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

== 5.2 Impacts of climate change on food systems ==

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There are many routes by which climate change can impact food security and thus human health (Watts et al. 2018 <sup>[[#fn:r156|156]]</sup> ; Fanzo et al. 2017 <sup>[[#fn:r157|157]]</sup> ). One major route is via climate change affecting the amount of food, both from direct impacts on yields (Section 5.2.2.1) and indirect effects through climate change’s impacts on water availability and quality, pests and diseases (Section 5.2.2.3), and pollination services (Section 5.2.2.4).Another route is via changing CO <sub>2</sub> in the atmosphere, affecting biomass and nutritional quality (Section 5.2.4.2). Food safety risks during transport and storage can also be exacerbated by changing climate (Section 5.2.4.1).

Further, the direct impacts of changing weather can affect human health through the agricultural workforce’s exposure to extreme temperatures (Section 5.2.5.1). Through changing metabolic demands and physiological stress for people exposed to extreme temperatures, there is also the potential for interactions with food availability; people may require more food to cope, whilst at the same time being impaired from producing it (Watts et al. 2018 <sup>[[#fn:r158|158]]</sup> ). All these factors have the potential to alter both physical health as well as cultural health, through changing the amount, safety and quality of food available for individuals within their cultural context.

This section assesses recent literature on climate change impacts on the four pillars of food security: availability (Section 5.2.2), access (Section 5.2.3), utilisation (Section 5.2.4), and stability (Section 5.2.5). It considers impacts on the food system from climate changes that are already taking place and how impacts are projected to occur in the future. See Supplementary Material Section SM5.2 for discussion of detection and attribution and improvement in projection methods.

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=== 5.2.1 Climate drivers important to food security ===

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Climate drivers relevant to food security and food systems include temperature-related, precipitation-related, and integrated metrics that combine these and other variables. These are projected to affect many aspects of the food security pillars (FAO 2018b <sup>[[#fn:r159|159]]</sup> ) (see Supplementary Material Table SM5.2, and Chapter 6 for assessment of observed and projected climate impacts). Climate drivers relevant to food production and availability may be categorised as modal climate changes (e.g., shifts in climate envelopes causing shifts in cropping varieties planted), seasonal changes (e.g., warming trends extending growing seasons), extreme events (e.g., high temperatures affecting critical growth periods, flooding/droughts), and atmospheric conditions for example, CO <sub>2</sub> concentrations, short-lived climate pollutants (SLCPs), and dust. Water resources for food production will be affected through changing rates of precipitation and evaporation, ground water levels, and dissolved oxygen content (Cruz-Blanco et al. 2015 <sup>[[#fn:r160|160]]</sup> ; Sepulcre-Canto et al. 2014 <sup>[[#fn:r161|161]]</sup> ; Huntington et al. 2017 <sup>[[#fn:r162|162]]</sup> ; Schmidtko et al. 2017 <sup>[[#fn:r163|163]]</sup> ). Potential changes in major modes of climate variability can also have widespread impacts such as those that occurred during late 2015 to early 2016 when a strong El Niño contributed to regional shifts in precipitation in the Sahel region. Significant drought across Ethiopia resulted in widespread crop failure and more than 10 million people in Ethiopia requiring food aid (U.S. Department of State 2016 <sup>[[#fn:r164|164]]</sup> ; Huntington et al. 2017 <sup>[[#fn:r165|165]]</sup> ) (Figure 5.3).

Other variables that affect agricultural production, processing, and/ or transport are solar radiation, wind, humidity, and (in coastal areas) salinisation and storm surge (Mutahara et al. 2016 <sup>[[#fn:r166|166]]</sup> ; Myers et al. 2017 <sup>[[#fn:r167|167]]</sup> ). Extreme climate events resulting in inland and coastal flooding, can affect the ability of people to obtain and prepare food (Rao et al. 2016 <sup>[[#fn:r168|168]]</sup> ; FAO et al. 2018 <sup>[[#fn:r169|169]]</sup> ). For direct effects of atmospheric CO <sub>2</sub> concentrations on crop nutrient status see Section 5.2.4.2.

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'''Figure 5.3'''

<span id="precipitation-anomaly-and-vegetation-response-in-eastern-africa.-a-sep-2015feb-2016-climate-hazards-group-infrared-precipitation-with-station-chirps-precipitation-anomaly-over-africa-relative-to-the-19812010-average-shows-that-large-areas-of-ethiopia-received-less-than-half-of-normal-precipitation.-consequently-widespread-impacts-to-agricultural-productivity-especially-within-pastoral-regions-were-present-across"></span>
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'''Precipitation anomaly and vegetation response in eastern Africa. (a) Sep 2015–Feb 2016 Climate Hazards Group Infrared Precipitation with Station (CHIRPS) precipitation anomaly over Africa relative to the 1981–2010 average shows that large areas of Ethiopia received less than half of normal precipitation. Consequently, widespread impacts to agricultural productivity, especially within pastoral regions, were present across […]'''
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[[File:db02ad47d5a517156b5402d134edcfbb Figure-5.3-1024x473.jpg]]

Precipitation anomaly and vegetation response in eastern Africa. (a) Sep 2015–Feb 2016 Climate Hazards Group Infrared Precipitation with Station (CHIRPS) precipitation anomaly over Africa relative to the 1981–2010 average shows that large areas of Ethiopia received less than half of normal precipitation. Consequently, widespread impacts to agricultural productivity, especially within pastoral regions, were present across Ethiopia as evidenced by (d) reduced greenness in remote sensing images. (b) MODIS NDVI anomalies for Sep 2015–Feb 2016 relative to 2000–2015 average are shown for the inset box in (a). (c) Landsat NDVI anomalies for Sep 2015–Feb 2016 relative to 2000–2015 average are shown for the inset box in (b) (Huntington et al. 2017).

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==== 5.2.1.1 Short-lived climate pollutants ====

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The important role of short-lived climate pollutants such as ozone and black carbon is increasingly emphasised since they affect agricultural production through direct effects on crops and indirect effects on climate (Emberson et al. 2018 <sup>[[#fn:r170|170]]</sup> ; Lal et al. 2017 <sup>[[#fn:r171|171]]</sup> ; Burney and Ramanathan 2014 <sup>[[#fn:r172|172]]</sup> ; Ghude et al. 2014 <sup>[[#fn:r173|173]]</sup> ) (Chapters 2 and 4). Ozone causes damage to plants through damages to cellular metabolism that influence leaf-level physiology to whole-canopy and root-system processes and feedbacks; these impacts affect leaf-level photosynthesis senescence and carbon assimilation, as well as whole-canopy water and nutrient acquisition and ultimately crop growth and yield (Emberson et al. 2018 <sup>[[#fn:r174|174]]</sup> ).

Using atmospheric chemistry and a global integrated assessment model, Chuwah et al. (2015) <sup>[[#fn:r175|175]]</sup> found that without a large decrease in air pollutant emissions, high ozone concentration could lead to an increase in crop damage of up to 20% in agricultural regions in 2050 compared to projections in which changes in ozone are not accounted for. Higher temperatures are associated with higher ozone concentrations; C3 crops are sensitive to ozone (e.g., soybeans, wheat, rice, oats, green beans, peppers, and some types of cottons) and C4 crops are moderately sensitive (Backlund et al. 2008 <sup>[[#fn:r176|176]]</sup> ).

Methane increases surface ozone which augments warming-induced losses and some quantitative analyses now include climate, long-lived (CO <sub>2</sub> ) and multiple short-lived pollutants (CH <sub>4</sub> , O <sub>3</sub> ) simultaneously (Shindell et al. 2017 <sup>[[#fn:r177|177]]</sup> ; Shindell 2016 <sup>[[#fn:r178|178]]</sup> ). Reduction of tropospheric ozone and black carbon can avoid premature deaths from outdoor air pollution and increases annual crop yields (Shindell et al. 2012 <sup>[[#fn:r179|179]]</sup> ). These actions plus methane reduction can influence climate on shorter time scales than those of carbon dioxide reduction measures. Implementing them substantially reduces the risks of crossing the 2°C threshold and contributes to achievement of the SDGs (Haines et al. 2017 <sup>[[#fn:r180|180]]</sup> ; Shindell et al. 2017 <sup>[[#fn:r181|181]]</sup> ).

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=== 5.2.2 Climate change impacts on food availability ===

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Climate change impacts food availability through its effect on the production of food and its storage, processing, distribution, and exchange.

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==== 5.2.2.1 Impacts on crop production ====

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'''Observed impacts''' . Since AR5, there have been further studies that document impacts of climate change on crop production and related variables (Supplementary Material Table SM5.3). There have also been a few studies that demonstrate a strengthening relationship between observed climate variables and crop yields that indicate future expected warming will have severe impacts on crop production (Mavromatis 2015 <sup>[[#fn:r182|182]]</sup> ; Innes et al. 2015 <sup>[[#fn:r183|183]]</sup> ). At the global scale, Iizumi et al. (2018) <sup>[[#fn:r184|184]]</sup> used a counterfactual analysis and found that climate change between 1981 and 2010 has decreased global mean yields of maize, wheat, and soybeans by 4.1, 1.8 and 4.5%, respectively, relative to preindustrial climate, even when CO <sub>2</sub> fertilisation and agronomic adjustments are considered. Uncertainties (90% probability interval) in the yield impacts are –8.5 to +0.5% for maize, –7.5 to +4.3% for wheat, and –8.4 to –0.5% for soybeans. For rice, no significant impacts were detected. This study suggests that climate change has modulated recent yields on the global scale and led to production losses, and that adaptations to date have not been sufficient to offset the negative impacts of climate change, particularly at lower latitudes.

