2.1.1 Reference barn Dummerstorf – central European maritime region

The naturally ventilated dairy building is located in northeast Germany close to the Baltic Sea (Gut Dummerstorf GmbH in Mecklenburg-Western Pomerania, 42 m above sea level; see Hempel et al., 2018). It is approx. 96 m long and 34 m wide. The roof height varies from approx. 4 to 10.5 m. The internal room volume is approximately 25 000 m³, and the barn was designed for 364 dairy cows (i.e., approx. 70 m³ per animal). The barn has an open ridge slot, partly closed gable walls and long open sidewalls protected by nets and adjustable curtains (see Fig. 1). It represents a typical building design for moderate climate used, for example, in northern Germany, the Netherlands or northern USA (Mendes et al., 2015; Wang et al., 2016; Hempel et al., 2016b; Kafle et al., 2018).

Figure 1Outer view of the reference barn (source: ATB), aerial photo of the associated farm (source: Google Maps, © 2018 DigitalGlobe; panels a and b: GeoBasis-DE/BKG, GeoContent, Kartendaten, © 2018 GeoBasis-DE/BKG (© 2009), © Google; panel c: Kartendaten © 2018 Google, Inst. Geogr. Nacional) and distribution of the sensor positions during different measurement campaigns for the three reference barns in Dummerstorf (a), Groß Kreutz (b) and Bétera (c). In addition, the roof shapes are sketched. Details on the measurement campaigns can be found in Table. 1 (cf. IDs).

Table 1Overview of on-farm measurement campaigns. Measurements were conducted approximately 3 m above the floor of the barns. The horizontal distribution of measurement points is sketched in Fig. 1 (cf. IDs). Device specifications: Comark Diligence EV N2003 sensors (Comark Limited, Hertfordshire, UK) logged temperature and relative humidity every 10 min (instantaneous value for the second). EasyLog USB 2+ sensors (Lascar Electronics Inc., USA) logged temperature and relative humidity every 5 min (instantaneous value, shortest logging rate 10 s). Three-axis ultrasonic anemometers of the Wind Master type (Gill Instruments Limited, Hampshire, UK) logged air velocity every second. Begin and end of the measurement periods are provided in the format dd-mm-yyyy.

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Temperature, relative humidity and air velocity were logged at various positions inside the barn at approximately 3 m height during measurement campaigns from summer 2015 until summer 2017 (see Table 1 and Fig. 1).

2.1.2 Reference barn Groß Kreutz – central European continental region

This naturally ventilated dairy building is located in eastern Germany (Teaching and Research Institute for Animal Breeding and Animal Husbandry, Groß Kreutz, Brandenburg, 32 m above sea level; see Hempel et al., 2018). It is approx. 39 m long and 18 m wide. The height of the roof varies from approx. 3.5 to 6 m. The internal room volume is approximately 4500 m³, designed for 50 dairy cows (i.e., ca. 90 m³ per animal). The barn has a closed roof and partly closed gable walls (see Fig. 1). One long sidewall is open up to about 1.5 m height, and the opposite side is open up to the roof.

Temperature, relative humidity and air velocity were logged continuously at eight positions inside the barn at approximately 3 m height from summer 2015 until summer 2017 (see Table 1 and Fig. 1). In addition, at three of the sensor positions in the center of the barn, temperature and relative humidity were measured at four other heights between approximately 4 and 6 m.

2.1.3 Reference barn Bétera - western Mediterranean region

The commercial naturally ventilated building is located in eastern Spain (More Holstein S. L., Bétera, Valencia, 125 m above sea level). It is approx. 137 m long and 18 m wide with open walls (fences) and a broad roof opening (see Fig. 1). The roof height varies from approx. 4.5 to 6 m. The internal room volume is approximately 12700 m³, designed for 192 dairy cows (i.e., approx. 66 m³ per animal).

Temperature and relative humidity were logged at various positions inside the barn at approximately 3 m height during two measurement campaigns in summer 2016 and 2017 (see Table 1 and Fig. 1).

3.2.1 Risk under different RCPs

In order to assess the effect of different atmospheric greenhouse gas concentrations on the heat stress risk, we considered the example of the reference barn Groß Kreutz (Germany, central European continental region), as for this location we could make use of the most comprehensive and homogeneous data set. The heat stress risks under RCP 2.6, RCP 4.5 and RCP 8.5 were evaluated using the number and duration of anticipated heat stress events based on the projected indoor THI as described in Sect. 2.5 (see Fig. 4).

Figure 4Projected change in heat stress events in the reference barn in central Europe (annual changes in panels a and b and summer changes in panels c and d). Modifications in average duration (average consecutive hours) and number (number of consecutive hours) of moderate stress events under RCP 2.6 (green), RCP 4.5 (blue) and RCP 8.5 (red) are plotted based on the evolution of the temperature humidity index (THI) with a threshold of 72.

