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ANIMAL PRODUCTION |
* The University of Queensland, Gatton, Queensland, Australia, 4343;
and
Haskell Agricultural Laboratory, University of Nebraska-Lincoln, 57905 866 Rd., Concord 68728; and
Hubbard Feeds Inc., PO Box 8500, Mankato, MN 56002-8500
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Abstract |
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 Top Abstract INTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Key Words: bioclimatic index • beef cattle • feedlot • heat stress
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INTRODUCTION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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) has been widely used as an indicator of thermal stress in livestock (Ingraham et al., 1974; Ibrahim et al., 1975; Hahn and Mader, 1997; Gaughan et al., 1999), and the THI forms the basis of the Livestock Weather Safety Index (Livestock Conservation Incorporated, 1970). However, the THI has limitations because it does not account for solar radiation or wind speed (St-Pierre et al., 2003; Brown-Brandl et al., 2005b; Mader et al., 2006). Various THI have been developed by using dry bulb temperature in combination with wet bulb temperature, relative humidity, or dew point (Buffington et al., 1981; Baeta et al., 1987; Roseler et al., 1997). Recently, wind and solar radiation adjustments have been developed based on changes in respiratory dynamics (Mader et al., 2006) and on a respiration rate index using dry bulb temperature, relative humidity, wind speed, and solar radiation (Eigenberg et al., 2005).
Current indices do not account for the cumulative effects of heat load, natural cooling, or both. Cattle may accumulate heat during the day (the body temperature rises) and dissipate the heat at night. If there is insufficient night cooling, cattle may enter the following day with an accumulated heat load (AHL; Hahn and Mader, 1997). The THI-hours model was developed to account for the impact of intensity x duration on thermal status (Hahn and Mader, 1997). Similarly, St-Pierre et al. (2003) developed models using combinations of the maximum THI, daily duration of heat stress, and a heat load index (HLI). Neither model accounts for air movement or solar radiation.
Therefore, the objectives of this study were to develop and validate a new HLI for cattle based on respiratory dynamics and tympanic temperature. Heat load thresholds were also determined for different genotypes, and an AHL model was developed to predict the heat balance of cattle.
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MATERIALS AND METHODS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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HLI Development
Data from 13 feedlots were used in this study. Ten of these data sets were obtained from Australia (9 commercial feedlots and 1 research feedlot) and 3 were obtained from research feedlots in the United States. The data collection periods for the commercial Australian feedlots were January to March 2000, 2002, 2004, 2005, and 2006. Data collection for the research feedlots occurred from January to February 2003 (Australia) and from July to August 2002, 2004, and 2005 (United States). These data were used to develop and evaluate the HLI.
Cattle (n = 2,490) for the initial data collection period (January to March 2000) were selected for consistency in terms of genotype (black Angus), days on feed (100 d as of January 1), BCS (4+; based on the Australian body condition score of 1 = lean to 5 = very fat), no access to shade, and sex (steers); this was the reference animal. The predominant breed across all feedlots for the post-2000 studies was Angus (n = 6,585). Sixteen additional genotypes, Brahman (n = 1,403), Santa Gertrudis (n = 1,039), Hereford (n = 1,011), Waygu (n = 894), Hereford x Angus (n = 704), Hereford x Brahman (n = 608), European-cross (with unidentified Bos taurus; n = 587), European-cross (with unidentified Bos indicus; n = 429), Angus x Charolais (n = 298), Charolais (n = 293), Santa Gertrudis x Charolais (n = 293), Shorthorn (n = 206), Droughtmaster (n = 192), Santa Gertrudis x Hereford (n = 191), Santa Gertrudis-cross (with unidentified B. indicus; n = 190), and Shorthorn x Hereford (n = 147), were used to evaluate the HLI. From these, 7 genotypic categories were defined: B. taurus (British), B. taurus (European), Waygu, and B. indicus (25, 50, 75, or 100%). Factors considered in the development of the heat load model included genotype, coat color, health status, access to shade, area of shade, days on feed, manure management, and drinking water temperature. Pen size, stocking rate, feed bunk space, water trough space, shade design, and area under shade were not standardized among feedlots.
