
==== Front
Poult Sci
Poult Sci
Poultry Science
0032-5791
1525-3171
Elsevier

S0032-5791(24)00764-8
10.1016/j.psj.2024.104185
104185
MANAGEMENT AND PRODUCTION
Study on the changing patterns of production performance of laying hens and their relationships with environmental factors in a large-scale henhouse
Li Yan ⁎
Ma Ruiyu *
Qi Renrong *
Li Hualong †
Li Junying ⁎
Liu Wei *
Wan Yi *
Liu Zhen *
Li Sanjun *
Chang Xueling ‡
Yuan Zhengdong §
Liu Xuming §
Wang Xinsheng ║
Zhan Kai zhankai633@126.com
⁎1
⁎ Anhui Key Laboratory of Livestock and Poultry Product Safety Engineering, Institute of Animal Husbandry and Veterinary Medicine, Anhui Academy of Agricultural Science, Hefei, Anhui, 230031, China
† Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Anhui, 230031, China
‡ College of Animal Science, Anhui Science and Technology University, Chuzhou, Anhui, 233100, China
§ Beijing Deqingyuan Agricultural Technology Co. Ltd, Beijing 100089, China
║ Xunwu Deqingyuan Agricultural Technology Co. Ltd, Ganzhou, Jiangxi, 342200, China
1 Corresponding author. zhankai633@126.com
20 8 2024
11 2024
20 8 2024
103 11 10418513 6 2024
2 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
The production performance of laying hens is influenced by various environmental factors within the henhouse. The intricate interactions among these factors make the impact process highly complicated. The exact relationships between production performance and environmental variables are still not well understood. In this study, we measured the production performance of laying hens and various environmental variables across different parts of the henhouse, evaluated the weight of each environmental variable, and constructed a laying rate prediction model. Results displayed that body weight, laying rate, egg weight and eggshell thickness of hens decrease gradually from WCA to FA (P < 0.05). Serum levels of FSH and LH, as well as antibody level of H5 Re-13, gradually decrease from WCA to FA (P < 0.05). Moreover, the values for temperature (T), temperature-humidity index (THI), air velocity (AV), carbon dioxide (CO2), and particulate matter (PM2.5) gradually increase from WCA to FA (P < 0.05). Conversely, the relative humidity (RH) value gradually decreases from FA to WCA (P < 0.05). Additionally, the weights of the environmental variables, determined using a combination of the grey relational analysis (GRA) and analytic hierarchy process (AHP), were as follows in descending order: RH, THI, T, light intensity (LI), AV, PM2.5, NH3, and CO2. When the number of decision trees in the laying rate prediction model was set to 2,500, the results displayed a high level of agreement between the model's predictions and the observed outcomes. The model's performance evaluation yielded an R2 value of 0.89995 for the test set, suggesting strong predictive effects. In conclusion, the current study revealed significant differences in both the production performance of laying hens and the environmental variables across different parts of the henhouse. Furthermore, the study demonstrated that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate.

Key words

production performance
environmental variable
laying hen
weight
model
==== Body
pmcINTRODUCTION

In recent years, China has promoted standardized large-scale livestock and poultry breeding to accelerate the transformation of animal husbandry. This approach offers several advantages. Firstly, it enhances efficiency through standardized and automated processes, allowing for economies of scale in the procurement of feed, equipment, and other resources, thereby reducing costs. Additionally, large-scale breeding enables more effective environmental control. For instance, negative pressure ventilation with an evaporative cooling system is widely used during the summer months. This system typically consists of a wet curtain at the front of the henhouse and exhaust fans at the back. The exhaust fans force dirty air out, creating slight negative pressure that draws fresh air into the henhouse. This helps maintain appropriate environmental conditions, thereby enhancing production performance. Large-scale breeding has become a popular trend.

Production performance is important indicator to measure the production status of poultry farms, directly influencing their economic benefits. Production performance of laying hens is influenced by various factors such as genetics, nutrition, diseases, and the environment (Wilson, 2017; Hocking, 2010; Nys, 2017; Bryden et al., 2021; Desbruslais et al., 2021; Kocaman et al., 2006; Abdisa and Tagesu, 2017; Jatav and Verma, 2021; Abd El-Hack et al., 2022). Among these, the impact of environmental factors has been receiving increasing attention in recent years.

Henhouse environment is a complex and dynamic system composed of various contributing factors, including temperature (T), relative humidity (RH), and air velocity (AV), light intensity (LI) and particulate matter etc. Furthermore, hens also release many substances affecting environmental conditions from their metabolism and various activities, such as ammonia (NH3), Hydrogen sulfide (H2S), carbon dioxide (CO2). A comfortable environment is vital for achieving optimal production performance. An unsuitable environment can trigger a chain reaction that adversely affects caged hens, leading to reduced overall production efficiency (Kocaman et al., 2006; Zhao et al., 2013; Liu et al., 2022). Although large-scale henhouses typically maintain environmental conditions within a reasonable range, environmental conditions are not the same in every part of the house in actual production. Production performance of laying hens in different part of the house is also different (Şekeroğlu et al., 2014; Liu et al., 2022). Meanwhile, due to the complex interactions among environmental factors, it is challenging to accurately evaluate the impact of each one. Therefore, it's crucial to analyze the changing patterns of environmental factor, comprehensively evaluate their relative importance, and explore the subtle relationships between these changes and production performance.

