Method and device for evaluating heat stress resistance of boar semen traits

Abstract

The invention relates to the technical field of boar breeding, and discloses a boar semen trait heat stress resistance evaluation method which comprises the following steps: step 1, acquiring historical boar semen phenotypic data and historical temperature and humidity data, and establishing a thermal index according to the historical temperature and humidity data; step 2, adopting a quantile regression method to carry out 5% quantile regression on the warming index according to the historical boar semen phenotype data, delimiting the warming index 72 as a heat stress critical point, and constructing a heat stress resistance reference model; according to the heat stress resistance reference model, constructing a heat stress resistance evaluation model; and step 3, inputting to-be-evaluated boar semen phenotypic data and the corresponding thermal index into the heat stress resistance evaluation model, and outputting a heat stress resistance index by the heat stress resistance evaluation model. According to the method, the breeding boars with high heritability and high heat stress resistance can be accurately bred. Meanwhile, the invention further provides a device for evaluating the heat stress resistance of the boar semen traits.

Description

Technical Field

[0001] The present invention relates to the technical field of breeding pigs, and in particular to a method and device for evaluating heat stress resistance of boar semen. Background Art

[0002] Boar stations are crucial for promoting regional joint pig breeding. Stable and high-quality semen is crucial for elite boars to realize their genetic potential. Heat stress induced by high temperatures leads to seasonal variations in boar semen quality, causing varying degrees of distress for both the pig industry and pig genetic improvement efforts, and attracting significant attention in production. Although boar houses often utilize cooling measures such as water curtains and sprinklers during hot weather, continuous monitoring of indoor and outdoor temperature and humidity during summer months revealed a significant positive correlation between the indoor and outdoor heat indices (r=0.70, P<0.01), indicating that the temperature control system is unable to maintain consistent indoor temperature and humidity, causing boars to experience heat stress even in hot weather, which in turn impairs semen quality.

[0003] In studies of conventional boar semen quality selection, the heritability of various parameters has been accurately estimated using various methods: semen volume 0.14-0.25, sperm density 0.13-0.26, sperm motility 0.05-0.18, and sperm abnormality rate 0.12-0.43. The abnormality rate is negatively correlated with volume, density, and motility. Using this research method, using seasonal or monthly temperature divisions, the heritability of boar semen quality is 0.16-0.27 for semen volume, 0.12-0.22 for sperm density, 0.08-0.25 for sperm motility, and 0.15-0.25 for sperm abnormality rate. These heritabilities are not significantly different from those of conventional boar semen quality, indicating that genetic selection can improve boar semen quality and heat stress resistance.

[0004] The heat stress resistance of boar semen quality is generally roughly divided into environmental temperature and humidity by month or season. my country's climate regions are mainly divided into five temperature zones: tropical, subtropical, warm temperate, mid-temperate and cold temperate. This makes the environmental temperature divided by month or season vary greatly in different regions. In this way, the screening results fluctuate greatly. Moreover, there is a lack of unified standards for the heat stress resistance of boar semen, making it difficult to breed boars with high heritability and high heat stress resistance.

[0005] The technical problem to be solved by the present invention is: how to establish an evaluation method that can screen out boars with high heat stress resistance and stable inheritance. Summary of the Invention

[0006] The main purpose of the present invention is to provide a method for evaluating the heat stress resistance of boar semen traits. According to the boar semen phenotypic data and the heat index, a quantile regression method is used to construct a heat stress resistance benchmark model. The heat stress resistance index is obtained through the heat stress resistance benchmark model. The semen phenotypic data of the boar to be evaluated is compared with the heat stress resistance index through the heat stress resistance evaluation model to obtain the heat stress resistance index. In this way, the strength of the boar's heat stress resistance can be intuitively and accurately obtained, providing a reference for subsequent boar breeding.

[0007] At the same time, a device for evaluating the heat stress resistance of boar semen properties is also provided.

[0008] To achieve the above objectives, the technical solutions adopted in this application are:

[0009] A method for evaluating heat stress resistance of boar semen comprises the following steps:

[0010] Step 1: Obtain historical boar semen phenotypic data and historical temperature and humidity data, and establish a thermal index based on the historical temperature and humidity data;

[0011] Step 2: Using the quantile regression method, the historical boar semen phenotypic data were regressed against the heat index at the 5% quantile. A heat index of 72 was defined as the heat stress critical point, and a heat stress resistance benchmark model was constructed. Based on the heat stress resistance benchmark model, a heat stress resistance evaluation model was constructed.

