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Estimating sensible heat loss in laying hens through thermal imaging
Computers and Electronics in Agriculture 166 (2019) 105038
Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Estimating sensible heat loss in laying hens through thermal imaging a,⁎
b
T
c
João Batista Freire Souza-Junior , Karim El-Sabrout , Alex Martins Varela de Arruda , Leonardo Lelis de Macedo Costaa a b c
Laboratory of Biometeorology and Environmental Biophysics, Universidade Federal Rural do Semi-Árido, Mossoró, Brazil Department of Poultry Production, Faculty of Agriculture, University of Alexandria, Egypt Department of Animal Sciences, Universidade Federal Rural do Semi-Árido, Mossoró, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Biophysical equations Hot environments Heat transfer Infrared thermography Poultry
The thermal challenge caused by high temperatures is a barrier to efficient poultry production. As a non-invasive method, infrared thermography can be valuable in the routine of a poultry system. To this end, the sensible heat loss (SHL) was estimated in sixty naked-neck laying hens (heterozygous) using biophysical equations and thermal imaging. The temperature during this study ranged from 24.0 °C to 31.0 °C and we divided it into three classes: 24.0–26.0 °C, 26.1–29.0 °C, and 29.1–31.0 °C. To assess the effect of air temperature on the SHL and its potential differences between body regions, an ANOVA using a general linear model was performed, which included all two-way interactions. Significant effects of air temperature class, body region and their interaction on the SHL (P < 0.05) were observed. The results showed that SHL in the body trunk (area covered by feathers) was considered incipient. The neck and face had the highest SHL values. We highlight that the mapping of body surface temperature using infrared thermography proved to be efficient in the estimative of the SHL because provides more accurate thermal imaging than thermocouple measurements.
1. Introduction Heat stress is a barrier to efficient poultry production (Silva et al., 2017; Saeed et al., 2019). The thermal challenge caused by the high temperatures, regardless of the farming system (intensive or semi-intensive) (El-Sabrout, 2018), decreases the productive performance of birds (Mascarenhas et al., 2018) and, consequently, generate productive losses in the poultry farming. Adapting to thermally stressful situations can be an advantage for raising the productive potential of rearing systems, where birds with the Na gene (naked neck) appear as an alternative (Souza Jr et al., 2015). The adaptation of this poultry to high-temperature conditions is related to the absence of feathers in the neck region (Sharifi et al., 2010), which acts as a thermolysis site, i.e. a body region used for sensible heat dissipation (Queiroz et al., 2019). In this scenario, recent studies have been performed using infrared thermography (Souza Jr et al., 2018; Scoley et al., 2019; Carvalho et al., 2019; Glavaš et al., 2019; Peng et al., 2019), where the thermal imaging obtained give the possibility of body surface temperature mapping, being a facilitator in the estimation of the sensible heat loss. A concept widely explored in recent years is that of body thermal Windows (Codde et al., 2016; Van de Vem et al., 2016; Thompson et al., 2017), which are body regions without feathers or hair coat and highly
⁎
vascularized used to maximize sensible heat loss and consequent in the body temperature regulation. In this way, the knowledge of how to process the sensible mechanisms of heat transfer, convection and long-wave radiation, in naked-neck laying hens becomes necessary, particularly with respect to the magnitude of these responses in the natural environment of rearing system. This environment (without human control over meteorological variables) is complex, because the wind speed, radiation, air temperature, and air humidity continually change and can cause significant changes in bird's heat dissipation. Thus, the aim of this study was to estimate the sensible heat loss in naked-neck laying hens using biophysical equations and thermal imaging. 2. Materials and methods Animal care and handling procedures followed the guidelines of the Ethics Committee on the Use of Animals in Experiments of Universidade Federal Rural do Semi-Árido (CEUA-UFERSA).
Corresponding author. E-mail address: [email protected] (J.B.F. Souza-Junior).
https://doi.org/10.1016/j.compag.2019.105038 Received 9 September 2019; Received in revised form 29 September 2019; Accepted 30 September 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
Computers and Electronics in Agriculture 166 (2019) 105038
J.B.F. Souza-Junior, et al.
Yijk = μ + Ci + Rj + Iij + εijk
2.1. Location and animals
(1)
where Yijk is the kth SHL data recorded from the ith TA class at jth body region, μ is the overall mean, C is the fixed effect of the TA class, R is the fixed effect of the body region, Iij is the effect of the interaction between the ith TA class and the jth body region, and εijk is the residual effect which includes the other sources of variation.
