J. Dairy Sci. 92:109–116 doi:10.3168/jds.2008-1370 © American Dairy Science Association, 2009. Is the temperature-humidity index the best indicator of heat stress in lactating dairy cows in a subtropical environment? S. Dikmen* and P. J. Hansen†1 *Department of Animal Science, Faculty of Veterinary Medicine, University of Uludag, Bursa, 16059, Turkey †Department of Animal Sciences, University of Florida, Gainesville 32611-0910 ABSTRACT environmental conditions to the magnitude of hyper- thermia experienced by heat-stressed cows. Several temperature-humidity indexes (THI) have Key words: temperature-humidity index, dairy cattle, been used to estimate the degree of thermal stress ex- rectal temperature, heat stress perienced by dairy cows. The present objectives were to develop equations using meteorological variables that predicted rectal temperature of lactating cows in INTRODUCTION a subtropical environment and compare the goodness Heat stress has adverse effects on milk production of fit of these equations to those using 8 different THI. and reproduction of dairy cattle (Kazdere et al., 2002; Rectal temperature was measured between 1500 and West, 2003; Hansen, 2007). As noted by Hansen (2007), 1700 h in 1,280 lactating Holstein cows in north central the problem of heat stress is a growing one because Florida between August and December. Meteorologi- increases in milk yield result in greater metabolic heat cal data recorded in the barn where cows were located production and because of anticipated changes in the included dry bulb temperature (Tdb), relative humidity global climate. The magnitude of heat stress, defined (RH), dew point temperature, and wind speed. Wet here as the sum of forces external to the animal that act bulb temperature was calculated. In the first series of to displace body temperature from set point, is caused analyses, regression analysis was used to model rectal by the combined effects of dry bulb temperature (Tdb), temperature using the meteorological variables as well 2 humidity, solar radiation, and wind speed (WS). A va-as THI. The r using Tdb (0.41) was slightly less than riety of indices were used to estimate the degree of heat for models using all but one THI (r2 between 0.42 and 2 stress affecting cattle and other animals. The most com-0.43). The r for equations using Tdb could be improved 2 2 2 mon of these, the temperature-humidity index (THI), by adding RH (r = 0.43) or RH and RH (r = 0.44) uses Tdb and wet bulb temperature (Twb) to estimate to the model. In the second analysis, regression analy- the magnitude of heat stress (Thom, 1959). Conceptu- sis was performed using forward selection, backward ally, it is difficult to ascertain whether THI is the most elimination, and stepwise selection procedures with appropriate measurement of heat stress in dairy cattle. the meteorological variables. All models gave a similar There was no explanation given in the original paper goodness of fit (r2 = 0.44). An analysis of variance with for the basis of the relative weighting of Tdb and Twb in rectal temperature as a class variable was performed the THI. Other THI were formulated empirically and to determine the least squares means of meteorological often without reference to body temperatures of cattle. measurements associated with hyperthermia. A Tdb of Nonetheless, the original THI and several variations of 29.7°C was associated with rectal temperature of 39°C, it have been used extensively to estimate the degree of and a Tdb of 31.4°C was associated with rectal tempera- heat stress in dairy and beef cattle (Mader et al., 2006; ture of 39.5°C. In conclusion, Tdb is nearly as good a Bohmanova et al., 2007; Morton et al., 2007). predictor of rectal temperatures of lactating Holsteins Despite not being formulated using cow data, THI in a subtropical environment as THI. Estimates of val- are related to body temperatures of cattle exposed to ues of meteorological variables associated with specific heat stress (Ingraham et al., 