# Economic Risk Assessment by Weather-Related Heat Stress Indices for Confined Livestock Buildings: A Case Study for Fattening Pigs in Central Europe

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Likelihood of HS Inside Livestock Builings

_{WB}+ 40.6, where T

_{WB}represents the wet bulb temperature. For a certain threshold X, the exceedance frequency P

_{X}and the intensity of HS A

_{X}, using the aggregated values between X and the time course of the HS index (i.e., the area under the time course), were calculated. P

_{X}(h a

^{−1}) gives the number of hours per year during by which the selected threshold is exceeded, A

_{X}(Kh a

^{−1}for temperature and h a

^{−1}for THI) includes the differences of the instantaneous values and the threshold. Two threshold values were selected for fattening pigs: the temperature X = 25 °C and the THI X = 75, which presents an alert situation of the thermal environment [42]. These two thresholds resulted in four HS indices P

_{T25}, A

_{T25}, P

_{THI75}, and A

_{THI75}. The HS indices were further processed as annual sums.

_{INT}denotes for P

_{T25}, A

_{T25}, P

_{THI75}, and A

_{THI75}inside the building. For the outdoor parameters, using meteorological measurements easily available from a nearby weather station, the vector I

_{EXT}denotes for P

_{T25}, A

_{T25}, P

_{THI75}, and A

_{THI75}.

_{t}of a HS index of a certain year t and the variability s

^{2}. The expected value I

_{t}is calculated by a linear regression of the logarithmically transformed HS index according to log I

_{t}= kt + d, with the slope k and the intercept d. The deviation of the HS index from the linear trend I

_{t}results in the variance s

^{2}. The detrended (and logarithmically transformed) HS indices, according to Δ

_{EXT,t}= log T

_{t}– log I

_{EXT,t}(deviation from the trend) were fitted to the Weibull, the Gumbel, and the Gauss (normal) distributions. The quality of the fit was determined by the Akaike information criterion AIC.

_{EXT}was tested by the Breusch-Pagan test for heteroskedasticity, assuming that the residuals Δ

_{EXT}are normally distributed. Using a χ

^{2}test, it was tested whether the variance of the errors from the regression depends on the values of the independent variable, the time t.

_{INT}and I

_{EXT}) was investigated by a linear regression analysis, using the model calculations by Mikovits, Zollitsch, Hörtenhuber, Baumgartner, Niebuhr, Piringer, Anders, Andre, Hennig-Pauka, Schönhart and Schauberger [11] for the indoor related vector I

_{INT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] and the corresponding vector I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}], calculated by the outdoor (meteorological) parameters. The linear regression was evaluated by the p-value of the coefficient of determination r

^{2}.

#### 2.2. Economic Impact of HS

^{−1}) (revenue), the reduction of dry matter intake (kg a

^{−1}) (variable costs) and the increase of mortality (%) (revenue). These three parameters were updated by data for 2020 from the Federal Institute of Agricultural Economics (AWI http://www.awi.bmnt.gv.at/) for feed at 0.25 € kg

^{−1}, the revenue for a fattening pig at 1.7 € kg

^{−1}, and the cost of a slaughtered pig (75 kg) at 100 €. The original predictor of the impact function for growing-fattening pigs of St-Pierre, Cobanov and Schnitkey [36] is the area under the curve A

_{X}for a THI threshold of X = 72. By the dataset of the HS index using the two THI thresholds, X = 72 (growing-fattening pigs) and X = 74 (sows), the impact function was modified for the THI threshold X = 75 used in this paper.

^{−1}) related to one animal place as a linear function of the HS index A

_{THI75}(h a

^{−1}) reads as follows: IMP = 0.0016 A

_{THI75}with r

^{2}= 0.9897 (Figure 1).