Based on the uniformity of body weight, sixty naked-necked hens (Label Rouge strain) were selected and evaluated between 32 and 35 weeks of age. Data collection took place once a week and was conducted in the poultry production sector of the Universidade Federal Rural do Semi-Árido, located in Mossoró, Brazil (latitude 05°11′S, longitude 37°22′W, and altitude of 16 m). In a shed (W 4.0 m × L 18.0 m × H 2.5 m), these laying hens were individually housed in galvanized wire cages (L 0.50 m × W 0.45 m × H 0.40 m) and fed with mash ration for semi-heavy laying hens Rostagno (2011). The cages were equipped with feeders, nipple drinkers and egg trimmer.
3. Results TA and RH ranged from 24 °C to 31 °C, and 67% and 95%, respectively. MRT values were approximately 1 °C higher than TA (mean of 28.9 °C). The U, however, did not exceed 1.5 m s−1. Significative effects of air temperature class, body region and their interaction on HC, HR, and SHL (P < 0.05) were observed. For HC (Fig. 4), comparing the same body region in the different TA classes, there was no significant difference in the neck between 26.1 and 29.0 °C and 29.1–31.0 °C, but both differed from 24.0 to 26.0 °C, which presented the highest average (66.53 W m−2). In the face, HC differed between 24.0 and 26.0 °C and 26.1–29.0 °C, but not between 24.0 and 26.0 °C and 29.1–31.0 °C. In this same body region, HC did not differ between 26.1 and 29.0 °C and 29.1–31.0 °C. In the legs and body trunk, there was no difference (P > 0.05) between the three TA classes, even though there was a slight increase in the legs at 29.1–31.0 °C (29.49 W m−2). Comparing body regions in each TA class, there was a significant difference between all body regions at 24.0–26.0 °C and 26.1–29.0 °C, where the neck presented the highest averages. At 29.1–31.0 °C, the neck did not differ from the face (P = 0.5656), but both differed from the legs and body trunk. The neck and face presented higher HR averages at 24.0–26.0 °C (65.42 and 53.25 W m−2, respectively), which differed from 26.1 to 29.0 °C and 29.1–31.0 °C, which showed no significant difference between them (Fig. 5). In the legs, 24.0–26.0 °C and 29.1–31.0 °C showed no difference but were different from 26.1 to 29.0 °C. There was no difference between TA classes for HR in the body trunk. The results also showed that all body regions differed in the three TA classes. The highest values were found in the neck. In general, SHL results showed similar behavior to HC and HR (Fig. 6). At 24.0–26.0 °C, the neck and face accounted for 83% of SHL, dropping to 75% at higher temperatures (29.1–31.0 °C). The other body region without feathers (legs) accounted for only 16% of SHL at 24.0–26.0 °C, increasing to 21% at 29.1–31.0 °C. The body trunk corresponded to a maximum of 3% at 29.1–31.0 °C.
2.2. Environmental analysis Environmental data were obtained once a week for 4 weeks starting at 07:00 until all hens were measured. The air temperature (TA, °C) and relative humidity (RH, %) were measured using a digital thermohygrometer (Instrutherm, HT-300, São Paulo, Brazil). Wind speed (U, m s−1) was measured with a precision anemometer (Lutron, YK-2005AH, Kolkata, India). The mean radiant temperature (MRT, °C) was estimated from the data from TA, U and black globe temperature (Da Silva et al., 2010), where the latter was measured with a thermocouple (type K) connected to a Data Logger digital thermometer (Minipa, MT-600, São Paulo, Brazil) and inserted in the center of the black globe. All these equipment were installed in the center of the shed where the animals were evaluated. 2.3. Thermal imaging In order to map the sensible heat transfer, as well as quantifying the contribution of each body region in the heat loss to the environment, thermal images were obtained using a portable thermographic camera (model b60, FLIR Systems; emissivity of biological tissue = 0.98) (Fig. 2). The thermal images obtained were analyzed with the software Flir ThermaCAM Researcher Pro 2.10 (Flir Systems, Wilsonville, United States) and the body regions analyzed were the face, legs, body trunk, and neck. 2.4. Sensible heat loss (SHL) The long-wave radiation heat transfer (HR, W m−2) and convective heat loss (HC, W m−2) from the body surface to the surrounding air was determined by biophysical equations according to Da Silva and Maia (2013). Thus, sensible heat loss (SHL, W m−2) was calculated for each body region measured with infrared thermography and is the sum of long-wave radiation heat transfer and convective heat loss (Fig. 3). To estimate HC, body regions were compared with geometric figures of approximate characteristics: vertical cylinder (legs and neck) and spheres (facial area and body trunk), as described in Fig. 1. For this, the four body regions chosen were measured with the aid of a tape measure.
