Heat load index during heat waves as an indicator of thermal comfort of Hereford heifers with access to natural shade on native grasslands

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Saravia, E. vanLier, C. Munka, O. Bentancur, R. Iribarne, R. Rodríguez-Palma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3913892/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Aug, 2024 Read the published version in Agroforestry Systems → Version 1 posted 9 You are reading this latest preprint version Abstract The quantification of the environmental conditions to predict the effect of extreme events (heat waves: HW), is important in animal welfare and performance. The aim of this study was to evaluate the impact of the meteorological environment on physiological and productive variables on heifers, either with or without access to natural shade on rangelands, using the Heat Load Index (HLI), a biometeorological index that allows the comparison between environments. The experiment was carried out at Estación Experimental de la Facultad de Agronomía en Salto, Uruguay, during two summers (Year 1, Year 2). The treatments were voluntary access to natural shade (Shade) and full sun (Sun). Three HW: Severe, Strong, Mild and a not HW (NHW) occurred in Year 1, but only the latter in Year 2. The HLI categories warm and very warm (HLI ≥ 77.1) daily hours percentages were 68 and 67 during Severe HW, 56 and 49 in Strong + Mild HW, 48 and 38 in NHW in Year 1 and 12 and 4 in NHW in Year 2, in the Sun and Shade treatment, respectively. During Severe and Strong HW, the Shade was not beneficial because the animal experimented thermoneutrality only for a few hours. During Mild HW and NHW, the HLI in the Shade was mainly thermoneutral and temperate, which would explain the higher animal weight gain, compared to the Sun. In Year 2, the predominant conditions were thermoneutral, with heifers maintaining normothermia during a major part of the day in both treatments, resulting in similar weight gain. heat stress animal welfare biometeorology index vaginal temperature silvopastoral systems Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction In predominantly grazing production systems, such as in Uruguay, cattle are continuously exposed to environmental factors, which directly affect their physiology and productivity or, indirectly, through nutrition by variations in forage quantity and quality (Hafez 1972 ). During summer, the combined effects of high solar radiation, temperature and humidity result in a meteorological environment that lies outside of the thermal comfort zones of the animals and may reduce their productivity (Cruz and Saravia 2008 ). Although animals have adapted to their environmental conditions, periods of thermal stress do occur, either due to pronounced temperature variations or a combination of meteorological factors, which may negatively impact productive performance and lead to economic losses (Nienaber et al. 2003 ). In cattle, body temperature ranges from 37.5 to 38.9 (Finch 1986 ) and outside of this range, if the animal does not reestablish equilibrium, it will show typical thermal stress symptoms and reduce productivity (Bianca 1965 ). High air temperatures reduce weight gain during post-weaning and fattening stages, and this is mainly due to increased body temperatures which lead to reduced feed intake (Johnson 1987 ). Abnormally warm and generally humid periods of three or more consecutive days are called heat waves (IPCC 2013 ) and cause economic losses in animal production throughout the World (Nienaber and Hahn 2007 ), either by reducing animal health and productivity or by death of the animals (St-Pierre et al. 2003 ; Lees et al. 2019 ). The expected climate conditions for the end of the 21st century, as modeled by the IPCC, project the following situations: a) increases in the extreme temperatures towards the end of the century, b) an increase in frequency and scale of the high extreme temperatures and a decrease of the number of extremely cold days and c) high probability of increases in duration, frequency and/or intensity of heat waves in the majority of areas on Earth (Seneviratne et al. 2012 ). For South America, analysis of series of extreme temperatures from 1950 to 2008 show evidence of heating, with a weak to moderate increase in the trend of calculated indexes, with an effect throughout South America of less cold daytime temperatures and warmer nighttime temperatures (Skansi et al. 2013 ). The estimated indexes of minimum temperatures show major increase rates, both for tropical nights (minimum registered temperatures above 20ºC) and warm nights (minimum registered temperatures above the 90 percentile of the data series). While maximum day temperature indexes also indicate an increase in number of days with maximum temperatures above 25ºC, but with lower increase rates than the minimum temperatures (Skansi et al. 2013 ). The predicted increase in air temperature for the first fifty years of the 21st century is expected to cause physiological and metabolic damage and reduce animal welfare (Lacetera et al. 2003 ). In terms of animal welfare and performance, it is important to be able to predict the effect of extreme meteorological variables on cattle (Gaughan et al. 2008 ). Quantification of environmental conditions through biometeorological indexes helps to relate the impact of meteorological conditions to animal performance. The Temperature-Humidity Index (THI, Thom 1959 ) has been used for over six decades to evaluate heat stress in beef and dairy cattle, and to establish alert and prevention systems. However, the THI has been validated in climate chambers with controlled temperature and humidity (Johnson et al. 1961 ) and its usefulness for describing highly variable meteorological environments, as in pastoral and silvopastoral systems, is under constant revision. More complete biometeorological indexes are needed for research on the effects of climate change on animal production (Nardone et al. 2010 ). The Heat Load Index (HLI, Gaughan et al. 2008 ) is estimated using the black globe temperature (BG, °C), the relative humidity (RH, %) of the air and the wind speed (WS, m/s). The HLI was validated in 2.490 Bos taurus steers (Aberdeen Angus and other breeds) observing their respiratory frequency using a subjective panting scale (1 to 5, Gaughan et al. 2008 ). The advantage of the HLI is that it can be estimated form locally generated meteorological data, allowing the comparison between environments, that either include modifications for understatement of heat stress or not. Calves and heifers are more tolerant to heat stress than adult cattle, however, their acclimatation involves short and long-term energetic costs (reducing weight gain and reproductive efficiency) resulting in a need to implement adaptation strategies for these animal categories (Wang et al. 2020 ). The main strategies for improving production under heat stress conditions are genetic selection, physical modification of the environment and nutrition (Beede and Collier 1986 ). Which strategy to adopt and develop will depend on the production system and the expected productive benefits (Wang et al. 2020 ). The focus of intensive livestock systems has been on environmental adaptation including cooling systems to ensure thermoneutrality for housed animals resulting in increased production costs through energy consumption (Nardone et al. 2010 ). In contrast, conditions of thermal comfort for cattle in agroforestry systems are improved due to the microclimate generated by the presence of trees in pastoral systems, resulting in less restrictive production environments, and at the same time having a positive impact on climate change as a carbon sink (Magalhães et al. 2020 ). Beef cattle exposed to periods of heat stress in northern Uruguay, but with voluntary access to natural shade, improved their comfort and weight gain (Becoña and Casella 1999 ; Simeone et al. 2010 ). The aim of this study was to evaluate the impact of the meteorological environment during heat waves on physiological and productive variables of Hereford heifers, either with or without voluntary access to natural shade on native grasslands in northern Uruguay, using the HLI. Material and methods Location and treatments The experiment was done at Estación Experimental de la Facultad de Agronomía en Salto, Universidad de la República , Uruguay (-31°23’ -57°57’, Fig. 1 ), for two summers (December, January, and February; Year 1: 2019–2020 and Year 2: 2020–2021). The experimental area consisted of 8 ha of native grasslands on Basalt soils (dominant soils: typical eutric Brunosol and melanic eutric Litosol) and was divided in four plots of 2 ha each. The plots were grazed by Hereford heifers with a body weight (BW ± SD) of 264 ± 24.6 kg (Year 1) and 281 ± 32.2 kg (Year 2) at the beginning of the experiment. The heifers were allocated to one of two treatments: Shade or Sun. Shade: grazing plots with voluntary access to natural shade of Eucalyptus tereticornis (298 trees/ha) E-W oriented tree lines (plot 1 with 0.29 ha of wood, and plot 2 with 0.28 ha of wood, plots 3 and 4 without access to shade). Characterization of the meteorological environment Heat Load Index The air temperature (ºC), the air humidity (%) and the wind speed (m/s) were registered hourly by two automatic meteorological stations (HOBO U30 USB Weather Station Data Logger, Onset Computer Corporation, Bourne, MA, 2019), one for each treatment, located in the Sun and in the Shade. Furthermore, black globe (BG, Berbigier, 1988 ) temperatures were registered hourly with Kooltrak sensors (i-Buttons-TMEX modelo DS1921, Dallas Semiconductors, Dallas, TX). The globes were 16 cm in diameter and made of copper and painted matt black and located at a height of 1.5 m in the Sun and in the Shade. The HLI was estimated with the data collected from the stations and the globes, as proposed by Gaughan et al. ( 2008 ): (if BG > 25): HLI = 8.62 + (0.38 × RH) + (1.55 × BG) - (0.5 × WS) + e (2.4 − WS) (if BG < 25): HLI = 10.66 + (0.28 × RH) + (1.3 × BG) - WS. Where: BG = Black globe temperature (ºC) at a height of 1.5 m RH = Air humidity (%) at a height of 1.5 m WS = Wind speed (m/s) at a height of 2.0 m The estimated values of HLI were divided in four categories of animal comfort, as proposed by Gaughan et al. ( 2008 ) for Bos taurus : HLI ≤ 70.0: thermoneutral conditions HLI 70.1 to 77.0: temperate conditions HLI 77.1 to 86.0: warm conditions HLI ≥ 86.1: very warm conditions Temperature-Humidity Index The Temperature-Humidity Index (THI) was estimated with the data collected from the meteorological stations, as proposed by Thom ( 1959 ): THI = (1.8 × AT + 32) - (0.55–0.55 × RH) × (1.8 × AT − 26) Where: AT = Air temperature (ºC) at a height of 1.5 m RH = Air humidity (%) at a height of 1.5 m Heat waves An extreme weather event “is an event that is rare at a particular place and time of year. Definitions of rare vary, but an extreme weather event would normally be as rare or rarer that the 10th or 90th percentile of a probability density function estimated from observations of a data set of at least thirty years” (IPCC glossary 2013). To identify extreme events of air temperature the Della-Marta and Beniston ( 2008 ) criterion was used, where a warm day was defined as a day in which the maximum temperature exceeded the 95th percentile of the maximum daily temperatures. In analogy with this criterion, a warm night can be defined as a night in which the minimum temperature exceeds the 95th percentile of the minimum daily temperatures of that place in a series of 30 years or more. For Salto, Uruguay, the temperature corresponding to the 95th percentile of the minimum air temperature (Tmin) and the maximum air temperature (TMAX) was estimated form data of a time series 1970–2022 of the Uruguayan Institute of Meteorology ( INUMET, Instituto Uruguayo de Meteorología ). Four Meteorological Conditions (MC) were defined considering the criteria defined by Della-Marta and Beniston ( 2008 ) and Nienaber et al ( 2003 ), based on the occurrence of TMAX and Tmin equal to or above the 95th percentile (p95) to characterize the occurrence of heat waves (HW) and the duration (hours) of each THI category according to their severity: Severe HW: when during three or more consecutive days the minimum air temperatures were equal or higher than the p95 of the Tmin, and the maximum air temperatures were equal to or higher than the p95 of the TMAX, with little possibility of recovery of normothermia (THI ≤ 72 of 0 to 2 hours a day). Further criteria were a) THI ≥ 84 for 3 to 15 hours per day during three consecutive days, b) a duration of the event of 6 to 8 days, and c) an accumulation of hours with THI ≥ 79 of 40 to 80 during the entire event. Strong HW: when during three or more