[0012] Step 3: Input the semen phenotypic data of the boar to be evaluated and the corresponding heat index into the heat stress resistance evaluation model, and the heat stress resistance evaluation model outputs the heat stress resistance index; the higher the heat stress resistance index, the stronger the boar's heat stress resistance.

[0013] Preferably, the method further comprises step 4: using ASReml software to calculate the heritability and genetic correlation of heat stress resistance in boar semen phenotypic data, and establishing genetic parameters for heat stress resistance.

[0014] Preferably, the boar semen phenotypic data include semen volume, sperm motility, sperm density, and sperm deformity rate.

[0015] Preferably, the formula of the heat stress resistance benchmark model is:

[0016]

[0017] in, For X ijk Down Y ijk τ-conditional quantile (τ∈(0,1)), X ijk Y is the ambient temperature index on the day of semen collection, ijkis the semen volume, semen density, sperm motility or sperm deformity rate of the boar semen of the i-th boar, the j-th semen collection, and the k-th farm-year-season (HYS), HYS m,ijk is the field-year-season dummy variable, the field is two independent production units in the same area, and the season is divided into cold season and hot season, γ m (τ) is the fixed effect of the mth field-year-season, β0(τ) is the intercept of the τ-quantile regression, β1(τ) is the fixed effect of the thermal index on the τ-quantile of the semen index, is the individual random intercept of the i-th boar, ∈ ijk is a random error.

[0018] Preferably, the formula of the heat stress resistance evaluation model is:

[0019] When the boar semen phenotypic data to be evaluated is the sperm deformity rate,

[0020]

[0021] When the boar semen phenotypic data to be evaluated is semen volume, sperm motility or sperm density,

[0022]

[0023] Among them, TTI i is the heat stress resistance index of the i-th boar, ranging from [0, 1], X ij is the thermal index, n i For the boar exposed to high temperature and high humidity (such as X ij ≥72), I is the indicator function (takes 1 when the condition is met, otherwise takes 0), X is the predicted value of the heat stress resistance benchmark model ij The corresponding 5% conditional quantile.

[0024] Preferably, the step 1 comprises the following steps:

[0025] Step A1: Obtain historical boar semen phenotypic data, process the historical boar semen phenotypic data, and exclude semen phenotypic data of boars with less than 50 semen collections;

[0026] Step A2: Obtain historical temperature and humidity data, input the historical temperature and humidity data into the heat index formula, and establish the heat index.

[0027] Preferably, the step 1 further comprises step A3: using a thermometer and a hygrometer to measure the indoor and outdoor temperature and humidity of the boar station where the boar semen phenotype data is located, and calculating the correlation between the indoor and outdoor temperature and humidity by Pearson correlation.

[0028] ASReml Software: ASReml is an excellent data analysis software for fitting linear mixed-effects models, suitable for analyzing large data sets and complex statistical models. ASReml is widely used by scientists and researchers in the biological sciences. ASReml is used in crop and livestock genetics and breeding, for example, to evaluate the growth and disease resistance of different crop varieties and estimate the heritability of growth and survival traits in species such as pigs, cattle, and fish and shrimp.

[0029] At the same time, a device for evaluating the heat stress resistance of boar semen is provided, comprising the following units:

[0030] Data acquisition unit: used to obtain historical boar semen phenotypic data and historical temperature and humidity data, and establish a thermal index based on the historical temperature and humidity data;

[0031] Model building unit: used to perform 5% quantile regression on the heat index using the quantile regression method to delineate a heat index of 72 as the heat stress critical point, and to construct a heat stress resistance benchmark model; based on the heat stress resistance benchmark model, a heat stress resistance evaluation model is constructed;

[0032] Data evaluation unit: used to input the semen phenotypic data of the boar to be evaluated and the corresponding heat index into the heat stress resistance evaluation model, and the heat stress resistance evaluation model outputs the heat stress resistance index; the higher the heat stress resistance index, the stronger the boar's heat stress resistance.