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Until the mid-21st century (≈2040) there is no significant difference between the projections under different RCP scenarios. The average duration of the stress events is expected to increase up to approximately 1 h in all scenarios, while the number of events is expected to increase by up to approximately 150 (i.e., up to approximately 2 % of all hours of a year, corresponding to 6 % of all hours of a summer, will be classified as at least moderate stress events in addition to the current situation).

For the second half of the 21st century, the average duration of heat stress events is expected to stay at the mid-century level under RCP 2.6 and RCP 4.5. For the extreme scenario RCP 8.5, the increase continues up to approximately 3 h. The number of heat stress events is projected to stay approximately at the mid-century level under RCP 2.6 and increases only slightly during the second half of the century under RCP 4.5 (approximately 150 additional heat stress events for RCP 2.6 and 200 for RCP 4.5). Under RCP 8.5, however, the number of additional heat stress events increases up to approximately 600, i.e., nearly 7 % of all hours of a year will be additionally classified as heat stress events, most pronounced in the summer season. Hence, approximately 27 % of all summer hours, i.e., more than every fourth hour, will be characterized by at least moderate heat stress conditions in addition by the end of the century.

Despite the described relations between the ensemble averages for each RCP scenario, there was a range of uncertainty of approximately ±1 h regarding the duration and ±200 regarding the number.

3.2.2 Regional differences

In order to evaluate the regional differences in climate change impacts on dairy farming, we considered the example of two reference barns in maritime regions, one in central Europe and one in the Mediterranean region (see Fig. 5). As in Sect. 3.2.1 the heat stress risks under RCP 2.6, RCP 4.5 and RCP 8.5 were evaluated in terms of number and duration of heat stress events as described in Sect. 2.5.

Figure 5Projected change in heat stress events in the reference barns in maritime regions in central Europe (top two rows) versus Mediterranean region (bottom two rows). Modifications in annual (first row) and summer (second row) average of duration (average consecutive hours) and number (number of consecutive hours) of moderate stress events under RCP 2.6 (green), RCP 4.5 (blue) and RCP 8.5 (red) are plotted based on the temperature humidity index (THI) with a threshold of 72.

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Although the annual temperature increase does not differ a lot among the regions (Fig. 3), the implications in terms of critical THI values were rather different for the two regions. By the mid-21st century we found an increase in the duration of heat stress events of approximately 1.5 h for central Europe and 2.5 h for the Mediterranean region under all RCPs. For RCP 2.6 this is also true until the end of the century. Under RCP 4.5 in the second half of the century an increase of 2 h for central Europe versus 5 h for the Mediterranean region was projected. For RCP 8.5 the deviation between the regions is even larger with an increase of 4 h in central Europe versus 17 h in the Mediterranean region. The latter implies that there will be barely any recovery phases for the cows in the reference barn in the Mediterranean region.

The change in the number of heat stress events was even more diverse among the regions and RCPs. While for the barn in central Europe up to approximately 200 additional hours of heat stress are expected (i.e., even less than for the barn in the central European continental region described in the previous subsection), for the Mediterranean region the increase was much stronger: under RCP 2.6 approximately 300 additional heat stress events (i.e., 4 % of all hours of a year), under RCP 4.5 approximately 800 additional heat stress events (i.e., nearly 9 % of all hours of a year) and under RCP 8.5 approximately 1800 additional heat stress events (i.e., nearly 21 % of all hours of a year) were projected. In addition, in central Europe most of the additional heat stress events occurred in the summer season. In the Mediterranean region, only approximately half of the increase was anticipated for summer, while there was a significant increase in the number of heat stress events projected for spring and autumn.

Again, we focused the comparison on the ensemble averages, and there was a range of uncertainty of ±200 regarding the number and approximately ±2 h (central Europe) and ±5 h (Mediterranean) regarding the duration. Towards the end of the century, the ranges tend to further increase, particularly for the RCP 8.5 scenario.

3.2.3 The effect of air movement

We investigated the impact of wind as additional environmental parameter to evaluate the heat stress risk using the example of the reference barn Groß Kreutz (Germany, central European continental region) and the heat stress index ETIC.

Figure 6Projected change in heat stress events in the reference barn in central Europe without (a, b) and with (c, d) consideration of near-surface wind. Modifications in annual average of duration (average consecutive hours) and number (number of consecutive hours) of at least moderate stress events under RCP 2.6 (green), RCP 4.5 (blue) and RCP 8.5 (red) are plotted based on the equivalent temperature index for cattle (ETIC) with a threshold of 20.

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The change in duration and number of heat stress events was investigated using ETIC with and without wind, neglecting radiation effects in both cases (see Fig. 6). We found a general tendency towards an increase in the duration and number of heat stress events by the end of the century, which was on the same order of magnitude as predicted using the THI (see Fig. 6 compared to Fig. 4).

