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

=== 5.4.3 Projected Impacts ===

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<span id="advances-in-the-characterisation-of-the-effects-of-elevated-atmospheric-co-2"></span>
==== 5.4.3.1 Advances in the characterisation of the effects of elevated atmospheric CO 2 ====

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Elevated CO 2 concentrations stimulate photosynthesis rates and biomass accumulation of C 3 crops, and enhance crop water use efficiency of various crop species, including C 4 crops ( ''high confidence'' ) ( [[#Kimball--2016|Kimball, 2016]] ; [[#Toreti--2020|Toreti et al., 2020]] ). Perennial crops and root crops may have a greater capacity for enhanced biomass under elevated CO 2 concentrations, although this does not always result in higher yields ( [[#Glenn--2013|Glenn et al., 2013]] ; [[#Kimball--2016|Kimball, 2016]] ).

Recent FACE studies found that the effects of elevated CO 2 are greater under water-limited conditions ( ''medium confidence'' ) ( [[#Manderscheid--2014|Manderscheid et al., 2014]] ; [[#Fitzgerald--2016|Fitzgerald et al., 2016]] ; [[#Kimball--2016|Kimball, 2016]] ), which was generally reproduced by crop models ( [[#Deryng--2016|Deryng et al., 2016]] ). However, drought sometimes negates the CO 2 effects ( [[#Jin--2018|Jin et al., 2018]] ).

There are significant interactions between CO 2 , temperature, cultivars, nitrogen and phosphorous nutrients ( [[#Kimball--2016|Kimball, 2016]] ; [[#Toreti--2020|Toreti et al., 2020]] ): positive effects of rising CO 2 on yield are significantly reduced by higher temperatures for soybean, wheat and rice ( ''medium confidence'' ) ( [[#Ruiz-Vera--2013|Ruiz-Vera et al., 2013]] ; [[#Cai--2016|Cai et al., 2016]] ; [[#Gray--2016|Gray et al., 2016]] ; [[#Hasegawa--2016|Hasegawa et al., 2016]] ; [[#Obermeier--2016|Obermeier et al., 2016]] ; [[#Purcell--2018|Purcell et al., 2018]] ; [[#Wang--2018|Wang et al., 2018]] ). In above-ground vegetables, elevated CO 2 can in some cases reduce the impact of other climate stressors, while in others the negative impacts of other abiotic factors negate the potential benefit of elevated CO 2 ( [[#Bourgault--2017|Bourgault et al., 2017]] ; [[#Bourgault--2018|Bourgault et al., 2018]] ; [[#Parvin--2018|Parvin et al., 2018]] ; [[#Parvin--2019|Parvin et al., 2019]] ). Significant variation exists among cultivars in yield response to elevated CO 2 , which is positively correlated with yield potential in rice and soybean, suggesting the potential to develop cultivars for enhanced productivity under future elevated [CO 2 ] ( [[#Ainsworth--2021|Ainsworth and Long, 2021]] ).

Elevated CO 2 reduces some important nutrients such as protein, iron, zinc and some grains, fruit or vegetables to varying degrees depending on crop species and cultivars ( ''high confidence'' ) ( [[#Mattos--2014|Mattos et al., 2014]] ; [[#Myers--2014|Myers et al., 2014]] ; [[#Dong--2018|Dong et al., 2018]] ; [[#Scheelbeek--2018|Scheelbeek et al., 2018]] ; [[#Zhu--2018a|Zhu et al., 2018a]] ; [[#Jin--2019|Jin et al., 2019]] ; [[#Ujiie--2019|Ujiie et al., 2019]] ). This is of particular relevance for fruit and vegetable crops given their importance in human nutrition ( ''high confidence'' ) (see [[#5.12.4|Section 5.12.4]] for potential impacts on nutrition; [[#Nelson--2018|Nelson et al., 2018]] ; [[#Springmann--2018|Springmann et al., 2018]] ). Recent experimental studies ( [[#5.3.2|Section 5.3.2]] ), however, show some complex and counteracting interactions between CO 2 and temperature in wheat, soybean and rice; heat stress negates the adverse effect of elevated CO 2 on some nutrient elements ( [[#Macabuhay--2018|Macabuhay et al., 2018]] ; [[#Kohler--2019|Kohler et al., 2019]] ; [[#Wang--2019b|Wang et al., 2019b]] ). The CO 2 by temperature interaction for grain quality needs to be better understood quantitatively to predict food nutritional security in the future.

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<span id="projected-impacts-on-major-crop-production"></span>
==== 5.4.3.2 Projected impacts on major crop production ====

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AR5 [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] estimated global crop yield reduction due to climate change to be about 1% per decade ( [[#Porter--2014|Porter et al., 2014]] ), similar to the previous assessment reports ( [[#Porter--2019|Porter et al., 2019]] ). Additional research confirms that climate change will disproportionately affect crop yields among regions, with more negative than positive effects being expected in most areas, especially in currently warm regions, including Africa and Central and South America ( ''high confidence'' ).