Dryland settlements are perceived as vulnerable to climate change with regard to food security, particularly in developing countries; such areas are known to have low capacities to cope effectively with decreasing crop yields (Shah et al. 2008 <sup>[[#fn:r185|185]]</sup> ; Nellemann et al. 2009 <sup>[[#fn:r186|186]]</sup> ). This is of concern because drylands constitute over 40% of the earth’s land area, and are home to 2.5 billion people (FAO et al. 2011 <sup>[[#fn:r187|187]]</sup> ).

''Australia''

In Australia, declines in rainfall and rising daily maximum temperatures based on simulations of 50 sites caused water-limited yield potential to decline by 27% from 1990 to 2015, even though elevated atmospheric CO <sub>2</sub> concentrations had a positive effect (Hochman et al. 2017 <sup>[[#fn:r188|188]]</sup> ). In New South Wales, high-temperature episodes during the reproduction stage of crop growth were found to have negative effects on wheat yields, with combinations of low rainfall and high temperatures being the most detrimental (Innes et al. 2015 <sup>[[#fn:r189|189]]</sup> ).

''Asia''

There are numerous studies demonstrating that climate change is affecting agriculture and food security in Asia. Several studies with remote sensing and statistical data have examined rice areas in north-eastern China, the northernmost region of rice cultivation, and found expansion over various time periods beginning in the 1980s, with most of the increase occurring after 2000 (Liu et al. 2014 <sup>[[#fn:r190|190]]</sup> ; Wang et al. 2014 <sup>[[#fn:r191|191]]</sup> ; Zhang et al. 2017 <sup>[[#fn:r192|192]]</sup> ). Rice yield increases have also been found over a similar period (Wang et al. 2014 <sup>[[#fn:r193|193]]</sup> ). Multiple factors, such as structural adjustment, scientific and technological progress, and government policies, along with regional warming (1.43°C in the past century) (Fenghua et al. 2006 <sup>[[#fn:r194|194]]</sup> ) have been put forward as contributing to the observed expanded rice areas and yield in the region. Shi et al. (2013) <sup>[[#fn:r195|195]]</sup> indicate that there is a partial match between climate change patterns and shifts in extent and location of the rice-cropping area (2000–2010).

There have also been documented changes in winter wheat phenology in Northwest China (He 2015 <sup>[[#fn:r196|196]]</sup> ). Consistent with this finding, dates of sowing and emergence of spring and winter wheat were delayed, dates of anthesis and maturity was advanced, and length of reproductive growth period was prolonged from 1981–2011 in a study looking at these crops across China (Liu et al. 2018b <sup>[[#fn:r197|197]]</sup> ). Another study looking in Northwest China demonstrated that there have been changes in the phenology and productivity of spring cotton (Huang and Ji 2015 <sup>[[#fn:r198|198]]</sup> ). A counterfactual study looking at wheat growth and yield in different climate zones of China from 1981–2009 found that impacts were positive in northern China and negative in southern China (Tao et al. 2014 <sup>[[#fn:r199|199]]</sup> ). Temperature increased across the zones while precipitation changes were not consistent (Tao et al. 2014 <sup>[[#fn:r200|200]]</sup> ).

Similar crop yield studies focusing on India have found that warming has reduced wheat yields by 5.2% from 1981 to 2009, despite adaptation (Gupta et al. 2017 <sup>[[#fn:r201|201]]</sup> ), and that maximum daytime temperatures have risen along with some night-time temperatures (Jha and Tripathi 2017 <sup>[[#fn:r202|202]]</sup> ).

Agriculture in Pakistan has also been affected by climate change. From 1980 to 2014, spring maize growing periods have shifted an average of 4.6 days per decade earlier, while sowing of autumn maize has been delayed 3.0 days per decade (Abbas et al. 2017 <sup>[[#fn:r203|203]]</sup> ). A similar study with sunflower showed that increases in mean temperature from 1980 to 2016 were highly correlated with shifts in sowing, emergence, anthesis, and maturity for fall and spring crops (Tariq et al. 2018 <sup>[[#fn:r204|204]]</sup> ).

Mountain people in the Hindu-Kush Himalayan region encompassing parts of Pakistan, India, Nepal, and China, are particularly vulnerable to food insecurity related to climate change because of poor infrastructure, limited access to global markets, physical isolation, low productivity, and hazard exposure, including Glacial Lake Outburst Floods (GLOFs) (Rasul et al. 2019 <sup>[[#fn:r205|205]]</sup> ; Rasul 2010 <sup>[[#fn:r206|206]]</sup> ; Tiwari and Joshi 2012 <sup>[[#fn:r207|207]]</sup> ; Huddleston et al. 2003 <sup>[[#fn:r208|208]]</sup> ; Ward et al. 2013 <sup>[[#fn:r209|209]]</sup> ; FAO 2008 <sup>[[#fn:r210|210]]</sup> ; Nautiyal et al. 2007 <sup>[[#fn:r211|211]]</sup> ; Din et al. 2014 <sup>[[#fn:r212|212]]</sup> ). Surveys have been conducted to determine how climate-related changes have affected food security (Hussain et al. 2016 <sup>[[#fn:r213|213]]</sup> ; Shrestha and Nepal 2016 <sup>[[#fn:r214|214]]</sup> ) with results showing that the region is experiencing an increase in extremes, with farmers facing more frequent floods as well as prolonged droughts with ensuing negative impacts on agricultural yields and increases in food insecurity (Hussain et al. 2016 <sup>[[#fn:r215|215]]</sup> ; Manzoor et al. 2013 <sup>[[#fn:r216|216]]</sup> ).

''South America''

In another mountainous region, the Andes, inhabitants are also beginning to experience changes in the timing, severity, and patterns of the annual weather cycle. Data collected through participatory workshops, semi-structured interviews with agronomists, and qualitative fieldwork from 2012 to 2014 suggest that in Colomi, Bolivia, climate change is affecting crop yields and causing farmers to alter the timing of planting, their soil management strategies, and the use and spatial distribution of crop varieties (Saxena et al. 2016 <sup>[[#fn:r217|217]]</sup> ). In Argentina, there has also been an increase in yield variability of maize and soybeans (Iizumi and Ramankutty 2016 <sup>[[#fn:r218|218]]</sup> ). These changes have had important implications for the agriculture, human health, and biodiversity of the region (Saxena et al. 2016 <sup>[[#fn:r219|219]]</sup> ).

''Africa''

In recent years, yields of staple crops such as maize, wheat, sorghum, and fruit crops, such as mangoes, have decreased across Africa, widening food insecurity gaps (Ketiem et al. 2017 <sup>[[#fn:r220|220]]</sup> ). In Nigeria, there have been reports of climate change having impacts on the livelihoods of arable crop farmers (Abiona et al. 2016 <sup>[[#fn:r221|221]]</sup> ; Ifeanyi-obi et al. 2016 <sup>[[#fn:r222|222]]</sup> ; Onyeneke 2018 <sup>[[#fn:r223|223]]</sup> ). The Sahel region of Cameroon has experienced an increasing level of malnutrition. This is partly due to the impact of climate change since harsh climatic conditions leading to extreme drought have a negative influence on agriculture (Chabejong 2016 <sup>[[#fn:r224|224]]</sup> ).

Utilising farmer interviews in Abia State, Nigeria, researchers found that virtually all responders agreed that the climate was changing in their area (Ifeanyi-obi et al. 2016 <sup>[[#fn:r225|225]]</sup> ). With regard to management responses, a survey of farmers from Anambra State, Nigeria, showed that farmers are adapting to climate change by utilising such techniques as mixed cropping systems, crop rotation, and fertiliser application (Onyeneke et al. 2018 <sup>[[#fn:r226|226]]</sup> ). In Ebonyi State, Nigeria, Eze (2017) <sup>[[#fn:r227|227]]</sup> interviewed 160 women cassava farmers and found the major climate change risks in production to be severity of high temperature stress, variability in relative humidity, and flood frequency.

''Europe''

The impacts of climate change are varied across the continent. Moore and Lobell (2015) <sup>[[#fn:r228|228]]</sup> showed via counterfactual analysis that climate trends are affecting European crop yields, with long-term temperature and precipitation trends since 1989 reducing continent-wide wheat and barley yields by 2.5% and 3.8%, respectively, and having slightly increased maize and sugar beet yields. Though these aggregate affects appear small, the impacts are not evenly distributed. In cooler regions such as the United Kingdom and Ireland, the effect of increased warming has been ameliorated by an increase in rainfall. Warmer regions, such as Southern Europe, have suffered more from the warming; in Italy this effect has been amplified by a drying trend, leading to yield declines of 5% or greater.

Another study examining the impacts of recent climate trends on cereals in Greece showed that crops are clearly responding to changes in climate – and demonstrated (via statistical analysis) that significant impacts on wheat and barley production are expected at the end of the 21st century (Mavromatis 2015 <sup>[[#fn:r229|229]]</sup> ). In the Czech Republic, a study documented positive long-term impacts of recent warming on yields of fruiting vegetables (cucumbers and tomatoes) from 4.9 to 12% per 1°C increase in local temperature, but decreases in yield stability of traditionally grown root vegetables in the warmest areas of the country (Potopová et al. 2017 <sup>[[#fn:r230|230]]</sup> ). A study in Hungary also indicated the increasingly negative impacts of temperature on crops and indicated that a warming climate is at least partially responsible for the stagnation in crop yields since the mid-1980s in Eastern Europe (Pinke and Lövei 2017 <sup>[[#fn:r231|231]]</sup> ).

In summary, climate change is already affecting food security ( ''high confidence'' ). Recent studies in both large-scale and smallholder farming systems document declines in crop productivity related to rising temperatures and changes in precipitation. Evidence for climate change impacts (e.g., declines and stagnation in yields, changes in sowing and harvest dates, increased infestation of pests and diseases, and declining viability of some crop varieties) is emerging from detection and attribution studies and ILK in Australia, Europe, Asia, Africa, North America, and South America ( ''medium evidence, robust agreement'' ).