1979; Buffington et al., rectal temperatures should prove valuable in relating 1981; Gaughan et al., 2008). Recently, Bohmanova et al. (2007) showed that various THI were predictive of milk yield in cows in the southeastern United States. It is not obvious that THI are better predictors of body Received May 15, 2008. temperature in heat-stressed cows than other measure- Accepted September 2, 2008. ments of environmental conditions. In addition, it is 1 Corresponding author: hansen@animal.ufl.edu possible that regression analysis using actual data on 109 110 DIKMEN AND HANSEN environmental conditions and cow body temperature Calculation of THI can result in development of heat stress indices that are better predictors of heat stress than THI. Wet bulb temperature was derived using the Psy- There were 2 objectives of the current study. The first Func program (Linric Co., Bedford, NH) that included was to compare the effectiveness of THI models cur- a package of psychrometric function programs for use rently being used to predict heat stress in dairy cattle with Excel 7.0 or greater (Microsoft, Redmond, WA). with other prediction equations based on environmental Values for altitude and barometric pressure for Gaines- variables. The second was to calculate values of various ville, Florida, provided to the program were 30 m and meteorological variables associated with specific rectal 101.325 kPa, respectively. temperatures to allow prediction of cow body tempera- A total of 8 THI were calculated as follows: ture under various environmental conditions. THI1 = (1.8 × Tdb + 32) − [(0.55 − 0.0055 × RH) MATERIALS AND METHODS × (1.8 × Tdb − 26.8)] Data Collection (NRC, 1971); The experiment was conducted with lactating Hol- THI2 = T + 0.36 × T + 41.2 stein cows at 3 dairies: the University of Florida Dairy db dp Research Unit (Hague, FL; 29°46′N and 82°24′ W), (Yousef, 1985); Alliance Dairy (Trenton, FL; 29°35′N and 82°51′W), and Hilltop Dairy (Trenton, FL; 29°35′N and 82°52′W). In each farm, cows were housed in free-stall barns THI3 = (0.35 × Tdb + 0.65 × Twb) × 1.8 + 32 that were equipped with fans and sprinklers. Rectal temperature was measured in 1,280 lactating Holstein (Bianca, 1962); cows between August and December 2007. Rectal tem- peratures were recorded manually between 1500 and THI4 = (0.55 × Tdb + 0.2 × Tdp) × 1.8 + 32 + 17.5 1700 h using a digital GLA M750 thermometer (GLA Agricultural Electronics, San Luis Obispo, CA). Cows (NRC, 1971); were measured while in head locks in the free-stall barn and while under shade. Various environmental THI5 = (0.15 × Tdb + 0.85 × Twb) × 1.8 + 32 measurements were taken in the barns where cows were housed. Measurements of T (Bianca, 1962);db, relative humidity (RH), dew point temperature (Tdp), and black globe temperature (Tbg) were measured at 1-min intervals THI6 = [0.4 × (Tdb + Twb)] × 1.8 + 32 + 15 between 1500 and 1700 h using a HOBO-U12 data log- ger (Tdb, RH, and Tdp) and a HOBO Water Temp Pro (Thom, 1959); V2 data logger (Tbg; Onset Company, Bourne, MA) that were both located in the center of the barn at 2 THI7 = (Tdb + Twb) × 0.72 + 40.6 m from the ground. Rectal temperature was matched with the measurements of T , RH, T , and T (NRC, 1971); anddb dp bg to the nearest minute at which environmental variables were recorded. Wind speed was measured at the time rectal THI8 = (0.8 × Tdb) + [(RH/100) temperature was measured using an Extech AN400 cup × (Tdb − 14.4)] + 46.4 thermo-anemometer (Extech, Melrose, MA) positioned within arm’s length of the cow being examined for (Mader et al., 2006). rectal temperature and at a height of ~2 m from the Following the use by Bohmanova et al. (2007), tem- ground. Retrospectively, farm, parity, and DIM were peratures were in degrees Celsius for calculation of all recorded for each cow. In addition, milk yield data at THI. the closest test date was available for a subset of 822 cows. For cows included in the study, milk yield aver- Statistical Analysis aged 24.8 kg/d (range 4.5 to 44.0 kg/d), parity aver- aged 2.3 (range 1 to 8), and DIM averaged 186 (range Regression analysis was performed using the PROC 7 to 737). GLM procedure of SAS (version 9.1, SAS Institute Inc., Journal of Dairy Science Vol. 92 No. 1, 2009 TEMPERATURE-HUMIDITY INDEX VERSUS OTHER MEASUREMENTS 111 Cary, NC) with rectal temperature as the dependent added to the BM or when added to a model including variable. Each model included effects of parity, stage farm effects, but not stage of lactation. Accordingly, of lactation (separated into 3 stage of lactation classes: milk yield was not included in further analyses. DIM <100, DIM between 100 and 200, and DIM >200), Addition of a single meteorological variable (Table and farm as class variables. These terms as well as all 1) increased the r2, with the greatest improvement oc- interactions between these terms were included in all curring for addition of T 2db (r = 0.41), with smaller mathematical models and are referred to as the base improvements for Twb (r 2 = 0.36) and RH (r2 = 0.29), model (BM). Regression analyses were performed us- and with little improvement when adding Tdp or WS 0.5. ing the BM with various combinations of environmen- There was little or no improvement in r2 when adding tal variables as continuous variables chosen a priori. the quadratic and cubic functions of any single me- These variables included T , RH, T 0.5db dp, WS , Twb, 8 teorological variable to the regression equation (Table separate THI, and the quadratic and cubic functions 1). In addition to models considering a single environ- of these variables. The term WS0.5 was used instead of mental variable, other models were fitted that included WS because of previous findings that it represents the combinations of environmental variables. The simplest best fit for predicting heat flow from WS (Léger and model with the greatest fit included Tdb and the linear Larochelle, 2006) and because the r2 values obtained and quadratic effects of RH (r2 = 0.44). from our analyses were greater for WS0.5 than for WS. A second series of regression analysis was performed Analyses were performed with Tbg. The Tbg were very using forward selection, backward elimination, and similar to the Tdb as were results of regression analysis stepwise selection procedures with parity, stage of lac- with Tbg and so data are not presented. tation, farm, Tdb, RH, Tdp, WS 0.5, and Twb as variables In a second series of analyses, stepwise regression in the model. All selection procedures gave an r2 = analyses were performed using the PROC REG pro- 0.44. In the forward selection model, the terms retained cedure of SAS. Variables in the initial model included in the model were farm, parity, stage of lactation, Tdb, parity, stage of lactation, farm, Tdb, RH, Tdp, WS 0.5, and RH, Tdp, and Twb, whereas in the backward and step- Twb. Analyses were performed using forward selection, wise selection models, farm, parity, stage of lactation, backward elimination, and stepwise selection methods. RH, Tdp, and Twb remained in the model. Variables with significance level <0.15 were retained in the model. Comparison of THI with Other Models The PROC GLM procedure of SAS was used to esti- mate the mean and standard deviation of Tdb, Tdp, Twb, Graphs illustrating the relationship between rectal and each of the THI that were associated with rectal temperature and THI1 and THI3 (shown as representa- temperatures of 38.5, 39.0, 39.5, 40.0, 40.5, and 41.0°C. tives of THI) are in Figure 2. The estimations of r2 for The mathematical model included Tdb, Tdp, Twb, or THI modeling rectal temperature using THI are in Table 1. as the dependent variable and the BM and rectal tem- Except for THI5, the r2 values were similar for all THI perature as independent class variables. Least squares and ranged between 0.42 and 0.43. The r2 for THI5 means for each independent variable were calculated was lower than for other THI (r2 = 0.39) because of for each rectal temperature. the greater