THERMAL PERFORMANCE OF ROOFING MATERIALS AND SHADE NETS IN POULTRY SHEDS

ABSTRACT

The use of reduced-scale physical models is highly valuable, as it is based on the theory of similarity. These models have become an important factor in evaluating different roofing materials for intensive animal farming. In light of this, this study aims to evaluate the thermal performance of different types of roofing, with various roof modification techniques (ceiling and roof shading). Fifteen reduced-scale models of broiler houses (1:10) were employed to evaluate the following treatments: roofing materials (ceramic, fiber-cement, and metal) and adaptations (control, thermal shading net 70% coverage on the roof, black shading net 70% coverage on the roof, ceiling with thermal shading net 70%, and ceiling with black shading net 70%). The results indicate that the utilization of ceilings, especially ceilings with black shading net 70%, is the most effective treatment in reducing all thermal comfort indices. However, none of the treatments were efficient enough to reduce the indices to a level that would provide a comfortable environment for broiler chickens.

rural constructions; thermal comfort; ceiling

INTRODUCTION

Brazil’s chicken meat production has surged by 112%, establishing the country as the leading exporter and the third-largest producer globally. The São Francisco Valley, a growing agricultural region, particularly its sub-middle area in the northeastern part, is a crucial hub for fruit and animal production. However, the absence of region-specific research, especially regarding environmental conditions and facility standardization, impedes the expansion of poultry farming in the Northeast. Furthermore, variability in facilities poses challenges in adopting effective practices to minimize production losses.

Thermal comfort is crucial for maintaining high performance in poultry production. Several significant studies have been conducted in this field. Mascarenhas et al. (2022) conducted a study on variables influencing thermal environment characteristics in chicken coops. Bilal (2021) and Yousaf et al. (2019) assessed the impact of heat stress on poultry production.

According to Paulino et al. (2019), an environment conducive to the thermal comfort of chickens allows the birds to express their full genetic potential. Furthermore, Ribeiro & Yanagi Junior (2022) emphasized that the construction characteristics and materials used in poultry houses are significant for the proper maintenance of the thermal environment, which can be monitored through internal microclimate technologies.

Therefore, along with the rising global temperature, an increase in temperature in agricultural sheds can lead to heat stress in birds. This is a serious issue in the poultry industry as it negatively impacts the physiology and immunology of the birds, impairing their productivity (Morais et al., 2020).

Thus, the use of insulating materials in roofing for animal confinement has been a significant focus of research in tropical regions. It is considered a relevant investment since the majority of radiant heat load comes through the roofs (Brito et al., 2020).

Hence, the optimal roofing solution should possess elevated solar reflectivity and thermal emissivity (Anand et al., 2021). There are significant studies on this topic, as exemplified by the research conducted by Neto et al. (2022), who investigated the internal temperature of attics in reduced-scale models of poultry houses using nine thermal insulation models on the roof. These models were categorized into three groups: metal roof, metal roof with thermal blanket (asphalt blanket), and metal roof with Extruded Polystyrene (XPS) panels. The results showed that the XPS panel models had attic temperatures 3.4°C lower than the zinc roof when the external temperature was higher. Furthermore, Gonçalves et al. (2022) assessed the thermal performance of a poultry house using four types of roofing (thermal-acoustic, ceramic, fiber-cement, and metal). The analysis showed a good approximation between experimental and simulated data, with Pearson correlation coefficients (r) above 0.95. The simulated results showed better thermal performance for thermal-acoustic roofing, followed by ceramic, fiber-cement, and metal roofing.

Conducting research on large-scale agricultural facilities is expensive. Therefore, the use of reduced-scale physical models is economically viable. Stringari et al. (2020) conducted a study on a smaller scale to assess the thermal performance of roofing materials in sheds.

In light of the above, this study aims to evaluate the thermal performance of different types of roofing materials (ceramic, fiber-cement, and metal) in distorted-scale prototypes of poultry houses, with various roof modifications, including the addition of ceilings and roof coverings (70% black shading net and 70% thermal shading net), in the context of the sub-middle São Francisco Valley.

MATERIAL AND METHODS

This study was conducted at the Department of Postgraduate Studies in Agricultural Engineering at the Federal University of São Francisco Valley (UNIVASF), Juazeiro Campus, in the municipality of Juazeiro, Bahia, in September 2020. The municipality is situated in the northern region of the state of Bahia, within the sub-basin area of the São Francisco River. Located at an elevation of 368 meters, Juazeiro has a geographical coordinates of 9°24' S latitude and 40°00' W longitude. The region’s climate is classified as hot and dry semi-arid (BSh), according to the Köppen-Geiger climate classification.

