Heat stress amelioration for pasture-based dairy cattle: challenges and opportunities

Abstract

The mental effects of heat stress in dairy cattle are poorly understood. Providing effective heat abatement while allowing cows to engage in highly motivated behaviors is important to maintain good welfare. On-animal sensors will help understand, monitor, and predict individual cow heat load. Mitigation now and in future should use known strategies, while research into novel mitigations continues. Advancing adaptation using genetic selection requires novel phenotypes for heat tolerance to be identified. Societal scrutiny of animal welfare in food production systems is intensifying. In the dairy sector, the impact of heat stress on cow productivity, health, and welfare is a growing global concern, particularly with increasing temperatures and weather variability predicted to become more extreme (Nguyen et al., 2016). Excessive heat can negatively impact biological functioning including milk production, health, and reproduction (Kadzere et al., 2002), and in severe circumstances, it can cause suffering, reduce quality of life, or even lead to death (Polsky and von Keyserlingk, 2017). The cost of unmitigated heat stress to the global dairy industry could reach $30 billion (USD) by 2050 (Allen, 2024). This global challenge requires locally tailored solutions due to variations in animal characteristics, farm systems, locations, and climatic drivers. Dairy farming occurs across the Northern and Southern Hemispheres, encompassing temperate, cold, and hot climates. Temperate climates provide conditions that fall mostly within the thermoneutral zone for modern dairy cattle breeds, which is ideal for dairy farming since cows do not need to expend additional energy to regulate their core temperature (Figure 1: Kadzere et al., 2002). Temperate climates also support low-cost, seasonal-calving systems reliant on seasonal pasture growth, common in New Zealand and Australia (Figure 2). A schematic of the influence of environmental temperature on cattle metabolic rate (balance between heat loss and gain) and core temperature. According to the environmental temperature on the x-axis moving away from the center toward the left of the image indicates cooler temperatures and moving away from the center toward the right of the image indicates warmer temperatures. Adapted from Kadzere et al. (2002). Key dairying areas in Australia (NT: Northern Territory; QLD: Queensland; SA: South Australia; NSW: New South Wales; VIC: Victoria; TAS: Tasmania) and New Zealand (NL: Northland; BP: Bay of Plenty; WK: Waikato; EC: East Coast; TA: Taranaki; MA: Manawatu; WC: West Coast; CA: Canterbury; OS: Otago-Southland) and climate classifications according to their location based on Köppen–Geiger classification between 1991 and 2020. Adapted from Woodward et al. (2024a), Dairy Australia (2023), and https://commons.wikimedia.org/w/index.php?curid=146726177, CC BY 4.0, via Wikimedia Commons. According to the Köppen–Geiger climate classification, dairying areas in New Zealand and Australia are mostly temperate, with average ambient temperatures from 0 to 20 °C (min: –40 °C; max: 40 °C) (Figure 2: Peel et al., 2007). About 5% of Australia’s and 5% of New Zealand’s dairy production occurs outside this zone, mainly in subtropical regions (i.e., Queensland and Northern New South Wales, Australia and Northland, New Zealand; Figure 2). All dairying areas in New Zealand and Australia, however, experience days when ambient temperatures exceed the cows’ thermoneutral range. Solar radiation, humidity, and wind speed also impact heat load by affecting radiative heat transfer and evaporative cooling (Blackshaw and Blackshaw, 1994). Indices for predicting conditions associated with increased heat stress risk have been developed using environmental factors as predictors (Ji et al., 2020). The commonly used Temperature Humidity Index (THI) combines air temperature and humidity but omits weather variables such as wind speed and solar radiation, critical for outdoor-grazing cows (Blackshaw and Blackshaw, 1994). Indices that include these factors better predict outdoor cattle thermal load. New Zealand’s Grazing Heat Load Index (GHLI) uses such factors to predict dairy cattle respiration rates (Bryant et al., 2022). On the contrary, Australia’s mixed systems, where cows are pasture-fed or have pasture access with partial mixed rations, utilize both the THI and dairy heat load index (DHLI) (Lees et al., 2018). While there is a need for more accurate indices in both New Zealand and Australia, available indices have modeled scenarios to define current and predict future heat stress risk. In New Zealand and Australia, where large populations of dairy cows are kept outdoors, it is recognized that heat stress is a significant and ongoing issue facing the dairy sector (Jago et al., 2023). Recent modeling indicates that dairy cows are at risk of heat stress annually for 40 to 85 days in New Zealand and 100 to 300 days in Australia, regardless of the indices or thresholds used in prediction (Nidumolu et al., 2010; Nguyen et al., 2016; Woodward et al., 2024a). By 2050, under low to high emission futures scenarios, the number of heat stress risk days is predicted to rise even in regions previously less prone to such conditions (Nidumolu et al., 2010; Woodward et al., 2024a). This review highlights areas that require ongoing research to better understand the onset and effects of heat stress on dairy cows kept predominantly outdoors in New Zealand and Australia. We summarize the challenges and opportunities in developing fit-for-purpose