''Projected impacts''

Climate change effects have been studied on a global scale following a variety of methodologies that have recently been compared (Lobell and Asseng 2017 <sup>[[#fn:r232|232]]</sup> ; Zhao et al. 2017a <sup>[[#fn:r233|233]]</sup> and Liu et al. 2016 <sup>[[#fn:r234|234]]</sup> ). Approaches to study global and local changes include global gridded crop model simulations (e.g., Deryng et al. 2014 <sup>[[#fn:r235|235]]</sup> ), point-based crop model simulations (e.g., Asseng et al. 2015 <sup>[[#fn:r236|236]]</sup> ), analysis of point-based observations in the field (e.g., Zhao et al. 2016 <sup>[[#fn:r237|237]]</sup> ), and temperature-yield regression models (e.g., Auffhammer and Schlenker 2014 <sup>[[#fn:r238|238]]</sup> ). For an evaluation of model skills see example used in AgMIP (Müller et al. 2017b <sup>[[#fn:r239|239]]</sup> ).

Results from Zhao et al. (2017a <sup>[[#fn:r240|240]]</sup> ) across different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. A limitation of Zhao et al. (2017a) is that it is based on the assumption that yield responses to temperature increase are linear, while yield response differs depending on growing season temperature levels. Iizumi et al. (2017) <sup>[[#fn:r241|241]]</sup> showed that the projected global mean yields of maize and soybean at the end of this century do decrease monotonically with warming, whereas those of rice and wheat increase with warming but level off at about 3°C (2091–2100 relative to 1850–1900).

Empirical statistical models have been applied widely to different cropping systems, at multiple scales. Analyses using statistical models for maize and wheat tested with global climate model scenarios found that the RCP4.5 scenario reduced the size of average yield impacts, risk of major slowdowns, and exposure to critical heat extremes compared to RCP8.5 in the latter decades of the 21st century (Tebaldi and Lobell 2018 <sup>[[#fn:r242|242]]</sup> ). Impacts on crops grown in the tropics are projected to be more negative than in mid – to high-latitudes as stated in AR5 and confirmed by recent studies (e.g., Levis et al. 2018 <sup>[[#fn:r243|243]]</sup> ). These projected negative effects in the tropics are especially pronounced under conditions of explicit nitrogen stress (Rosenzweig et al. 2014 <sup>[[#fn:r244|244]]</sup> ) (Figure 5.4).

Reyer et al. (2017b) examined biophysical impacts in five world regions under different warming scenarios: 1°C, 1.5°C, 2°C, and 4°C warming. For the Middle East and northern African region a significant correlation between crop yield decrease and temperature increase was found, regardless of whether the effects of CO <sub>2</sub> fertilisation or adaptation measures are taken into account (Waha et al. 2017 <sup>[[#fn:r245|245]]</sup> ). For Latin America and the Caribbean the relationship between temperature and crop yield changes was only significant when the effect of CO <sub>2</sub> fertilisation is considered (Reyer et al. 2017a <sup>[[#fn:r246|246]]</sup> ).

A review of recent scientific literature found that projected yield loss for West Africa depends on the degree of wetter or drier conditions and elevated CO <sub>2</sub> concentrations (Sultan and Gaetani 2016 <sup>[[#fn:r247|247]]</sup> ). Faye et al. (2018b) in a crop modelling study with RCPs 4.5 and 8.5 found that climate change could have limited effects on peanut yield in Senegal due to the effect of elevated CO <sub>2</sub> concentrations.

'''Crop productivity changes for 1.5°C and 2.0°C''' . The IPCC Special Report on global warming of 1.5°C found that climate-related risks to food security are projected to increase with global warming of 1.5°C and increase further with 2°C (IPCC 2018b <sup>[[#fn:r248|248]]</sup> ). These findings are based among others on Schleussner et al. (2018); Rosenzweig et al. (2018a) <sup>[[#fn:r249|249]]</sup> ; Betts et al. (2018) <sup>[[#fn:r250|250]]</sup> , Parkes et al. (2018) <sup>[[#fn:r251|251]]</sup> and Faye et al. (2018a) <sup>[[#fn:r252|252]]</sup> . The importance of assumptions about CO <sub>2</sub> fertilisation was found to be significant by Ren et al. (2018) and Tebaldi and Lobell (2018).

AgMIP coordinated global and regional assessment (CGRA) results confirm that at the global scale, positive and negative changes are mixed in simulated wheat and maize yields, with declines in some breadbasket regions, at both 1.5°C and 2.0°C (Rosenzweig et al. 2018a <sup>[[#fn:r253|253]]</sup> ). In conjunction with price changes from the global economics models, productivity declines in the Punjab, Pakistan resulted in an increase in vulnerable households and poverty rate (Rosenzweig et al. 2018a).

'''Crop suitability''' . Another method of assessing the effects of climate change on crop yields that combined observations of current maximum-attainable yield with climate analogues also found strong reductions in attainable yields across a large fraction of current cropland by 2050 (Pugh et al. 2016 <sup>[[#fn:r254|254]]</sup> ). However, the study found the projected total land area in 2050, including regions not currently used for crops, climatically suitable for a high attainable yield similar to today. This indicates that large shifts in land-use patterns and crop choice will likely be necessary to sustain production growth and keep pace with current trajectories of demand.

'''Fruits and vegetables''' . Understanding the full range of climate impacts on fruits and vegetables is important for projecting future food security, especially related to dietary diversity and healthy diets.

However, studies for vegetables are very limited (Bisbis et al. 2018 <sup>[[#fn:r255|255]]</sup> ). Of the 174 studies considered in a recent review, only 14 described results of field or greenhouse experiments studying impacts of increased temperatures on yields of different root and leafy vegetables, tomatoes and legumes (Scheelbeek et al. 2018 <sup>[[#fn:r256|256]]</sup> ). Bisbis et al. (2018) found similar effects for vegetables as have been found for grain crops. That is, the effect of increased CO <sub>2</sub> on vegetables is mostly beneficial for production, but may alter internal product quality, or result in photosynthetic down-regulation. Heat stress reduces fruit set of fruiting vegetables, and speeds up development of annual vegetables, shortening their time for photoassimilation. Yield losses and impaired product quality result, thereby increasing food loss and waste. On the other hand, a longer growing season due to warmer temperatures enables a greater number of plantings and can contribute to greater annual yields. However, some vegetables, such as cauliflower and asparagus, need a period of cold accumulation to produce a harvest and warmer winters may not provide those requirements.

For vegetables growing in higher baseline temperatures (&gt;20°C), mean yield declines caused by 4°C warming were 31.5%; for vegetables growing in cooler environments (≤20°C), yield declines caused by 4°C were much less, on the order of about 5% (Scheelbeek et al. 2018 <sup>[[#fn:r257|257]]</sup> ). Rippke et al. (2016) <sup>[[#fn:r258|258]]</sup> found that 30–60% of the common bean growing area and 20–40% of the banana growing areas in Africa will lose viability in 2078–2098 with a global temperature increase of 2.6°C and 4°C respectively. Tripathi et al. (2016) <sup>[[#fn:r259|259]]</sup> found fruits and vegetable production to be highly vulnerable to climate change at their reproductive stages and also due to potential for greater disease pressure.

In summary, studies assessed find that climate change will increasingly be detrimental to crop productivity as levels of warming progress ( ''high confidence'' ). Impacts will vary depending on CO <sub>2</sub> concentrations, fertility levels, and region. Productivity of major commodity crops as well as crops such as millet and sorghum yields will be affected. Studies on fruits and vegetables find similar effects to those projected for grain crops in regard to temperature and CO <sub>2</sub> effects. Total land area climatically suitable for high attainable yield, including regions not currently used for crops, will be similar in 2050 to today.

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'''AgMIP median yield changes (%) for RCP8.5 (2070–2099 in comparison to 1980–2010 baseline) with CO2 effects and explicit nitrogen stress over five GCMs 힆 four Global Gridded Crop Models (GGCMs) for rainfed maize, wheat, rice, and soy (20 ensemble members from EPIC, GEPIC, pDSSAT, and PEGASUS; except for rice which has 15). Grey areas indicate […]'''
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[[File:63de8b189ca1afd3873414bb46e938b6 Figure-5.4.jpg]]

AgMIP median yield changes (%) for RCP8.5 (2070–2099 in comparison to 1980–2010 baseline) with CO <sub>2</sub> effects and explicit nitrogen stress over five GCMs 힆 four Global Gridded Crop Models (GGCMs) for rainfed maize, wheat, rice, and soy (20 ensemble members from EPIC, GEPIC, pDSSAT, and PEGASUS; except for rice which has 15). Grey areas indicate historical areas with little to no yield capacity. All models use a 0.5°C grid, but there are differences in grid cells simulated to represent agricultural land. While some models simulated all land areas, others simulated only potential suitable cropland area according to evolving climatic conditions. Others utilised historical harvested areas in 2000 according to various data sources (Rosenzweig et al. 2014) <sup>[[#fn:r1422|1422]]</sup> .