For this study, 15 reduced-scale models of broiler chicken houses with distorted scale (1:10) were used. Different roof types and various adaptations with shading nets were employed as under-roof coverings and ceilings (see Table 1).

TABLE 1
Treatments and their specifications.

Treatment	Roof Type	Type of Adaptation
1	Ceramic	C
2	Ceramic	TSNCR
3	Ceramic	BSNCR
4	Ceramic	CTSN
5	Ceramic	CBSN
6	Fiber-cement	C
7	Fiber-cement	TSNCR
8	Fiber-cement	BSNCR
9	Fiber-cement	CTSN
10	Fiber-cement	CBSN
11	Metal	C
12	Metal	TSNCR
13	Metal	BSNCR
14	Metal	CTSN
15	Metal	CBSN

• CTSN: Ceiling with 70% thermal shading net, CBSN: Ceiling with 70% black shading net, TSNCR: 70% thermal shading net on the roof, BSNCR: 70% black shading net on the roof, and C: Control

The floor-to-ceiling walls of the prototypes were constructed using waterproof plywood measuring 10 mm in thickness, and the legs and support beams were made from wooden slats (5 mm x 10 mm) to elevate them to a height of 1.20 meters above the ground. The dimensions of a typical commercial poultry house were used as a reference, so the models had a 0.30 meter ceiling height, a 0.80 meter width, and a 1.20 meter length, representing a section of the actual facility (see Figure 1). Ceramic tiles (type B ridge cap), fiber-cement (corrugated, 2.44 m x 0.50 m, 4 mm thick), and metal (galvanized, 1 mm thick) were used for the roofing. Table 2 presents their thermal properties. The shading nets (black and thermal at 70%) were installed 0.05 meters below the roof when used as roof shading and at the end of the ceiling height when used as ceiling shading. Following the recommendation of Fonseca et al. (2011), the models were assembled on a flat, elevated terrain; free from shading; oriented in an east-west direction; and spaced one and a half meters apart from each other.

FIGURE 1
Sketch with the dimensions of a prototype (Unit: mm).

TABLE 2
Thermal property of the tiles.

Material	Apparent Mass Density [kg m−3]	Thermal Conductivity [W m−1 K−1]	Specific Heat [J kg−1 K−1]
Ceramic	2000	1.05	0.92
Fiber-cement	1900	0.95	0.84
Metal	7800	55	0.46

Wind speed data were collected using the Testo 404i hot wire anemometer (accuracy of ± 3% + 1 m/s) from Testo SE & Co. KGaA, Titisee-Neustadt, Germany; dry bulb temperature (tbs); black globe temperature (tgn); and relative air humidity with the assistance of Onset HOBO® U12-013 data loggers (accuracy of ± 0.35°C and ± 5% for relative humidity) and with Onset® model TMC6-HD thermocouples (accuracy of ± 0.25°C) attached to black globes placed at the geometric center of each physical installation model. Data were collected over 10 non-consecutive days in September 2020 for 1 hour in the morning (8:30 AM to 9:30 AM) and 1 hour in the afternoon (2:30 PM to 3:30 PM), at 10-minute intervals. To assess the environment, the Globe Temperature and Humidity Index (GTHI) (Buffington et al., 1981), enthalpy (H) (Albright, 1990), and radiant thermal load (RTL) (Esmay, 1969) were calculated using eqs (1), (2), and (3).

$\mathrm{GTHI}=t_{\mathrm{bg}}+0,36t_{\mathrm{dp}}+41,5$ (1)

$H=1,006t_{\mathrm{db}}+W(2501+1,805t_{\mathrm{db}})$ (2)

$\mathrm{RTL}=\sigma (\mathrm{MRT}{)}^4\text{, where MRT}=100\left\{{[2,51{\left(V_w\right)}^{0,5}(t_{\mathrm{bg}}-t_{dd})+{\left(t_{bg}/100\right)}^4]}^{0,25}\right\}$ (3)

In the above equations, t_db is the dry bulb temperature (°C), t_dp is the dew point temperature (°C), t_bg is the black globe temperature (°C), W is the humidity ratio (kPa), V_w is the wind velocity (m s¹), MRT is the mean radiant temperature, and σ is equal to 5.67 x 10–8 K⁴ Wm² (Stefan-Boltzmann constant).