Without the wind effect (i.e., ETIC with v=0 m s⁻¹ at all time points; see Fig. 6a, b) the projected increase in the duration of heat stress was under RCP 8.5 on average 4 h (ETIC) compared to 3 h (THI). Taking the range of the model ensemble into consideration, it was even 6 h (ETIC) versus 4.5 h (THI). Similar deviations were observed for the estimated increase in the number of heat stress events. Here, ETIC under RCP 8.5 resulted in on average 800 more heat stress events (up to 1200 considering the uncertainty of the model ensemble), while THI resulted in on average 600 more heat stress events (up to 900 considering the uncertainty in the model ensemble).

Taking into account the wind in the ETIC calculation (see Fig. 6c, d), the heat stress risk decreased as the wind increased (particularly in summer), which had a cooling effect. While ETIC under RCP 8.5 projected on average approximately 800 additional heat stress events without wind effect, on average approximately 600 additional heat stress events were projected with wind effect. This is a relative reduction in the number of heat stress events of approximately 25 % due to changes in the wind regime. However, the projected heat stress risk considering ETIC with the wind effect was almost the same as that projected with the THI which does not include the wind effect at all.

3.3.1 Climate model ensembles

The span of anticipated climate signals was well represented in our ensemble with GCMs projecting strong and weak changes in the global mean temperature and precipitation, respectively (Warszawski et al., 2014). The RCM–GCM combinations capture the edges of the intermodel variability in the CORDEX-EUR ensemble in terms of biases in temperature and precipitation (see Dosio, 2016).

However, only a limited number of runs per RCP was available for our study, namely 11 for RCP 2.6, 12 for RCP 4.5 and 21 for RCP 8.5. Increasing the number of simulations might increase our estimated uncertainty ranges. In order to account for the imbalanced sampling among the possible climate signals (see sparse modeling matrix in Table 3), we used a bootstrapping algorithm for the statistical analysis to evaluate the heat stress risk. Our analysis shows a similar range of the anticipated changes in the number of heat stress events for all three barns as well as all RCPs and time slices. The uncertainty in the anticipated duration of heat stress events was also similar among the RCPs and time slices.

The uncertainty in the duration, however, varied regionally. In the Mediterranean region, where the largest impacts are expected, it was particularly high, yielding additional challenges for adaption. These regional differences in the uncertainty might be, however, to some degree also caused by the fact that the calibration data sets for the indoor-THI model differed in regard to comprehensiveness and homogeneity.

3.3.2 RCP scenarios

Earlier other authors used one climate model, one emission scenario and the outdoor THI to perform a similar study for Spain (Segnalini et al., 2013). Our study, on the other hand, was based on a model ensemble and used an indoor THI together with multiple recent emission scenarios to cover the full range of future socioeconomic pathways.

Segnalini et al. (2013) derived a slight increase in the heat stress risk mainly in summer. Our results based on the indoor THI under RCP 2.6 were well in line with this former study. However, based on the more recent climate projections, a larger ensemble of climate models and incorporating the effect of housing on the THI, our results also indicated that the former projections can be understood as some kind of best case scenario. The projected indoor temperature change by the end of the 21st century deviated in our study among the RCPs by approximately 2 ^∘C in the barns in central Europe and 4 ^∘C in the barn in the Mediterranean region (see Fig. 3). At the same time, the projected change of annual relative humidity deviated by less then 3 %. Thus, the THI increase is likely to be higher than previously assumed.

Moreover, the projected change in temperature, humidity and wind resulted in an increase in the duration of heat stress events, which was approximately 3 h (central European regions) and 17 h (Mediterranean region) less under RCP 2.6 than under RCP 8.5, respectively. In addition, approximately 5 % heat stress hours less per year (relative to the total number of hours in a year) were projected under RCP 2.6 compared to RCP 8.5 for the central European regions by the end of the century. In the Mediterranean region the deviation between RCP 2.6 and RCP 8.5 was approximately 17 %. This implies that with the continuation of global warming the regional differences will be amplified with higher greenhouse gas concentrations.

The RCP scenarios considered in our study were associated with different socioeconomic, technological and political developments that yield different atmospheric greenhouse gas concentrations. Feedback loops induced by changing duration and number of heat stress events were not included. However, it is known that the net emissions from cattle husbandry (dependent on the storage, treatment and application of manure and slurry as well as on the production level and feed of the ruminants) are influenced by environmental parameters such as temperature and humidity (Monteny et al., 2001). Increased heat stress may yield higher ammonia and methane emissions affecting aerosol formation and amplifying the increase in greenhouse gas concentration as discussed in Sect. 3.4.2.

Such effects are, however, underexplored so far and should be addressed in future studies to further develop representative greenhouse gas and ammonia concentration scenarios for climate impact assessments.

3.3.3 Stress index

A lot of cattle-related thermal indices have been proposed in literature, two of which we considered in our study (Bianca, 1962; Mader et al., 2006; Gaughan et al., 2008; Mader et al., 2010; Da Silva et al., 2015; Lees et al., 2018; Wang et al., 2018b). The genetic basis used to derive the indices relative to the dominating genotypic conditions in our study (including resilience types and future adaptation) represents additional incalculable factors. Our results (see Sect. 3.2.3) indicated that the uncertainty that was introduced by choosing one of the stress indices (THI or ETIC with or without wind) together with a particular threshold was on the same order of magnitude as the wind effect. This leads to the assumption that the effect of wind speed could be neglected in heat stress risk projections using the common indices.

