Climate change impacts on livestock in Brazil

Climate change impacts on livestock in Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Climate change impacts on livestock in Brazil Nicole Ferreira, Rafaella Resende Andrade, Leonardo Nascimento Ferreira This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3941355/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Sep, 2024 Read the published version in International Journal of Biometeorology → Version 1 posted 4 You are reading this latest preprint version Abstract Brazilian livestock provides a significant fraction of the food consumed globally, making the country one of the largest producers and exporters of meat, milk and eggs. However, current advances in the production of protein from Brazilian animal origin may be directly impacted by climate change and the resulting biophysical effects. Therefore, it is strategically consistent to develop measures to deal with the resulting environmental heat stress on domesticated animal species, especially the need in developing countries. This work aims to (1) evaluate the impacts of climate change on livestock in different regions of Brazil and (2) discuss possible response strategies, associated with animal comfort and welfare. From our results, we can draw better strategies to mitigate the impacts of climate change on livestock production. The results presented show an increase of high heat stress in South and Southeast and an increase of extreme heat stress in North and Central-West areas of Brazil. The rise in extreme heat stress tends to occur mostly during spring and summer and tends to vary considering the different evaluated species. Within the evaluated species, the ones that seem to be more affected by climate changes are Poultry , pigs , cattle-beef and general (temperature-humidity index value). The differences between the results for the five geographic regions in Brazil suggests that different mitigation measures need to be considered to cope with future heat stress in livestock. To ensure the long-term success of Brazil's influence on the global market for proteins of animal origin, it must achieve sustainable production systems more intensively. climate change food security animal heat stress livestock temperature-humidity index Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Climate change and extreme events affect different regions globally, with a range of negative impacts affecting multiple sectors, such as health (Vicedo-Cabrera et al., 2021 ), energy (Jong et al., 2019 ; Ferreira et al., 2023a ), and agriculture (Zilli et al., 2020 ; Appiah et al. 2023 ; Ferreira et al., 2023b ). As an impact of climate change, we expect an increase in temperature, changes in relative humidity and heatwaves. All of these changes will affect livestock differently across the world (Allen et al., 2015 ).The main consequence is a stronger heat stress (Vitali et al., 2015 ). Heat stress in domesticated livestock occurs when environmental conditions challenge the animal's thermoregulatory mechanisms (Thornton et al., 2021 ). The effects of heat stress include reduced production, reproduction, fertility, animal welfare, increased susceptibility to disease and, in some cases, increased mortality (Herbut et al., 2021 ; Andrade et al., 2022a ). If our global society does not reduce protein of animal origin consumption in the near future, then the additional heat stress might also compromise food security in the coming years. Climate models are important tools to assess climate change impacts. Projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6) provide climate scenarios based on different Shared Socio-Economic Pathways (SSP), and are widely used to assess climate change impacts in different sectors (Thornton et al., 2021 ; Zeng et al., 2022 ). Using climate data, available as an output of climate models, heat stress indices can be calculated to investigate the impact of climate change on livestock, in different regions, and different species (Berman, 2019 ). The temperature-humidity index (THI) is one of the indexes that are frequently used to represent the heat stress for different species (Allen et al., 2015 ; Oliveira et al., 2022 ). Exploiting existing variation in heat tolerance among different animal species could be a key adaptation strategy (Thornton et al., 2021 ). Measures that assess animal welfare, environmental and economic issues are little investigated in the context of climate change (Hempel et al., 2019 ). At the same time, there are still rare studies in the literature that estimate the effect of climate change on Brazilian livestock, especially paying attention to the different regions of the country, taking into account the large territorial extension. Brazilian agricultural production provides a significant fraction of the food consumed worldwide (Zilli et al., 2020 ). The goal of this paper is to evaluate the impacts of climate change by using the THI and specific thresholds to different livestock species. This impact was evaluated by using CMIP6 ensemble models for historical period, and short-, medium- and long-term projections. For this analysis, we used the SSP5-8.5 scenario which assumes a business as usual scenario. We focused on the five geographical regions in Brazil, as different regions have different productions and will be affected differently by climate change. 2. Material and Methods 2.1. Datasets: CMIP6 Climate change projections Projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6) provide climate scenarios based on different Shared Socio-Economic Pathways (SSP) (O’Neill et al. 2014 , 2020 ). The SSP scenarios were created from long-term integrations with possible greenhouse gas emission scenarios in the atmosphere and their impacts on climate variables. These scenarios can be used to investigate the implications of long term climatic changes for designing robust policies in an environment of interacting complex systems and uncertainty (Hall et al. 2016 ; Harrison et al. 2015 ; O’Neil et al. 2014). These scenarios are widely used in the literature, which makes the comparison with other research results easier. In this paper, four CMIP6 models were used to create an ensemble model, which represents the daily median values across the models. More information about the models can be found in Table 1 . Table 1 Climate models used to create the ensemble model. Model Institution More information CanESM5 Canadian Centre for Climate Modelling and Analysis Swart et al. 2019 GFDL-ESM4 Geophysical Fluid Dynamics Laboratory (GFDL) Horowitz et al. ( 2018 ) MPI-ESM1-2-LR Max-Planck Institut für Meteorologie Wieners et al. 2019 MRI-ESM2-0 Japan Meteorological Research Institute Yukimoto et al. 2019 We evaluate the impacts of climate change on livestock, using historical simulations (from 1991 to 2010) and climate projections (scenario SSP5-8.5, from 2021 to 2080). The scenario SSP5-8.5 is considered as a pessimist scenario, with a higher increase of temperature by the end of the century, compared to other scenarios. We divided the future projections into short- (2021–2040), medium- (2041–2060) and long-term (2061–2080). The variables used in this methodology are the daily temperature (tas) and the near-surface relative humidity (hurs). This dataset will be used to calculate the THI, as described in section 2.3. 2.2. Study area: Brazil According to FAO (2021), livestock occupy about 26% of the global ice-free land with one-third of the cropland being used for feed production. In Brazil, livestock assumes an important position in the economy of the country. In 2021, crop and livestock production accounted for eight percent of Brazil’s Gross Domestic Product (GDP) (USDA, 2022). According to the USDA (2022), the value of Brazil’s agriculture, including cultivation of crops and livestock production, grew an average of eight percent annually over the past two decades (2000–2020), with agricultural output doubling and livestock production increasing threefold. Brazil is divided in five geographical regions: North, South, Southeast, Center West, and Northeast, as indicated in Fig. 1 . Due to the country's large territorial extension, animal production and the thermal environment differ between regions. Brazil stands out among the largest producers and exporters of protein of animal origin, with emphasis on the production of cattle dairy, cattle beef, goats, sheep, pigs, poultry general, the focus of this research. Milk production in the country, estimated at 35.30 billion liters in 2021 (IBGE, 2022 ), is distributed throughout almost the entire country. The Southeast, Central-West and South regions with the greatest production, mainly the states of Minas Gerais, Goiás, Paraná, Santa Catarina and Rio Grande do Sul (EMBRAPA, 2023 ). According to data from IBGE ( 2022 ), in the first quarter of 2022, 1.55 billion heads of broiler chickens were slaughtered. In this period, the South Region accounted for 60.2% of the national chicken slaughter, followed by the Southeast (19.2%), Central-West (14.7%), Northeast (4.3%) and North (1.6%). The production of chicken eggs was 977.20 million dozen, with the southeast and south regions standing out as the largest producers. For pig production, 13.64 million heads of pigs were slaughtered, with the South Region accounting for 66.0% of the national pig slaughter, in the 1st quarter of 2022, followed by the Southeast (18.8%), Central-West (13.9%), Northeast (1.2%) and North (0.1%). Brazil is the largest beef exporter in the world and has a cattle herd of 234.3 million heads. Beef cattle production in the country is predominantly based on pastures. In the 1st quarter of 2022, 6.96 million heads of beef cattle were slaughtered. The Central-West Region presented the highest proportion of cattle slaughter in the period, 37.1% of the total, followed by the North (21.7%), Southeast (21.3%), South (11.4%) and Northeast (8.5%) (IBGE, 2022 ). 