MY or FPCM, raw CH₄ emission, and CH₄ emission intensity (expressed on a MY basis) during the 4-wk period before the disturbance (before disturbance) for each feeding system (AE vs. IN) or during the last 4-wk period of the experiment (during disturbance) for each feeding system (AE vs. IN) subjected or not to the disturbance (Con vs. Dis)<fnref n="1" /></caption><thead><tr><th rowspan="3">Item</th><th colspan="8">Before disturbance</th><th colspan="6">During disturbance</th></tr><tr><th colspan="4">P-value</th><th colspan="2">AE</th><th colspan="2">IN</th><th colspan="2">P-value</th><th colspan="2">Dist</th><th colspan="2">Sys × dist</th></tr><tr><th>AE</th><th>IN</th><th>SEM</th><th>Breed</th><th>Sys</th><th>Con</th><th>Dis</th><th>Con</th><th>Dis</th><th>Breed</th><th>Sys</th><th>Dist</th><th>Sys × dist</th></tr></thead><tbody><tr><td>Feed efficiency<br/>kg MY/kg DMI</td><td>1.18</td><td>1.13</td><td>0.140</td><td>0.445</td><td>0.328</td><td>0.79<sup>a</sup></td><td>0.94<sup>b</sup></td><td>1.09<sup>c</sup></td><td>0.90<sup>ab</sup></td><td>0.115</td><td>0.569</td><td>0.002</td><td>0.889</td><td>&lt;0.001</td></tr><tr><td>kg FPCM/kg DMI</td><td>1.07</td><td>1.00</td><td>0.106</td><td>0.686</td><td>0.076</td><td>0.76<sup>a</sup></td><td>0.90<sup>bc</sup></td><td>1.00<sup>b</sup></td><td>0.87<sup>ab</sup></td><td>0.097</td><td>0.816</td><td>0.004</td><td>0.516</td><td>&lt;0.001</td></tr><tr><td>Methane emission<br/>g CH<sub>4</sub>/d</td><td>412<sup>b</sup></td><td>387<sup>a</sup></td><td>15.3</td><td>0.939</td><td>0.033</td><td>396<sup>ab</sup></td><td>360<sup>a</sup></td><td>378<sup>ab</sup></td><td>417<sup>b</sup></td><td>14.4</td><td>0.631</td><td>0.091</td><td>0.904</td><td>0.003</td></tr><tr><td>g CH<sub>4</sub>/kg MY</td><td>17.93</td><td>18.01</td><td>2.268</td><td>0.676</td><td>0.921</td><td>27.69<sup>b</sup></td><td>22.17<sup>a</sup></td><td>19.06<sup>a</sup></td><td>20.62<sup>a</sup></td><td>3.415</td><td>0.764</td><td>&lt;0.001</td><td>0.045</td><td>0.004</td></tr></tbody></table>

but disturbance-induced decreases were greater for IN versus AE milks (P = 0.038). The other color parameters of milk did not show significant responses to disturbance (Table 4).

Levels of total germs in blended milks were higher on average in the AEC group compared with the other 3 groups (Figure 6). In terms of fecal indicators and pathogenic germs, β-glucuronidase positive *Escherichia coli* and *Listeria monocytogenes* were detected in only 2 IN milk samples (below 20 cfu/mL; Supplemental Table S4, see Notes).

The levels of both yeasts and facultative heterofermentative lactobacilli were higher in AE milk compared with IN milk (+0.62 log₁₀ cfu/mL and 0.49 log₁₀ cfu/mL, respectively; P < 0.001). These system effects were driven by higher microbial counts in AEC milks, compared with those in the 3 other groups (Figure 6).

Disturbance reduced the microbial levels in AE milk, whereas levels in IN milks were unchanged. The levels of yeasts, heterofermentative lactobacilli and ripening bacteria in AED milk were reduced by -1, -0.9, and -1.4 log₁₀ cfu/mL, respectively, compared with AEC. Microbial levels in IND and INC milk were very similar (Figure 6).

## Cheeses

Cheese dry matter was highest for AEC cheese compared with the other system × disturbance combinations (+4.6%; P < 0.001), whereas the fat in dry ratio was highest for INC (P < 0.001; Table 4). Fat and NaCl contents were similar for both feeding systems and decreased in response to disturbance (P < 0.001). Total nitrogen was higher for all IN cheeses (+0.11 g/kg; P < 0.001) but decreased in response to disturbance in both systems (P = 0.006). Primary proteolysis estimated by the ratio WSN/TN was higher for AE versus IN cheese (P < 0.001) and was decreased by the disturbance across systems (P < 0.001). However, responses of secondary proteolysis (estimated by the PTSN/TN ratio) to disturbance varied depending on system; secondary proteolysis was increased by disturbance in AE cheese but decreased in IN cheese (P = 0.002; Table 4). For cheese FA, the effects of systems and disturbance mirrored response patterns described for blended milk. It is noteworthy to observe that for absolute values of the main families of FA, the differences between milk and cheeses do not exceed 5% to 10% of the FA expressed in grams per 100 g total FA (Figure 4) and that the calculated melting points are the same.

The proportion of κ-CN Gly and total κ-CN were generally 23% and 14% higher in AE versus IN cheeses (P = 0.034 and P = 0.038, respectively). Irrespective of system, disturbance increased the proportion of intact

<footnote>a-Mean values in the same row with different superscripts differ (P < 0.05) for the interaction between system and disturbance for the “during disturbance” period.</footnote>

<footnote n="1">¹MY = milk yield; FPCM = fat- and protein-corrected milk.</footnote>
<figure><img src="image_6.png" /></figure>