consecutive days the minimum air temperatures were equal or higher than the p95 of the Tmin, and the maximum air temperatures were equal to or higher than the p95 of the TMAX, with little possibility of recovery of normothermia (THI ≤ 72 of 0 to 4 hours a day). Further criteria were a) THI ≥ 84 for 6 hours per day, b) a duration of the event of 5 to 7 days, and c) an accumulation of hours with THI ≥ 79 of 33 to 65 during the entire event. Mild HW: when during three or more consecutive days the minimum air temperatures were equal or higher than the p95 of the Tmin, or the maximum air temperatures were equal to or higher than the p95 of the TMAX (only one of the two criteria), with at least 5 hours of possible recovery of normothermia (THI ≤ 72 of 3 to 8 hours a day). No HW: when none of the above criteria were met. Measurements in heifers In Year 1, the number of heifers in the Sun treatment was 12 and 11 in the Shade treatment, while in Year 2 each treatment consisted of eight heifers during the entire experimental period. Every 21 days the heifers were weighed without fasting on an electronic scale with a precision of 0.5 kg. Average daily weight gain (ADG) (kg/animal/day) was determined by linear regression of the evolution of body weight (BW) within each period. The vaginal temperature is an indicator of thermal balance in cattle and can be used to evaluate the impact of heat stress on thermoregulation and animal welfare (Cvetkovic et al. 2005 ). Each summer, eight heifers per treatment were fitted with an intravaginal device (CIDR) of inert silicone to which a Kooltrak temperature sensor (17.4 mm diameter; 3.3 g and ± 0.5°C precision; i-Buttons Thermochron DS1921G-F5, Texas Instruments, Dallas) was fixed with hypoallergenic tape. Vaginal temperature was registered hourly. The devices were held in a non-irritating antiseptic solution (Dichloro-M-Xylenol diluted to 10%), and before insertion into the vagina the device was sprinkled with oxytetracycline (1 mL). After 21 days in the vagina of the heifer, the device was retrieved for data extraction, disinfected, and inserted in the vagina of another heifer of the same treatment, to keep collecting data. These procedures were approved by the Comisión de Ética en el Uso de Animales (CEUA) of the Centro Universitario Regional (CENUR) Litoral Norte (file Nº 311170-000156-23) and approved by the Board of CENUR Litoral Norte, Universidad de la República , Uruguay. Measurements in pastures Grazing management was done with the continuous presence of animals with a variable stocking rate (Wheeler et al. 1973 ) with fixed (test) animals and put-and-take animals to obtain similar sward heights among plots, and the average sward heights (cm ± SD) were 9.6 ± 2.45 cm in Year 1 and 9.0 ± 2.33 cm in Year 2. Sward height was measured weekly with 50 measurements on each plot, with a graded measuring device (HFRO sward height stick, Barthram 1986 ). Forage allowance (kg DM/kg of BW, Sollenberger et al. 2005 ) was calculated based on the measurement of the amount of forage biomass at the beginning of each period, plus forage growth during the period and stocking rate (test animals and put-and-take animals). The amount of forage biomass (kg DM/ha) in each plot was estimated with the comparative yield method (Haydock and Shaw 1975 ), with 40 subjective determinations per plot in 0.2 by 0.2 m squares, determining forage present in 14 of them. Forage growth rate (kg DM/ha/day) was measured by the cutting of pasture in three mobile grazing exclusion cages per plot every 45 days (Gardner 1967 ; Frame 1993 ) with an area of 0.4 m 2 per cage. In both determinations forage is cut at 1.5-2.0 cm from the soil. Harvested forage is freshly weighed and then dried in an oven with forced air at 60ºC to constant weight. Statistical analysis Statistical analysis was done using the GLIMMIX procedure of SAS® University Edition (SAS OnDemand Institute, Cary, CN, 2023 edition). The means were compared using the Tukey test, with a repeated measurements in time and an alpha level of 0.05. The model to analyze forage allowance, summer forage growth rate and daily weight gain (ADG) of the heifers included the effects of year (n = 2, Year 1 and Year 2) and treatment (n = 2, Sun and Shade). For the analysis of vaginal temperature, the Meteorological Conditions (MC, n = 4) were added to the moment in which they occurred: Severe HW in December Year 1, Strong HW in February Year 1, Mild HW in February Year 1, and without HW (No HW), as well as six-hour intervals of the day (Interval 04–09, Interval 10–15, Interval 16–21 and Interval 22 − 03 hours) and the Day in the MC: Day(MC). In order to detect significant differences in the series of black globe temperatures, ARIMA (Auto Regressive Integrated Moving Average) models were adjusted with a seasonal component, and for both treatments the selected model was of the multiplicative type ARIMA(1,0,0)(1,1,1) 24 , i.e. an autoregressive model with a seasonal component and a 24 hour lag (as hourly data sets for all the experimental days were available). Results Meteorological environment Daily mean minimum (Tmin) and maximum (TMAX) temperatures of December, January, and February were lower than the time series of Salto (Climate Normal 1991–2020, INUMET, 2023) for both years (Fig. 2 ). The Tmin temperatures of Year 2 were lower than Tmin of Year 1. Figure 2 includes the monthly extreme maximum of the minimum temperatures (Fig. 2 A) and the absolute maximum temperatures (Fig. 2 B). Based on the information of the time series of Salto (Climate Normal 1991–2020, INUMET, 2023), the p95 for Tmin was 21.8 ºC and the p95 of TMAX was 35.3 ºC. Based on these thresholds, the occurrence of HW was identified, and then HW were classified according to severity using the criteria of Nienaber et al. ( 2003 ): a Severe HW (27/12/2019 to 01/01/2020), a Strong HW (04/02/2020 to 7/02/2020) and a Mild HW (13/02/2020 to 17/02/2020) in Year 1; the rest of the days corresponded to No HW. During Year 2 no heat wave occurred, therefore all the days corresponded to No HW. The results of the comparison of the BG temperature series for the Sun and Shade treatments are shown in Fig. 3 . Minimum BG temperatures were not different between treatments (p > 0.05) for both Year 1 and Year 2 (Fig. 3 ), while during daytime the maximum BG temperatures in the Sun treatment were significantly higher than in the Shade treatment for most of the days. The number of hours per day that the BG temperatures were significantly different (p < 0.05) between treatments ranged from 0 to 9 hours in Year 1 and from 0 to 9 hours in Year 2, according to the day, and always during daytime. When comparing MC within Year 1, the BG temperatures were significantly different between treatments for 0 to 3 h per day during the Severe HW and for 0 to 5 h per day during both the Strong and the Mild HW (Fig. 3 ). In Year 1, the highest BG temperatures were concentrated in the Interval from 10 to 15 h and were significantly higher in the Sun treatment than in the Shade treatment. In Year 2, the BG temperatures were different between treatments for more hours per day, which occurred in all Intervals, but mainly during daytime Intervals. During the Severe HW the highest BG temperatures of the summer of Year 1 were registered (52 ºC on two consecutive days), but during the five days of this event only 0 to 4 h per day were different between treatments, indicating similar conditions for both treatments. Although the BG temperatures in the other HW were not that high, the same pattern of no difference between the treatments of 0 to 5 h consecutive hours per day was found. This pattern only occurred in some days during No HW in both years. For each MC the HLI was grouped according to severity categories (thermoneutral: ≤70.0, temperate: 70.1 to 77.0, warm: 77.1 to 86.0 and very warm: ≥86.1) and Fig. 4 shows the distribution of hours (± SD) in the day for each category. The distribution of the number of hours per day according to HLI category within each MC were different, but not between treatments during the heat waves. During the days of No HW, the Shade treatment had more thermoneutral hours (14 vs 12 h in Year 1 and 23 vs 18 h in Year 2, Sun and Shade, respectively), and few (6 vs 10 h in Year 1, Sun and Shade, respectively) or null (0 vs 2 h in Year 2, Sun and Shade, respectively) warm or very warm hours. The hours of HLI categories warm and very warm (HLI ≥ 77.1) were grouped and their proportion (%) for each six-hour interval according to MC and Year are shown in Table 1 . Table 1 Percentage of hours per day with HLI above 77.1 (warm to very warm conditions) according to the six-hours interval of the day (from 04 to 09, 10 to 15, 16 to 21 and 22 to 03 hour), for each treatment (Sun and Shade) and according to the meteorological conditions (Severe HW, Strong HW, Mild HW and No HW) for Year 1 and Year 2 Year 1 Year 2 MC Severe HW Strong HW Mild HW No HW No HW Treatment Sun Shade Sun Shade Sun Shade Sun Shade Sun Shade Interval 04–09 13 11 14 11 4 4 6 2 1 0.5 Interval 10–15 24 22 21 18 22 18 25 24 2 0.1 Interval 16–21 22 22 13 13 23 21 14 11 4 1 Interval 22 − 03 9 12 8 7 8 6 3 1 5 2 During intervals 10–15 and 16–21 more hours with HLI warm to very warm occurred during heat waves. In the Sun treatment in Year 1, during the Severe HW 68% of the hours corresponded to HLI ≥ 77.1 in the Sun and 67% in the Shade, while during the Strong and Mild HW, 56% of the hours in the Sun and 49% of the hours in the Shade corresponded to HLI ≥ 77.1. In Year 1, during No HW, more warm and very warm hours compared to Year 2 (Year 1: Sun 48% vs Shade 38%; Year 2: Sun 12% vs Shade 4%). Heifers Vaginal temperature The vaginal temperatures were significantly different between treatments (Sun vs . Shade, mean ± SEM) in Year 2: 38.45 ºC vs. 38.68ºC ± 0.02 (p < 0.0001), but not in Year 1: 38.6 ºC ± 0.05 (overall mean for both treatments, p = 0.393). The interaction Interval × Treatment for vaginal temperatures was significant both in Year 1 (p = 0.028) and Year 2 (p = 0.002) (Fig. 5 ). Vaginal temperatures were significantly different among MC (p < 0.0001, Year 1) and among the six-hour intervals (p < 0.0001), as well as Day(MC) (p < 0.0001). The interactions MC × six-hour interval (p < 0.0001) were also significantly different, but not Treatment × MC (p = 0.167) or Treatment × MC × six-hour interval (p = 0.386) (Fig. 5 ). In Year 1, the highest vaginal temperatures were registered in Interval 16–21 in the Sun treatment (39.4 ºC). During Interval 04–09, the heifers had the lowest vaginal temperatures (38.1 ºC to 38.3 ºC), which correspond to the hours of the day with a thermoneutral environment. The lowest vaginal temperatures were registered during the night-time (Interval 22 − 03 and 04–09), corresponding with the thermoneutral HLI category (Fig. 4 and Table 1 ). Although the effect of treatment was not significant in Year 1, the effect of Day(MC) was significant, and therefore the vaginal temperature of heifers in the Sun treatment was significantly higher than those of heifers in the Shade treatment during Intervals 10–15 and 16–21. The highest vaginal temperatures were registered during Interval 16–21 and were significantly higher during Severe and Strong HW, than during Mild and No HW. The lowest vaginal temperatures were registered during Interval 04–09 on the days of No HW. For each MC the 24-hour evolution of the HLI, the THI and the vaginal temperatures average of the heifers according to treatment (Sun and Shade) are presented in Fig. 6 . As shown in Fig. 6 , during the Severe HW the highest vaginal temperatures occurred, ranging from 38 to 40 ºC during the whole day, for both treatments, although in the Shade treatment the vaginal temperatures were lower. The increase in vaginal temperature showed a lag of several hours in relation to the increase in HLI, which was similar in both treatments, but with lower vaginal temperatures for the heifers in the Shade treatment, even in the hours that the HLI was high. The heifers in the Sun treatment increased their temperature earlier than the heifers in the Shade treatment during the Severe HW and Strong. Maximum vaginal temperatures during the Mild HW were registered during Interval 16–21 but was less pronounced than for the other two HW as the HLI was less challenging. During No HW the hourly variation of the vaginal temperatures was less related to the HLI. Average daily gain The ADG of BW (kg/animal/d) was significantly different between treatments in Year 1 (p = 0.042), but not in Year 2 (p = 0.502). Average daily gain was significantly higher in Year 2 compared to Year 1 (p˂0.0001) (Fig. 7 ). Pastures The forage growth rate was not different between treatments (p = 0.471) or years (p = 0.627). The mean sward height was higher in the Sun treatment than in the