2.3. Temperature Humidity Index (THI) The thermal environment is one of the major climatic factors that affects animal production, and can be reproduced as a combination of air temperature, humidity, and air movement (Ames, 1980 ). There is a thermal zone, where the animals exhibit optimum performance and minimal energy expenditure (Nardone et al., 2006 ). When the animal is suffering from an individual source of stress, the phenotypic response is called acclimation (Nardone et al., 2010 ). Considering the impacts of climate change, it is likely that the animals across the world are outside this thermal zone. This means that extra energy will be required to maintain thermoregulation and production processes may become less effective (Joy et al., 2020 ; Godde et al., 2021 ). Research shows that milk production tends to be constant when the ambient temperature is within the thermoneutral zone, but drops linearly as the THI increases (Hempel et al., 2019 ; Lobeck et al., 2012 ). Therefore, when an animal is exposed to a heat stress they are not able to dissipate sufficient heat to keep homeothermy, leading to an increasing in respiration, pulse, heart rate, and body temperatures (Fregly, 2011; Nardone et al., 2010 ; Kadzere et al., 2002 ). This can lead to a reduction in the feed intake, reproduction efficiency, as well as changes in mortality and immune system function (Das et al., 2016 ; Sejian et al., 2018 ). This may become an additional challenge to a world that is already concerned with future food security under scenarios of climate change. Cheng et al. ( 2022 ) produced a literature review highlighting the Climate Change and Livestock Production. According to the authors, adaptation measures are essential to sustain the growing demand for livestock products, however their relevance depends on climatic conditions, the management of local production, as well as ensuring comfort and well-being conditions for the animals. At the same time, mitigation is key to limiting the future worsening of climate change and there are a number of possible strategies. The environmental conditions that induce heat stress can be calculated using the temperature humidity index (THI), which is determined with a combination of ambient temperature and relative humidity (NRC, 1971 ). The THI can be defined as NRC ( 1971 ). $$THI=\left(1.8*T+32\right)-\left[\left(0.55-0.0055*RH\right)*\left(1.8+T-26\right)\right],$$ 1 where T is the air temperature (°C), RH is the relative humidity (%) and THI is the Temperature humidity index. The THI was applied in several researches across the world (Andrade et al., 2022b ; Kang et al., 2020 ; Lallo et al., 2018 ). THI varies according to the animal species, as each animal species has different mechanisms to cope with high air temperature and relative humidity. Table 2 presents a compilation of thresholds for THI, classified as Moderate , High and Extreme heat stress (adapted from Thornton et al., 2021 ), considering relevant domesticated animal species in the Brazilian livestock context. Table 2 THI onset of the stress level for different species (cattle dairy, cattle beef, goats, sheep, pigs, poultry-geral), classified as Moderate, High and Extreme heat stress. Species Onset of the stress level References Moderate High Extreme General¹ 72 78 90 Fuquay ( 1981 ) Cattle-dairy 72 79 89 Mader et al. ( 2006 ); Dunn et al. ( 2014 ); Dash et al. ( 2016 ); Ranjitkar et al. ( 2020 ); Rahimi et al. ( 2021 ) Cattle beef 72 82 94 Valente et al. ( 2015 ) Goats 70 79 89 Serradilla et al. ( 2018 ) Sheep 72 78 90 McManus et al. ( 2016 ); Belhadj Slimen et al. ( 2019 ) Pigs 75 79 84 Xin & Harmon ( 1998 ); Lallo et al. ( 2018 ); Mutua et al. ( 2020 ) Poultry-general 73 81 85 Moraes et al. ( 2008 )² ¹General - THI value considered by the literature for all animals. ²THI limit table adapted from Thornton et al. ( 2021 ). Moraes et al. ( 2008 ) used five different categories for poultry-light and moderate discomfort were merged here. To evaluate the impact of climate change in the livestock in Brazil, we used the THI onset of stress levels presented in Table 2 and calculated the number of days with mild stress, moderate stress, severe stress for different animals in historical simulations and future projections from CMIP6. We focused on days with extreme and high stress, and how they change according to each species and considering different time-slices. 3. Results and discussions 3.1. Climate change projections As described in the section, two climate variables were evaluated: mean daily temperature (tas) and near-surface relative humidity (hurs). Four models were used in this analysis to calculate an ensemble model, based on the medium daily value across the four models. Figure 2 shows the climatology of temperature and relative humidity for the ensemble model (left) and the anomalies between the future projections (SSP5-8.5) and the historical period (right). As previously mentioned, the historical period included data from 1991 to 2010, and the future projections are divided into short- (2021–2040), medium- (2041–2060), and long-term (2061–2080). The anomalies are calculated based on the difference between future projection and historical period. According to the climatology of temperature and relative humidity, we can notice that there is a great spatial variability of these variables across Brazil. For temperature, the lowest temperatures are found in South of Brazil, while for relative humidity the lowest relative humidity are found in central regions in Brazil. In terms of anomalies, we can expect an increase in temperature in all projections, especially towards the end of the century. In the short-term, we expect an increase of 1°C across the country. In the long-term however, we find more variability of this increase across the country, with highest values in the North and Central West part of Brazil, reaching values up to 4°C. The increasing of temperature can be problematic to livestock, especially in the production phase, as it will require adaptation measures to provide comfortable for the animals. We highlight that the values in Fig. 2 represent the average for the 20-years period evaluated. In terms of extreme events, the increase of temperature can be even higher, which will also have an impact on the livestock. For animals kept outdoors, for example in pastures, an adaptation method with an adequate cost-benefit ratio is the provision of shade to reduce exposure to solar radiation and reduce thermal stress (Cheng et al., 2022 ). Sprinklers and foggers can also help reduce heat stress and are more effective in drier climates. Another example is the interaction of different methods, for example, the combination of sprinkling and a covered pen without an outdoor yard leads to a higher daily gain for hogs than sprinkling alone (Huynh et al., 2005). For animals kept indoors, physical modification options may involve the use or addition of (1) ventilation systems, (2) heat-reducing construction materials (e.g., insulation), (3) orientation, and (3) forced air velocity associated with evaporative cooling (for example, misting, spraying and pad cooling). However, the cooling system has the best performance in terms of reducing thermal stress in hot and dry environments. In terms of relative humidity, we mainly expect decreasing of this variable in the future, being this decreasing more pronounced towards the end of the century. The North and Central West regions are the regions where we expect the highest differences, with a decreasing of relative humidity of around 8%. For the Central West region this adds an additional challenge in the livestock production, as they already face problems with low relative humidity in the region, as the region tends to become drier and hotter (Hoffman, 2021). 