4. Discussion Poultry breeding systems characterized by thermally stressful conditions cause physiological discomfort and poor welfare, culminating in low productive performance (Gonçalves et al., 2017). Heat-stressed poultry has a hard time finding the thermal balance with the environment. The reason for this is the sweat glands absence, which serves to dissipate excess body heat through the evaporation of sweat, and, according to Zhao et al., (2013), a thick layer of feathers that covers almost every body surface, acting as a resistance to body heat loss. In this scenario, as a remote method for obtaining body surface temperature by imaging, infrared thermography has been shown to be useful in evaluating the thermophysiological status of farm animals (McManus et al., 2016). Sensible heat loss is closely dependent on the gradient formed between the air and body surface (Yahav et al., 2008) and, in addition, convection can be maximized when air movement is high (Silva, 2008). In this sense, birds with higher capacity for sensible heat loss at higher temperatures are more thermotolerant than birds without this capacity (Nascimento et al., 2011). Birds with a smaller body area covered by feathers are more advantageous. The laying hens' body surface temperature in the feather-covered body areas approximates the air temperature, regardless of the thermal
2.5. Statistical Analysis To include TA in the statistical model (ANOVA) as a fixed effect, three classes were broken down as follows: Class 1 (TA = 24.0–26.0 °C), Class 2 (TA = 26.1–29.0 °C) and Class 3 (TA = 29.1–31.0 °C). Thus, to assess the effect of air temperature on the SHL and its potential differences between body regions, an ANOVA using the PROC GLM of the Statistical Analysis System, version 8.0 (SAS, 1999) was performed, which included all two-way interactions (Eq. (1)). We used the Tukey's multiple comparison tests to verify the differences among the means (P ≤ 0.05) after having observed significant differences through the F test of the ANOVA (P < 0.05). 2
Computers and Electronics in Agriculture 166 (2019) 105038
J.B.F. Souza-Junior, et al.
Fig. 1. Thermal imaging obtained during the study and body model of a hen made from basic geometric shapes (spheres and vertical cylinders) used to estimate the convective heat loss (HC).
Fig. 3. Block diagram explaining the estimation of sensible heat loss through biophysical equations according Da Silva and Maia (2013).
condition in which they were submitted (Mutaf et al., 2008). In our study, the body trunk (covered by feathers) presented incipient HC and HR values, despite the increase from 24.0–26.0 °C to 29.1–31.0 °C where, the thermal gradient formed between the TS and the TA, which is the driving force for SHL, was low in this body area. This also culminated in a low SHL. Warm conditions are known to cause depression in the bird's
Fig. 2. Thermal imaging combined with data collection image during the experimental period.
3
Computers and Electronics in Agriculture 166 (2019) 105038
J.B.F. Souza-Junior, et al.
Fig. 6. Least squares means of the sensible heat loss (SHL) for the interaction between the body regions and the ambient temperature class. The different lowercase letters indicate a significant difference between the body regions in the same ambient temperature class. The different capital letters in the same body region indicate a difference between ambient temperature classes (Tukey’s test, P < 0.05).
Fig. 4. Least squares means of the convective heat loss for the interaction between the body regions and the ambient temperature class. The different lowercase letters indicate a significant difference between the body regions in the same ambient temperature class. The different capital letters in the same body region indicate a difference between ambient temperature classes (Tukey’s test, P < 0.05).
(HR + HC) from 24.0 to 26.0 °C to 29.1–31.0 °C, it remained elevated throughout the study. This result becomes important because in birds with necks covered by feathers, the sensible heat loss would be reduced because there is a resistance to sensible heat transfer in this body region due to the feather layer, as previously observed by Nääs et al., (2010). These high SHL values, regardless of the thermal condition, were already expected. Carotid arteries are located in the neck, which supplies highly vascularized organs located in the head, including the eyes and brain (Queiroz et al., 2019). The legs, which behaved unexpectedly in our study, only represented 21% of SHL. We did not observe significant difference in SHL between low and high air temperatures for this body area. In other studies (Šumbera et al., 2007; Shinder et al., 2007; Queiroz et al., 2019), the legs functioned as thermal windows, i.e., body regions without feathers or hair coat used in the body temperature regulation process (Thompson et al., 2017; Queiroz et al., 2019). We believe that the non-acting of the legs as a thermoregulatory organ is due to the efficiency of the other body parts to maintain homeothermy, as was the case with the without feather neck.
