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

== 5.4 Crop-Based Systems ==

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Crops such as cereals, vegetables, fruit, roots, tubers, oilseeds and sugar account for about 80% of the dietary energy supply (FAO, 2019 f). Crops are a significant source of food and income for about 600 million farms in the world, 90% of which are family farms ( [[#Lowder--2019|Lowder et al., 2019]] ). Previous assessment reports focused on yields of staple crops such as maize, wheat and rice, but studies are emerging on climate change impacts on other crops.

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=== 5.4.1 Observed Impacts ===

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==== 5.4.1.1 Observed impacts on major crops ====

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AR5 [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] ( [[#Porter--2014|Porter et al., 2014]] ) stated with confidence that warmer temperatures have benefited agriculture in the high latitudes, and more evidence has been published to support this statement. Typical examples include pole-ward expansion of growing areas and reduction of cold stress in East Asia and North America (Table SM5.1).

Recent warming trends have generally shortened the life cycle of major crops ( ''high confidence'' ) ( [[#Zhang--2014|Zhang et al., 2014]] ; [[#Shen--2015|Shen and Liu, 2015]] ; [[#Ahmed--2018|Ahmed et al., 2018]] ; [[#Liu--2018c|Liu et al., 2018c]] ; [[#Tan--2021|Tan et al., 2021]] ). Some studies, however, observed prolonged crop growth duration despite the warming trends ( [[#Mueller--2015|Mueller et al., 2015]] ; [[#Tao--2016|Tao et al., 2016]] ; [[#Butler--2018|Butler et al., 2018]] ; [[#Zhu--2018b|Zhu et al., 2018b]] ) because of shifts in planting dates and/or adoption of longer-duration cultivars in mid-to-high latitudes. Conversely, in mid-to-low latitudes in Asia, a review study found that farmers favoured early maturing cultivars to reduce risks of damages due to drought, flood and/or heat ( [[#Shaffril--2018|Shaffril et al., 2018]] ), suggesting that region-specific adaptations are already occurring in different parts of the world ( ''high confidence'' ).

Global yields of major crops per unit land area have increased 2.5- to 3-fold since 1960. Plant breeding, fertilisation, irrigation and integrated pest management have been the major drivers, but many studies have found significant impacts from recent climate trends on crop yield ( ''high confidence'' ) (Figure 5.3; see [[#5.2.1|Section 5.2.1]] for the change attributable to anthropogenic climate change).

Climate impacts for the past 20–50 years differ by crops and regions. Positive effects have been identified for rice and wheat in Eastern Asia, and for wheat in Northern Europe. The effects are mostly negative in Sub-Saharan Africa, South America and Caribbean, Southern Asia, and Western and Southern Europe. Climate factors that affected long-term yield trends also differ between regions. For example, in Western Africa, 1°C warming above preindustrial climate has increased heat and rainfall extremes, and reduced yields by 10–20% for millet and 5–15% for sorghum (Sultan et al., 2019). In Australia, declined rainfall and increased temperatures reduced yield potential of wheat by 27%, accounting for the low yield growth between 1990 and 2015 ( [[#Hochman--2017|Hochman et al., 2017]] ). In Southern Europe, climate warming has negatively impacted yields of almost all major crops, leading to recent yield stagnation ( [[#Moore--2015|Moore and Lobell, 2015]] ; [[#Agnolucci--2020|Agnolucci and De Lipsis, 2020]] ; [[#Brás--2021|Brás et al., 2021]] ).

[[#Ortiz-Bobea--2021|Ortiz-Bobea et al. (2021)]] analysed agricultural total factor productivity (TFP), defined as the ratio of all agricultural outputs to all agricultural inputs, and found that, while TFP has increased between 1961 and 2015, the climate change trends reduced global TFP growth by a cumulative 21% over a 55-year period relative to TFP growth under counterfactual non-climate change conditions. Greater effects (30–33%) were observed in Africa, Latin America and the Caribbean (Figure 5.3).

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[[File:dbf41cc585e3e4c6655b7bef7aa97b71 IPCC_AR6_WGII_Figure_5_003.png]]

'''Figure 5.3 |''' '''Synthesis of literature on observed impacts of climate change on productivity by crop type and region.''' The figure draws on &gt;150 articles categorized by: agriculture total factor productivity including literature estimating all agricultural outputs in a region; major crop species including literature assessing yield changes in the four major crops; crop categories including productivity changes (yield, quality and other perceived changes) in a range of crops with different growth habits. The assessment uses literature published since AR5, although the timespan often extends prior to 2014. The direction of the effect and the confidence are based on the reported impacts and attribution, and on the number of articles. See SM5.1 and SM5.2 for details.

Climate variability is a major source of variation in crop production ( [[#Ray--2015|Ray et al., 2015]] ; [[#Iizumi--2016|Iizumi and Ramankutty, 2016]] ; [[#Frieler--2017|Frieler et al., 2017]] ; [[#Cottrell--2019|Cottrell et al., 2019]] )(Table SM5.1). Weather signals in yield variability are generally stronger in productive regions than in the less productive regions ( [[#Frieler--2017|Frieler et al., 2017]] ), where other yield constraints exist such as pests, diseases and poor soil fertility ( [[#Mills--2018|Mills et al., 2018]] ; 5.2.2). Nevertheless, yield variability in less productive regions has severe impacts on local food availability and livelihood ( ''high confidence'' ) ( [[#FAO--2021|FAO, 2021]] ).

Climate-related hazards that cause crop losses are increasing ( ''medium evidence'' , ''high agreement'' ) ( [[#Cottrell--2019|Cottrell et al., 2019]] ; [[#Mbow--2019|Mbow et al., 2019]] ; [[#Brás--2021|Brás et al., 2021]] ; [[#FAO--2021|FAO, 2021]] ; [[#Ranasinghe--2021|Ranasinghe et al., 2021]] ). Drought-related yield losses have occurred in about 75% of the global harvested area ( [[#Kim--2019b|Kim et al., 2019b]] ) and increased in recent years ( [[#Lesk--2016|Lesk et al., 2016]] ). Heatwaves have reduced yields of wheat ( [[#Zampieri--2017|Zampieri et al., 2017]] ) and rice ( [[#Liu--2019b|Liu et al., 2019b]] ) ''.'' The combined effects of heat and drought decreased global average yields of maize, soybeans and wheat by 11.6%, 12.4% and 9.2%, respectively ( [[#Matiu--2017|Matiu et al., 2017]] ). In Europe, crop losses due to drought and heat have tripled over the last five decades ( [[#Brás--2021|Brás et al., 2021]] ), pointing to the importance of assessing multiple stresses. Globally, floods also increased in the past 50 years, causing direct damages to crops and indirectly reduced yields by delaying planting, which cost 4.5 billion USD in the 2010 flood in Pakistan and 572 million USD in the 2015 flood in Myanmar ( [[#FAO--2021|FAO, 2021]] ).

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==== 5.4.1.2 Observed impacts on other crops (vegetables, fruit, nut and fibre) ====

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The impact of climate change on these diverse crop types is under-researched and uncertain ( [[#Manners--2018|Manners and van Etten, 2018]] ; [[#Alae-Carew--2020|Alae-Carew et al., 2020]] ); there are reports of positive impacts in some cases, but overall the observed impacts are negative across all crop categories (Figure 5.3).

Above-ground annual crops consumed as vegetables, fruits or salad are essential for food security and nutrition (5.12). In temperate regions, climate change can result in higher yields ( [[#Potopová--2017|Potopová et al., 2017]] ; [[#Bisbis--2018|Bisbis et al., 2018]] ), while in subtropical/tropical regions, negative impacts from heat and drought take precedence ( [[#Scheelbeek--2018|Scheelbeek et al., 2018]] ). Different species have different sensitivities to heat and drought ( [[#Prasad--2017|Prasad et al., 2017]] ; [[#Scheelbeek--2018|Scheelbeek et al., 2018]] ) and to combinations of stresses ( [[#Zandalinas--2018|Zandalinas et al., 2018]] ). Above-ground vegetables are especially vulnerable to heat and drought stress during pollination and fruit set, resulting in negitive impacts on yield ( [[#Daryanto--2017|Daryanto et al., 2017]] ; [[#Sita--2017|Sita et al., 2017]] ; [[#Brás--2021|Brás et al., 2021]] ) and harvest quality ( [[#Mattos--2014|Mattos et al., 2014]] ; [[#Bisbis--2018|Bisbis et al., 2018]] ). Growers have already seen negative impacts from the expansion of pest and disease agents due to warming ( [[#5.4.1.3|Section 5.4.1.3]] ; Figure 5.3).