3.2. Climatology of THI and future projections To assess the impact of climate change on heat stress, the THI was calculated for historical and future projections. Figure 3 shows the THI climatology for historical period, and anomalies between future projections and historical. From Fig. 3 , we can identify that the historical simulations show that THI is higher in the North, Northeast and Central West of Brazil. Considering the anomalies between future projections and historical simulations, we identify that in the time-slice 2021–2040, the THI may have an increasing of 2 [-]. For the time-slice from 2061–2080, we estimate higher increase of THI (up to 6 [-]) and higher spatial variability of index, compared to historical simulations. The results are aligned with results presented in Fig. 2 , which also indicates that these regions are getting hotter and drier in the future. As defined in the methodology section, the risk can be divided into moderate, high and extreme stress. Figure 4 and Fig. 5 shows the number of days (average for 20-years) with high and extreme THI (respectively) for historical projections (left), and the anomalies for short-, medium-, and long-term, for different species (right). In Fig. 4 we can observe that the highest occurrence of high heat stress are found in North, Northeast and Central West of Brazil, considering all the evaluated species. For all species, the number of days with high heat stress increases as we move towards the end of the century. The 2061–2080 time-slice indicates the worst case scenario, where most affected regions are South and Southeast, with an increase in the number of days per year with high heat stress higher than 200 days (for all considered species). For some species (e.g. poultry general , cattle beef and cattle dairy ), there will be also an increase in the days with high heat stress in coastal areas of Northeast. The general results show a decrease of days with high heat stress in North (2061–2080 time slice), which is not seem for other specific species. This shows the importance of looking at different onset, more specific for the considered species. In Fig. 5 , we can observe that the highest occurrence of extreme heat stress are found in North of Brazil ( general , cattle beef , pigs and poultry general ). Similarly as presented in Fig. 4 , the number of days with extreme heat stress increases as we move towards the end of the century. The 2061–2080 time-slice indicates the worst case scenario, where most affected region is North, with an increase in the number of days per year with extreme heat stress higher than 200 days. Figure 6 and Fig. 7 shows the number of days with high (extreme) heat stress for different geographical regions in Brazil. According to Fig. 6 , the North and Northeast show an increase of high heat stress for cattle dairy , cattle beef , goats , sheep , pigs and poultry in general . However, for the species ( general) , we identified decreasing trends for these regions. This can be explained in Fig. 7 , where the extreme heat stress is increased in these regions for the general category . For the South, Southeast and Central West regions no relevant trends of high heat stress are identified for cattle dairy , cattle beef , goats , sheep , pigs and poultry general . In Fig. 7 we identify increasing trends of extreme heat stress for all regions. However, the magnitude is different according to the region and the species evaluated. The regions with higher number of days with extreme heat stress are Southeast and Central West. These numbers are especially high for the species: general , cattle beef , pigs , poultry general . More than a third of the beef cattle herd is raised in the Central-West region of Brazil (PAM-IBGE, 2019 ). According to a study carried out in Brazil by Zilli et al. ( 2020 ), the impacts of climate change affect the livestock sector through productivity losses and, to a lesser extent, through losses in the production of soybeans and corn used as livestock feed. This indicates the need for greater strategies on the part of rural producers to maintain better solutions for construction materials, shading, ventilation and cooling systems to ensure greater comfort and welfare for the animals. To evaluate the effect of climate change on the seasonality of the extreme heat stress , we also evaluate the different seasons for each region (Fig. 8 ). The heatmap presented in Fig. 8 shows the number of days of extreme heat stress for each specie, considering the historical simulation and the projections for the SSP5-8.5 scenario for short- (2021–2040), medium- (2041–2060), and long-term (2061–2080). The seasons were defined as DJF (summer), MAM (autumn), JJA (winter) and SON (spring). Figure 8 shows that the season with higher extreme heat stress is spring (SON), followed by summer (DJF). Southeast and Central-West will be the most affected areas, according to future projections. Poultry , pigs , cattle-beef and general are the species with higher impact due to climate changes. 4. Conclusions The goal of this paper is to evaluate the impacts of climate change by using the THI, which is widely used in research for regions with tropical and temperate climates. Based on the temperature and relative humidity of the air, the calculated THI values ​​reflect exposure to recorded heat levels. We evaluated the THI projections by using CMIP6 ensemble models for historical period, and short-, medium- and long-term projections in a pessimist scenario of climate change (SSP5-8.5). It is important to remember that this scenario, and therefore our results, can be seen as the worst case. Still, our results can help livestock producers to better prepare for impacts of climate change on the production. The results presented in this paper show an increase of high heat stress in South and Southeast, and an increase of extreme heat stress in the North and Central-West areas of Brazil. The increase in extreme heat stress tends to occur mostly during spring and summer. This increase tends to vary considering the different evaluated species. Within the evaluated animal species, the species that seem to be more affected by climate changes are Poultry , pigs , cattle-beef and general. The differences between the results for the five geographic regions in Brazil suggests that different mitigation measures need to be considered to cope with future heat stress in livestock. To survive in unfavorable environmental conditions, animals seek to modify their behavior and physiology to resist stressful conditions (Santos et al., 2021 ). In situations of thermal discomfort, animals activate thermoregulatory mechanisms to regulate internal temperature that remain within acceptable physiological limits (Godyń et al. 2019 ). Stressful environments impair agricultural production, that is, animal growth, production and quality of milk and meat, egg production, weight, reproductive quality and performance, and metabolic and health status. Therefore, it is strategically efficient to use measures to deal with environmental thermal stress (Berman, 2019 ). With climate changes indicating drier and hotter future conditions in Brazil, the heat stress in livestock may become an additional challenge to a world that is already concerned with future food security under scenarios of climate change. This situation may be even more problematic if the global society does not reduce meat consumption. Regional changes in production in Brazil, observed in all regions and species considered, raise concerns regarding the availability of infrastructure and resources to accommodate them. The aspect to consider is that, due to climate changes, there will be an even greater need for cooling systems, with attention to rising water and electricity costs. More intense insertion of mechanized systems powered by renewable energy sources is also likely to reduce costs and potential increases in greenhouse gas emissions that would otherwise result in the use of fossil fuels. At the same time, food production in Brazil, the world's largest exporter of beef and soy, has been responsible for a large part of the country's greenhouse gas emissions. Most emissions are directly related to deforestation to convert native vegetation into pastures, being the main source of carbon released by Brazil into the atmosphere. Pollution from beef packing plants is also quite significant in the country. Measures related to food safety, animal welfare practices, societal acceptability and greenhouse effect reduction measures are essential for the food production chain as a whole. Declarations Acknowledgments: The authors would like to thank the Federal University of Goiás (UFG), their support is appreciated. 