importance of Twb for THI5. Addition of the quadratic and cubic functions of THI did not cause a substantial increase in r2 (results not shown). Simi- RESULTS larly, addition of other meteorological variables to THI Comparison of Various Environmental Variables models did not result in a large change in goodness for Modeling Rectal Temperature of fit. The largest r 2 for the most complicated model, including the BM, THI, THI2, T 2db, Tdb , RH, RH 2, and The range of meteorological variables measured in the RH3 was 0.45 for all THI models. course of the experiment and their correlation to rectal Simple correlations between meteorological values temperature is in Figure 1. The r2 obtained from regres- are shown in Table 2. Note that there was a correlation sion analyses for modeling rectal temperature with vari- between Tdb with THI1 to THI8 (r = 0.84 for THI5 and ous environmental variables are in Table 1. The r2 value r ≥ 0.92 for other THI). Moreover, correlations between for the BM only (farm, parity, stage of lactation, and THI were r = 0.932 to 0.99998. These correlations were interactions) was 0.24. Initial analyses indicated that the reason all of these variables gave a similar goodness there was no significant effect of milk yield (entered of fit when included in regression analyses for rectal as a continuous variable) on rectal temperature when temperature. Journal of Dairy Science Vol. 92 No. 1, 2009 112 DIKMEN AND HANSEN Figure 1. Relationship between rectal temperature and meteorological measurements. Points represent individual observations, lines rep- resent simple linear regression equations, and r represents the simple correlation coefficient. Note that the negative correlation between rectal temperature and relative humidity reflects a correlation of r = −0.744 between dry bulb temperature and relative humidity. Journal of Dairy Science Vol. 92 No. 1, 2009 TEMPERATURE-HUMIDITY INDEX VERSUS OTHER MEASUREMENTS 113 Table 1. Coefficients of determination for predicting rectal temperature using various environmental variables Model1 r2 Base model (BM) 0.24 BM + Tdb 0.41 BM + Tdb + T 2 db 0.41 BM + Tdb + T 2 db + T 3 db 0.41 BM + RH 0.29 BM + RH + RH2 0.29 BM + RH + RH2 + RH3 0.32 BM + Tdp 0.27 BM + Tdp + T 2 dp 0.28 BM + Tdp + T 2 dp + T 3 dp 0.28 BM + WS0.5 0.25 BM + WS0.5 + (WS0.5)2 0.25 BM + WS0.5 + (WS0.5)2 + (WS0.5)3 0.26 BM + Twb 0.36 BM + Twb + T 2 wb 0.37 BM + Twb + T 2 wb + T 3 wb 0.38 BM + Tdb + RH + Twb 0.43 BM + Tdb + RH + RH 2 + Twb 0.44 BM + Tdb + RH 0.43 BM + T 2db + RH + RH 0.44 BM + Tdb + RH + RH 2 + RH3 0.44 BM + Tdb + Tdp + T 2 dp + T 3 dp 0.43 BM + T + T + T 2 + T 3+ WS0.5 0.5 2 0.5 3db dp dp dp + (WS ) + (WS ) 0.43 BM + Tdb + T 2 db + RH 0.43 BM + Tdb + T 2 db + Tdp 0.43 BM + T 2 0.5db + Tdb + WS 0.41 BM + Tdb + RH + Tdp + Twb 0.44 BM + THI1 0.43 BM + THI2 0.42 BM + THI3 0.42 BM + THI4 0.43 BM + THI5 0.39 BM + THI6 0.43 BM + THI7 0.43 BM + THI8 0.43 1Base model includes adjustments of rectal temperature for parity, stage of lactation, farm, and all interactions between parity, stage of lactation, and farm. Tdb = dry bulb temperature; RH = relative humidity; Tdp = dew point temperature; Twb = wet-bulb temperature; WS = wind speed; THI = temperature-humidity index. Predicted Values of Environmental Variables the amount of latent heat loss; wind speed, which af- That Cause Hyperthermia fects rate of sensible and latent heat loss; and radia- An ANOVA was used to estimate the least squares tion. Estimates of the degree of heat stress affecting means and standard errors of T , T , and THI as- animal regulation of body temperature were made by db wb sociated with average rectal temperatures of 38.5, 39.0, developing mathematical formulae that combine one or 39.5, 40.0, 40.5, and 41.0°C. Results are in Table 3. more meteorological variables. Here we show, that for lactating dairy cows in a subtropical environment, the DISCUSSION most commonly used indices, collectively termed THI, explain much of the variation between cows in