Below-ground vegetables include starchy roots and tubers that form a regular diet in many parts of the tropics and subtropics. Warming and climate variability has altered the rate of tuber development, with yield impacts varying by location, including yield increases in some cases ( [[#Shimoda--2018|Shimoda et al., 2018]] ; [[#Ray--2019|Ray et al., 2019]] ). These crops are considered stress tolerant but are more sensitive to drought than cereals ( [[#Daryanto--2017|Daryanto et al., 2017]] ). Impacts on water supply are critical as root crops are water-demanding for long periods, and highly sensitive to drought and heat events during tuber initiation ( [[#Dua--2013|Dua et al., 2013]] ; [[#Potopová--2017|Potopová et al., 2017]] ; [[#Brás--2021|Brás et al., 2021]] ).

Among perennial tree crops, only grapevine, olive, almond, apple, coffee and cocoa have received significant research attention. Concerns about climate impacts on harvest quality are widespread (Figure 5.3) ( [[#Barnuud--2014|Barnuud et al., 2014]] ; [[#Bonada--2015|Bonada et al., 2015]] ). In higher-latitude regions, the primary concern is the effect of temperature variability on harvest stability, pests and diseases and phenology (including fulfilment of winter chill requirements and risks due to early emergence in spring), ( [[#El%20Yaacoubi--2014|El Yaacoubi et al., 2014]] ; [[#Ramírez--2015|Ramírez and Kallarackal, 2015]] ; [[#Santos--2017|Santos et al., 2017]] ; [[#Gitea--2019|Gitea et al., 2019]] ). In lower-latitude regions, information is limited, but studies are focused on increased tree mortality and yield loss due to drought, heat and impacts from variability in the timing of the wet and dry seasons ( [[#Glenn--2013|Glenn et al., 2013]] ; [[#Ramírez--2015|Ramírez and Kallarackal, 2015]] ); see Box 5.7). In fruit trees, warming and climate variability have already affected fruit quality, such as acidity and texture in apples, or skin colour in grape berries ( [[#Sugiura--2013|Sugiura et al., 2013]] ; [[#Sugiura--2018|Sugiura et al., 2018]] ). The reliability and stability of harvests has been impacted by climate variability, changes in the distribution of pests and pathogens ( [[#Seidel--2014|Seidel, 2014]] ; [[#Bois--2017|Bois et al., 2017]] ), and the mismatch of important phenological events (such as bud emergence and flowering) ( [[#Guo--2015|Guo and Shen, 2015]] ; [[#Legave--2015|Legave et al., 2015]] ; [[#Ito--2018|Ito et al., 2018]] ; [[#Vitasse--2018|Vitasse et al., 2018]] ). Perennial crops are particularly vulnerable to these impacts as they are exposed throughout the year, with little potential for growers to adjust planting date or location. Negative impacts via disruption to phenology and pest dynamics are best studied in grapevine (see Box 5.2).

Among the fibre crops, cotton is particularly well studied. As cotton is heat tolerant and yield increases with extra plant growth, positive effects of increasing temperature are expected, but observed impacts have been mixed due to negative impacts on phenology and plant water status ( [[#Traore--2013|Traore et al., 2013]] ; [[#Chen--2015a|Chen et al., 2015a]] ; [[#Cho--2017|Cho and McCarl, 2017]] ). Negative impacts of climate change due to proliferation of the pest cotton bollworm are widely reported ( [[#Ouyang--2014|Ouyang et al., 2014]] ; [[#Huang--2020|Huang and Hao, 2020]] ).

The impacts of climate change on water availability (rainfall and irrigation supply) are an emerging issue. Increased occurrence of drought combined with limited access to irrigation water is already a key constraint; for example, Californian almonds are predicted to increase their potential geographical range under climate warming ( [[#Parker--2018|Parker, 2018]] ), yet a trend of increasing drought has already resulted in trees being removed due to lack of access to irrigation water ( [[#Keppen--2015|Keppen and Dutcher, 2015]] ; [[#Kerr--2018|Kerr et al., 2018]] ; [[#Reisman--2019|Reisman, 2019]] ).

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==== 5.4.1.3 Observed impacts on pests, diseases and weeds ====

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AR5 and SRCCL (IPCC, 2019) indicated that more frequent outbreaks and area expansion of pests and diseases are serious concerns under climate change but are under-researched because of the difficulties in assessing multi-species interactions ( [[#Porter--2014|Porter et al., 2014]] ; [[#Mbow--2019|Mbow et al., 2019]] ). High-quality historical and current observational data to detect changes in pests and diseases attributable to recent trends in climate are still limited.

Bebber (2013) found significant poleward expansions of many important groups of crop pests and pathogens since 1960, with an average shift of 2.7 km yr −1 . Different pest species populations respond differently to ongoing climate change, with some shifting, contracting or expanding their current distribution range and others persisting or disappearing in their current range ( ''high confidence'' ). These asymmetric distribution changes can create novel species combinations or decouple existing ones ( [[#Pecl--2017|Pecl et al., 2017]] ; [[#Hobbs--2018|Hobbs et al., 2018]] ), but their consequences on future crop production and food security are hard to predict. Multi-species climate change experiments are rare ( [[#Bonebrake--2018|Bonebrake et al., 2018]] ), but one study shows that under future climates different pest assemblages of interacting species may alter levels of damage to crops compared with that by only one species ( [[#Crespo-Perez--2015|Crespo-Perez et al., 2015]] ). Some studies highlight the importance of location-specific species interactions for more realistic projections of pest distribution, performance and damage to crops, which in turn would allow more effective prevention and pest control strategies ( [[#Wilson--2015|Wilson et al., 2015]] ; [[#Carrasco--2018|Carrasco et al., 2018]] ).

Weeds are recognised as a primary constraint on crop production ( [[#Oerke--2006|Oerke, 2006]] ), rangelands ( [[#DiTomaso--2017|DiTomaso et al., 2017]] ) and forests ( [[#Webster--2006|Webster et al., 2006]] ). Climate change could favour the growth and development of weeds over crops with negative consequences for desired plants in managed systems ( ''medium evidence'' , ''high agreement'' ) ( [[#Peters--2014|Peters et al., 2014]] ; [[#Ziska--2016|Ziska and McConnell, 2016]] ). First, changes in temperature and precipitation alter the range, composition and competitiveness of native and invasive weeds ( [[#Bradley--2010|Bradley et al., 2010]] ). Second, rising concentrations of CO 2 enhance growth of C 3 species (~85% of plant species, including many weeds) ( [[#Ogren--1982|Ogren and Chollet, 1982]] ; [[#Ziska--2003|Ziska, 2003]] ), and increase plant water use efficiency with potentially strong effects on invasive plant species establishment ( [[#Smith--2000|Smith et al., 2000]] ; [[#Belote--2004|Belote et al., 2004]] ; [[#Blumenthal--2013|Blumenthal et al., 2013]] ).

Some invasive species within unmanaged areas will expand further, proliferate and be more competitive under climate change as they may benefit from increased resource ability (e.g., additional CO 2 , enhanced precipitation) ( [[#Bradley--2010|Bradley et al., 2010]] ; [[#Kathiresan--2016|Kathiresan and Gualbert, 2016]] ; [[#Merow--2017|Merow et al., 2017]] ; [[#Ramesh--2017|Ramesh et al., 2017]] ; [[#Waryszak--2018|Waryszak et al., 2018]] ), which will make chemical weed control more problematic ( ''medium evidence'' , ''high agreement'' ) ( [[#Waryszak--2018|Waryszak et al., 2018]] ; [[#Ziska--2020|Ziska, 2020]] ). The range of other invasive weeds may become static, or even decline ( [[#Bradley--2016|Bradley et al., 2016]] ; [[#Buckley--2017|Buckley and Csergo, 2017]] ). A recent meta-analysis also supports that invasive plants respond more favourably to elevated CO 2 concentrations and elevated temperatures than native plants ( [[#Korres--2016|Korres et al., 2016]] ; [[#Liu--2017|Liu et al., 2017]] ). Movement of invasive species into low-fertility areas, however, could provide resource opportunities, especially if agriculture in those areas is limited ( [[#Randriambanona--2019|Randriambanona et al., 2019]] ).