A systematic literature search between 2014 and 2020 resulted in about 100 peer-reviewed papers that simulated crop yields of four major crops (maize, rice, soybean and wheat) using Coupled Model Intercomparison Project Phase 5 (CMIP5) data ( [[#Hasegawa--2021b|Hasegawa et al., 2021b]] ). Most studies focus on the relative change in crop yields due to climate change but do not consider technological advances. Nevertheless, they provide useful insights into time-, scenario- and warming-degree-dependent impacts of climate change.

The impact of climate change on crop yield without adaptation projected in the 21st century is generally negative even with the CO 2 fertilisation effects, with the overall median per-decade effect being −2.3% for maize, −3.3% for soybean, −0.7% for rice and −1.3% for wheat, which is consistent with previous IPCC assessments ( [[#Porter--2014|Porter et al., 2014]] ). The effects vary greatly within each crop, timeframe and RCP, but show a few common features across crops (Figure 5.6a ''')''' . Differences in the projected impacts between RCPs are not pronounced by mid-century. From then onward, the negative effect becomes more pronounced under RCP8.5, notably in maize. Rice yields show less variation across models than other crops presumably because simulations are mostly under irrigated conditions. A part of the uncertainty in the projection is due to regional differences (Figure 5. 6b). Negative impacts on cereals are projected in Africa and Central and South America at the end of the century, which agrees with the previous studies ( [[#Aggarwal--2019|Aggarwal et al., 2019]] ; [[#Porter--2019|Porter et al., 2019]] ).

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[[File:6b70db295b948bb407847e44a782e2bf IPCC_AR6_WGII_Figure_5_006.png]]

'''Figure 5.6 |''' '''Projected yield changes relative to the baseline period (2001–2010) without adaptation and with CO''' '''2''' '''fertilisation effects (Hasegawa et al.''' , '''2021b).''' The box is the interquartile range (IQR), and the middle line in the box represents the median. The upper and lower end of whiskers are median 1.5 × IQR ± median. Open circles are values outside the 1.5 × IQR. '''(a)''' At different time periods (near future, NF, baseline to 2039; mid-century, MC, 2040–2069; end-century, EC, 2070–2100) under three RCPs, and '''(b)''' at different regions at EC.

The differences due to regions, RCPs and timeframes are related to the current temperature level and degree of warming (Figure 5.7). The projected effects of climate change are positive where current annual mean temperatures ( ''T'' ave ) are below 10°C, but they become negative with ''T'' ave above around 15°C. At ''T'' ave &gt; 20°C, even a small degree of warming could result in adverse effects. In maize, negative effects are apparent at almost all temperature zones. A new study using the latest climate scenarios (Coupled Model Intercomparison Project Phase 6, CMIP6) and global gridded crop model ensemble projected that climate change impacts on major crop yields appear sooner than previously anticipated, mainly because of warmer climate projections and improved crop model sensitivities ( [[#Jägermeyr--2021|Jägermeyr et al., 2021]] ).

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[[File:0e423cd61ac5542bcb906d247461e7dd IPCC_AR6_WGII_Figure_5_007.png]]

'''Figure 5.7 |''' '''Projected yield changes relative to the baseline period (2001–2010) without adaptation and with CO''' '''2''' '''fertilisation effects (Hasegawa et al''' '''.''' ''', 2021b).''' '''(a)''' Mid-century (MC, 2040–2069) and end-century (EC, 2070–2100) projections under three RCP scenarios as a function of current annual temperature ( ''T'' ave ), '''(b)''' as a function of global temperature rise from the baseline period by three ''T'' ave levels. See Figure. 5.6 for legends.

As noted in [[#5.3.1|Section 5.3.1]] , most simulations do not fully account for responses to pests, diseases, long-term change in soil, and some climate extremes ( [[#Rosenzweig--2014|Rosenzweig et al., 2014]] ), but studies are emerging to include some of these effects. For example, based on the temperature response of insect pest population and metabolic process, global yield losses of rice, maize and wheat are projected to increase by 10–25% per degree Celsius of warming ( [[#Deutsch--2018|Deutsch et al., 2018]] ). Rising temperatures reduce soil carbon and nitrogen, which in turn exacerbate the negative effects of +3°C warming on yield from 9% to 13% in wheat and from 14% to 19% in maize ( [[#Basso--2018|Basso et al., 2018]] ).

A few studies have examined possible occurrences of tele-connected yield losses (5.4.1.2) using future climate scenarios. Tigchelaar (2018) estimated that, for the top four maize-exporting countries, the probability that simultaneous production losses greater than 10% occur in any given year increases from 0% to 7% under 2°C warming and to 86% under 4°C warming. Gaupp (2019) estimated that risks of simultaneous failure in maize would increase from 6% to 40% at 1.5°C and to 54% at 2°C warming, relative to the historical baseline climate. Large-scale changes in SST are the major factors causing simultaneous variation in climate extremes, which are projected to intensify under global warming ( [[#Cai--2014|Cai et al., 2014]] ; [[#Perry--2017|Perry et al., 2017]] ). Consequently, risks of simultaneous yield losses in major food-producing regions will also increase with global warming levels above 1.5°C ( ''medium confidence'' ). Further examination is needed for the effects of spatial patterns of these extremes on breadbaskets in relation to SST anomalies under more extreme climate scenarios.