<table><thead><tr><th rowspan="2">Item</th><th colspan="3">Sys</th><th colspan="3">Sys × Dist</th><th colspan="3">Sys × Dist</th></tr><tr><th>Br</th><th>Sys</th><th>Dist</th><th>Br</th><th>Sys</th><th>Dist</th><th>Br</th><th>Sys</th><th>Dist</th></tr></thead><tbody><tr><td>SFA</td><td>0.435</td><td>&lt;0.001</td><td>0.266</td><td>0.863</td><td>&lt;0.001</td><td>0.005</td><td>0.260</td><td>&lt;0.001</td><td>0.007</td><td>0.256</td></tr><tr><td>MUFA</td><td>0.711</td><td>&lt;0.001</td><td>0.494</td><td>0.956</td><td>&lt;0.001</td><td>0.012</td><td>0.319</td><td>&lt;0.001</td><td>0.011</td><td>0.286</td></tr><tr><td>PUFA</td><td>0.041</td><td>&lt;0.001</td><td>0.013</td><td>0.462</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.101</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.130</td></tr><tr><td>n-3</td><td>0.190</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.037</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.014</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.013</td></tr><tr><td>n-6</td><td>0.055</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.135</td><td>0.001</td><td>0.007</td><td>0.172</td><td>&lt;0.001</td><td>0.002</td><td>0.290</td></tr><tr><td>Spreadability index</td><td>0.779</td><td>&lt;0.001</td><td>0.123</td><td>0.258</td><td>&lt;0.001</td><td>0.006</td><td>0.931</td><td>&lt;0.001</td><td>0.006</td><td>0.932</td></tr><tr><td>Fat MP</td><td>0.138</td><td>0.021</td><td>0.584</td><td>0.397</td><td>0.002</td><td>0.151</td><td>0.426</td><td>0.004</td><td>0.332</td><td>0.145</td></tr><tr><td>FA&gt;37/FA&lt;37</td><td>0.618</td><td>0.006</td><td>0.423</td><td>0.572</td><td>&lt;0.001</td><td>0.081</td><td>0.566</td><td>&lt;0.001</td><td>0.850</td><td>0.294</td></tr><tr><td>FA&gt;45/FA&lt;15</td><td>0.593</td><td>&lt;0.001</td><td>0.659</td><td>0.648</td><td>&lt;0.001</td><td>0.053</td><td>0.596</td><td>&lt;0.001</td><td>0.048</td><td>0.297</td></tr></tbody></table>

**Figure 4.** Major fatty acid (FA) family and melting point (MP) properties (in rows) at the end of the experimentation, illustrated per group (light blue-green = AEC; dark blue-green = AED, light brown = INC; dark brown = IND) and per matrix (in columns). The table presents the *P*-value of the effect tested in the model for each matrix. Br: Breed (Prim'Holstein vs. Montbéliarde); Sys: system (AE vs. IN); Dist: disturbance (Con vs. Dis). Upper whisker: maximum; lower whisker: minimum; points (●): outliers; cross (×): mean; middle line: median; upper end of the box: 75th percentile; lower end of the box: 25th percentile.
<figure><img src="image_7.png" /></figure>

<table><thead><tr><th rowspan="2">Item</th><th colspan="3">Sys × Dist</th><th colspan="3">Sys × Dist</th><th colspan="3">Sys × Dist</th></tr><tr><th>Br</th><th>Sys</th><th>Dist</th><th>Sys</th><th>Dist</th><th>Dist</th><th>Sys</th><th>Dist</th><th>Dist</th></tr></thead><tbody><tr><td>Total αS1-CN</td><td>0.057</td><td>0.013</td><td>0.920</td><td>0.090</td><td>0.147</td><td>0.926</td><td>0.872</td><td>0.265</td><td>0.807</td></tr><tr><td>Pep.C αS1-CN</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.256</td><td>0.016</td><td>0.099</td></tr><tr><td>Total αS2-CN</td><td>0.885</td><td>0.002</td><td>0.137</td><td>0.232</td><td>0.029</td><td>0.908</td><td>0.025</td><td>&lt; 0.001</td><td>0.029</td></tr><tr><td>Total β-CN</td><td>0.877</td><td>0.241</td><td>0.830</td><td>0.003</td><td>0.214</td><td>0.129</td><td>0.709</td><td>0.011</td><td>0.502</td></tr><tr><td>Pep.P β-CN</td><td>0.293</td><td>0.878</td><td>0.146</td><td>0.127</td><td>0.004</td><td>0.315</td><td>0.162</td><td>0.793</td><td>0.766</td></tr><tr><td>Total κ-CN</td><td>0.174</td><td>0.933</td><td>0.710</td><td>0.224</td><td>0.446</td><td>0.451</td><td>0.038</td><td>0.321</td><td>0.649</td></tr><tr><td>α-LG</td><td>0.432</td><td>0.001</td><td>0.330</td><td>0.001</td><td>0.089</td><td>0.072</td><td></td><td></td><td></td></tr><tr><td>β-LA</td><td>0.271</td><td>0.091</td><td>0.503</td><td>0.001</td><td>0.034</td><td>0.147</td><td></td><td></td><td></td></tr></tbody></table>