Rising CO 2 concentrations and climate change could reduce herbicide efficacy ( ''medium evidence'' , ''high agreement'' ). These reductions may be associated with physical environmental changes (precipitation, wind speed) that influence herbicide coverage ( [[#Ziska--2016|Ziska, 2016]] ) as well as direct effects of CO 2 on plant biochemistry and herbicide resistance ( [[#Refatti--2019|Refatti et al., 2019]] ). Increasing CO 2 levels and altered temperature and precipitation are therefore projected to affect all aspects of weed biology ( [[#Peters--2014|Peters et al., 2014]] ; [[#Ziska--2016|Ziska and McConnell, 2016]] ), including establishment ( [[#Bradley--2016|Bradley et al., 2016]] ), competition ( [[#Fernando--2019|Fernando et al., 2019]] ), distribution, ( [[#Castellanos-Frías--2016|Castellanos-Frías et al., 2016]] ) and management ( [[#Waryszak--2018|Waryszak et al., 2018]] ).

A warmer climate increases the need for pesticides ( [[#Shakhramanyan--2013|Shakhramanyan et al., 2013]] ; [[#Ziska--2014|Ziska, 2014]] ; [[#Delcour--2015|Delcour et al., 2015]] ; [[#Zhang--2018|Zhang et al., 2018]] ). Increases in temperature and CO 2 concentration may reduce pesticide efficiency by altering its metabolism, or accelerating detoxification ( [[#Matzrafi--2016|Matzrafi et al., 2016]] ; [[#Matzrafi--2019|Matzrafi, 2019]] ). Intense rainfall also reduces persistence ( [[#Delcour--2015|Delcour et al., 2015]] ). Invasive pests and pathogens impose an additional cost for the society ( [[#Bradshaw--2016|Bradshaw et al., 2016]] ). Rapid and large-scale dispersal of pests is already a major threat to food security, as exemplified by the recent outbreak of desert locusts (see Box 5.8), indicating the importance of international cooperation. Taken together, the need for control of pests, disease and weeds will increase under climate change ( ''medium evidence'' , ''high agreement'' ). The use of toxic agricultural chemicals also has human health and environmental risks ( [[#Whitmee--2015|Whitmee et al., 2015]] ; [[#IPBES--2019|IPBES, 2019]] ). Surveillance for monitoring pest distribution and damages, climate-relevant pest risk analysis, and climate-smart strategies for controlling pests with minimal impacts on human and environmental health are important tools in the face of climate change ( [[#IPPC%20Secretariat--2021|IPPC Secretariat, 2021]] ).

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==== 5.4.1.4 Observed impacts of ozone on crops ====

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Tropospheric (i.e., the lowest 6–10 km of the atmosphere) ozone exacerbates negative impacts of climate change ( ''high confidence'' ) ( [[#Mattos--2014|Mattos et al., 2014]] ; [[#Chuwah--2015|Chuwah et al., 2015]] ; [[#McGrath--2015|McGrath et al., 2015]] ; [[#Bisbis--2018|Bisbis et al., 2018]] ; [[#Mills--2018|Mills et al., 2018]] ; [[#Scheelbeek--2018|Scheelbeek et al., 2018]] ). Ozone is an air pollutant and short-lived GHG that affects air quality and global climate. It is a strong oxidant that reduces physiological functions, yield and quality of crops and animals. Surface ozone concentration has increased substantially since the late 19 th century ( [[#Cooper--2014|Cooper et al., 2014]] ; Forster et al., 2021; [[#Gulev--2021|Gulev et al., 2021]] ; [[#Szopa--2021|Szopa et al., 2021]] ) and in some locations and times reaches levels that harm plants, animals and human ( ''high confidence'' ) ( [[#Fleming--2018|Fleming et al., 2018]] ).

[[#Mills--2018|Mills (2018)]] estimated global distributions of current yield losses of major crops due to ozone, pest and diseases, heat, and aridity (Figure 5.4). Ozone-induced yield losses in 2010–2012 averaged 12.4%, 7.1%, 4.4% and 6.1% for soybean, wheat, rice and maize, respectively. Spatial variation in yield losses is similar among different stresses; areas with a large loss due to ozone are also at high risk of yield losses due to pest and diseases and heat. Many vegetable crops are also susceptible to ozone, which will adversely impact quality and quantity ( [[#Mattos--2014|Mattos et al., 2014]] ; [[#Bisbis--2018|Bisbis et al., 2018]] ; [[#Scheelbeek--2018|Scheelbeek et al., 2018]] ).

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[[File:9cb2c3164051a26bb5ff3bf260e4b9b7 IPCC_AR6_WGII_Figure_5_004.png]]

'''Figure 5.4 |''' '''The global effects of five biotic and abiotic stresses on soybean and wheat.''' All data are presented for the 1 × 1° (latitude and longitude) grid squares where the mean production of soybean or wheat was &gt;500 tonnes (0.0005 Tg). The effect of each stress on yield is presented as a Yield Constraint Score (YCS) on a scale of 1–5, where 5 is the highest level of stress from ozone, pests and diseases, heat stress and aridity ( [[#Mills--2018|Mills et al., 2018]] ). Data are available at [[#Sharps--2020|Sharps et al. (2020)]] . See Annex I: Global to Regional Atlas for all four crops.

The estimated yield loss does not account for interactions with other climatic factors. Temperatures enhance not only ozone production but also ozone uptake by plants, exacerbating yield and quality damage. Burney (2014) estimated current yield losses due to the combined effects of ozone and heat in India at 36% for wheat and 20% for rice. [[#Schauberger--2019a|Schauberger et al. (2019a)]] found global yield losses, ranging from 2% to 10% for soybean and 0% to 39% for wheat with a model that accounts for temperature, water and CO 2 concentration on ozone uptake.

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'''Box 5.1: Evidence for Simultaneous Crop Failures Due to Climate Change'''
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Simultaneous yield losses across major producing regions can be a threat to food security but had not been quantified by the time of AR5. Large-scale sea surface temperature (SST) oscillations greatly influence global yield of major crops ( ''high confidence'' ) ( [[#Anderson--2019b|Anderson et al., 2019b]] ; [[#Najafi--2019|Najafi et al., 2019]] ; [[#Ubilava--2019|Ubilava and Abdolrahimi, 2019]] ; [[#Heino--2020|Heino et al., 2020]] ; [[#Iizumi--2021b|Iizumi et al., 2021b]] ) and food prices ( [[#Ubilava--2018|Ubilava, 2018]] ). Some studies showed that crop yields in different regions covaried with SST oscillations, suggesting occurrences of tele-connected yield failures (crop losses caused by related factors in distant regions; Table Box 5.1.1) ( ''medium confidence'' ). Evidence of synchronised crop failures increasing with ongoing climate change is still limited.

'''Table Box 5.1.1 |''' A summary of peer-review papers detecting synchronised yield losses.

{| class="wikitable"
|-
! Regions/ commodities

! Period studied

! Observed impacts

! Climate driver

! Evidence for multiple breadbasket failures

! Evidence for increasing risks due to multiple breadbasket failures

! Reference

|-
| Global breadbaskets for maize, rice, sorghum and soybean

| 1961–2013

| Not only yields of each crop covaried in many countries, but also those of different crops, maize in particular, covaried with other crops.

| SST anomalies, atmospheric and oceanic in- dices, air temperature anomalies and Palmer Drought Severity Index

| High

| NA

| Najafi et al. (2019)

|-
| Global breadbaskets for wheat, soybean and maize

| 1980–2010

| Climate modes (El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), tropical Atlantic variability (TAV) and the North Atlantic Oscillation (NAO)) account for 18%, 7% and 6% of global maize, wheat and soybean production variability, respectively. ENSO events sometimes offset yield reductions in some places by increases in other places (e.g., soybean yields in the USA and southeast South America).

Since 1961, ENSO in 1983 was the only climate mode that showed global synchronous crop failures.

| Climate modes

| Medium (1983)

| NA

| [[#Anderson--2019b|Anderson et al. (2019b)]]

|-
| Global breadbaskets for wheat, soybean and maize

|
| Climate modes induce yield variability in major breadbaskets, e.g., ENSO affects about half of maize and wheat areas. IOD and ENSO influence wheat in Australia. ENSO affects soybean in northern South America.

| Climate modes

| Medium

| NA

| [[#Heino--2020|Heino et al. (2020)]]

|-
| 67 maize producing countries

| 1961–2017

| SST anomalies from the 1980–2010 base period in the Niño3.4 region, a rectangular area bounded by 120°W–170°W and 5°S–5° is used as a driver. Maize yields are tele-connected among the southeastern tier of Sub-Saharan Africa, as well as Central America, South Asia and Australia. A 1° increase in SST reduced maize yield by up to 20% in these countries.

| Climate modes (SST), precipitation

| Medium

| NA

| [[#Ubilava--2019|Ubilava and Abdolrahimi (2019)]]

|-
| Global breadbasket (the USA, Argentina, Europe, Russia/Ukraine, China, India, Australia, Indonesia and Brazil)

| 1967–2012

| Likelihood of simultaneous climate risks increased from 1967–1990 to 1991–2012 in the global breadbasket (lower 25 th yield deviation percentile events at province level) for wheat, soybean and maize, but not rice.

