Editing IPCC:AR6/WGII/Chapter-5 (section)

== 5.5 Livestock-Based Systems ==

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Livestock systems may be classified as industrial (monogastric, ruminant), grassland-based in which crop-based agriculture is absent or minimal (pastoralism, agro-pastoralism), mixed rainfed combining mostly rainfed cropping with livestock, and mixed irrigated systems with a significant proportion of irrigated cropping interspersed with livestock. Livestock systems are located widely across all regions of the world, and animal-sourced food provides humans with 39% of their protein and 18% of their calorie intake (FAO, 2019 f). Some 400 million people depend on livestock for a substantial part of their livelihood ( [[#Robinson--2011|Robinson et al., 2011]] ).

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=== 5.5.1 Observed Impacts ===

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Climate change affects livestock productivity and production in many ways ( [[#Porter--2014|Porter et al., 2014]] ; [[#Rojas-Downing--2017|Rojas-Downing et al., 2017]] ). Evidence is accumulating that rising temperatures are increasing heat stress in domestic species and affecting productivity ( ''high confidence'' ) ( [[#Das--2016b|Das et al., 2016b]] ; [[#Godde--2021|Godde et al., 2021]] ).

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==== 5.5.1.1 Pastoral systems ====

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Many grassland-based livestock systems are vulnerable to climate change and increases in climate variability ( ''high confidence'' ) ( [[#Dasgupta--2014|Dasgupta et al., 2014]] ; [[#Sloat--2018|Sloat et al., 2018]] ; [[#Stanimirova--2019|Stanimirova et al., 2019]] ). Decadal vegetation changes from warming and drying trends have been detected in North American grasslands, with implications for species composition, rangeland quality and economic viability of grazing livestock ( [[#Rondeau--2018|Rondeau et al., 2018]] ; [[#Reeves--2020|Reeves et al., 2020]] ). Feed quality in South Asian grasslands has been negatively affected, reducing food security ( [[#Rasul--2019|Rasul et al., 2019]] ). Increased grassland degradation has been observed in parts of Inner Mongolia ( [[#Nandintsetseg--2021|Nandintsetseg et al., 2021]] ). Changing seasonality, increasing frequency of drought and rising temperatures are affecting pastoral systems globally ( ''high confidence'' ). These and other drivers are reducing herd mobility, decreasing productivity, increasing incidence of vector borne diseases and parasites, and reducing access to water and feed ( ''high agreement'' , ''medium evidence'' ) ( [[#López-i-Gelats--2016|López-i-Gelats et al., 2016]] ; [[#Vidal-González--2018|Vidal-González and Nahhass, 2018]] ; [[#de%20Leeuw--2020|de Leeuw et al., 2020]] ).

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==== 5.5.1.2 Livestock distribution and climate variability ====

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There is ''limited evidence'' of observed distributional changes in livestock species due to climate changes. Asian buffalo and yak breeds in China over the past 50 years have shifted distribution partly because of increases in heat stress ( [[#Wu--2015|Wu, 2015]] ; [[#Wu--2016|Wu, 2016]] ). Nepalese cattle numbers have declined, attributed to increases in the number of hot days ( [[#Koirala--2017|Koirala and Shrestha, 2017]] ).

Climate variability has been identified as the primary cause of vegetation cover changes on the Tibetan Plateau since 2000 ( [[#Lehnert--2016|Lehnert et al., 2016]] ). Increasing inter-annual variability is a driver of farm extensification in Mediterranean dairy systems ( [[#Dono--2016|Dono et al., 2016]] ). In Australian rangelands ( [[#Godde--2019|Godde et al., 2019]] ) and dairy systems ( [[#Harrison--2016|Harrison et al., 2016]] ; [[#Harrison--2017|Harrison et al., 2017]] ), increasing rainfall variability contributes more to stocking rate and profitability variability than changes in mean rainfall.

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==== 5.5.1.3 Diseases and disease vectors ====

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Climate change is affecting the transmission of vector-borne diseases ( [[#Hutter--2018|Hutter et al., 2018]] ; [[#Semenza--2018|Semenza and Suk, 2018]] ) and parasites ( [[#Rinaldi--2015|Rinaldi et al., 2015]] ) in high latitudes ( ''high confidence'' ). Different processes link climate change and infectious diseases in domesticated livestock. Some show a positive association between temperature and range expansion of arthropod vectors that spread the bluetongue virus. Others show a contraction, such as tsetse flies that transmit trypanosome parasites of several livestock species. Positive associations have been found between temperature and the spread of pathogens such as anthrax, and droughts and ENSO weather patterns and Rift Valley fever outbreaks in East Africa ( [[#Bett--2017|Bett et al., 2017]] ). Observed range expansion of economically important tick disease vectors in North America (Sonenshine, 2018) and Africa ( [[#Nyangiwe--2018|Nyangiwe et al., 2018]] ) are presenting new public health threats to humans and livestock.