[0033] Compared with the existing technology, this solution has the following beneficial effects:

[0034] The evaluation method of this scheme constructs a heat stress resistance benchmark model based on the quantile regression method. This model utilizes boar semen phenotypic data, thermal index, environmental fixed effects, and the fixed effect of thermal index on semen index τ-quantile to obtain a benchmark that can accurately measure boar heat stress resistance. Then, by inputting the semen phenotypic data of the boar to be evaluated and the corresponding thermal index into the heat stress resistance evaluation model, the heat stress resistance index is obtained. By observing the size of the heat stress resistance index, breeding boars with high heat stress resistance and stable inheritance can be intuitively and accurately screened out, thereby improving the efficiency and benefits of the pig farming industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a flow chart of the method for evaluating the heat stress resistance trait of boar semen in Example 1;

[0036] Figure 2 This is a data graph of historical temperatures in Example 1;

[0037] Figure 3 This is a data graph of historical humidity in Example 1;

[0038] Figure 4 This is a data graph of the correlation between indoor and outdoor temperature and humidity in Example 1;

[0039] Figure 5 This is a block diagram of the evaluation device for the boar semen trait heat stress resistance in Example 2. DETAILED DESCRIPTION

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Generally, the components of the present application described and shown in the drawings herein can be arranged and designed in various different configurations.

[0041] Example 1

[0042] refer to Figure 1-4 A method for evaluating heat stress resistance of boar semen comprises the following steps:

[0043] Step 1: Obtain historical boar semen phenotypic data and historical temperature and humidity data, and establish a thermal index based on the historical temperature and humidity data;

[0044] Preferably, the step 1 comprises the following steps:

[0045] Step A1: Obtain historical boar semen phenotypic data, process the historical boar semen phenotypic data, and exclude semen phenotypic data of boars with less than 50 semen collections; boar semen phenotypic data includes semen volume, sperm motility, sperm density, and sperm deformity rate.

[0046] In this embodiment, semen collection data of 2,467 Duroc boars from a large boar breeding station in my country from 2016 to 2021 were collected, totaling 274,332 phenotypic data of semen collection tests. The phenotypic data were sorted, and the phenotypic data of boars with less than 50 semen collection data were eliminated. The phenotypic data are shown in Table 1.

[0047] Table 1 Phenotypic data

[0048] type quantity Minimum Maximum average value Standard deviation Coefficient of variation (%) Semen volume (L) 2467 0.02 1.959 0.163 0.003 38.428 <![CDATA[Sperm density (10 9 / mL)]]> 2467 0.01 19.97 5.063 2.303 45.485 Sperm motility 2467 0.5 1 0.899 0.058 6.466 Sperm deformity rate (%) 2467 0 0.6 0.0999 0.068 1.986

[0049] Step A2: Obtain historical temperature and humidity data, input the historical temperature and humidity data into the heat index formula, and establish the heat index.

[0050] In this embodiment, the historical temperature and humidity data sets of the stations near the boar station where the phenotypic data were collected were collected from 2016 to 2021 and published by the European Centre for Medium-Range Weather Forecasts. The temperature data and humidity data at 15:00 were sorted. The temperature data were as follows: Figure 2 As shown, humidity data is as follows Figure 3 As shown, the temperature data and humidity data at 15:00 are input into the heat index formula to establish the heat index.

[0051] The formula for the heat index is:

[0052] THI=T-0.55×(1-0.01×RH)×(T-14.5)

[0053] Where THI is the heat index, T is the temperature in degrees Celsius (°C), and RH is the relative humidity (%).

[0054] Step A3: Use a thermometer and a hygrometer to measure the indoor and outdoor temperature and humidity of the boar stud where the boar semen phenotyping data is collected, and calculate the correlation between indoor and outdoor temperature and humidity using Pearson correlation.

[0055] In this embodiment, a thermometer and a hygrometer were used to measure the indoor and outdoor temperature and humidity of the boar station, and the correlation between the indoor and outdoor temperature and humidity was calculated by Pearson correlation. The correlation coefficient of the indoor and outdoor temperature and humidity of the boar station was found to be r = 0.7 (P < 0.07). Figure 4 As shown in the figure, it is proved that the correlation between indoor and outdoor temperature and humidity of the boar station is high and positive. Therefore, using outdoor temperature and humidity for calculation can also produce the same expected results as indoor temperature and humidity. Moreover, outdoor temperature and humidity data are relatively easy to obtain.