The experiment was set up following a randomized block design using a split-plot scheme. Roofing materials (ceramic, fiber-cement, and metal) were allocated in the main plot. The adaptations (C, TSNCR, BSNCR, CTSN, CBSN) were placed in the sub-plot, and the factor period was allocated in the sub-sub-plot, with data collected on average at 9 AM and 3 PM. Measurements were taken over 10 non-consecutive days, with each day considered a block. Statistical analyses were carried out utilizing the SISVAR software platform.

RESULTS AND DISCUSSION

The GTHI showed a significant interaction between adaptation and period (p < 0.01, F-test) (see Table 3), where all treatments performed better in the morning without significant differences. For the afternoon period, CBSN showed the best result. The environment can be classified as comfortable only in the morning, with GTHI ranging from 69 to 77. These values are consistent with those found in the work conducted by Akamine & Passini (2017) in which they evaluated recycled and bamboo tiles. They obtained an average GTHI value of 77 for broiler chickens throughout the day, necessitating the use of other means to reduce thermal indices inside the facilities.

TABLE 3
Average GTHI (dimensionless) values in the interaction between period and the addition of shading and ceiling

Adaptation	Period
	Morning	Afternoon
CTSN	76,31 A a	85,90 B b
CBSN	76,74 A a	84,75 B a
TSNCR	76,76 A a	86,25 B b
BSNCR	76,30 A a	86,05 B b
C	77,07 A a	86,94 B c

Means distinguished by distinct uppercase letters within the row and lowercase letters within the column indicate significant differences according to the Tukey test, at a 5% probability level. CV = 1.45%; SD = 4,97. (CTSN: Ceiling with 70% thermal shading net, CBSN: Ceiling with 70% black shading net, TSNCR: 70% thermal shading net on the roof, BSNCR: 70% black shading net on the roof, and C: Control).

For the afternoon period, CBSN achieved the best result, followed by CTSN, TSNCR, and BSNCR, with statistically equal means, and lastly, control. This result validates the study’s hypothesis, which states that the use of shading nets tends to reduce the transfer of radiation from the roof to the interior of the facilities.

Fonseca et al. (2011) obtained similar GTHI results when evaluating roofs with different roofing materials in zinc barns for sheltering dairy calves in Gameleira de Goiás. The indices remained in the comfort range only in the morning, with values between 74 and 77, while in the afternoon, the values varied between 79 and 85.

With regard to the enthalpy variable (H), a significant difference was noted in the adaptation treatment (p < 0.01, F-test) and in the interaction between roofing and period (p < 0.05, F-test). CTSN, CBSN, and BSNCR achieved the best H results (see Table 4), which can be attributed to the fact that shading nets reduce the passage of radiation into the facilities.

TABLE 4
Average enthalpy (H) values (kJ kg of dry air−1) with the addition of shading and ceiling.

Adaptation	H
CTSN	72,56 a
CBSN	72,50 a
TSNCR	73,05 b
BSNCR	72,75 a
C	73,14 b

• Means labeled with distinct letters demonstrate statistically significant differences from one another according to the Tukey test, with a 5% significance level. CV = 1.46%; SD = 3,99. (CTSN: Ceiling with 70% thermal shading net, CBSN: Ceiling with 70% black shading net, TSNCR: 70% thermal shading net on the roof, BSNCR: 70% black shading net on the roof, and C: Control).

According to Barbosa Filho et al. (2006), birds raised in environments with enthalpy values above 54.7 kJ kg of dry air¹ are in a situation of thermal discomfort, demonstrating that no type of roofing was effective in creating a comfortable thermal environment in relation to enthalpy. In addition, Costa et al. (2017) achieved reductions in indices using ceilings in reduced-scale models, concluding that on days with higher enthalpy, the use of such ceilings can hinder the loss of moisture from the environment.

An analysis of the interaction between roofing and period (see Table 5) showed that all treatments performed better in the morning without significant differences. During the afternoon, the fiber-cement roofing treatment showed the best result, with a difference between 0.48 and 0.68 kJ kg of dry air¹. This difference is considered very small since the roofing was new and the natural aging of fiber-cement tiles negatively impacts their thermal performance (Coelho et al., 2017).

TABLE 5
Average values of enthalpy (H) (kJ kg of dry air−1) in the interaction between roofing and period.

Roofing	Morning	Afternoon
Ceramic	68,94 A a	76,60 B b
Fiber-cement	69,23 A a	76,12 B a
Metal	69,10 A a	76,80 B b

• Means distinguished by unique uppercase letters in the rows and lowercase letters in the columns exhibit significant differences according to the Tukey test, with a 5% significance level. CV = 1.46%; SD = 3,99.

