Article Request Page ASABE Journal Article
Characterizing Feedlot Heifer Response to Environmental Temperature
T. M. Brown-Brandl, D. D. Jones
Published in Transactions of the ASABE 59(2): 673-680 (doi: 10.13031/trans.59.10855). Copyright 2016 American Society of Agricultural and Biological Engineers.
Submitted for review in July 2014 as manuscript number PAFS 10855; approved for publication by the Plant, Animal, & Facility Systems Community of ASABE in October 2015.
Mention of company or trade names is for description only and does not imply endorsement by the USDA. The USDA is an equal opportunity provider and employer.
The authors are Tami M. Brown-Brandl, ASABE Member, Agricultural Engineer, USDA-ARS Meat Animal Research Center, Clay Center, Nebraska; David D. Jones, ASABE Fellow, Professor, College of Engineering, University of Nebraska-Lincoln, Lincoln, Nebraska. Corresponding author: Tami M. Brown-Brandl, Meat Animal Research Center, P.O. Box 166, Clay Center, NE 68933; phone: 402-762-4279; e-mail: Tami.BrownBrandl@ars.usda.gov.
Abstract. It has been shown that feedlot cattle vary in their response to environmental temperature. There is a need to develop a parameter to summarize those varied responses, i.e., a heat stress phenotype. The goal of this investigation is to quantify animal response to environment by describing each animal with a parameter and to investigate how this parameter is impacted by management practices. Responsiveness was determined to be a useful parameter to describe the impact of dry-bulb temperature (tdb) on respiration rate (RR) of feedlot heifers. Responsiveness was defined as the slope of RR to tdb. It is a valid and useful parameter because it expresses a single value for each animal that includes the dynamic interaction of RR and tdb. Using the responsiveness parameter, it was determined that unshaded feedlot cattle had a lower responsiveness than shaded cattle. It was noted that there were a range of responsiveness values for all colors of cattle tested; thus, it is likely that there is genetic variation in this parameter. This parameter may prove useful for genomic analysis of heat stress. In shaded animals, the effects of color were minimized. Therefore, dark breeds and composite breeds (Angus and dark red MARC III composite) showed more of a reduction in responsiveness than tan-colored MARC I composites, while Charolais heifers showed no response to shade. While responsiveness was shown to be a useful parameter, it may not be optimal, and other candidate parameters need to be explored.
Keywords.Cattle, Feedlot cattle, Heat stress, Model, Shade.
Periods of extreme heat have negative impacts on an animal’s growth, performance, and well-being and can ultimately cause death in severe cases. Economic losses associated with heat stress in feedlot cattle average $369 million for a single year in the U.S. (St-Pierre et al., 2003). Heat waves are a reoccurring phenomenon in the cattle producing regions of the U.S. Heat waves occur regularly in the Midwestern U.S., resulting in substantial death losses for the cattle industry. These events can result in the death of thousands of feedlot cattle and the loss of millions of dollars in revenue to the cattle industry, both in direct animal losses and indirect performance losses (Busby and Loy, 1996; Hahn, 1999; Hubbard et al., 1999). The total impact of a heat wave is dependent on the interaction of several factors, including environment, animal, and management (Brown-Brandl, 2008). These factors interact, and each needs to be considered when making management decisions.
Environmental factors contributing to the total heat load include a combination of dry-bulb temperature, relative humidity, wind speed, and solar radiation (Eigenberg et al., 2005; Gaughan et al., 2008; Mader et al., 2006). In addition to the current conditions, the previous day’s environmental conditions plus overnight temperatures contribute to the total heat load (Gaughan et al., 2008; Hahn et al., 1999; Hubbard et al., 1999; Nienaber et al., 2007).
Management of feedlot animals also contributes to the overall heat load that an animal experiences. Researchers have been looking for management options to reduce heat stress for many years, and many management strategies have been investigated. Strategies include adjustments to feed composition or timing of feeding events (Brosh et al., 1998; Holt et al., 2004; Mader et al., 2002; MLA, 2006), water temperature or inclusion of additional drinking space (Beck et al., 2000; Bicudo and Gates, 2002), environmental modifications including adding shade or sprinkle cooling (ground or animal) (Blackshaw and Blackshaw, 1994; Garner et al., 1989; Mader et al., 2007; Sullivan et al., 2011), and changing handling practices (Brown-Brandl et al., 2010; Mader et al., 2007). While each studied strategy has some positive benefits, no strategy has eliminated the effects of heat. In addition, all strategies have negative aspects associated with their implementation. Negative aspects include extra cost, management time, added odor generation, and possibly others.