## 3. Results

#### 3.1. Relationship between Indoor-Related and Weather-Related Heat Stress Indices

_{INT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}], calculated by indoor parameters, and the vector of HS indices I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}], calculated by the outdoor (meteorological) parameters, shows a high linear correlation. The statistical parameters of the linear regression are summarized in Table 2 and graphically presented in Figure 2. All HS indices of the vector I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] show a high explanatory power for the indoor-related vector I

_{INT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}], explaining more than 80% of the variance (r

^{2}> 0.80, p < 0.001) in each case. The intercept of the regression can be interpreted as the impact resulting from an increase of the indoor temperature and indoor humidity by the sensible and latent heat release of the farm animals. The slope of the two exceedance frequencies P

_{T25}and P

_{THI75}are closer to the line of identity (1:1) compared to the HS intensities A

_{T25}and A

_{THI75}.

^{2}> 0.80, p < 0.001) (Table 2), the vector of the HS indices I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] can be used as a proxy to quantify the HS impact on farm animals without the need of a determination of the indoor parameters. This is an advantage for manageability, e.g., for weather-index based insurance, because the meteorological parameters are easily available from national weather services and no further model calculations are required.

#### 3.2. Consequence of Global Warming on the Likelihood of Heat Stress Indices

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] has been confirmed by the signal-to-noise ratio SNR and the Mann-Kendall trend test on a high level of significance with p < 0.04 and p <0.001, respectively (Table 3).

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] for a certain year t was fitted by the empirical data to determine the expected value I

_{t}and the variability s

^{2}. The expected value I

_{t}of the distribution and the statistical parameters of the linear trend are given in Table 4. The results of all four weather-related HS indices of the vector I

_{EXT}show a high significance with p < 0.001 and a coefficient of determination r

^{2}between 30% and 33%.

^{2}showing a homoscedasticity of the four detrended HS indices Δ

_{EXT}, which was confirmed by the Breusch-Pagan test for all four weather-related HS indices of the vector I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] at the 5% level. The detrended (and logarithmically transformed) HS indices were fitted to the Weibull distribution, the Gumbel distribution and the Gauss (normal) distribution. The last distribution showed the best overall fit, assessed by the Akaike information criterion AIC. In Table 5, the standard deviation s and the AIC are summarized. Figure 3 compares the empirically detrended (and logarithmically transformed) HS indices Δ

_{EXT}with the three fitted CDFs: Gauss, Weibull, and Gumbel distributions. Especially the right tail of the detrended HS indices Δ

_{EXT}is fitted well by the normal distribution.

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] were used to determine the likelihood of their occurrence in a certain year t. In Figure 4 the likelihood for the past (t = 1980 and t = 2020) and for the near future (t = 2030) is shown for the exceedance probability for P

_{T25}, and P

_{THI75}in panel A and the area under the curve A

_{T25}and A

_{THI75}in panel B, using the cumulative distribution function (CDF) of the log-normal distribution.

#### 3.3. Consequence of Global Warming on Extreme Values of Heat Stress Indices

_{E}of 10% and a return period of 10 years), according to the weather-VaR concept.

_{t,10%}of each weather-related HS index was determined by the cumulative distribution function shown in Figure 4 for the year t = 1980 E

_{1980,10%}and for t = 2020 E

_{2020,10%}and by the 90-percentile of the CDF.

_{E}= 10% the corresponding RP = 1/P

_{E}resulted in RP = 10 a (one in10-years event), as the length of an average time interval between the occurrences of two years with an HS level that exceeds the extreme value E

_{t,10%}.

_{E}for t = 2020 and t = 2030 with P

_{E,2020}and P

_{E,2030}was calculated according to the temporal trend I

_{t}(Table 4) and the standard deviation of the detrended values s (Table 5). The RP (in years) and P

_{E,t}are presented for the time shift in the past and for the time shift in the future in Table 6.

_{T25}) and 957% (A

_{THI75}). In Figure 5A the scenario of the weather-related HS index P

_{T25}is shown exemplarily for the past (t = 1980 and t = 2020). t = 1980, P

_{E}= 10% (p

_{CDF}= 90%) results in E

_{1980,10%}= 283 h/a (Table 7). Forty years later, the corresponding P

_{E}(t = 2020) is P

_{E}= 93%. This means, that the extreme value of the year t = 1980 E

_{1980,10%}= 283 h/a will be exceeded with a probability of 93% in 2020, or 13 a out of 14 a. The mean value is more than doubled from 190 h/a (in 1980) to 448 h/a 40 a later (Figure 5A).