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=== 5.5.2 Assessing Vulnerabilities ===

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==== 5.5.2.1 Rising temperature and heat stress ====

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Most domestic livestock have comfort zones in the range 10–30°C, depending on species and breed ( [[#Nardone--2006|Nardone et al., 2006]] ). At higher temperatures, animals eat 3–5% less per additional degree of temperature, reducing their productivity and fertility. Heat stress suppresses the immune and endocrine system, enhancing susceptibility of the animal to disease ( [[#Das--2016b|Das et al., 2016b]] ). Recent stagnation in dairy production in West Africa and China may be associated with increased periods of high daily temperatures ( ''low confidence'' ) ( [[#Rahimi--2020|Rahimi et al., 2020]] ; [[#Ranjitkar--2020|Ranjitkar et al., 2020]] ). Increases in the productive capacity of domestic animals can compromise thermal acclimation and plasticity, creating further loss. Escalating demand for livestock products in low-to-middle-income countries (LMICs) may necessitate considerable adaptation in the face of new thermal environments ( ''medium confidence'' ) ( [[#Collier--2015|Collier and Gebremedhin, 2015]] ; [[#Theusme--2021|Theusme et al., 2021]] ). Heat effects on productivity have been summarised for pigs ( [[#da%20Fonseca%20de%20Oliveira--2019|da Fonseca de Oliveira et al., 2019]] ), sheep and goats ( [[#Sejian--2018|Sejian et al., 2018]] ), and cattle ( [[#Herbut--2019|Herbut et al., 2019]] ). The direct effects of higher temperatures on the smaller ruminants (sheep and goats) are relatively muted, compared with large ruminants; goats are better able to cope with multiple stressors than sheep ( [[#Sejian--2018|Sejian et al., 2018]] ). Under SSP5-8.5 to mid-century, land suitability for livestock production will decrease because of increased heat stress prevalence in mid and lower latitudes ( ''high confidence'' ) ( [[#Thornton--2021|Thornton et al., 2021]] ).

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==== 5.5.2.2 Livestock water needs ====

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Livestock production may account for 30% of all water (blue, green and grey) used in agriculture ( [[#Mekonnen--2010|Mekonnen and Hoekstra, 2010]] ) and can negatively affect water quality. Cropland feed production accounts for 38% of crop water consumption ( [[#Weindl--2017|Weindl et al., 2017]] ). High-input livestock systems may consume more water than grazing or mixed systems, though water used per kg beef produced, for example, depends on country, context and system ( [[#Noya--2019|Noya et al., 2019]] ). In systems where feed production is rainfed, livestock and crop water productivity may be comparable ( [[#Haileslassie--2009|Haileslassie et al., 2009]] ). Direct water consumption by livestock is &lt;1–2% of global water consumption ( [[#Hejazi--2014|Hejazi et al., 2014]] ). Rising temperatures increase animal water needs, potentially affecting access of herders and livestock to drinking water sources ( [[#Flörke--2018|Flörke et al., 2018]] ).

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==== 5.5.2.3 Rising temperatures and livestock disease ====

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Climate change will have effects on future distribution, incidence and severity of climate-sensitive infectious diseases of livestock ( ''high confidence'' ) ( [[#Bett--2017|Bett et al., 2017]] ). In an assessment of climate sensitivity of European human and domestic animal infectious pathogens, 63% were sensitive to rainfall and temperature, and zoonotic pathogens were more climate-sensitive than human- or animal-only pathogens ( [[#McIntyre--2017|McIntyre et al., 2017]] ). Over the last 75 years, &gt;220 emerging zoonotic diseases, some associated with domesticated livestock, have been identified, several of which may be affected by climate change, particularly vector-borne diseases ( [[#Vaillancourt--2016|Vaillancourt and Ogden, 2016]] ; see Cross-Chapter Box ILLNESS in Chapter 2). Walsh et al. (2018) identified both temperature and rainfall as influential factors in predicting increasing anthrax outbreaks in northern latitudes. Growing infectious disease burdens in domesticated animals may have wide-ranging impacts on the vulnerability of rural livestock producers in the future, particularly related to human health and projected increases in zoonoses ( ''high confidence'' ) ( [[#Bett--2017|Bett et al., 2017]] ; [[#Heffernan--2018|Heffernan, 2018]] ; [[#Rushton--2018|Rushton et al., 2018]] ; [[#Meade--2019|Meade et al., 2019]] ).

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==== 5.5.2.4 Livestock and socioeconomic vulnerability to climate change ====

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There is ''limited evidence'' about the role of livestock in addressing socioeconomic vulnerability. Although agriculture in parts of North America has become more sensitive to climate over the last 50 years, livestock have helped to moderate this effect, being less sensitive to increasing temperatures than some specialised crop systems ( [[#Ortiz-Bobea--2018|Ortiz-Bobea et al., 2018]] ). Increasing frequency and severity of droughts will affect the future economic viability of grassland-based livestock production in the North American Great Plains ( [[#Briske--2021|Briske et al., 2021]] ). Purchasing more forage and selling more livestock have reduced household vulnerability in semi-arid parts of China over the last 35 years ( [[#Bai--2019|Bai et al., 2019]] ). A greater focus on sheep production away from cropping has increased the resilience of farming systems in Western Australia in low-rainfall years, although with mixed environmental effects ( [[#Ghahramani--2018|Ghahramani and Bowran, 2018]] ). More insights are needed as to where and how livestock can affect the vulnerability of farmers and pastoralists.