**Figure 5.** Major proteins (in rows) expressed in g/100 g proteins at the end of the experimentation illustrated per group (light blue-green = AEC; dark blue-green = AED, light brown = INC; dark brown = IND) and per matrix (in columns). The table presents the *P*-value of the effect tested in the model for each matrices. Br: breed (Prim’Holstein vs. Montbéliarde); Sys: system (AE vs. IN); Dist: disturbance (Con vs. Dis); Pep.C: chymosin peptides; Pep.P: plasmin peptides. Upper whisker: maximum; lower whisker: minimum; points (●): outliers; cross (×): mean; middle line: median; upper end of the box: 75th percentile; lower end of the box: 25th percentile.
<table><caption>**Table 4.** Effect of feeding system (Sys) and simulated forage shortage (i.e., disturbance [Dist]) on physicochemical properties of milk and Cantal-type cheeses after 9 wk of ripening, according to initial feeding system (AE vs. IN) and absence or presence of the disturbance (Con vs. Dis)</caption><thead><tr><th rowspan="2">Item<sup>1</sup></th><th colspan="2">AE</th><th colspan="2">IN</th><th colspan="4">P-value</th></tr><tr><th>Con</th><th>Dis</th><th>Con</th><th>Dis</th><th>SEM</th><th>Sys</th><th>Dist</th><th>Sys × dist</th></tr></thead><tbody><tr><td>Milk turned to cheese</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Fat (g/kg)</td><td>35.20</td><td>34.20</td><td>34.80</td><td>35.33</td><td>1.394</td><td>0.665</td><td>0.782</td><td>0.377</td></tr><tr><td>Protein (g/kg)</td><td>33.33<sup>ab</sup></td><td>32.67<sup>a</sup></td><td>33.53<sup>bc</sup></td><td>35.87<sup>c</sup></td><td>0.559</td><td>0.002</td><td>0.042</td><td>0.003</td></tr><tr><td>Lactose (g/kg)</td><td>47.07</td><td>49.20</td><td>48.17</td><td>49.97</td><td>0.850</td><td>0.106</td><td>0.007</td><td>0.746</td></tr><tr><td>Casein (g/kg)</td><td>27.75<sup>ab</sup></td><td>27.41<sup>a</sup></td><td>27.88<sup>bc</sup></td><td>29.95<sup>c</sup></td><td>0.435</td><td>0.002</td><td>0.014</td><td>0.003</td></tr><tr><td>Urea (mg/kg)</td><td>305<sup>c</sup></td><td>250<sup>b</sup></td><td>250<sup>b</sup></td><td>195<sup>a</sup></td><td>36.6</td><td>0.032</td><td>0.032</td><td>0.999</td></tr><tr><td>SCC (×1,000/mL)</td><td>215<sup>b</sup></td><td>70<sup>a</sup></td><td>228<sup>b</sup></td><td>113<sup>a</sup></td><td>52.5</td><td>0.353</td><td>&lt;0.001</td><td>0.625</td></tr><tr><td>Total microbial count, log<sub>10</sub> (cfu/mL)</td><td>4.13<sup>b</sup></td><td>4.07<sup>b</sup></td><td>4.01<sup>a</sup></td><td>3.92<sup>a</sup></td><td>3.402</td><td>0.062</td><td>0.219</td><td>0.999</td></tr><tr><td>Color</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L*</td><td>82.16</td><td>82.65</td><td>82.03</td><td>81.84</td><td>0.535</td><td>0.168</td><td>0.636</td><td>0.305</td></tr><tr><td>a*</td><td>-2.31</td><td>-2.42</td><td>-2.40</td><td>-2.64</td><td>0.152</td><td>0.124</td><td>0.09</td><td>0.455</td></tr><tr><td>b*</td><td>7.41<sup>c</sup></td><td>6.16<sup>b</sup></td><td>6.27<sup>b</sup></td><td>4.01<sup>a</sup></td><td>0.330</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.038</td></tr><tr><td>Cheeses</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>pH</td><td>5.60<sup>b</sup></td><td>5.37<sup>a</sup></td><td>5.33<sup>a</sup></td><td>5.32<sup>a</sup></td><td>0.100</td><td>&lt;0.001</td><td>0.001</td><td>0.003</td></tr><tr><td>DM (%)</td><td>65.3<sup>c</sup></td><td>63.0<sup>b</sup></td><td>61.7<sup>a</sup></td><td>63.0<sup>b</sup></td><td>0.70</td><td>&lt;0.001</td><td>0.039</td><td>&lt;0.001</td></tr><tr><td>Fat (g/kg)</td><td>31.33<sup>b</sup></td><td>30.03<sup>a</sup></td><td>31.67<sup>b</sup></td><td>30.02<sup>a</sup></td><td>0.006</td><td>0.393</td><td>&lt;0.001</td><td>0.393</td></tr><tr><td>Fat in DM (%)</td><td>48.20<sup>a</sup></td><td>47.67<sup>a</sup></td><td>51.08<sup>b</sup></td><td>47.66<sup>a</sup></td><td>0.100</td><td>&lt;0.001</td><td>0.001</td><td>0.003</td></tr><tr><td>Chlorides (g NaCl/kg)</td><td>1.97<sup>b</sup></td><td>1.90<sup>ab</sup></td><td>1.97<sup>b</sup></td><td>1.87<sup>a</sup></td><td>0.053</td><td>0.353</td><td>&lt;0.001</td><td>0.353</td></tr><tr><td>TN (%)</td><td>6.79<sup>a</sup></td><td>6.89<sup>b</sup></td><td>6.94<sup>b</sup></td><td>6.97<sup>b</sup></td><td>0.061</td><td>&lt;0.001</td><td>0.006</td><td>0.081</td></tr><tr><td>WSN (% TN)</td><td>17.3<sup>c</sup></td><td>16.0<sup>b</sup></td><td>16.3<sup>b</sup></td><td>14.7<sup>a</sup></td><td>0.72</td><td>&lt;0.001</td><td>&lt;0.001</td><td>1.000</td></tr><tr><td>PTSN (% TN)</td><td>7.1<sup>ab</sup></td><td>7.3<sup>a</sup></td><td>7.7<sup>b</sup></td><td>6.0<sup>a</sup></td><td>0.88</td><td>0.267</td><td>0.031</td><td>0.002</td></tr><tr><td>Color</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>L*</td><td>75.3</td><td>75.8</td><td>76.2</td><td>77.7</td><td>1.63</td><td>0.543</td><td>0.756</td><td>0.589</td></tr><tr><td>a*</td><td>3.22<sup>b</sup></td><td>2.47<sup>b</sup></td><td>2.80<sup>b</sup></td><td>0.98<sup>a</sup></td><td>0.342</td><td>0.168</td><td>0.027</td><td>0.027</td></tr><tr><td>b*</td><td>22.5<sup>b</sup></td><td>19.8<sup>b</sup></td><td>20.5<sup>b</sup></td><td>13.9<sup>a</sup></td><td>1.34</td><td>0.103</td><td>0.035</td><td>0.037</td></tr><tr><td>Rheology</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Young's modulus, MPa</td><td>0.28<sup>a</sup></td><td>0.34<sup>a</sup></td><td>0.37<sup>a</sup></td><td>0.48<sup>b</sup></td><td>0.036</td><td>0.018</td><td>0.060</td><td>0.310</td></tr><tr><td>Strength to (%) deformation, N/cm<sup>2</sup></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>20%</td><td>4.46<sup>a</sup></td><td>6.64<sup>ab</sup></td><td>7.52<sup>ab</sup></td><td>8.71<sup>b</sup></td><td>1.188</td><td>0.014</td><td>0.054</td><td>0.491</td></tr><tr><td>40%</td><td>6.76<sup>a</sup></td><td>9.57<sup>a</sup></td><td>10.82<sup>b</sup></td><td>11.53<sup>b</sup></td><td>2.110</td><td>0.046</td><td>0.142</td><td>0.416</td></tr><tr><td>60%</td><td>8.56<sup>a</sup></td><td>12.19<sup>ab</sup></td><td>13.21<sup>ab</sup></td><td>14.85<sup>b</sup></td><td>2.067</td><td>0.025</td><td>0.063</td><td>0.429</td></tr></tbody></table>