tools to predict, monitor, and mitigate heat load, and strategies to adapt to heat load. Our goal is to support the development of sustainable and climate-resilient farm systems for outdoor dairy cows. Heat load and heat stress are often used synonymously in the literature, with definitions differing among authors. Both terms relate to environmental conditions (temperature, humidity, solar radiation, and wind speed) and animal factors (genotype, coat color, breed, age, and stage of lactation) affecting cows’ thermal balance and tolerance (Kadzere et al., 2002). Cattle maintain a stable core temperature within the range 38 to 39.3 °C for optimal functioning (Bewley et al., 2008). Heat exchange between an animal’s body and its environment can lead to heat loss or gain through radiation, convection, conduction, and evaporation. Cattle respond to environmental conditions through hormonal, metabolic, behavioral, and physiological responses to balance heat loss with heat gain. An imbalance leads to hyperthermia (increased core temperature) or hypothermia (decreased core temperature) (Figure 1). We define increased heat load as the thermal state where an animal must respond to environmental conditions physiologically (altered respiration, panting, and blood flow to the skin) or behaviorally (altered dry matter intake, activity, and lying time) to maintain thermal balance and normal functioning. We define heat stress as the physiological state where the animal’s adaptive mechanisms to dissipate heat to maintain thermal balance have been overcome, which can lead to reduced biological functioning (Kadzere et al., 2002). Signs of heat stress may include hyperthermia (elevated core temperature beyond critical limits) (Bewley et al., 2008), reduced milk production (Chen et al., 2024), impaired fertility (Kadzere et al., 2002), drooling, and open-mouthed breathing, alongside behavioral changes that also occur during increasing heat load. Developing tools to predict heat stress risk and monitor heat load and stress, while also exploring options to mitigate elevated heat load in dairy cows, requires cow-level responses, and environmental and system-level drivers of heat load to be understood (Ji et al., 2020). Ambient temperature is the primary driver used to predict heat stress risk in housed (Ji et al., 2020) and grazing cows (Bryant et al., 2022; Hitchman et al., 2024). However, solar radiation, humidity, and wind speed also contribute to heat load by influencing radiative heat transfer and the efficiency of evaporative cooling (Blackshaw and Blackshaw, 1994). Previous research has focused on developing indices that capture the interaction of environmental conditions to predict heat stress risk for dairy cows housed indoors. The THI is one of the most widely used indices that was initially developed for humans and later modified by others (Ji et al., 2020) for use in housed cows. However, the THI is less applicable to cows outdoors (Nguyen et al., 2016; Bryant et al., 2022) due to solar radiation exposure, which can be the largest single contributor to heat load for these animals (Blackshaw and Blackshaw, 1994). Furthermore, grazing cows can also be exposed to wind, which can have a cooling effect (Hitchman et al., 2024). Given the limitations of the THI for outdoor conditions, New Zealand and Australia have developed indices that are more accurate for their environments and systems. In Australia, Lees et al., (2018) developed the DHLI using panting score and environmental in dairy cows with pasture and The DHLI ambient humidity, solar radiation, and wind In New Bryant et al. developed the using respiration and environmental and this index was using a more from grazing dairy cows across in New Zealand (Hitchman et al., 2024). The combines ambient solar radiation, and wind speed (Bryant et al., with humidity due to in predicting While impact on heat load is in hot its from the New Zealand index the need for developed using to capture of environmental factors on heat stress risk. of highly indices in both housed and grazing cows, as by the development of indices and the of more 20 indices in the by et al. and et al. (2018) and et al., Bryant et al., 2022; Hitchman et al., Both New Zealand and Australia need to and their indices can on for can the of should capture system-level factors to where the indices will be and and cow-level responses to environmental conditions et al., 2008). and cow-level factors may the limitations when indices and thresholds developed for systems to New Zealand and grazing systems. factors include but are not the that cows for and to through grazing that metabolic heat production et al., 2008), and increased metabolic heat production through of with a of et al., 2018). factors may include in breed, production and and cow-level factors not be in used to for housed cows (Hitchman et al., 2024). the of and cow-level between and outdoor dairy systems the need for indices developed using of current indices is their development from of respiration rate and (Ji et al., 2020). are in biological between environmental conditions and cow-level responses such as effects of heat load across days et al., 2008). Cattle may an heat load into the due to which may be a for indices et al. 2023). of cow-level using sensors highly and could in indices et al., 2024a). provide the for that the of the to with et al., Woodward et al., systems are and are not for (Hitchman et al., 2024). Woodward et al. used to capture temperature at from grazing dairy cows and weather at to predict heat stress using both and with and the was better at heat stress by increased