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==== 5.5.2.5 Effects of climate on the health and vulnerability of livestock keepers ====

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Vulnerability to the health impacts of climate change will be shaped by existing burdens of ill health and is expected to be highest in poor and socioeconomically marginalised populations ( ''high agreement'' , ''limited evidence'' ) ( [[#Labbé--2016|Labbé et al., 2016]] ). In addition to projected changes in infectious disease burdens, labour capacity in a warming climate is anticipated to decrease further, beyond the &gt;5% drop estimated since 2000 ( [[#Watts--2018|Watts et al., 2018]] ). Loss of labour capacity may greatly increase the vulnerability of subsistence livestock keepers ( ''high agreement'' , ''limited evidence'' ).

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==== 5.5.2.6 Gender and other social inequities ====

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Vulnerability to climate change depends on demography and social roles ( [[#Mbow--2019|Mbow et al., 2019]] ). Gender inequities can act as a risk multiplier, with women being more vulnerable than men to climate-change-induced food insecurity and related risks ( ''high confidence'' ) (Cross-Chapter Box GENDER in Chapter 18). Women and men often have differential and unequal control over different productive assets and the benefits they provide, such as income from livestock ( [[#Ngigi--2017|Ngigi et al., 2017]] ; [[#Musinguzi--2018|Musinguzi et al., 2018]] ). Indigenous livestock keepers can be more vulnerable to climate change, partly due to ongoing processes of land fragmentation ( [[#Hobbs--2008|Hobbs et al., 2008]] ), historical land dispossession, discrimination and colonialisation, creating greater levels of poverty and marginalisation ( [[#Stephen--2018|Stephen, 2018]] ). Adaptation actions may also be affected by gender and other social inequities ( [[#Balehey--2018|Balehey et al., 2018]] ; [[#Dressler--2019|Dressler et al., 2019]] ). Men and women heads of household may access institutional support for adaptation in different ways ( [[#Assan--2018|Assan et al., 2018]] ). Further research is warranted to evaluate alternative gendered and equity-based approaches that can address differences in adaptive capacity within communities.

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=== 5.5.3 Projected Impacts ===

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There is ''limited evidence'' on future impact of climate change on livestock production, particularly in LMICs ( [[#Rivera-Ferre--2016|Rivera-Ferre et al., 2016]] ).

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==== 5.5.3.1 Impacts on rangelands, feeds and forages ====

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Uncertainties persist regarding estimates of net primary productivity (NPP) in grazing lands ( [[#Fetzel--2017|Fetzel et al., 2017]] ; [[#Chen--2018b|Chen et al., 2018b]] ), so estimation of climate change impacts on grasslands is challenging. Mean global annual NPP is projected to decline 10 gC m −2 yr −1 in 2050 under RCP8.5, although herbaceous NPP is projected to increase slightly ( [[#Boone--2018|Boone et al., 2018]] ; see Figure 5.11). Similar estimates were made by [[#Havlik--2014|Havlik et al. (2014)]] : large increases in projected NPP in higher northern latitudes (21% increase in the USA and Canada) and large declines in western Africa (−46%) and Australia (−17%). The cumulative effects of impacts on forage productivity globally are projected to result in 7–10% declines in livestock numbers by 2050 for warming of ~2°C, representing a loss of livestock assets ranging from USD 10 to 13 billion ( [[#Boone--2018|Boone et al., 2018]] ). Changes to African grassland productivity will have substantial, negative impacts on the livelihoods of &gt;180 million people.

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[[File:bbbc672a5e564302999be245928fdce7 IPCC_AR6_WGII_Figure_5_011.png]]

'''Figure 5.11 |''' '''Regional percent changes in land cover and soil carbon from ensemble simulation results in 2050 under emissions scenario RCP8''' '''.''' '''5 compared with 1971–2000.''' Plant responses were enhanced by CO 2 fertilisation. The larger chart (lower left) shows mean changes for all rangelands, and all charts are scaled to −60% to +60% change. Shown are annual net primary productivity (ANPP), herbaceous net primary productivity (HNPP), bare ground, herbaceous (herb), shrub, and tree cover, SOC (soil carbon), above-ground live biomass and below-ground live biomass. Regions as defined by the United Nations Statistics Division. The bar for above-ground live biomass in Western Asia (*) is truncated and is 82% ( [[#Boone--2018|Boone et al., 2018]] ).