Shade treatment only in Year 1 (p˂0.001) and was not different in Year 2 (p = 0.211) (Table 2 ). However, forage allowance (kg DM/100 kg of BW) was not different between treatments (p = 0.789) in each year but tended to differ between years (p = 0.069). Table 2 Mean values (± SEM) of forage growth rate (kg DM/ha/d), forage allowance (kg DM/100 kg of BW) and sward height (cm) by Year (1 and 2) and treatment (Sun and Shade) Year 1 Year 2 Treatment Sun Shade Sun Shade Growth rate 31.7 ± 0.94 27.0 ± 0.94 27.6 ± 2.12 29.1 ± 2.12 Forage allowance 7.0 ± 1.53 6.9 ± 1.53 12.4 ± 2.94 11.3 ± 2.94 Sward height 10.7 ± 0.23 a 8.5 ± 0.19 b 9.2 ± 0.30 8.8 ± 0.28 a,b within year indicate significant differences (p < 0.05) Discussion Meteorological environment The meteorological environment was different between years as Year 1 had three extreme temperature events (heat waves) and Year 2 had none. This was somewhat unexpected as there is a 94% probability of at least one extreme temperature event per summer when analyzing the maximum and minimum temperatures of a 52-year data series (1971–2022) for areas of Western Uruguay (Saravia and de Souza 2023 ) and in accordance with Rusticucci et al. ( 2016 ) who describe an increase in number of days of Extreme HW (Tmin and TMAX above p90) for the Province of Entre Ríos, Argentina (from 10 to 40 days between 1961 and 2010) during the warm months. The three extreme events during the summer of Year 1 were of different intensity and duration according to the classification of Nienaber et al. ( 2003 ). Specifically, a Severe HW was registered of four days with THI above 84 for 6 to 9 h a day, and above 72 (12 to 24 h a day), even during the night, which complicates nighttime recovery of normothermia (Hahn and Mader 1997 ; Hahn 1999 ; Nienaber and Hahn 2007 ). However, THI has not been precise in characterizing heat load in very warm environments, while other indices, such as the HLI, are better predictors of the effect of the meteorological environment on the response of the animal (Silva et al. 2007 ). The black globe temperature (BGt), employed as an indirect measurement of the radiant heat load, was used to characterize the thermal environment at open pasture or at under the shade of the trees (Fig. 3 ). Estimation of heat gain and heat loss through BG needs to be integrated (Berbigier, 1988 ), as well as the influence of wind speed and air humidity, to identify the warm and very warm conditions (through the HLI), as this better reflects the increased risk of heat load because of increased radiation and the marginal possibility of heat dissipation by advection of warm and humid air, which reduces the efficacy of thermoregulatory mechanisms (like panting and sweating) (Berman, 2005 ). The black globe temperature were relatively similar between Sun and Shade during the heat waves, and only differed between treatments for 0 to 3 h per day during the Severe HW and 0 to 5 hours per day during both the Strong and the Mild HW (Fig. 3 ). In the same sense, the HLI shows little variation between treatments during the Severe and the Strong HW (Fig. 4 ), particularly regarding the number of hours below the threshold of 70 (thermoneutrality). This indicates that the shade provided by the trees in this experiment were not enough to distinguish the meteorological environment (temperature and air humidity) from the environment of the open pasture during the Severe and Strong HW, and therefore hampering thermoregulatory mechanisms for maintaining thermal balance (Hahn 1999 ). This differs from de Souza et al. ( 2010 ) and Magalhães et al. ( 2020 ), who, working in warm environments with biometeorological indices including BGt, reported bigger differences between Sun and Shade than found in this experiment. The microclimate changes when trees are included, improving thermal comfort of the animals because of reduced direct solar radiation resulting in a lower heat load, and this is reflected in lower biometeorological indexes based on the black globe temperature and air humidity (Pezzopane et al. 2019 ). Shade trees reduce air temperatures by reducing reflected radiation and because of a major role that evaporation of water plays in heat transfer (6 to 9 ºC lower black globe temperatures in the shade than in the sun, depending on the spatial arrangement of the trees, Munka et al. 2019 ). However, the air temperature at a specific site is the result of the integration of a radiation component (local radiation balance) and an advection component (advection of a warmer mass of air). Of the heat waves in subtropical South America, 73% develop in association with an active South Atlantic Convergence Zone (SACZ, Cerne and Vera 2011 ). The development of anticyclonic circulation result in an increase of temperature in the subtropical region, weakening the SACZ activity and producing a progressive increase in temperature in the region, dominated by warmer and more humid air, with anomaly advection from the North (Cerne y Vera 2011). Analysis of the atmospheric behavior of patterns of circulation that determine the occurrence of heat waves in Salto indicated that the persistence of surface winds in the North of Uruguay with high humidity, is boosted by the location of this region West of the High-Pressure System of the South Atlantic (Caffera and Salaberry 2005 ). During the summer of 2019–2020, the dominant wind direction during the Severe and Strong HW was N-NW with a mean daily wind speed of 8 to 14 m/s, mainly during the Interval 10–15, which is consistent with synoptic situation described above. This stresses the importance of the effect of advection of warm and humid air from other regions North of Uruguay, as indicated by Cerne et al. ( 2007 ) for the Extreme HW in Rosario, Argentina, in January 2006. Our results confirm the importance of the SACZ activity in the induction of anomalies in air circulation in subtropical regions, which can lead to persistent heat waves with very high daily temperatures (Cerne and Vera 2011 ). The events Severe HW and Strong HW presented this kind of anomalous circulation, which explains why the shade of the wooded areas (number and density of trees) was not enough to reduce the HLI in this treatment compared to the Sun Treatment. In this study, the HLI measured in two summers differentiated the conditions of the environments for heifers with or without access to natural Shade only in days of No HW (Fig. 4 ), while during the extreme events the number of warm and very warm hours was similar between treatments (Table 1 ). Physiological variable The variation of vaginal temperature during the Severe HW was related to the variation of HLI, but with a time lag of several hours (Fig. 6 ). During the day, cattle accumulate heat by increasing body temperature, which is dissipated during the night. However, if nighttime recovery is insufficient, the animal starts the day with an accumulated heat load as a carry-over effect from the previous day increasing susceptibility to heat stress at lower HLI values (Gaughan et al. 2008 ). The thermoregulatory strategy of the heifers in the Sun treatment was to increase heat load by absorbing radiation, while in the Shade treatment part of this radiation was intercepted by the tree canopy and air temperature was lower because of evapotranspiration of the leaves, leading to more comfortable HLI for the heifers. The distribution of the different HLI categories on the average day of each MC (Fig. 4 ) and along the hours of the day (Table 1 and Fig. 6 ) shows the challenge to which the animals were subjected to maintain thermal balance during extreme temperature events when no protective measure such as shade was available. The difference between the Sun and Shade treatments in daily hours were like other studies which included biometeorological indexes based on black globe temperatures (de Souza et al. 2010 ; Magalhães et al. 2020 ). The highest vaginal temperatures were registered during interval 16–21 in all HW (39.4ºC, 39.3ºC, 38.7ºC, in Severe, Strong, Mild HW respectively, Fig. 5 ) in Year 1, but only during Severe and Strong HW the vaginal temperatures were above normothermia of heifers; above the threshold of 38.9ºC described by Finch ( 1986 ). This is consistent with the delay in increase in heat load in response to the HLI categories warm and very warm which begins during interval 10–15 and is maintained during interval 16 a 21(in al HW Table 1 ), when increases in the vaginal temperature become evident during interval 16–21 (Fig. 6 ). Similar responses have been seen in cows when panting was increased several hours after the highest HLI (Kaufman et al. 2018 ), and elevated conditions of air temperature and humidity increased vaginal temperatures in dry cows, but more in Hereford cows compared to their crosses with Bonsmara (Fedrigo et al. 2021 ). However, the vaginal temperatures were within the range of normothermia (37.5 to 38.9ºC) during Mild HW, No HW Year 1 and No HW Year 2 (Finch 1986 ), possibly because of nighttime recovery (very low percentage of hours with HLI ≥ 77.1 during interval 4–9), which was limited or absent in the Severe and Strong HW. During Year 2 during all hours of the day the vaginal temperatures of the heifers of the Sun treatment were lower than those of the Shade treatment (Fig. 6 ), which is in agreement with other reports (Muller et al. 1994 ; Saravia 2009 ), and heifers in the Shade treatment also experienced less diurnal temperature variation. The thermoregulatory mechanisms of animals exposed to the sun remain active because of overstimulation and cause a decrease of body temperature, even in environments of thermal comfort (Johnson and Vanjonack 1976 ). Others explain these findings as an advantage of cooling directly under an open nighttime sky on the open grasslands. (Peent et al. 2021). In this experiment the BGt did not differ significantly between the Sun and Shade treatments during nighttime and therefore the vaginal temperatures are probably due to thermoregulation physiology of the species and category (Wang et al. 2020 ). Productive performance In Year 1, heifers in the Shade treatment gained more weight than the heifers in the Sun treatment (Fig. 7 ), which cannot be explained by forage allowance, which was not different between the treatments (Table 2 ). However, differences in ADG between heifers in the Sun and Shade treatment could be a consequence of the different meteorological environments as expressed in the different HLI, particularly during the 70 days of No HW (Table 1 ). The heat load of animals can be reduced up to 30% because of the physical interception of solar radiation by natural or artificial shade (Blackshaw and Blackshaw 1994 ). During the summer season, the effects of high temperatures are evident in the reduced performance of the animals (lower ADG) because of lower feed intake as well as increased energy expenditure (increased metabolic rate and heat dissipation mechanisms) (Fox et al. 1998; Pigurina et al. 1998 ; CSIRO 2007 ; O’Brien et al. 2010). These negative effects on energy balance reduce the energy available for growth, which is reflected in reduced ADG (St-Pierre et al. 2003 ; Gaughan et al. 2008 ). In warm climates, heifers with access to natural shade in silvopastoral systems had lower rectal temperatures, leading to an increase in food intake and a higher body weight (Lemes et al. 2021 ). Access to natural shade during warm conditions of sumer in the North of Uruguay has resulted in higher ADG in growing heifers and steers (Simeone et al. 2010 ; Beretta et al. 2013 ). Forage allowance was not restrictive in any of the two years and tended to be higher in Year 2, which explains the higher ADG of the heifers in Year 2 compared to Year 1. The summer forage growth rates reported for similar plant communities (17.2 kg DM/ha/d Berretta and Bemhaja 1998 ) are lower than observed in this study. The higher forage growth rates in this study can be attributed to the defoliation management based on sward height, with a target range of 6 to 12 cm, with a variable stocking rate (Wheeler et al. 1973 ). Forage allowance in this study was at a level that maximizes animal performance (weight gain) in growing cattle (heifers) (Carvalho 2017 ), especially in Year 2. Conclusions The extreme temperature events (Severe and Strong HW as a result of anomaly air circulation) during nine days in the summer of Year 1 induced hyperthermia in the heifers along with similar hours of HLI in the categories of warm and very warm, and few hours of thermoneutral temperatures, affecting recovery of normothermia. In the conditions of this experiment, the shade provided was not enough to ensure thermal comfort to the heifers. During the environmental conditions of Mild HW and No HW (seventy-five days of the summer), the HLI was mainly in the categories of thermoneutral and temperate in both treatments, but for more hours in the Shade treatment. This may have improved the welfare of the heifers with access to Shade and would explain the higher weight gain compared to the heifers of the Sun treatment, which needed more energy for thermoregulation. In Year 2, the predominant conditions were that of thermoneutrality, and these conditions of comfort allowed the heifers to maintain normothermia during a major part of the day in both treatments, resulting in similar energy partitioning and weight gain. Combined, these findings stress the need to include natural shade in grassland cattle production improving animal welfare during the warm days of summer, and the HLI appears to be a better predictor of physiological and productive responses to extreme temperature events. Which is able to contribute to minimizing production losses in a scenario of increasing variability and climate change. Declarations Author Contribution Celmira Saravia: Project administration, Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Supervision, Resources, Funding acquisition; Elize van Lier: Conceptualization, Data curation, Writing - original draft, Supervision, Funding acquisition;Carolina Munka: Resources, Conceptualization, Methodology, Validation, Data curation, Investigation;Oscar Bentancur: Methodology, Validation, Formal analysis; Rodrigo Iribarne: Investigation, Formal analysis;Ricardo Rodríguez Palma: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Supervision, Resources; Laura Astigarraga: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Supervision, Resources, Funding acquisition Acknowledgements: Partial financial support was received from Estación Experimental de Facultad de Agronomía Salto, Author C. 