Individual animal response is quite varied, given the same environmental conditions and management, thus making animal response to changing temperature a good candidate for genetic evaluation and possible phenotype development. Animal characteristics contributing to this variability include cattle breed, hide color, hair thickness or type, health issues, amount of fat cover, and temperament. Bos taurus breeds are more vulnerable to heat stress than either Bos indicus or Bos indicus × Bos taurus breeds (Beatty et al., 2004, 2006; Cartwright, 1955; Carvalho et al., 1995; De Azevedo et al., 2005). Cattle with dark or black hides tend to be more affected by heat stress than those with light-colored hides (Busby and Loy, 1996; Hungerford et al., 2000). A common perception is that a compromised immune system and/or prior cases of pneumonia (Brown-Brandl et al., 2006) can cause an increased response to hot weather. Animals that approach finishing weight or have more fat cover (Brown-Brandl et al., 2006) were reported to have higher respiration rates under hot conditions. Animals that have not had adequate time to acclimate to hot weather are more impacted by the heat than those that are acclimated (Robinson et al., 1986). The last factor that has been documented to increase an animal’s response is an excitable temperament (Brown-Brandl et al., 2006).
The need for a heat stress phenotype exists. A phenotype is an observable trait that results from the expression of an organism’s genes as well the influences of environmental factors and the interactions between the two. Common cattle phenotypes include coat color (Joerg et al., 1996), meat tenderness (Page et al., 2004), and feed efficiency (Arthur et al., 2001), among many others. An animal’s response to the thermal environment has been a target of geneticists for many years (Collier et al., 2008). In a review article, Collier et al. (2008) discussed the genetic evaluation phenotypes. Heat stress phenotypes have been limited to what can be observed or measured, including hair coat characteristic and heat shock and other metabolites. However, heat stress is a complex interaction of many responses that impact an animal’s heat loss and heat production. A single parameter, such as a hair coat characteristic or a blood parameter that can change over time, may not be the best parameter to monitor. Therefore, there is a need to develop a single parameter to summarize the complex thermal response of an individual animal.
While several animal characteristics have been identified, it is difficult to understand the relative importance of each. Brown-Brandl and Jones (2011) attempted to summarize these animal characteristics into a single parameter called animal susceptibility. Their work was limited in that the animal susceptibility parameter could not be validated since there was no quantifiable measure of animal susceptibility. Additionally, the ultimate goal of the Brown-Brandl and Jones (2011) study was to consolidate the impact of environment, animal, and management factors. This knowledge would allow producers to customize management to maximize the benefits of different options while minimizing negative aspects. This would provide an appropriate level of care for individual animals while minimizing inputs from the producer’s side.
The goal of this investigation is to quantify animal response to the thermal environment by describing each animal with a single parameter. The specific objectives are to develop and evaluate a parameter of beef cattle response to ambient temperature and to investigate how this parameter is impacted by management practices.
Materials and Methods
The data used in this investigation were collected and published previously by Brown-Brandl et al. (2013). The methods used in that study are summarized below. A study was conducted over three consecutive summers (May to August) at the USDA-ARS Meat Animal Research Center (MARC) feedlot near Clay Center, Nebraska (98.055 W, 42.522 N). A total of 384 yearling feedlot heifers (beginning weight 423 ±47 kg) from the MARC population were selected for this study (128 heifers per year). Four distinct breeds or composites were selected based on their hide color and included: Angus (black), MARC III composite (dark red) [1/4 Pinzgauer, 1/4 Red Poll, 1/4 Hereford, and 1/4 Angus], MARC I composite (tan) [1/4 Charolais, 1/4 Braunvieh, 1/4 Limousin, 1/8 Angus, and 1/8 Hereford], and Charolais (white). Details about the animals can be found in Brown-Brandl et al. (2013).