It has, however, to be noted that these results referred only to an averaged wind speed in the barn under consideration, while the distribution of the air flow inside the building is very sensitive to changes in the inflow conditions (e.g., surrounding building or planting) as well as the building design (see Sect. 3.3.4) (Hempel et al., 2015b; Yi et al., 2018). The impact of including or neglecting the wind may be different for other barns or even when considering heat stress levels in individual locations of the same barn.

The location of the animals inside the barn is also crucial in regards to shading as the lack of shade shifts the heat stress threshold towards lower ambient temperatures (e.g., body temperature increases earlier) (Berman, 2005; Kendall et al., 2006). This effect was neglected in our study as the cows were free to move inside the barns and could look for shade almost all the time. The roof height and insulation were considered sufficient to avoid direct radiation effects. The validity of this approximation depends on the building design. Larger radiation effects can be expected in the reference barn in Bétera (Spain, western Mediterranean region), which has a wide roof opening compared to the reference barn in Groß Kreutz (Germany, central European continental region), which has no roof opening but only roof lights.

Moreover, the projected indoor temperature, humidity and wind were not linearly translated into heat stress events, as in our study heat stress was evaluated in terms of critical thresholds. The choice of the threshold affects the risk assessment and depends on the considered indicator (e.g., milk yield, body temperature, respiration rate, rumination or lying time). The threshold we used for THI was based on milk-yield depression and related to respiration rate in literature (Bohmanova et al., 2007; Collier et al., 2012). Although based on the same indicator, the thresholds for THI and ETIC differ slightly due to the imperfect correlation and rounding when ETIC thresholds were derived from THI thresholds with a linear transfer function (Wang et al., 2018b).

In addition, the thresholds provided in literature only refer to some kind of prototypical cattle. The actual threshold is animal-specific and depends on the local environment to which the animals are adapted (Bohmanova et al., 2007). Referring to the thresholds defined by Collier (see Sect. 2.5), for example, Israeli Holstein in summer in Israel are permanently under heat stress conditions, while producing a similar amount of milk or even more than Holstein Frisian in Germany, as breed, barn design and cooling management are already adapted to the local climate conditions (Pinto et al., 2019a, c). As a consequence, due to the regional adaptation the threshold for moderate stress in our central European focus regions may relate only to mild stress in our Mediterranean focus region.

Furthermore, under heat stress conditions cows tend to adapt their body posture together with the respiration rate (Pinto et al., 2019b). As laying down decreases the body surface area of cows that is exposed to air by approximately 42 %, the effect of wind differs depending on the body posture. As a consequence, lying cows increase the respiration rate significantly more than cows in standing posture and cows with higher core body temperature tend to stand up (Allen et al., 2015; Hempel et al., 2016a). As body posture changes, depending on the barn management (e.g., feeding or milking times) and in reaction to the ambient climate conditions, may occur simultaneously, additional uncertainty in the threshold selection is introduced.

Finally, milk yield, lactation phase and age influence directly the heat stress susceptibility. Because of the metabolized energy used for milk production, high-yielding cows have significantly more heat to dissipate than low-yielding cows and thus are more susceptible to heat stress (Kadzere et al., 2002; West, 2003; Spiers et al., 2004). Cows at the beginning of lactation tend to produce more milk than cows during a late lactation period, yielding higher heat stress susceptibility. In addition, there are pieces of evidence that older (multiparous) cows are more affected by heat load than young (primiparous) ones (Bernabucci et al., 2014). As the herds in our reference barns consist of cows of different ages and lactation phases, and hence different milk yields, the projected heat stress risks will apply only for herd averages.

With the breeding towards higher milk yields of the individual cows, as observed in the past, the thresholds for heat stress can be expected to further decrease on a herd level (Carabano et al., 2016). This will amplify the projected heat stress risks. However, further quantitative studies are required to evaluate to which degree the thresholds will decrease in the future in the focus regions.

3.3.4 Calibration data

Besides systematic errors related to the measurement device accuracy and long-term stability, the spatial and temporal variability in the outdoor and indoor measurements, which were used to derive and calibrate our indoor climate model, introduces uncertainty related to the sampling procedure.

For outdoor measurements (i.e., weather observations from meteorological observation stations) this relates mainly to the question of if the weather conditions at the meteorological station are connected to those near the barn. Here the aspect of closeness is crucial, as well as topographic and orographic considerations which affect particularly the dynamics of the humidity time series (e.g., the autocorrelation function decreases faster for Rostock-Warnemünde than for Dummerstorf potentially because it is closer to the sea; Hempel et al., 2018). In order to minimize the impact of such complex effects, we applied the artificial neural network approach and considered a time lag of ±2 h (see Sect. 2.3).