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Munka","email":"","orcid":"","institution":"Departamento de Sistemas Ambientales, Facultad de Agronomía, Montevideo","correspondingAuthor":false,"prefix":"","firstName":"C.","middleName":"","lastName":"Munka","suffix":""},{"id":270400943,"identity":"c7058103-c805-49fc-b261-3ee54f809e71","order_by":3,"name":"O. Bentancur","email":"","orcid":"","institution":"Estación Experimental Mario A. Cassinoni, Universidad de la República","correspondingAuthor":false,"prefix":"","firstName":"O.","middleName":"","lastName":"Bentancur","suffix":""},{"id":270400944,"identity":"2ed39c6f-e5b1-4a8f-b7f1-038b20e759d5","order_by":4,"name":"R. Iribarne","email":"","orcid":"","institution":"Estación Experimental de la Facultad de Agronomía en Salto","correspondingAuthor":false,"prefix":"","firstName":"R.","middleName":"","lastName":"Iribarne","suffix":""},{"id":270400945,"identity":"8f36ebe0-dca6-4ca8-9606-4cb8c1423b7e","order_by":5,"name":"R. 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Astigarraga","email":"","orcid":"","institution":"Departamento de Producción Animal y Pasturas, Facultad de Agronomía, Montevideo, Uruguay","correspondingAuthor":false,"prefix":"","firstName":"L.","middleName":"","lastName":"Astigarraga","suffix":""}],"badges":[],"createdAt":"2024-01-31 13:01:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3913892/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3913892/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10457-024-01039-x","type":"published","date":"2024-08-16T15:57:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50551135,"identity":"e01b9d0e-08c1-4ced-bf29-5d3c68891641","added_by":"auto","created_at":"2024-02-02 11:02:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":9987,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the experiment in the \u003cem\u003eEstación Experimental de la Facultad de Agronomía en Salto (EEFAS), Universidad de la República, \u003c/em\u003eUruguay within South America.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/5d6caabde2abb93f86189d52.png"},{"id":50551131,"identity":"1d4aa9a4-30b3-406f-b6c9-8a5e013b5d1c","added_by":"auto","created_at":"2024-02-02 11:02:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":9609,"visible":true,"origin":"","legend":"\u003cp\u003eAverage air temperatures (ºC) in December, January, and February during the two years of the experiment: A) minimum temperatures and B) maximum temperatures of the Salto time series (Climate Normal 1991-2020, INUMET, 2023, grey bars), Year 1 (2019-2020, white bars) and Year 2 (2020-2021, black bars). Horizontal bars and values indicate A) extreme maximum of the minimum temperatures and B) absolute maximum temperatures.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/713cd45cde9d06a3af90b1c9.png"},{"id":50551134,"identity":"c88d0523-4b1b-4934-9766-b86fd7324411","added_by":"auto","created_at":"2024-02-02 11:02:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":26644,"visible":true,"origin":"","legend":"\u003cp\u003eDaily minimum (dotted lines) and maximum (solid lines) black globe temperatures for each treatment (Sun: grey lines and Shade: black lines) (upper panels) and the number of hours per day in which the black globe temperatures in the Sun were significantly higher (p˂0.05) than the black globe temperatures in the Shade (lower panel) for Year 1 and Year 2. Meteorological Conditions (Severe HW, Strong HW, and Mild HW) are indicated by vertical boxes.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/a81000a91ee59cc370082ebe.png"},{"id":50551139,"identity":"c6b1eef3-2909-4e21-a0e5-9983e8ebf4a1","added_by":"auto","created_at":"2024-02-02 11:02:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":11065,"visible":true,"origin":"","legend":"\u003cp\u003eMean number of hours (± SD) in the day according to HLI categories (thermoneutral: ≤70.0, temperate: 70.1 to 77.0, warm: 77.1 to 86.0 and very warm: ≥86.1) for each type of meteorological condition (Severe HW, Strong HW, Mild HW, No HW 1 in Year 1, and No HW 2 in Year 2) in the Sun (white bars) and Shade (black bars) treatments.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/410e7c91a67467d624e3acdf.png"},{"id":50551132,"identity":"029883e5-5b73-4721-80db-841e96e6186b","added_by":"auto","created_at":"2024-02-02 11:02:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":8993,"visible":true,"origin":"","legend":"\u003cp\u003eMean (± SEM) vaginal temperatures (ºC) for each type of meteorological condition (MC: Severe HW, Strong HW, Mild HW and No HW) in Year 1, according to Interval six-hours of the day (from 04 to 09 h, 10 to 15 h, 16 to 21 h and 22 to 03 h), and to treatment (Sun: white bars and Shade: black bars). a,b,c,d indicate significant differences between Interval six-hours within MC (p\u0026lt;0.0001), and A,B indicate significant differences among MC (p\u0026lt;0.0001).\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/8c3d5fa4fafefd8873a10718.png"},{"id":50551138,"identity":"247dd6d2-8f95-47e0-8ec4-e473a182d150","added_by":"auto","created_at":"2024-02-02 11:02:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":18520,"visible":true,"origin":"","legend":"\u003cp\u003eHourly Heat Load Index for the Sun and Shade treatment (HLI, solid lines with dots, Sun grey and Shade black) and Temperature-Humidity Index (THI, dark grey dotted line) (upper panel) and mean (± SD) hourly vaginal temperatures during the average day of each MC (Severe HW, Strong HW, Mild HW, No HW1 in Year 1 and No HW2 in Year 2) and each treatment (Sun grey lines and Shade black lines).\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/c17ef23c7044c3b63ae81f53.png"},{"id":50551140,"identity":"ddd125f9-0b2a-4ddb-8788-56146aeb41eb","added_by":"auto","created_at":"2024-02-02 11:02:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4896,"visible":true,"origin":"","legend":"\u003cp\u003eAverage daily gain (kg/animal/d ± SEM) by treatment Sun (white bars) and Shade (black bars) and by Year (1 and 2). a,b indicate significant differences within Year, and A,B indicate significant differences between Years (p\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/90968426c7d964cae7c8e648.png"},{"id":63071141,"identity":"cabfb1e1-acd1-4ad9-bfef-846aee4347cf","added_by":"auto","created_at":"2024-08-22 20:04:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":677746,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3913892/v1/a44663be-46d1-4ffc-8124-17dcac994ac0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heat load index during heat waves as an indicator of thermal comfort of Hereford heifers with access to natural shade on native grasslands","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn predominantly grazing production systems, such as in Uruguay, cattle are continuously exposed to environmental factors, which directly affect their physiology and productivity or, indirectly, through nutrition by variations in forage quantity and quality (Hafez \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1972\u003c/span\u003e). During summer, the combined effects of high solar radiation, temperature and humidity result in a meteorological environment that lies outside of the thermal comfort zones of the animals and may reduce their productivity (Cruz and Saravia \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Although animals have adapted to their environmental conditions, periods of thermal stress do occur, either due to pronounced temperature variations or a combination of meteorological factors, which may negatively impact productive performance and lead to economic losses (Nienaber et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In cattle, body temperature ranges from 37.5 to 38.9 (Finch \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) and outside of this range, if the animal does not reestablish equilibrium, it will show typical thermal stress symptoms and reduce productivity (Bianca \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1965\u003c/span\u003e). High air temperatures reduce weight gain during post-weaning and fattening stages, and this is mainly due to increased body temperatures which lead to reduced feed intake (Johnson \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1987\u003c/span\u003e). Abnormally warm and generally humid periods of three or more consecutive days are called heat waves (IPCC \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and cause economic losses in animal production throughout the World (Nienaber and Hahn \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), either by reducing animal health and productivity or by death of the animals (St-Pierre et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Lees et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe expected climate conditions for the end of the 21st century, as modeled by the IPCC, project the following situations: a) increases in the extreme temperatures towards the end of the century, b) an increase in frequency and scale of the high extreme temperatures and a decrease of the number of extremely cold days and c) high probability of increases in duration, frequency and/or intensity of heat waves in the majority of areas on Earth (Seneviratne et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). For South America, analysis of series of extreme temperatures from 1950 to 2008 show evidence of heating, with a weak to moderate increase in the trend of calculated indexes, with an effect throughout South America of less cold daytime temperatures and warmer nighttime temperatures (Skansi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The estimated indexes of minimum temperatures show major increase rates, both for tropical nights (minimum registered temperatures above 20\u0026ordm;C) and warm nights (minimum registered temperatures above the 90 percentile of the data series). While maximum day temperature indexes also indicate an increase in number of days with maximum temperatures above 25\u0026ordm;C, but with lower increase rates than the minimum temperatures (Skansi et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The predicted increase in air temperature for the first fifty years of the 21st century is expected to cause physiological and metabolic damage and reduce animal welfare (Lacetera et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn terms of animal welfare and performance, it is important to be able to predict the effect of extreme meteorological variables on cattle (Gaughan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Quantification of environmental conditions through biometeorological indexes helps to relate the impact of meteorological conditions to animal performance. The Temperature-Humidity Index (THI, Thom \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1959\u003c/span\u003e) has been used for over six decades to evaluate heat stress in beef and dairy cattle, and to establish alert and prevention systems. However, the THI has been validated in climate chambers with controlled temperature and humidity (Johnson et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1961\u003c/span\u003e) and its usefulness for describing highly variable meteorological environments, as in pastoral and silvopastoral systems, is under constant revision. More complete biometeorological indexes are needed for research on the effects of climate change on animal production (Nardone et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Heat Load Index (HLI, Gaughan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) is estimated using the black globe temperature (BG, \u0026deg;C), the relative humidity (RH, %) of the air and the wind speed (WS, m/s). The HLI was validated in 2.490 \u003cem\u003eBos taurus\u003c/em\u003e steers (Aberdeen Angus and other breeds) observing their respiratory frequency using a subjective panting scale (1 to 5, Gaughan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The advantage of the HLI is that it can be estimated form locally generated meteorological data, allowing the comparison between environments, that either include modifications for understatement of heat stress or not.