temperature. and novel and opportunities to and for grazing dairy cows. respond to increased heat load through changes in biological functioning and the of with the of these responses to heat load influencing the cows’ mental A in the is the at which increasing heat load stress based on a of biological and behavioral responses and their impact on responses to increasing heat load that have been to or have the most to the with housed dairy cows have used environmental indices the to heat load thresholds associated with physiological responses and used these thresholds to define when should occur for respiration for for and for et al., et al., thresholds have not been for grazing dairy cows outdoors, with common thresholds in grazing dairy cows to milk production to Hitchman et al., or the onset of increased panting and Bryant et al., 2022). responses have been to of animals with increasing heat however, these have also less in grazing dairy cows. their use and and and lying et al., as of the of thermal and these responses cows there are production, health, and welfare responses to heat these of heat stress in housed cows and associated environmental thresholds at which cows research in grazing dairy cows should be based on the of cow-level responses within seasonal dairy systems. seasonal systems, cows are outdoors for most of the and within an in a by an of to Heat stress risk the by but in New Zealand and Australia, the risk for heat stress is in the from to (Nguyen et al., 2016; Woodward et al., The impact of heat stress and with the stage of or by the of to be as will with housed cows. In housed systems where cows heat stress can occur within a as heat can occur during physiological may in or more severe within seasonal with to with heat stress risk in grazing dairy cows in New Zealand and Australia, of heat stress on milk production are a in grazing dairy cows in Australia et al., et al., 2020) and one in (Bryant et al., and in cows in New Zealand et al., et al., the of heat stress on milk and and milk with increasing THI in grazing cows. a of housed cows that milk by and milk by in with thermoneutral cows (Chen et al., 2024). in a New Zealand and are and more from grazing dairy cows is to understand these Furthermore, on animal responses in to milk production dry matter intake, body and under differing scenarios could modeling of heat stress are important for options and future research as as a for The impact of and of heat stress on of biological functioning such as and has also less in grazing dairy cows with in housed cows et al., et al., The and are when the of heat stress may be their as future research to be within a seasonal cows, the number of heat stress is during the and of milk production are to be the known biological these of heat stress during have not been in housed or grazing dairy cows which may be important for On the contrary, for cows, the of heat stress during on the and the during the may be important et al., et al., but to be more understood in these In New are a of and future research should areas to in Australia where are more common of or this may be an important for future Furthermore, future due to changes in climate should to be within New Zealand and Australia and by et al., 2020). While cow-level responses to heat stress that impact biological functioning have important and also contribute to an animal’s mental and quality of (Polsky and von Keyserlingk, 2017). due to heat stress may occur due to impaired biological functioning to such as and by and von Keyserlingk, and et al., 2018). on the of particularly behaviors and on increasing heat load, dairy cows respond by in behaviors such as even at the of lying et al., 2008), and and (Kadzere et al., 2002). The to engage in behaviors that animals are highly motivated to or to for access to can lead to as or even (Polsky and von Keyserlingk, 2017). However, it is when the of responses to increasing heat load stress in dairy cows grazing outdoors, not in terms of the biological functioning but also the to engage in and their on on dairy cows outdoors should the environmental conditions that behaviors to better understand thermal and heat indices are commonly used to predict heat load. in milk and (Nguyen et al., to the of (Ji et al., 2020). and not for in grazing cows. Furthermore, do not an animal’s experience of heat load due to animal and individual variability that influence and tolerance to heat stress (Ji et al., et al., an individual animal is (i.e., are conditions of increasing heat load to maintain thermal will require individual to capture the responses of cows to heat load not through (Hitchman et al., Woodward et al., However, will important for do not have access to cow-level Our of heat stress and is due to responses to increasing ambient temperature et al., the of for cow-level should on that to be in and with a of that the of more the and of heat stress at cow-level et al., 2022). This could provide for to cows at risk of heat stress or to the effects of heat stress, (i.e., on an individual and the of at to the of grazing dairy cows a large grazing sensors such as and for use in housed cows may have limitations et al., On-animal sensors may include sensors panting, and and and use (i.e., and behaviors (i.e., et al., et al., 2022). sensors may individual variability in heat stress to be for et al., factors including breed, coat color, stage of milk and available mitigations and opportunities to access (i.e., and impact a to heat stress which be within et al., 2018). cow-level is to for the of and the and factors that access to pasture animal and welfare by