The commercial feedlots ranged in capacity from 9,000 to 50,000 cattle. The Australian research feedlot had a capacity of 200 cattle. Two of the US research feedlots had capacities of 325 cattle and 1 had a capacity of 720 cattle. Across all feedlots, stocking densities varied from 12.5 to 22 m2/animal. In feedlots that provided shade, the shaded areas varied from 1.1 to 5.3 m2/animal (at 1200). Shade materials used included shade cloth (70 to 90% solar block) and steel (various combinations of open spacing between solid and open areas to solid shade). The height of the shade structures ranged from 2 to 5.4 m. Manure depth (mm) was measured at 5 feedlots (20 pens; 4,000 cattle). This was done by taking 5 measures from the front to the rear of a pen at approximately 15-m intervals. Measures were made at the beginning, approximately midway, and at the end of the data collection period. Values were then averaged. Drinking water temperature was measured at 3 feedlots (6 pens; 1,080 unshaded Angus steers) at approximately 1000, 1200, 1400, and 1600 on days when cattle were heat stressed. Water temperature was measured by using a thermistor attached to a data logger (YSI 400, Mini-Mitter, Sun River, OR).
Automated weather stations were located at each feedlot. At each location, air temperature (Ta, °C), solar radiation (W/m), wind speed (m/s), relative humidity (%), and black globe (BG) temperature (°C) were recorded at 10-min intervals. Rainfall (mm) was also recorded. From 2000 to 2002, the THI was calculated for each weather station by using the following equation: THI = (0.8 x ambient temperature) + {[(relative humidity/100) x (ambient temperature – 14.4)] + 46.4} (adapted from Thom, 1959). In addition, THI-hours were calculated by using the method of Hahn and Mader (1997). After 2002, in addition to THI and THI-hours, the new HLI and AHL units were calculated (see below for details).
Within each commercial data set, the panting scores (Table 1) of cattle were recorded for 54 d. Cattle were assessed 3 times each day at approximately 0600, 1200, and 1600. Thus, approximately 162 observations were made per animal. During periods of extreme weather, observations were made at 2-h intervals between 0600 and 1800. Panting score was the key physiological and behavioral factor used in the development of the HLI and in establishing the heat load thresholds. Mean panting score was calculated according to the following formula:
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 | [1] |
where Ni = the number of cattle observed at panting score i.
Additional data collected at the research feedlots were respiration rates (15-min intervals; Australian facility) and panting scores at 2-h intervals from 0600 to 1800. Tympanic temperatures were recorded from cattle (n = 90) at the US 720-capacity feedlot at 30-min intervals over three 6-d heat waves, from 80 cattle at the US 325-capacity feedlots at 30-min intervals, and from 20 cattle at the Australian research feedlot at 15-min intervals on four 5-d heat waves. Tympanic temperature was measured by using the procedure of Mader et al. (2002). The thermistors remained in the ear for a maximum of 7 d.
Development of Thresholds
After development of the HLI, a threshold value for the reference animal was developed. The HLI value at which body heat is readily dissipated into the environment is influenced by a number of factors. The major nonclimatic factors that influence heat dissipation were identified, and HLI thresholds were determined for these factors. Data collected after the first study in 2000 were used to identify the major thresholds. The major thresholds were identified as genotype (B. taurus, B. indicus, and crossbred cattle), coat color (black, red, and white), health status, degree of acclimatization, access to shade, area of shade available, days on feed, depth of manure, and water trough temperature. The influence of previously mentioned factors on alleviating or contributing to heat load was assessed primarily on changes in mean panting score. Adjustments to the reference animal threshold (positive or negative) were made on the basis of 
20% of the cattle in a pen having a panting score of 1. This value was determined on the basis of the majority of reference cattle in a pen moving from a panting score of 1 to 2 very quickly when more than 20% of the cattle in a pen had a panting score of 1.
AHL Model Development
Following the development and validation of the HLI, the AHL model was developed. The AHL is a 2-dimensional function incorporating time and animal heat balance (the amount of time the animal is exposed to an HLI above its threshold, the upper threshold). When this occurs, the animal is not dissipating sufficient body heat into the environment and therefore core body temperature increases above its normal range. Alternatively, if the HLI falls below the upper threshold, then the animal is able to dissipate body heat into the environment, and core body temperature will return to the normal range. The threshold value is genotype specific and is also affected by management factors such as access to shade and drinking water temperature. The upper threshold was defined as the HLI, where 20% of unshaded cattle had a panting score of 1.
Statistical Analysis
Because of the uneven number of animals per pen within and across feedlots, all observational data were converted from the actual observation number to the proportion of animals in the pen. For statistical analysis, the percentages of cattle recorded for each panting score measure (within a feedlot, and then within a genotype across and within feedlots) were transformed to a normalized distribution by using a square root-arcsine transformation.
The HLI was developed by using regression analysis (PROC REG, RSREG; SAS Inst. Inc., Cary, NC). The regression analysis was used to determine the relationship between mean panting score (2,490 cattle, 403,380 observations) and climatic parameters (ambient temperature, relative humidity, wind speed, solar radiation, and BG temperature). Solar radiation and ambient temperature were eliminated from the model by the backward elimination procedure.