_{T25}) to 80% (A

_{THI75}) of the mean value in relation to 2020 can be expected for the upcoming decade. In Figure 5B the likelihood of P

_{T25}for the near future is shown. The exceedance probability P

_{E}= 10% (p

_{CDF}= 90%) in the year t = 2020 E

_{2020,10%}= 668 h/a is shifted for the year t = 2030 to P

_{E}= 28%. The mean value increases from 448 h/a to 556 h/a.

_{THI75}and A

_{THI75}) compared to the temperature index (P

_{T25}and A

_{T25}). This means that not only the air temperature, but also the humidity, which is part of the THI index, will increase with time. Otherwise, the temperature-based index would grow proportional to the THI index.

#### 3.4. Economic Risk for Pig Farms due to Global Warming

_{THI75}, shown in Figure 1.

_{THI75}is shown by the probability density function (PDF) in Figure 6. It defines the temporal trend I

_{t}which gives the expected value of the HS index for a certain year t (mean value (maximum) of the PDF) and the standard deviation s.

^{2}of the detrended and logarithmically transformed HS index ΔA

_{THI75}(Table 4). Due to the temporal trend of the HS indices (Table 5), the probability shifts from 1980 to 2030. The statistics of the reduction of the gross margin per animal place (about three fattening periods per year) due to HS is shown in Table 7. The median moves from 0.08 € a

^{−1}to 1.57 € a

^{−1}per animal place. The weather-VaR values were calculated for a 90 and 95 percentile, corresponding to a return period of RP = 10 a and RP = 20 a, respectively. For years with HS with a probability of RP = 10 a, the economic risk grows from 0.27 € a

^{−1}to 5.13 € a

^{−1}per animal place. For RP = 20 a, the economic risk is 0.38 € a

^{−1}for 1980 and 7.18 € a

^{−1}per animal place for 2030, which is a 20-fold increase.

## 4. Discussion

_{EXT}with a Gaussian distribution was demonstrated by the low values of the AIC and graphically shown in Figure 3. Especially the right tail was fitted well by the selected normal distribution.

_{THI75}heat stress index. The predictor of the cost function in St-Pierre, Cobanov and Schnitkey [36] was the HS index calculated by the THI and the threshold of X

_{THI}= 75. The variability of this HS index was caused by differences in local climates across the entire USA (spatial variability), whereas the variability of the HS indices presented here was caused by the trend due to global warming. The assessment of the economic impact following the approach of St-Pierre, Cobanov and Schnitkey [36] included the reduction of the animals’ live mass at the end of the fattening period, the related reduction of feed required and the increased mortality of the animals. These traits were parameterized by linear functions. It is however not plausible that the impact of HS will be the same for the temperature range of 25 °C to 28 °C and for 30 °C to 33 °C. This fact was investigated by several authors [15,58,59]. In a European study about risk factors for mortality in fattening pigs, a significant seasonal impact with higher mortalities of pigs placed at the end of the year was found, which was associated with infectious diseases [60]. This finding was in contrast to a higher mortality in summer in the Midwest in the USA [23], which might be associated with a higher frequency of HS in this region. To the authors’ knowledge, no evaluation of mortality in fatteners during extreme weather periods has been performed so far. For this reason, the economic assessment of the impact of heat stress was mainly based on the decrease in livestock growth parameters. Other variable costs such as the market driven costs for piglets, veterinary services, energy for the ventilation system, and water consumption were not taken into account in this study, despite the effect of HS on these [11].