[0056] Step 2: Using the quantile regression method, the historical boar semen phenotypic data were regressed against the heat index at the 5% quantile. A heat index of 72 was defined as the heat stress critical point, and a heat stress resistance benchmark model was constructed. Based on the heat stress resistance benchmark model, a heat stress resistance evaluation model was constructed.

[0057] In this embodiment, the formula of the heat stress resistance benchmark model is:

[0058]

[0059] in, For X ijk Down Y ijk τ-conditional quantile (τ∈(0,1)), X ijk Y is the ambient temperature index on the day of semen collection, ijk is the semen volume, semen density, sperm motility or sperm deformity rate of the boar semen of the i-th boar, the j-th semen collection, and the k-th farm-year-season (HYS), HYS m,ijk is the field-year-season dummy variable, the field is two independent production units in the same area, and the season is divided into cold season and hot season, γ m(τ) is the fixed effect of the mth field-year-season, β0(τ) is the intercept of the τ-quantile regression, β1(τ) is the fixed effect of the thermal index on the τ-quantile of the semen index, is the individual random intercept of the i-th boar, ∈ ijk The cold season is from November to April, and the hot season is from May to October.

[0060] The historical boar semen phenotype data were regressed on the heat index at the 5% quantile level. 5% corresponds to a P value of less than 0.05 in the hypothesis test. The corresponding data were input into the heat stress resistance benchmark model and the model was trained. The heat stress resistance benchmark model obtained was:

[0061]

[0062] It should be noted that HYS m,ijk , γ m (0.05), β0 (0.05), β1 (0.05), α i (0.05) can be obtained based on the boar semen phenotypic data and the corresponding boar station environmental parameters (temperature, humidity, time, and breeding distribution location of the boar station).

[0063] After obtaining the trained heat stress resistance benchmark model, a heat stress evaluation model is constructed based on the heat stress resistance benchmark model. The formula of the heat stress resistance evaluation model is:

[0064] When the boar semen phenotypic data to be evaluated is the sperm deformity rate,

[0065]

[0066] When the boar semen phenotypic data to be evaluated is semen volume, sperm motility or sperm density,

[0067]

[0068] Among them, TTI i is the heat stress resistance index of the i-th boar, ranging from [0, 1], X ij is the thermal index, n i For the boar exposed to high temperature and high humidity (such as X ij ≥72), I is the indicator function (takes 1 when the condition is met, otherwise takes 0), X is the predicted value of the heat stress resistance benchmark model ij The corresponding 5% conditional quantile.

[0069] Step 3: Input the semen phenotypic data of the boar to be evaluated and the corresponding heat index into the heat stress resistance evaluation model, and the heat stress resistance evaluation model outputs the heat stress resistance index; the higher the heat stress resistance index, the stronger the boar's heat stress resistance.

[0070] In this embodiment, the boar semen phenotypic data to be evaluated and the corresponding heat index are input into the heat stress resistance evaluation model. The heat stress resistance evaluation model inputs the heat index into the heat stress resistance benchmark model to obtain the corresponding 5% conditional quantile of the heat index, that is, the corresponding heat stress resistance benchmark. Then, by using an indicator function, the boar semen phenotypic data to be evaluated is compared with the heat stress resistance benchmark. When the boar semen phenotypic data meets the condition, the output is 1, and when the boar semen phenotypic data does not meet the condition, the output is 0. By inputting the phenotypic data multiple times, the heat stress resistance index of the boar is obtained. The value range of the heat stress resistance index is [0, 1]. i The closer the value is to 1, the stronger the boar's heat stress resistance is, and the boar can stably maintain a low sperm deformity rate, high sperm motility, high sperm density or high semen volume under high temperature. i A value close to 0 indicates that boars are highly sensitive to heat stress, and semen parameters are easily affected by high temperatures. The heat stress resistance index allows researchers to intuitively and accurately understand the strength of each boar's heat stress resistance, laying the foundation for subsequent research related to heat stress resistance.