Based on the statistical analysis of panting score (4,200 observations) and body temperature (3,148 observations) data of unshaded Angus steers (n = 190) at the research feedlots, the HLI was divided into 4 categories: 1) thermoneutral conditions, when the HLI is <70.0; 2) warm conditions, when the HLI is 70.1 to 77.0; 3) hot conditions, when the HLI is 77.1 to 86.0; and 4) very hot conditions, when HLI is >86.0. Accumulated heat load was divided into 5 categories: 1) thermoneutral conditions, when the AHL is <1; 2) mild conditions, when the AHL is 1 to 10; 3) warm conditions, when the AHL is 10.1 to 20; 4) hot conditions, when the AHL is 20.1 to 50; and 5) very hot conditions, when the AHL is >50. These thresholds were identified by fitting polynomial equations by using PROC REG. The thresholds identified marked upward or downward shifts in panting score and body temperature of unshaded Angus steers.
The panting score data from the post-2000 studies were analyzed by using 
2 analysis and the PROC CORR, PROC NLIN, PROC SORT, PROC MIXED, PROC REG, and PROC GLM options of SAS. The models used were the effects of HLI, AHL, HLI category, AHL category, THI, and THI-hours on panting scores (14,481 cattle, 1,563,948 observations). Pen effects were considered where the same genotype was in shaded and unshaded pens within a feedlot. Interactions among genotype, pen, time of day (0601 to 1200; 1201 to 1700; 1701 to 0600), HLI, AHL, THI, and THI-hours were analyzed, and the effects of those individual variables on panting scores were determined. Statistical models for mean panting score included genotype x feedlot x pen x HLI x time of day and genotype x feedlot x pen x AHL x time of day. The HLI x AHL category interactions on panting score was also investigated. Similar models were used for THI and THI-hours. Independent data sets comprising the 1,200 to 1,800 observations were used to validate the HLI, AHL, and threshold values.
Tympanic temperature data (3,148 observations) were analyzed by using Fourier frequencies. Each 30-min time point represented a proportion of a complete cycle. The linear regression model (PROC REG) used was as follows:
 | [2] |
where h = time in hours.
A fraction of a day (h/24) was multiplied by 2
, which translated the time into radians. Having both sine and cosine components allows the cycle to shift left or right, as required. The intercept B0 estimates the average temperature around which the cycle oscillates.
When Fourier frequencies are fitted, the model becomes:
 | [3] |
This equation adjusts the diurnal cycle by making the oscillations in tympanic temperature less symmetric.
The parameter associated with HLI in the regression models is thus the elevation in tympanic temperature for each "unit" of heat load. An increase of 10 in the HLI should result in an increase of 0.3°C in tympanic temperature.
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RESULTS |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Analysis of the panting score data determined that there was a BG temperature threshold (25°C) above which panting score increased from 0 to 1 by 20% of the cattle. Two multiple regression models were developed by using the panting score data from unshaded Angus steers (n = 2,490). The first model (equation [4]) was a nonlinear regression model, which was applied when BG temperature was greater than 25°C. The second linear model (equation [5]) was applied when BG temperature was less than 25°C. Both models were developed by using relative humidity (in decimal form), BG temperature, and wind speed. All parameters were significant (P < 0.001).
 | [4] |
 | [5] |
where e = the base of the natural logarithm (approximate value of e = 2.71828).
AHL
For the reference animal, the upper threshold at which the animal accumulates heat was established at HLI = 86, and the lower threshold was 77. For a Brahman, the upper threshold was defined as HLI = 96 (Table 2). Over a 24-h period, the AHL may be increasing or may be decreasing. However the AHL value does not fall below zero. A zero value indicates that the animal is in thermal balance. The following equation was used to calculate the AHL:
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 | [6] |
where HLIACC = the actual HLI value at a point in time; HLILower Threshold = the HLI threshold below which cattle in a particular class will dissipate heat (e.g., 77 for the reference animal); HLIUpper Threshold = the HLI threshold above which cattle in a particular class will gain heat (e.g., 86 for the reference animal); and M = measures per hour (i.e., how often HLI data are collected per hour). If every 10 min, then M = 6.