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<div id="section-5-2-2-2-impacts-on-livestock-production-systems"></div>

<span id="impacts-on-livestock-production-systems"></span>
==== 5.2.2.2 Impacts on livestock production systems ====

<div id="section-5-2-2-2-impacts-on-livestock-production-systems-block-1"></div>

Livestock systems are impacted by climate change mainly through increasing temperatures and precipitation variation, as well as atmospheric carbon dioxide (CO <sub>2</sub> ) concentration and a combination of these factors. Temperature affects most of the critical factors of livestock production, such as water availability, animal production and reproduction, and animal health (mostly through heat stress) (Figure 5.5). Livestock diseases are mostly affected by increases in temperature and precipitation variation (Rojas-Downing et al. 2017 <sup>[[#fn:r260|260]]</sup> ). Impacts of climate change on livestock productivity, particularly of mixed and extensive systems, are strongly linked to impacts on rangelands and pastures, which include the effects of increasing CO <sub>2</sub> on their biomass and nutritional quality. This is critical considering the very large areas concerned and the number of vulnerable people affected (Steinfeld 2010 <sup>[[#fn:r261|261]]</sup> ; Morton 2007 <sup>[[#fn:r262|262]]</sup> ). Pasture quality and quantity are mainly affected through increases in temperature and CO <sub>2</sub> , and precipitation variation.

<div id="section-5-2-2-2-impacts-on-livestock-production-systems-block-2"></div>

<span id="figure-5.5"></span>
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'''Figure 5.5'''

<span id="impacts-of-climate-change-on-livestock-based-on-rojas-downing-et-al.-2017"></span>
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'''Impacts of climate change on livestock (based on Rojas-Downing et al. 2017)'''
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[[File:a4de770f2066bcabf96bc4ac56c0150b Figure-5.5-1024x899.jpg]]

Impacts of climate change on livestock (based on Rojas-Downing et al. 2017) <sup>[[#fn:r1423|1423]]</sup>

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<div id="section-5-2-2-2-impacts-on-livestock-production-systems-block-3"></div>

Among livestock systems, pastoral systems are particularly vulnerable to climate change (Dasgupta et al. 2014 <sup>[[#fn:r263|263]]</sup> ) (see Section 5.2.2.6 for impacts on smallholder systems that combine livestock and crops). Industrial systems will suffer most from indirect impacts leading to rises in the costs of water, feeding, housing, transport and the destruction of infrastructure due to extreme events, as well as an increasing volatility of the price of feedstuff which increases the level of uncertainty in production (Rivera-Ferre et al. 2016b <sup>[[#fn:r264|264]]</sup> ; Lopez-i-Gelats 2014 <sup>[[#fn:r265|265]]</sup> ). Mixed systems and industrial or landless livestock systems could encounter several risk factors mainly due to the variability of grain availability and cost, and low adaptability of animal genotypes (Nardone et al. 2010 <sup>[[#fn:r266|266]]</sup> ).

Considering the diverse typologies of animal production, from grazing to industrial, Rivera-Ferre et al. (2016b) <sup>[[#fn:r267|267]]</sup> distinguished impacts of climate change on livestock between those related to extreme events and those related to more gradual changes in the average of climate-related variables. Considering vulnerabilities, they grouped

the impacts as those impacting the animal directly, such as heat and cold stress, water stress, physical damage during extremes; and others impacting their environment, such as modification in the geographical distribution of vector-borne diseases, location, quality and quantity of feed and water and destruction of livestock farming infrastructures.

With severe negative impacts due to drought and high frequency of extreme events, the average gain of productivity might be cancelled by the volatility induced by increasing variability in the weather. For instance, semi-arid and arid pasture will likely have reduced livestock productivity, while nutritional quality will be affected by CO <sub>2</sub> fertilisation (Schmidhuber and Tubiello 2007 <sup>[[#fn:r268|268]]</sup> ).

'''Observed impacts''' . Pastoralism is practiced in more than 75% of countries by between 200 and 500 million people, including nomadic communities, transhumant herders, and agropastoralists (McGahey et al. 2014 <sup>[[#fn:r269|269]]</sup> ). Observed impacts in pastoral systems reported in the literature include decreasing rangelands, decreasing mobility, decreasing livestock numbers, poor animal health, overgrazing, land degradation, decreasing productivity, decreasing access to water and feed, and increasing conflicts for the access to pasture land ( ''high confidence'' ) (López-i-Gelats et al. 2016 <sup>[[#fn:r270|270]]</sup> ; Batima et al. 2008 <sup>[[#fn:r271|271]]</sup> ; Njiru 2012 <sup>[[#fn:r272|272]]</sup> ; Fjelde and von Uexkull 2012 <sup>[[#fn:r273|273]]</sup> ; Raleigh and Kniveton 2012 <sup>[[#fn:r274|274]]</sup> ; Egeru 2016 <sup>[[#fn:r275|275]]</sup> ).

Pastoral systems in different regions have been affected differently. For instance, in China changes in precipitation were a more important factor in nomadic migration than temperature (Pei and Zhang 2014 <sup>[[#fn:r276|276]]</sup> ). There is some evidence that recent years have already seen an increase in grassland fires in parts of China and tropical Asia (IPCC 2012 <sup>[[#fn:r277|277]]</sup> ). In Mongolia, grassland productivity has declined by 20–30% over the latter half of the 20th century, and ewe average weight reduced by 4 kg on an annual basis, or about 8% since 1980 (Batima et al. 2008 <sup>[[#fn:r278|278]]</sup> ). Substantial decline in cattle herd sizes can be due to increased mortality and forced off-take (Megersa et al. 2014 <sup>[[#fn:r279|279]]</sup> ). Important, but less studied, is the impact of the interaction of grazing patterns with climate change on grassland composition. Spence et al. (2014) <sup>[[#fn:r280|280]]</sup> showed that climate change effects on Mongolia mountain steppe could be contingent on land use.

Conflicts due to resource scarcity, as well as other socio-political factors (Benjaminsen et al. 2012 <sup>[[#fn:r281|281]]</sup> ) aggravated by climate change, has differentiated impact on women. In Turkana, female-headed households have lower access to decision-making on resource use and allocation, investment and planning (Omolo 2011 <sup>[[#fn:r282|282]]</sup> ), increasing their vulnerability (Cross-Chapter Box 11 in Chapter 7, Section 5.1.3).

Non-climate drivers add vulnerability of pastoral systems to climate change (McKune and Silva 2013 <sup>[[#fn:r283|283]]</sup> ). For instance, during environmental disasters, livestock holders have been shown to be more vulnerable to food insecurity than their crop-producing counterparts because of limited economic access to food and unfavourable market exchange rates (Nori et al. 2005 <sup>[[#fn:r284|284]]</sup> ). Sami reindeer herders in Finland showed reduced freedom of action in response to climate change due to loss of habitat, increased predation, and presence of economic and legal constraints (Tyler et al. 2007 <sup>[[#fn:r285|285]]</sup> ; Pape and Löffler 2012 <sup>[[#fn:r286|286]]</sup> ). In Tibet, emergency aid has provided shelters and privatised communally owned rangeland, which have increased the vulnerability of pastoralists to climate change (Yeh et al. 2014 <sup>[[#fn:r287|287]]</sup> ; Næss 2013 <sup>[[#fn:r288|288]]</sup> ).

'''Projected impacts''' . The impacts of climate change on global rangelands and livestock have received comparatively less attention than the impacts on crop production. Projected impacts on grazing systems include changes in herbage growth (due to changes in atmospheric CO <sub>2</sub> concentrations and rainfall and temperature regimes) and changes in the composition of pastures and in herbage quality, as well as direct impacts on livestock (Herrero et al. 2016b <sup>[[#fn:r289|289]]</sup> ). Droughts and high temperatures in grasslands can also be a predisposing factor for fire occurrence (IPCC 2012 <sup>[[#fn:r290|290]]</sup> ).

'''Net primary productivity, soil organic carbon, and length of growing period''' . There are large uncertainties related to grasslands and grazing lands (Erb et al. 2016) <sup>[[#fn:r291|291]]</sup> , especially in regard to net primary productivity (NPP) (Fetzel et al. 2017 <sup>[[#fn:r292|292]]</sup> ; Chen et al. 2018 <sup>[[#fn:r293|293]]</sup> ). Boone et al. (2017) estimated that the mean global annual net primary production (NPP) in rangelands may decline by 10 gC m <sup>–2</sup> yr <sup>–1</sup> in 2050 under RCP8.5, but herbaceous NPP is likely to increase slightly (i.e., average of 3 gC m <sup>–2</sup> yr <sup>–1</sup> ) (Figure 5.6). Results of a similar magnitude were obtained by Havlík et al. (2015) <sup>[[#fn:r294|294]]</sup> , using EPIC and LPJmL on a global basis. According to Rojas-Downing et al. (2017) <sup>[[#fn:r295|295]]</sup> , an increase of 2°C is estimated to negatively impact pasture and livestock production in arid and semi-arid regions and positively impact humid temperate regions.

<div id="section-5-2-2-2-impacts-on-livestock-production-systems-block-4"></div>

<span id="figure-5.6"></span>
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'''Figure 5.6'''

<span id="ensemble-simulation-results-for-projected-annual-net-primary-productivity-of-rangelands-as-simulated-in-2000-top-and-their-change-in-2050-bottom-under-emissions-scenario-rcp-8.5-with-plant-responses-enhanced-by-co2-fertilisation.-results-from-rcp-4.5-and-8.5-with-and-without-positive-effects-of-atmospheric-co2-on-plant-production-differed-considerably-in-magnitude"></span>
<!-- IMG CAPTION -->
'''Ensemble simulation results for projected annual net primary productivity of rangelands as simulated in 2000 (top) and their change in 2050 (bottom) under emissions scenario RCP 8.5, with plant responses enhanced by CO2 fertilisation. Results from RCP 4.5 and 8.5, with and without positive effects of atmospheric CO2 on plant production, differed considerably in magnitude […]'''
<!-- IMG FILE -->
[[File:179d1fa9d08265a7a2ce0afd43b2200e Figure-5.6-1024x1020.jpg]]

Ensemble simulation results for projected annual net primary productivity of rangelands as simulated in 2000 (top) and their change in 2050 (bottom) under emissions scenario RCP 8.5, with plant responses enhanced by CO <sub>2</sub> fertilisation. Results from RCP 4.5 and 8.5, with and without positive effects of atmospheric CO <sub>2</sub> on plant production, differed considerably in magnitude but had similar spatial patterns, and so results from RCP 8.5 with increasing production are portrayed spatially here and in other figures. Scale bar labels and the stretch applied to colours are based on the spatial mean value plus or minus two standard deviations (Boone et al. 2017) <sup>[[#fn:r1424|1424]]</sup> .