[0071] Preferably, the method further comprises step 4: using ASReml software to calculate the heritability and genetic correlation of heat stress resistance in boar semen phenotypic data, and establishing genetic parameters for heat stress resistance.

[0072] In this example, ASReml-R (v4.2) was used to calculate the heritability and genetic correlation of semen volume, sperm motility, sperm density, and sperm deformity rate in the 5% conditional quantile of the heat stress resistance benchmark model, and to establish the genetic parameters of heat stress resistance of boar semen quality. The specific data are shown in Table 2:

[0073] Table 2 Genetic parameters of boar semen quality and heat stress resistance

[0074]

[0075] Among them, QR VOL 、QR CONCT 、QR MOTL 、QR ABN are the semen volume, sperm motility, sperm density, and sperm deformity rate at the 5% conditional quantile in the heat stress resistance benchmark model, respectively. 2 For heritability.

[0076] The heritabilities of heat stress resistance of boar semen indicators (semen volume, sperm motility, sperm density, and sperm deformity rate) calculated by this method were: semen volume 0.28±0.05, sperm density 0.27±0.06, sperm motility 0.29±0.06, and sperm deformity rate 0.54±0.05, respectively. This indicates that the heat stress resistance trait defined by this method has medium to high heritability and can be genetically improved, which is beneficial to improving the efficiency and benefits of the pig farming industry.

[0077] Example 2

[0078] refer to Figure 5 , a device for evaluating the heat stress resistance of boar semen, comprising the following units:

[0079] Data acquisition unit: used to obtain historical boar semen phenotypic data and historical temperature and humidity data, and establish a thermal index based on the historical temperature and humidity data;

[0080] Model building unit: used to perform 5% quantile regression on the heat index using the quantile regression method to delineate a heat index of 72 as the heat stress critical point, and to construct a heat stress resistance benchmark model; based on the heat stress resistance benchmark model, a heat stress resistance evaluation model is constructed;

[0081] Data evaluation unit: used to input the semen phenotypic data of the boar to be evaluated and the corresponding heat index into the heat stress resistance evaluation model, and the heat stress resistance evaluation model outputs the heat stress resistance index; the higher the heat stress resistance index, the stronger the boar's heat stress resistance.

[0082] In this embodiment, the specific operation process of the evaluation device is as follows: the data acquisition unit acquires historical boar semen phenotypic data and historical temperature and humidity data, organizes the historical boar semen phenotypic data, screens and eliminates phenotypic data of boars with a small number of semen collections, establishes a thermal index based on the historical temperature and humidity data, and sends the organized historical boar semen phenotypic data and thermal index to the model establishment unit;

[0083] After receiving the historical boar semen phenotypic data and the heat index, the model building unit uses the quantile regression method to perform a 5% quantile regression on the heat index for the historical boar semen phenotypic data, thereby constructing a heat stress resistance benchmark model. The heat stress resistance benchmark module outputs the corresponding heat stress resistance benchmark according to the heat index, and then constructs a heat stress resistance evaluation model based on the heat stress resistance benchmark model. The heat stress resistance evaluation model can calculate the heat stress resistance benchmark according to the input boar semen phenotypic data and the corresponding heat index according to the heat index, and then compare the boar semen phenotypic data with the heat stress resistance. After multiple comparisons, the sum is divided by the number of times to obtain the heat stress resistance index. The model building unit sends the heat stress resistance evaluation model to the data evaluation unit;

[0084] The data evaluation unit receives the semen phenotypic data of the boar to be evaluated and the corresponding thermal index, and then inputs the semen phenotypic data of the boar to be evaluated and the corresponding thermal index into the heat stress resistance evaluation model. The heat stress resistance evaluation model outputs the heat stress resistance index for the boar. The value range of the heat stress resistance index is [0, 1]. i The closer the value is to 1, the stronger the boar's heat stress resistance is, and the boar can stably maintain a low sperm deformity rate, high sperm motility, high sperm density or high semen volume under high temperature. i Close to 0, it indicates that the boar is highly sensitive to heat stress and semen indicators are easily affected by high temperatures.