Future surface ozone concentration is highly uncertain ( [[#Fiore--2012|Fiore et al., 2012]] ; [[#Turnock--2018|Turnock et al., 2018]] ); it is projected to increase under RCP8.5 and decrease under other RCPs depending largely on different methane emission trajectories because methane is an important precursor of ozone. Methane, therefore, reduces crop yield both from climate warming and ozone increase ( [[#Avnery--2013|Avnery et al., 2013]] ). [[#Shindell--2016|Shindell (2016)]] estimated yield losses of four major crops (to be 25±11% by 2100 under RCP8.5, as a net balance of the positive effect of CO 2 (15±2%) and negative effects of warming (35±10%) and ozone (4.0±1.3%), and that 62% of the yield loss was attributable to methane. This points to the importance of reducing methane and other precursors of ozone as an effective adaptation strategy ( ''medium evidence'' , ''high agreement'' ).

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<span id="projected-impacts-on-other-crops"></span>
==== 5.4.3.3 Projected impacts on other crops ====

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Yield projections for crops other than cereals indicate mostly negative impacts on production due to a range of climate drivers ( ''high confidence'' ), with yield reductions similar to that of cereals expected in tropical, subtropical and semi-arid areas ( [[#Mbow--2019|Mbow et al., 2019]] ). [[#Springmann--2016|Springmann et al. (2016)]] , compared the projected global food availability for different food groups under the SSP2 2050 scenario and found reductions in availability were similar in cereals, fruit and vegetables, and root and tubers (with legumes and oilseed crops showing a smaller reduction).

Fruit and vegetables have not been subject to extensive or coordinated yield projections (Figure 5.8). Yield projections have been performed for individual crops and locations ( [[#Ruane--2014|Ruane, 2014]] ; [[#Adhikari--2015|Adhikari et al., 2015]] ; [[#Awoye--2017|Awoye et al., 2017]] ; [[#Ramachandran--2017|Ramachandran et al., 2017]] ), but more often crop suitability models have been used (SM5.3). Zhao (2019) introduced a modelling approach that could be used to generate yield projections for a wider range of annual crops. The discussion here also draws on reviews of more restricted experimental studies. Negative impacts of climate change on crop production are expected across many cropping systems (Figure 5.8). Apart from the direct effects of elevated carbon dioxide, most changes are expected to have negative effects on crop production. Changes in temperature and rainfall are most often mentioned as drivers of climate impacts, but expected changes in phenology, pests and diseases are also raising concerns. [[#Scheelbeek--2018|Scheelbeek et al. (2018)]] synthesised projections for vegetables and legumes, based on their response to climate factors under experimental conditions; in most cases, the magnitude of the changes is comparable to the RCP8.5 2100 forecasts. [[#Scheelbeek--2018|Scheelbeek et al. (2018)]] projected yield changes of: +22.0% (+11.6% to +32.5%) for a 250 ppm increase in CO 2 concentration; −34.7% (−44.6% to −24.9%) for a 50% reduction in water availability; −8.9% (−15.6% to −2.2%) for a 25% increase in ozone concentration; −31.5% for a 4°C increase in temperature (in papers with a baseline temperature of &gt;20°C). Overall, impacts are expected to be largely negative in regions where the temperature is currently above 20°C, while some yield gains are expected in cooler regions (provided that water availability and other conditions are maintained). [[#Scheelbeek--2018|Scheelbeek et al. (2018)]] did not consider changes in pest and disease pressure, which are projected to increase with warming (see SM5.3).

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[[File:b09525ce8811a74fff0d9f60c9266326 IPCC_AR6_WGII_Figure_5_008.png]]

'''Figure 5.8 |''' '''Synthesis of literature on the projected impacts of climate change on different cropping systems.''' The assessment includes projections of impacts on crop productivity over a range of emission scenarios and time periods. The projected impacts are disaggregated by the different climate and climate-related drivers. Impacts are reported as positive, negative or mixed. The assessment draws on &gt;60 articles published since AR5. The confidence is based on the evidence given in individual articles and on the number of articles. See '''SM5.2''' information for details.

Systematic assessments of climate response for root crops as a group are lacking ( [[#Raymundo--2014|Raymundo et al., 2014]] ; [[#Knox--2016|Knox et al., 2016]] ; [[#Manners--2018|Manners and van Etten, 2018]] ). Climate suitability is projected to increase for tropical root crops (SM5.3), and some studies have found that root crops will be less negatively impacted than cereals, but there is no consensus on this ( [[#Brassard--2008|Brassard and Singh, 2008]] ; [[#Adhikari--2015|Adhikari et al., 2015]] ; [[#Schafleitner--2016|Schafleitner, 2016]] ; [[#Manners--2021|Manners et al., 2021]] ). For potato, [[#Raymundo--2018|Raymundo et al. (2018)]] projected global yield reductions of 2–6% by 2055 under different RCPs, but with important differences among regions; tuber dry weight may experience reductions of 50–100% in marginal growing areas such as central Asia, while increases of up to 25% are expected in many high-yielding environments. Projections show yield increases of 6% per 100 ppm elevation in CO 2 but declines of 4.6% per degree Celsius and 2% per 10% decrease in rainfall ( [[#Fleisher--2017|Fleisher et al., 2017]] ). [[#Jennings--2020|Jennings et al. (2020)]] projected an overall increase in global potato production, but only if widespread adoption of adaptation measures is achieved. Although increases in CO 2 could produce positive yield responses, the effects of temperature may offset these potential benefits ( [[#Dua--2013|Dua et al., 2013]] ; [[#Raymundo--2014|Raymundo et al., 2014]] ). Warming offers the potential of longer growing seasons but can also have negative impacts through disrupted phenology and interactions with pests (Figure 5.8, [[#Bebber--2015|Bebber, 2015]] ; [[#Pulatov--2015|Pulatov et al., 2015]] ).