Increases in above-ground NPP, and woody cover at the expense of grassland, are projected in some of the tropical and subtropical drylands ( [[#Doherty--2010|Doherty et al., 2010]] ; [[#Ravi--2010|Ravi et al., 2010]] ; [[#Saki--2018|Saki et al., 2018]] ), in Mediterranean wood pastures ( [[#Rolo--2019|Rolo and Moreno, 2019]] ) and in the northern Great Plains of North America ( [[#Klemm--2020|Klemm et al., 2020]] ). [[#Godde--2021|Godde et al. (2021)]] projected that woody encroachment would occur on 51% of global rangeland area by 2050 under RCP8.5. The future makeup of grasslands under climate change is uncertain, given the variation in responses of the component species, though this variation may provide a climate buffer ( [[#Jones--2019|Jones, 2019]] ) ( ''low confidence'' ). C4 grass species are regarded as less responsive to elevated carbon dioxide than C3 species, though this is not always the case ( [[#Reich--2018|Reich et al., 2018]] ).

There are other interactions between climate change and grazing effects on grasslands. Li (2018a) reported strong negative responses of NPP and species richness to 4°C warming, a 50% precipitation decrease, and high grazing intensity. Changes in grassland composition will inevitably change their suitability for different grazing animal species, with switches from herbaceous grazers such as cattle to goats and camels to take advantage of increases in shrubland ( [[#Kagunyu--2014|Kagunyu and Wanjohi, 2014]] ). Rangeland feed quality may also be reduced via invasive species of lower quality than native species ( [[#Blumenthal--2016|Blumenthal et al., 2016]] ).

Warming and water deficits impair the quality and digestibility of a C4 tropical forage grass, ''Panicum maximum'' , because of increases in leaf lignin ( [[#Habermann--2019|Habermann et al., 2019]] ). A metanalysis by Dellar (2018) of climate change impacts on European pasture yield and quality found an increase in above-ground dry weight under increased CO 2 concentrations for forbs, legumes, graminoids and shrubs with reductions in N concentrations in all plant functional groups. Temperature increases will increase yields in alpine and northern areas (+82.6%) but reduce N concentrations for shrubs (−13.6%) and forbs (−18.5%).

Increased temperatures and CO 2 concentrations may increase herbaceous growth and favour legumes over grasses in mixed pastures ( [[#He--2019|He et al., 2019]] ). These effects may be modified by changes in rainfall patterns, plant competition, perennial growth habits and plant–animal interactions. The cumulative effect of these factors is uncertain. Large, persistent declines in forage quality are projected, irrespective of warming, under elevated CO 2 conditions (600 ppm and +1.5°C day/3°C night temperature increases) in North American grasslands ( [[#Augustine--2018|Augustine et al., 2018]] ). Rising CO 2 concentrations may result in losses of iron, zinc and protein in plants by up to 8% by 2050 ( [[#Smith--2018|Smith and Myers, 2018]] ). Little information is available on possible impacts on carbon-based micronutrients, such as vitamins. About 57% of grasses globally are C3 plants and thus susceptible to CO 2 effects on their nutritional quality ( [[#Osborne--2014|Osborne et al., 2014]] ). These impacts will result in greater nutritional stress in grazing animals as well as reduced meat and milk production (quality and quantity) ( ''high confidence'' , ''medium evidence'' ).

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==== 5.5.3.2 Impacts of increased temperature on livestock ====

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Recent research confirms the seriousness of the heat stress issue ( ''medium evidence'' , ''high agreement'' ). Considerable increases are projected during this century in the number of ‘extreme stress’ days per year for cattle, chicken, goat, pig and sheep populations with SSP5-8.5 but many fewer with SSP1-2.6 ( [[#Thornton--2021|Thornton et al., 2021]] : Figure 5.12; see Cross-Chapter Box MOVING PLATE in this chapter). Resulting impacts on livestock production and productivity may be large, particularly for cattle throughout the tropics and subtropics and for goats in parts of Latin America and much of Africa and Asia. Pigs are projected to be particularly affected in the mid-latitudes of Europe, East Asia and North America. [[#Lallo--2018|Lallo et al. (2018)]] estimated that global warming of 1.5°C and 2°C may exceed limits for normal thermo-regulation of livestock animals and result in persistent heat stress for animals in the Caribbean. Breed differences in heat stress resistance in dairy animals are now being quantified ( [[#Gantner--2017|Gantner et al., 2017]] ), as are effects on sow reproductive performance in temperate climates ( [[#Wegner--2016|Wegner et al., 2016]] ). Estimates of losses in milk production due to heat stress in parts of the USA, UK and West Africa to the end of the century range from 1% to 17% ( [[#Hristov--2018|Hristov et al., 2018]] ; [[#Fodor--2018|Fodor et al., 2018]] ; Wreford and Topp, 2020; [[#Rahimi--2020|Rahimi et al., 2020]] ). Much larger losses in dairy and beef production due to heat stress are projected for many parts of the tropics and subtropics: these could amount to USD 9 billion per year for dairy and USD 31 billion per for beef to end-century under SSP5-8.5, approximately 5% and 14% of the global value of production of these commodities in constant 2005 dollars.