^{−1}to 1.57 € a

^{−1}per animal place. Taking into account the likelihood of the occurrence of heat stress, the economic risk was determined for a repeat period of 20 years with 0.38 € a

^{−1}for 1980 and 7.18 € a

^{−1}per animal place for 2030. These additional costs due to HS can be compared to the cost of a misting system to reduce HS. Bridges, et al. [61] simulated the costs for Kentucky for 1983 (warm year) at 10.2 $ a

^{−1}per animal place and 1995 (close to normal) at 1.5 $ a

^{−1}per animal place, which lie in the same range.

^{−1}and 42 € a

^{−1}per animal place [62] for Germany and about 50 to 70 € a

^{−1}per animal place for Austria (Federal Institute of Agricultural Economics AWI www.awi.bmnt.gv.at). For the US, Jacobson, et al. [63] estimated a gross margin of about 98 $ a

^{−1}per animal place (without building costs).

^{2}per pig) was found to be connected with an increased adipose iodine value and a decreased saturated:unsaturated fatty acids ratio. Pigs kept at higher temperature showed changes in carcass lipid and bacon quality, e.g., lean:fat ratio of bacon slices and increased quantity of collagen in belly fat [67].

## 5. Conclusions

^{−1}to 5.13 € a

^{−1}per animal place, which is around 5% to 10% of gross margins for a typical farm. For farmers, such a risk assessment is an essential tool for management decisions like the implementation of adaption measures to reduce HS, thermal-tolerant and adapted breeds or feeding strategies by adjusting diet composition.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Economic impact IMP (€/a per animal place), described by the reduction of the gross margin per animal place as a linear function of the heat stress index A

_{THI75}(h/a) derived from St-Pierre, Cobanov and Schnitkey [36] with IMP = 0.0016 A

_{THI75}.

**Figure 2.**Relationship between the weather-related vector of heat stress indices I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] and the indoor-related vector of HS indices I

_{INT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}]. The exceedance frequencies of a threshold P

_{T25}and P

_{THI75}(h/a) are presented in panel (

**A**), the heat stress intensities (area under the curve for a threshold) A

_{T25}(Kh/a) and A

_{THI75}(h/a) in panel (

**B**). The parameters of the linear regression are summarized in Table 2.

**Figure 3.**Cumulative distribution of the detrended (and logarithmically transformed) heat stress indices Δ

_{EXT}and three cumulative distribution functions: Gauss, Weibull and Gumbel distribution for the four weather-related heat stress indices P

_{T25}(

**A**), A

_{T25}(

**B**), P

_{THI75}(

**C**), and A

_{THI75}(

**D**), describing the variability of HS.

**Figure 4.**Likelihood for the occurrence of the weather-related heat stress indices I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] shown by the cumulative distribution function (CDF) of a log-normal distribution for t = 1980 (dark color), t = 2020 (light color), and t = 2030 (very dark color) for the exceedance frequency P

_{T25}, and P

_{THI75}(panel (

**A**)) and the area under the curve A

_{T25}, and A

_{THI75}(panel (

**B**)).

**Figure 5.**The cumulative distribution function showing the likelihood of the occurrence of P

_{T25}. The shift of the mean value (p

_{CDF}= 0.5) is presented by the black arrow. In panel (

**A**) the scenario of the past (t = 1980 and t = 2020) is shown, and in panel (

**B**) the scenario of the near future (t = 2020 and t = 2030) is shown.

**Figure 6.**Likelihood of the occurrence of the weather-related heat stress index A

_{THI75}for t = 1980, t = 2020, and t = 2030.

**Figure 7.**Distribution of the economic risk (€/a) per animal place by the reduction of the gross margin for t = 1980, t = 2020 and t = 2030 shown by probability density functions (PDFs panel (

**A**)) and cumulative distribution functions (CDFs panel (

**B**)).

**Table 1.**System parameters for livestock, building, and ventilation system related to one animal place for the indoor climate simulation of a conventional livestock building [11].