Global yield modelling is lacking for woody perennial crops. Experimental studies suggest negative impacts on yields due to reduced water supply and increased soil salinity, as well as from warming and ozone (although evidence was limited for these) ( [[#Alae-Carew--2020|Alae-Carew et al., 2020]] ). Increasing CO 2 is expected to increase yields, but only where other factors, such as warming, do not become yield-limiting ( [[#Alae-Carew--2020|Alae-Carew et al., 2020]] ). Many local projections include large uncertainty because of a lack of observational data and reliable parametrisation ( [[#Moriondo--2015|Moriondo et al., 2015]] ; [[#Mosedale--2016|Mosedale et al., 2016]] ; [[#Kerr--2018|Kerr et al., 2018]] ; [[#Mayer--2019b|Mayer et al., 2019b]] ). Most perennial crop models have found large negative impacts on yield and suitability, although CO 2 fertilisation and phenology are not always considered ( [[#Lobell--2011|Lobell and Field, 2011]] ; [[#Glenn--2013|Glenn et al., 2013]] ). Perennial crops are often grown in dryland areas where rainfall or irrigation water can be critical ( [[#Mrabet--2020|Mrabet et al., 2020]] ). Valverde (2015) found that yield losses in the Mediterranean region were largely driven by reduced rainfall, with maximum estimated yield losses of 5.4% for grape, 14.9% for olive and 27.2% for almond under a relatively hot and dry scenario (by 2041–2070). Moriondo (2015) highlight the need for perennial crop models to incorporate phenology and extreme climate events. Equally challenging is the need to estimate the impact of biotic changes, particularly climate-driven movement of pests and diseases ( [[#Ponti--2014|Ponti et al., 2014]] ; [[#Bosso--2016|Bosso et al., 2016]] ; [[#Schulze-Sylvester--2019|Schulze-Sylvester and Reineke, 2019]] ).

For cotton, experimental studies suggest positive impacts from rising CO 2 and temperature ( [[#Zhang--2017a|Zhang et al., 2017a]] ; [[#Jans--2021|Jans et al., 2021]] ), but projections show mixed impacts on yield, including large negative impacts in warmer regions due to heat, drought and the interaction of temperature with phenology ( [[#Yang--2014|Yang et al., 2014]] ; [[#Williams--2015|Williams et al., 2015]] ; [[#Adhikari--2016|Adhikari et al., 2016]] ; [[#Rahman--2018|Rahman et al., 2018]] ). Climate change is also expected to increase the demand for irrigation water, which will likely limit production ( [[#Jans--2021|Jans et al., 2021]] ). There are also concerns that fibre quality may deteriorate (e.g., air permeability of compressed cotton fibers) ( [[#Luo--2016|Luo et al., 2016]] ).

Higher temperatures and altered moisture levels are expected to present a food safety risk, particularly for above-ground harvested vegetables (Figures 5.8; 5.10). Warmer and wetter weather is anticipated to increase fungal and microbial growth on leaves and fruit, while altered flooding regimes increase the risk of crop contamination ( [[#Liu--2013|Liu et al., 2013]] ; [[#Uyttendaele--2015|Uyttendaele et al., 2015]] ). This is also true for perennial crops; for example, warming and climate variability can increase fungal contamination of grapes, including that associated with mycotoxins ( [[#Battilani--2016|Battilani, 2016]] ; Paterson, 2018).

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==== 5.4.3.4 Observed and projected impacts on cultural ecosystem service ====

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Cultural ecosystem services (CES) are those non-material benefits, such as aesthetic experiences, recreation, spiritual enrichment, social relations, cultural identity, knowledge and other values ( [[#Millennium%20Ecosystem%20Assessment--2005|Millennium Ecosystem Assessment, 2005]] ), which support physical and mental health and human well-being ( [[#Chan--2012|Chan et al., 2012]] ; [[#Triguero-Mas--2015|Triguero-Mas et al., 2015]] ). CES in agricultural and wild landscapes include recreational activities, access to wild or cultivated products, and cultural foods, spiritual rituals, heritage and memory dimensions, and aesthetic experiences ( [[#Daugstad--2006|Daugstad et al., 2006]] ; [[#Calvet-Mir--2012|Calvet-Mir et al., 2012]] ; [[#Ruoso--2015|Ruoso et al., 2015]] ). Relative to other ecosystem services, CES in agricultural landscapes have been less researched ( [[#Merlín-Uribe--2012|Merlín-Uribe et al., 2012]] ; [[#Milcu--2013|Milcu et al., 2013]] ; [[#Bernues--2014|Bernues et al., 2014]] ; [[#Plieninger--2014|Plieninger et al., 2014]] ; [[#van%20Berkel--2014|van Berkel and Verburg, 2014]] ; [[#Ruoso--2015|Ruoso et al., 2015]] ; [[#Quintas-Soriano--2016|Quintas-Soriano et al., 2016]] ). Agricultural heritage is a key aspect of CES and plays an important role in maintaining agrobiodiversity ( [[#Hanaček--2018|Hanaček and Rodríguez-Labajos, 2018]] ).