\u003c/p\u003e \u003cp\u003eCalves and heifers are more tolerant to heat stress than adult cattle, however, their acclimatation involves short and long-term energetic costs (reducing weight gain and reproductive efficiency) resulting in a need to implement adaptation strategies for these animal categories (Wang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The main strategies for improving production under heat stress conditions are genetic selection, physical modification of the environment and nutrition (Beede and Collier \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Which strategy to adopt and develop will depend on the production system and the expected productive benefits (Wang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The focus of intensive livestock systems has been on environmental adaptation including cooling systems to ensure thermoneutrality for housed animals resulting in increased production costs through energy consumption (Nardone et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). In contrast, conditions of thermal comfort for cattle in agroforestry systems are improved due to the microclimate generated by the presence of trees in pastoral systems, resulting in less restrictive production environments, and at the same time having a positive impact on climate change as a carbon sink (Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Beef cattle exposed to periods of heat stress in northern Uruguay, but with voluntary access to natural shade, improved their comfort and weight gain (Beco\u0026ntilde;a and Casella \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Simeone et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe aim of this study was to evaluate the impact of the meteorological environment during heat waves on physiological and productive variables of Hereford heifers, either with or without voluntary access to natural shade on native grasslands in northern Uruguay, using the HLI.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLocation and treatments\u003c/h2\u003e \u003cp\u003eThe experiment was done at \u003cem\u003eEstaci\u0026oacute;n Experimental de la Facultad de Agronom\u0026iacute;a en Salto, Universidad de la Rep\u0026uacute;blica\u003c/em\u003e, Uruguay (-31\u0026deg;23\u0026rsquo; -57\u0026deg;57\u0026rsquo;, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), for two summers (December, January, and February; Year 1: 2019\u0026ndash;2020 and Year 2: 2020\u0026ndash;2021). The experimental area consisted of 8 ha of native grasslands on Basalt soils (dominant soils: typical eutric Brunosol and melanic eutric Litosol) and was divided in four plots of 2 ha each. The plots were grazed by Hereford heifers with a body weight (BW\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) of 264\u0026thinsp;\u0026plusmn;\u0026thinsp;24.6 kg (Year 1) and 281\u0026thinsp;\u0026plusmn;\u0026thinsp;32.2 kg (Year 2) at the beginning of the experiment. The heifers were allocated to one of two treatments: Shade or Sun. Shade: grazing plots with voluntary access to natural shade of \u003cem\u003eEucalyptus tereticornis\u003c/em\u003e (298 trees/ha) E-W oriented tree lines (plot 1 with 0.29 ha of wood, and plot 2 with 0.28 ha of wood, plots 3 and 4 without access to shade).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCharacterization of the meteorological environment\u003c/h2\u003e \u003cp\u003eHeat Load Index\u003c/p\u003e \u003cp\u003eThe air temperature (\u0026ordm;C), the air humidity (%) and the wind speed (m/s) were registered hourly by two automatic meteorological stations (HOBO U30 USB Weather Station Data Logger, Onset Computer Corporation, Bourne, MA, 2019), one for each treatment, located in the Sun and in the Shade. Furthermore, black globe (BG, Berbigier, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) temperatures were registered hourly with Kooltrak sensors (i-Buttons-TMEX modelo DS1921, Dallas Semiconductors, Dallas, TX). The globes were 16 cm in diameter and made of copper and painted matt black and located at a height of 1.5 m in the Sun and in the Shade. The HLI was estimated with the data collected from the stations and the globes, as proposed by Gaughan et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e):\u003c/p\u003e \u003cp\u003e(if BG\u0026thinsp;\u0026gt;\u0026thinsp;25): HLI\u0026thinsp;=\u0026thinsp;8.62 + (0.38 \u0026times; RH) + (1.55 \u0026times; BG) - (0.5 \u0026times; WS)\u0026thinsp;+\u0026thinsp;e \u003csup\u003e(2.4 \u0026minus; WS)\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(if BG\u0026thinsp;\u0026lt;\u0026thinsp;25): HLI\u0026thinsp;=\u0026thinsp;10.66 + (0.28 \u0026times; RH) + (1.3 \u0026times; BG) - WS.\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eBG\u0026thinsp;=\u0026thinsp;Black globe temperature (\u0026ordm;C) at a height of 1.5 m\u003c/p\u003e \u003cp\u003eRH\u0026thinsp;=\u0026thinsp;Air humidity (%) at a height of 1.5 m\u003c/p\u003e \u003cp\u003eWS\u0026thinsp;=\u0026thinsp;Wind speed (m/s) at a height of 2.0 m\u003c/p\u003e \u003cp\u003eThe estimated values of HLI were divided in four categories of animal comfort, as proposed by Gaughan et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) for \u003cem\u003eBos taurus\u003c/em\u003e:\u003c/p\u003e \u003cp\u003eHLI\u0026thinsp;\u0026le;\u0026thinsp;70.0: thermoneutral conditions\u003c/p\u003e \u003cp\u003eHLI 70.1 to 77.0: temperate conditions\u003c/p\u003e \u003cp\u003eHLI 77.1 to 86.0: warm conditions\u003c/p\u003e \u003cp\u003eHLI\u0026thinsp;\u0026ge;\u0026thinsp;86.1: very warm conditions\u003c/p\u003e \u003cp\u003eTemperature-Humidity Index\u003c/p\u003e \u003cp\u003eThe Temperature-Humidity Index (THI) was estimated with the data collected from the meteorological stations, as proposed by Thom (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1959\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eTHI = (1.8 \u0026times; AT\u0026thinsp;+\u0026thinsp;32) - (0.55\u0026ndash;0.55 \u0026times; RH) \u0026times; (1.8 \u0026times; AT \u0026minus;\u0026thinsp;26)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eAT\u0026thinsp;=\u0026thinsp;Air temperature (\u0026ordm;C) at a height of 1.5 m\u003c/p\u003e \u003cp\u003eRH\u0026thinsp;=\u0026thinsp;Air humidity (%) at a height of 1.5 m\u003c/p\u003e \u003cp\u003eHeat waves\u003c/p\u003e \u003cp\u003eAn extreme weather event \u0026ldquo;is an event that is \u003cem\u003erare\u003c/em\u003e at a particular place and time of year. Definitions of \u003cem\u003erare\u003c/em\u003e vary, but an extreme weather event would normally be as rare or rarer that the 10th or 90th percentile of a probability density function estimated from observations of a data set of at least thirty years\u0026rdquo; (IPCC glossary 2013). To identify extreme events of air temperature the Della-Marta and Beniston (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) criterion was used, where a warm day was defined as a day in which the maximum temperature exceeded the 95th percentile of the maximum daily temperatures. In analogy with this criterion, a warm night can be defined as a night in which the minimum temperature exceeds the 95th percentile of the minimum daily temperatures of that place in a series of 30 years or more. For Salto, Uruguay, the temperature corresponding to the 95th percentile of the minimum air temperature (Tmin) and the maximum air temperature (TMAX) was estimated form data of a time series 1970\u0026ndash;2022 of the Uruguayan Institute of Meteorology (\u003cem\u003eINUMET, Instituto Uruguayo de Meteorolog\u0026iacute;a\u003c/em\u003e). Four Meteorological Conditions (MC) were defined considering the criteria defined by Della-Marta and Beniston (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and Nienaber et al (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), based on the occurrence of TMAX and Tmin equal to or above the 95th percentile (p95) to characterize the occurrence of heat waves (HW) and the duration (hours) of each THI category according to their severity:\u003c/p\u003e \u003cp\u003eSevere HW: when during three or more consecutive days the minimum air temperatures were equal or higher than the p95 of the Tmin, and the maximum air temperatures were equal to or higher than the p95 of the TMAX, with little possibility of recovery of normothermia (THI\u0026thinsp;\u0026le;\u0026thinsp;72 of 0 to 2 hours a day). Further criteria were a) THI\u0026thinsp;\u0026ge;\u0026thinsp;84 for 3 to 15 hours per day during three consecutive days, b) a duration of the event of 6 to 8 days, and c) an accumulation of hours with THI\u0026thinsp;\u0026ge;\u0026thinsp;79 of 40 to 80 during the entire event.\u003c/p\u003e \u003cp\u003eStrong HW: when during three or more consecutive days the minimum air temperatures were equal or higher than the p95 of the Tmin, and the maximum air temperatures were equal to or higher than the p95 of the TMAX, with little possibility of recovery of normothermia (THI\u0026thinsp;\u0026le;\u0026thinsp;72 of 0 to 4 hours a day). Further criteria were a) THI\u0026thinsp;\u0026ge;\u0026thinsp;84 for 6 hours per day, b) a duration of the event of 5 to 7 days, and c) an accumulation of hours with THI\u0026thinsp;\u0026ge;\u0026thinsp;79 of 33 to 65 during the entire event.\u003c/p\u003e \u003cp\u003eMild HW: when during three or more consecutive days the minimum air temperatures were equal or higher than the p95 of the Tmin, or the maximum air temperatures were equal to or higher than the p95 of the TMAX (only one of the two criteria), with at least 5 hours of possible recovery of normothermia (THI\u0026thinsp;\u0026le;\u0026thinsp;72 of 3 to 8 hours a day).\u003c/p\u003e \u003cp\u003eNo HW: when none of the above criteria were met.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurements in heifers\u003c/h2\u003e \u003cp\u003eIn Year 1, the number of heifers in the Sun treatment was 12 and 11 in the Shade treatment, while in Year 2 each treatment consisted of eight heifers during the entire experimental period. Every 21 days the heifers were weighed without fasting on an electronic scale with a precision of 0.5 kg. Average daily weight gain (ADG) (kg/animal/day) was determined by linear regression of the evolution of body weight (BW) within each period.\u003c/p\u003e \u003cp\u003eThe vaginal temperature is an indicator of thermal balance in cattle and can be used to evaluate the impact of heat stress on thermoregulation and animal welfare (Cvetkovic et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Each summer, eight heifers per treatment were fitted with an intravaginal device (CIDR) of inert silicone to which a Kooltrak temperature sensor (17.4 mm diameter; 3.3 g and \u0026plusmn;\u0026thinsp;0.5\u0026deg;C precision; i-Buttons Thermochron DS1921G-F5, Texas Instruments, Dallas) was fixed with hypoallergenic tape. Vaginal temperature was registered hourly. The devices were held in a non-irritating antiseptic solution (Dichloro-M-Xylenol diluted to 10%), and before insertion into the vagina the device was sprinkled with oxytetracycline (1 mL). After 21 days in the vagina of the heifer, the device was retrieved for data extraction, disinfected, and inserted in the vagina of another heifer of the same treatment, to keep collecting data. These procedures were approved by the \u003cem\u003eComisi\u0026oacute;n de \u0026Eacute;tica en el Uso de Animales\u003c/em\u003e (CEUA) of the \u003cem\u003eCentro Universitario Regional (CENUR) Litoral Norte\u003c/em\u003e (file N\u0026ordm; 311170-000156-23) and approved by the Board of \u003cem\u003eCENUR Litoral Norte, Universidad de la Rep\u0026uacute;blica\u003c/em\u003e, Uruguay.