<footnote>a: Mean values in the same row with different superscripts differ (P < 0.05) for the interaction between system and disturbance.</footnote>

<footnote n="1">¹L* = lightness; a* = redness; b* = yellowness; TN = total nitrogen; WSN = water-soluble nitrogen; PTSN = phosphotungstic acid-soluble nitrogen.</footnote>
<figure><img src="image_8.png" /></figure>

<table><thead><tr><th rowspan="2">Item</th><th colspan="3">Sys</th><th colspan="3">Sys × Dist</th><th colspan="3">Sys × Dist</th></tr><tr><th>Sys</th><th>Dist</th><th>Sys × Dist</th><th>Sys</th><th>Dist</th><th>Sys × Dist</th><th>Sys</th><th>Dist</th><th>Sys × Dist</th></tr></thead><tbody><tr><td>Yeast</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>0.636</td><td>0.177</td><td>0.044</td><td>0.117</td><td>0.235</td><td>0.507</td></tr><tr><td>Heteroferm.lact.</td><td>0.001</td><td>0.008</td><td>0.001</td><td>&lt; 0.001</td><td>0.013</td><td>0.240</td><td>0.075</td><td>0.191</td><td>0.389</td></tr><tr><td>Lactococcus lactis</td><td>0.599</td><td>0.276</td><td>0.624</td><td>&lt; 0.001</td><td>&lt; 0.001</td><td>0.006</td><td>0.270</td><td>0.080</td><td>0.413</td></tr><tr><td>Ripening bacteria</td><td>0.151</td><td>&lt; 0.001</td><td>0.006</td><td>0.123</td><td>0.001</td><td>0.009</td><td>0.101</td><td>0.780</td><td>0.645</td></tr><tr><td>Enterococci</td><td>0.685</td><td>0.437</td><td>0.256</td><td>0.063</td><td>0.034</td><td>0.012</td><td>0.168</td><td>0.020</td><td>0.022</td></tr><tr><td>Pseudomonas</td><td></td><td></td><td></td><td>0.348</td><td>0.304</td><td>0.078</td><td>0.021</td><td>0.039</td><td>0.408</td></tr><tr><td>Mold</td><td></td><td></td><td></td><td>0.023</td><td>0.039</td><td>0.005</td><td>0.545</td><td>0.381</td><td>0.973</td></tr></tbody></table>