Parameter | Value | |
---|---|---|

Animal | ||

Body mass | 30–120 kg | |

Service period (building emptied for cleaning and disinfection) per fattening period. | 10 days | |

Building | ||

Area of the building oriented to the outside (wall, ceiling, door, windows). | 1.41 m^{2} | |

Mean thermal transmission coefficient weighted by the area of the construction elements (wall, ceiling, door, windows) which are oriented to the outside. | 0.41 W m^{−2} K^{−1} | |

Ventilation system | ||

Set point temperature of the ventilation control unit. | 16–20 °C | |

Proportional range (band width) of the control unit. | 4 K | |

Minimum volume flow rate of the ventilation system, for maximum CO_{2} concentration of 3000 ppm and a body mass of 30 kg [41]. | 8.62 m^{3} h^{−1} | |

Maximum volume flow rate for a maximum temperature difference between indoor and outdoor of 3 K [41]. | 107 m^{3} h^{−1} |

**Table 2.**Linear regression results of vector I

_{INT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] (calculated by the indoor parameters) as a function of the vector I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] (calculated by weather data) with the coefficient of determination r

^{2}of the linear regression with the slope k and the intercept d. The p-values are <0.001.

Heat Stress Index I _{INT} | Coefficient of Determination Adjusted r ^{2} | Linear Regression I _{INT} = k I_{EXT} + d |
---|---|---|

P_{T25} (h/a) | 0.8354 | =1.166 P_{T25} + 405.0 |

A_{T25} (Kh/a) | 0.9084 | =1.505 A_{T25} + 1219.7 |

P_{THI75} (h/a) | 0.8032 | =1.495 P_{THI75} + 212.9 |

A_{THI75} (h/a) | 0.8862 | =2.130 A_{THI75} + 489.9 |

**Table 3.**Statistical analysis of the temporal trend of the weather-related vector of heat stress (HS) indices I

_{EXT}= [P

_{T25}, A

_{T25}, P

_{THI75}, A

_{THI75}] by the signal-to-noise ratio SNR and the Mann-Kendall Trend Test with the test statistics τ with the corresponding p-values.

Weather-Related Heat Stress Indices I_{EXT} | Signal-to-Noise Ratio | Mann-Kendall Trend Test | ||
---|---|---|---|---|

SNR | p | τ | p | |

P_{T25} (h a^{−1}) | 2.001 | 0.023 | 0.4271 | <0.001 |

A_{T25} (Kh a^{−1}) | 1.790 | 0.037 | 0.3982 | <0.001 |

P_{THI75} (h a^{−1}) | 2.071 | 0.019 | 0.4174 | <0.001 |

A_{THI75} (h a^{−1}) | 1.832 | 0.033 | 0.3832 | <0.001 |

**Table 4.**Temporal trend of the weather-related heat stress indices log I

_{t}= kt + d, calculated by the logarithmically transformed heat stress indices log I

_{EXT}with the slope k, the intercept d, the coefficient of determination r

^{2}, and the p value.

Weather-Related Heat Stress Indices I _{EXT} | Linear Regression of the Temporal Trend log I _{t} | |||
---|---|---|---|---|

Trend k | Intercept d | Coef Det r ^{2} | p | |

P_{T25} (h a^{−1}) | 0.009326 | −16.187 | 0.333 | <0.001 |

A_{T25} (Kh a^{−1}) | 0.013745 | −24.643 | 0.314 | <0.001 |

P_{THI75} (h a^{−1}) | 0.019806 | −37.658 | 0.334 | <0.001 |

A_{THI75} (h a^{−1}) | 0.025600 | −48.987 | 0.296 | <0.001 |

**Table 5.**Standard deviation s of the detrended (and logarithmically transformed) weather-related heat stress indices Δ

_{EXT}and the Akaike information criteria AIC.

Weather-Related Heat Stress Indices Δ_{EXT} | Standard Deviation s | AIC |
---|---|---|

P_{T25} (h a^{−1}) | 0.1354 | −38.98 |

A_{T25} (Kh a^{−1}) | 0.2075 | −7.36 |

P_{THI75} (h a^{−1}) | 0.2867 | 16.54 |

A_{THI75} (h a^{−1}) | 0.4024 | 41.64 |

**Table 6.**Exceedance probability P

_{E}and return period RP using the 90-percentile p

_{CDF}= 90% (P

_{E}= 10%) of the cumulative distribution function. The time shift was calculated for the past t = 1980 and t = 2020, and the near future t = 2020 and t = 2030. The shift of the mean values MV (expected value of the HS index for an exceedance probability of 50%) shows the consequence of global warming.