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National Academy of Sciences, Washington Oliveira CEA, Tinôco I de FF, Damasceno FA, et al (2022) Mapping of the Thermal Microenvironment for Dairy Cows in an Open Compost-Bedded Pack Barn System with Positive-Pressure Ventilation. Animals 12:2055. https://doi.org/10.3390/ani12162055 O’Neill, B. C., Kriegler, E., Riahi, K., Ebi, K. L., Hallegatte, S., Carter, T. R., ... & Van Vuuren, D. P. (2014). A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic change, 122, 387-400. O’Neill, B. C., Carter, T. R., Ebi, K., Harrison, P. A., Kemp-Benedict, E., Kok, K., ... & Pichs-Madruga, R. (2020). Achievements and needs for the climate change scenario framework. Nature climate change, 10(12), 1074-1084. PAM-IBGE (2019) Produção Agrícola Munucipal – Instituto Brasileiro de Geografia e Estatística [database; in portugguese] Retrived from https://sidra.ibge.gov.br/pesquisa/pam/tabelas. Accessed 08 November 2023 Rahimi J, Mutua JY, Notenbaert AMO, et al (2021) Heat stress will detrimentally impact future livestock production in East Africa. Nat Food 2:88-96 https://doi.org/10.1038/s43016-021-00226-8 Ranjitkar S, Bu D, Van Wijk M, et al (2020) Will heat stress take its toll on milk production in China. Clim Change 16: 637-652. https://doi.org/10.1007/s10584-020-02688-4 Santos MM, Souza-Junior JBF, Dantas MRT, de Macedo Costa LL (2021) An updated review on cattle thermoregulation: physiological responses, biophysical mechanisms, and heat stress alleviation pathways. Environmental Science and Pollution Research 28:30471–30485. https://doi.org/10.1007/s11356-021-14077-0 Sejian V, Bhatta R, Gaughan JB, et al (2018) Review: Adaptation of animals to heat stress. Animal 12:s431-s444. https://doi.org/10.1017/S1751731118001945 Serradilla JM, Carabaño MJ, Ramón M, et al (2018) Characterisation of Goats’ Response to Heat Stress: Tools to Improve Heat Tolerance. In: Goat Science. https://doi.org/10.5772/intechopen.70080 Swart NC, Cole JNS, Kharin V V., et al (2019) The Canadian Earth System Model version 5 (CanESM5.0.3). Geosci Model Dev 12:4823-4873. https://doi.org/10.5194/gmd-12-4823-2019 Thornton P, Nelson G, Mayberry D, Herrero M (2021) Increases in extreme heat stress in domesticated livestock species during the twenty-first century. Glob Chang Biol 27:5762-5772. https://doi.org/10.1111/gcb.15825 Valente ÉEL, Chizzotti ML, De Oliveira CVR, et al (2015) Intake, physiological parameters and behavior of Angus and Nellore bulls subjected to heat stress. Semina:Ciencias Agrarias 36:4565–4574. https://doi.org/10.5433/1679-0359.2015v36n6Supl2p4565 Vicedo-Cabrera AM, Scovronick N, Sera F, et al (2021) The burden of heat-related mortality attributable to recent human-induced climate change. Nat Clim Chang 11:. https://doi.org/10.1038/s41558-021-01058-x Vitali A, Felici A, Esposito S, et al (2015) The effect of heat waves on dairy cow mortality. J Dairy Sci 98:4572–4579. https://doi.org/10.3168/jds.2015-9331 Wieners KH, Giorgetta M, Jungclaus J, et al (2019) MPI-M MPI-ESM1.2-LR model output prepared for CMIP6 CMIP historical. Version 20230703. Earth System Grid Federation. https://doi.org/10.22033/ESGF/CMIP6.6595 Xin H, Harmon JD (1998) Livestock Industry Facilities and Environment: Heat Stress Indices for Livestock. Agriculture and Environment Extension Publications 163. http://lib.dr.iastate.edu/extension_ag_pubs/163 Yukimoto S, Kawai H, Koshiro T, et al (2019) The Meteorological Research Institute Earth System Model version 2.0, MRI-ESM2. 0: Description and basic evaluation of the physical component. Journal of the Meteorological Society of Japan. Ser. II, 97:931-965. Zeng J, Li J, Lu X, et al (2022) Assessment of global meteorological, hydrological and agricultural drought under future warming based on CMIP6. Atmospheric and Oceanic Science Letters 15:100143. https://doi.org/10.1016/j.aosl.2021.100143 Zilli M, Scarabello M, Soterroni AC, et al (2020) The impact of climate change on Brazil’s agriculture. Science of the Total Environment 740:139384. https://doi.org/10.1016/j.scitotenv.2020.139384 Cite Share Download PDF Status: Published Journal Publication published 23 Sep, 2024 Read the published version in International Journal of Biometeorology → Version 1 posted Reviewers agreed at journal 08 May, 2024 Reviewers invited by journal 04 Mar, 2024 Editor assigned by journal 08 Feb, 2024 First submitted to journal 07 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3941355","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276296895,"identity":"cf3d8688-73a9-47e7-9572-70bc5359a5ba","order_by":0,"name":"Nicole Ferreira","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYDACZiB+AOPwMNgwMEjAeBLY1EO1JCC0pBGhhQFVy2HCWszZmR8+SKhgyOfnP3zswZuK84n9s5sPPmCosYlmkO59gE2LZTObsUHCGQbLmTPS0g3nnLmdOOPOsWQDhmNpuQ0yxw2waTE4zMMmkdjGYGBwg8dMmrftdmLDjRwzCcaGw7kNEmlYHQbR8o/BwP78+W/SvP/OJc4nTksD0BaGHDZp3oYDiRsIawH55ZiEgcSNNDPJOceSjTfeSEsGiqTltskcw67l/OGHDz7U2Bjw9x9+JvGmxk523o3kgyCR3H7pNqxaoAARBY4NIDIBiNnwaUAG9sQqHAWjYBSMgpEDAADlXc4uplweAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3098-5993","institution":"University of Goettingen","correspondingAuthor":true,"prefix":"","firstName":"Nicole","middleName":"","lastName":"Ferreira","suffix":""},{"id":276296896,"identity":"c58fa2ba-35b6-41f0-931b-4ecd6b0c94f2","order_by":1,"name":"Rafaella Resende Andrade","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rafaella","middleName":"Resende","lastName":"Andrade","suffix":""},{"id":276296897,"identity":"b9313913-974c-4a19-aa21-bb1a2fedf2b2","order_by":2,"name":"Leonardo Nascimento Ferreira","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Leonardo","middleName":"Nascimento","lastName":"Ferreira","suffix":""}],"badges":[],"createdAt":"2024-02-08 23:03:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3941355/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3941355/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00484-024-02778-3","type":"published","date":"2024-09-23T15:57:58+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":52078425,"identity":"e86cf134-ad65-49f6-bfa3-7cacfb6e388d","added_by":"auto","created_at":"2024-03-06 10:52:20","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":350414,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eGeographical regions in Brazil: North (N), Northeast (NE), Central-West (CW), Southeast (SE) and South (S).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/a80e4c6cb398f0841c8dba77.jpg"},{"id":52078279,"identity":"91e9cd46-2990-4fd6-9978-1aab9383724d","added_by":"auto","created_at":"2024-03-06 10:44:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":608696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDaily mean temperature (tas, °C) and relative humidity (hurs, %) climatology for the baseline period (1991-2010) and anomalies between future and baseline period, considering short- (2021-2040), medium- (2041-2060) and long-term (2061-2080) simulations for SSP5-8.5.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/725d655cc53d7fe5d9b10321.jpg"},{"id":52078280,"identity":"ba149201-4956-4498-9c8a-a348222e1b60","added_by":"auto","created_at":"2024-03-06 10:44:20","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":329619,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eDaily mean THI (-) climatology for the baseline period (1991-2010) and anomalies between future and baseline period, considering short- (2021-2040), medium- (2041-2060) and long-term (2061-2080) simulations for SSP5-8.5.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/32a93f7646646168af7a6c44.jpg"},{"id":52078284,"identity":"72388f17-314f-4726-b3e3-cf6bb1ee889e","added_by":"auto","created_at":"2024-03-06 10:44:21","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1182251,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNumber of days with high heat stress for each species (Historical, 1991-2010) and anomalies between future and historical for the short- (2021-2040), medium- (2041-2060), and long-term (2061-2080).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/47d9912ecbda5441a74bf7b0.jpg"},{"id":52078285,"identity":"5327708b-2a15-4c23-9829-52f6c588d322","added_by":"auto","created_at":"2024-03-06 10:44:21","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1164445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNumber of days with extreme heat stress for each species (Historical, 1991-2010) and anomalies between future and historical for the short- (2021-2040), medium- (2041-2060), and long-term (2061-2080).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/d1a0c35f38e536620be31627.jpg"},{"id":52078426,"identity":"5eb3f234-de33-4750-a97a-e47a4d1bb9a5","added_by":"auto","created_at":"2024-03-06 10:52:21","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":367508,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNumber of days with high heat stress for different geographical regions in Brazil. Line colors correspond to the regions with the same color in the brazilian map (upper-right)\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/232cfc38ae894aeb04993123.jpg"},{"id":52078283,"identity":"32aca561-db48-4af5-bfdc-d2825ff94aee","added_by":"auto","created_at":"2024-03-06 10:44:20","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":268091,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNumber of days with extreme heat stress for different geographical regions in Brazil. Line colors correspond to the regions with the same color in the brazilian map (upper-right).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/fa2a680810ceca151e2b0146.jpg"},{"id":52078282,"identity":"04d982ec-bbdc-4c63-bae5-23625da6c02c","added_by":"auto","created_at":"2024-03-06 10:44:20","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":606606,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eNumber of days with extreme heat stress per season (DJF, MAM, JJA, and SON) for each specie, considering the historical simulation (1) and the projections for the SSP5-8.5 scenario for (2) short- (2021-2040), (3) medium- (2041-2060), and (4) long-term (2061-2080).