Climate change is projected to have negative impacts on CES ( ''medium confidence'' ) (Table 5.4). There is limited evidence that climate change has been the main driver affecting CES of agroecosystems confounded by other drivers such as migration and changing farming patterns ( [[#Hanaček--2018|Hanaček and Rodríguez-Labajos, 2018]] ; [[#Dhakal--2019|Dhakal and Kattel, 2019]] ). Recent studies observed declines in CES in alpine pastures and floodplains in Europe in part due to climate change impacts ( [[#Probstl-Haider--2016|Probstl-Haider et al., 2016]] ; [[#Schirpke--2019|Schirpke et al., 2019]] ). Another study estimated that the scenic beauty enjoyed by those who visit the vineyards in central Chile will decline by 18–28% by 2050 owing to a combination of reduced precipitation, increased temperatures and natural fire cycles ( [[#Martinez-Harms--2017|Martinez-Harms et al., 2017]] ). More research is needed, however, particularly on cultural heritage and spiritually significant places and in low-income countries.

'''Table 5.4 |''' Projected impacts on CES from climate change.

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

! '''CES'''

! '''Climate change scenario'''

! '''Projected impacts from climate change'''

! '''References'''

|-
| Central Chile, South America

| Aesthetic experience of scenic beauty in vine-growing region.

| RCP2.6 and 8.5.

| Increased temperature, reduced precipitation and increased fires will damage scenic beauty of vineyards. Participatory scenario analysis estimated reduction in aesthetic experience from scenic beauty by 18–28% by 2050 for RCP2.6, with greater impacts under RCP8.5.

| [[#Martinez-Harms--2017|Martinez-Harms et al. (2017)]]

|-
| Mountainous regions of Austria

| Cultural and aesthetic experiences in alpine pastures and diverse agricultural landscapes.

| Temperature +1.5°C from 2008 to 2040 and four precipitation scenarios (high, similar '','' seasonal shift and low).

| Some decline in CES, with trade-offs between diversity and CES and provisioning services depending upon the scenario.

| [[#Kirchner--2015|Kirchner et al. (2015)]]

|-
| Forest and agricultural landscapes in southern Saxony-Anhalt in Germany

| Recreation, scenic landscape beauty and spiritual value of agricultural landscapes and forests.

| Regional scenarios, do not specify RCPs.

| Not anticipated to be significantly changed by climate change under most scenarios, except for intensification scenario, which would lead to a decline in the forest cultural services as they provide important historical and cultural ties.

| Gorn et al. (2018)

|-
| Northeast Austria floodplains (grasslands and wetlands)

| Tourism, recreation, cultural heritage.

| Increased temperature by 2050 and 2100 and seasonal shifts in precipitation.

| Increased agricultural intensification due to shifts in climate and decline in CES is predicted, based on farmer interviews.

| [[#Probstl-Haider--2016|Probstl-Haider et al. (2016)]]

|-
| Mount Kenya, Kenya

| Tourism, recreation, spiritual and cultural values.

| Not specified.

| Glacier disappearance may lead to reduced mountain trekking and other tourism and recreational activities.

| [[#Evaristus--2014|Evaristus (2014)]]

|-
| Philippines

| Nature-based tourism in agri-tourism.

| Not specified.

| Risk of typhoon, drought and strong wind, grass fire, heavy rains. Anticipated to increase vulnerability in terms of human health services and energy use in tourism.

| [[#Hidalgo--2015|Hidalgo (2015)]]

|}

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'''Box 5.2: Case Study: Wine'''
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Wine-growing regions cover 7.4 million ha, with a value of 35 billion USD in 2018 (OIV, 2019). Important regions (Italy, France, Spain, USA, Argentina, Australia, South Africa, Chile, Germany, China, Argentina) are located in areas where mean annual temperature roughly varies between 10°C and 20°C ( [[#Schultz--2010|Schultz and Jones, 2010]] ; [[#Mosedale--2016|Mosedale et al., 2016]] ).