Numerous studies have investigated these issues. To understand the changing patterns of environmental factors, researchers like Li Junying (Li et al., 2017), Prodanov (Prodanov et al., 2016), and Wei and Haizhu (2020) measured various parameters such as T, RH, CO2 and AV, and analyzed their changing trends. To evaluate the relative importance of environmental factors, Liu et al. (Liu et al., 2022) used the analytic hierarchy process (AHP) to weigh each factor. Furthermore, Wen et al. (Wen et al., 2022) went a step further by combining grey relational analysis (GRA) with the AHP method to assign weight to each environmental factor, thereby minimizing the subjective influence of decision-makers. In addition, Samadpour et al. (Samadpour et al., 2018) and Chen et al. (Chen et al., 2022) also conducted similar research. However, the aforementioned studies are primarily confined to the impacts of individual environmental factors' impacts on poultry production, addressing each topic in isolation, especially that there is a lack of predictive analysis connecting environmental variables with production performance. To date, little information is available that simultaneously addresses these issues. A systematic investigation is therefore necessary to fill this gap.

The current study was designed to initially investigate the changing patterns of production performance and environmental factors in a large-scale henhouse. Subsequently, we compared and evaluated the relative importance of each environmental variable using a combination of GRA and AHP. Finally, we developed a model to predict laying rate based on multiple environmental variables, with the goal of providing practical guidance for poultry farming.

MATERIALS AND METHODS

Birds and Experimental Design

The experiments were conducted at Xunwu Deqingyuan Agricultural Technology Co. Ltd, located at latitude 114°57′ and longitude 25°51′. All birds (47-wk-old Hy-Line Grey laying hens) were housed in 7 rows of 8-tier cages, with 4 tiers on the second floor and 4 tiers on the first floor. Each row consisted of 180 cages measuring 60 cm in length, 64 cm in width, and 46 cm in height on each side of each tier. Each cage housed 8 laying hens, resulting in an approximate total of 160,000 laying hens in the henhouse. The hen house adopted the longitudinal ventilation mode, and fresh air entered the henhouse through inlets on both sides of the gable wall, while exhaust occurs at the end of the henhouse (Figure 1). Artificial light was provided using LED strips and photoperiod was maintained at 16L:8D. Feed and water were provided ad libitum, and all diets were presented in mash form. All diets were formulated to meet the nutrient requirement for laying hens suggested by National Research Council (1994).Figure 1 Schematic diagram of measurement points. (A) In the horizontal direction, cages from 22 to 24 were designated as the wet curtain area (WCA) of the henhouse, cages from 67 to 69 as the near wet curtain area (NWCA), cages from 112 to 114 as the near fan area (NFA), and cages from 157 to 159 as the fan area (FA). (B) In the vertical direction, the numbers 1, 3, 5, and 7 correspond to the first, third, fifth, and seventh tiers, respectively.

Figure 1

Due to the symmetrical structure of the henhouse, we only measured the 4 rows of cages on one side. To select more representative measurement points, we divided it into 4 parts. For each part, we selected 3 cages positioned at the center horizontally to represent the area. The study involved a total of 1,536 birds distributed across 64 measurement points, as shown in Figure 1. Specifically, in the horizontal direction, cages from 22 to 24 were designated as the wet curtain area (WCA) of the henhouse, cages from 67 to 69 as the near wet curtain area (NWCA), cages from 112 to 114 as the near fan area (NFA), and cages from 157 to 159 as the fan area (FA). In the vertical direction, the numbers 1, 3, 5, and 7 correspond to the first, third, fifth, and seventh tiers, respectively, with the first tier being the floor directly above ground level. The measurement point's height is approximately at the back height of the chickens near the cage door. The entire experiment lasted for 1 mo.

Approval for all experimental procedures involving animals was granted by the Animal Care and Use Committee of the Anhui Academy of Agricultural Sciences (approval number A11-CS06; date of approval: September 21, 2011).

Measurement of Production Performances and Egg quality Analysis

Production performances of laying hens, including hen-day egg production, the number of defective eggs (misshapen eggs, gross-cracked, broken, softshell and shell-less eggs, excessively small or large eggs), and the number of dead birds, were recorded daily for each measurement point. Before the end of the experiment, forty-eight eggs per group were collected for determination of egg quality. Egg weight, albumen height, yolk color, and Haugh units were assessed with an automatic egg analyzer (EA-01, Tenovo International Co., Ltd., Beijing, China) following the manufacturer's instructions. Shell color was measured using a reflectometer (PRS-Evans Electroselenium Ltd, Halstead, Essex) on a scale from 0 (black) to 79.9 (white), with measurements taken at the top, middle, and bottom of the eggs. The mean of 3 measurements represented eggshell color. Each egg's length and width were measured using a digital vernier caliper (DL91150, Deli Group Ltd., Ningbo, China), and the egg shape index was calculated accordingly. Shell thickness was measured at the bottom, top, and middle of the eggshell using a shell thickness meter (ESTG-1, ORKA Food Technology Co., Ltd., Ramat Hasharon, Israel). The yolks were separated from albumen and weighed.