Animals were assigned to one of 16 pens, eight shaded and eight unshaded, based on breed, individual weight, and previous cases of pneumonia (two heifers of each breed or composite per pen). The pens measured 7.3 m × 20.7 m, thus providing 18.8 m2 per animal. Each pen had 4.27 m of feed bunk length, and watering tanks were shared between two pens. The eight shaded pens accommodated free access to shade, and the shade covered 50% of the total pen area. The shade structure was constructed of timber and galvanized steel. The feed bunks and water tanks were located beneath the shade.
Throughout the study, dry-bulb temperature was collected every 15 min using an automated weather station (Vantage Pro, Davis Instruments Corp., Hayward, Cal.). The weather station was located approximately in the center of the set of 16 pens.
Measurements of respiration rate were conducted twice daily (between 0800 and 1000 h and between 1300 and 1500 h) five days a week during the experiment. Approximately two weeks prior to initiating the experiments, the cattle were preconditioned to the presence of observers. During the pre-conditioning period, two observers spent 1 h twice daily walking outside the pens. This was done to ensure that the cattle were acclimated to observers so that accurate measurements of respiration rate could be taken.
On days when data collection was scheduled, two observers, working independently, recorded data on a preselected 64 of the 128 experimental animals. For each selected animal, the identification number and respiration rate were recorded. Respiration rates were determined by visual observation of flank movement. The time required for ten breaths was recorded using a stopwatch. Dry-bulb temperature was recorded before the beginning and at the conclusion of animal observations. The dry-bulb temperature readings were averaged and served as weather data for the statistical analysis. A database was developed that included animal ID, pen, shade or no shade treatment, date and time of respiration rate measurement, dry-bulb temperature at that time, and individual animal respiration rate.
The GLM procedure in SAS (2010) was used to test significant differences in responsiveness by year, treatment, and breed or composite breed of heifers, and the interaction of breed/composite and treatment.
To illustrate the distribution, histograms were created for different subsets of the populations using the Pivot Tables function in Microsoft Excel. Two different types of histograms were created: (1) a histogram for each animal’s average respiration rate throughout the season and (2) histograms describing the distributions of slope categories. The histogram of the average respiration rate throughout the season (RRavg) was created by categorizing the average respiration rate into one of 16 categories (RRavg greater than or equal to 45 breaths min-1 (bmp) and less than 50 bmp had a category center at 47.5, 50 bmp = RRavg > 55 bmp was centered at 52.5, 55 bmp = RRavg > 60 bmp was centered at 57.5, …, 115 bmp = RRavg > 120 bmp was centered at 117.5, and 120 bmp = RRavg > 125 bmp was centered at 122.5). In creating the histograms for the slope of respiration rate response to changes in ambient temperature, the slopes were categorized into one of nine slope categories (0 to less than 1.5 = 1, greater than 1.5 to less than 2.5 = 2, …, and greater than 8.5 and less than 9.5 = 9). Histograms were used to visually describe the distribution of all heifers, shaded and unshaded heifers, and the effects of breed and treatment (shade or unshaded).
The distribution of animals in the two shade treatments was further described by determining average temperament score, condition score, weight gain, and number of animals treated for pneumonia with each of nine different slope categories. To help quantify factors within each responsiveness category, the GLM procedure in SAS was used to determine significance of condition score, weight gain, animals who had been treated for pneumonia, and temperament between the different slope categories in each treatment group.
Results and Discussion
Parameter Criteria and Development
The data describing each animal’s response consist of between 23 and 43 data points, with the number of points depending on the year. Data collected during 2004 contained between 24 and 26 data points collected at different temperatures. There were a similar number of points collected for each animal during 2005 (22 to 27 points per animal). The experiment was conducted for a longer duration during 2006; therefore, more data points were collected (40 to 43 points per animal). The raw data (11,704 observations) are shown in figure 1 and illustrate the respiration rate (RR) of each animal as a function of dry-bulb temperature (tdb). RR is the measure of animal responsiveness, and tdb is the measure of the environment. RR has been shown to be a good indicator of heat stress (Brown-Brandl et al., 2005).