For indoor measurements the topic of data variability relates to the question of if point measurements are representative for the barn average. All measurement devices were mounted in such a way that they were as much as possible exposed to free inflow in order to minimize systematic errors induced by the construction material of the barns (e.g., wind shading or conduction). Moreover, to reduce the spatial and temporal variability in the relevant variables, we focused our modeling on an hourly averaged value. Direct air movement and turbulent diffusion, which are mainly responsible for the high spatial variability, can not be resolved on hourly timescale, which justifies also our choice of focusing on spatial averages over the whole barn.

The calibration data used for our statistical indoor models differed in homogeneity and extent between the individual barns. The most comprehensive data set was the one for the reference barn Groß Kreutz where wind, temperature and humidity were measured at about 3 m height approximately every 10 m. In contrast, in the reference barns Dummerstorf and Bétera the spatial resolution of the measurements and the spatial coverage of individual barn locations changed over the measurement periods and differed between the variables. This introduces additional uncertainty in the spatial averages. In addition, three vertical indoor profiles for temperature and relative humidity were incorporated into the spatial averaging in Groß Kreutz, which were not available for the other barns. The observed vertical gradients in temperature and relative humidity are, however, much smaller than the horizontal variability. Thus, the effect of this additional data can be neglected for the total barn average.

Preceding studies indicated that there is a high degree of turbulence yielding to significant spatial inhomogeneities in the velocity field inside the barn (Fiedler et al., 2014; Yi et al., 2018). Depending on the opening sizes of the inlets, eddies of different sizes propagate towards the outlets (Hempel et al., 2015a; Yi et al., 2018). The spatial spreading of the wind speed $\stackrel{\mathrm{̃}}{v}$ increases typically with the average wind speed $\stackrel{\mathrm{‾}}{v}$ (i.e., $\stackrel{\mathrm{̃}}{v}\approx \mathrm{6.96}\cdot \stackrel{\mathrm{‾}}{v}$ for the barns under consideration). This means the spatial variability in the wind depends on the actual inflow speed. Considering the median of the distribution of the observed hourly averaged wind speed values, the typical spatial wind speed variance is approximately 2.4 m s⁻¹. The anticipated changes in the near-surface wind, which governs the inflow, are, however, regionally very diverse (Kjellström et al., 2018).

Considering the threshold ETIC =20 (as used in our study), a decrease in the wind speed from 2.4 to 0 m s⁻¹ results in an increase in the ETIC value between approximately 1.5 (for very low relative humidity) and 4 for very high humidity. This means under arid climate cows in some locations of the barn, which are particularly exposed to the wind, will only exceed the threshold for mild stress, while others in calm locations suffer from already moderate stress. Under humid climate, the effect is even stronger as cows in locations that are particularly exposed to the wind may not even be under heat stress at all (ETIC with wind approximately 16 instead of 20), while others are suffering from already moderate heat stress.

The inhomogeneous distribution of heat and humidity sources, related to farm management and the turbulent inflow associated with the meteorological boundary conditions, yield also high spatial and temporal variability in air temperature (approximately ±2 ^∘C) and relative humidity (up to ±20 % relative humidity) (Herbut et al., 2015; Hempel et al., 2018). For THI values close to the threshold of 72, each of those uncertainty values corresponds approximately to ±2 THI units. However, this estimated spatial variability refers to measurements with a temporal resolution of 5 to 10 min.

However, it has to be noted that the spatial variability in the THI inside the barn can be up to ±4 THI units from the projected average value (considering the spatial variation in temperature and humidity as independent of each other). This is almost a difference of one THI class, which implies that events classified as moderate based on the spatial average might correspond to already severe heat stress in some locations and only mild stress in other parts of the building.

3.4.1 Economic impact

Heat stress affects the reproductive performance of cows and decreases fertility (De Rensis et al., 2015; Schüller, 2015). In addition, elevated temperatures may increase disease pressure and lead on to more health treatment (see Sect. 3.4.3). In particular, the occurrence of heat stress events can be translated into losses in milk yield and quality, where the decline of milk yield begins directly after being exposed to uncomfortable environmental conditions (Rushen et al., 2001). In literature it is often assumed that milk yield stays almost constant until a certain threshold and then linearly declines with increasing degree of THI (Ravagnolo and Misztal, 2000; Bohmanova et al., 2007). The decline rate per cow and day per THI unit with the onset of heat stress has been estimated for Holstein dairy cattle (the breed in our reference barns) to be, for example, between 0.3 and 0.39 kg for cows with 28 kg daily milk yield in the US and around 0.41 kg for cows with 20 kg milk yield in the Mediterranean region (Bouraoui et al., 2002; Bohmanova et al., 2007). These cows can be considered average-producing cows. Higher-yielding cows are known to be more susceptible to heat stress (Kadzere et al., 2002; Nardone et al., 2010; West, 2003). It has been shown that at the same THI threshold the most productive cows (yield average 42.5 kg d⁻¹) of the same Holstein breed lost 0.174 kg d⁻¹ per unit of THI more than the average cows (31.5 kg d⁻¹) (Carabano et al., 2016). As the average milk yield of the cows in our focus regions is higher than in the mentioned studies about milk-yield depression (approximately 35 to 45 kg d⁻¹ milk yield), it is reasonable to scale the decline rates accordingly leading to an excepted decline range between 0.474 and 0.584 kg d⁻¹ per THI unit. As a consequence, we assumed an average decline of approximately 0.5 kg d⁻¹ and per cow for each THI unit above the heat stress threshold. That means 2.0 kg less milk per day and per cow for each additional day with moderate heat stress can be expected (see Sect. 2.6).