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eMeasurements in pastures\u003c/h2\u003e \u003cp\u003eGrazing management was done with the continuous presence of animals with a variable stocking rate (Wheeler et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1973\u003c/span\u003e) with fixed (test) animals and put-and-take animals to obtain similar sward heights among plots, and the average sward heights (cm\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) were 9.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45 cm in Year 1 and 9.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33 cm in Year 2. Sward height was measured weekly with 50 measurements on each plot, with a graded measuring device (HFRO sward height stick, Barthram \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Forage allowance (kg DM/kg of BW, Sollenberger et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) was calculated based on the measurement of the amount of forage biomass at the beginning of each period, plus forage growth during the period and stocking rate (test animals and put-and-take animals). The amount of forage biomass (kg DM/ha) in each plot was estimated with the comparative yield method (Haydock and Shaw \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), with 40 subjective determinations per plot in 0.2 by 0.2 m squares, determining forage present in 14 of them. Forage growth rate (kg DM/ha/day) was measured by the cutting of pasture in three mobile grazing exclusion cages per plot every 45 days (Gardner \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1967\u003c/span\u003e; Frame \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1993\u003c/span\u003e) with an area of 0.4 m\u003csup\u003e2\u003c/sup\u003e per cage. In both determinations forage is cut at 1.5-2.0 cm from the soil. Harvested forage is freshly weighed and then dried in an oven with forced air at 60\u0026ordm;C to constant weight.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was done using the GLIMMIX procedure of SAS\u0026reg; University Edition (SAS OnDemand Institute, Cary, CN, 2023 edition). The means were compared using the Tukey test, with a repeated measurements in time and an alpha level of 0.05. The model to analyze forage allowance, summer forage growth rate and daily weight gain (ADG) of the heifers included the effects of \u003cem\u003eyear\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2, Year 1 and Year 2) and \u003cem\u003etreatment\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2, Sun and Shade). For the analysis of vaginal temperature, the Meteorological Conditions (MC, n\u0026thinsp;=\u0026thinsp;4) were added to the moment in which they occurred: Severe HW in December Year 1, Strong HW in February Year 1, Mild HW in February Year 1, and without HW (No HW), as well as six-hour intervals of the day (Interval 04\u0026ndash;09, Interval 10\u0026ndash;15, Interval 16\u0026ndash;21 and Interval 22\u0026thinsp;\u0026minus;\u0026thinsp;03 hours) and the Day in the MC: Day(MC). In order to detect significant differences in the series of black globe temperatures, ARIMA (Auto Regressive Integrated Moving Average) models were adjusted with a seasonal component, and for both treatments the selected model was of the multiplicative type ARIMA(1,0,0)(1,1,1)\u003csub\u003e24\u003c/sub\u003e, \u003cem\u003ei.e.\u003c/em\u003e an autoregressive model with a seasonal component and a 24 hour lag (as hourly data sets for all the experimental days were available).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMeteorological environment\u003c/h2\u003e \u003cp\u003eDaily mean minimum (Tmin) and maximum (TMAX) temperatures of December, January, and February were lower than the time series of Salto (Climate Normal 1991\u0026ndash;2020, INUMET, 2023) for both years (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The Tmin temperatures of Year 2 were lower than Tmin of Year 1. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e includes the monthly extreme maximum of the minimum temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) and the absolute maximum temperatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBased on the information of the time series of Salto (Climate Normal 1991\u0026ndash;2020, INUMET, 2023), the p95 for Tmin was 21.8 \u0026ordm;C and the p95 of TMAX was 35.3 \u0026ordm;C. Based on these thresholds, the occurrence of HW was identified, and then HW were classified according to severity using the criteria of Nienaber et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e): a Severe HW (27/12/2019 to 01/01/2020), a Strong HW (04/02/2020 to 7/02/2020) and a Mild HW (13/02/2020 to 17/02/2020) in Year 1; the rest of the days corresponded to No HW. During Year 2 no heat wave occurred, therefore all the days corresponded to No HW.\u003c/p\u003e \u003cp\u003eThe results of the comparison of the BG temperature series for the Sun and Shade treatments are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Minimum BG temperatures were not different between treatments (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for both Year 1 and Year 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), while during daytime the maximum BG temperatures in the Sun treatment were significantly higher than in the Shade treatment for most of the days. The number of hours per day that the BG temperatures were significantly different (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between treatments ranged from 0 to 9 hours in Year 1 and from 0 to 9 hours in Year 2, according to the day, and always during daytime. When comparing MC within Year 1, the BG temperatures were significantly different between treatments for 0 to 3 h per day during the Severe HW and for 0 to 5 h per day during both the Strong and the Mild HW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Year 1, the highest BG temperatures were concentrated in the Interval from 10 to 15 h and were significantly higher in the Sun treatment than in the Shade treatment. In Year 2, the BG temperatures were different between treatments for more hours per day, which occurred in all Intervals, but mainly during daytime Intervals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the Severe HW the highest BG temperatures of the summer of Year 1 were registered (52 \u0026ordm;C on two consecutive days), but during the five days of this event only 0 to 4 h per day were different between treatments, indicating similar conditions for both treatments. Although the BG temperatures in the other HW were not that high, the same pattern of no difference between the treatments of 0 to 5 h consecutive hours per day was found. This pattern only occurred in some days during No HW in both years. For each MC the HLI was grouped according to severity categories (thermoneutral: \u0026le;70.0, temperate: 70.1 to 77.0, warm: 77.1 to 86.0 and very warm: \u0026ge;86.1) and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the distribution of hours (\u0026plusmn;\u0026thinsp;SD) in the day for each category.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution of the number of hours per day according to HLI category within each MC were different, but not between treatments during the heat waves. During the days of No HW, the Shade treatment had more thermoneutral hours (14 \u003cem\u003evs\u003c/em\u003e 12 h in Year 1 and 23 \u003cem\u003evs\u003c/em\u003e 18 h in Year 2, Sun and Shade, respectively), and few (6 \u003cem\u003evs\u003c/em\u003e 10 h in Year 1, Sun and Shade, respectively) or null (0 \u003cem\u003evs\u003c/em\u003e 2 h in Year 2, Sun and Shade, respectively) warm or very warm hours. The hours of HLI categories warm and very warm (HLI\u0026thinsp;\u0026ge;\u0026thinsp;77.1) were grouped and their proportion (%) for each six-hour interval according to MC and Year are shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage of hours per day with HLI above 77.1 (warm to very warm conditions) according to the six-hours interval of the day (from 04 to 09, 10 to 15, 16 to 21 and 22 to 03 hour), for each treatment (Sun and Shade) and according to the meteorological conditions (Severe HW, Strong HW, Mild HW and No HW) for Year 1 and Year 2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c9\" namest=\"c2\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eYear 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSevere HW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eStrong HW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMild HW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eNo HW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eNo HW\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTreatment\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSun\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eShade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSun\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eShade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eSun\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eShade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eSun\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e\u003cem\u003eShade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cem\u003eSun\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003eShade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval 04\u0026ndash;09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval 10\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval 16\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterval 22\u0026thinsp;\u0026minus;\u0026thinsp;03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDuring intervals 10\u0026ndash;15 and 16\u0026ndash;21 more hours with HLI warm to very warm occurred during heat waves. In the Sun treatment in Year 1, during the Severe HW 68% of the hours corresponded to HLI\u0026thinsp;\u0026ge;\u0026thinsp;77.1 in the Sun and 67% in the Shade, while during the Strong and Mild HW, 56% of the hours in the Sun and 49% of the hours in the Shade corresponded to HLI\u0026thinsp;\u0026ge;\u0026thinsp;77.1. In Year 1, during No HW, more warm and very warm hours compared to Year 2 (Year 1: Sun 48% \u003cem\u003evs\u003c/em\u003e Shade 38%; Year 2: Sun 12% \u003cem\u003evs\u003c/em\u003e Shade 4%).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eHeifers\u003c/h2\u003e \u003cp\u003eVaginal temperature\u003c/p\u003e \u003cp\u003eThe vaginal temperatures were significantly different between treatments (Sun \u003cem\u003evs\u003c/em\u003e. Shade, mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SEM) in Year 2: 38.45 \u0026ordm;C vs. 38.68\u0026ordm;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), but not in Year 1: 38.6 \u0026ordm;C\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05 (overall mean for both treatments, p\u0026thinsp;=\u0026thinsp;0.393). The interaction Interval \u0026times; Treatment for vaginal temperatures was significant both in Year 1 (p\u0026thinsp;=\u0026thinsp;0.028) and Year 2 (p\u0026thinsp;=\u0026thinsp;0.002) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Vaginal temperatures were significantly different among MC (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, Year 1) and among the six-hour intervals (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), as well as Day(MC) (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The interactions MC \u0026times; six-hour interval (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were also significantly different, but not Treatment \u0026times; MC (p\u0026thinsp;=\u0026thinsp;0.167) or Treatment \u0026times; MC \u0026times; six-hour interval (p\u0026thinsp;=\u0026thinsp;0.386) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Year 1, the highest vaginal temperatures were registered in Interval 16\u0026ndash;21 in the Sun treatment (39.4 \u0026ordm;C). During Interval 04\u0026ndash;09, the heifers had the lowest vaginal temperatures (38.1 \u0026ordm;C to 38.3 \u0026ordm;C), which correspond to the hours of the day with a thermoneutral environment. The lowest vaginal temperatures were registered during the night-time (Interval 22\u0026thinsp;\u0026minus;\u0026thinsp;03 and 04\u0026ndash;09), corresponding with the thermoneutral HLI category (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the effect of treatment was not significant in Year 1, the effect of Day(MC) was significant, and therefore the vaginal temperature of heifers in the Sun treatment was significantly higher than those of heifers in the Shade treatment during Intervals 10\u0026ndash;15 and 16\u0026ndash;21. The highest vaginal temperatures were registered during Interval 16\u0026ndash;21 and were significantly higher during Severe and Strong HW, than during Mild and No HW. The lowest vaginal temperatures were registered during Interval 04\u0026ndash;09 on the days of No HW. For each MC the 24-hour evolution of the HLI, the THI and the vaginal temperatures average of the heifers according to treatment (Sun and Shade) are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, during the Severe HW the highest vaginal temperatures occurred, ranging from 38 to 40 \u0026ordm;C during the whole day, for both treatments, although in the Shade treatment the vaginal temperatures were lower. The increase in vaginal temperature showed a lag of several hours in relation to the increase in HLI, which was similar in both treatments, but with lower vaginal temperatures for the heifers in the Shade treatment, even in the hours that the HLI was high. The heifers in the Sun treatment increased their temperature earlier than the heifers in the Shade treatment during the Severe HW and Strong. Maximum vaginal temperatures during the Mild HW were registered during Interval 16\u0026ndash;21 but was less pronounced than for the other two HW as the HLI was less challenging. During No HW the hourly variation of the vaginal temperatures was less related to the HLI.