Fig. 5. Least squares means of the long-wave radiation for the interaction between the body regions and the ambient temperature class. The different lowercase letters indicate a significant difference between the body regions in the same ambient temperature class. The different capital letters in the same body region indicate a difference between ambient temperature classes (Tukey’s test, P < 0.05).
5. Conclusions It concludes with this study that SHL in the body trunk (area covered by feathers) was considered incipient. The neck and face regions had the highest SHL values. We highlight that the mapping of body surface temperature using infrared thermography proved to be efficient in the estimative of the SHL because provides accurate thermal imaging. For future research, body surface temperature obtained through thermal imaging should be correlated with core body temperature on thermal physiology studies and, especially, with productive indices (feed efficiency, weight gain, feed intake, laying rate, egg weight) on poultry production studies.
performance. In this context, the inclusion of genes that reduce the body area covered by feathers has been a strategy used to reduce productive losses under high-temperature conditions (Hadad et al., 2014). Body areas that are not covered by feathers, i.e., the face, legs and, for the hens in our study, the neck, are fully committed to sensible heat transfer (Yahav et al., 2005). In this study, HR from the face area decreased as TA increased and this can be explained by the fact that, during the study, the MRT was elevated when TA was also high. In this situation, there is a decrease in the TS – MRT gradient, which leads to a lower HR at 29.1–31.0 °C. HC showed a high value at 24.0–26.0 °C. At 29.1–31.0 °C, HC was similar to that observed at 24.0–26.0 °C. This may have been due to the fact that at 29.1–31.0 °C the wind speed averaged 0.5 m s−1 higher than at 24.0–26.0 °C and 26.1–29.0 °C, which maximized HC despite the smallest difference between TS and TA (24.0–26.0 °C: 10.5 °C and 29.1–31.0 °C: 7.1 °C). In the neck, despite the decrease in SHL
Declaration of Competing Interest The authors declare that they have no competing interests. References Carvalho, C.C.S., Souza, A.C., Ferreira, H.C., Rocha Júnior, V.R., Ruas, J.R.M., Costa, M.D., Guimarães, C.C.F., Durães, H.F., Gonçalves, M.C.M., 2019. Using infrared
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thermography to measure the body surface temperature of crossbred cows and calves in the semi-arid region of Minas Gerais, Brazil. J. Anim. Behav. Biometeorol. 7, 132–136. Codde, S.A., Allen, S.G., Houser, D.S., Crocker, D.E., 2016. Effects of environmental variables on surface temperature of breeding adult female northern elephant seals, Mirounga angustirostris, and pups. J. Therm. Biol. 61, 98–105. Da Silva, R.G., 2008. Biofísica ambiental: os animais e seu ambiente. Funep, Jaboticabal. Da Silva, R.G., Guilhermino, M.M., Morais, D.A.E.F., 2010. Thermal radiation absorbed by dairy cows in pasture. Int. J. Biometeorol. 54, 5–11. Da Silva, R.G., Maia, A.S.C., 2013. Principles of Animal Biometeorology. Springer, New York. El-Sabrout, K., 2018. Effect of rearing system and season on behaviour, productive performance and carcass quality of rabbit: a review. J. Anim. Behav. Biometeorol. 6, 102–108. Glavaš, H., Bobić, T., Dorić, D., Božić Lenard, D., 2019. Infrared thermography camera protection in dairy farming management. Comput. Electron. Agric. 157, 604–615. Gonçalves, S.A., Ferreira, R.A., Pereira, I.G., Oliveira, C.C., Amaral, P.I.S., Garbossa, C.A.P., Fonseca, L.S., 2017. Behavioral and physiological responses of different genetic lines of free-range broiler raised on a semi-intensive system. J. Anim. Behav. Biometeorol. 