Likelihood of simultaneous climate risks increased from 1967–1990 to 1991–2012 in China (lower 25 th yield deviation percentile events at province level).

| Unspecified

| Medium

| Medium

| [[#Gaupp--2020|Gaupp et al. (2020)]]

|-
| Global

| 1961–2008

| Synchronous yield losses among major breadbaskets within each commodity, such as maize and soybean, decreased between 1961 and 2008. In contrast, synchronous yield variation between crops has increased. Under a scenario of synchronisation of all four crops, the global maximum production losses for rice, wheat, soybean and maize are estimated to reach between −17% and −34%.

| Unspecified

| Medium

| Medium

| [[#Mehrabi--2019|Mehrabi and Ramankutty (2019)]]

|}

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=== 5.4.2 Assessing Vulnerabilities within Production Systems ===

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Since AR5, vulnerability assessment has become a pivotal component of risk analysis associated with climate hazards, climate change and climate variability ( [[#UNDRR--2019|UNDRR, 2019]] ) ''.'' Vulnerability assessment can be sectoral or regional but involves social and ecological indicators. This section presents examples of vulnerability assessment to climatic hazards and social vulnerabilities.

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==== 5.4.2.1 Vulnerability to climatic hazards ====

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Drought is a major risk component in cropping systems globally, with substantial economic loss ( [[#Kim--2019b|Kim et al., 2019b]] ), livelihood impacts ( [[#Shiferaw--2014|Shiferaw et al., 2014]] ; [[#Miyan--2015|Miyan, 2015]] ) and ultimately health risks such as malnutrition ( [[#Phalkey--2015|Phalkey et al., 2015]] ; [[#Cooper--2019|Cooper et al., 2019]] ). Vulnerability to drought can be estimated with a range of indicators ( [[#Hagenlocher--2019|Hagenlocher et al., 2019]] ). Meza (2020) showed that drought risks could be exacerbated or moderated by regional differences in vulnerability (Figure 5.5). For instance, high-level risks observed in southern Africa, western Asia and central Asia result from high vulnerability (low coping capacity), whereas risk levels are relatively low despite the high exposure by relatively high adaptive capacity to drought in other regions.

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[[File:476b52ef017212d4e44c55cc4346ac26 IPCC_AR6_WGII_Figure_5_005.png]]

'''Figure 5.5 |''' '''Hazard and exposure indicator score (a), vulnerability index (b) and drought risk index (c), for rainfed agricultural systems between 1986 and 2015.''' Drought hazard indicator is defined as the ratio of actual crop evapotranspiration to potential crop evapotranspiration, calculated for 24 crops. Vulnerability index is the country-scale weighted average of a total of 64 indicators including social and ecological susceptibility indicators, and coping capacity. Risk index is calculated by multiplying hazard/exposure indicator score and vulnerability index ( [[#Meza--2020|Meza et al., 2020]] ).

Regional-scale assessment also highlights the importance of adaptive capacity. For instance, rice and maize production in Viet Nam Mekong Delta has high exposure to multiple climate hazards such as flooding, sea level rise, salinity intrusion and drought ( [[#Parker--2019|Parker et al., 2019]] ). Risks can be moderated by a relatively high adaptive capacity because of infrastructure, resources and high education levels ( [[#Parker--2019|Parker et al., 2019]] ). Another regional study demonstrated that erratic rains and high temperatures in southern and southeastern Africa increased the vulnerability of agricultural soils, thereby exacerbating impacts of prolonged and frequent droughts (Sonwa et al., 2017a; See also Box 5.4).

Farm-scale assessment exemplifies context-sensitive vulnerability to climate hazards. Studies of coffee growers in Central America demonstrated that key vulnerability indicators varied greatly between regions and between farms, ranging from a lack of labour, postharvest infrastructure, conservation practices and transport that limits access to market, technical and financial assistance ( [[#Baca--2014|Baca et al., 2014]] ; [[#Bouroncle--2017|Bouroncle et al., 2017]] ). These region- and scale-specific vulnerability indicators assist in identifying ways to enhance resilience to climate hazards ( ''high confidence'' ).

<div id="5.4.2.2" class="h3-container"></div>

<span id="inequities-in-cropping-systemsother-crops-and-regional-disparities"></span>
==== 5.4.2.2 Inequities in cropping systems—other crops and regional disparities ====

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While those working with major crops have benefited from the release of new cultivars, those growing other crops are typically reliant on a heritage cultivars or landraces. While Indigenous knowledge and local smallholder knowledge and practices play an important role in supporting agrobiodiversity which provides genetic diversity resistant to climate-related stresses, a global and national focus in international research, subsidies and support for a few crop species has contributed to an overall decline in agrobiodiversity ( [[#FAO--2019e|FAO, 2019e]] ; [[#Song--2019|Song et al., 2019]] ) Similarly, there is a lack of agronomic innovation and research to service ‘minor’ crops ( [[#Moriondo--2015|Moriondo et al., 2015]] ; [[#Manners--2018|Manners and van Etten, 2018]] ). Even some high-value commodities grown outside high-income countries suffer from imbalances in the focus of available credit, research and innovation ( [[#5.4.4.3|Section 5.4.4.3]] ; [[#Glover--2014|Glover, 2014]] ; [[#Fischer--2016|Fischer, 2016]] ; [[#Farrell--2018|Farrell et al., 2018]] ). There is a possibility that a lack of adaptive capacity and policy support will drive these growers to move away from these diverse crops, further reducing the resilience of food systems by increasing risk of crop loss from pests, disease and drought and potential loss of Indigenous or local knowledge ( [[#5.13|Section 5.13.5]] , Table Box 5.1.1). In the Andean Altiplano of Bolivia, for example, Indigenous farmers have traditionally managed a diverse set of native crops which are drought and frost-tolerant, using cultural practices of seed selection and exchange, but have faced an increase in pests and diseases and a decline of traditional crops due to climate-change-related stresses, out-migration and intensification drivers ( [[#Meldrum--2018|Meldrum et al., 2018]] ).

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<span id="gender-and-other-social-inequities"></span>
==== 5.4.2.3 Gender and other social inequities ====

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Social inequities such as gender, ethnicity and income level, which vary by time and place and may overlap, can compound vulnerability to climate change for producers within cropping systems ( ''high confidence'' ) (Table 5.3, [[#Arora-Jonsson--2011|Arora-Jonsson, 2011]] ; [[#Djoudi--2013|Djoudi et al., 2013]] ; [[#Carr--2014|Carr and Thompson, 2014]] ; [[#Mbow--2019|Mbow et al., 2019]] ; [[#Rao--2019a|Rao et al., 2019a]] ; [[#Nyantakyi-Frimpong--2020a|Nyantakyi-Frimpong, 2020a]] ). Rather than binary and static categories (i.e., men versus women), social vulnerabilities are dynamic and intersect; to understand vulnerability, the specific socio-cultural identities and political and environmental context need to be studied in relation to climate stress (Thompson- [[#Hall--2016|Hall et al., 2016]] ; [[#Rao--2019a|Rao et al., 2019a]] ; [[#Nyantakyi-Frimpong--2020a|Nyantakyi-Frimpong, 2020a]] ).

'''Table 5.3 |''' Examples of social inequities in cropping systems that compound climate change vulnerability.