Measurement of Serum Biochemical Indicators and Antibody Titers

Blood samples (3 ml/bird, 48 birds/group) were collected by puncturing the brachial vein with a sterilized syringe. Following collection, the samples were centrifuged at 4000 rpm for 10 min, and the clear supernatants were transferred to 1.5 ml microcentrifuge tubes. A portion was delivered to Xinle Biotechnology Laboratories (Anhui Xinle Biotechnology Co., Ltd, Hefei, China) for the determination of follicle-stimulating hormone (FSH), luteinizing hormone (LH), total antioxidant capacity (TAOC), superoxide dismutase (SOD), malondialdehyde (MDA), heat shock protein 70 (HSP 70), glutathione peroxidase (GPX), cortisol, total cholesterol (TC), triglyceride (TG), and blood urea nitrogen (BUN). The remaining supernatants were used to measure antibody titers against H5 (Re-13, Re-14), H7 and H9 subtypes of avian influenza virus (AIV) and Newcastle disease virus (NDV) using hemagglutination inhibition (HI) tests as described by Xie et al. (Xie, Wang et al. 2008). The antibod titers were expressed as log2 of the reciprocal of the last serum dilution showing hemagglutation inhibition.

Environmental Data Collection

Environmental variables, including T, RH, AV, LI, and concentrations of NH3, CO2, and PM2.5 were collected 3 times daily at 6:00, 12:00, and 18:00 using an intelligent measuring instrument jointly developed by the Institute of Animal Husbandry and Veterinary Medicine at Anhui Academy of Agricultural Science and the Hefei Institute of Intelligent Machines at the Chinese Academy of Sciences. THI was calculated following the method outlined by Wang et al. (2018). The environmental variables were calculated by averaging the data collected during 3 periods, which were then used for subsequent analysis.

Weight Determination of Environmental Variables

Considering the potential differential effects of various environmental variables on laying hen performance, we evaluated the weight of each environmental variable. Specifically, we initially employed GRA to evaluate the relationship between each variable and laying rate, which was then integrated into AHP to improve the construction of the judgment matrix. This process may be described as follows.

GRA

GRA is a statistical method used to measure the degree of correlation between different factors. In this study, GRA was employed to analyze the degree of influence of each factor on the laying rate, aiming to overcome the shortcomings of AHP. This process may be described as follows.(1) Determination of reference sequence and comparison sequence

Usually, researchers designate the evaluation variables as the comparison sequence, while selecting the target sequence based on the objective of the studied problem as the reference sequence. In this article, comparison sequence was defined as environmental variables, abbreviated as Xij,(1) Xij=[x1(1),x2(1)…,xi(j)]

Where i=1, 2, ...64, j=1, 2, ...7 while reference sequence was defined as laying rate, abbreviated as X0.(2) X0=[x1(0),x2(0)…,xi(0)]

where i=1, 2, ...64

‘i’ is the number of measurement points and, ‘j’ is the number of parameters.(2) Nondimensionalization of variables

Nondimensionalization is a technique used to simplify mathematical models by removing units of measurement. Environmental variables were measured in different units, requiring nondimensionalization to streamline their analysis and comparison. Nondimensionalization process involves calculating the mean of each parameter column and then dividing each element in that column by its respective mean.(3) Calculation of grey relational coefficient and grey relational degree

The grey relational coefficient γ(xi(j), xi(0)) indicates the degree of correlation between 2 sequences being compared. It ranges from 0 to 1, with 1 signifying a perfect correlation or similarity between the sequences, while 0 indicates no correlation.

The grey relational coefficient is defined as follows.(3) γ(xi(j),xi(0))=miniminj|xi(j)−xi(0)|+ρ*maximaxj|xi(j)−xi(0)||xi(j)−xi(0)|+ρ*maximaxj|xi(j)−xi(0)|

Where ρ is the distinguishing coefficient, ρ∈ [0,1] and usually ρ=0.5.

The grey relational degree γ(x(j), x(0)) is the average value of a series of grey relational coefficients, indicating the degree of association between the reference sequence and the evaluation objects. The grey relational coefficient is defined as follows:(4) γ(x(j),x(0))=1i∑k=1iγ(xi(j),xi(0))

Construction of Improved Judgment Matrix and Determination of Weight

In this process, GRA’ results were used to construct the improved judgment matrix. This approach aimed to reduce subjectivity and enhance both the objectivity and reliability of the evaluation system. The specific steps were as follows:(1) Construction of improved judgment matrix

Currently, AHP was employed to construct the judgment matrix using Saaty's (1–9) scale (Table 1) (Saaty, 1994). Environmental variables were compared pairwise based on gray relation degree, which lead to the construction of an improved judgment matrix. Subsequently, the weights of each variable were calculated to form a weight vector B.(2) Consistency check of the judgment matrix

Table 1 Saaty's (1–9) scale.