Table 6The number of heat stress events (HSE) and their average duration (HSED) based on the projections under RCP 8.5 were considered. The number of heat stress days (HSD) was estimated as the ratio HSE/HSED assuming one period of consecutive heat stress hours per day. Milk-yield loss (MYL) per country was extrapolated for the countries where the reference barns are located and given relative to a reference milk yield. The assumed total amount of cows was 4.2×10⁶ in Germany and 0.8×10⁶ in Spain (2017 level according to http://de.statistica.com/). For Germany half of the cows were allocated to the projected change for the reference barn Groß Kreutz and the other half to the reference barn Dummerstorf. A reference annual European milk yield of 168×10⁶ t was assumed (see the Introduction section).

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Our results imply an increase of, on average, approximately 120 heat stress days with THI ≥72 by the end of the century (see Table 6). Eventually, the estimated milk-yield losses in Germany and Spain, where our reference barns were located, accumulate to approximately 0.68 % of the annual European milk yield today, which is approximately 168×10⁶ t (see Table 6). As Germany and Spain provide together approximately 24 % of the European milk yield, assuming our reference barns and the focus regions to be representative for the average change of heat stress events in Europe, the total loss can be extrapolated to be about 2.8 % of the annual milk yield today under the RCP 8.5 scenario.

In addition to the milk yield, a decrease in the milk fat (approximately 0.34 % to 0.4 %) and protein (approximately 0.08 % to 0.2 %) contents (with milk fat content typically around 3.5 % and milk protein content typically around 2.9 %) can be expected according to literature (Bouraoui et al., 2002; Collier et al., 2012; Carabano et al., 2016). Under contemporary market conditions, higher percentages of fat and proteins increase the milk price (Bailey et al., 2005). The amount of these price corrections depends on the local markets. In addition to losing these potential bonuses, low fat and protein contents increase the risk of rejection from the buyer of the milk lot.

Heat stress events and thus financial losses are concentrated in the summer. An increase of 120 additional heat stress days per year, at least half of which are expected to occur in summer, implies that in each summer month approximately 20 additional heat stress days can be expected, thus affecting liquidity of farms in summer. In the worst summer month, farmers may lose approximately EUR 14 per cow assuming an average milk price of EUR 0.35 per kilogram of milk. With our estimates, a month with mild stress would be equivalent to losing 5.4 % of the monthly income. For an average farm in Germany and Spain that would involve losing 30 % and 26 %, respectively, of the monthly farm gross margin without coupled payments from the year 2016. This yields particular challenges for the survival of dairy specialized farms, of which only 2 % and 21 % in Germany and Spain, respectively, have positive net economic margin (European Commission – EU FADN, 2018).

Finally, in countries with already pronounced hot summer periods, like in the Mediterranean region where farms already manage calving seasonally in order to avoid the lower summer fertility rates, the increase in the number of heat stress events in spring or autumn may be particularly damaging.

3.4.2 Environmental impact

Lower productivity per cow, as expected under heat stress (see Sect. 3.4.1), has been linked with increased ammonia emission intensity in the literature (Groenestein et al., 2019; Sajeev et al., 2018; Sanchis et al., 2019). The ammonia release from manure increases with temperature by approximately 1.5 g. Hence, the increase in heat stress events can be translated into the number of hours with at least 4.5 g per cow and per day higher ammonia emissions. With approximately 120 more heat stress days (see estimation in Sect. 3.4.1), 540 g per cow and per year (which is about 10⁶ mg per barn and per year) higher ammonia emissions can be expected as a result of climate change. With around 30×10⁶ dairy cattle in Europe this would pile up to additional annual ammonia emissions from European dairy cattle husbandry of about 16 Gg yr⁻¹ (i.e., approximately 2.9 % (550 Gg) of the German and 4.5 % (353 Gg) of the Spanish, or 0.4 % (3624 Gg) of the EU-28, national emission ceilings (NEC) target; see https://www.eea.europa.eu, last access: 11 April 2019). This implies further impacts as ammonia contributes to the formation of secondary particulate matter, which is relevant to respiratory health issues and the Earth's radiation budget (Lelieveld et al., 2015). Moreover, ammonia reacts to chemical compounds that lead to acidification of soil and water (Sutton et al., 2013).