{| class="wikitable"
|-
! '''Social inequity'''

! '''How social inequity increases vulnerability to climate change in cropping systems'''

|-
| '''Gender inequity''' can create and worsen social vulnerability to climate change impacts within cropping systems ( ''high confidence'' ) ( [[#Carr--2014|Carr and Thompson, 2014]] ; [[#Sugden--2014|Sugden et al., 2014]] ; [[#Nyantakyi-Frimpong--2015|Nyantakyi-Frimpong and Bezner-Kerr, 2015]] ; [[#Rao--2019a|Rao et al., 2019a]] ; [[#Ebhuoma--2020|Ebhuoma et al., 2020]] ; [[#Nyantakyi-Frimpong--2020a|Nyantakyi-Frimpong, 2020a]] ; see Cross-Chapter Box GENDER in Chapter 18).

| * Men and women have different access to and decision-making control over resources such as seeds, systemic differences in land tenure and agricultural employment, and their responsibilities, workloads and response to climate stresses differ due to systemic gender inequities and socio-cultural norms, which intersect with other inequities (e.g., income level, ethnicity) to compound vulnerability ( [[#Rao--2019a|Rao et al., 2019a]] ; [[#Ebhuoma--2020|Ebhuoma et al., 2020]] ; [[#Nyantakyi-Frimpong--2020a|Nyantakyi-Frimpong, 2020a]] ).
* In a study in northern Ghana, for example, poor widows with poor health had fewer resources to rely on during droughts than married women, particularly those married to local leaders; in contrast, due to gendered expectations, during floods low-income men suffered greater consequences ( [[#Nyantakyi-Frimpong--2020a|Nyantakyi-Frimpong, 2020a]] ).
* Adaptation strategies such as migration can compound that vulnerability, but importantly, the specific gendered vulnerability intersects with other inequities which are context specific ( [[#Sugden--2014|Sugden et al., 2014]] ; [[#Nyantakyi-Frimpong--2020a|Nyantakyi-Frimpong, 2020a]] ; Cross-Chapter Box MIGRATE in Chapter 7).

|-
| Globally, '''smallholder food producers''' are more vulnerable than large-scale producers to climate change impacts ( ''high confidence'' ).

| * Smallholder food producers are more vulnerable in part because of limited policy, infrastructure and institutional support, low credit access, viable markets and limited political voice in policy debates ( [[#HLPE--2013|HLPE, 2013]] ; [[#Karttunen--2017|Karttunen et al., 2017]] ; [[#Mbow--2019|Mbow et al., 2019]] ; [[#Nyantakyi-Frimpong--2020a|Nyantakyi-Frimpong, 2020a]] ).
* Smallholder producers’ vulnerability may be increased by heavy reliance on one crop for income, particularly if the crop requires significant capital investments ( ''medium confidence'' ) ( [[#Toufique--2014|Toufique and Belton, 2014]] ; [[#Craparo--2015|Craparo et al., 2015]] ; [[#Ovalle-Rivera--2015|Ovalle-Rivera et al., 2015]] ).
* For example, smallholder coffee producers in southern Mexico and Central America are more vulnerable due to a range of factors, including unstable and low coffee prices, limited institutional support for small-scale producers, low negotiation capacity and access to markets, and heavy reliance on one crop for income (Economic Commission for Latin America and the Caribbean and System, 2014; [[#Ovalle-Rivera--2015|Ovalle-Rivera et al., 2015]] ; [[#Ruiz%20Meza--2015|Ruiz Meza, 2015]] ; [[#Hannah--2017|Hannah et al., 2017]] ; [[#Bacon--2021|Bacon et al., 2021]] ). Pest and disease outbreaks such as coffee leaf rust, extreme climatic events, ongoing conflict, poor governance and low viability of livelihoods increased migration and high levels of food insecurity for this group ( [[#Robalino--2015|Robalino et al., 2015]] ; [[#Hannah--2017|Hannah et al., 2017]] ; [[#Donatti--2019|Donatti et al., 2019]] ) which also varied by institutional- and farm-level responses, land size and income level ( [[#Quiroga--2020|Quiroga et al., 2020]] ; [[#Bacon--2021|Bacon et al., 2021]] ).

|-
| '''Farmworkers''' are another social group with heightened vulnerability to climate change ( ''medium confidence'' ).

| * Farmworkers often experience job insecurity, food insecurity, poor working conditions, poverty and social marginalisation. Climate change impacts can compound their vulnerability, for example by worsening working conditions through increased temperatures and humidity ( [[#5.12.3.1|Section 5.12.3.1]] ), or increase unreliability of work due to rainfall irregularity, flooding or drought, and can put them more at risk during climatic extreme events such as wildfires ( [[#Turhan--2015|Turhan et al., 2015]] ; [[#Greene--2018|Greene, 2018]] ; [[#Mendez--2020|Mendez et al., 2020]] ; [[#Tigchelaar--2020|Tigchelaar et al., 2020]] ).

|}

<div id="5.4.3" class="h2-container"></div>

<span id="projected-impacts-1"></span>
=== 5.4.3 Projected Impacts ===

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<div id="5.4.3.1" class="h3-container"></div>

<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.

<div id="5.4.3.2" class="h3-container"></div>

<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]] ).

<div id="_idContainer018" class="Figure"></div>

[[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]] ).

<div id="_idContainer020" class="Figure"></div>

[[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'' ).

<div id="5.4.3.3" class="h3-container"></div>

<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).

<div id="5.4.3.4" class="h3-container"></div>

<span id="observed-and-projected-impacts-on-cultural-ecosystem-service"></span>
==== 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)]]

|}

<div id="box-5.2:-case-study:-wine" class="h2-container box-container"></div>

'''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.

<div id="_idContainer024" class="Box_Header-continued"></div>

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>
=== 5.4.4 Adaptation Options ===

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Adaptation strategies in crop production range from field and farm-level technical options such as crop management and cultivar/crop options to livelihood diversification and income protection such as index-based insurance. This section assesses crop management options for different crop types. Feasibility of adaptation options in various systems is addressed in [[#5.1|Section 5.1.4]] .

<div id="5.4.4.1" class="h3-container"></div>

<span id="adaptation-options-for-major-crops"></span>
==== 5.4.4.1 Adaptation options for major crops ====

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Crop management practices are the most commonly studied adaptation measures ( [[#Shaffril--2018|Shaffril et al., 2018]] ; [[#Hansen--2019a|Hansen et al., 2019a]] ; [[#Muchuru--2019|Muchuru and Nhamo, 2019]] ), but quantitative assessments are mostly limited to existing agronomic options such as changes in planting schedules, cultivars and irrigation ( [[#Beveridge--2018a|Beveridge et al., 2018a]] ; [[#Aggarwal--2019|Aggarwal et al., 2019]] ). This section draws on the global data set used in [[#5.4.3.2|Section 5.4.3.2]] ( [[#Hasegawa--2021b|Hasegawa et al., 2021b]] ) to estimate adaptation potential, defined as the difference in simulated yields with and without adaptations. A caveat to the analysis is that the data set includes management options if the literature treats them as adaptation. They include intensification measures such as fertilizer and water management, not allowing for physical and economic feasibility.

The overall adaptation potential of existing farm management practices to reduce yield losses averaged 8% in mid-century and 11% in end-century (Figure 5.9), which is insufficient to offset the negative impacts from climate change, particularly in currently warmer regions ( [[#5.4.3.2|Section 5.4.3.2]] ). Emission scenarios, crop species, regions and adaptation options do not show discernible differences. Combinations of two or more options do not necessarily have greater adaptation potential than a single option, though a fair comparison is difficult in the data set from independent studies. One regional study in West Africa found that currently promising management would no longer be effective under future climate, suggesting the need to evaluate effectiveness under projected climate change.

<div id="_idContainer029" class="Figure"></div>

[[File:30aa91bdc5e38afd5b5a4d74aa701431 IPCC_AR6_WGII_Figure_5_009.png]]

'''Figure 5.9 |''' '''Adaptation potential, defined as the difference between yield impacts with and without adaptation in projected impacts (Hasegawa et al''' '''.''' ''', 2021b).''' '''(a)''' Projections under three RCP scenarios by regions and '''(b)''' by options at mid-century (MC, 2040–2069) and end-century (EC, 2070–2100). ''n'' is the number of simulations. See Figure 5.6 for legends.

A global-scale meta-analysis estimated a 3–7% yield loss per degree Celsius increase in temperature ( [[#Zhao--2017|Zhao et al., 2017]] ). Two global-scale studies using multiple global gridded crop models found that growing-season adaptation through cultivar changes offsets global production losses up to 2°C of temperature increase ( [[#Minoli--2019|Minoli et al., 2019]] ; [[#Zabel--2021|Zabel et al., 2021]] ). While these studies do not account for CO 2 fertilisation effects, another global-scale study with the CO 2 fertilisation effects ( [[#Iizumi--2020|Iizumi et al., 2020]] ) showed that residual damage (climate change impacts after adaptation) would start to increase almost exponentially from 2040 towards the end of the century under RCP8.5. The cost required for adaptation and due to residual damage is projected to rise from USD 63 billion at 1.5°C to USD 80 billion at 2°C and to USD 128 billion at 3°C ( [[#Iizumi--2020|Iizumi et al., 2020]] ). All these global studies project that risks and damages are greater in tropical and arid regions, where crops are exposed to heat and drought stresses more often than in temperate regions ( [[#Sun--2019|Sun et al., 2019]] ; [[#Kummu--2021|Kummu et al., 2021]] ; SM5.4). There are still large uncertainties in the crop model projections ( [[#Müller--2021a|Müller et al., 2021a]] ), but these multiple lines of evidence suggest that warming beyond +2°C (projected to be reached by mid-century under high-emission scenarios) will substantially increase the cost of adaptation and the residual damage to major crops ( ''high confidence'' ). The residual damage will prevail much sooner in currently warmer regions, where the effect of even a modest temperature increase is greater ( [[#5.4.3.2|Section 5.4.3.2]] ).