Temperature is the primary determinant for vine development. Recent warming trends have advanced flowering, maturity and harvest ( ''high confidence'' ) ( [[#Koufos--2014|Koufos et al., 2014]] ; [[#Cook--2016|Cook and Wolkovich, 2016]] ; [[#Hall--2016|Hall et al., 2016]] ; [[#Ruml--2016|Ruml et al., 2016]] ; [[#van%20Leeuwen--2017|van Leeuwen and Destrac-Irvine, 2017]] ; [[#Koufos--2020|Koufos et al., 2020]] ; [[#Wang--2020b|Wang et al., 2020b]] ; Wang and Li, 2020), and wine-growing regions have expanded outside the normal temperature bounds of locally grown varieties ( ''limited evidence'' , ''high agreement'' ) ( [[#Kryza--2015|Kryza et al., 2015]] ; [[#Irimia--2018|Irimia et al., 2018]] ). Milder winters have affected harvest in ice-wine growing regions ( [[#Pickering--2015|Pickering et al., 2015]] ). Higher temperatures have mixed effects depending on site, but generally decrease grape quality ( [[#Barnuud--2014|Barnuud et al., 2014]] ; [[#Morales--2014|Morales et al., 2014]] ; [[#Sweetman--2014|Sweetman et al., 2014]] ; [[#Kizildeniz--2015|Kizildeniz et al., 2015]] ; [[#Kizildeniz--2018|Kizildeniz et al., 2018]] ). Warming increases sugar accumulation and decreases acidity ( [[#Leolini--2019|Leolini et al., 2019]] ). Secondary metabolites are negatively affected ( [[#Biasi--2019|Biasi et al., 2019]] ; [[#Teslić--2019|Teslić et al., 2019]] ). Developmental phases are projected to proceed faster in response to warming ( ''high confidence'' ) ( [[#Fraga--2016a|Fraga et al., 2016a]] ; [[#Fraga--2016b|Fraga et al., 2016b]] ; [[#García%20de%20Cortázar-Atauri--2017|García de Cortázar-Atauri et al., 2017]] ; [[#Costa--2019|Costa et al., 2019]] ; [[#Molitor--2019|Molitor and Junk, 2019]] ; Sánchez, 2019). However extreme high temperatures may have inhibitory effects on development ( [[#Cuccia--2014|Cuccia et al., 2014]] ).

In some cases, irrigation is required, and more frequent droughts are a key concern for yield and fruit quality ( [[#Morales--2014|Morales et al., 2014]] ; [[#Bonada--2015|Bonada et al., 2015]] ; [[#Kizildeniz--2015|Kizildeniz et al., 2015]] ; Salazar-Parra, 2015; [[#Kizildeniz--2018|Kizildeniz et al., 2018]] ; [[#Funes--2020|Funes et al., 2020]] ). Water stress reduces shoot growth and berry size, and increases tannin and anthocyanin content ( [[#van%20Leeuwen--2016|van Leeuwen and Darriet, 2016]] ). However, controlled water stress produces positive impacts on wine quality, increasing skin phenolic compounds ( [[#van%20Leeuwen--2017|van Leeuwen and Destrac-Irvine, 2017]] ). The level of stress will depend on soil type, texture and organic matter content ( [[#Fraga--2016a|Fraga et al., 2016a]] ; [[#Fraga--2016b|Fraga et al., 2016b]] ; Bonfante, 2017; [[#García%20de%20Cortázar-Atauri--2017|García de Cortázar-Atauri et al., 2017]] ; [[#Leibar--2017|Leibar et al., 2017]] ; [[#Costa--2019|Costa et al., 2019]] ; [[#Molitor--2019|Molitor and Junk, 2019]] ; Sánchez, 2019). Increases in water demands with potential negative effects from increased soil salinity are among the most common effects of climate change in irrigated regions ( ''medium evidence'' , ''high agreement'' ) ( [[#Mirás-Avalos--2018|Mirás-Avalos et al., 2018]] ; [[#Phogat--2018|Phogat et al., 2018]] ).

Rising CO 2 will have mixed effects on vine growth and quality ( ''medium evidence, high agreement'' ) ( [[#Martínez-Lüscher--2016|Martínez-Lüscher et al., 2016]] ; [[#Edwards--2017|Edwards et al., 2017]] ; [[#van%20Leeuwen--2017|van Leeuwen and Destrac-Irvine, 2017]] ). Rising CO 2 concentrations will negatively affect wine quality by reducing anthocyanin concentration and colour intensity ( [[#Leibar--2017|Leibar et al., 2017]] ).

Suitability responses to warming are region-specific. In regions where low temperature is a limiting factor, warming will enable growers to grow a wider range of varieties and obtain better-quality wines ( ''high confidence'' ) ( [[#Fuhrer--2014|Fuhrer et al., 2014]] ; [[#Mosedale--2015|Mosedale et al., 2015]] ; [[#Mosedale--2016|Mosedale et al., 2016]] ; [[#Meier--2018|Meier et al., 2018]] ; [[#Jobin%20Poirier--2019|Jobin Poirier et al., 2019]] ; [[#Maciejczak--2019|Maciejczak and Mikiciuk, 2019]] ). Subtropical and Mediterranean regions will experience major declines in fruit quality for high-quality wines ( ''high confidence'' ) ( [[#Resco--2016|Resco et al., 2016]] ; [[#Lazoglou--2018|Lazoglou et al., 2018]] ; [[#Cardell--2019|Cardell et al., 2019]] ; [[#Fraga--2019a|Fraga et al., 2019a]] ; [[#Fraga--2019b|Fraga et al., 2019b]] ; [[#Teslić--2019|Teslić et al., 2019]] ). These changes will also affect wine tourism ( [[#Nunes--2016|Nunes and Loureiro, 2016]] ).