Development of Threshold Adjustments
The critical HLI threshold value of 86 was determined based on panting score observations (n = 4,200) of unshaded Angus steers. However, the HLI value at which body heat is dissipated into the environment is influenced by a number of factors, including, but not limited to, genotype, coat color, health status, degree of acclimatization, and access to shade. The influence of the previously mentioned factors on alleviating or contributing to heat load was assessed primarily based on changes in mean panting score. Adjustments (either positive or negative) were made on the basis of 20% of cattle in a pen having a panting score of 1. Adjustments to the reference threshold were made and new thresholds for the different management strategies and genotypes observed were developed (Table 2
). A positive value indicates that the threshold has been increased, and a negative value indicates that the threshold has been reduced. For example, the HLI threshold for pure-bred B. indicus is 96 (86 + 10). The threshold for these animals may be greater than 96; however, there were not sufficient data where HLI > 95.
Relationships Among Mean Panting Score, HLI, and AHL
Effects of the HLI category on the panting scores of 6 genotypes are presented in Table 3. Both HLI and AHL had an effect (P < 0.001) on mean panting score. The R2 were high, at 0.93 and 0.92 for HLI and AHL, respectively. The R2 for THI was 0.6 (P < 0.001), and was 0.37 (P < 0.001) for THI-hours. The HLI x AHL interactions were a good predictor (P < 0.001; R2 = 0.92) of panting score (all genotypes) when pen within feedlot and feedlot location were considered. The effects of the HLI x AHL on Angus and Brahman steers are presented in Figure 1.
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). Nevertheless, they were not immune to extreme conditions. Increased panting scores were observed when the AHL exceeded 10 and the HLI was greater than 86. However, the percentage of Brahman with a panting score of 0 was higher (P < 0.05) when compared with Angus exposed to similar climatic conditions. Tympanic Temperature
The relationship between tympanic temperature and the average HLI for the previous 24 h was moderate (R2 = 0.67; P < 0.001), and was considerably better than the relationship between tympanic temperature and THI (R2 = 0.26; P < 0.001). A linear model was developed for tympanic temperature by using time and the average HLI over the previous 24 h (equation [7]):
 | [7] |
where TT = tympanic temperature; T = hour of the day in half-hour increments (1300 = 13, 1330 = 13.5, 0100 = 1); and HLI24 = the average HLI over the previous 24 h.
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DISCUSSION |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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Development of a thermal stress index for cattle should be based on biological factors (Nienaber et al., 1999; Hahn et al., 2003). The need for a large data set to develop and test an index necessitates that the biological parameter used must be easy to measure and be a good indicator of heat load. Behavioral changes are reliable indicators of heat load status. Feedlot location, feedlot layout, and pen microclimate influence the behavior of cattle (Casta
eda et al., 2004). However, measuring climatic conditions within pens is difficult and is not practical under most conditions. Therefore, the location of a weather station at a feedlot needs to be representative of the average climatic conditions to which cattle are exposed. Changes in DMI when cattle are exposed to hot conditions are well documented (NRC, 1981; Roseler et al., 1997; Holt et al., 2004). However, on its own DMI is not a good indicator of heat load status. Body temperature and respiration rate are reliable indicators of heat load but are difficult to measure under field conditions (Hahn et el., 1997; Gaughan et al., 2000; Gaughan et al., 2002; Brown-Brandl et al., 2005a), especially where large numbers of animals are involved. An alternative method is the use of panting scores (Mader et al., 2001). Panting scores have been used to evaluate the heat load status of feedlot cattle under commercial and research conditions, and are a reliable indicator of heat load status (Mader et al., 2001, 2006; Davis et al., 2001; Gaughan et al., 2002, 2004; Brown-Brandl et al., 2006). In the current study, panting scores served as the basis for the development of the HLI.
There are temperature thresholds above which respiration rate and panting score increase. The thresholds are somewhat genotype specific. Threshold values are defined as the climatic values, in this case HLI values, that trigger a response (Hahn et al., 1992; St-Pierre et al., 2003). In the current study, a threshold of 25°C (BG temperature) was determined for increasing respiration rates. A lower value (21°C; dry bulb temperature) was reported by Brown-Brandl et al. (2006). Similar threshold values for respiration rate have been reported by Hahn et al. (1997; 21°C; dry bulb temperature) and Eigenberg et al. (2005) with a threshold range of 25 to 30°C (dry bulb temperature).