An analysis of the RTL variable showed a significant interaction between roofing and adaptation (p < 0.01, F-test) and between adaptation and period (p < 0.01, F-test). A look at the roofing and adaptation interaction (see Table 6) revealed that for ceramic roofing, the treatments that showed the best result were CBSN, TSNCR, and BSNCR, followed by CTSN and C, which had the highest RTL mean. CBSN was the best treatment for fiber-cement roofing, followed by the others with statistically equal means. Finally, for metal roofing, all treatments had statistically equal results, except for control, which had the worst RTL. This result can be elucidated by the fact that metal roofing has high thermal conductivity, so it does not differentiate small reductions that shading nets may have provided.

TABLE 6
Average RTL values (W m−2) in the interaction between roofing and the addition of shading and ceiling

Adaptation	Roofing
	Ceramic	Fiber-cement	Metal
CTSN	519,09 A b	516,20 A b	514,23 A a
CBSN	511,42 A a	507,17 A a	519,10 B a
TSNCR	515,17 A a	521,07 A b	519,49 A a
BSNCR	512,00 A a	516,70 A b	520,12 A a
C	523,93 A b	521,57 A b	526,41 A b

• Means denoted by dissimilar uppercase letters within the row and lowercase letters within the column exhibit statistically significant differences according to the Tukey test, at a 5% probability level. CV = 1.68%; SD = 39,42. (CTSN: Ceiling with 70% thermal shading net, CBSN: Ceiling with 70% black shading net, TSNCR: 70% thermal shading net on the roof, BSNCR: 70% black shading net on the roof, and C: Control).

The only treatment that showed a significant difference in roofing was CBSN, where ceramic and fiber-cement tiles obtained the best results, as they have a thermal conductivity of 1.05 and 0.95 [W/(m K)], respectively, which is lower than metal roofing with 55 [W/(m K)]. However, we must take into account the fact that the fiber-cement roofing is new, and its natural aging process detrimentally affects its thermal efficiency (Coelho et al., 2017).

Regarding the interaction between adaptation and period (see Table 7), all treatments performed better in the morning without significant differences. For the afternoon period, CBSN showed the best average, followed by CTSN, TSNCR, and BSNCR, which had statistically equal results, and lastly, control, which exhibited the worst result. These results are attributed to the shading nets that reduce the passage of radiation into the facilities.

TABLE 7
Average RTL values (W.m−2) in the interaction between period and the addition of shading and ceiling

Adaptation	Period
	Morning	Afternoon
CTSN	478,35 A a	554,67 B b
CBSN	481,37 A a	543,77 B a
TSNCR	480,97 A a	556,18 B b
BSNCR	477,31 A a	555,25 B b
C	484,51 A a	563,45 B c

• Means designated by distinct uppercase letters within the row and lowercase letters within the column exhibit significant differences according to the Tukey test, at a 5% confidence level. CV = 1.68%. (CTSN: Ceiling with 70% thermal shading net, CBSN: Ceiling with 70% black shading net, TSNCR: 70% thermal shading net on the roof, BSNCR: 70% black shading net on the roof, and C: Control).

For all indices, CBSN proved to be the most effective adaptation in reducing values, corroborating the statement by Souza et al. (2015) who concluded that the use of ceilings reduces heat transfer into the building. However, it was ineffective in promoting the ideal thermal comfort environment for broiler chickens.

Thus, the best way to reduce the thermal load inside the facility is the use of black shading nets with 70% light retention, associated with materials of high thermal inertia and good thermal insulation that have low thermal conductivity (Paulino et al., 2019).

CONCLUSIONS

The use of ceilings, especially ceilings with 70% black shading net (CBSN), proved to be the most efficacious treatment in reducing all thermal comfort indices. However, none of the treatments were efficient enough to reduce the indices to a level where it would ensure a comfortable environment for broiler chickens.

ACKNOWLEDGMENTS

The authors would like to thank the National Council for Scientific and Technological Development (CNPq), the Bahia State Research Support Foundation (FAPESB), and the Coordination for the Improvement of Higher Education Personnel (CAPES) for their support in this research.

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Edited by

• Area Editor:
  Héliton Pandorfi

Publication Dates

• Publication in this collection
  29 Nov 2024
• Date of issue
  2024

History

• Received
  7 Feb 2024
• Accepted
  9 Sept 2024