rectal The ability of homeotherms to successfully maintain temperature. At a practical level, the predictive value a constant body temperature can be compromised when of THI is only slightly better than Tdb alone, because environmental conditions limit the loss of metabolic there were very high correlations between Tdb and THI1 heat or contribute to the heat load of the animal (i.e., to THI8. Thus, little is to be gained in terms of evaluat- when surrounding temperature is greater than surface ing an environment by using THI instead of Tdb. temperature or heat is gained from other objects by Data for the current study were collected in a sub- radiation). These environmental conditions include not tropical environment in which high humidity was a only Tdb, which affects sensible heat loss via conduction prevailing characteristic (Figure 1). It is possible that and convection, but also humidity, because it affects the effectiveness of various heat stress indices will vary Journal of Dairy Science Vol. 92 No. 1, 2009 114 DIKMEN AND HANSEN where cows received extensive shade (Hansen, 1990). In environments characterized by intense solar radiation, it is likely that use of Tbg will improve ability of heat stress indices to predict rectal temperature (Buffington et al., 1981). It was expected that regulation of body temperature during hyperthermia would be decreased as milk yield increased because of the metabolic heat output associ- ated with lactation. This was demonstrated experimen- tally in dairy cows in Israel (Berman et al., 1985) and theoretically using mathematical models of heat balance in dairy cows (Berman, 2005). In addition, the summer depression in fertility in dairy cows was more pro- nounced for cows with greater milk yield (Al-Katanani et al., 1999). Given these observations, it was a surprise that there was no significant relationship between milk yield and rectal temperature. Perhaps, the failure to find a relationship was because cows that have greater capacity for regulation of body temperature, through genetic inheritance (Ravagnolo and Misztal, 2000) or otherwise, may have greater milk yield as a result. Al- ternatively, because milk yield was not measured on the day of rectal temperature measurement, this may have obscured an effect of milk yield. Temperature humidity indices are often placed into classes to indicate the degree of heat stress (Armstrong, 1994; Mader et al., 2006). The terms used to describe these classes and the ranges of THI used to define each class are arbitrary. In the current study, average me- teorological variables were determined associated with specific rectal temperatures. This approach has the ad- Figure 2. Relationship between rectal temperature and tempera- vantage over previous classifications in that the specific ture humidity index (THI)1 and THI3. Points represent individual rectal temperature likely experienced by animals can observations, lines represent simple linear regression equations, and r represents the simple correlation coefficient. be estimated at various environmental conditions. This information can be combined with experimental data indicating at which body temperature deviations in milk yield and reproduction occur to more accurately assess with climate. Using milk yield data as an end point, thermal environment and its effect on animal health Bohmanova et al. (2007) demonstrated that THI that and production. For example, it was estimated that weighted humidity more heavily were better for cattle conception rate declined by 12% for each 0.5°C increase in Georgia, where humidity is high, whereas THI that in uterine temperature above 38.6°C (Gwazdauskas weighted humidity to a lesser degree were better for et al., 1973). Accordingly, decreased fertility could be cattle in Arizona, where humidity is low. Wind speed expected at environmental conditions causing a rectal was not an important determinant of rectal tempera- temperature of 39°C. Data in Table 1 indicate such a ture in the