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[[File:3750c6aa5275494e5a09d0fbf3baa129 IPCC_AR6_WGII_Figure_5_012.png]]

'''Figure 5.12 |''' '''Change in the number of days per year above ‘extreme stress’ values from the early 21st century (1991–2010) to end of century (2081–2100), estimated under SSP1-2''' '''.''' '''6 and SSP5-8.5 using the Temperature Humidity Index (THI).''' Mapped for species current global distribution ( [[#Gilbert--2018|Gilbert et al., 2018]] ) (grey areas, no change). ( [[#Thornton--2021|Thornton et al., 2021]] ), Also see Annex I: Global to Regional Atlas.

In many LMICs, poultry contribute significantly to rural livelihoods, including via modest improvements in nutritional outcomes of household children ( [[#de%20Bruyn--2018|de Bruyn et al., 2018]] ). Rural poultry are generally assumed to be hardy and well adapted to stressful environments, but little information exists regarding their performance under warmer climates or interactions with other production challenges ( [[#Nyoni--2019|Nyoni et al., 2019]] ).

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==== 5.5.3.3 Impacts on livestock diseases ====

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The impacts of climate change on livestock diseases remain highly uncertain ( ''medium evidence'' , ''high agreement'' ). [[#Bett--2017|Bett et al. (2017)]] showed positive associations between rising temperature and expansion of the geographical ranges of arthropod vectors such as ''Culicoides imicola'' , which transmits the bluetongue virus. A 1-in-20-year bluetongue outbreak at present-day temperatures is projected to increase in frequency to 1-in-5 to 1-in-7 years by the 2050s, under RCP4.5 and RCP8.5, although animal movement restrictions can prevent devastating outbreaks ( [[#Jones--2019|Jones et al., 2019]] ).

The prevalence and occurrence of some livestock diseases are positively associated with extreme weather events ( ''high confidence'' ). There are high risks of future Rift Valley fever (RVF) outbreaks under both RCP4.5 and RCP8.5 this century in East Africa and beyond ( [[#Taylor--2016|Taylor et al., 2016]] ; [[#Mweya--2017|Mweya et al., 2017]] ).

Few studies explicitly consider the biotic and abiotic factors that interact additively, multiplicatively or antagonistically to influence host–pathogen dynamics ( [[#Cable--2017|Cable et al., 2017]] ). Integrative concepts that aim to improve the health of people, animals and the environment such as One Health may offer a framework for enhancing understanding of these complex interactions ( [[#Zinsstag--2018|Zinsstag et al., 2018]] ). Much remains unknown concerning disease transmission dynamics under a warming climate ( [[#Heffernan--2018|Heffernan, 2018]] ), highlighting the need for effective monitoring of livestock disease ( [[#Brito--2017|Brito et al., 2017]] ; [[#Hristov--2018|Hristov et al., 2018]] ).

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==== 5.5.3.4 Impacts on livestock and water resources ====

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Water resources for livestock may decrease in places because of increased runoff and reduced groundwater resources, as well as decreased groundwater availability in some environments (AR5). Increased temperatures will cause changes in river flow and the amount of water stored in basins, potentially leading to increased water stress in dry areas such as parts of the Volta River Basin ( [[#Mul--2015|Mul et al., 2015]] ). Toure (2017) estimated decreases in groundwater recharge rates of 49% and of stored groundwater by 24% to the 2030s in the Klela Basin in Mali under both RCP4.5 and RCP8.5, with potentially serious consequences for water availability for livestock and irrigation.

Water intake by livestock is related to species, breed, animal size, age, diet, animal activity, temperature and physiological status of animals ( [[#Henry--2018|Henry et al., 2018]] ). Direct water use by cattle may increase by 13% for a temperature increase of 2.7°C in a subtropical region ( [[#Harle--2007|Harle et al., 2007]] ). Changes in water availability may arise because of decreased supply or increased competition from other sectors. Availability changes may be accompanied by shifts in water quality, such as increased levels of microorganisms and algae, that can negatively affect livestock health ( [[#Naqvi--2015|Naqvi et al., 2015]] ). In arid lands, projected decreases in water availability will severely compromise reproductive performance and productivity in sheep ( [[#Naqvi--2017|Naqvi et al., 2017]] ). In higher-input livestock systems, water costs may increase substantially owing to increased competition for water ( [[#Rivera-Ferre--2016|Rivera-Ferre et al., 2016]] ).