<!-- END IMG -->
<div id="section-5-2-2-2-impacts-on-livestock-production-systems-block-5"></div>

Boone et al. (2017) <sup>[[#fn:r296|296]]</sup> identified significant regional heterogeneity in responses, with large increases in annual productivity projected in northern regions (e.g., a 21% increase in productivity in the USA and Canada) and large declines in western Africa (–46% in Sub-Saharan western Africa) and Australia (–17%). Regarding the length of growing period (LGP, average number of growing days per year) Herrero et al. (2016b) <sup>[[#fn:r297|297]]</sup> projected reductions in lower latitudes due to changes in rainfall patterns and increases in temperatures, which indicate increasing limitations of water. They identified 35°C as a critical threshold for rangeland vegetation and heat tolerance in some livestock species.

'''Rangeland composition''' . According to Boone et al. (2017), the composition of rangelands is projected to change as well (Chapter 3). Bare ground cover is projected to increase, averaging 2.4% across rangelands, with increases projected for the eastern Great Plains, eastern Australia, parts of southern Africa, and the southern Tibetan Plateau. Herbaceous cover declines are projected in the Tibetan Plateau, the eastern Great Plains, and scattered parts of the Southern Hemisphere. Shrub cover is projected to decline in eastern Australia, parts of southern Africa, the Middle East, the Tibetan Plateau, and the eastern Great Plains. Shrub cover could also increase in much of the Arctic and some parts of Africa. In mesic and semi-arid savannas south of the Sahara, both shrub and tree cover are projected to increase, albeit at lower productivity and standing biomass. Rangelands in western and south-western parts of the Isfahan province in Iran were found to be more vulnerable to future drying–warming conditions (Saki et al. 2018 <sup>[[#fn:r298|298]]</sup> ; Jaberalansar et al. 2017 <sup>[[#fn:r299|299]]</sup> ).

Soil degradation and expanding woody cover suggest that climate-vegetation-soil feedbacks catalysing shifts toward less productive, possibly stable states (Ravi et al. 2010 <sup>[[#fn:r300|300]]</sup> ) may threaten mesic and semi-arid savannas south of the Sahara (Chapters 3 and 4). This will also change their suitability for grazing different animal species; switches from cattle, which mainly consume herbaceous plants, to goats or camels are likely to occur as increases in shrubland occur.

'''Direct and indirect effects on livestock''' . Direct impacts of climate change in mixed and extensive production systems are linked to increased water and temperature stress on the animals potentially leading to animal morbidity, mortality and distress sales. Most livestock species have comfort zones between 10oC–30oC, and at temperatures above this animals reduce their feed intake 3–5% per additional degree of temperature (NRC 1981 <sup>[[#fn:r301|301]]</sup> ). In addition to reducing animal production, higher temperatures negatively affect fertility (HLPE 2012 <sup>[[#fn:r302|302]]</sup> ).

Indirect impacts to mixed and extensive systems are mostly related to the impacts on the feed base, whether pastures or crops, leading to increased variability and sometimes reductions in availability and quality of the feed for the animals (Rivera-Ferre et al. 2016b <sup>[[#fn:r303|303]]</sup> ). Reduced forage quality can increase CH <sub>4</sub> emissions per unit of gross energy consumed. Increased risk of animal diseases is also an important impact to all production systems (Bett et al. 2017 <sup>[[#fn:r304|304]]</sup> ). These depend on the geographical region, land-use type, disease characteristics, and animal susceptibility (Thornton et al. 2009 <sup>[[#fn:r305|305]]</sup> ). Also important is the interaction of grazing intensity with climate change. Pfeiffer et al. (2019) <sup>[[#fn:r306|306]]</sup> estimated that, in a scenario of mean annual precipitation below 500 mm, increasing grazing intensity reduced rangeland productivity and increased annual grass abundance.

Pastoral systems. In Kenya, some 1.8 million extra cattle could be lost by 2030 because of increased drought frequency, the value of the lost animals and production foregone amounting to 630 million USD (Herrero et al. 2010 <sup>[[#fn:r307|307]]</sup> ). Martin et al. (2014) <sup>[[#fn:r308|308]]</sup> assessed impacts of changing precipitation regimes to identify limits of tolerance beyond which pastoral livelihoods could not be secured and found that reduced mean annual precipitation always had negative effects as opposed to increased rainfall variability. Similarly, Martin et al. (2016) <sup>[[#fn:r309|309]]</sup> found that drought effects on pastoralists in High Atlas in Morocco depended on income needs and mobility options (see Section 5.2.2.6 for additional information about impacts on smallholder farmers).

In summary, observed impacts in pastoral systems include changes in pasture productivity, lower animal growth rates and productivity, damaged reproductive functions, increased pests and diseases, and loss of biodiversity ( ''high confidence'' ). Livestock systems are projected to be adversely affected by rising temperatures, depending on the extent of changes in pasture and feed quality, spread of diseases, and water resource availability ( ''high confidence'' ). Impacts will differ for different livestock systems and for different regions ( ''high confidence'' ). Vulnerability of pastoral systems to climate change is very high ( ''high confidence'' ), and mixed systems and industrial or landless livestock systems could encounter several risk factors mainly due to variability of grain availability and cost, and low adaptability of animal genotypes. Pastoral system vulnerability is exacerbated by non-climate factors (land tenure issues, sedentarisation programmes, changes in traditional institutions, invasive species, lack of markets, and conflicts) ( ''high confidence'' ).

<div id="section-5-2-2-3-impacts-on-pests-and-diseases"></div>

<span id="impacts-on-pests-and-diseases"></span>
==== 5.2.2.3 Impacts on pests and diseases ====

<div id="section-5-2-2-3-impacts-on-pests-and-diseases-block-1"></div>

Climate change is changing the dynamics of pests and diseases of both crops and livestock. The nature and magnitude of future changes is likely to depend on local agroecological and management context. This is because of the many biological and ecological mechanisms by which climate change can affect the distribution, population size, and impacts of pests and diseases on food production (Canto et al. 2009 <sup>[[#fn:r310|310]]</sup> ; Gale et al. 2009 <sup>[[#fn:r311|311]]</sup> ; Thomson et al. 2010 <sup>[[#fn:r312|312]]</sup> ; Pangga et al. 2011 <sup>[[#fn:r313|313]]</sup> ; Juroszek and von Tiedemann 2013 <sup>[[#fn:r314|314]]</sup> ; Bett et al. 2017 <sup>[[#fn:r315|315]]</sup> ).

These mechanisms include changes in host susceptibility due to CO <sub>2</sub> concentration effects on crop composition and climate stresses; changes in the biology of pests and diseases or their vectors (e.g., more generational cycles, changes in selection pressure driving evolution); mismatches in timing between pests or vectors and their ‘natural enemies’; changes in survival or persistence of pests or disease pathogens (e.g., changes in crop architecture driven by CO <sub>2</sub> fertilisation and increased temperature, providing a more favourable environment for persistence of pathogens like fungi), and changes in pest distributions as their ‘climate envelopes’ shift. Such processes may affect pathogens, and their vectors, as well as plant, invertebrate and vertebrate pests (Latham et al. 2015 <sup>[[#fn:r316|316]]</sup> ).

Furthermore, changes in diseases and their management, as well as changing habitat suitability for pests and diseases in the matrix surrounding agricultural fields, have the ability to reduce or exacerbate impacts (Bebber 2015 <sup>[[#fn:r317|317]]</sup> ). For example, changes in water storage and irrigation to adapt to rainfall variation have the potential to enhance disease vector populations and disease occurrence (Bett et al. 2017 <sup>[[#fn:r318|318]]</sup> ).

There is ''robust evidence'' that pests and diseases have already responded to climate change (Bebber et al. 2013 <sup>[[#fn:r319|319]]</sup> ), and many studies have now built predictive models based on current incidence of pests, diseases or vectors that indicate how they may respond in future (e.g., Caminade et al. 2015 <sup>[[#fn:r320|320]]</sup> ; Kim et al. 2015 <sup>[[#fn:r321|321]]</sup> ; Kim and Cho 2016 <sup>[[#fn:r322|322]]</sup> ; Samy and Peterson 2016 <sup>[[#fn:r323|323]]</sup> ; Yan et al. 2017 <sup>[[#fn:r324|324]]</sup> ). Warren et al. (2018) <sup>[[#fn:r325|325]]</sup> estimate that about 50% of insects, which are often pests or disease vectors, will change ranges by about 50% by 2100 under current GHG emissions trajectories. These changes will lead to crop losses due to changes in insect pests (Deutsch et al. 2018) and weed pressure (Ziska et al. 2018 <sup>[[#fn:r326|326]]</sup> ), and thus affect pest and disease management at the farm level (Waryszak et al. 2018 <sup>[[#fn:r327|327]]</sup> ). For example, Samy and Peterson (2016) <sup>[[#fn:r328|328]]</sup> modelled bluetongue virus (BTV), which is spread by biting ''Culicodes'' midges, finding that the distribution of BTV is likely to be extended, particularly in Central Africa, the USA, and Western Russia.