Most crop modelling studies on adaptation are still limited to a handful of options for each crop type ( [[#Beveridge--2018a|Beveridge et al., 2018a]] ). A range of other options are possible not just to reduce yield losses but to diversify risks to livelihoods, which are partially assessed in Sections 5.4.4.4 and 5.14.1. Current modelling approaches are not suited for the assessment of multiple dimensions of adaptation options. New studies are emerging that evaluate multiple options for productivity, sustainability and GHG emission ( [[#Xin--2019|Xin and Tao, 2019]] ; [[#Smith--2020b|Smith et al., 2020b]] ), but local- and household-scale assessment, taking account of future climatic variability, needs to be enhanced ( [[#Beveridge--2018a|Beveridge et al., 2018a]] ).

<div id="5.4.4.2" class="h3-container"></div>

<span id="adaptation-options-for-other-crops"></span>
==== 5.4.4.2 Adaptation options for other crops ====

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Across this diverse group of cropping systems, distinct adaptation options and adaptation limits have emerged (Figure 5.10; [[#Acevedo--2020|Acevedo et al., 2020]] ; [[#Berrang-Ford--2021b|Berrang-Ford et al., 2021b]] ). Some crop types have already seen widescale implementation of climate adaptation (e.g., grapevines), while others show little evidence of preparation for climate change (e.g., leafy salad crops). Many adaptation responses are shared with the major crops, but prominent options such as plant breeding are underutilised and there is a lack of evidence for assessing adaptation for many crops ( [[#Bisbis--2018|Bisbis et al., 2018]] ; [[#Gunathilaka--2018|Gunathilaka et al., 2018]] ; [[#Manners--2018|Manners and van Etten, 2018]] ). Figure 5.10 assesses several adaptation options based on the perceived importance of each in the literature. Fruit and vegetable crops tend to be more reliant on ecosystem services in the form of pollination, biocontrol and other resources (water, nutrients, microbes, etc.), and ecosystem-based adaptation options are prominent. The range of crops means that there is great potential for crop switching, but cultural and economic barriers will make such options difficult to implement, with barriers to entry for production and marketing ( [[#Waha--2013|Waha et al., 2013]] ; [[#Magrini--2016|Magrini et al., 2016]] ; [[#Kongsager--2017|Kongsager, 2017]] ; [[#Rhiney--2018|Rhiney et al., 2018]] ). Perennial crops are exposed to a wide range of climate factors throughout the year and have significant barriers to implementing some of the common adaptation options, such as relocation or replacing tree species/cultivar; agronomic interventions on-farm are well used in high-value tree crops and provide some climate resilience, but longer-term options will be needed ( [[#Glenn--2013|Glenn et al., 2013]] ; [[#Mosedale--2016|Mosedale et al., 2016]] ; [[#Gunathilaka--2018|Gunathilaka et al., 2018]] ; [[#Sugiura--2019|Sugiura, 2019]] ).

<div id="_idContainer031" class="Figure"></div>

[[File:567738060f3c2f67f853d06bbdac5c91 IPCC_AR6_WGII_Figure_5_010.png]]

'''Figure 5.10 |''' '''Synthesis of literature on the implementation of on-farm adaptation options across different cropping systems.''' Adaptation options that have been implemented by growers are considered ‘tested’, while those that have not are considered ‘untested’. Untested options are those that appear in studies as suggestions by stakeholder or experts but were not implemented within the study. The assessment draws on &gt;200 articles published since AR5. The confidence is based on the evidence given in individual articles and on the number of articles. See SM5.2 for details.

Many fruit and vegetable crops are water demanding, and adaptation responses relating to water management and access to irrigation water are crucial. Rainwater storage and deficit irrigation techniques are frequently mentioned as adaptation options and can minimise the burden on off-farm water supplies ( [[#Bisbis--2018|Bisbis et al., 2018]] ; [[#Acevedo--2020|Acevedo et al., 2020]] ).

<div id="5.4.4.3" class="h3-container"></div>

<span id="cultivar-improvements"></span>
==== 5.4.4.3 Cultivar improvements ====

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As stated in AR5, cultivar improvements are one effective countermeasure against climate change ( [[#Porter--2014|Porter et al., 2014]] ; [[#Challinor--2016|Challinor et al., 2016]] ; [[#Atlin--2017|Atlin et al., 2017]] ). Plant breeding biotechnology for climate change adaptation draws upon modern biotechnology and conventional breeding, with the latter often assisted by genomics and molecular markers. Plant breeding biotechnology will contribute to adaptation for large-scale producers ( ''high confidence'' ). However, in addition to inconsistencies in meeting farmer expectations, a variety of socioeconomic and political variables strongly influence, and limit, uptake of climate-resilient crops ( [[#Acevedo--2020|Acevedo et al., 2020]] ; [[#Rhoné--2020|Rhoné et al., 2020]] ).

Genome sequencing significantly increases the rate and accuracy for identifying genes of agronomic traits that are relevant to climate change, including adaptation to stress from pests and disease, temperature and water extremes ( ''high confidence'' ) ( [[#Brozynska--2016|Brozynska et al., 2016]] ; [[#Scheben--2016|Scheben et al., 2016]] ; [[#Voss-Fels--2016|Voss-Fels and Snowdon, 2016]] ). Access to this information where it is needed and in practical timeframes, as well as the expertise to use it, will limit the sharing of benefits by the most vulnerable groups and countries ( ''high agreement'' , ''limited evidence'' ) ( [[#Heinemann--2018|Heinemann et al., 2018]] ).

Genetic improvements for climate change adaptation using modern biotechnology have not reliably translated into the field ( [[#Hu--2014|Hu and Xiong, 2014]] ; [[#Nuccio--2018|Nuccio et al., 2018]] ; [[#Napier--2019|Napier et al., 2019]] ), but good progress has been made by conventional breeding. Desirable traits that adapt plants to environmental stress are inherited as a complex of genes, each of which makes a small contribution to the trait ( [[#Negin--2017|Negin and Moshelion, 2017]] ). Adaptation by conventional breeding requires making rapid incremental changes in the best germplasm to keep pace with the environment ( [[#Millet--2016|Millet et al., 2016]] ; [[#Atlin--2017|Atlin et al., 2017]] ; [[#Cobb--2019|Cobb et al., 2019]] ). Further improvements would be difficult without ''in situ'' and ''ex situ'' conservation of plant genetic resources to maintain critical germplasm for breeding ( [[#Dempewolf--2014|Dempewolf et al., 2014]] ; [[#Castañeda-Álvarez--2016|Castañeda-Álvarez et al., 2016]] ).

Despite the advances in sequencing, phenotyping remains a significant bottleneck ( [[#Ghanem--2015|Ghanem et al., 2015]] ; [[#Negin--2017|Negin and Moshelion, 2017]] ; [[#Araus--2018|Araus and Kefauver, 2018]] ); the emergence of high-throughput phenotyping platforms may reduce this bottleneck in future. Emerging modern biotechnology such as gene/genome editing may in the future increase the ability to better translate genetic improvements into the field ''(medium agreement'' , ''limited evidence)'' ( [[#Puchta--2017|Puchta, 2017]] ; [[#Yamamoto--2018|Yamamoto et al., 2018]] ; [[#Friedrichs--2019|Friedrichs et al., 2019]] ; [[#Kawall--2019|Kawall, 2019]] ; [[#Zhang--2019b|Zhang et al., 2019b]] ).