Table 1Scale	Meaning	
1	i is equally important as j	
3	i is as moderately more important as j	
5	i is strongly more important as j	
7	i is very strongly more important as j	
9	i is extremely more important as j	
2, 4, 6, 8	Intermediate values between the 2 adjacent judgments	
The inverse of the scale	If activity i has one of the above numbers assigned to it when compared with activity j, then j has the reciprocal values when compared with i	

A consistency check was performed by calculating the consistency ratio (CR = CI/RI), where RI represented the random index (Table 2) and CI stands for the consistency index. RI denoted the variability with changes in dimensions, while CI can be calculated as CI = (λmax - n)/(n-1), λmax was the maximum eigenvalue of judgment matrix, n was the number of environmental factors. If CI was not greater than 0.10, the pairwise comparison matrix's consistency was deemed acceptable.Table 2 Values of the random index for different matrix orders.

Table 2n	1,2	3	4	5	6	7	8	
RI	0	0.58	0.90	1.12	1.24	1.32	1.41	
Abbreviations: n, the number of environmental factors; RI, random index.

Construction and Performance Evaluation of Laying Rate Prediction Model

The multi-variable prediction model was constructed using the random forest algorithm to achieve robust and accurate predictions of laying rate. The construction of the prediction model proceeded as follows:Dataset partitioning. In this study, we randomly divided the collected data into a training dataset, which comprised 75% of the data, and an independent testing dataset, which comprised the remaining 25% of the data. T, RH, THI, AV, LI, NH3, CO2, and PM2.5 were used as input features, while laying rate was chosen as the output feature. The programs were written and ran in MATLAB R2019a software (Mathworks Inc., Matick, MA).

Construction of laying rate prediction model. The TreeBagger function was employed to construct a random forest model, with certain parameters specified to regulate its behavior, including the number of decision trees, minleaf node size, etc. These parameters can be dynamically adjusted during program runtime to enhance prediction accuracy. Random forest prediction models constructed numerous individual decision trees at training. Predictions from all trees were pooled to make the final prediction. After numerous attempts, the program parameters were set as follows: the number of decision trees=2500, min leaf size =1, method=regression.

Evaluation indicators. Coefficient of determination (R2), mean squared error (MSE), root mean squared error (RMSE) and mean absolute error (MAE) were used to evaluate the model's performance and prediction accuracy based on the actual and predictive values. R2, also known as the coefficient of determination, was utilized to evaluate the proportion of variance in the dependent variable that can be accounted for by the independent variable. Values of R2 that are closer to one indicate a better-fitting model.

The function can be expressed as:(5) R2=1−∑i=1m(yi−y^)2∑i=1m(yi−y¯)2

RMSE represents the square root of the mean squared error between predicted and observed outcomes. The lower the RMSE, the better the model's ability to predict accurately. The function is formulated as:(6) RMSE=∑i=1m(yi−y^)2m

MAE measures the average magnitude of errors between predicted and observed outcomes, and A value closer to 0 indicates a better fit. The function can be expressed as:(7) MAE=∑i=1m|yi−y^|m

m represents the total number of test samples, yi denotes the observed outcomes, y^ signifies the predicted outcomes, and y¯ represents the mean value.

Due to the stochastic nature of the random forest algorithm's search mechanism, the program was repeated 10 times, and the best result was adopted.

Statistical Analysis

Statistical analysis was performed using Origin Pro 8.0 software (OriginLab Corporation, Northampton, USA). GRA, AHP, and construction and performance evaluation of laying rate prediction models were conducted using MATLAB R2019a software. Results (mean ± standard error) with different superscripts within the same row indicate significant differences at P < 0.05.

RESULTS

Production Performances and Egg Quality

Body weight and laying rate of hens decreased gradually from WCA to FA (P < 0.05, Table 3). Similarly, egg weight and eggshell thickness also decreased gradually from WCA to FA (P < 0.05, Table 3). Eggshell strength and yolk weight in FA exhibited the lowest values (P < 0.05, Table 3). There were no significant differences in other egg quality (P > 0.05).Table 3 Production performances and egg quality of laying hens in different areas.

Table 3Performances	WCA	NWCA	NFA	FA	
Body weight (g)	1824.44 ± 20.99a	1752.13 ± 24.18ab	1729.54 ± 23.58b	1728.13 ± 25.17b	
Laying rate (%)	89.35 ± 0.52a	82.69 ± 0.52b	77.95 ± 0.59c	74.96 ± 0.63d	
Egg weight (g)	62.11 ± 0.51a	60.28 ± 0.61ab	59.24 ± 0.55bc	57.58 ± 0.82c	
Egg shape index (mm:mm)	1.31 ± 0.01	1.33 ± 0.01	1.34 ± 0.01	1.33 ± 0.01	
Eggshell color	48.97 ± 0.56	48.31 ± 0.78	48.90 ± 0.84	48.68 ± 0.68	
Eggshell thickness (mm)	0.35 ± 0.00a	0.35 ± 0.00ab	0.34 ± 0.00bc	0.32 ± 0.00c	
Shell strength (kg/cm2)	4.43 ± 0.07a	4.34 ± 0.07a	4.22 ± 0.11a	3.75 ± 0.09b	
Yolk weight (g)	16.88 ± 0.18a	16.13 ± 0.22a	16.27 ± 0.21a	15.11 ± 0.30b	
Yolk color	11.35 ± 0.12	11.38 ± 0.09	11.30 ± 0.12	11.08 ± 0.10	
Albumen height (mm)	6.27 ± 0.14	6.16 ± 0.17	6.46 ± 0.19	6.51 ± 0.14	
Haugh units	76.88 ± 1.27	76.72 ± 1.38	79.22 ± 1.39	80.26 ± 1.13	
Abbreviations: WCA, the wet curtain area; NWCA, the near wet curtain area; NFA, the near fan area; FA, the fan area. Results (mean ± standard error) with different superscripts within the same row indicate significant differences at P < 0.05.