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==== 5.5.3.5 Livestock and climate variability ====

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Information on future climate variability changes on livestock system productivity does not exist yet. Increases in climate variability may increase food insecurity in the future, mediated through increased crop and livestock production variability ( [[#Thornton--2014|Thornton and Herrero, 2014]] ) in LMICs. Rainfall variability increases in pastoral lands have been linked to declining cattle numbers ( [[#Megersa--2014|Megersa et al., 2014]] ). Changes in future climate variability may have large negative impacts on livestock system outcomes ( [[#Sloat--2018|Sloat et al., 2018]] ; [[#Stanimirova--2019|Stanimirova et al., 2019]] ); these effects can be larger than those associated with gradual climate change ( ''limited evidence'' , ''medium agreement'' ) ( [[#Godde--2019|Godde et al., 2019]] ). In grasslands, [[#Chang--2017|Chang et al. (2017)]] (Europe) and [[#Godde--2020|Godde et al. (2020)]] (globally) projected increases in biomass inter-annual variability, the worst effects occurring in rangeland communities that are already vulnerable. Ways in which climate variability impacts have been addressed in the past, such as via herd mobility, may become increasingly unviable in the future ( [[#Hobbs--2008|Hobbs et al., 2008]] ).

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==== 5.5.3.6 Societal impacts within the production system ====

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Livestock play important social ( [[#Kitalyi--2005|Kitalyi et al., 2005]] ) and cultural ( [[#Gandini--2003|Gandini and Villa, 2003]] ) roles in many societies. Climate change will negatively affect the provisioning of social benefits in many of the world’s grasslands ( ''medium confidence'' ). Examples include moving to semi-private land ownership models, driven in part by climate change, that are changing social networks and limiting socio-ecological resilience in pastoral systems in East Africa ( [[#Kibet--2016|Kibet et al., 2016]] ; [[#Bruyere--2018|Bruyere et al., 2018]] ) and Asia ( [[#Cao--2018a|Cao et al., 2018a]] ); altering traditional food, resource and medicine sharing mechanisms in West Africa ( [[#Boafo--2016|Boafo et al., 2016]] ); and the limited ability of current livestock systems to satisfy societies’ demand for CES in Northwest Europe ( [[#Bengtsson--2019|Bengtsson et al., 2019]] ). The societal impacts of climate change on livestock systems may interact with drivers of change and increase herders’ vulnerability via processes of sedentarisation and land fragmentation, both of which may result in decreased animal access to rangelands ( [[#Adhikari--2015|Adhikari et al., 2015]] ; Cross-Chapter Box MOVING PLATE this chapter). Stronger linkages are needed between ecosystem service and food security research and policy to address these challenges ( [[#Gentle--2016|Gentle and Thwaites, 2016]] ; [[#Bengtsson--2019|Bengtsson et al., 2019]] ).

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=== 5.5.4 Adaptation in Livestock-Based Systems ===

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Livestock adaptation options are increasingly being studied with methods such as agent-based household models ( [[#Hailegiorgis--2018|Hailegiorgis et al., 2018]] ), household models that disaggregate climate scenarios as well as differentiate farms of varying types and farmer attributes ( [[#Descheemaeker--2018|Descheemaeker et al., 2018]] ), new meso-scale grassland models ( [[#Boone--2018|Boone et al., 2018]] ) and modelling approaches that capture decision making at the farm level for sample populations ( [[#Henderson--2018|Henderson et al., 2018]] ).

Many grassland-based livestock systems have been highly resilient to past climate risk, providing a sound starting point for current and future climate change adaptation ( [[#Hobbs--2008|Hobbs et al., 2008]] ). These adaptations include more effective matching of stocking rates with pasture or other feed production; adjusting herd and watering point management to altered seasonal and spatial patterns of forage production; managing diet quality, which also helps reduce enteric fermentation in ruminants and thus GHG emissions (using diet supplements, legumes, choice of introduced pasture species and pasture fertility management); more effective use of silage, rotational grazing or other forms of pasture spelling; fire management to control woody thickening; using better-adapted livestock breeds and species; restoration of degraded pastureland; migratory pastoralist activities; and a wide range of biosecurity activities to monitor and manage the spread of pests, weeds and diseases ( [[#Herrero--2015|Herrero et al., 2015]] ; [[#Godde--2020|Godde et al., 2020]] ). Combining adaptations can result in increases in benefits in terms of production and livelihoods over and above those attainable from single adaptations ( ''high confidence'' ) ( [[#Bonaudo--2014|Bonaudo et al., 2014]] ; [[#Thornton--2015|Thornton and Herrero, 2015]] ; [[#ul%20Haq--2021|ul Haq et al., 2021]] ).