Other breeding approaches assisted by genomics have been making steady gains in introducing traits that adapt crops to climate change ( ''high confidence'' ). DNA sequence information is used to identify markers of desirable traits that can be enriched in breeding programmes, as well as to quantify the genetic variability in species ( [[#Gepts--2014|Gepts, 2014]] ; [[#Brozynska--2016|Brozynska et al., 2016]] ; [[#Voss-Fels--2016|Voss-Fels and Snowdon, 2016]] ). However, breeding for smallholder farmers and the stresses caused by climate change are unlikely to be addressed by the private sector and will require more public investment and adjusting to the local social-ecological system ( [[#Glover--2014|Glover, 2014]] ; [[#Heinemann--2014|Heinemann et al., 2014]] ; [[#Acevedo--2020|Acevedo et al., 2020]] ). Modern biotechnology has not demonstrated the scale neutrality needed to serve smallholder-dominated agroecosystems, due to a combination of the kinds of traits and restrictions that come from the predominant intellectual property rights instruments used in their commercialisation, as well as the focus on a small number of major crop species ( ''medium confidence)'' ( [[#Fischer--2016|Fischer, 2016]] ; [[#Montenegro%20de%20Wit--2020|Montenegro de Wit et al., 2020]] ).

Globally, there is a notable lack of programmes aimed specifically at breeding for climate resilience in fruits and vegetables, although there have been calls to begin this process ( [[#Kole--2015|Kole et al., 2015]] ). Breeding for climate resilience in vegetables has great potential given the range of crop species available. Tolerance to abiotic stress is reasonably advanced in pulses ( [[#Araújo--2015|Araújo et al., 2015]] ; [[#Varshney--2018|Varshney et al., 2018]] ), but examples of translation to commercial cultivars are still limited ( [[#Varshney--2018|Varshney et al., 2018]] ; [[#Varshney--2019|Varshney et al., 2019]] ). The infrastructure for germplasm collection, maintenance, testing and breeding lags behind that of major crops (partly because of the large number of species involved) ( [[#Keatinge--2016|Keatinge et al., 2016]] ; [[#Atlin--2017|Atlin et al., 2017]] ).

Participatory plant breeding (PPB) facilitates interaction between Indigenous and local knowledge systems and scientific research and can be an effective adaptation strategy in generating varieties well adapted to the socio-ecological context and climate hazards ( ''high confidence'' ) (Table 5.5, Westengen and Brysting, 2014; [[#Humphries--2015|Humphries et al., 2015]] ; [[#Anderson--2016|Anderson et al., 2016]] ; [[#Migliorini--2016|Migliorini et al., 2016]] ; [[#Leitão--2019|Leitão et al., 2019]] ; [[#Ceccarelli--2020|Ceccarelli and Grando, 2020]] ; [[#Singh--2020|Singh et al., 2020]] ).

'''Table 5.5 |''' PPB as cultivar improvement adaptation method.

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

! '''Crop(s) used for breeding'''

! '''Results'''

|-
| West Africa

| Sorghum and pearl millet

| * Released sorghum and millet varieties which were selected for climate variability (e.g., drought), low soil fertility, pest and disease resistance, gendered preferences for processing, and nutrition ( [[#Camacho-Henriquez--2015|Camacho-Henriquez et al., 2015]] ; [[#Weltzien--2019|Weltzien et al., 2019]] ).
* Farmers who adopted these varieties increased yield, income and food security, alongside increased technical knowledge of plant breeding, and increased breeders’ understanding of local farmers’ varietal requirements ( [[#Trouche--2016|Trouche et al., 2016]] ).
* Joint learning with scientists led to increased genetic gain both in terms of operational scale and focused breeding for diverse farmer priorities ( [[#Weltzien--2019|Weltzien et al., 2019]] ).

|-
| South America (Andes)

| Potato

| * PPB with Indigenous Quechua and Aymara farmers resulted in potato varieties with traits from wild relatives, with yield stability, higher yields under low input use and disease resistance under climate change impacts such as increased hail or frost events and upward expansion of pests and diseases ( [[#Camacho-Henriquez--2015|Camacho-Henriquez et al., 2015]] ; [[#Scurrah--2019|Scurrah et al., 2019]] ).

|-
| Asia (southwest China)

| Maize

| * PPB done primarily with women farmers, led to 1500 landraces safeguarded, 12 farmer-preferred varieties released and 30 landraces released, bred for improved yield (15–20% increases), drought resistance, taste, market potential and other priority traits ( [[#Song--2019|Song et al., 2019]] ).
* Studies suggest PPB improved farmer knowledge, income and access to resilient seeds, and strengthened institutions such as women-led farmer cooperatives and Farmers’ Seed Network of China ( [[#Song--2019|Song et al., 2019]] ).

|}

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<span id="integrated-approach-to-enhance-agroecosystem-resilience"></span>
==== 5.4.4.4 Integrated approach to enhance agroecosystem resilience ====

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Diversifying agricultural systems is an adaptation strategy that can strengthen resilience to climate change, with socioeconomic and environmental co-benefits, but trade-offs and benefits vary by socio-ecological context ( ''high confidence'' ) (Table 5.6, [[#M’Kaibi--2015|M’Kaibi et al., 2015]] ; [[#Bellon--2016|Bellon et al., 2016]] ; [[#Jones--2017b|Jones, 2017b]] ; [[#Schulte--2017|Schulte et al., 2017]] ; [[#Jarecki--2018|Jarecki et al., 2018]] ; [[#Jones--2018|Jones et al., 2018]] ; [[#Luna-Gonzalez--2018|Luna-Gonzalez and Sorensen, 2018]] ; [[#Sibhatu--2018|Sibhatu and Qaim, 2018]] ; [[#Renard--2019|Renard and Tilman, 2019]] ; [[#Rosa-Schleich--2019|Rosa-Schleich et al., 2019]] ; [[#Bozzola--2020|Bozzola and Smale, 2020]] ; [[#Mulwa--2020|Mulwa and Visser, 2020]] ). Crop diversification alongside livestock, fish and other species can be applied at various scales in a range of systems, from rainfed or irrigated to urban and home gardens in multiple spatial and temporal arrangements such as mixed planting, intercrops, crop rotation, diversified management of field margins, agroforestry ( [[#5.10.1.3|Section 5.10.1.3]] ) and integrated crop livestock systems ( [[#5.10.1.1|Section 5.10.1.1]] , [[#Isbell--2017|Isbell et al., 2017]] ; [[#Kremen--2018|Kremen and Merenlender, 2018]] ; [[#Dainese--2019|Dainese et al., 2019]] ; [[#Rosa-Schleich--2019|Rosa-Schleich et al., 2019]] ; [[#Hussain--2020|Hussain et al., 2020]] ; [[#Renwick--2020|Renwick et al., 2020]] ; [[#Tamburini--2020|Tamburini et al., 2020]] ; [[#Snapp--2021|Snapp et al., 2021]] ; see [[#5.1|Section 5.1]] 4 and Cross-Chapter Box NATURAL in Chapter 2).

'''Table 5.6 |''' Agroecosystem diversification practices, climate change adaptation mechanisms, trade-offs, co-benefits and constraints to implementation.

{| class="wikitable"
|-
! '''Agroecosystem diversification practice and'''

'''mechanism for climate change adaptation'''

! '''Benefits, trade-offs and constraints to implementation with examples'''

|-
| '''''Crop diversification'''''

* Diversifying revenue streams and food supply (portfolio effect).
* Can impact multiple plant and soil biological and physicochemical properties associated with building SOM, improving soil structure and water conservation.