Figure 1 highlights the challenge of using the direct and simple measure of RR as a useful parameter since RR changes with temperature, resulting in multiple values for each animal. Even though RR is a useful measure, a single value cannot describe how the RR changes as tdb changes. A parameter that combines all the RR values for each animal into a single value is desirable. A number of candidates exist, such as minimum, maximum, average, and median RR. An example of such a parameter is shown in figure 2. Figure 2 shows a histogram of the average RR for each of the 384 animals. This histogram is shown as a bar graph; all subsequent histograms will be shown as line graphs. Even though this parameter provides a single value for each animal, it fails because it does not capture the range of influence of tdb.
Figure 1. Respiration rate data collected from a total of 384 heifers over a three-year period (128 heifers per year). Heifers were from four distinct breeds or composites selected based on their hide color and included: Angus (black), MARC III composite (dark red) [1/4 Pinzgauer, 1/4 Red Poll, 1/4 Hereford, and 1/4 Angus], MARC I composite (tan) [1/4 Charolais, 1/4 Braunvieh, 1/4 Limousin, 1/8 Angus, and 1/8 Hereford], and Charolais (white).
Figure 2. Histogram (line and bar graph) of average respiration rate (breaths min-1) for each of the 384 heifers, showing the number of animals in each of 16 different respiration rate categories. Subsequent histograms are shown using a line graph only.
Figure 3. Respiration rate responses using slope as a method for depicting thermal response. The slope of RR to tdb is shown for two heifers. Heifer A was more responsive to changes in tdb than heifer B. Heifer A has a slope of RR to tdb of 9.3 breaths min-1 °C-1, and heifer B has a slope of RR to tdb of 1.6 breaths min-1 °C-1. Therefore, heifer A has a responsiveness of 9.3 breaths min-1 °C-1, and heifer B has a responsiveness of 1.6 breaths min-1 °C-1. Heifer A was a dark red MARC III composite sampled during 2004, and heifer B was a tan MARC I composite sampled during 2005. Neither animal had access to shade.
For a parameter to be useful, it must combine RR and tdb in a way that differentiates each animal and accounts for the influence of the range of tdb. Consider heifers A and B, as shown in figure 3. Notice that the RR for each tdb for each animal is shown. It is clear that heifer A was more responsive to higher tdb than heifer B. A useful parameter is the slope of each individual animal’s RR to changes in tdb. The slopes for heifers A and B are 9.3 and 1.64 breaths min-1 °C-1, respectively. The slope values are unique for each animal, consider the response of each animal over all temperatures experienced by the animal, and therefore describe the dynamic response of RR and tdb. The slope of RR to tdb is a useful parameter that will be used throughout this study and is referred to as “responsiveness.”
The responsiveness (slope of individual animal respiration rate to dry-bulb temperature) of 384 animals was determined (128 heifers in each of three years, with each year balanced for each of the four breeds or composites). A histogram of the responsiveness is shown in figure 4. The animals in this project were equally proportioned among four Bos taurus breeds or composite breeds and two treatments (shaded and unshaded pens).
Figure. 4. Histogram showing the distribution of responsiveness (slope of individual animal respiration rate to dry-bulb temperature) of 384 feedlot heifers. The data are from animals equally distributed among four Bos taurus breeds or composite breeds and two treatments (shaded and unshaded pens) over the three years of the study.
Impacts of Breed and Management
Weather in the summer of 2004 was cooler on average than in the 2005 and 2006 summer periods. The average temperature was 20.7°C (5.5°C min., 35.4°C max.) in 2004, 24.1°C (8.7°C min., 37.7°C max.) in 2005, and 23.0°C (4.9°C min., 39.6°C max.) in 2006. Respiration rates during 2004 were collected at dry-bulb temperatures ranging from 13.8°C to 36.2°C, while the temperature range during data collection was 22.7°C to 39.5°C in 2005 and 16.9°C to 40.8°C in 2006.