Biochemical Indicators and Antibody Levels in Laying Hens

Serum levels of FSH and LH gradually decreased from WCA to FA (P < 0.05, Table 4). TAOC activity and SOD content was relatively low at NFA and FA (P < 0.05, Table 4). No significant difference was observed in other indicators (P > 0.05).Table 4 Biochemical indicators of laying hens in different areas.

Table 4Biochemical indicators	WCA	NWCA	NFA	FA	
FSH (ng/mL)	37.27 ± 2.05a	37.30 ± 1.3a	32.05 ± 0.95b	31.19 ± 1.14b	
LH (pg/mL)	3367.72 ± 191.71a	3300.29 ± 152.39a	2965.79 ± 147.71ab	2561.83 ± 156.5b	
TAOC (mM)	0.34 ± 0.05ab	0.42 ± 0.06a	0.23 ± 0.05b	0.22 ± 0.04b	
SOD (U/mgprot)	18.17 ± 0.89ab	19.62 ± 0.79a	15.65 ± 1.09b	14.23 ± 1.37b	
MDA (nmol/mL)	9.39 ± 1.67	9.01 ± 1.83	13.22 ± 2.70	12.59 ± 2.75	
HSP 70 (ng/mL)	11.6 ± 0.73	12.11 ± 0.84	11.36 ± 0.74	10.27 ± 0.81	
GPX	317.09 ± 17.72	334.51 ± 18.81	304.55 ± 20.98	305.25 ± 40.22	
Cortisol (ng/mL)	44.74 ± 5.11	56.00 ± 7.14	47.68 ± 5.06	43.44 ± 5.27	
TG (mmol/L)	13.91 ± 1.36	13.32 ± 1.54	16.06 ± 1.08	16.70 ± 1.31	
TC (mmol/L)	1.52 ± 0.19	1.38 ± 0.15	1.61 ± 0.12	1.45 ± 0.12	
BUN (mmol/L)	1.13 ± 0.25	0.76 ± 0.11	1.18 ± 0.11	1.33 ± 0.19	
Abbreviations: WCA, the wet curtain area; NWCA, the near wet curtain area; NFA, the near fan area; FA, the fan area. Results (mean ± standard error) with different superscripts within the same row indicate significant differences at P < 0.05.

The antibody levels of H5 Re-13 gradually decreased from WCA to FA (P < 0.05, Table 5). In FA, the antibody levels of H7 were the lowest (P < 0.05, Table 5). There were no significant differences in different areas regarding the antibody levels of H5 Re-14, H9, and ND (P > 0.05).Table 5 Antibody levels of laying hens in different areas (log2).

Table 5Antibody levels	WCA	NWCA	NFA	FA	
H5 Re-13	8.22 ± 0.17a	8.00 ± 0.18ab	7.81 ± 0.20ab	7.46 ± 0.18b	
H5 Re-14	8.91 ± 0.15	8.77 ± 0.16	8.60 ± 0.17	8.37 ± 0.18	
H7	8.69 ± 0.17a	8.43 ± 0.18ab	8.71 ± 0.19a	7.97 ± 0.20b	
H9	9.61 ± 0.17	9.63 ± 0.16	9.27 ± 0.21	9.76 ± 0.17	
ND	8.07 ± 0.20	7.85 ± 0.18	7.63 ± 0.18	7.84 ± 0.20	
Abbreviations: WCA, the wet curtain area; NWCA, the near wet curtain area; NFA, the near fan area; FA, the fan area. Results (mean ± standard error) with different superscripts within the same row indicate significant differences at P < 0.05.

Changing Patterns of Environmental Variables

Values for T, THI, AV, CO2, and PM2.5 gradually increased from WCA to FA (P < 0.05, Table 6). Conversely, the value for RH gradually decreased from FA to CA (P < 0.05). Additionally, the value for the light intensity was the highest in FA. there was no remarkable difference in NH3 concentration (P > 0.05).Table 6 Changing patterns of environmental variables in different areas.

Table 6Environmental variables	WCA	NWCA	NFA	FA	
T (°C)	28.29 ± 0.07d	29.61 ± 0.05c	30.29 ± 0.05b	30.99 ± 0.07a	
RH (%)	86.45 ± 0.38a	79.24 ± 0.26b	77.96 ± 0.32c	76.40 ± 0.28d	
THI	80.95 ± 0.09d	82.13 ± 0.08c	83.01 ± 0.07b	83.86 ± 0.09a	
AV(m/s)	1.31 ± 0.02c	2.03 ± 0.03b	2.11 ± 0.02ab	2.15 ± 0.03a	
LI (lx)	6.26 ± 0.13c	7.33 ± 0.14b	6.31 ± 0.11c	8.26 ± 0.14a	
NH3 (mg/m3)	0.99 ± 0.05	1.05 ± 0.05	1.05 ± 0.05	0.98 ± 0.06	
CO2 (mg/m3)	1753.56 ± 8.66d	1985.79 ± 12.51c	2228.93 ± 14.42b	2407.98 ± 14.87a	
PM2.5 (μg/m3)	1.44 ± 0.02d	1.72 ± 0.02c	1.93 ± 0.03b	2.13 ± 0.03a	
Abbreviations: WCA, the wet curtain area; NWCA, the near wet curtain area; NFA, the near fan area; FA, the fan area. Results (mean ± standard error) with different superscripts within the same row indicate significant differences at P < 0.05.