| * Crop diversification reduces cereal crop sensitivity to '''precipitation variability''' , yield losses and crop insurance payouts under '''drought''' ( ''high confidence'' ) ( [[#McDaniel--2014|McDaniel et al., 2014]] ; [[#Williams--2016|Williams et al., 2016]] ; [[#Iizumi--2019|Iizumi and Wagai, 2019]] ; [[#Renwick--2020|Renwick et al., 2020]] ; [[#Huang--2021|Huang et al., 2021]] ; [[#Kane--2021|Kane et al., 2021]] ).
* For example, a study in Canada comparing diversified rotations and monoculture corn found significant positive yield impacts, yield stability and increased SOC under both RCP4.5 and RCP8.5 by 2100 ( [[#Jarecki--2018|Jarecki et al., 2018]] ).
* Diverse agroecosystems with a range of native, neglected and introduced species, often maintained through Indigenous knowledge and farmer seed systems, offer adaptation opportunities in some regions ( ''medium evidence'' , ''high agreement'' ) ( [[#Bezner%20Kerr--2014|Bezner Kerr, 2014]] ; Westengen and Brysting, 2014; [[#Camacho-Henriquez--2015|Camacho-Henriquez et al., 2015]] ; [[#Ghosh-Jerath--2015|Ghosh-Jerath et al., 2015]] ; [[#Adhikari--2017|Adhikari et al., 2017]] ; Li and Siddique, 2018; [[#Scurrah--2019|Scurrah et al., 2019]] ).
* Diversified landscapes can also enhance CES, by supporting cultural heritage crops, recreational and aesthetic experiences ( ''medium confidence'' ) ( [[#Novikova--2017|Novikova et al., 2017]] ; [[#Martínez-Paz--2019|Martínez-Paz et al., 2019]] ; [[#Alcon--2020|Alcon et al., 2020]] ).
* Diversified cropping systems often require new knowledge, equipment access to inputs and viable markets for new products ( [[#van%20Zonneveld--2020|van Zonneveld et al., 2020]] ). Barriers to diversification, or those which support agroecosystem simplification, include environmental constraints such as elevation or soil type, along with institutional constraints such as low research investment, limited policy support, subsidies that encourage monocrops, poor market access, market instability and limited access to seeds ( [[#Kaushal--2015|Kaushal and Muchomba, 2015]] ; [[#DeLonge--2016|DeLonge et al., 2016]] ; [[#Burchfield--2018|Burchfield and de la Poterie, 2018]] ).

|-
| '''Legume diversification''' can be effective for both mitigation and adaptation, by reducing '''use of nitrogen derived from fossil fuels''' , and meat consumption, and providing ecosystem services through '''nutrient cycling, increasing soil biological activity and erosion control''' ( [[#Snapp--2019|Snapp et al., 2019]] ).

| * Can increase food security and nutrition by increasing cereal productivity and stability in intercropped systems, diversify diets and increase income in crop sales ( ''high agreement, medium evidence'' ) ( [[#Snapp--2019|Snapp et al., 2019]] ; [[#Steward--2019|Steward et al., 2019]] ; [[#Renwick--2020|Renwick et al., 2020]] ), but legume production may be constrained by pest, disease, limited access to genetic material, market access and food preferences ( [[#Anders--2020|Anders et al., 2020]] ).

|-
| '''Organic amendments, no/low tillage or crop residue retention''' may increase diversity in soil biological organisms, which might be important in building resilience to multiple stresses such as '''drought and pest pressure''' ( [[#Furze--2017|Furze et al., 2017]] ; [[#Blundell--2020|Blundell et al., 2020]] ; [[#de%20Vries--2020|de Vries et al., 2020]] ; [[#Stefan--2021|Stefan et al., 2021]] ; [[#Yang--2021|Yang et al., 2021]] ).

| * Higher organic matter does not consistently improve soil hydraulic properties ( [[#Minasny--2018|Minasny and McBratney, 2018]] ; [[#Basche--2019|Basche and DeLonge, 2019]] ).
* Can decrease '''yield variability under dry conditions''' and increase rainfed annual crop yield productivity ( ''high agreement'' ) ( [[#Pittelkow--2014|Pittelkow et al., 2014]] ; [[#Williams--2016|Williams et al., 2016]] ; [[#Williams--2018|Williams et al., 2018]] ; [[#Degani--2019|Degani et al., 2019]] ; [[#Steward--2019|Steward et al., 2019]] ; [[#Bowles--2020|Bowles et al., 2020]] ; [[#Marini--2020|Marini et al., 2020]] ; [[#Sanford--2021|Sanford et al., 2021]] ).

|-
| '''Livestock integration''' . Inclusion of legumes and other forage into crop rotation allows mixed crop and livestock operations to '''mitigate farm-level risk and ecosystem buffering''' .

| * Benefits to productivity and stability of annual crop yields in some contexts (see [[#5.10.3|Section 5.10.3]] , ''high agreement'' , ''medium evidence'' ) ( [[#Stark--2018|Stark et al., 2018]] ; [[#Peterson--2020|Peterson et al., 2020]] ; [[#de%20Albuquerque%20Nunes--2021|de Albuquerque Nunes et al., 2021]] ).

|-
| Traditional and locally adapted '''mixed cropping and agroforestry practices''' which include leguminous trees can improve soil fertility and microclimate ( [[#Sida--2018|Sida et al., 2018]] ; [[#Amadu--2020|Amadu et al., 2020]] ).

| Benefits: resilience to extreme events such as hurricanes can be promoted by supporting ecosystem functions to mitigate impacts and accelerate recovery ( ''high agreement'' , ''medium evidence'' ) ( [[#Altieri--2015|Altieri et al., 2015]] ; [[#Simelton--2015|Simelton et al., 2015]] ; [[#Sida--2018|Sida et al., 2018]] ; [[#Perfecto--2019|Perfecto et al., 2019]] ).

* Can increase food security, livelihoods and productivity, but local context and resource availability must be considered to optimise species arrangement and benefits and can have considerable implementation barriers and costs ( ''high confidence'' ) (see Sections 5.10.3, 5.14 and Cross-Chapter Box NATURAL in Chapter 2). ( [[#Altieri--2015|Altieri et al., 2015]] ; [[#Simelton--2015|Simelton et al., 2015]] ; [[#Sida--2018|Sida et al., 2018]] ; [[#Perfecto--2019|Perfecto et al., 2019]] ).

|}

Diversification improves regulating and supporting ecosystem services such as pest control, soil fertility and health, pollination, nutrient cycling, water regulation and buffering of temperature extremes ( ''high confidence'' ) ( [[#Barral--2015|Barral et al., 2015]] ; [[#Prieto--2015|Prieto et al., 2015]] ; [[#Tiemann--2015|Tiemann et al., 2015]] ; [[#Schulte--2017|Schulte et al., 2017]] ; [[#Beillouin--2019a|Beillouin et al., 2019a]] ; [[#Dainese--2019|Dainese et al., 2019]] ; [[#Kuyah--2019|Kuyah et al., 2019]] ; [[#Tamburini--2020|Tamburini et al., 2020]] ), which can in turn mediate yield stability and reduced risk of crop loss according to socio-ecological contexts and time since adoption ( ''high confidence'' ) ( [[#Prieto--2015|Prieto et al., 2015]] ; [[#Roesch-McNally--2018|Roesch-McNally et al., 2018]] ; [[#Sida--2018|Sida et al., 2018]] ; [[#Williams--2018|Williams et al., 2018]] ; [[#Birthal--2019|Birthal and Hazrana, 2019]] ; [[#Degani--2019|Degani et al., 2019]] ; [[#Amadu--2020|Amadu et al., 2020]] ; [[#Bowles--2020|Bowles et al., 2020]] ; [[#Li--2020|Li et al., 2020]] ; [[#Sanford--2021|Sanford et al., 2021]] ).

Agroecosystem diversification often has variable impacts depending on crop combination, agro-ecological zone and soil types, and rigorous assessments of adaptive gains with traditional and locally diversified systems and potential trade-offs still need to be conducted across socio-ecological contexts. The quantitative upstanding will assist in enhancing multiple benefits of diversification tailored for each condition (Table 5.6). Progress is also needed via breeding and/or agronomy to adapt underutilised as well as major food crops to diversified agroecosystems and optimise management of nutrients, pest and disease pressure and other socio-ecological constraints ( [[#Araújo--2015|Araújo et al., 2015]] ; [[#Foyer--2016|Foyer et al., 2016]] ; [[#Adams--2018|Adams et al., 2018]] ; [[#Pang--2018|Pang et al., 2018]] ).

Managing for diversity and flexibility at multiple scales is central to developing adaptive capacity. Policies to support diversification include shifting subsidies towards diversified systems, public procurement for diverse foods for schools and other public institutions, investment in shorter value chains, lower insurance premiums and payments for ecosystem services that include diversification ( [[#Sorensen--2015|Sorensen et al., 2015]] ; [[#Guerra--2017|Guerra et al., 2017]] ; [[#Nehring--2017|Nehring et al., 2017]] ; [[#Valencia--2019|Valencia et al., 2019]] ). Integrated landscape approaches involving multiple stakeholders ( [[#Reed--2016|Reed et al., 2016]] ) including urban governments can support diversification at a regional scale through public and private sector investment in extension services, regional supply chains, agritourism and other incentives for diversified landscapes ( [[#Milder--2014|Milder et al., 2014]] ; [[#Münke--2015|Münke et al., 2015]] ; [[#Sorensen--2015|Sorensen et al., 2015]] ; [[#Pérez-Marin--2017|Pérez-Marin et al., 2017]] ; [[#Caron--2018|Caron et al., 2018]] ; 5.14.1.5).

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