The adaptations that livestock keepers have been undertaking in Asia ( [[#Hussain--2016|Hussain et al., 2016]] ; [[#Li--2017|Li et al., 2017]] ) and Africa ( [[#Belay--2017|Belay et al., 2017]] ; [[#Ouédraogo--2017|Ouédraogo et al., 2017]] ) are largely driven by their perceptions of climate change. Keeping two or more species of livestock simultaneously on the same farm can confer economic and sustainability benefits to European farmers ( [[#Martin--2020|Martin et al., 2020]] ). Some livestock producers are changing and diversifying management practices, improving access to water sources, increasing uptake of off-farm activities, trading short-term profits for longer-term resilience benefits and migrating out of the area ( [[#Hussain--2016|Hussain et al., 2016]] ; [[#Berhe--2017|Berhe et al., 2017]] ; [[#Merrey--2018|Merrey et al., 2018]] ; [[#Thornton--2018|]] [[#Thornton--2018|Thornton et al., 2018]] ; [[#Espeland--2020|Espeland et al., 2020]] ). Others are adopting more climate-resilient livestock species such as camels ( [[#Watson--2016a|Watson et al., 2016a]] ), using climate forecasts at differing time scales, and benefitting from innovative livestock insurance schemes, though challenges remain in their use at scale ( [[#Dayamba--2018|Dayamba et al., 2018]] ; [[#Hansen--2019a|Hansen et al., 2019a]] ; [[#Johnson--2019|Johnson et al., 2019]] ).

In West Africa, cattle and small ruminant producers and traders are changing strategies in response to emerging market opportunities as well as to multiple challenges including climate change ( [[#Gautier--2016|Gautier et al., 2016]] ; [[#Ouédraogo--2017|Ouédraogo et al., 2017]] ). Niles (2017) found that reduced food insecurity in 12 countries was associated with livestock ownership, providing cash for food purchases. Livestock ownership or switching to smaller, local breeds does not automatically translate into positive nutrition outcomes for women and children, although it may if communities see such animals as suitable for husbandry by women ( [[#Chanamuto--2015|Chanamuto and Hall, 2015]] ); the relationship is complex ( [[#Nyantakyi-Frimpong--2015|Nyantakyi-Frimpong and Bezner-Kerr, 2015]] ; [[#Dumas--2018|Dumas et al., 2018]] ).

Options for adapting domestic livestock systems to increased exposure to heat stress (Table 5.7) include breeding and crossbreeding strategies, species switching, low-cost shading alternatives and ventilation and building-design options ( [[#Chang-Fung-Martel--2017|Chang-Fung-Martel et al., 2017]] ; [[#Godde--2021|Godde et al., 2021]] ). ''In utero'' exposure to heat stress may increase adaptive capacity in later life, though the underlying mechanisms are incompletely understood ( [[#Skibiel--2018|Skibiel et al., 2018]] ). For confined livestock systems in temperate regions, the economic consequences of adapting to heat stress are still being quantified.

'''Table 5.7 |''' Selected adaptations to heat stress in livestock systems.

{| class="wikitable"
|-
! '''Adaptation'''

! '''Example'''

! '''Reference'''

|-
| Breeding for heat stress tolerance

| Sheep and cattle farming systems in southern Australia under IPCC Special Report on Emissions Scenarios (SRES) A2. Projected not to improve livestock productivity by 2070, even in drier locations.

| [[#Moore--2014|Moore and Ghahramani (2014)]]

|-
| ‘Slick hair’ breeding

| In the Caribbean, introduction of a ‘slick hair’ gene into Holstein cows by crossbreeding with Senepols to increase thermo-tolerance and productivity. An integrated approach to heat stress adaptation will still be needed, including shading strategies, for example.

| ( [[#Ortiz-Colón--2018|Ortiz-Colón et al. (2018)]]

|-
| Crossbreeding

| Crossbreeding with Indigenous sheep breeds as an adaptation option in Mongolia produced some benefits in productivity and improved adaptation to winter cold. Best combined with other improved management interventions. In general, effectiveness of crossbreeding as an adaptation strategy will be dependent on context.

| [[#Wilkes--2017|Wilkes et al. (2017)]]

|-
| Species switching

| Switching from large ruminants to more heat-resilient goats for dairy production in Mediterranean systems to adapt to increasing heat stress.

Switching from cattle to more heat- and drought-resilient camels in pastoral systems of southern Ethiopia as an adaptation to increasing drought.

| [[#Silanikove--2015|Silanikove and Koluman (2015)]]

Wako et al. (2017)

|-
| Shading, fanning, bathing

| Low-capital relief strategies (shading with trees or different types of shed; bathing animals several times each day; installing electric fans in sheds) are effective at reducing heat stress impacts on household income in smallholder dairy systems in India.

Different tree arrangements in silvopastoral systems in Brazil were effective in reducing thermal loads by up to 22% for animals compared with full-sun pasture.

| [[#York--2017|York et al. (2017)]]

[[#Pezzopane--2019|Pezzopane et al. (2019)]]

|-
| Ventilation and cooling systems

| A wide range of different ventilation systems, cooling systems and building designs for confined and seasonally confined intensive livestock systems (pigs, poultry, beef, dairy) in temperate regions. Economic consequences and profitability of different options under different RCPs are still being assessed.

| [[#Vitt--2017|Vitt et al. (2017)]]

[[#Derner--2018|Derner et al. (2018)]] ,

[[#Hempel--2019|Hempel and Menz (2019)]] , [[#Mikovits--2019|Mikovits et al. (2019)]] , [[#Schauberger--2019b|Schauberger et al. (2019b)]]

|-
| ''In utero'' exposure to heat stress

| Potential as an adaption option is uncertain, as there are different effects of ''in utero'' heat stress exposure and the mechanisms are not completely understood:

* Cows may be better adapted to heat stress conditions at maturity via improved regulation of core body temperature
* Cow milk yield at first lactation was reduced
* Nutrient partitioning and carcass composition were altered in pigs

| [[#Ahmed--2017|Ahmed et al. (2017)]]

[[#Monteiro--2016|Monteiro et al. (2016)]] ,

[[#Boddicker--2014|Boddicker et al. (2014)]]

|}

New research is investigating the prospects for accelerating traditional and novel breeding processes for animal traits that may be effective in improving livestock adaptation as well as production ( [[#Stranden--2019|Stranden et al., 2019]] ; [[#Barbato--2020|Barbato et al., 2020]] ). Even if the technical challenges of using new tools such as CRISPR-Cas9 for genome editing in livestock are overcome, the granting of societal approval to operate in this research space may be elusive ( [[#Herrero--2020|Herrero et al., 2020]] ; [[#Menchaca--2020|Menchaca et al., 2020]] ).

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==== 5.5.4.1 Contributions of Indigenous knowledge and local knowledge ====

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Indigenous knowledge has a role to play in helping livestock keepers adapt ( ''medium confidence'' ), though the transferability of this knowledge is often unclear. Pastoralists’ local knowledge of climate and ecological change can complement scientific research ( [[#Klein--2014|Klein et al., 2014]] ), and local knowledge can be mobilised to inform adaptation decision making ( [[#Klenk--2017|Klenk et al., 2017]] ). While Indigenous weather forecasting systems among pastoralists in Ethiopia ( [[#Balehegn--2019|Balehegn et al., 2019]] ; [[#Iticha--2019|Iticha and Husen, 2019]] ) and Uganda ( [[#Nkuba--2020|Nkuba et al., 2020]] ) are effective, synergies can be gained by combining traditional and modern knowledge to help pastoralists adapt. Sophisticated knowledge of feed resources among agro-pastoralists in West Africa is being used to increase system resilience ( [[#Naah--2019|Naah and Braun, 2019]] ). Understanding local knowledge for adaptation can present research challenges, for which new multi-disciplinary research methods may be needed ( [[#Reyes-Garcia--2016|Reyes-Garcia et al., 2016]] ; Roncoli et al., 2016). In particular, the complexities of knowledge, practice, power, local governance and politics need to be addressed ( [[#Hopping--2016|Hopping et al., 2016]] ; [[#Scoville-Simonds--2020|Scoville-Simonds et al., 2020]] ).

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'''Box 5.5: Alternative Sources of Protein for Food and Feed'''
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Alternative protein sources for human food and livestock feed are receiving considerable attention. Laboratory or ‘clean meat’ is one potential contributor to the human demand for protein in the future (SRCLL). Such technology may be highly disruptive to existing value chains but could lead to significant reduction in land use for pastures and crop-based animal feeds ( [[#Burton--2019|Burton, 2019]] ; [[#Rosenzweig--2020|Rosenzweig et al., 2020]] ). The impacts on GHG emissions depend on the meat being substituted and the trade-off between industrial energy consumption and agricultural land requirements ( [[#Mattick--2015|Mattick et al., 2015]] ; [[#Alexander--2017|Alexander et al., 2017]] ; [[#Rubio--2020b|Rubio et al., 2020b]] ; [[#Santo--2020|Santo et al., 2020]] ). Livestock feeds can make use of other protein sources: insects are generally rich in protein and can be a significant source of vitamins and minerals. Black soldier fly, yellow mealworm and the common housefly have been identified for potential use in feed products in the EU, for example ( [[#Henchion--2017|Henchion et al., 2017]] ). Replacing land-based crops in livestock diets with some proportion of insect-derived protein may reduce the GHG emissions associated with livestock production, though these and other potential effects have not yet been quantified ( [[#Parodi--2018|Parodi et al., 2018]] ; [[#5.13.2|Section 5.13.2]] ). Other sources are high-protein woody plants such as paper mulberry ( [[#Du--2021|Du et al., 2021]] ) and algae, including seaweed. While microalgae and cyanobacteria are mainly sold as a dietary supplement for human consumption, they are also used as a feed additive for livestock and aquaculture, being nutritionally comparable to vegetable proteins. The potential for cultivated seaweed as a feed supplement may be even greater: some red and green seaweeds are rich in highly digestible protein. ''Asparagopsis taxiformis'' , for example, also decreases methane production in both cattle and sheep when used as a feed supplement ( [[#Machado--2016|Machado et al., 2016]] ; [[#Li--2018b|Li et al., 2018b]] ). Novel protein sources may have considerable potential for sustainably delivering protein for food and feed alike, though their nutritional, environmental, technological and socioeconomic impacts at scale need to be researched and evaluated further.

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