Grey Relational Degree and Weights of Environmental Variables

Figure 2 visually illustrated the differential relationship between environmental variables and laying rate. Further analysis indicated that the grey relational degree, ranked from high to low, was as follows: RH, THI, T, LI, AV, PM2.5, NH3 and CO2 (Table 7).Figure 2 Comparison chart of environmental variables and laying rate. The X-axis represents the 1st to 64th measurement points, while the Y-axis represents the value of the measured indicator.

Figure 2

Table 7 The ranking of grey relational degree of environmental variable.

Table 7Environmental variables	RH	THI	T	LI	AV	PM2.5	NH3	CO2	
Grey relational degree	0.9929	0.9915	0.9313	0.9038	0.8977	0.8976	0.8971	0.4182	
The ranking of grey relational degree	1	2	3	4	5	6	7	8	

An improved judgment matrix was constructed based on the grey relational degree. The elements of this matrix, arranged from the upper left to the lower right, were RH, THI, T, LI, AV, PM2.5, NH3 and CO2. The consistency of the improved judgment matrix was evaluated using the CR, which yielded the following results: λmax = 8.6898, CI = 0.0985, and CR = 0.0699 < 0.1. The weight values were displayed and sorted in Figure 3.(8) A=[122344470.512334470.50.51234460.330.330.5124460.250.330.330.514460.250.250.250.250.251150.250.250.250.250.251150.140.140.170.170.170.20.21]

(9) B=[0.270.210.170.120.100.050.050.02]

Figure 3 Radar plot of rank-ordered weights of environmental variables.

Figure 3

Construction and Performance Evaluation of Laying Rate Prediction Model

Determination of the optimal number of decision trees

As depicted in Figure 4, the error peaks at 3 decision trees. With a subsequent increase in the number of decision trees, the error gradually diminished. After repeated attempts, it was conclusively established that the optimal number of decision trees was 2500, resulting in favorable prediction performance.Figure 4 Prediction error curves for various numbers of decision trees.

Figure 4

Prediction effect of laying rate prediction model

The model's prediction effect and error on laying rate were illustrated in Figure 5, Figure 6, respectively. The results revealed a high level of agreement between the model's predictions and observed outcomes, with a predictive error range of [−12.77%, 11.15%]. The error for the majority of samples was consistently within 7% (93.84%), and the distribution of positive and negative errors was relatively balanced.Figure 5 Observed versus predicted outcomes of laying rate prediction model.

Figure 5

Figure 6 Prediction error of laying rate prediction model.

Figure 6

Performance evaluation of laying rate prediction model

Performance evaluation of laying rate prediction model revealed that the R2, MSE, RMSE, and MAE for training set were 0.90404, 14.991, 3.8718 and 3.0279, respectively (Table 8). Furthermore, the R2, MSE, RMSE, and MAE for test set were 0.89995, 14.2608, 3.7764 and 0.1116, respectively (Table 8).Table 8 Error statistics of model predictions.

Table 8Dataset	R2	MSE	RMSE	MAE	
Training set	0.90404	14.991	3.8718	3.0279	
Testing set	0.89995	14.2608	3.7764	2.9961	

DISCUSSION

The current study aims to prove 3 key hypotheses: (1) Production performance and environmental conditions differ across different parts of the henhouse; (2) Each environmental variable has a distinct impact on laying rate; (3) Laying rate and environmental variable are closely linked, allowing for the development of a robust predictive model. To validate our hypotheses, we initiated an investigation into the production performance of laying hens in different areas. We observed differences in production performance. Numerous studies have demonstrated that the production performance of laying hens is influenced by various factors, including genetics, nutrition, diseases, and the environment (Wilson, 2017; Hocking, 2010; Nys, 2017; Bryden et al., 2021; Desbruslais et al., 2021; Kocaman et al., 2006; Abdisa and Tagesu, 2017; Jatav and Verma, 2021; Abd El-Hack et al., 2022). In this study, however, the chickens were of the same breed and fed diets with similar nutrient composition and form. Therefore, we speculate that the observed variations are likely due to differences in local environmental conditions. More specifically, body weight and laying rate of hens decreased gradually from WCA to FA. Many studies indicate that poor environmental conditions, particularly heat stress, could lead to a reduction in body weight and laying rate (Kursun, 2019 ; Kuchmistov 2022). Our results demonstrate that environmental conditions might gradually worsen from the wet curtain area to the fan area of the henhouse, where conditions could be even more unfavorable. However, the precise mechanism remains to be explored. Moreover, the average laying rate for the entire henhouse was 81%, which is below the standard laying rate for Hy-Line Grey, typically between 88% and 91%. The experiment was conducted during the high temperatures in summer months when birds have high metabolic activity, are covered in feathers, and lack sweat glands, making them susceptible to heat stress (Kim et al., 2021; Kim et al., 2022). In these conditions, birds alter their behavior and physiological responses to maintain thermoregulation. For example, they may spend less time feeding, more time drinking and panting, and more time resting (Lara and Rostagno, 2013). This can ultimately lead to a decrease in laying rate. Meanwhile, we observed a gradual decrease in egg weight and eggshell thickness from WCA to FA, and eggshell strength and yolk weight in FA exhibited the lowest values. Numerous studies have reported that heat stress significantly reduces egg weight, shell thickness, shell strength, and yolk weight (Nordstrom, 1973; Tanor et al., 1984; Lin et al., 2004; Chung et al., 2005; Franco-Jimenez et al., 2007; Allahverdi et al., 2013; Mignon-Grasteau et al., 2015, Bleu et al., 2019; Radwan 2020). A possible explanation is the reduction in available calcium, since heat stress reduces calcium intake and absorption in the gut (Quinteiro-Filho et al., 2010; Kim et al., 2020). There were no significant differences in other egg quality parameters. It is worth noting that although there was nearly a 4-point change in the Haugh unit value, the large standard error rendered this change statistically insignificant. We believe that this may be attributed to the limited number of eggs analyzed. Further investigation is warranted by increasing the number of eggs.