5, 112–117. Hadad, Y., Cahaner, A., Halevy, O., 2014. Featherless and feathered broilers under control versus hot conditions. 2. Breast muscle development and growth in pre- and posthatch periods. Poult. Sci. 93, 076–1088. Mascarenhas, N.M.H., Costa, A.N.L., Pereira, M.L.L., Caldas, A.C.A., Batista, L.F., Andrade, E.L.G., 2018. Thermal conditioning in the broiler production: challenges and possibilities. J. Anim. Behav. Biometeorol. 6, 52–55. McManus, C., Tanure, C.B., Peripolli, V., Seixas, L., Fischer, V., Gabbi, A.M., Menegassi, S.R.O., Stumpf, M.T., Kolling, G.J., Dias, E., Costa, J.B.G., 2016. Infrared thermography in animal production: an overview. Comput. Electron. Agric. 123, 10–16. Mutaf, S., Seber Kahraman, N., Firat, M.Z., 2008. Surface wetting and its effect on body and surfaces temperatures of domestic laying hens at different thermal conditions. Poult. Sci. 87, 2441–2450. Nääs, I.A., Romanini, C.E.B., Neves, D.P., 2010. Broiler surface temperature distribution of 42 day old chickens. Sci. Agric. 67, 497–502. Nascimento, G.R.I., Nääs, I.A.I., Pereira, D.F.I.I., Baracho, M.S.I., Garcia, R., 2011. Assessment of broiler surface temperature variation when exposed to different air temperatures. Rev. Bras. Cienc. Avic. 13, 259–263. Peng, D., Chen, S., Li, G., Cheng, J., Wang, J., Gu, X., 2019. Infrared thermography measured body surface temperature and its relationship with rectal temperature in dairy cows under different temperature-humidity indexes. Int. J. Biometeorol. 63, 327–336. Queiroz, J.P.A.F., Souza Jr, J.B.F., Morais Oliveira, V.R., Castelo, T.S., Dantas, M.R.T., Costa, L.L.M., Oliveira, M.F., 2019. Sensible heat transfer and thermal windows in
5
Contents lists available at ScienceDirect
Computers and Electronics in Agriculture journal homepage: www.elsevier.com/locate/compag
Estimating sensible heat loss in laying hens through thermal imaging a,⁎
b
T
c
João Batista Freire Souza-Junior , Karim El-Sabrout , Alex Martins Varela de Arruda , Leonardo Lelis de Macedo Costaa a b c
Laboratory of Biometeorology and Environmental Biophysics, Universidade Federal Rural do Semi-Árido, Mossoró, Brazil Department of Poultry Production, Faculty of Agriculture, University of Alexandria, Egypt Department of Animal Sciences, Universidade Federal Rural do Semi-Árido, Mossoró, Brazil
A R T I C LE I N FO
A B S T R A C T
Keywords: Biophysical equations Hot environments Heat transfer Infrared thermography Poultry
The thermal challenge caused by high temperatures is a barrier to efficient poultry production. As a non-invasive method, infrared thermography can be valuable in the routine of a poultry system. To this end, the sensible heat loss (SHL) was estimated in sixty naked-neck laying hens (heterozygous) using biophysical equations and thermal imaging. The temperature during this study ranged from 24.0 °C to 31.0 °C and we divided it into three classes: 24.0–26.0 °C, 26.1–29.0 °C, and 29.1–31.0 °C. To assess the effect of air temperature on the SHL and its potential differences between body regions, an ANOVA using a general linear model was performed, which included all two-way interactions. Significant effects of air temperature class, body region and their interaction on the SHL (P < 0.05) were observed. The results showed that SHL in the body trunk (area covered by feathers) was considered incipient. The neck and face had the highest SHL values. We highlight that the mapping of body surface temperature using infrared thermography proved to be efficient in the estimative of the SHL because provides more accurate thermal imaging than thermocouple measurements.