Table 7The change in methane release (corresponding to annual emissions) is estimated as the product of the average livestock unit (LU_avg) in the barn and the estimated number of additional heat stress events (HSE). A 1 g LU⁻¹ h⁻¹ higher methane emission during heat stress conditions is assumed (Hempel et al., 2016b). The average livestock unit of the reference barns was estimated as the number of animals in the barn times 500 kg divided by the average body weight. Considering a total mass of the Earth's atmosphere of approximately 5×10²¹ g, this methane release was converted into a concentration increase, neglecting other processes that affect the methane concentration (Trenberth and Smith, 2005).

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Besides ammonia, the emission of greenhouse gases, particularly methane, is a crucial topic. Although its average atmospheric concentration is only a small fraction of that of carbon dioxide (1800 ppb compared to 390 ppm), methane is initially far more harmful (Pachauri et al., 2014). Methane production by ruminants is associated with microbial fermentation of hydrolyzed carbohydrates and influenced by many factors including the ambient temperature (Broucek, 2014). An optimal ambient temperature (i.e., no climatic stress) corresponds to minimal methane emissions from dairy cattle, while each heat stress event is expected to lead to a few grams per livestock unit and per hour higher methane emissions (Hempel et al., 2016b). In addition, the higher temperatures will yield a considerable increase in methane emissions from manure (particularly liquid manure) (Amon et al., 2007). The total methane increase implies a positive feedback loop as the increased methane concentration in the atmosphere will contribute to an acceleration of climate change. Based on the expected increase in the number of heat stress events, the impact of heat stress on methane emissions is about 10⁶ mg yr⁻¹ per dairy barn (see Table 7). With around 30×10⁶ dairy cattle in Europe and assuming an average barn size of 200 cattle, this corresponds to an increase of approximately 0.15 Gg yr⁻¹ (i.e., on average about 10⁻³ ppb higher methane concentrations).

3.4.3 Welfare impact

Heat stress introduces some physiological impacts as cattle try to adapt themselves by increasing respiration rate (panting in extreme situations) and reducing milk yield and reproductive performance as well as by changing the feeding behavior, decreasing activity and increasing standing time (De Rensis and Scaramuzzi, 2003; West, 2003; Schütz et al., 2008; Dikmen and Hansen, 2009). Behavior, health and productivity provide information to evaluate the welfare.

The respiration rate, measured in breaths per minute (bpm), is particularly valuable to evaluate instantaneous heat stress as it is affected by the ambient conditions with little or no time lag (Pinto et al., 2019a; Galán et al., 2018; Brown-Brandl et al., 2005). While under thermoneutral conditions the respiration rate ranges from 15 to 36 bpm, high-yielding dairy cattle tend to increase their respiration rate by 27 to 39 bpm if THI increases from THI ≤68 to THI ≥80 (i.e., 2–4 bpm per THI unit) (Dirksen et al., 1990; Jackson and Cockcroft, 2008; Pinto et al., 2019b; Berman et al., 1985; Ominski et al., 2002). As a consequence, based on our results an increase of approximately 9 bpm (i.e., 25 % to 60 % relative to the normal respiration rate) can be expected under RCP 8.5 during one-tenth of all hours of a year or one-fourth of all summer hours in addition to the current situation in the reference barns in our focus regions. The initial response is, however, part of a homeostatic mechanism which, besides increased respiration rate, includes also increased water intake, increased loss of body fluids due to sweating and panting, reduction in fecal and urinary water losses, reduced feed intake, and increased heart rate during short-term exposure to heat (Kadzere et al., 2002). If the heat stress persists, the muscles of the animal tend to fatigue and the respiration rate tends to decrease again for a short time. Thus, with persisting heat load accumulation, the respiration rate will level off at an intermediate value which can, however, still be considerably above the normal state. These changes promote diseases, such as disorders of the acid–base budget (alkalosis), which increase the probability for medical treatments, and in the long term may negatively affect longevity (West, 2003).

The most common behavioral indicator for heat stress is the time spent standing, where the lying posture is considered a cow comfort indicator (Galán et al., 2018; Acatincăi et al., 2010; Herbut and Angrecka, 2018). The average daily lying time decreases by approximately 10 to 20 min per THI unit under heat stress conditions, resulting in an increase in the standing time on the same order of magnitude to improve the wind convection and thus increase the heat dissipation (Cook et al., 2007; Allen et al., 2015; Heinicke et al., 2018). This increased standing time is an effect of aggregated heat stress events and is typically associated with daily averaged THI. Our results imply approximately 120 additional heat stress days with prolonged periods of THI ≥72 (i.e., nearly one-third of the year). At each of those days an approximately 1 h longer standing time can be expected. This significantly contributes to a higher risk of lameness (Cook et al., 2007; Allen et al., 2013).

The changes in the average duration of heat stress events further imply that in the Mediterranean region cattle are potentially under permanent heat stress in summer. Especially during the night, the decrease in the ambient temperature is too low for recovering (Polsky and von Keyserlingk, 2017). Hence, a behavioral adaption in terms of shifting activity towards non-heat-stress hours might become impossible. Although short-term adaptation of the physiology of the cows might be supported by the increased duration to some degree, the effect of the daily heat load duration amplifies the effect of the average daily THI up to a point at which the cows can not further adapt their activity changes (Heinicke et al., 2018, 2019). As a consequence milk yield is expected to decrease significantly if no additional cooling is provided.