\u003c/p\u003e \u003cp\u003eAverage daily gain\u003c/p\u003e \u003cp\u003eThe ADG of BW (kg/animal/d) was significantly different between treatments in Year 1 (p\u0026thinsp;=\u0026thinsp;0.042), but not in Year 2 (p\u0026thinsp;=\u0026thinsp;0.502). Average daily gain was significantly higher in Year 2 compared to Year 1 (p˂0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePastures\u003c/h2\u003e \u003cp\u003eThe forage growth rate was not different between treatments (p\u0026thinsp;=\u0026thinsp;0.471) or years (p\u0026thinsp;=\u0026thinsp;0.627). The mean sward height was higher in the Sun treatment than in the Shade treatment only in Year 1 (p˂0.001) and was not different in Year 2 (p\u0026thinsp;=\u0026thinsp;0.211) (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, forage allowance (kg DM/100 kg of BW) was not different between treatments (p\u0026thinsp;=\u0026thinsp;0.789) in each year but tended to differ between years (p\u0026thinsp;=\u0026thinsp;0.069).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values (\u0026plusmn;\u0026thinsp;SEM) of forage growth rate (kg DM/ha/d), forage allowance (kg DM/100 kg of BW) and sward height (cm) by Year (1 and 2) and treatment (Sun and Shade)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eYear 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eYear 2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eTreatment\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSun\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eShade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eSun\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eShade\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrowth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForage allowance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.4\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSward height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 \u003cb\u003ea\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 \u003cb\u003eb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ea,b within year indicate significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eMeteorological environment\u003c/h2\u003e \u003cp\u003eThe meteorological environment was different between years as Year 1 had three extreme temperature events (heat waves) and Year 2 had none. This was somewhat unexpected as there is a 94% probability of at least one extreme temperature event per summer when analyzing the maximum and minimum temperatures of a 52-year data series (1971\u0026ndash;2022) for areas of Western Uruguay (Saravia and de Souza \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and in accordance with Rusticucci et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who describe an increase in number of days of Extreme HW (Tmin and TMAX above p90) for the Province of Entre R\u0026iacute;os, Argentina (from 10 to 40 days between 1961 and 2010) during the warm months. The three extreme events during the summer of Year 1 were of different intensity and duration according to the classification of Nienaber et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Specifically, a Severe HW was registered of four days with THI above 84 for 6 to 9 h a day, and above 72 (12 to 24 h a day), even during the night, which complicates nighttime recovery of normothermia (Hahn and Mader \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Hahn \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Nienaber and Hahn \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, THI has not been precise in characterizing heat load in very warm environments, while other indices, such as the HLI, are better predictors of the effect of the meteorological environment on the response of the animal (Silva et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The black globe temperature (BGt), employed as an indirect measurement of the radiant heat load, was used to characterize the thermal environment at open pasture or at under the shade of the trees (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Estimation of heat gain and heat loss through BG needs to be integrated (Berbigier, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1988\u003c/span\u003e), as well as the influence of wind speed and air humidity, to identify the warm and very warm conditions (through the HLI), as this better reflects the increased risk of heat load because of increased radiation and the marginal possibility of heat dissipation by advection of warm and humid air, which reduces the efficacy of thermoregulatory mechanisms (like panting and sweating) (Berman, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The black globe temperature were relatively similar between Sun and Shade during the heat waves, and only differed between treatments for 0 to 3 h per day during the Severe HW and 0 to 5 hours per day during both the Strong and the Mild HW (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the same sense, the HLI shows little variation between treatments during the Severe and the Strong HW (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), particularly regarding the number of hours below the threshold of 70 (thermoneutrality). This indicates that the shade provided by the trees in this experiment were not enough to distinguish the meteorological environment (temperature and air humidity) from the environment of the open pasture during the Severe and Strong HW, and therefore hampering thermoregulatory mechanisms for maintaining thermal balance (Hahn \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This differs from de Souza et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Magalh\u0026atilde;es et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), who, working in warm environments with biometeorological indices including BGt, reported bigger differences between Sun and Shade than found in this experiment. The microclimate changes when trees are included, improving thermal comfort of the animals because of reduced direct solar radiation resulting in a lower heat load, and this is reflected in lower biometeorological indexes based on the black globe temperature and air humidity (Pezzopane et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Shade trees reduce air temperatures by reducing reflected radiation and because of a major role that evaporation of water plays in heat transfer (6 to 9 \u0026ordm;C lower black globe temperatures in the shade than in the sun, depending on the spatial arrangement of the trees, Munka et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, the air temperature at a specific site is the result of the integration of a radiation component (local radiation balance) and an advection component (advection of a warmer mass of air). Of the heat waves in subtropical South America, 73% develop in association with an active South Atlantic Convergence Zone (SACZ, Cerne and Vera \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The development of anticyclonic circulation result in an increase of temperature in the subtropical region, weakening the SACZ activity and producing a progressive increase in temperature in the region, dominated by warmer and more humid air, with anomaly advection from the North (Cerne y Vera 2011). Analysis of the atmospheric behavior of patterns of circulation that determine the occurrence of heat waves in Salto indicated that the persistence of surface winds in the North of Uruguay with high humidity, is boosted by the location of this region West of the High-Pressure System of the South Atlantic (Caffera and Salaberry \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). During the summer of 2019\u0026ndash;2020, the dominant wind direction during the Severe and Strong HW was N-NW with a mean daily wind speed of 8 to 14 m/s, mainly during the Interval 10\u0026ndash;15, which is consistent with synoptic situation described above. This stresses the importance of the effect of advection of warm and humid air from other regions North of Uruguay, as indicated by Cerne et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) for the Extreme HW in Rosario, Argentina, in January 2006. Our results confirm the importance of the SACZ activity in the induction of anomalies in air circulation in subtropical regions, which can lead to persistent heat waves with very high daily temperatures (Cerne and Vera \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The events Severe HW and Strong HW presented this kind of anomalous circulation, which explains why the shade of the wooded areas (number and density of trees) was not enough to reduce the HLI in this treatment compared to the Sun Treatment. In this study, the HLI measured in two summers differentiated the conditions of the environments for heifers with or without access to natural Shade only in days of No HW (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), while during the extreme events the number of warm and very warm hours was similar between treatments (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePhysiological variable\u003c/h2\u003e \u003cp\u003eThe variation of vaginal temperature during the Severe HW was related to the variation of HLI, but with a time lag of several hours (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). During the day, cattle accumulate heat by increasing body temperature, which is dissipated during the night. However, if nighttime recovery is insufficient, the animal starts the day with an accumulated heat load as a carry-over effect from the previous day increasing susceptibility to heat stress at lower HLI values (Gaughan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The thermoregulatory strategy of the heifers in the Sun treatment was to increase heat load by absorbing radiation, while in the Shade treatment part of this radiation was intercepted by the tree canopy and air temperature was lower because of evapotranspiration of the leaves, leading to more comfortable HLI for the heifers. The distribution of the different HLI categories on the average day of each MC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) and along the hours of the day (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) shows the challenge to which the animals were subjected to maintain thermal balance during extreme temperature events when no protective measure such as shade was available. The difference between the Sun and Shade treatments in daily hours were like other studies which included biometeorological indexes based on black globe temperatures (de Souza et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Magalh\u0026atilde;es et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The highest vaginal temperatures were registered during interval 16\u0026ndash;21 in all HW (39.4\u0026ordm;C, 39.3\u0026ordm;C, 38.7\u0026ordm;C, in Severe, Strong, Mild HW respectively, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) in Year 1, but only during Severe and Strong HW the vaginal temperatures were above normothermia of heifers; above the threshold of 38.9\u0026ordm;C described by Finch (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). This is consistent with the delay in increase in heat load in response to the HLI categories warm and very warm which begins during interval 10\u0026ndash;15 and is maintained during interval 16 a 21(in al HW Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), when increases in the vaginal temperature become evident during interval 16\u0026ndash;21 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Similar responses have been seen in cows when panting was increased several hours after the highest HLI (Kaufman et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and