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/50083d1cffea6830a9f11405.jpg"},{"id":65628087,"identity":"0ababb5a-74bc-4be9-9ae0-cf24459880d6","added_by":"auto","created_at":"2024-09-30 16:17:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5330666,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3941355/v1/7edaa376-3c72-4949-a16f-2ba30f23dcb5.pdf"}],"financialInterests":"","formattedTitle":"Climate change impacts on livestock in Brazil","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eClimate change and extreme events affect different regions globally, with a range of negative impacts affecting multiple sectors, such as health (Vicedo-Cabrera et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), energy (Jong et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ferreira et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e), and agriculture (Zilli et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Appiah et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ferreira et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e). As an impact of climate change, we expect an increase in temperature, changes in relative humidity and heatwaves. All of these changes will affect livestock differently across the world (Allen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).The main consequence is a stronger heat stress (Vitali et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Heat stress in domesticated livestock occurs when environmental conditions challenge the animal's thermoregulatory mechanisms (Thornton et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The effects of heat stress include reduced production, reproduction, fertility, animal welfare, increased susceptibility to disease and, in some cases, increased mortality (Herbut et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Andrade et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). If our global society does not reduce protein of animal origin consumption in the near future, then the additional heat stress might also compromise food security in the coming years.\u003c/p\u003e \u003cp\u003eClimate models are important tools to assess climate change impacts. Projections from Phase Six of the Coupled Model Intercomparison Project (CMIP6) provide climate scenarios based on different Shared Socio-Economic Pathways (SSP), and are widely used to assess climate change impacts in different sectors (Thornton et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zeng et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Using climate data, available as an output of climate models, heat stress indices can be calculated to investigate the impact of climate change on livestock, in different regions, and different species (Berman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The temperature-humidity index (THI) is one of the indexes that are frequently used to represent the heat stress for different species (Allen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Exploiting existing variation in heat tolerance among different animal species could be a key adaptation strategy (Thornton et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Measures that assess animal welfare, environmental and economic issues are little investigated in the context of climate change (Hempel et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). At the same time, there are still rare studies in the literature that estimate the effect of climate change on Brazilian livestock, especially paying attention to the different regions of the country, taking into account the large territorial extension. Brazilian agricultural production provides a significant fraction of the food consumed worldwide (Zilli et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe goal of this paper is to evaluate the impacts of climate change by using the THI and specific thresholds to different livestock species. This impact was evaluated by using CMIP6 ensemble models for historical period, and short-, medium- and long-term projections. For this analysis, we used the SSP5-8.5 scenario which assumes a business as usual scenario. We focused on the five geographical regions in Brazil, as different regions have different productions and will be affected differently by climate change.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Datasets: CMIP6 Climate change projections\u003c/h2\u003e \u003cp\u003eProjections from Phase Six of the Coupled Model Intercomparison Project (CMIP6) provide climate scenarios based on different Shared Socio-Economic Pathways (SSP) (O\u0026rsquo;Neill et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The SSP scenarios were created from long-term integrations with possible greenhouse gas emission scenarios in the atmosphere and their impacts on climate variables. These scenarios can be used to investigate the implications of long term climatic changes for designing robust policies in an environment of interacting complex systems and uncertainty (Hall et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Harrison et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; O\u0026rsquo;Neil et al. 2014). These scenarios are widely used in the literature, which makes the comparison with other research results easier. In this paper, four CMIP6 models were used to create an ensemble model, which represents the daily median values across the models. More information about the models can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClimate models used to create the ensemble model.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInstitution\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMore information\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanadian Centre for Climate Modelling and Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSwart et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeophysical Fluid Dynamics Laboratory (GFDL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHorowitz et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax-Planck Institut f\u0026uuml;r Meteorologie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWieners et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJapan Meteorological Research Institute\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYukimoto et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe evaluate the impacts of climate change on livestock, using historical simulations (from 1991 to 2010) and climate projections (scenario SSP5-8.5, from 2021 to 2080). The scenario SSP5-8.5 is considered as a pessimist scenario, with a higher increase of temperature by the end of the century, compared to other scenarios.\u003c/p\u003e \u003cp\u003eWe divided the future projections into short- (2021\u0026ndash;2040), medium- (2041\u0026ndash;2060) and long-term (2061\u0026ndash;2080). The variables used in this methodology are the daily temperature (tas) and the near-surface relative humidity (hurs). This dataset will be used to calculate the THI, as described in section 2.3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Study area: Brazil\u003c/h2\u003e \u003cp\u003eAccording to FAO (2021), livestock occupy about 26% of the global ice-free land with one-third of the cropland being used for feed production. In Brazil, livestock assumes an important position in the economy of the country. In 2021, crop and livestock production accounted for eight percent of Brazil\u0026rsquo;s Gross Domestic Product (GDP) (USDA, 2022). According to the USDA (2022), the value of Brazil\u0026rsquo;s agriculture, including cultivation of crops and livestock production, grew an average of eight percent annually over the past two decades (2000\u0026ndash;2020), with agricultural output doubling and livestock production increasing threefold.\u003c/p\u003e \u003cp\u003eBrazil is divided in five geographical regions: North, South, Southeast, Center West, and Northeast, as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Due to the country's large territorial extension, animal production and the thermal environment differ between regions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBrazil stands out among the largest producers and exporters of protein of animal origin, with emphasis on the production of cattle dairy, cattle beef, goats, sheep, pigs, poultry general, the focus of this research. Milk production in the country, estimated at 35.30\u0026nbsp;billion liters in 2021 (IBGE, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), is distributed throughout almost the entire country. The Southeast, Central-West and South regions with the greatest production, mainly the states of Minas Gerais, Goi\u0026aacute;s, Paran\u0026aacute;, Santa Catarina and Rio Grande do Sul (EMBRAPA, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to data from IBGE (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), in the first quarter of 2022, 1.55\u0026nbsp;billion heads of broiler chickens were slaughtered. In this period, the South Region accounted for 60.2% of the national chicken slaughter, followed by the Southeast (19.2%), Central-West (14.7%), Northeast (4.3%) and North (1.6%). The production of chicken eggs was 977.20\u0026nbsp;million dozen, with the southeast and south regions standing out as the largest producers. For pig production, 13.64\u0026nbsp;million heads of pigs were slaughtered, with the South Region accounting for 66.0% of the national pig slaughter, in the 1st quarter of 2022, followed by the Southeast (18.8%), Central-West (13.9%), Northeast (1.2%) and North (0.1%).