1. Introduction Heat stress is a barrier to efficient poultry production (Silva et al., 2017; Saeed et al., 2019). The thermal challenge caused by the high temperatures, regardless of the farming system (intensive or semi-intensive) (El-Sabrout, 2018), decreases the productive performance of birds (Mascarenhas et al., 2018) and, consequently, generate productive losses in the poultry farming. Adapting to thermally stressful situations can be an advantage for raising the productive potential of rearing systems, where birds with the Na gene (naked neck) appear as an alternative (Souza Jr et al., 2015). The adaptation of this poultry to high-temperature conditions is related to the absence of feathers in the neck region (Sharifi et al., 2010), which acts as a thermolysis site, i.e. a body region used for sensible heat dissipation (Queiroz et al., 2019). In this scenario, recent studies have been performed using infrared thermography (Souza Jr et al., 2018; Scoley et al., 2019; Carvalho et al., 2019; Glavaš et al., 2019; Peng et al., 2019), where the thermal imaging obtained give the possibility of body surface temperature mapping, being a facilitator in the estimation of the sensible heat loss. A concept widely explored in recent years is that of body thermal Windows (Codde et al., 2016; Van de Vem et al., 2016; Thompson et al., 2017), which are body regions without feathers or hair coat and highly
⁎
vascularized used to maximize sensible heat loss and consequent in the body temperature regulation. In this way, the knowledge of how to process the sensible mechanisms of heat transfer, convection and long-wave radiation, in naked-neck laying hens becomes necessary, particularly with respect to the magnitude of these responses in the natural environment of rearing system. This environment (without human control over meteorological variables) is complex, because the wind speed, radiation, air temperature, and air humidity continually change and can cause significant changes in bird's heat dissipation. Thus, the aim of this study was to estimate the sensible heat loss in naked-neck laying hens using biophysical equations and thermal imaging. 2. Materials and methods Animal care and handling procedures followed the guidelines of the Ethics Committee on the Use of Animals in Experiments of Universidade Federal Rural do Semi-Árido (CEUA-UFERSA).
Corresponding author. E-mail address: [email protected] (J.B.F. Souza-Junior).
https://doi.org/10.1016/j.compag.2019.105038 Received 9 September 2019; Received in revised form 29 September 2019; Accepted 30 September 2019 0168-1699/ © 2019 Elsevier B.V. All rights reserved.
Computers and Electronics in Agriculture 166 (2019) 105038
J.B.F. Souza-Junior, et al.
Yijk = μ + Ci + Rj + Iij + εijk
2.1. Location and animals
(1)
where Yijk is the kth SHL data recorded from the ith TA class at jth body region, μ is the overall mean, C is the fixed effect of the TA class, R is the fixed effect of the body region, Iij is the effect of the interaction between the ith TA class and the jth body region, and εijk is the residual effect which includes the other sources of variation.
Based on the uniformity of body weight, sixty naked-necked hens (Label Rouge strain) were selected and evaluated between 32 and 35 weeks of age. Data collection took place once a week and was conducted in the poultry production sector of the Universidade Federal Rural do Semi-Árido, located in Mossoró, Brazil (latitude 05°11′S, longitude 37°22′W, and altitude of 16 m). In a shed (W 4.0 m × L 18.0 m × H 2.5 m), these laying hens were individually housed in galvanized wire cages (L 0.50 m × W 0.45 m × H 0.40 m) and fed with mash ration for semi-heavy laying hens Rostagno (2011). The cages were equipped with feeders, nipple drinkers and egg trimmer.
3. Results TA and RH ranged from 24 °C to 31 °C, and 67% and 95%, respectively. MRT values were approximately 1 °C higher than TA (mean of 28.9 °C). The U, however, did not exceed 1.5 m s−1. Significative effects of air temperature class, body region and their interaction on HC, HR, and SHL (P < 0.05) were observed. For HC (Fig. 4), comparing the same body region in the different TA classes, there was no significant difference in the neck between 26.1 and 29.0 °C and 29.1–31.0 °C, but both differed from 24.0 to 26.0 °C, which presented the highest average (66.53 W m−2). In the face, HC differed between 24.0 and 26.0 °C and 26.1–29.0 °C, but not between 24.0 and 26.0 °C and 29.1–31.0 °C. In this same body region, HC did not differ between 26.1 and 29.0 °C and 29.1–31.0 °C. In the legs and body trunk, there was no difference (P > 0.05) between the three TA classes, even though there was a slight increase in the legs at 29.1–31.0 °C (29.49 W m−2). Comparing body regions in each TA class, there was a significant difference between all body regions at 24.0–26.0 °C and 26.1–29.0 °C, where the neck presented the highest averages. At 29.1–31.0 °C, the neck did not differ from the face (P = 0.5656), but both differed from the legs and body trunk. The neck and face presented higher HR averages at 24.0–26.0 °C (65.42 and 53.25 W m−2, respectively), which differed from 26.1 to 29.0 °C and 29.1–31.0 °C, which showed no significant difference between them (Fig. 5). In the legs, 24.0–26.0 °C and 29.1–31.0 °C showed no difference but were different from 26.1 to 29.0 °C. There was no difference between TA classes for HR in the body trunk. The results also showed that all body regions differed in the three TA classes. The highest values were found in the neck. In general, SHL results showed similar behavior to HC and HR (Fig. 6). At 24.0–26.0 °C, the neck and face accounted for 83% of SHL, dropping to 75% at higher temperatures (29.1–31.0 °C). The other body region without feathers (legs) accounted for only 16% of SHL at 24.0–26.0 °C, increasing to 21% at 29.1–31.0 °C. The body trunk corresponded to a maximum of 3% at 29.1–31.0 °C.