Impacts on suitability may reshape the geographical distribution of wine regions. Viability of the wine-growing regions will depend on the knowledge of local climatic variability ( [[#Neethling--2019|Neethling et al., 2019]] ; [[#Rességuier--2020|Rességuier et al., 2020]] ) and the implementation of adaptation strategies such as use of adapted plant material rootstocks, cultivars and clones, viticultural techniques (e.g., changing trunk height, leaf area to fruit weight ratio, timing of pruning), irrigation, enological interventions to control alcohol and acidity, and policy incentives and support (Callen et al., 2016; [[#Ollat--2016|Ollat and Leeuwen, 2016]] ; [[#van%20Leeuwen--2017|van Leeuwen and Destrac-Irvine, 2017]] ; [[#Merloni--2018|Merloni et al., 2018]] ; [[#Alikadic--2019|Alikadic et al., 2019]] ; [[#del%20Pozo--2019|del Pozo et al., 2019]] ; [[#Fraga--2019b|Fraga et al., 2019b]] ; [[#Santillan--2019|Santillan et al., 2019]] ; [[#Morales-Castilla--2020|Morales-Castilla et al., 2020]] ; [[#Marín--2021|Marín et al., 2021]] ).

<div id="box-5.3:-pollinators" class="h2-container box-container"></div>

'''Box 5.3: Pollinators'''
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Climate change will reduce the effectiveness of pollinator agents as species are lost from certain areas, or the coordination of pollinator activity and flower receptiveness is disrupted in some regions ( ''high confidence'' ) ( [[#Potts--2010|Potts et al., 2010]] ; [[#Gonzalez-Varo--2013|Gonzalez-Varo et al., 2013]] ; [[#Polce--2014|Polce et al., 2014]] ; [[#Kerr--2015|Kerr et al., 2015]] ; [[#Potts--2016|Potts et al., 2016]] ; [[#Settele--2016|Settele et al., 2016]] ; [[#Giannini--2017|Giannini et al., 2017]] ; [[#Mbow--2019|Mbow et al., 2019]] ). A modelling study estimates that complete removal of pollinators could reduce global fruit supply by 23%, vegetables by 16%, and nuts and seeds by 22%, leading to significant increases in nutrient-deficient population and malnutrition-related diseases ( [[#Smith--2015|Smith and Haddad, 2015]] ), highlighting the importance of this ecosystem service for human health.

Bees are an essential agricultural pollinator, widely recognised for their role in the fertilisation of many domesticated plants. The observed widespread decline in native bees and honeybee colony numbers, particularly in the USA and Europe, has been associated with a number of environmental stressors in addition to climate change, such as neonicotinoids and varroa mites, and has raised concerns regarding plant–pollinator networks, the stability of pollination services, global food production and the prevalence of malnutrition ( [[#Williams--2009|Williams and Osborne, 2009]] ; [[#Potts--2010|Potts et al., 2010]] ; [[#Chaplin-Kramer--2014|Chaplin-Kramer et al., 2014]] ).

Any climatic influence on floral phenology or physiology could, potentially, alter bee biology. At present, there is evidence that climate-change-induced asynchrony in pollen and pollinators can occur ( [[#Stemkovski--2020|Stemkovski et al., 2020]] ). In addition, the nutritional composition of floral pollen may also affect bees’ health at the global level ( ''low evidence'' ). For example, goldenrod ( ''Solidago'' spp.), a ubiquitous pollen source for bees just prior to winter, has experienced a ~30% drop in protein since the onset of CO 2 emissions from the industrial revolution ( [[#Ziska--2016|Ziska et al., 2016]] ).

Climate extremes could pose risks to pollinators when species tolerance is exceeded, with subsequent reduction in populations and potential extirpation ( [[#Nicholson--2020|Nicholson and Egan, 2020]] ; [[#Soroye--2020|Soroye et al., 2020]] ). The rate of climate change may induce potential mismatches in the timing of flowering and pollinator activity depending on the species ( [[#Bartomeus--2011|Bartomeus et al., 2011]] ). For instance, Miller-Struttmann (2015) showed that long-tongued bumblebees may be at a disadvantage as warming temperatures are reducing their floral hosts, making generalist bumblebees more successful.

Overall, there is ''medium confidence'' that long-term mutualisms may be impacted directly by CO 2 increases in terms of nutrition, or by temperature and other climatic shifts that may alter floral emergence relative to pollinator life cycles. Additional research is needed to further our understanding of the biological basis for these effects, and their consequence for pollination services.

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Box 5.3

<div id="box-5.4:-soil-health" class="h2-container box-container"></div>

'''Box 5.4: Soil Health'''
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Soil health, defined as an integrative property that reflects the capacity of soil to respond to land management, continues to support provisioning ecosystem services ( [[#Kibblewhite--2008|Kibblewhite et al., 2008]] ). Climate change will have significant impacts on soil health indicators such as soil organic matter (SOM). For example, precipitation extremes can reduce soil biological functions, and increase surface flooding, waterlogging, soil erosion and susceptibility to salinisation ( [[#Herbert--2015|Herbert et al., 2015]] ; [[#Chen--2018|Chen and Mueller, 2018]] ; [[#Akter--2019|Akter et al., 2019]] ; Sánchez- [[#Rodríguez--2019|Rodríguez et al., 2019]] ).