There is some evidence ( ''medium confidence'' ) that exposure will, on average, increase (Bebber and Gurr 2015 <sup>[[#fn:r329|329]]</sup> ; Yan et al. 2017 <sup>[[#fn:r330|330]]</sup> ), although there are a few examples where changing stresses may limit the range of a vector. There is also a general expectation that perturbations may increase the likelihood of pest and disease outbreaks by disturbing processes that may currently be at some quasi-equilibrium (Canto et al. 2009 <sup>[[#fn:r331|331]]</sup> ; Thomson et al. 2010 <sup>[[#fn:r332|332]]</sup> ; Pangga et al. 2011 <sup>[[#fn:r333|333]]</sup> ). However, in some places, and for some diseases, risks may decrease as well as increase (e.g., drying out may reduce the ability of fungi to survive) (Kim et al. 2015 <sup>[[#fn:r334|334]]</sup> ; Skelsey and Newton 2015 <sup>[[#fn:r335|335]]</sup> ), or tsetse fly’s range may decrease (Terblanche et al. 2008 <sup>[[#fn:r336|336]]</sup> ; Thornton et al. 2009 <sup>[[#fn:r337|337]]</sup> ).

Pests, diseases, and vectors for both crop and livestock diseases are likely to be altered by climate change ( ''high confidence'' ). Such changes are likely to depend on specifics of the local context, including management, but perturbed agroecosystems are more likely, on theoretical grounds, to be subject to pest and disease outbreaks ( ''low confidence'' ). Whilst specific changes in pest and disease pressure will vary with geography, farming system, pest/pathogen – increasing in some situations decreasing in others – there is ''robust evidence'' , with high agreement, that pest and disease pressures are likely to change; such uncertainty requires robust strategies for pest and disease mitigation.

<div id="section-5-2-2-4-impacts-on-pollinators"></div>

<span id="impacts-on-pollinators"></span>
==== 5.2.2.4 Impacts on pollinators ====

<div id="section-5-2-2-4-impacts-on-pollinators-block-1"></div>

Pollinators play a key role on food security globally (Garibaldi et al. 2016 <sup>[[#fn:r338|338]]</sup> ). Pollinator-dependent crops contribute up to 35% of global crop production volume and are important contributors to healthy human diets and nutrition (IPBES 2016 <sup>[[#fn:r339|339]]</sup> ). On a global basis, some 1500 crops require pollination (typically by insects, birds and bats) (Klein et al. 2007 <sup>[[#fn:r340|340]]</sup> ). Their importance to nutritional security is therefore perhaps under-rated by valuation methodologies, which, nonetheless, include estimates of the global value of pollination services at over 225 billion USD2010 (Hanley et al. 2015 <sup>[[#fn:r341|341]]</sup> ). As with other ecosystem processes affected by climate change (e.g., changes in pests and diseases), how complex systems respond is highly context dependent. Thus, predicting the effects of climate on pollination services is difficult (Tylianakis et al. 2008 <sup>[[#fn:r342|342]]</sup> ; Schweiger et al. 2010 <sup>[[#fn:r343|343]]</sup> ) and uncertain, although there is ''limited evidence'' that impacts are occurring already (Section 5.2.2.4), and ''medium evidence'' that there will be an effect.

Pollination services arise from a mutualistic interaction between an animal and a plant – which can be disrupted by climate’s impacts on one or the other or both (Memmott et al. 2007 <sup>[[#fn:r344|344]]</sup> ). Disruption can occur through changes in species’ ranges or by changes in timing of growth stages (Settele et al. 2016 <sup>[[#fn:r345|345]]</sup> ). For example, if plant development responds to different cues (e.g., day length) from insects (e.g., temperature), the emergence of insects may not match the flowering times of the plants, causing a reduction in pollination. Climate change will affect pollinator ranges depending on species, life-history, dispersal ability and location. Warren et al. (2018) <sup>[[#fn:r346|346]]</sup> estimate that under a 3.2°C warming scenario, the existing range of about 49% of insects will be reduced by half by 2100, suggesting either significant range changes (if dispersal occurs) or extinctions (if it does not). However, in principle, ecosystem changes caused by invasions, in some cases, could compensate for the decoupling generated between native pollinators and pollinated species (Schweiger et al. 2010 <sup>[[#fn:r347|347]]</sup> ).

Other impacts include changes in distribution and virulence of pathogens affecting pollinators, such as the fungus ''Nosema cerana,'' which can develop at a higher temperature range than the less-virulent ''Nosema apis'' ; increased mortality of pollinators due to higher frequency of extreme weather events; food shortage for pollinators due to reduction of flowering length and intensity; and aggravation of other threats, such as habitat loss and fragmentation (González-Varo et al. 2013 <sup>[[#fn:r348|348]]</sup> ; Goulson et al. 2015 <sup>[[#fn:r349|349]]</sup> ; Le Conte and Navajas 2008 <sup>[[#fn:r350|350]]</sup> ; Menzel et al. 2006 <sup>[[#fn:r351|351]]</sup> ; Walther et al. 2009 <sup>[[#fn:r352|352]]</sup> ; IPBES, 2016 <sup>[[#fn:r353|353]]</sup> ). The increase in atmospheric CO <sub>2</sub> is also reducing the protein content of pollen, with potential impact on pollination population biology (Ziska et al. 2016 <sup>[[#fn:r354|354]]</sup> ).

In summary, as with other complex agroecosystem processes affected by climate change (e.g., changes in pests and diseases), how pollination services respond will be highly context dependent. Thus, predicting the effects of climate on pollination services is difficult and uncertain, although there is ''medium evidence'' that there will be an effect.

<div id="section-5-2-2-5-impacts-on-aquaculture"></div>

<span id="impacts-on-aquaculture"></span>
==== 5.2.2.5 Impacts on aquaculture ====

<div id="section-5-2-2-5-impacts-on-aquaculture-block-1"></div>

This report focuses on land-based aquaculture; for assessment of impacts on marine fisheries both natural and farmed see the IPCC Special Report on the ocean and cryosphere in a changing climate (SROCC).

Aquaculture will be affected by both direct and indirect climate change drivers, both in the short and the long-term. Barange et al. (2018) <sup>[[#fn:r355|355]]</sup> provides some examples of short-term loss of production or infrastructure due to extreme events such as floods, increased risk of diseases, toxic algae and parasites; and decreased productivity due to suboptimal farming conditions. Long-term impacts may include scarcity of wild seed, limited access to freshwater for farming due to reduced precipitation, limited access to feeds from marine and terrestrial sources, decreased productivity due to suboptimal farming conditions, eutrophication and other perturbations.

FAO (2014a) <sup>[[#fn:r357|357]]</sup> assessed the vulnerability of aquaculture stakeholders to non-climate change drivers, which add to climate change hazards. Vulnerability arises from discrimination in access to inputs and decision-making; conflicts; infrastructure damage; and dependence on global markets and international pressures. Other non-climate drivers identified by McClanahan et al. (2015) <sup>[[#fn:r1425|1425]]</sup> include: declining fishery resources; a North–South divide in investment; changing consumption patterns; increasing reliance on fishery resources for coastal communities; and inescapable poverty traps created by low net resource productivity and few alternatives. In areas where vulnerability to climate change is heightened, increased exposure to climate change variables and impacts is likely to exacerbate current inequalities in the societies concerned, penalising further already disadvantaged groups such as migrant fishers (e.g., Lake Chad) or women (e.g., employees in Chile’s processing industry) (FAO 2014a) <sup>[[#fn:r1426|1426]]</sup> .

In many countries the projected declines co-occur across both marine fisheries and agricultural crops (Blanchard et al. 2017 <sup>[[#fn:r358|358]]</sup> ), both of which will impact the aquaculture and livestock sectors (Supplementary Material Figure SM5.1). Countries with low Human Development Index, trade opportunities and aquaculture technologies are likely to face greater challenges. These cross-sectoral impacts point to the need for a more holistic account of the inter-connected vulnerabilities of food systems to climate and global change.

<div id="section-5-2-2-6-impacts-on-smallholder-farming-systems"></div>

<span id="impacts-on-smallholder-farming-systems"></span>
==== 5.2.2.6 Impacts on smallholder farming systems ====

<div id="section-5-2-2-6-impacts-on-smallholder-farming-systems-block-1"></div>

New work has developed farming system approaches that take into account both biophysical and economic processes affected by climate change and multiple activities. Farm households in the developing world often rely on a complex mix of crops, livestock, aquaculture, and non-agricultural activities for their livelihoods (Rosenzweig and Hillel 2015 <sup>[[#fn:r359|359]]</sup> ; Antle et al. 2015 <sup>[[#fn:r360|360]]</sup> ). Across the world, smallholder farmers are considered to be disproportionately vulnerable to climate change because changes in temperature, rainfall and the frequency or intensity of extreme weather events directly affect their crop and animal productivity as well as their household’s food security, income and well-being (Vignola et al. 2015 <sup>[[#fn:r361|361]]</sup> ; Harvey et al. 2014b <sup>[[#fn:r362|362]]</sup> ). For example, smallholder farmers in the Philippines, whose survival and livelihood largely depend on the environment, constantly face risks and bear the impacts of the changing climate (Peria et al. 2016 <sup>[[#fn:r363|363]]</sup> ).

Smallholder farming systems have been recognised as highly vulnerable to climate change (Morton, 2007 <sup>[[#fn:r364|364]]</sup> ) because they are highly dependent on agriculture and livestock for their livelihood ( ''high confidence'' ) (Dasgupta et al. 2014 <sup>[[#fn:r365|365]]</sup> ). In Zimbabwe, farmers were found vulnerable due to their marginal location, low levels of technology, and lack of other essential farming resources. Farmers observed high frequency and severity of drought; excessive precipitation; drying of rivers, dams and wells; and changes in timing and pattern of seasons as evidence of climate change, and indicated that prolonged wet, hot, and dry weather conditions resulted in crop damage, death of livestock, soil erosion, bush fires, poor plant germination, pests, lower incomes, and deterioration of infrastructure (Mutekwa 2009 <sup>[[#fn:r366|366]]</sup> ).