**Figure 6.** Microbial numerations (in rows) expressed in $log_{10}$ cfu/mL in milk and in $log_{10}$ cfu/g in cheese at the end of the experimentation illustrated per group (light blue-green = AEC; dark blue-green = AED; light brown = INC; dark brown = IND) and per matrix (in columns). The table presents the *P*-value of the effect tested in the model for each matrix. Sys = system (AE vs. IN); Dist = disturbance (Con vs. Dis). Heteroferm.lact. = heterofermentative lactobacilli. Upper whisker: maximum; lower whisker: minimum; points (●): outliers; cross (×): mean; middle line: median; upper end of the box: 75th percentile; lower end of the box: 25th percentile.
<table><caption>**Table 5.** Effect of feeding system (Sys) and simulated forage shortage (i.e. disturbance [Dist]), on the intensity of sensory attributes of Cantal-type cheeses after 9 wk of ripening, graded from 0 (low intensity) to 10 (high intensity) by 10 trained panelists, according to initial feeding system (AE vs. IN) and application of the disturbance (Con vs. Dis)</caption><thead><tr><th rowspan="2">Sensory profile</th><th colspan="2">AE</th><th colspan="2">IN</th><th rowspan="2">SEM</th><th colspan="3">P-value</th></tr><tr><th>Con</th><th>Dis</th><th>Con</th><th>Dis</th><th>Sys</th><th>Dist</th><th>Sys × dist</th></tr></thead><tbody><tr><td><strong>Texture and color</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Smoothness</td><td>4.32<sup>c</sup></td><td>3.50<sup>b</sup></td><td>3.24<sup>b</sup></td><td>2.39<sup>a</sup></td><td>1.229</td><td>&lt;0.001</td><td>&lt;0.001</td><td>0.912</td></tr><tr><td>Firm</td><td>4.88<sup>a</sup></td><td>5.29<sup>a</sup></td><td>5.81<sup>b</sup></td><td>5.97<sup>b</sup></td><td>1.109</td><td>&lt;0.001</td><td>0.052</td><td>0.400</td></tr><tr><td>Melty</td><td>3.86<sup>c</sup></td><td>3.28<sup>ab</sup></td><td>3.50<sup>bc</sup></td><td>2.84<sup>a</sup></td><td>1.186</td><td>0.010</td><td>&lt;0.001</td><td>0.794</td></tr><tr><td>Fatty</td><td>4.41<sup>b</sup></td><td>4.20<sup>b</sup></td><td>3.95<sup>ab</sup></td><td>3.64<sup>a</sup></td><td>0.985</td><td>&lt;0.001</td><td>0.042</td><td>0.675</td></tr><tr><td>Grainy</td><td>3.60<sup>a</sup></td><td>3.87<sup>a</sup></td><td>3.72<sup>a</sup></td><td>4.06<sup>a</sup></td><td>1.144</td><td>0.282</td><td>0.041</td><td>0.817</td></tr><tr><td>Yellow</td><td>6.29<sup>c</sup></td><td>5.23<sup>b</sup></td><td>5.08<sup>b</sup></td><td>2.75<sup>a</sup></td><td>1.131</td><td>&lt;0.001</td><td>&lt;0.001</td><td>&lt;0.001</td></tr><tr><td><strong>Taste</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Salty</td><td>5.70</td><td>5.61</td><td>5.47</td><td>5.38</td><td>1.233</td><td>0.154</td><td>0.555</td><td>0.979</td></tr><tr><td>Bitter</td><td>3.96</td><td>3.64</td><td>3.81</td><td>3.57</td><td>1.289</td><td>0.512</td><td>0.093</td><td>0.822</td></tr><tr><td>Spicy</td><td>3.01</td><td>2.83</td><td>2.9</td><td>2.77</td><td>1.083</td><td>0.552</td><td>0.269</td><td>0.877</td></tr><tr><td>Persistence</td><td>4.6</td><td>4.49</td><td>4.67</td><td>4.41</td><td>1.168</td><td>0.960</td><td>0.211</td><td>0.631</td></tr><tr><td><strong>Odor</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Rind</td><td>5.50<sup>ab</sup></td><td>5.58<sup>ab</sup></td><td>5.75<sup>b</sup></td><td>5.32<sup>a</sup></td><td>0.907</td><td>0.983</td><td>0.142</td><td>0.034</td></tr><tr><td>Global</td><td>5.41<sup>ab</sup></td><td>5.32<sup>ab</sup></td><td>5.57<sup>b</sup></td><td>5.01<sup>a</sup></td><td>0.901</td><td>0.529</td><td>0.006</td><td>0.046</td></tr><tr><td>Dry fruit</td><td>2.81</td><td>2.64</td><td>2.70</td><td>2.78</td><td>1.100</td><td>0.925</td><td>0.725</td><td>0.373</td></tr><tr><td>Animal</td><td>3.59<sup>ab</sup></td><td>3.96<sup>b</sup></td><td>3.75<sup>ab</sup></td><td>3.36<sup>a</sup></td><td>1.215</td><td>0.153</td><td>0.932</td><td>0.017</td></tr><tr><td>Lactic</td><td>4.05</td><td>4.15</td><td>4.28</td><td>4.12</td><td>1.222</td><td>0.544</td><td>0.870</td><td>0.414</td></tr><tr><td>Butter</td><td>2.44</td><td>2.78</td><td>2.67</td><td>2.58</td><td>1.340</td><td>0.912</td><td>0.456</td><td>0.210</td></tr><tr><td>Mushroom</td><td>2.92</td><td>3.09</td><td>3.25</td><td>2.91</td><td>1.104</td><td>0.595</td><td>0.563</td><td>0.076</td></tr><tr><td>Ammonia</td><td>1.79</td><td>1.55</td><td>1.81</td><td>1.58</td><td>1.034</td><td>0.856</td><td>0.083</td><td>0.935</td></tr><tr><td>Dry plant</td><td>2.99</td><td>2.98</td><td>3.14</td><td>2.79</td><td>0.802</td><td>0.872</td><td>0.078</td><td>0.109</td></tr><tr><td>Wet plant</td><td>2.03</td><td>2.16</td><td>2.11</td><td>1.88</td><td>0.860</td><td>0.361</td><td>0.664</td><td>0.097</td></tr><tr><td><strong>Aroma</strong></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Global</td><td>5.59</td><td>5.61</td><td>5.52</td><td>5.28</td><td>0.925</td><td>0.102</td><td>0.354</td><td>0.274</td></tr><tr><td>Dry fruit</td><td>2.84</td><td>2.65</td><td>2.58</td><td>2.75</td><td>0.955</td><td>0.548</td><td>0.930</td><td>0.151</td></tr><tr><td>Animal</td><td>3.15</td><td>3.29</td><td>3.14</td><td>2.90</td><td>1.056</td><td>0.134</td><td>0.732</td><td>0.172</td></tr><tr><td>Lactic</td><td>3.76</td><td>3.91</td><td>3.70</td><td>3.90</td><td>1.129</td><td>0.810</td><td>0.222</td><td>0.873</td></tr><tr><td>Butter</td><td>2.30</td><td>2.48</td><td>2.32</td><td>2.20</td><td>1.114</td><td>0.370</td><td>0.858</td><td>0.301</td></tr><tr><td>Mushroom</td><td>3.12</td><td>3.21</td><td>3.14</td><td>3.03</td><td>1.267</td><td>0.625</td><td>0.959</td><td>0.535</td></tr><tr><td>Dry plant</td><td>2.94<sup>ab</sup></td><td>3.18<sup>b</sup></td><td>2.74<sup>a</sup></td><td>2.76<sup>a</sup></td><td>1.016</td><td>0.020</td><td>0.323</td><td>0.417</td></tr><tr><td>Wet plant</td><td>2.01<sup>c</sup></td><td>1.81<sup>b</sup></td><td>1.99<sup>c</sup></td><td>1.61<sup>a</sup></td><td>0.953</td><td>0.358</td><td>0.019</td><td>0.465</td></tr></tbody></table>

κ-CN (+6%) and β-CN (+11%), of total β-CN (+7%) and α<sub>S2</sub>-CN (+27%) but decreased the proportion of α<sub>S1</sub>-CN chymosin peptides (−10%) and β-CN plasmin peptides (−22%; Figure 5; Supplemental Table S3).

Cheese made from AE milk was perceived to be more yellow (b*) and red (a*) than cheese made from IN milk. Disturbance increased luminousness and decreased red and yellow indexes, with greatest reductions between IND and INC rather than between AED and AEC cheeses (b*: −6.6 vs. −2.7; a*: −1.8 vs. −0.8; P = 0.037 and P = 0.027, respectively; Table 4). A similar trend was observed for the yellowness perceived by the panelists (Table 5).

Forces exerted to deform the cheeses and Young's modulus were lower for cheese in AE compared with IN. Disturbance tended to lower cheese stretch (i.e., increase forces), with greater differences observed between AEC and AED compared with differences between INC and IND (Table 4). Based on panelists' perceptions, the AE cheeses felt smoother to the touch and were described as less firm, more melty, and fattier in the mouth. Dis-

turbance decreased smoothness and the fatty and melty perceptions but increased graininess (Table 4).

For taste attributes, no differences were observed between the 2 management systems and disturbance tended to reduce the bitter taste of cheese across systems (Table 5).

For odor attributes, IND cheese distinguished itself from AEC, AED, and INC. The IND cheeses were scored with a lower global odor of both curd and rind, and a lower animal odor. They also tended to have the lowest wet plant odor. Disturbance decreased ammonia odor in cheeses from both systems and tended to decrease the wet plant odor in IND cheeses compared with INC. For aroma, AE cheeses scored higher for global, animal, and plant aromas compared with IN, but disturbance decreased the wet plant aroma in both AE and IN systems (Table 5).