2.2. Environmental analysis Environmental data were obtained once a week for 4 weeks starting at 07:00 until all hens were measured. The air temperature (TA, °C) and relative humidity (RH, %) were measured using a digital thermohygrometer (Instrutherm, HT-300, São Paulo, Brazil). Wind speed (U, m s−1) was measured with a precision anemometer (Lutron, YK-2005AH, Kolkata, India). The mean radiant temperature (MRT, °C) was estimated from the data from TA, U and black globe temperature (Da Silva et al., 2010), where the latter was measured with a thermocouple (type K) connected to a Data Logger digital thermometer (Minipa, MT-600, São Paulo, Brazil) and inserted in the center of the black globe. All these equipment were installed in the center of the shed where the animals were evaluated. 2.3. Thermal imaging In order to map the sensible heat transfer, as well as quantifying the contribution of each body region in the heat loss to the environment, thermal images were obtained using a portable thermographic camera (model b60, FLIR Systems; emissivity of biological tissue = 0.98) (Fig. 2). The thermal images obtained were analyzed with the software Flir ThermaCAM Researcher Pro 2.10 (Flir Systems, Wilsonville, United States) and the body regions analyzed were the face, legs, body trunk, and neck. 2.4. Sensible heat loss (SHL) The long-wave radiation heat transfer (HR, W m−2) and convective heat loss (HC, W m−2) from the body surface to the surrounding air was determined by biophysical equations according to Da Silva and Maia (2013). Thus, sensible heat loss (SHL, W m−2) was calculated for each body region measured with infrared thermography and is the sum of long-wave radiation heat transfer and convective heat loss (Fig. 3). To estimate HC, body regions were compared with geometric figures of approximate characteristics: vertical cylinder (legs and neck) and spheres (facial area and body trunk), as described in Fig. 1. For this, the four body regions chosen were measured with the aid of a tape measure.
4. Discussion Poultry breeding systems characterized by thermally stressful conditions cause physiological discomfort and poor welfare, culminating in low productive performance (Gonçalves et al., 2017). Heat-stressed poultry has a hard time finding the thermal balance with the environment. The reason for this is the sweat glands absence, which serves to dissipate excess body heat through the evaporation of sweat, and, according to Zhao et al., (2013), a thick layer of feathers that covers almost every body surface, acting as a resistance to body heat loss. In this scenario, as a remote method for obtaining body surface temperature by imaging, infrared thermography has been shown to be useful in evaluating the thermophysiological status of farm animals (McManus et al., 2016). Sensible heat loss is closely dependent on the gradient formed between the air and body surface (Yahav et al., 2008) and, in addition, convection can be maximized when air movement is high (Silva, 2008). In this sense, birds with higher capacity for sensible heat loss at higher temperatures are more thermotolerant than birds without this capacity (Nascimento et al., 2011). Birds with a smaller body area covered by feathers are more advantageous. The laying hens' body surface temperature in the feather-covered body areas approximates the air temperature, regardless of the thermal
2.5. Statistical Analysis To include TA in the statistical model (ANOVA) as a fixed effect, three classes were broken down as follows: Class 1 (TA = 24.0–26.0 °C), Class 2 (TA = 26.1–29.0 °C) and Class 3 (TA = 29.1–31.0 °C). Thus, to assess the effect of air temperature on the SHL and its potential differences between body regions, an ANOVA using the PROC GLM of the Statistical Analysis System, version 8.0 (SAS, 1999) was performed, which included all two-way interactions (Eq. (1)). We used the Tukey's multiple comparison tests to verify the differences among the means (P ≤ 0.05) after having observed significant differences through the F test of the ANOVA (P < 0.05). 2
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Fig. 1. Thermal imaging obtained during the study and body model of a hen made from basic geometric shapes (spheres and vertical cylinders) used to estimate the convective heat loss (HC).