[0085] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for evaluating heat stress resistance of boar semen, characterized in that: The following steps are involved: Step 1: Obtain historical boar semen phenotypic data and historical temperature and humidity data, and establish a thermal index based on the historical temperature and humidity data; Step 2: Using the quantile regression method, the historical boar semen phenotypic data were regressed against the heat index at the 5% quantile. A heat index of 72 was defined as the heat stress critical point, and a heat stress resistance benchmark model was constructed. Based on the heat stress resistance benchmark model, a heat stress resistance evaluation model was constructed. Step 3: Input the semen phenotypic data of the boar to be evaluated and the corresponding heat index into the heat stress resistance evaluation model, and the heat stress resistance evaluation model outputs the heat stress resistance index; The higher the heat stress resistance index, the stronger the boar's heat stress resistance.

2. The method for evaluating heat stress resistance of boar semen according to claim 1, characterized in that: The method also includes step 4: using ASReml software to calculate the heritability and genetic correlation of heat stress resistance in boar semen phenotypic data and establish genetic parameters for heat stress resistance.

3. The method for evaluating heat stress resistance of boar semen according to claim 1, characterized in that: The boar semen phenotypic data include semen volume, sperm motility, sperm density, and sperm deformity rate.

4. The method for evaluating heat stress resistance of boar semen according to claim 3, characterized in that: The formula for the heat stress resistance benchmark model is: in, For X ijk Down Y ijk τ-conditional quantile (τ∈(0,1)), X ijk Y is the ambient temperature index on the day of semen collection, ijk is the semen volume, semen density, sperm motility or sperm deformity rate of the boar semen of the i-th boar, the j-th semen collection, and the k-th farm-year-season (HYS), HYS m,ijk is the field-year-season dummy variable, the field is two independent production units in the same area, and the season is divided into cold season and hot season, γ m (τ) is the fixed effect of the mth field-year-season, β0(τ) is the intercept of the τ-quantile regression, β1(τ) is the fixed effect of the thermal index on the τ-quantile of the semen index, is the individual random intercept of the i-th boar, ∈ ijk is a random error.

5. The method for evaluating heat stress resistance of boar semen according to claim 4, characterized in that: The formula of the heat stress resistance evaluation model is: When the boar semen phenotypic data to be evaluated is the sperm deformity rate, When the boar semen phenotypic data to be evaluated is semen volume, sperm motility or sperm density, Among them, TTI i is the heat stress resistance index of the i-th boar, ranging from [0, 1], X ij is the thermal index, n i For the boar exposed to high temperature and high humidity (such as X ij ≥72), I is the indicator function (takes 1 when the condition is met, otherwise takes 0), X is the predicted value of the heat stress resistance benchmark model ij The corresponding 5% conditional quantile.

6. The method for evaluating heat stress resistance of boar semen according to claim 1, characterized in that: The step 1 comprises the following steps: Step A1: Obtain historical boar semen phenotypic data, process the historical boar semen phenotypic data, and exclude semen phenotypic data of boars with less than 50 semen collections; Step A2: Obtain historical temperature and humidity data, input the historical temperature and humidity data into the heat index formula, and establish the heat index.

7. The method for evaluating heat stress resistance of boar semen according to claim 6, characterized in that: The step 1 further includes step A3: using a thermometer and a hygrometer to measure the indoor and outdoor temperature and humidity of the boar station where the boar semen phenotype data is located, and calculating the correlation between the indoor and outdoor temperature and humidity by Pearson correlation.

8. An evaluation device for heat stress resistance of boar semen, characterized in that: The following units are included: Data acquisition unit: used to obtain historical boar semen phenotypic data and historical temperature and humidity data, and establish a thermal index based on the historical temperature and humidity data; Model building unit: used to perform 5% quantile regression on the heat index using the quantile regression method to delineate a heat index of 72 as the heat stress critical point, and to construct a heat stress resistance benchmark model; based on the heat stress resistance benchmark model, a heat stress resistance evaluation model is constructed; Data evaluation unit: used to input the boar semen phenotypic data to be evaluated and the corresponding heat index into the heat stress resistance evaluation model, and the heat stress resistance evaluation model outputs the heat stress resistance index; The higher the heat stress resistance index, the stronger the boar's heat stress resistance.

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