In terms of microbial composition, none of the pathogens *L. monocytogenes*, *Salmonella*, coagulase positive *Staphylococcus* and shigatoxin-producing *E. coli* were detected in any of the cheeses (Supplemental Table S4).

<footnote><sup>a-c</sup>Mean values in the same row with different superscripts differ (P < 0.05) for the interaction between system and disturbance.</footnote>
<figure><img src="image_9.png" /><figcaption>**Figure 7.** Multiple factorial analysis of physicochemical, microbial, and sensory properties of the Cantal-type cheeses after 9 wk of ripening. (A) Plot of the individuals: light blue-green = AEC; dark blue-green = AED; light brown = INC; dark brown = IND. (B) Plot of the variables. Each category of variables (n = 9) is represented in a different color.</figcaption></figure>

Levels of *Pseudomonas* in cheese rind and levels of *Lactococcus lactis* and molds in cheese core were higher in AE than in IN systems. Levels of heterofermentative lactobacilli in AE systems were also significantly higher in the cheese core (+0.2 log₁₀ cfu/g) and rind (+0.4 log₁₀ cfu/g) compared with their IN counterparts (Figure 6).

The dietary disturbance reduced the levels of most bac-
terial groups in AE cheeses, whereas those in IN cheeses
remained unchanged (Figure 6). Levels of *Lactococcus
lactis*, ripening bacteria, and heterofermentative lactoba-
cilli in AED cheese cores were reduced by 0.61, 1.16,
and 0.51 log₁₀ cfu/g, respectively, compared with those
in AEC. Levels of enterococci were reduced by 0.64
log₁₀ cfu/g both in the core and the rind of AED cheese
compared with AEC. Only the level of *Pseudomonas* was
significantly reduced by 0.58 log₁₀ cfu/g in the rind of
IND cheese compared with other cheeses.

Links Between Physicochemical Characteristics,
Microbial Levels, and Sensory Properties
of the Cheeses

Texture attributes were mainly explained by the sum
of FA with high melting points for the firmer cheeses and
FA with low melting points for the softer cheeses (Figure
7B; Supplemental Table S5, see Notes). Firmer cheeses
were also linked to a low spreadability index, whereas the
softer cheeses were correlated with the ratio of n-3 FA to

n-6 FA and the levels of PUFA. When projected on axis
1 of the multifactor analysis (Figure 7), levels of intact
β-CN were correlated with firmer cheeses and associated
with grainy texture. Nearly all other sensory attributes
were located in the same quarter of the correlation circle.
Regarding aroma and odor, wet plant and animal aromas
as well as global odor were the main contributors of axis
1. When projected on axis 1, these latter parameters were
close to heterofermentative lactobacilli and enterobacte-
ria, as well as to primary proteolysis.

Lactic and butter odors (and aroma to a lesser extent) were positively correlated with the fat content in cheese dry mass but negatively correlated with heterofermentative lactobacilli, ripening bacteria, *Lactococcus lactis*, and κ-casein (Figure 7B).

Overall, INC and AEC cheeses were most similar,
whereas AEC and IND cheeses were most distinct and
presented contrasting characteristics (Figure 7A).

DISCUSSION

Evidence-based recommendations for effective adap-
tation strategies to climate change are in clear demand
among European dairy farmers. In this paper, we focused
on part-time grazing, corn-based systems because they
are gaining in popularity in semi-mountainous areas
in comparison to more traditional grass-based systems
(INOSYS, 2020; Allart et al., 2024b). In addition, we
explored the effects of short-term dietary changes that are commonly implemented in situations of summer forage shortage. Our comprehensive approach accounted for animal performance, CH₄ emissions as well as microbiological, nutritional and sensory qualities of milk and cheese. Key findings are that feeding cows on extensively-managed permanent grasslands generated cheeses with more desirable nutritional and sensory qualities but reduced milk production and feed efficiency, and therefore increased CH₄ emission intensity, compared with corn-fed cows. Performance in grass-based systems was more variable during the season and linked to the variability in grass nutritional value. Under a simulated summer forage shortage, increasing indoor feeding in the grass-based system enhanced animal performance, feed efficiency, and CH₄ emission intensity. Cheese nutritional quality, rather than sensory quality, was also reduced under simulated fodder shortage. In the corn-based system, the complete removal of grass linked to a forage shortage had no effects on animal performance, feed efficiency, and CH₄ emission intensity, but instead impaired cheese nutritional quality and dramatically affected its sensory quality.

One originality of this study is that all groups were conducted at the same time in the same environment (similar paddocks for each system, same barn for all cows) and milked in the same milking parlor. Differences observed can be directly linked to the differences in terms of diets distributed over several weeks, providing valuable insights into the broader implications of short- and mid-term dairy farm adaptation strategies to cope with climate change.

## Feeding System Effects

Grass-fed cow performance decreased during the experiment in line with the advancement of the lactation stage and shifts in grass phenology inherent to pastures under low management intensity (Delagarde et al., 2011; Jacquot et al., 2015). By comparison, the higher MY and FPCM of corn-fed cows during the second part of the experiment was explained by a higher and more stable nutritional value of the diet, including rotationally grazed grass (INRAE, 2018). The higher CP content of corn-fed cows at the end of the experiment was consistent with the higher energy intake (especially starch; INRAE, 2018). Our estimated raw methane emissions were in line with the literature. Various authors have reported values ranging between 260 and 420 g/d in dairy cows grazing multispecies sown grasslands including tannin-rich plant species (Jonker et al., 2019; Carmona-Flores et al., 2020; Loza et al., 2021). Our estimated methane emission intensities are in the upper range of the ones reported by these authors (from 11.9 g/kg FPCM to 22.6

g/kg FPCM) because of the low MY of the cows. Overall, FE and CH₄ emission intensity were better for IN cows compared with AE cows, driven by the increase in animal performance (Fresco et al., 2023) and the lower NDF/starch ratio of the IN diet, which promotes propionate pathways in the rumen (Sauvant et al., 2011).