In Madagascar, Harvey et al. (2014b) <sup>[[#fn:r367|367]]</sup> surveyed 600 small farmers and found that chronic food insecurity, physical isolation and lack of access to formal safety nets increased Malagasy farmers’ vulnerability to any shocks to their agricultural system, particularly extreme events. In Chitwan, Nepal, occurrence of extreme events and increased variability in temperature has increased the vulnerability of crops to biotic and abiotic stresses and altered the timing of agricultural operations; thereby affecting crop production (Paudel et al. 2014 <sup>[[#fn:r368|368]]</sup> ). In Lesotho, a study on subsistence farming found that food crops were the most vulnerable to weather, followed by soil and livestock. Climate variables of major concern were hail, drought and dry spells which reduced crop yields. In the Peruvian Altiplano, Sietz et al. (2012) <sup>[[#fn:r369|369]]</sup> evaluated smallholders’ vulnerability to weather extremes with regard to food security and found that resource scarcity (livestock, land area), diversification of activities (lack of alternative income, education deprivation) and income restrictions (harvest failure risk) shaped the vulnerability of smallholders. See Section 5.2.2.2 for observed impacts on smallholder pastoral systems.

'''Projected impacts''' . By including regional economic models, integrated methods take into account the potential for yield declines to raise prices and thus livelihoods (up to a certain point) in some climate change scenarios. Regional economic models of farming systems can be used to examine the potential for switching to other crops and livestock, as well as the role that non-farm income can play in adaptation (Valdivia et al. 2015 <sup>[[#fn:r370|370]]</sup> ; Antle et al. 2015 <sup>[[#fn:r371|371]]</sup> ). On the other hand, lost income for smallholders from climate change-related declines (for example, in coffee production), can decrease their food security (Hannah et al. 2017 <sup>[[#fn:r372|372]]</sup> ).

Farming system methods developed by AgMIP (Rosenzweig et al. 2013 <sup>[[#fn:r373|373]]</sup> ) have been used in regional integrated assessments in Sub-Saharan Africa (Kihara et al. 2015 <sup>[[#fn:r374|374]]</sup> ), West Africa (Adiku et al. 2015 <sup>[[#fn:r375|375]]</sup> ); East Africa (Rao et al. 2015 <sup>[[#fn:r376|376]]</sup> ), South Africa (Beletse et al. 2015 <sup>[[#fn:r377|377]]</sup> ), Zimbabwe (Masikati et al. 2015 <sup>[[#fn:r378|378]]</sup> ), South Asia (McDermid et al. 2015 <sup>[[#fn:r379|379]]</sup> ), Pakistan (Ahmad et al. 2015 <sup>[[#fn:r380|380]]</sup> ), the Indo-Gangetic Basin (Subash et al. 2015 <sup>[[#fn:r381|381]]</sup> ), Tamil Nadu (Ponnusamy et al. 2015) and Sri Lanka (Zubair et al. 2015 <sup>[[#fn:r382|382]]</sup> ). The assessments found that climate change adds pressure to smallholder farmers across Sub-Saharan Africa and South Asia, with winners and losers within each area studied. Temperatures are expected to increase in all locations, and rainfall decreases are projected for the western portion of West Africa and southern Africa, while increases in rainfall are projected for eastern West Africa and all studied regions of South Asia. The studies project that climate change will lead to yield decreases in most study regions except South India and areas in central Kenya, as detrimental temperature effects overcome the positive effects of CO <sub>2</sub> .

These studies use AgMIP representative agricultural pathways (RAPs) as a way to involve stakeholders in regional planning and climate resilience (Valdivia et al. 2015) <sup>[[#fn:r1427|1427]]</sup> . RAPs are consistent with and complement the RCP/SSP approaches for use in agricultural model intercomparisons, improvement, and impact assessments.

New methods have been developed for improving analysis of climate change impacts and adaptation options for the livestock component of smallholder farming systems in Zimbabwe (Descheemaeker et al. 2018 <sup>[[#fn:r383|383]]</sup> ). These methods utilised disaggregated climate scenarios, as well as differentiating farms with larger stocking rates compared to less densely stocked farms. By disaggregating climate scenarios, impacts, and smallholder farmer attributes, such assessments can more effectively inform decision-making towards climate change adaptation.

In Central Asia, a study using the bio-economic farm model (BEFM) found large differences in projected climate change impact ranging from positive income gains in large-scale commercial farms in contrast to negative impacts in small-scale farms (Bobojonov and Aw-Hassan 2014 <sup>[[#fn:r384|384]]</sup> ). Negative impacts may be exacerbated if irrigation water availability declines due to climate change and increased water demand in upstream regions. In Iran, changes in rainfall and water endowments are projected to significantly impact crop yield and water requirements, as well as income and welfare of farm families (Karimi et al. 2018 <sup>[[#fn:r385|385]]</sup> ).

Climate change impacts on food, feed and cash crops other than cereals, often grown in smallholder systems or family farms are less often studied, although impacts can be substantial. For example, areas suitable for growing coffee are expected to decrease by 21% in Ethiopia with global warming of 2.4°C (Moat et al. 2017) and more than 90% in Nicaragua (Läderach et al. 2017 <sup>[[#fn:r386|386]]</sup> ) with 2.2°C local temperature increase.

Climate change can modify the relationship between crops and livestock in the landscape, affecting mixed crop-livestock systems in many places. Where crop production will become marginal, livestock may provide an alternative to cropping. Such transitions could occur in up to 3% of the total area of Africa, largely as a result of increases in the probability of season failure in the drier mixed crop–livestock systems of the continent (Thornton et al. 2014 <sup>[[#fn:r387|387]]</sup> ).

In Mexico, subsistence agriculture is expected to be the most vulnerable to climate change, due to its intermittent production and reliance on maize and beans (Monterroso et al. 2014 <sup>[[#fn:r388|388]]</sup> ). Overall, a decrease in suitability and yield is expected in Mexico and Central America for beans, coffee, maize, plantain and rice (Donatti et al. 2018 <sup>[[#fn:r389|389]]</sup> ). Municipalities with a high proportional area under subsistence crops in Central America tend to have less resources to promote innovation and action for adaptation (Bouroncle et al. 2017 <sup>[[#fn:r390|390]]</sup> ).

In summary, smallholder farmers are especially vulnerable to climate change because their livelihoods often depend primarily on agriculture. Further, smallholder farmers often suffer from chronic food insecurity ( ''high confidence'' ). Climate change is projected to exacerbate risks of pests and diseases and extreme weather events in smallholder farming systems.

<span id="climate-change-impacts-on-access"></span>
=== 5.2.3 Climate change impacts on access ===

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Access to food involves the ability to obtain food, including the ability to purchase food at affordable prices.

<div id="section-5-2-3-1-impacts-on-prices-and-risk-of-hunger"></div>

<span id="impacts-on-prices-and-risk-of-hunger"></span>
==== 5.2.3.1 Impacts on prices and risk of hunger ====

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A protocol-based analysis based on AgMIP methods tested a combination of RCPs and SSPs to provide a range of projections for prices, risk of hunger, and land-use change (Hasegawa et al. 2018 <sup>[[#fn:r391|391]]</sup> ) (Figure 5.7 and Supplementary Material Table SM5.4.). Previous studies have found that decreased agricultural productivity will depress agricultural supply, leading to price increases. Despite different economic models with various representations of the global food system (Valin et al. 2014 <sup>[[#fn:r392|392]]</sup> ; Robinson et al. 2014 <sup>[[#fn:r393|393]]</sup> ; Nelson et al. 2013 <sup>[[#fn:r394|394]]</sup> ; Schmitz et al. 2014 <sup>[[#fn:r395|395]]</sup> ), as well as having represented the SSPs in different ways, for example, technological change, land-use policies, and sustainable diets (Stehfest et al. 2019 <sup>[[#fn:r396|396]]</sup> ; Hasegawa et al. 2018 <sup>[[#fn:r397|397]]</sup> ), the ensemble of participating models projected a 1–29% cereal price increase in 2050 across SSPs 1, 2 and 3 due to climate change (RCP 6.0). This would impact consumers globally through higher food prices, though regional effects will vary. The median cereal price increase was 7%, given current projections of demand. In all cases (across SSPs and global economic models), prices are projected to increase for rice and coarse grains, with only one instance of a price decline (–1%) observed for wheat in SSP1, with price increases projected in all other cases. Animal-sourced foods (ASFs) are also projected to see price increases (1%), but the range of projected price changes are about half those of cereals, highlighting that the climate impacts on ASFs will be felt indirectly, through the cost and availability of feed, and that there is significant scope for feed substitution within the livestock sector.

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<span id="figure-5.7"></span>
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'''Figure 5.7'''

<span id="implications-of-climate-change-by-2050-on-land-use-selected-agricultural-commodity-prices-and-the-population-at-risk-of-hunger-based-on-agmip-global-economic-model-analysis.a-projected-change-in-land-use-by-2050-by-land-type-cropland-grassland-and-forest-and-ssp.-b-projected-changes-in-average-world-prices-by-2050-for-cereals-rice"></span>
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'''Implications of climate change by 2050 on land-use, selected agricultural commodity prices, and the population at risk of hunger based on AgMIP Global Economic Model analysis.(A) Projected % change in land-use by 2050 by land type (cropland, grassland, and forest) and SSP. (B) Projected % changes in average world prices by 2050 for cereals (rice, […]'''
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[[File:ce630367ec4ed1e6520696ebf8911a99 Figure-5.7-724x1024.jpg]]

Implications of climate change by 2050 on land-use, selected agricultural commodity prices, and the population at risk of hunger based on AgMIP Global Economic Model analysis.(A) Projected % change in land-use by 2050 by land type (cropland, grassland, and forest) and SSP. (B) Projected % changes in average world prices by 2050 for cereals (rice, wheat, and coarse grains) and animal sourced foods (ruminant meat, monogastric, and dairy) by SSP. (C)Percentage change by 2050 in the global population at risk of hunger by SSP. (Hasegawa et al. 2018) <sup>[[#fn:r1428|1428]]</sup> .