The responsiveness (slope of respiration to temperature) was significantly impacted by year, breed/composite, shade treatment, and the interaction of breed and treatment (p < 0.0017). In 2004, the average responsiveness was 4.99 breaths min-1 C-1, the highest of the three years. The next highest responsiveness was 4.13 breaths min-1 C-1 in 2005, followed by 3.21 breaths min-1 C-1 in 2006. The differences in the responsiveness between years could be due to either of two factors: the animals or the environment. Unfortunately, using feedlot animals, determining which factor, or if a combination of the two factors, led to the significant year effect would be impossible.
Understanding the distributions of responsiveness proved useful in understanding factors that affected the severity of heat stress (fig. 4). Figure 4 shows the distribution of responsiveness of the animals observed in this study. The majority of animals (352 of 384) had a responsiveness between 2.5 and 6.5 breaths min-1 °C-1. Twenty-nine animals had a responsiveness greater than 6.5 breaths min-1 °C-1, while only three animals had a responsiveness less than 2.5 breaths min-1 °C-1.
To describe the distribution, histograms were created for different subsets of the animals. Histograms were used to visually describe the distribution of all heifers, shaded and unshaded, and the effects of breed/composite and treatment (shaded or unshaded). These histograms are shown as line graphs to easily compare different populations.
Shade significantly reduced the responsiveness of cattle (3.74 ±0.08 breaths min-1 °C-1 compared to 4.47 ±0.08 breaths min-1 °C-1). While this provides some information and agrees with other literature data (Blackshaw and Blackshaw, 1994; Brown-Brandl et al., 2005; Eigenberg and Brown-Brandl, 2011; Gaughan et al., 2010a), the fact remains that we know little about the change in distribution and which of the animals were most impacted by shade.
Figure 5 provides more information, showing that the shaded animals had a nearly perfectly normal distribution of responsiveness, ranging from 2 to 7 breaths min-1 °C-1, with all but four animals having a responsiveness between 3 and 6 breaths min-1 °C-1. The unshaded animals had a much larger range of responsiveness, ranging from 2 to over 9 breaths min-1 °C-1. Interestingly, it appears that shade had more of an impact on animals with high responsiveness than on animals with lower responsiveness. While the lower values of responsiveness were similar in both the shaded and unshaded treatments, it appears that shade moderated heat stress in highly responsive animals.
Figure 7. Distribution of responsiveness of different breeds or composites of cattle provided with access to shade: (a) Angus heifers, (b) MARC III composite heifers, (c) MARC I composite heifers, and (d) Charolais heifers. Each breed/treatment group is represented by 48 heifers.
Several heat stress risk factors have previously been identified, including color (Gaughan et al., 2008) and breed (Brown-Brandl et al., 2006; Gaughan et al., 2010b). Generally, light-colored heifers had lower responsiveness and dark-colored heifers had higher responsiveness (fig. 6). However, it is interesting to note that the range of responsiveness was the same regardless of color. Gaughan et al. (2010b) also noted large variations in heat tolerance of different genotypes of cattle. Animals demonstrating these extremes would be interesting to study from a genomic perspective.
Figure 5. Distribution of responsiveness of feedlot heifers when provided or not provided with access to shade. A total of 192 heifers were in each of the two groups. Data were collected over three summers (64 heifers each summer).
Figure 6. Distribution of the responsiveness of feedlot cattle with different coat colors. Dark breeds were represented by Angus and MARC III composite; light breeds were represented by Charolais and MARC I composite.
A comparison of responsiveness between animals of each breed/composite and treatment was performed. There were significant effects of breed/composite and treatment (p = 0.001). There was also a significant interaction effect of breed/composite × treatment (p = 0.0017) (fig. 7). Angus heifers without access to shade were more responsive than those with access to shade (4.89 breaths min-1 °C-1 compared to 3.71 breaths min-1 °C-1; p < 0.0001). The same response was observed in the MARC III composite heifers (4.91 breaths min-1 °C-1 for unshaded animals and 3.96 breaths min-1 °C-1 for shaded animals; p < 0.001). While a similar response was observed in MARC I composites, the difference was about half that of the other breeds or composites (4.27 breaths min-1 °C-1 for unshaded animals and 3.68 breaths min-1 °C-1 for shaded animals). An advantage of providing shade was not observed in Charolais heifers (3.83 compared to 3.62 breaths min-1 °C-1; p = 0.3904).