Fig. 3. Block diagram explaining the estimation of sensible heat loss through biophysical equations according Da Silva and Maia (2013).
condition in which they were submitted (Mutaf et al., 2008). In our study, the body trunk (covered by feathers) presented incipient HC and HR values, despite the increase from 24.0–26.0 °C to 29.1–31.0 °C where, the thermal gradient formed between the TS and the TA, which is the driving force for SHL, was low in this body area. This also culminated in a low SHL. Warm conditions are known to cause depression in the bird's
Fig. 2. Thermal imaging combined with data collection image during the experimental period.
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Fig. 6. Least squares means of the sensible heat loss (SHL) for the interaction between the body regions and the ambient temperature class. The different lowercase letters indicate a significant difference between the body regions in the same ambient temperature class. The different capital letters in the same body region indicate a difference between ambient temperature classes (Tukey’s test, P < 0.05).
Fig. 4. Least squares means of the convective heat loss for the interaction between the body regions and the ambient temperature class. The different lowercase letters indicate a significant difference between the body regions in the same ambient temperature class. The different capital letters in the same body region indicate a difference between ambient temperature classes (Tukey’s test, P < 0.05).
(HR + HC) from 24.0 to 26.0 °C to 29.1–31.0 °C, it remained elevated throughout the study. This result becomes important because in birds with necks covered by feathers, the sensible heat loss would be reduced because there is a resistance to sensible heat transfer in this body region due to the feather layer, as previously observed by Nääs et al., (2010). These high SHL values, regardless of the thermal condition, were already expected. Carotid arteries are located in the neck, which supplies highly vascularized organs located in the head, including the eyes and brain (Queiroz et al., 2019). The legs, which behaved unexpectedly in our study, only represented 21% of SHL. We did not observe significant difference in SHL between low and high air temperatures for this body area. In other studies (Šumbera et al., 2007; Shinder et al., 2007; Queiroz et al., 2019), the legs functioned as thermal windows, i.e., body regions without feathers or hair coat used in the body temperature regulation process (Thompson et al., 2017; Queiroz et al., 2019). We believe that the non-acting of the legs as a thermoregulatory organ is due to the efficiency of the other body parts to maintain homeothermy, as was the case with the without feather neck.
Fig. 5. Least squares means of the long-wave radiation for the interaction between the body regions and the ambient temperature class. The different lowercase letters indicate a significant difference between the body regions in the same ambient temperature class. The different capital letters in the same body region indicate a difference between ambient temperature classes (Tukey’s test, P < 0.05).
5. Conclusions It concludes with this study that SHL in the body trunk (area covered by feathers) was considered incipient. The neck and face regions had the highest SHL values. We highlight that the mapping of body surface temperature using infrared thermography proved to be efficient in the estimative of the SHL because provides accurate thermal imaging. For future research, body surface temperature obtained through thermal imaging should be correlated with core body temperature on thermal physiology studies and, especially, with productive indices (feed efficiency, weight gain, feed intake, laying rate, egg weight) on poultry production studies.
performance. In this context, the inclusion of genes that reduce the body area covered by feathers has been a strategy used to reduce productive losses under high-temperature conditions (Hadad et al., 2014). Body areas that are not covered by feathers, i.e., the face, legs and, for the hens in our study, the neck, are fully committed to sensible heat transfer (Yahav et al., 2005). In this study, HR from the face area decreased as TA increased and this can be explained by the fact that, during the study, the MRT was elevated when TA was also high. In this situation, there is a decrease in the TS – MRT gradient, which leads to a lower HR at 29.1–31.0 °C. HC showed a high value at 24.0–26.0 °C. At 29.1–31.0 °C, HC was similar to that observed at 24.0–26.0 °C. This may have been due to the fact that at 29.1–31.0 °C the wind speed averaged 0.5 m s−1 higher than at 24.0–26.0 °C and 26.1–29.0 °C, which maximized HC despite the smallest difference between TS and TA (24.0–26.0 °C: 10.5 °C and 29.1–31.0 °C: 7.1 °C). In the neck, despite the decrease in SHL
Declaration of Competing Interest The authors declare that they have no competing interests. References Carvalho, C.C.S., Souza, A.C., Ferreira, H.C., Rocha Júnior, V.R., Ruas, J.R.M., Costa, M.D., Guimarães, C.C.F., Durães, H.F., Gonçalves, M.C.M., 2019. Using infrared
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