Blood is a bodily fluid in animals that is composed of blood cells and plasma. Hematological indices are useful in assessment of the health status, disease and other physiological functions. In the current study, serum levels of FSH and LH gradually decreased from WCA to FA. It is widely acknowledged that FSH and LH play a particularly important role in follicular development and ovulation. Their levels have been considered sensitive indicators of laying performance. Ragil et al. validated that optimal levels of FSH and LH can stimulate follicle growth fairly rapidly, leading to increased egg production due to the increased number of follicles developed and ovulated. The remarkable decrease in FSH and LH levels indicates a gradual decline in laying rate, which aligns with the previously described results on laying rate (Long et al., 2017; Cui et al., 2019; Barros et al., 2020; Prastiya et al., 2022). TAOC activity and SOD content were significantly lower at NFA and FA in the present study. TAOC and SOD are most widely used as the indicators of the body's antioxidant capability (An et al., 2019; Huang et al., 2019). The reduction in TAOC activity indicates that the balance between free radical generation and scavenging has been disrupted, with an increase in harmful free radicals exceeding the scavenging capacity of SOD. This leads to a decrease in antioxidant enzyme activity, which can ultimately impact overall health (Hu et al., 2021; Sun et al., 2023). We speculate that this decrease could be related to the lower air quality near the exhaust vent. The results were consistent with Sun et al., who reported that heat stress reduced TAOC levels and SOD activity (Sun et al., 2023).

Avian influenza and newcastle disease are 2 of the most devastating diseases of poultry and impose a severe economic burden on the poultry industry throughout the world. It is of fundamental importance to monitor the antibody titers of the flock. Our results revealed a gradual decline in the antibody levels of H5 Re-13 from WCA to FA. Moreover, the levels of H7 antibody in FA were the lowest among all areas. Given their crucial role in immune response and body protection, this phenomenon raises concerns regarding the effectiveness of immune protection for chickens at the fan area. Studies have demonstrated that chronic heat stress adversely affects animals' immune responses, heightening their susceptibility to infections (Mashaly et al., 2004; Jin et al., 2011; Meng et al., 2013; Goel 2021). There is a subtle relationship between immune response and regional heat stress although the precise link requires further investigation.

To better understand the reasons for performance variations among laying hens in different areas, the experiment subsequently investigated various environmental factors. The value for T gradually increased from WCA to FA, whereas RH exhibited the opposite trend. As the airflow passes through the wet curtain, which is positioned at the air inlet of the longitudinal ventilation system in the henhouse, the water absorbs heat from the air and introduces water vapor into the henhouse. Consequently, this lowers the air temperature and increases relative humidity. THI is a single value representing the combined effects of air temperature and humidity associated with the level of thermal stress. The THI can be categorized in 4 classes, as presented in the existing literature: normal (THI ≤ 74), moderate (75 ≤ THI ≤ 78), severe (79 ≤ THI ≤ 83), and very severe (THI ≥ 84) (Menezes et al., 2010; Lallo et al., 2018; Wang et al., 2018; Shin et al., 2024). Our findings displayed that THI value gradually increase from WCA to FA, indicating that the heat stress at the end of the henhouse is more severe compared to the front. This observation was highly consistent with the results of production performance mentioned above. Additionally, the average THI of the entire henhouse was approximately in the range of 80-84, hinting that chickens experience severe heat stress. Numerous studies have shown that heat stress is one of the most significant environmental stressors affecting poultry production worldwide. It causes various physiological changes, including oxidative stress, acid-base imbalance, and suppressed immune competence, leading to increased mortality, reduced feed efficiency, lower body weight, decreased feed intake, and poor laying rate, as well as lower egg weight and shell quality in laying hens (Lin et al., 2006; Lara and Rostagno 2013; Wasti et al., 2020; Goel 2021). Therefore, measures should be taken to alleviate its occurrence. AV value gradually increased from WCA to FA as the distance from fan decreased, CO2 value gradually increased from WCA to FA. CO2, originating from animal respiration as well as from manure breakdown. CO2 accumulation can occur due to the cumulative increase in the number of chickens and feces closer to the end of henhouse. Furthermore, the average CO2 concentration surpassed 1,500 mg/m3, which was higher than the recommended range (China, 1999), indicating the need to take measures to reduce CO2 concentration. LI value was the highest in FA, likely due to natural daylight entering the henhouse through the exhaust fan.