It has to be noted that dairy husbandry in the Mediterranean region, as well as in countries in Middle Eastern or tropical regions, is already faced with extended periods of heat load conditions during the year (Honig et al., 2012; da Costa et al., 2015; Ortiz et al., 2015). The associated reactions to heat stress conditions have a strong genetic component (Broucek et al., 2007; Bernabucci et al., 2014). If climate changes slow enough, dairy herds will genetically adapt by nature to some degree to the elevated temperatures. In addition, most of the hot countries already search actively for adaptation measures to alleviate the cows’ heat stress (see Sect. 3.4.4). Those measures include, for example, promoting cross breeds adapted to the heat load conditions (da Costa et al., 2015) and evaporative cooling systems to provide refreshment for the cows, especially during the day when the environment temperature is particularly high (Honig et al., 2012; Ortiz et al., 2015; Pinto et al., 2019a; Broucek et al., 2007). These efforts need to be further intensified in the future.

3.4.4 Adaptation options for animal housing

Moderate changes in the heat stress risk can be addressed by short-term measures which optimize the already implemented control mechanisms such as shading, fans, adjustable opening, or cow showers and fogging devices (St-Pierre et al., 2003; Galán et al., 2018; Davison et al., 2016; Polsky and von Keyserlingk, 2017; Honig et al., 2012; Anderson et al., 2013; Valtorta and Gallardo, 2004). Depending on the boundary conditions some of those measures may become more suitable than others.

If the annual variability in temperature is rather small and the overall heat stress risk is high, such as in Mediterranean regions, open barns can be considered an adaptation measure. Under such conditions, as in the example of the reference barn Bétera, natural ventilation offers almost no possibility for further adaptation by adjusting the opening configurations. In such buildings, fans and showers or fogging devices have been implemented in the past to alleviate the heat load and enable high milk yields during hot periods (Ortiz et al., 2015; Fournel et al., 2017). The fans can in principle decrease or increase the air speed in the animal-occupied zone as they induce a flow that can be aligned or opposed to the naturally induced flow (Anderson et al., 2013). The speed and direction of the fan-induced flow could be optimized via velocity measurements inside the barns. High relative humidity can additionally promote the cooling via the air flow. Similarly, showering and fogging contribute to the reduction in heat stress, particularly under dry weather conditions. If the ambient relative humidity is already high these measures are less efficient, but it has been shown that frequent showering can be still valuable (Honig et al., 2012). In terms of costs and benefits, recurring cooling sessions instead of constant cooling are reasonable, where the number of sessions is an important factor (Pinto et al., 2019c). With increasing temperatures and decreasing humidity (as projected in our study; see Fig. 3) frequent showering, as common for example in the Israeli husbandry system, might become more valuable for the western Mediterranean region.

For the central European regions, in general the cooling by showering and fogging can be expected to be less efficient than in the Mediterranean region due to the higher relative humidity. However, as hot and dry periods are expected to become more frequent, such devices can still be a valuable investment (, ). Smart regulation of the fans and the opening configurations will be, however, even more valuable. The position of curtains and the opening ratio have a crucial impact on the flow pattern and the air speed in the animal-occupied zone. It has been shown that the average horizontal velocity in the animal-occupied zone could be varied in a range of −4 % up to 70 % of the incident flow velocity (Yi et al., 2018). This can reduce emissions, because the airflow can be controlled precisely and the overflowing of emission-active surfaces can be minimized. Moreover, the local air exchange rates in the animal-occupied zones can be significantly improved.

As a mid-term adaption strategy, a sensor-based control of openings, fans and fogging devices should be implemented, including a smart control of the fogging times governed by the actual relative humidity. Active cooling may involve also the use of tubes for targeted supply of (potentially pre-cooled) air in the animal-occupied zone. The speed and orientation of the mechanical ventilation support (e.g., by fans or tubes) should be regulated automatically based on wind speed measurements in the barn. In addition, further improvement of the roof insulation and the use of cooling pads in the cubicles may reduce the thermal load. The number and timing of the cooling sessions, the operation of the fans or tubes, and the control of the curtains should be based on cow-specific indicators such as respiration rate or body temperature. In this context, respiration rate is the more direct indicator, but it is much harder to measure automatically at the moment (Strutzke et al., 2018).

Finally, considering the large probability of highly increasing heat stress risk by the end of the century, the investment in hybrid ventilation systems for cattle husbandry might become valuable. An engineering solution of a precision air supply system with additional mechanical ventilation could provide a better local thermal environment for cows and remove considerably more heat from the animals than currently available systems, especially under calm conditions (Wang et al., 2018c). Such a system may be combined with smart fogging devices and air flushing systems as well as adjustable opening configurations and a number of fans permitting operators to switch between natural ventilation, mechanically supported natural ventilation and mechanical ventilation in a smart, automated way.