elevated conditions of air temperature and humidity increased vaginal temperatures in dry cows, but more in Hereford cows compared to their crosses with Bonsmara (Fedrigo et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the vaginal temperatures were within the range of normothermia (37.5 to 38.9\u0026ordm;C) during Mild HW, No HW Year 1 and No HW Year 2 (Finch \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), possibly because of nighttime recovery (very low percentage of hours with HLI\u0026thinsp;\u0026ge;\u0026thinsp;77.1 during interval 4\u0026ndash;9), which was limited or absent in the Severe and Strong HW. During Year 2 during all hours of the day the vaginal temperatures of the heifers of the Sun treatment were lower than those of the Shade treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), which is in agreement with other reports (Muller et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Saravia \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), and heifers in the Shade treatment also experienced less diurnal temperature variation. The thermoregulatory mechanisms of animals exposed to the sun remain active because of overstimulation and cause a decrease of body temperature, even in environments of thermal comfort (Johnson and Vanjonack \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1976\u003c/span\u003e). Others explain these findings as an advantage of cooling directly under an open nighttime sky on the open grasslands. (Peent et al. 2021). In this experiment the BGt did not differ significantly between the Sun and Shade treatments during nighttime and therefore the vaginal temperatures are probably due to thermoregulation physiology of the species and category (Wang et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eProductive performance\u003c/h2\u003e \u003cp\u003eIn Year 1, heifers in the Shade treatment gained more weight than the heifers in the Sun treatment (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which cannot be explained by forage allowance, which was not different between the treatments (Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, differences in ADG between heifers in the Sun and Shade treatment could be a consequence of the different meteorological environments as expressed in the different HLI, particularly during the 70 days of No HW (Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The heat load of animals can be reduced up to 30% because of the physical interception of solar radiation by natural or artificial shade (Blackshaw and Blackshaw \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the summer season, the effects of high temperatures are evident in the reduced performance of the animals (lower ADG) because of lower feed intake as well as increased energy expenditure (increased metabolic rate and heat dissipation mechanisms) (Fox et al. 1998; Pigurina et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; CSIRO \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; O\u0026rsquo;Brien et al. 2010). These negative effects on energy balance reduce the energy available for growth, which is reflected in reduced ADG (St-Pierre et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Gaughan et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In warm climates, heifers with access to natural shade in silvopastoral systems had lower rectal temperatures, leading to an increase in food intake and a higher body weight (Lemes et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Access to natural shade during warm conditions of sumer in the North of Uruguay has resulted in higher ADG in growing heifers and steers (Simeone et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Beretta et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eForage allowance was not restrictive in any of the two years and tended to be higher in Year 2, which explains the higher ADG of the heifers in Year 2 compared to Year 1. The summer forage growth rates reported for similar plant communities (17.2 kg DM/ha/d Berretta and Bemhaja \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) are lower than observed in this study. The higher forage growth rates in this study can be attributed to the defoliation management based on sward height, with a target range of 6 to 12 cm, with a variable stocking rate (Wheeler et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1973\u003c/span\u003e). Forage allowance in this study was at a level that maximizes animal performance (weight gain) in growing cattle (heifers) (Carvalho \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), especially in Year 2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe extreme temperature events (Severe and Strong HW as a result of anomaly air circulation) during nine days in the summer of Year 1 induced hyperthermia in the heifers along with similar hours of HLI in the categories of warm and very warm, and few hours of thermoneutral temperatures, affecting recovery of normothermia. In the conditions of this experiment, the shade provided was not enough to ensure thermal comfort to the heifers.\u003c/p\u003e \u003cp\u003eDuring the environmental conditions of Mild HW and No HW (seventy-five days of the summer), the HLI was mainly in the categories of thermoneutral and temperate in both treatments, but for more hours in the Shade treatment. This may have improved the welfare of the heifers with access to Shade and would explain the higher weight gain compared to the heifers of the Sun treatment, which needed more energy for thermoregulation.\u003c/p\u003e \u003cp\u003eIn Year 2, the predominant conditions were that of thermoneutrality, and these conditions of comfort allowed the heifers to maintain normothermia during a major part of the day in both treatments, resulting in similar energy partitioning and weight gain.\u003c/p\u003e \u003cp\u003eCombined, these findings stress the need to include natural shade in grassland cattle production improving animal welfare during the warm days of summer, and the HLI appears to be a better predictor of physiological and productive responses to extreme temperature events. Which is able to contribute to minimizing production losses in a scenario of increasing variability and climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCelmira Saravia: Project administration, Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Supervision, Resources, Funding acquisition; Elize van Lier: Conceptualization, Data curation, Writing - original draft, Supervision, Funding acquisition;Carolina Munka: Resources, Conceptualization, Methodology, Validation, Data curation, Investigation;Oscar Bentancur: Methodology, Validation, Formal analysis; Rodrigo Iribarne: Investigation, Formal analysis;Ricardo Rodr\u0026iacute;guez Palma: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Supervision, Resources; Laura Astigarraga: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing - original draft, Supervision, Resources, Funding acquisition\u003c/p\u003e\u003ch2\u003eAcknowledgements:\u003c/h2\u003e \u003cp\u003ePartial financial support was received from Estaci\u0026oacute;n Experimental de Facultad de Agronom\u0026iacute;a Salto, Author C. Saravia has received research support from Comisi\u0026oacute;n Central de Dedicaci\u0026oacute;n Total, Universidad de la Rep\u0026uacute;blica, Uruguay. The authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBarthram GT (1986) Experimental techniques: the HFRO sward stick. In Alcock MM (ed) The Hill Farming Research Organization Biennial Report 1984-1985. Haddington, U.K. pp. 29-30.\u003c/li\u003e\n\u003cli\u003eBeco\u0026ntilde;a G, Casella M (1999) Efecto de la sombra sobre el comportamiento animal en terneros Holando y Hereford en el per\u0026iacute;odo estival. Tesis Ing. Agr. Facultad de Agronom\u0026iacute;a, Universidad de la Rep\u0026uacute;blica, Uruguay. Montevideo. 97 p.\u003c/li\u003e\n\u003cli\u003eBeede DK, Collier RJ (1986) Potential nutritional strategies for intensively managed cattle during thermal stress. 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Global and Planetary Change 100:295-307 https://doi.org/10.1016/j.gloplacha.2012.11.004.\u003c/li\u003e\n\u003cli\u003eSollenberger LE, Moore JE, Allen VG, Pedreira CGS (2005) Reporting forage allowance in grazing experiments. Crop Science 45:896-900 https://doi.org/10.2135/cropsci2004.0216\u003c/li\u003e\n\u003cli\u003eSt-Pierre NR, Cobanov B, Scnitkey G (2003) Economic losses from heat stress by US Livestock Industries. Journal of Dairy Science 86(E. suppl.): E52-E77 https://doi.org/10.3168/jds.S0022-0302(03)74040-5\u003c/li\u003e\n\u003cli\u003eThom E (1959) The discomfort index. Weatherwise, 12:57-59 https://doi.org/10.1080/00431672.1959.9926960 \u003c/li\u003e\n\u003cli\u003eWang J, Li J, Wang F, Xiao J, Wang Y, Yang H, Li S, Cao Z (2020) Heat stress on calves and heifers: a review. Journal of Animal Science and Biotechnology 11:79 https://doi.org/10.1186/s40104-020-00485-8\u003c/li\u003e\n\u003cli\u003eWheeler JL, Burns JC, Mochrie RD, Gross HD (1973) The choice of fixed or variable stocking rates in grazing experiments. Experimental Agriculture 9: 289-302 https://doi.org/10.1017/S0014479700010085.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"heat stress, animal welfare, biometeorology index, vaginal temperature, silvopastoral systems","lastPublishedDoi":"10.21203/rs.3.rs-3913892/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3913892/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quantification of the environmental conditions to predict the effect of extreme events (heat waves: HW), is important in animal welfare and performance. The aim of this study was to evaluate the impact of the meteorological environment on physiological and productive variables on heifers, either with or without access to natural shade on rangelands, using the Heat Load Index (HLI), a biometeorological index that allows the comparison between environments. The experiment was carried out at Estaci\u0026oacute;n Experimental de la Facultad de Agronom\u0026iacute;a en Salto, Uruguay, during two summers (Year 1, Year 2). The treatments were voluntary access to natural shade (Shade) and full sun (Sun). Three HW: Severe, Strong, Mild and a not HW (NHW) occurred in Year 1, but only the latter in Year 2. The HLI categories warm and very warm (HLI\u0026thinsp;\u0026ge;\u0026thinsp;77.1) daily hours percentages were 68 and 67 during Severe HW, 56 and 49 in Strong\u0026thinsp;+\u0026thinsp;Mild HW, 48 and 38 in NHW in Year 1 and 12 and 4 in NHW in Year 2, in the Sun and Shade treatment, respectively. During Severe and Strong HW, the Shade was not beneficial because the animal experimented thermoneutrality only for a few hours. During Mild HW and NHW, the HLI in the Shade was mainly thermoneutral and temperate, which would explain the higher animal weight gain, compared to the Sun. In Year 2, the predominant conditions were thermoneutral, with heifers maintaining normothermia during a major part of the day in both treatments, resulting in similar weight gain.\u003c/p\u003e","manuscriptTitle":"Heat load index during heat waves as an indicator of thermal comfort of Hereford heifers with access to natural shade on native grasslands","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 11:01:51","doi":"10.21203/rs.3.rs-3913892/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-05-16T12:02:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-05-13T07:48:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10759645-05c8-4f3d-9095-a3bcc0e17728","date":"2024-04-01T15:22:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-20T15:47:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"67d573eb-7357-4cd6-b54b-b8cc08df92b4","date":"2024-02-09T14:59:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-02-07T08:21:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-02T17:27:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-02-01T07:25:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2024-01-31T12:51:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"80baafe4-efea-494d-ac58-21a0b26383f6","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-22T19:31:52+00:00","versionOfRecord":{"articleIdentity":"rs-3913892","link":"https://doi.org/10.1007/s10457-024-01039-x","journal":{"identity":"agroforestry-systems","isVorOnly":false,"title":"Agroforestry Systems"},"publishedOn":"2024-08-16 15:57:52","publishedOnDateReadable":"August 16th, 2024"},"versionCreatedAt":"2024-02-02 11:01:51","video":"","vorDoi":"10.1007/s10457-024-01039-x","vorDoiUrl":"https://doi.org/10.1007/s10457-024-01039-x","workflowStages":[]},"version":"v1","identity":"rs-3913892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3913892","identity":"rs-3913892","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}