\u003c/p\u003e \u003cp\u003eBrazil is the largest beef exporter in the world and has a cattle herd of 234.3\u0026nbsp;million heads. Beef cattle production in the country is predominantly based on pastures. In the 1st quarter of 2022, 6.96\u0026nbsp;million heads of beef cattle were slaughtered. The Central-West Region presented the highest proportion of cattle slaughter in the period, 37.1% of the total, followed by the North (21.7%), Southeast (21.3%), South (11.4%) and Northeast (8.5%) (IBGE, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Temperature Humidity Index (THI)\u003c/h2\u003e \u003cp\u003eThe thermal environment is one of the major climatic factors that affects animal production, and can be reproduced as a combination of air temperature, humidity, and air movement (Ames, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1980\u003c/span\u003e). There is a thermal zone, where the animals exhibit optimum performance and minimal energy expenditure (Nardone et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). When the animal is suffering from an individual source of stress, the phenotypic response is called acclimation (Nardone et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsidering the impacts of climate change, it is likely that the animals across the world are outside this thermal zone. This means that extra energy will be required to maintain thermoregulation and production processes may become less effective (Joy et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Godde et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Research shows that milk production tends to be constant when the ambient temperature is within the thermoneutral zone, but drops linearly as the THI increases (Hempel et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lobeck et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, when an animal is exposed to a heat stress they are not able to dissipate sufficient heat to keep homeothermy, leading to an increasing in respiration, pulse, heart rate, and body temperatures (Fregly, 2011; Nardone et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Kadzere et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). This can lead to a reduction in the feed intake, reproduction efficiency, as well as changes in mortality and immune system function (Das et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sejian et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This may become an additional challenge to a world that is already concerned with future food security under scenarios of climate change. Cheng et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) produced a literature review highlighting the Climate Change and Livestock Production. According to the authors, adaptation measures are essential to sustain the growing demand for livestock products, however their relevance depends on climatic conditions, the management of local production, as well as ensuring comfort and well-being conditions for the animals. At the same time, mitigation is key to limiting the future worsening of climate change and there are a number of possible strategies.\u003c/p\u003e \u003cp\u003eThe environmental conditions that induce heat stress can be calculated using the temperature humidity index (THI), which is determined with a combination of ambient temperature and relative humidity (NRC, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1971\u003c/span\u003e). The THI can be defined as NRC (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1971\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$THI=\\left(1.8*T+32\\right)-\\left[\\left(0.55-0.0055*RH\\right)*\\left(1.8+T-26\\right)\\right],$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere T is the air temperature (\u0026deg;C), RH is the relative humidity (%) and THI is the Temperature humidity index.\u003c/p\u003e \u003cp\u003eThe THI was applied in several researches across the world (Andrade et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e; Kang et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lallo et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). THI varies according to the animal species, as each animal species has different mechanisms to cope with high air temperature and relative humidity. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a compilation of thresholds for THI, classified as \u003cem\u003eModerate\u003c/em\u003e, \u003cem\u003eHigh\u003c/em\u003e and \u003cem\u003eExtreme\u003c/em\u003e heat stress (adapted from Thornton et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), considering relevant domesticated animal species in the Brazilian livestock context.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTHI onset of the stress level for different species (cattle dairy, cattle beef, goats, sheep, pigs, poultry-geral), classified as Moderate, High and Extreme heat stress.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eOnset of the stress level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eReferences\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExtreme\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFuquay (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1981\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCattle-dairy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMader et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2006\u003c/span\u003e); Dunn et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e); Dash et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); Ranjitkar et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Rahimi et al. (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCattle beef\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eValente et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoats\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSerradilla et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSheep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMcManus et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); Belhadj Slimen et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePigs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXin \u0026amp; Harmon (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e); Lallo et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e); Mutua et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoultry-general\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMoraes et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e\u0026sup1;General - THI value considered by the literature for all animals. \u0026sup2;THI limit table adapted from Thornton et al. (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Moraes et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) used five different categories for poultry-light and moderate discomfort were merged here.\u003c/p\u003e \u003cp\u003eTo evaluate the impact of climate change in the livestock in Brazil, we used the THI onset of stress levels presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and calculated the number of days with mild stress, moderate stress, severe stress for different animals in historical simulations and future projections from CMIP6. We focused on days with extreme and high stress, and how they change according to each species and considering different time-slices.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussions","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Climate change projections\u003c/h2\u003e \u003cp\u003eAs described in the section, two climate variables were evaluated: mean daily temperature (tas) and near-surface relative humidity (hurs). Four models were used in this analysis to calculate an ensemble model, based on the medium daily value across the four models.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the climatology of temperature and relative humidity for the ensemble model (left) and the anomalies between the future projections (SSP5-8.5) and the historical period (right). As previously mentioned, the historical period included data from 1991 to 2010, and the future projections are divided into short- (2021\u0026ndash;2040), medium- (2041\u0026ndash;2060), and long-term (2061\u0026ndash;2080). The anomalies are calculated based on the difference between future projection and historical period.\u003c/p\u003e \u003cp\u003eAccording to the climatology of temperature and relative humidity, we can notice that there is a great spatial variability of these variables across Brazil. For temperature, the lowest temperatures are found in South of Brazil, while for relative humidity the lowest relative humidity are found in central regions in Brazil.