In our experiment, the stronger dry plant aroma, the yellower color, and the softer texture of the cheeses from grass-fed cows (AE) were consistent with previous reports of the effect of grass versus corn diets on sensory properties of dairy products (Martin et al., 2005; Frétin et al., 2019; Cabiddu et al., 2022). In the present study, overall system effects represent the average of control and disturbed treatments with different proportions of grass in the diet; this may explain why differences in flavor were of a lower magnitude than those reported with more contrasted diets. Milk from grass-fed cows is known to having a greater carotenoid content that increases the yellowness of the products (Nozière et al., 2006; Agabriel et al., 2007; O'Callaghan et al., 2017), in agreement with our findings. The softer texture of AE versus IN cheeses cannot be explained by the differences found in DM or fat content in cheese dry mass. It may result from the higher proportions of MUFA and PUFA (n-3 FA in particular) in milk and cheese from grass-fed cows. Milk and cheese from corn-fed cows contained more n-6 FA and SFA, which is fully consistent with the literature (Ferlay et al., 2013; Frétin et al., 2017; O'Callaghan et al., 2017). More novel here is the use of the fat melting point calculated according to Toral et al. (2013), which was indeed lower in AE milk and cheeses compared with their IN counterparts. The lower melting point of AE milk and cheeses is consistent with their higher proportion of FA with low (<37 or <15°C) versus high (≥37 or ≥45°C) melting point. Moreover, in agreement with Lucas et al. (2006), milk and cheese FA profiles followed the same pattern as the calculated fat melting point. This confirms that the cheesemaking process in the present study had very little impact on FA profile. The softer texture of AE versus IN cheeses may also be linked to their higher primary proteolysis, as shown in Fenelon and Guinee (2000). This is consistent with the higher average proportion of peptides produced during the first stages of the breakdown of β- and α<sub>S1</sub>-casein in AE cheeses. Unlike FA, proteins profiles differed between blended milks and resulting cheeses. During the cheesemaking process, soluble proteins (β-LG and α-LA) and κ-CN casein-macropeptides, issued from κ-CN partial hydrolyzation by chymosin during the coagulation, are known to be lost through the whey (Emmons et al., 2003).

For milk, it is generally admitted that animal feeding (energy) affects milk casein content but not composition (Mackle et al., 1999; Leduc et al., 2022), which is mainly driven by animal genetics (Ng-Kwai-Hang et al.,
1987; Heck et al., 2009). Indeed, Amalfitano et al. (2020) showed that for most of the lactoproteins, up to 10% of the variance can be explained by environmental factors, including feeding. Differences in lactoproteins proportion found between IN and AE milks may therefore result from the higher proportion of (1) Prim'Holstein cows in IN groups, known to synthesize more α<sub>S2</sub>-CN, β-CN, and α-LA (Amalfitano et al., 2020), and (2) cows carrying the variant A of β-LG in IN groups, known to be related to higher β-LG concentrations in milk (Knutsen et al., 2022; Supplemental Table S6, see Notes). Because all cows carried the same variant of α-LA (Supplemental Table S6), higher proportions of lactoproteins in IN milk may be driven by the higher MY of IN cows because α-LA is a precursor of lactose, which drives milk production (Bleck and Bremel, 1993).

The softer texture and the stronger flavor of AE cheese reported here may also be explained by their higher level of certain microbes. Lactobacilli, higher in AE milk and cheese, are known to have a proteolytic activity in cheese (Courtin et al., 2002; Bergamini et al., 2006; Grizon et al., 2024), increasing the production of small peptides and resulting in a higher secondary proteolysis (Ismail and Nielsen, 2010). The higher levels in lactic acid bacteria in AE cheese may contribute to their stronger flavor, as they are involved in various biochemical processes producing volatile compounds (Le Quéré and Buchin, 2022; Li et al., 2022). Differences in microbial levels in cheese mirrored those observed in milk. Increased levels of lactobacilli and yeasts in AE milk compared with IN milk were consistent with those reported by Frétin et al. (2017) comparing an extensive grazing system with a semi-intensive grazing system.

## Effects of Diet Change Due to Simulated Forage Shortage

The increased proportion of hay and concentrates in the AED diet partially compensated for the loss of nutritional value of grass, promoting MY and FPCM in AED compared with AEC (INRAE, 2018). For corn-fed cows, diet quality remained better and more stable for resulting in similar milk yields at the end of the experiment, irrespective of disturbance treatment. In IND, the complete removal of grazed grass resulted in higher starch and NDF intakes, which improved protein and fat contents, respectively, and therefore FPCM (INRAE, 2018). This led to the lowest FE and the highest methane emission intensity for AEC cows, as expected. Differences between INC and IND did not affect methane intensity, probably due to the high intake of starch that reduced CH₄ production via an increase in propionate metabolism in IND cows (Sauvant et al., 2011).

The change in diet to adapt to the simulated forage shortages affected cheese texture in a similar manner in both feeding systems. The linear decrease of smoothness and melty attributes linked to disturbance in both systems may be related to the similar decrease in the proportion of grazed grass in the IN and AE diets (–25%), affecting FA profile and spreadability index (Couvreur et al., 2006; O’Callaghan et al., 2017). Conversely, the calculated melting points were similar in INC and IND: they seemed to reach a plateau when the proportion of grazed grass decreased in the diet. As suggested by Toral et al. (2013), the milk fat melting point regulating milk fat fluidity is probably involved in the control of diet-induced variations in milk fat secretion (Timmen and Patton, 1988). In this experiment, we found strong correlations between cheese softness and milk or cheese fat melting point ($r_{Softness-FatMeltingPoint} = -0.76$) and the proportions of FA having high or low melting point ($r_{Softness-FA with MP <15°C} = 0.88$; $r_{Softness-FA with MP >45°C} = -0.86$). It is nevertheless noteworthy that the spreadability index proposed by Couvreur et al. (2006) remains the best proxy of cheese softness, as we found higher correlations ($r_{Softness-SpreadIndex} = -0.92$).