Differences in temperament, condition score, weight gain, and number of animals with at least one treatment for pneumonia were analyzed to help quantify risk factors within each responsiveness category for the unshaded animals (table 1) Temperament score did not significantly change with responsiveness (p = 0.8770). Brown-Brandl et al. (2006) reported a trend toward slightly higher respiration rates for more excitable animals (higher temperament scores) than for calm animals (lower temperament scores). While this is a difference between the studies, the difference was small and was only found when the temperament scores were combined into two groups, and even then the effect was very small.
Table 1. Breakdown of risk factors in the different responsiveness categories for unshaded feedlot heifers.[a] Responsiveness
Angus MARC III
Charolais Pneumonia Average
2 1 0 0 1 (100%) 0 0 7.0 ±0.7 1.6 ±0.33 2.0 ±0.6 3 25 4 (16%) 5 (20%) 5 (20%) 11 (44%) 3 (12%) 7.4 ±0.1 1.6 ±0.07 2.0 ±0.1 4 52 9 (17%) 8 (15%) 14 (27%) 21 (40%) 10 (19%) 7.3 ±0.1 1.5 ±0.05 2.0 ±0.1 5 55 15 (27%) 13 (24%) 17 (31%) 10 (18%) 6 (11%) 7.5 ±0.1 1.5 ±0.05 2.0 ±0.1 6 32 7 (22%) 14 (44%) 7 (22%) 4 (12%) 3 (9%) 7.2 ±0.1 1.4 ±0.06 2.1 ±0.1 7 19 10 (53%) 6 (32%) 2 (11%) 1 (5%) 1 (5%) 7.1 ±0.2 1.3 ±0.08 2.2 ±0.1 8 4 0 1 (25%) 2 (50%) 1 (25%) 2 (50%) 7.3 ±0.4 1.4 ±0.17 1.8 ±0.3 9 4 3 (75%) 1 (25%) 0 0 0 7.3 ±0.4 1.3 ±0.17 2.0 ±0.3 Total count 192 48 48 48 48 25
[a] Pneumonia = number of heifers that had been treated for pneumonia, average condition = average condition score of heifers throughout the measurement period, weight gain = weight gain throughout the measurement period, and average temperament = average temperament score of heifers recorded throughout the measurement period.
Table 2. Break down of risk factors in the different responsiveness categories for shaded feedlot heifers.[a] Responsiveness
Angus MARC III
Charolais Pneumonia Average
2 2 1 (50%) 1 (50%) 0 0 1 (50%) 7.5 ±0.5 1.3 ±0.2 1.5 ±0.5 3 50 14 (28%) 8 (16%) 12 (24%) 16 (32%) 3 (6%) 7.5 ±0.1 1.7 ±0.05 1.8 ±0.1 4 69 13 (19%) 21 (30%) 18 (26%) 17 (24%) 5 (7%) 7.3 ±0.1 1.4 ±0.04 2.0 ±0.1 5 51 15 (29%) 12 (23%) 14 (27%) 10 (20%) 9 (17%) 7.2 ±0.1 1.4 ±0.05 2.0 ±0.1 6 18 5 (27%) 7 (39%) 2 (11%) 4 (22%) 5 (28%) 6.8 ±0.2 1.2 ±0.08 2.1 ±0.2 7 2 0 0 1 (50%) 1 (50%) 0 7.0 ±0.5 1.1 ±0.2 2.0 ±0.5 8 0 - - - - - - - - 9 0 - - - - - - - - Total count 192 48 48 48 48 23
[a] Pneumonia = number of heifers that had been treated for pneumonia, average condition = average condition score of heifers throughout the measurement period, weight gain = weight gain throughout the measurement period, and average temperament = average temperament score of heifers recorded throughout the measurement period.
Average condition score, or level of finish, also did not impact responsiveness (p = 0.5544). Many authors have noted an effect of condition score, or heavier animals had significantly higher stress levels or death losses (Brown-Brandl et al., 2006; Busby and Loy, 1996; Hungerford et al., 2000). It is uncertain why condition score did not impact the responsiveness of the cattle in this study. Gaughan et al. (2008) found differences in response with days on feed; however, the ranges were large (0 to 80 days, 80 to 130 days, and over 130 days). It is possible that the difference between animals in this study were not large enough to elicit a response because the animals were all spring-born calves that entered the feedlot at the same time.