\u003c/p\u003e \u003cp\u003eIn terms of anomalies, we can expect an increase in temperature in all projections, especially towards the end of the century. In the short-term, we expect an increase of 1\u0026deg;C across the country. In the long-term however, we find more variability of this increase across the country, with highest values in the North and Central West part of Brazil, reaching values up to 4\u0026deg;C. The increasing of temperature can be problematic to livestock, especially in the production phase, as it will require adaptation measures to provide comfortable for the animals. We highlight that the values in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e represent the average for the 20-years period evaluated. In terms of extreme events, the increase of temperature can be even higher, which will also have an impact on the livestock.\u003c/p\u003e \u003cp\u003eFor animals kept outdoors, for example in pastures, an adaptation method with an adequate cost-benefit ratio is the provision of shade to reduce exposure to solar radiation and reduce thermal stress (Cheng et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Sprinklers and foggers can also help reduce heat stress and are more effective in drier climates. Another example is the interaction of different methods, for example, the combination of sprinkling and a covered pen without an outdoor yard leads to a higher daily gain for hogs than sprinkling alone (Huynh et al., 2005). For animals kept indoors, physical modification options may involve the use or addition of (1) ventilation systems, (2) heat-reducing construction materials (e.g., insulation), (3) orientation, and (3) forced air velocity associated with evaporative cooling (for example, misting, spraying and pad cooling). However, the cooling system has the best performance in terms of reducing thermal stress in hot and dry environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of relative humidity, we mainly expect decreasing of this variable in the future, being this decreasing more pronounced towards the end of the century. The North and Central West regions are the regions where we expect the highest differences, with a decreasing of relative humidity of around 8%. For the Central West region this adds an additional challenge in the livestock production, as they already face problems with low relative humidity in the region, as the region tends to become drier and hotter (Hoffman, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Climatology of THI and future projections\u003c/h2\u003e \u003cp\u003eTo assess the impact of climate change on heat stress, the THI was calculated for historical and future projections. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the THI climatology for historical period, and anomalies between future projections and historical.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFrom Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, we can identify that the historical simulations show that THI is higher in the North, Northeast and Central West of Brazil. Considering the anomalies between future projections and historical simulations, we identify that in the time-slice 2021\u0026ndash;2040, the THI may have an increasing of 2 [-]. For the time-slice from 2061\u0026ndash;2080, we estimate higher increase of THI (up to 6 [-]) and higher spatial variability of index, compared to historical simulations. The results are aligned with results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which also indicates that these regions are getting hotter and drier in the future.\u003c/p\u003e \u003cp\u003eAs defined in the methodology section, the risk can be divided into moderate, high and extreme stress. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the number of days (average for 20-years) with high and extreme THI (respectively) for historical projections (left), and the anomalies for short-, medium-, and long-term, for different species (right).\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e we can observe that the highest occurrence of \u003cem\u003ehigh heat stress\u003c/em\u003e are found in North, Northeast and Central West of Brazil, considering all the evaluated species. For all species, the number of days with \u003cem\u003ehigh heat stress\u003c/em\u003e increases as we move towards the end of the century. The 2061\u0026ndash;2080 time-slice indicates the worst case scenario, where most affected regions are South and Southeast, with an increase in the number of days per year with \u003cem\u003ehigh heat stress\u003c/em\u003e higher than 200 days (for all considered species). For some species (e.g. \u003cem\u003epoultry general\u003c/em\u003e, \u003cem\u003ecattle beef\u003c/em\u003e and \u003cem\u003ecattle dairy\u003c/em\u003e), there will be also an increase in the days with \u003cem\u003ehigh heat stress\u003c/em\u003e in coastal areas of Northeast. The \u003cem\u003egeneral\u003c/em\u003e results show a decrease of days with \u003cem\u003ehigh heat stress\u003c/em\u003e in North (2061\u0026ndash;2080 time slice), which is not seem for other specific species. This shows the importance of looking at different onset, more specific for the considered species.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, we can observe that the highest occurrence of \u003cem\u003eextreme heat stress\u003c/em\u003e are found in North of Brazil (\u003cem\u003egeneral\u003c/em\u003e, \u003cem\u003ecattle beef\u003c/em\u003e, \u003cem\u003epigs\u003c/em\u003e and \u003cem\u003epoultry general\u003c/em\u003e). Similarly as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the number of days with \u003cem\u003eextreme heat stress\u003c/em\u003e increases as we move towards the end of the century. The 2061\u0026ndash;2080 time-slice indicates the worst case scenario, where most affected region is North, with an increase in the number of days per year with \u003cem\u003eextreme heat stress\u003c/em\u003e higher than 200 days.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the number of days with \u003cem\u003ehigh (extreme) heat stress\u003c/em\u003e for different geographical regions in Brazil. According to Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the North and Northeast show an increase of \u003cem\u003ehigh heat stress\u003c/em\u003e for \u003cem\u003ecattle dairy\u003c/em\u003e, \u003cem\u003ecattle beef\u003c/em\u003e, \u003cem\u003egoats\u003c/em\u003e, \u003cem\u003esheep\u003c/em\u003e, \u003cem\u003epigs\u003c/em\u003e and \u003cem\u003epoultry in general\u003c/em\u003e. However, for the species (\u003cem\u003egeneral)\u003c/em\u003e, we identified decreasing trends for these regions. This can be explained in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, where the \u003cem\u003eextreme heat stress\u003c/em\u003e is increased in these regions for the \u003cem\u003egeneral category\u003c/em\u003e. For the South, Southeast and Central West regions no relevant trends of \u003cem\u003ehigh heat stress\u003c/em\u003e are identified for \u003cem\u003ecattle dairy\u003c/em\u003e, \u003cem\u003ecattle beef\u003c/em\u003e, \u003cem\u003egoats\u003c/em\u003e, \u003cem\u003esheep\u003c/em\u003e, \u003cem\u003epigs\u003c/em\u003e and \u003cem\u003epoultry general\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e we identify increasing trends of \u003cem\u003eextreme heat stress\u003c/em\u003e for all regions. However, the magnitude is different according to the region and the species evaluated. The regions with higher number of days with \u003cem\u003eextreme heat stress\u003c/em\u003e are Southeast and Central West. These numbers are especially high for the species: \u003cem\u003egeneral\u003c/em\u003e, \u003cem\u003ecattle beef\u003c/em\u003e, \u003cem\u003epigs\u003c/em\u003e, \u003cem\u003epoultry general\u003c/em\u003e. More than a third of the beef cattle herd is raised in the Central-West region of Brazil (PAM-IBGE, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to a study carried out in Brazil by Zilli et al. (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the impacts of climate change affect the livestock sector through productivity losses and, to a lesser extent, through losses in the production of soybeans and corn used as livestock feed. This indicates the need for greater strategies on the part of rural producers to maintain better solutions for construction materials, shading, ventilation and cooling systems to ensure greater comfort and welfare for the animals.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo evaluate the effect of climate change on the seasonality of the \u003cem\u003eextreme heat stress\u003c/em\u003e, we also evaluate the different seasons for each region (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). The heatmap presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows the number of days of \u003cem\u003eextreme heat stress\u003c/em\u003e for each specie, considering the historical simulation and the projections for the SSP5-8.5 scenario for short- (2021\u0026ndash;2040), medium- (2041\u0026ndash;2060), and long-term (2061\u0026ndash;2080). The seasons were defined as DJF (summer), MAM (autumn), JJA (winter) and SON (spring).