The decrease in smoothness and melty attributes linked to the disturbance in each system may also be linked to the reduced level of primary proteolysis in milk as discussed previously for the feeding system effects. This reduced proteolysis linked to the disturbance is consistent with the lower levels of β-CN plasmin peptides and α<sub>S1</sub>-CN chymosin peptides found in the cheese. Disturbance effects appear to be more closely related to a lower protease activity in the corresponding cheeses rather than to differences in milk linked to cow genotypes or diets. For instance, the higher proportion of β-CN in IND individual milks could be linked to the proportion of cows carrying variant B of β-CN (Supplemental Table S6, see Notes), which is related to higher β-CN production (Ng-Kwai-Hang et al., 1987; Heck et al., 2009). The disturbance also tended to increase more α-LA in individual and blended milks from AED compared with IND cows, consistent with the greater differences in MY between AED versus AEC compared with IND versus INC (Larson, 1985; Bleck and Bremel, 1993).

Unlike cheese texture, cheese color and flavor showed stronger responses to disturbance in the corn-feeding system than the grass-feeding system. The complete removal of fresh grass from the IND diet resulted in less yellow milk and cheese. Yellowness is associated with the presence of fresh grass in the diet, even in low proportions (Nozière et al., 2006). Our results are consistent with Calderón et al. (2007) who reported a plateau effect in the positive relationship between carotenoids intake and milk carotenoid content and yellowness. The
complete removal of pasture in the IND diet also led to
cheeses with lower intensity of several flavors compared
with the other groups. This adds to the existing literature
that mainly compared highly contrasted diets (Martin et
al., 2005; Cabiddu et al., 2022). The less pronounced fla-
vor of IND cheeses may be explained by their lower level
of secondary proteolysis. The latter results mainly from
a lower microbial protease activity (Ismail and Nielsen,
2010), in line with the lower microbial counts we ob-
served in IND cheese. In the present work, AEC milk and
cheese were characterized by high levels of microbial
groups compared with the other treatment combinations.
We can hypothesize that the levels of microbes in milk
reflect the time spent on the pasture by AEC cows (no
barn housing during the experiment). Thus, the main
effect of disturbance on microbes may be linked to the
housing conditions, as previously suggested by several
authors (Doyle et al., 2017; Nikoloudaki et al., 2021). In
addition, grass surfaces are known to host lactobacilli (Li
et al., 2024), which may be transferred to milk via teat
skin (Verdier-Metz et al., 2012). Our results are also con-
sistent with Manzocchi et al. (2021) who hypothesized
that the stronger flavor of cheeses from cows fed fresh
grass at pasture versus fresh grass indoors could result
from differences in microbial species present in the dif-
ferent environments where the animals were kept, rather
than a direct effect of the diet.

CONCLUSIONS

Overall, we found that cheese quality from cows was
more sensitive to removal of grazed grass from corn-
based diets compared with reduction of grazed grass
in grass-fed cows. Our study suggests that maintaining
fresh herbage in corn-based diets, even in limited quan-
tity, is crucial for cheese nutritional and sensory quality,
and does not impair animal performance. Conversely, in
grass-based systems, reducing grass allowance by 25%
may better maintain animal performance. In our study,
samples were taken from the soil through to the ripened
cheeses, as well as from the feces of rats who fed on these
cheeses. The microbial communities in these samples
were characterized using high-throughput sequencing
approaches. This analysis will provide new insights on
product quality, microbial transfers along the agri-food
chain and the complex interactions likely to occur as a
function of all the parameters.

NOTES

Funding was obtained from INRAE (Paris, France)
Metaprograms Holoflux (TANDEM project, Microbial
fluxes at the heart of agroecological transitions of dairy
systems) and Syalsa (BestDairy project, Optimisation of

milk composition as well as nutritional, probiotic and
sensorial quality of dairy products through breeding
[upstream] and ferment use [downstream]). We thank
all researchers involved in the TANDEM and BestDairy
consortia for their significant contribution to the work
done. We thank all those helping during the experiment,
in particular R. Lavigne and F. Allegretti for cheesemak-
ing (INRAE, UMRF, Aurillac, France); E. Rispal, O. Tro-
quier, P. Tardif (INRAE, Herbipôle Marcenat, France),
and A. Salcedo (INRAE, UMR UREP, Clermont-Ferrand,
France) for technical assistance; I. Constant, E. Cuzol,
C. Labonne, E. Tixier (INRAE, UMRH, Theix, France),
B. Desserre (INRAE, UMRF, Aurillac, France), and L.
Bianchi (INRAE, UMR GABI, France) for laboratory
analysis. Many thanks also to the panelists participating
in the sensory evaluations. Supplemental material for this
article is available at https://doi.org/10.57745/0VWGZX.
None of the data were deposited in an official repository.
The data that support the study findings are available
upon reasonable request to the corresponding author.
All animal-related procedures were conducted in accor-
dance with the French guidelines for animal welfare and
were approved by the local ethics committee (approval
APAFIS#2015043014541577). There was no need for In-
stitutional Review Board approval because no clinical or
health data were involved in this study. Informed consent
was obtained from all subjects involved in the study. The
authors have not stated any conflicts of interest.

**Nonstandard abbreviations used:** a* = redness; AE = agroecological system; AEC = agroecological control; AED = agroecological disturbed; b* = yellowness; Br. = breed; Con = control; Dis = disturbed; Dist = disturbance; FA = fatty acid; FC = fat content; FE = feed efficiency; FPCM = fat- and protein-corrected milk; IN = intensive system; INC = intensive control; IND = intensive disturbed; L* = lightness; LC/ESI-MS = liquid chromatography/electrospray ionization-MS; MP = melting point; MY = milk yield; PC = protein content; PTSN = phosphotungstic acid-soluble nitrogen; Sys = system; TN = total nitrogen; WSN = water-soluble nitrogen.

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ORCIDS

M. Bouchon, https://orcid.org/0000-0002-7175-7233

B. Martin, https://orcid.org/0000-0003-2501-8306

A. Ferlay, https://orcid.org/0000-0002-0651-792X

J. M. G. Bloor, https://orcid.org/0000-0002-8668-1323

A. Delacroix-Buchet, https://orcid.org/0000-0002-8065-560X

C. Cebo, https://orcid.org/0000-0001-5071-9353

B. Graulet, https://orcid.org/0000-0002-6956-3448

I. Verdier-Metz, https://orcid.org/0000-0001-6002-3878

C. Delbès https://orcid.org/0000-0003-0142-537X