Responsiveness tended to be impacted by weight gain (p = 0.1049), with animals that were not as responsive having a higher weight gain than animals that were more impacted. It is likely that weight gain is a heat stress response rather than a risk factor. A possible scenario is that animals that were not as impacted by heat stress consumed more feed and grew faster than animals that were more impacted by heat stress.
Cases of pneumonia did not impact responsiveness (p = 0.2580), although health status or health history has been shown to impact the stress level of feedlot cattle (Brown-Brandl et al., 2006; Gaughan et al., 2008). This lack of significance could be a sign that we needed more animals to detect a difference or that the all animals infected with pneumonia were not necessarily treated, as found by Wittum et al. (1996).
A similar breakdown was performed to determine the effects of documented risk factors on the responsiveness categories of feedlot heifers with access to shade (table 2). This breakdown resulted in some interesting deviations from the results found for the unshaded cattle. Unlike the unshaded cattle, responsiveness did not seem to be impacted by breed. There seems to be an equal distribution of animals across the different categories. This seems logical because the coat colors of different breeds absorb different amounts of solar energy (da Silva et al., 2003; Hillman et al., 2001; Stewart, 1953). After eliminating a large portion of the solar load with shade, the different breeds responded similarly to heat stress.
Cases of pneumonia tended to increase with increasing responsiveness (p = 0.1144), meaning that more animals had been treated for pneumonia in the higher response categories than in the lower response categories. This is a different response from that of the unshaded cattle, which had no correlation between responsiveness and being treated for pneumonia. This is an interesting effect. It is possible that the effect of pneumonia on stress was masked by the strong impact of breed or coat color, but this is difficult to determine definitively. In addition, the relatively low numbers of animals that had been treated for pneumonia indicate that some infected animals may not have been treated, as noted earlier (Wittum et al., 1996).
Numerically, temperament score increased with responsiveness category, although not significantly (p = 0.3782). This result could possibly be attributed to the higher-temperament animals not utilizing shade as much as the lower-temperament animals. Although not measured in this study, this affect should be investigated in future studies to determine if this hypothesis is correct.
Interestingly, condition score decreased with increasing responsiveness (p = 0.0566). This is the opposite of the results reported in other studies. Additionally, weight gain was also found to be greater in the lower categories than in the higher categories. It is hypothesized that both of these results are indications of better performance of the lower responsiveness category, instead of indications of susceptibility to heat stress. Again, the animals were all of a similar age and entered the feedlot at the same time; therefore, this opposite result does not necessarily confound the literature, which indicates that cattle with higher condition scores, or more days on feed, have the potential to be more stressed.
Shade is one of many management options employed to reduce heat stress. This research shows that shade preferentially provides more relief to cattle with darker-colored hides. There are many different management options, including sprinkle cooling and diet manipulations. Similar studies could be conducted with each of these management options to find the distribution of responses to different breeds or composite breeds of cattle.
Responsiveness was determined to be a useful parameter to describe the impact of tdb on RR of feedlot heifers. Responsiveness was defined as the slope of RR to tdb. It is a valid and useful parameter because it expresses a single value for each animal that includes the dynamic interaction of RR and tdb.
For unshaded feedlot cattle, it was determined that lighter-colored breeds had a lower responsiveness than darker-colored breeds. However, it was noted that there were lighter-colored animals with high responsiveness and darker breeds with low responsiveness.
For shaded animals, the effects of color were minimized. Therefore, the dark breeds (Angus and dark red MARC III composite) showed more of a reduction in responsiveness than the tan-colored MARC I composites, while Charolais heifers show no response to shade.
While responsiveness was shown to be a useful parameter, it may not be optimal, and other candidate parameters need to be explored. For example, responsiveness only considered a linear response of RR through all tdb. In fact, RR is known to have a non-linear response to ambient temperature, with a typical inflection point around 25°C. This parameter only considered tdb as a descriptor of the environment. As discussed in this article, environment has been described by a variety of climatic descriptors. These descriptors and their combinations should be examined as potential parameters.
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