current study, although forced ventilation rectal temperature would occur at a Tdb of 29.7°C. decreased body temperature in dairy cows (Berman The Tdb at which rectal temperature was 38.5°C et al., 1985). Air velocity was low (<1 m/s) in most was 28.4°C. This value of 28.4°C represents the upper instances, and it is possible that the lack of relationship critical temperature, because hyperthermia would be between air velocity and rectal temperature reflected expected at Tdb above this value. Our estimate of upper a paucity of cows exposed to air velocities sufficient critical temperature is greater than the value of upper to alter thermal balance. Black globe temperature was critical temperature obtained by Berman et al. (1985) not a valuable meteorological trait in the current study, for lactating dairy cows in Israel (25 to 26°C). Simi- because the value was very similar to Tdb (data not larly, our estimate of the upper critical THI (the THI shown). Similar results were seen in other environments at which rectal temperature is 38.5°C) is greater than Journal of Dairy Science Vol. 92 No. 1, 2009 TEMPERATURE-HUMIDITY INDEX VERSUS OTHER MEASUREMENTS 115 Table 2. Simple correlations between environmental variables1,2 Item RH Tdp Twb WS THI1 THI2 THI3 THI4 THI5 THI6 THI7 THI8 Tdb −0.78 0.36 0.74 0.19 0.95 0.98 0.92 0.98 0.84 0.96 0.96 0.94 *** *** *** *** *** *** *** *** *** *** *** *** RH 0.32 −0.16 −0.23 −0.54 −0.63 −0.48 −0.63 −0.31 −0.58 −0.58 −0.54 *** *** *** *** *** *** *** *** *** *** *** Tdp 0.89 −0.10 0.63 0.55 0.69 0.55 0.81 0.60 0.60 0.63 *** *** *** *** *** *** *** *** *** *** Twb 0.02 0.91 0.86 0.94 0.86 0.99 0.89 0.89 0.91 NS *** *** *** *** *** *** *** *** WS 0.11 0.15 0.11 0.15 0.06 0.13 0.13 0.11 *** *** *** *** * *** *** *** THI1 0.99 0.99 0.99 0.96 1.00 1.00 1.00 *** *** *** *** *** *** *** THI2 0.98 1.000 0.93 1.00 1.00 0.99 *** *** *** *** *** *** THI3 0.98 0.98 0.99 0.99 0.99 *** *** *** *** *** THI4 0.93 1.00 1.00 0.99 *** *** *** *** THI5 0.95 0.95 0.97 *** *** *** THI6 1.000 1.00 *** *** THI7 1.00 *** 1Tdb = dry bulb temperature; Tdp = dew point temperature; Twb = wet bulb temperature; WS = wind speed; THI = temperature-humidity index. 2Correlations listed as 1.000 were actually slightly lower than 1. *P < 0.05; ***P < 0.001. the estimates by Bohmanova et al. (2007) for threshold the presence of these devices may have resulted in an THI above which milk yield declined. For example, the increase in upper critical temperature. upper critical THI using THI1 was 78.2°C in the cur- In conclusion, Tdb is nearly as good a predictor of rent study compared with 72°C for cows in Georgia and rectal temperatures of lactating Holsteins in a sub- 74°C for cows in Arizona (Bohmanova et al., 2007). tropical environment as THI. Estimates of values of Perhaps, cows in the current study were more adapted meteorological variables associated with specific rectal to heat stress or other features of their housing de- temperatures should prove valuable in relating environ- creased the magnitude of heat stress. In addition, cows mental conditions to the magnitude of hyperthermia were in free-stall barns with fans and sprinklers, and experienced by heat-stressed cows. Table 3. Least squares means (±SE) for environmental variables at various rectal temperatures1 Rectal Tdb (°C) Tdp (°C) Twb (°C) THI1 THI2 THI3 temperature (°C) Mean SE Mean SE Mean SE Mean SE Mean SE Mean SE 38.5 28.4 0.36 21.2 0.32 23.2 0.21 78.2 0.37 77.3 0.39 77.1 0.41 39 29.7 0.31 21.3 0.28 23.7 0.19 79.6 0.32 78.6 0.34 78.5 0.36 39.5 31.4 0.36 22.3 0.31 24.7 0.21 81.5 0.36 80.6 0.38 80.7 0.41 40 32.2 0.48 22.5 0.42 25.0 0.29 82.3 0.49 81.5 0.51 81.6 0.55 40.5 34.1 0.64 22.2 0.56 25.3 0.38 83.6 0.65 82.3 0.68 83.1 0.73 41 35.2 1.18 22.1 1.02 25.6 0.70 84.6 1.18 83.4 1.25 84.1 1.30 THI4 THI5 THI6 THI7 THI8 38.5 85.3 0.38 75.2 0.39 84.2 0.36 77.8 0.36 78.1 0.36 39 86.6 0.33 76.3 0.34 85.5 0.31 79.1 0.31 79.5 0.31 39.5 88.6 0.38 78.3 0.38 87.4 0.36 81.0 0.36 81.4 0.36 40 89.5 0.51 79.1 0.51 88.2 0.48 81.8 0.48 82.2 0.48 40.5 91.3 0.67 80.0 0.68 89.8 0.64 83.4 0.64 83.5 0.64 41 92.3 1.23 80.7 1.26 90.8 1.17 84.4 1.17 84.4 1.17 1Tdb = dry bulb temperature; Tdp = dew point temperature; Twb = wet bulb temperature; THI = temperature-humidity index. Journal of Dairy Science Vol. 92 No. 1, 2009 116 DIKMEN AND HANSEN ACKNOWLEDGMENTS Gaughan, J. B., T. L. Mader, S. M. Holt, and A. Lisle. 