Cattle adjust physiologically, behaviorally, and immunologically to minimize the adverse effects of thermal stress (Johnson, 1987; Hahn, 1999). Factors such as nutrition (Hahn et al., 1990; Hahn and Nienaber, 1993; Mader et al., 1999a; Mader et al., 2001; Gaughan et al., 2004; Holt et al., 2004), health status (Morrow-Tesch and Hahn, 1994; Brown-Brandl et al., 2006), BCS (Brown-Brandl et al., 2006), genotype or phenotype (Hammond et al., 1996, 1998; Gaughan et al., 1999; Brown-Brandl et al., 2006), magnitude of exposure (Hahn and Mader, 1997), and housing (Mader et al., 1999b; Mitlöhner et al., 2001) affect the responses of cattle when faced with a thermal challenge. Development of a predictive model that takes into account all the factors that are likely to affect heat tolerance is difficult (Nienaber et al., 1999). The HLI model can explain 93% of the variation in panting score and is a good predictor of the thermal status of various genotypes. As expected, British breeds (Angus and Hereford) had lower heat tolerance than Brahman and Waygu (Table 3). The percentage of cattle with a panting score of 0 decreased (more cattle had elevated panting scores) as the HLI categories moved through each stage from thermoneutral to very hot, except for Brahman and Waygu, where the percentage did not decrease until the very hot conditions were encountered. These data show that there are differences between genotypes; therefore, a single value to predict or measure the impact of heat stress is not valid.
The existing indices (THI) use a 1-dimensional approach, the thermal situation at a point in time (intensity only). They do not take into account the effect of exposure (duration) to adverse thermal conditions. Furthermore, there is no genotype distinction, so all cattle are assumed to respond the same. As such, THI may under- or overestimate the effect of an adverse heat event, especially if nighttime conditions are not considered. Nighttime recovery (or a lack thereof) is an important element when assessing the heat load status of cattle (Hahn and Mader, 1997). If nighttime conditions are not considered, the heat load status of cattle may be underestimated. If the day following a heat event is cool, then underestimation is not critical. However, if the following day is hot (HLI > threshold), then cattle may enter the day with a carryover heat load and may be susceptible to heat stress at lower HLI values than expected. In addition, Hahn et al. (1997) and Gaughan et al. (2000) reported that respiration rate may lag behind dry bulb temperature by up to 3 h when cattle are housed in climate chambers. A lag of 1 h for cattle housed in a feedlot was reported by Brown-Brandl et al. (2005a). It is clear that current ambient conditions may not have an immediate impact on the animal.
On a daily basis, cattle may be subjected to an HLI greater than 86 and yet have an AHL of less than 1. In addition, cattle may be exposed to an HLI of less than 70 but have an AHL greater than 50. In both cases, panting score will be elevated for B. taurus cattle (Figure 1). Cattle observed in the afternoon of a hot day continue to have elevated panting scores even if the HLI has decreased below the threshold, especially where they have considerable accumulated heat. This comes about because the cattle have not had sufficient time to offload the excessive heat gained during the day.
In conclusion, development of a dynamic thermal index will improve animal management during periods of adverse weather. The AHL model takes into account the magnitude of exposure (intensity x duration), the genotype or phenotype, coat color, the degree of acclimatization, and access to shade. The AHL index can be adjusted (by feedlot management) by using thresholds based on animal responses to observed conditions. Adjustments can be made on a pen by pen basis if required (newly arrived cattle vs. cattle at 150 d on feed). An on-site weather station will improve the accuracy of the HLI and AHL for a particular site.
The HLI and AHL have been incorporated into a Web-based heat load model (www.katestone.com.au) that allows feedlot managers to input their location, cattle type, days on feed, health status, and heat alleviation strategies, such as shade and manure management. Based on these inputs, a heat risk assessment is calculated. The model uses historical weather data for the specified locations. However, potential risk can also be calculated by using current weather conditions. A 6-d forecast is also provided. The model is dynamic and, as results from future studies involving both beef and dairy cattle and feedback from users are obtained, adjustments will be made.
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Footnotes |
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2 Corresponding author: j.gaughan{at}uq.edu.au
Received for publication May 28, 2007. Accepted for publication September 17, 2007.
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LITERATURE CITED |
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TopAbstractINTRODUCTIONMATERIALS AND METHODSRESULTSDISCUSSIONLITERATURE CITED |
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eda, C. A., J. B. Gaughan, and Y. Sakaguchi. 2004. Relationships between climatic conditions and the behaviour of feedlot cattle. Anim. Prod. Aust. 25:33–36.This article has been cited by other articles:
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
) and accumulated heat load (AHL, 
) between 0600 and 1700 on a day classified as very hot (HLI > 86, AHL > 50). HLIBG>25 = 8.62 + (0.38 x RH) + (1.55 x BG) – (0.5 x WS) + e2.4–WS; HLIBG<25 = 10.66 + (0.28 x RH) + (1.3 x BG) – WS. The AHL is a 2-dimensional function incorporating time and animal heat balance; that is, the amount of time the animal is exposed to an HLI above a threshold (the threshold for an unshaded Angus steer is 86).