behaviors and 2020). However, at of the it can also cows to thermal and pasture can be particularly under dry conditions, the of or the and is for the welfare and of dairy cows in these systems, on use of will require accurate indices in tools that heat stress risk which can use to provide opportunities for cows to engage in and New Zealand dairy provide and for their to heat should to support to known heat stress now and in future based on their environmental challenges while research novel options continues. include the of additional access to or in or in the a or of or to the of the options are more understood in housed cows mixed in grazing cows, where strategies and the in housed cows has focused on increasing with more and using et al., 2018). While use of could be a this is in systems requires through the will require additional research it not used by which may this as a future in systems requires due to the changes in and quality of that could from increased conditions (Jago et al., to increased use of such as which could metabolic heat production et al., 2018). research could the that pasture or in heat stress while also seasonal changes in pasture and quality that may become more in future (Jago et al., 2023). in (i.e., or requires due to cost et al., however, with options such as these are in the While these options have limitations in dairy systems, could be through a location that cows can to In New Zealand and Australia, are and and their and Dairy Australia, 2024). However, this while to be and do not the of highly motivated behaviors such as and could be to cows et al., While a may be effective in cooling a cow there are the and access to that grazing dairy cows require to their behavioral and biological research should the of and on a cows’ particularly when to engage in behaviors may in with important behaviors et al., for when a cows’ need to with need to Furthermore, heat load is not due to for (Polsky and von Keyserlingk, or cows not to utilize the cattle options for cooling such as this could lead to such as research options for grazing dairy cows should these to that effective while the cows’ behavioral, and mental can provide a at the et al., 2008), but it is a due to growing and has in dairy systems. While and of have from in their in New Zealand and Australia Dairy Australia, 2024), is known the of these as these are from can provide a of for dairy cattle under conditions by solar radiation et al., but can also reduce wind speed which has a The research to and based on and Furthermore, from the due to the of the in the of the when the and solar radiation are a (Figure the of will be critical for optimal of the in by single based on the of the from at and on New Zealand strategies will be an important of adaptation to heat A range of strategies and in an by et al. et al. and et al. We provide additional from the New Zealand based on options that are available or to be available in future but to these for an of genetic variability to to heat stress will be an important of of and selection for heat tolerance within are areas in New Zealand and Australia. known single associated with increased heat tolerance from a of through are and novel using are a et al., this is a for use in New Zealand and Australia and has However, since a from New and more in the and have this using to the and the of heat tolerance in dairy cattle et al., et al., 2022). research is to understand the of the and others in biological and both and to heat tolerance to the production and welfare of these are selection for cattle is a that a to heat stress at a low particularly in outdoor systems where the of heat abatement may be et al., 2022). Australia is of New Zealand and in the of heat tolerance in their dairy industry where Australia was the in the to for heat tolerance in (Nguyen et al., 2016; et al., 2022). This individual genetic variability in milk production responses to increasing temperature and humidity et al., however, milk production is one of thermal balance and is a to heat Furthermore, milk production is through et al., 2022). a for heat tolerance could be by responses to heat stress, particularly in dairy cows to et al., 2022). the of heat tolerance has been developed for cows outdoors using phenotypes milk production (Nguyen et al., 2016). production was a for developing the heat tolerance due to the of large and weather (Nguyen et al., 2016). However, as within this heat stress is and heat tolerance is to be by a of factors et al., 2022). phenotypes that could be in developing for heat tolerance include physiological such as respiration rate and core temperature or behaviors associated with heat these phenotypes are this may provide additional selection for cows. to the large for cow-level responses using sensors are et al., 2022). The of is research in this through the temperature to individual variability in dairy cows in to heat as in cattle et al., 2023). could be to the selection for cattle the increasing of sensors on there are significant opportunities in this to support the selection of cows. The increasing on animal welfare and climate challenges highlights the need for heat stress in dairy cows The of and cow-level factors and their a challenge in and predicting heat However, in and modeling opportunities to these By and can more accurate and tools that that cows can engage in highly motivated behaviors while effective heat abatement is important for animal welfare and research on conditions and cow-level responses is important to and of heat stress in outdoor grazing systems. 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