2008. A new heat load index for feedlot cattle. J. Anim. Sci. 86:226–234. Serdal Dikmen was supported by a grant from Gwazdauskas, F. C., W. W. Thatcher, and C. J. Wilcox. 1973. Physiological, environmental, and hormonal factors at insemination TUBITAK-BIDEB, Ankara, Turkey. We thank the fol- which may affect conception. J. Dairy Sci. 56:873–877. lowing individuals for providing access to cows and cow Hansen, P. J. 1990. Effects of coat colour on physiological and milk records: Eric Diepersloot of the University of Florida production responses to solar radiation in Holsteins. Vet. Rec. 127:333–334. Dairy Research Unit, Ron St. John and the employees Hansen, P. J. 2007. Exploitation of genetic and physiological of Alliance Dairy, and Pam Bliss and the employees determinants of embryonic resistance to elevated temperature of Hilltop Dairy. Thanks are expressed to Meghan M. to improve embryonic survival in dairy cattle during heat stress. Theriogenology 68S:S242–S249. Brennan of the Department of Statistics, University of Ingraham, R. H., R. W. Stanley, and W. C. Wagner. 1979. Seasonal Florida, for providing statistical advice. effects of tropical climate on shaded and nonshaded cows as measured by rectal temperature, adrenal cortex hormones, thyroid hormone, and milk production. Am. J. Vet. Res. 40:1792–1797. REFERENCES Kazdere, C. T., M. R. Murphy, N. Silanikove, and E. Maltz. 2002. Heat stress in lactating dairy cows: A review. Livest. Prod. Sci. Al-Katanani, Y. M., D. W. Webb, and P. J. Hansen. 1999. Factors 77:59–91. affecting seasonal variation in 90-day nonreturn rate to first Léger, J., and J. Larochelle. 2006. On the importance of radiative heat service in lactating Holstein cows in a hot climate. J. Dairy Sci. exchange during nocturnal flight in birds. J. Exp. Biol. 209:103– 82:2611–2616. 114. Armstrong, D. V. 1994. Heat stress interaction with shade and cooling. Mader, T. L., M. S. Davis, and T. Brown-Brandl. 2006. Environmental J. Dairy Sci. 77:2044–2050. factors influencing heat stress in feedlot cattle. J. Anim. Sci. Berman, A. 2005. Estimates of heat stress relief needs for Holstein 84:712–719. dairy cows. J. Anim. Sci. 83:1377–1384. Morton, J. M., W. P. Tranter, D. G. Mayer, and N. N. Jonsson. 2007. Berman, A., Y. Folman, M. Kaim, M. Mamen, Z. Herz, D. Wolfenson, Effects of environmental heat on conception rates in lactating dairy A. Arieli, and Y. Graber. 1985. Upper critical temperatures cows: Critical periods of exposure. J. Dairy Sci. 90:2271–2278. and forced ventilation effects for high-yielding dairy cows in a NRC. 1971. A Guide to Environmental Research on Animals. Natl. subtropical climate. J. Dairy Sci. 68:1488–1495. Acad. Sci., Washington, DC. Bianca, W. 1962. Relative importance of dry- and wet-bulb temperatures Ravagnolo, O., and I. Misztal. 2000. Genetic component of heat stress in causing heat stress in cattle. Nature 195:251–252. in dairy cattle, parameter estimation. J. Dairy Sci. 83:2126– Bohmanova, J., I. Misztal, and J. B. Cole. 2007. Temperature-humidity 2130. indices as indicators of milk production losses due to heat stress. Thom, E. C. 1959. The discomfort index. Weatherwise 12:57–59. J. Dairy Sci. 90:1947–1956. West, J. W. 2003. Effects of heat-stress on production in dairy cattle. Buffington, D. E., A. Collazo-Arocho, G. H. Canton, D. Pitt, W. J. Dairy Sci. 86:2131–2144. W. Thatcher, and R. J. Collier. 1981. Black globe-humidity Yousef, M. K. 1985. Stress Physiology in Livestock. CRC Press, Boca index (BGHI) as comfort equation for dairy cows. Trans. ASAE Raton, FL. 24:711–714. Journal of Dairy Science Vol. 92 No. 1, 2009