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e shows that the season with higher \u003cem\u003eextreme heat stress\u003c/em\u003e is spring (SON), followed by summer (DJF). Southeast and Central-West will be the most affected areas, according to future projections. \u003cem\u003ePoultry\u003c/em\u003e, \u003cem\u003epigs\u003c/em\u003e, \u003cem\u003ecattle-beef\u003c/em\u003e and \u003cem\u003egeneral\u003c/em\u003e are the species with higher impact due to climate changes.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eThe goal of this paper is to evaluate the impacts of climate change by using the THI, which is widely used in research for regions with tropical and temperate climates. Based on the temperature and relative humidity of the air, the calculated THI values ​​reflect exposure to recorded heat levels. We evaluated the THI projections by using CMIP6 ensemble models for historical period, and short-, medium- and long-term projections in a pessimist scenario of climate change (SSP5-8.5). It is important to remember that this scenario, and therefore our results, can be seen as the worst case. Still, our results can help livestock producers to better prepare for impacts of climate change on the production.\u003c/p\u003e \u003cp\u003eThe results presented in this paper show an increase of high heat stress in South and Southeast, and an increase of extreme heat stress in the North and Central-West areas of Brazil. The increase in extreme heat stress tends to occur mostly during spring and summer. This increase tends to vary considering the different evaluated species. Within the evaluated animal species, the species that seem to be more affected by climate changes are \u003cem\u003ePoultry\u003c/em\u003e, \u003cem\u003epigs\u003c/em\u003e, \u003cem\u003ecattle-beef\u003c/em\u003e and \u003cem\u003egeneral.\u003c/em\u003e The differences between the results for the five geographic regions in Brazil suggests that different mitigation measures need to be considered to cope with future heat stress in livestock.\u003c/p\u003e \u003cp\u003eTo survive in unfavorable environmental conditions, animals seek to modify their behavior and physiology to resist stressful conditions (Santos et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In situations of thermal discomfort, animals activate thermoregulatory mechanisms to regulate internal temperature that remain within acceptable physiological limits (Godyń et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Stressful environments impair agricultural production, that is, animal growth, production and quality of milk and meat, egg production, weight, reproductive quality and performance, and metabolic and health status. Therefore, it is strategically efficient to use measures to deal with environmental thermal stress (Berman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). With climate changes indicating drier and hotter future conditions in Brazil, the heat stress in livestock may become an additional challenge to a world that is already concerned with future food security under scenarios of climate change. This situation may be even more problematic if the global society does not reduce meat consumption.\u003c/p\u003e \u003cp\u003eRegional changes in production in Brazil, observed in all regions and species considered, raise concerns regarding the availability of infrastructure and resources to accommodate them. The aspect to consider is that, due to climate changes, there will be an even greater need for cooling systems, with attention to rising water and electricity costs. More intense insertion of mechanized systems powered by renewable energy sources is also likely to reduce costs and potential increases in greenhouse gas emissions that would otherwise result in the use of fossil fuels. At the same time, food production in Brazil, the world's largest exporter of beef and soy, has been responsible for a large part of the country's greenhouse gas emissions. Most emissions are directly related to deforestation to convert native vegetation into pastures, being the main source of carbon released by Brazil into the atmosphere. Pollution from beef packing plants is also quite significant in the country. Measures related to food safety, animal welfare practices, societal acceptability and greenhouse effect reduction measures are essential for the food production chain as a whole.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAcknowledgments: The authors would like to thank the Federal University of Goi\u0026aacute;s (UFG), their support is appreciated.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAllen JD, Hall LW, Collier RJ, Smith JF (2015) Effect of core body temperature, time of day, and climate conditions on behavioral patterns of lactating dairy cows experiencing mild to moderate heat stress. J Dairy Sci 98:118-127. https://doi.org/10.3168/jds.2013-7704\u003c/li\u003e\n\u003cli\u003eAmes D (1980) Thermal Environment Affects Production Efficiency of Livestock. 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Science of the Total Environment 740:139384. https://doi.org/10.1016/j.scitotenv.2020.139384\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"climate change, food security, animal heat stress, livestock, temperature-humidity index","lastPublishedDoi":"10.21203/rs.3.rs-3941355/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3941355/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBrazilian livestock provides a significant fraction of the food consumed globally, making the country one of the largest producers and exporters of meat, milk and eggs. However, current advances in the production of protein from Brazilian animal origin may be directly impacted by climate change and the resulting biophysical effects. Therefore, it is strategically consistent to develop measures to deal with the resulting environmental heat stress on domesticated animal species, especially the need in developing countries. This work aims to (1) evaluate the impacts of climate change on livestock in different regions of Brazil and (2) discuss possible response strategies, associated with animal comfort and welfare. From our results, we can draw better strategies to mitigate the impacts of climate change on livestock production. The results presented show an increase of high heat stress in South and Southeast and an increase of extreme heat stress in North and Central-West areas of Brazil. The rise in extreme heat stress tends to occur mostly during spring and summer and tends to vary considering the different evaluated species. Within the evaluated species, the ones that seem to be more affected by climate changes are \u003cem\u003ePoultry\u003c/em\u003e, \u003cem\u003epigs\u003c/em\u003e, \u003cem\u003ecattle-beef\u003c/em\u003e and \u003cem\u003egeneral\u003c/em\u003e (temperature-humidity index value). The differences between the results for the five geographic regions in Brazil suggests that different mitigation measures need to be considered to cope with future heat stress in livestock. To ensure the long-term success of Brazil's influence on the global market for proteins of animal origin, it must achieve sustainable production systems more intensively.\u003c/p\u003e","manuscriptTitle":"Climate change impacts on livestock in Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-06 10:44:16","doi":"10.21203/rs.3.rs-3941355/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2024-05-08T15:45:40+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-03-04T14:36:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-02-08T20:52:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Biometeorology","date":"2024-02-07T13:58:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-biometeorology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijbm","sideBox":"Learn more about [International Journal of Biometeorology](http://link.springer.com/journal/484)","snPcode":"484","submissionUrl":"https://www.editorialmanager.com/ijbm/default2.aspx","title":"International Journal of Biometeorology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e3acb48e-6f57-458e-9975-b6e417cf04a1","owner":[],"postedDate":"March 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-30T16:11:23+00:00","versionOfRecord":{"articleIdentity":"rs-3941355","link":"https://doi.org/10.1007/s00484-024-02778-3","journal":{"identity":"international-journal-of-biometeorology","isVorOnly":false,"title":"International Journal of Biometeorology"},"publishedOn":"2024-09-23 15:57:58","publishedOnDateReadable":"September 23rd, 2024"},"versionCreatedAt":"2024-03-06 10:44:16","video":"","vorDoi":"10.1007/s00484-024-02778-3","vorDoiUrl":"https://doi.org/10.1007/s00484-024-02778-3","workflowStages":[]},"version":"v1","identity":"rs-3941355","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3941355","identity":"rs-3941355","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}