Weather-Related Heat Stress Indices I_{EXT} | Expected Values for p = 10% E _{t,10%} | Exceedance Probability P_{E} and ReturnPeriod RP | |||
---|---|---|---|---|---|

P_{E}(%) | RP (a) | Shift of the MV | Shift % | ||

Time shift from 1980 to 2020 | E_{1980,10%} | for t = 2020 | |||

P_{T25} (h a^{−1}) | 283 | 93.0 | 1.1 | 258 | 136 |

A_{T25} (Kh a^{−1}) | 689 | 91.4 | 1.1 | 951 | 255 |

P_{THI75} (h a^{−1}) | 84 | 93.1 | 1.1 | 188 | 520 |

A_{THI75} (h a^{−1}) | 165 | 89.7 | 1.1 | 481 | 957 |

Time shift from 2020 to 2030 | E_{2020,10%} | for t = 2030 | |||

P_{T25} (h a^{−1}) | 668 | 27.7 | 3.6 | 107 | 24 |

A_{T25} (Kh a^{−1}) | 2442 | 26.8 | 3.7 | 493 | 37 |

P_{THI75} (h a^{−1}) | 520 | 27.7 | 3.6 | 129 | 58 |

A_{THI75} (h a^{−1}) | 1742 | 25.9 | 3.9 | 427 | 80 |

**Table 7.**Statistics of economic risk by the reduction of the gross margin (€ a

^{−1}) per animal place for t = 1980, t = 2020, and t = 2030. The weather-VaR values were calculated for a 90 and 95-percentile, corresponding to a return period of 10 a and 20 a, respectively.

Year t | Reduction of the Gross Margin (€ a^{−1}) per Animal Place | ||
---|---|---|---|

Median | Weather VaR | ||

90-Percentile (10 a Return Period) | 95-Percentile (20 a Return Period) | ||

1980 | 0.08 | 0.27 | 0.38 |

2020 | 0.87 | 2.86 | 4.00 |

2030 | 1.57 | 5.13 | 7.18 |

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**MDPI and ACS Style**

Schauberger, G.; Schönhart, M.; Zollitsch, W.; Hörtenhuber, S.J.; Kirner, L.; Mikovits, C.; Baumgartner, J.; Piringer, M.; Knauder, W.; Anders, I.; Andre, K.; Hennig-Pauka, I. Economic Risk Assessment by Weather-Related Heat Stress Indices for Confined Livestock Buildings: A Case Study for Fattening Pigs in Central Europe. *Agriculture* **2021**, *11*, 122.
https://doi.org/10.3390/agriculture11020122

**AMA Style**

Schauberger G, Schönhart M, Zollitsch W, Hörtenhuber SJ, Kirner L, Mikovits C, Baumgartner J, Piringer M, Knauder W, Anders I, Andre K, Hennig-Pauka I. Economic Risk Assessment by Weather-Related Heat Stress Indices for Confined Livestock Buildings: A Case Study for Fattening Pigs in Central Europe. *Agriculture*. 2021; 11(2):122.
https://doi.org/10.3390/agriculture11020122

**Chicago/Turabian Style**

Schauberger, Günther, Martin Schönhart, Werner Zollitsch, Stefan J. Hörtenhuber, Leopold Kirner, Christian Mikovits, Johannes Baumgartner, Martin Piringer, Werner Knauder, Ivonne Anders, Konrad Andre, and Isabel Hennig-Pauka. 2021. "Economic Risk Assessment by Weather-Related Heat Stress Indices for Confined Livestock Buildings: A Case Study for Fattening Pigs in Central Europe" *Agriculture* 11, no. 2: 122.
https://doi.org/10.3390/agriculture11020122