Given the potential differences in the impact of each environmental variable on production performance, we conducted a comprehensive evaluation of the weights for each environmental variable using the combination of GRA and AHP. It is well known that a poultry house can be considered a typical gray system. GRA, based on gray system theory, is a valuable tool for analyzing superficially inconsistent data. It enables researchers to identify key relationships among influential factors and determine which factors significantly impact the defined objectives. Meanwhile, AHP, developed by Saaty in 1971, is a systematic method for quantitatively analyzing complex, multicriteria systems. It allows for the decomposition of complex problems into simpler components, facilitating easy comparisons and calculations of factor weights. However, AHP tends to be highly subjective, which may impact the accuracy of the analysis. To address this, we combined GRA with AHP to reduce subjectivity and improve the reliability of the results. Our results confirmed the differential relationship between each environmental variable and laying rate, indicating that each environmental factor exerts a distinct impact on laying rate. Further analysis indicated that the grey relational degree, ranked from high to low, was as follows: RH, THI, T, LI, AV, PM2.5, NH3, and CO2. The grey relational degree indicates the degree of influence exerted by the comparability sequence on the reference sequence. The higher the grey relational degree, the closer the relationship between the 2 indexes and the greater the influence on each other. Result suggest that priority should be given to environmental variables with high grey relational degrees in regulating henhouse environment. The consistency of the improved judgment matrix displayed that CR = 0.0699 < 0.1, it means that the matrix passes the consistency test. The weights of environmental variables, obtained according to the improved judgment matrix and in descending order, were RH, THI, T, LI, AV, PM2.5, NH3 and CO2, which indicates that humidity and temperature are the primary indicators. In summary, the combination of GRA and AHP emphasizes objective data while incorporating the subjective flexibility of AHP. This integration enables the synthesis of subjective and objective characteristics to a certain extent, enhancing the rigor and scientific validity of the evaluation system.

To comprehend the complex relationship between environmental variables and laying rate and to achieve accurate prediction, we constructed a multi-variable prediction model using random forest algorithm. The results indicated that the error peaks at 3 decision trees, but gradually diminishes with an increase in the number of decision trees. After repeated attempts, the model yielded favorable results for R2, MSE, RMSE, and MAE with a set of 2,500 decision trees. Consequently, we decided to use 2,500 decision trees for subsequent analysis. Further investigation revealed that the model's predictions closely aligned with the actual outcomes, demonstrating a predictive error range of [−12.77%, 11.15%]. The error for the majority of samples consistently remained within 7%, and there was a relatively balanced distribution of positive and negative errors. These findings suggest that the model exhibits robust predictive performance on this dataset. Performance evaluation of laying rate prediction model was conducted to quantitatively assess its predictive capabilities. The results indicate that R2 values for the training set and the test set are 0.90404 and 0.89995, respectively, and other performance indicators were generally within the expected error range, further reinforcing the notion that the model demonstrates strong predictive effects. Conclusively, our findings strongly suggest that laying rate prediction model exhibits robust generalization ability and high accuracy in predicting laying rate though environmental variables

In conclusion, our study revealed significant differences in both the production performance of laying hens and the environmental conditions across different areas of the henhouse. we also found that different environmental factors have distinct impacts on laying rate, with humidity and temperature identified as the primary factors. Finally, a multi-variable prediction model was constructed, exhibiting high accuracy in predicting laying rate. Our findings offer valuable insights into understanding the relationship between the performance of laying hens and the microenvironment they inhabit.

DISCLOSURES

The authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.

ACKNOWLEDGMENTS

This study was supported by China Agriculture Research System of MOF and MARA [CARS-40-K21 ]; Anhui Provincial Key Laboratory of Livestock and Poultry Product Safety Engineering [XMT2022-3 ]; Joint research project of excellent broiler breed of Anhui province; Key research and development project of Anhui province [2023n06020002 ]. We want to express our sincere appreciation to the staff at Beijing Deqingyuan Agricultural Technology Co. Ltd and Xunwu Deqingyuan Agricultural Technology Co. Ltd for their assistance and contributions.

Ethics statement: Approval for all experimental procedures involving animals was granted by the Animal Care and Use Committee of the Anhui Academy of Agricultural Sciences (approval number A11-CS06; date of approval: September 21, 2011).

Author contributions: YL performed the animal trail, finished the statistical analysis and drafted the manuscript. KZ conceived and designed the entire experimental plan. RYM, RRQ, JYL, WL, YW & ZL were responsible for measuring egg quality and antibody titers. HLL contributed to the construction of laying rate prediction model. SJL and XLC performed on-site measurements of environmental variables. ZDY, XML & XSW provided essential experimental animals and site. All authors read and approved the final version of the manuscript.

Availability of data and materials: The data for the current study are available from the corresponding author upon reasonable request.
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