The most significant threat to soil health is the loss of SOM ( [[#FAO%20and%20ITPS--2015|FAO and ITPS, 2015]] ). SOM holds a great proportion of the nutrients, and regulates important soil physical, chemical and biological processes, such as cation exchange capacity, pH buffering, soil structure, water-holding capacity and microbial activity ( [[#FAO%20and%20ITPS--2015|FAO and ITPS, 2015]] ). Soils also hold the largest terrestrial organic carbon stock, three to four times greater than the atmosphere ( [[#Stoorvogel--2017|Stoorvogel et al., 2017]] ). At the global scale, climate and vegetation are the main drivers of soil organic carbon (SOC) storage ( [[#Wiesmeier--2019|Wiesmeier et al., 2019]] ). While organic matter input is the primary driver of SOC stocks ( [[#Fujisaki--2018|Fujisaki et al., 2018]] ), temperature and soil moisture play a key role in SOC storage at the local scale ( [[#Carvalhais--2014|Carvalhais et al., 2014]] ; [[#Doetterl--2015|Doetterl et al., 2015]] ). Soil type, land use and management practices also play important roles at the local scale.

Increase in soil temperature will negatively impact SOC, but primarily in higher latitudes ( ''medium confidence'' ) ( [[#Carey--2016|Carey et al., 2016]] ; [[#Qi--2016|Qi et al., 2016]] ; [[#Feng--2017|Feng et al., 2017]] ; [[#Gregorich--2017|Gregorich et al., 2017]] ; [[#Hicks%20Pries--2017|Hicks Pries et al., 2017]] ; [[#Melillo--2017|Melillo et al., 2017]] ; [[#Hicks%20Pries--2018|Hicks Pries et al., 2018]] ). Experiments have shown that warming can accelerate litter mass loss and soil respiration ( [[#Lu--2013|Lu et al., 2013]] ) and reduces the soil recalcitrant C pool ( [[#Chen--2020|Chen et al., 2020]] ). SOC losses may speed up soil structural degradation, changes in soil stoichiometry and function ( [[#Hakkenberg--2008|Hakkenberg et al., 2008]] ; [[#Tamene--2019|Tamene et al., 2019]] ), with downstream effects on aquatic ecosystems. The rate and extent of SOC losses vary greatly depending on the scale of measurement (local to global), soil properties, climate, land use and management practices ( [[#Sanderman--2017|Sanderman et al., 2017]] ; [[#Wiesmeier--2019|Wiesmeier et al., 2019]] ).

Adoption of practices that build SOC can improve crop resilience to climate-change-related stresses such as agricultural drought. [[#Iizumi--2019|Iizumi and Wagai (2019)]] found that a relatively small increase in topsoil (0–30 cm) SOC could reduce drought damages to crops over 70% of the global harvested area. The effects of increasing SOC are more positive in drylands owing to more efficient use of rainwater, which can increase drought tolerance ( [[#Iizumi--2019|Iizumi and Wagai, 2019]] ). Similarly, [[#Sun--2020|Sun et al. (2020)]] found that, relative to local conventional tillage, conservation agriculture has a win-win outcome of enhanced C sequestration and increased crop yield in arid regions. However, the impact of no-till may be minimal if not supplemented with residue cover and cover crops. As such, this is a highly debated area where some authors argue that no-till has limited effect and the evidence outside drylands is weak. Furthermore, the use of crop residues is constrained by its alternative uses (e.g., fuel, livestock feed, etc.) in much of the developing world. Practices that build up SOC may encourage soil microbial populations, which in turn can increase yield stability under drought conditions ( [[#Prudent--2020|Prudent et al., 2020]] ).

Soil C sequestration is an important strategy to improve crop and livestock production sustainably that could be applied at large scales and at a low cost, if there was adequate institutional support and labour, using agroforestry, conservation agriculture, mixed cropping and targeted application of fertilizer and compost ( ''high confidence'' ) ( [[#Paustian--2016|Paustian et al., 2016]] ; [[#Kongsager--2018|Kongsager, 2018]] ; [[#Nath--2018|Nath et al., 2018]] ; [[#Woolf--2018|Woolf et al., 2018]] ; [[#Corbeels--2019|Corbeels et al., 2019]] ; [[#Kuyah--2019|Kuyah et al., 2019]] ; [[#Corbeels--2020|Corbeels et al., 2020]] ; [[#Muchane--2020|Muchane et al., 2020]] ; [[#Sun--2020|Sun et al., 2020]] ; [[#Nath--2021|Nath et al., 2021]] ). For example, a widespread adoption of agroforestry, conservation agriculture, mixed cropping and balanced application of fertilizer and compost by India’s small landholders could increase annual C sequestration by 70–130 Tg CO 2 e ( [[#Nath--2018|Nath et al., 2018]] ; [[#Nath--2021|Nath et al., 2021]] ).

<div id="5.4.4" class="h2-container"></div>

<span id="adaptation-options"></span>