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<div id="section-5-2-3-1-impacts-on-prices-and-risk-of-hunger-block-3"></div>

Declining food availability caused by climate change is likely to lead to increasing food cost impacting consumers globally through higher prices and reduced purchasing power, with low-income consumers particularly at risk from higher food prices (Nelson et al. 2010 <sup>[[#fn:r398|398]]</sup> ; Springmann et al. 2016a <sup>[[#fn:r399|399]]</sup> and Nelson et al. 2018). Higher prices depress consumer demand, which in turn will not only reduce energy intake (calories) globally (Hasegawa et al. 2015 <sup>[[#fn:r400|400]]</sup> ; Nelson et al. 2010 <sup>[[#fn:r401|401]]</sup> ; Springmann et al. 2016a <sup>[[#fn:r402|402]]</sup> and Hasegawa et al. 2018 <sup>[[#fn:r403|403]]</sup> ), but will also likely lead to less healthy diets with lower availability of key micronutrients (Nelson et al. 2018 <sup>[[#fn:r404|404]]</sup> ) and increase diet-related mortality in lower and middle-income countries (Springmann et al. 2016a <sup>[[#fn:r405|405]]</sup> ). These changes will slow progress towards the eradication of malnutrition in all its forms.

The extent that reduced energy intake leads to a heightened risk of hunger varies by global economic model. However, all models project an increase in the risk of hunger, with the median projection of an increase in the population at risk of insufficient energy intake by 6, 14, and 12% in 2050 for SSPs 1, 2 and 3 respectively compared to a no climate change reference scenario. This median percentage increase would be the equivalent of 8, 24 and 80 million (full range 1–183 million) additional people at risk of hunger due to climate change (Hasegawa et al. 2018 <sup>[[#fn:r406|406]]</sup> ).

<div id="section-5-2-3-2-impacts-on-land-use"></div>

<span id="impacts-on-land-use"></span>
==== 5.2.3.2 Impacts on land use ====

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Climate change is likely to lead to changes in land use globally (Nelson et al. 2014 <sup>[[#fn:r407|407]]</sup> ; Schmitz et al. 2014 <sup>[[#fn:r408|408]]</sup> and Wiebe et al. 2015 <sup>[[#fn:r409|409]]</sup> ). Hasegawa et al. (2018) <sup>[[#fn:r410|410]]</sup> found that declining agricultural productivity broadly leads to the need for additional cropland, with 7 of 8 models projecting increasing cropland and the median increase by 2050 projected across all models of 2% compared to a no climate change reference (Figure 5.7). Not all regions will respond to climate impacts equally, with more uncertainty on regional land-use change across the model ensemble than the global totals might suggest. For example, the median land-use change for Latin America is an increase of cropland by 3%, but the range across the model ensemble is significant, with three models projecting declines in cropland (–25 to –1%) compared to the five models projecting cropland increase (0–5%). For further discussion on land-use change and food security see Section 5.6.

<span id="climate-change-impacts-on-food-utilisation"></span>
=== 5.2.4 Climate change impacts on food utilisation ===

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Food utilisation involves nutrient composition of food, its preparation, and overall state of health. Food safety and quality affects food utilisation.

<div id="section-5-2-4-1-impacts-on-food-safety-and-human-health"></div>

<span id="impacts-on-food-safety-and-human-health"></span>
==== 5.2.4.1 Impacts on food safety and human health ====

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Climate change can influence food safety through changing the population dynamics of contaminating organisms due to, for example, changes in temperature and precipitation patterns, humidity, increased frequency and intensity of extreme weather events, and changes in contaminant transport pathways. Changes in food and farming systems, for example, intensification to maintain supply under climate change, may also increase vulnerabilities as the climate changes (Tirado et al. 2010 <sup>[[#fn:r411|411]]</sup> ).

Climate-related changes in the biology of contaminating organisms include changing the activity of mycotoxin-producing fungi, changing the activity of microorganisms in aquatic food chains that cause disease (e.g., dinoflagellates, bacteria like ''Vibrio'' ), and increasingly heavy rainfall and floods causing contamination of pastures with enteric microbes (like ''Salmonella'' ) that can enter the human food chain. Degradation and spoilage of products in storage and transport can also be affected by changing humidity and temperature outside of cold chains, notably from microbial decay but also from potential changes in the population dynamics of stored product pests (e.g., mites, beetles, moths) (Moses et al. 2015 <sup>[[#fn:r412|412]]</sup> ).

Mycotoxin-producing fungi occur in specific conditions of temperature and humidity, so climate change will affect their range, increasing risks in some areas (such as mid-temperate latitudes) and reducing them in others (e.g., the tropics) (Paterson and Lima 2010 <sup>[[#fn:r413|413]]</sup> ). There is ''robust evidence'' from process-based models of particular species ( ''Aspergillus'' /Aflatoxin B1, ''Fusarium'' /deoxynivalenol), which include projections of future climate that show that aflatoxin contamination of maize in Southern Europe will increase significantly (Battilani et al. 2016 <sup>[[#fn:r414|414]]</sup> ), and deoxynivalenol contamination of wheat in Northwestern Europe will increase by up to three times current levels (van der Fels-Klerx et al. 2012b, a) <sup>[[#fn:r1429|1429]]</sup>

Whilst downscaled climate models make any specific projection for a given geography uncertain (Van der Fels-Klerx et al. 2013) <sup>[[#fn:r1430|1430]]</sup> , experimental evidence on the small scale suggests that the combination of rising CO <sub>2</sub> levels, affecting physiological processes in photosynthetic organisms, and temperature changes, can be significantly greater than temperature alone (Medina et al. 2014 <sup>[[#fn:r415|415]]</sup> ). Risks related to aflatoxins are likely to change, but detailed projections are difficult because they depend on local conditions (Vaughan et al. 2016 <sup>[[#fn:r416|416]]</sup> ).

Foodborne pathogens in the terrestrial environment typically come from enteric contamination (from humans or animals), and can be spread by wind (blowing contaminated soil) or flooding – the incidence of both of which are likely to increase with climate change (Hellberg and Chu 2016 <sup>[[#fn:r417|417]]</sup> ). Furthermore, water stored for irrigation, which may be increased in some regions as an adaptation strategy, can become an important route for the spread of pathogens (as well as other pollutants). Contaminated water and diarrheal diseases are acute threats to food security (Bond et al. 2018 <sup>[[#fn:r418|418]]</sup> ). Whilst there is little direct evidence (in terms of modelled projections) the results of a range of reviews, as well as expert groups, suggest that risks from foodborne pathogens are likely to increase through multiple mechanisms (Tirado et al. 2010 <sup>[[#fn:r419|419]]</sup> ; van der Spiegel et al. 2012 <sup>[[#fn:r420|420]]</sup> ; Liu et al. 2013 <sup>[[#fn:r421|421]]</sup> ; Kirezieva et al. 2015 <sup>[[#fn:r422|422]]</sup> ; Hellberg and Chu 2016 <sup>[[#fn:r423|423]]</sup> ).

An additional route to climate change impacts on human health can arise from the changing biology of plants altering human exposure levels. This may include climate changing how crops sequester heavy metals (Rajkumar et al. 2013 <sup>[[#fn:r424|424]]</sup> ), or how they respond to changing pest pressure (e.g., cassava produces hydrogen cyanide as a defence against herbivore attack).

All of these factors will lead to regional differences regarding food safety impacts (Paterson and Lima 2011 <sup>[[#fn:r425|425]]</sup> ). For instance, in Europe it is expected that most important food safety-related impacts will be mycotoxins formed on plant products in the field or during storage; residues of pesticides in plant products affected by changes in pest pressure; trace elements and/or heavy metals in plant products depending on changes in abundance and availability in soils; polycyclic aromatic hydrocarbons in foods following changes in long-range atmospheric transport and deposition; and presence of pathogenic bacteria in foods following more frequent extreme weather, such as flooding and heat waves (Miraglia et al. 2009 <sup>[[#fn:r426|426]]</sup> ).

In summary, there is ''medium evidence,'' with ''high agreement'' that food utilisation via changes in food safety (and potentially food access from food loss) will be impacted by climate change, mostly by increasing risks, but there is ''low confidence'' , exactly how they may change for any given place.

<div id="section-5-2-4-2-impacts-on-food-quality"></div>

<span id="impacts-on-food-quality"></span>
==== 5.2.4.2 Impacts on food quality ====

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There are two main routes by which food quality may change. First, the direct effects of climate change on plant and animal biology, such as through changing temperatures changing the basic metabolism of plants. Secondly, by increasing carbon dioxide’s effect on biology through CO <sub>2</sub> fertilisation.

'''Direct effects on plant and animal biology''' . Climate affects a range of biological processes, including the metabolic rate in plants and ectothermic animals. Changing these processes can change growth rates, and therefore yields, but can also cause organisms to change relative investments in growth vs reproduction, and therefore change the nutrients assimilated. This may decrease protein and mineral nutrient concentrations, as well as alter lipid composition (DaMatta et al. 2010 <sup>[[#fn:r427|427]]</sup> ). For example, apples in Japan have been exposed to higher temperatures over 3–4 decades and have responded by blooming earlier. This has led to changes in acidity, firmness, and water content, reducing quality (Sugiura et al. 2013 <sup>[[#fn:r428|428]]</sup> ). In other fruit, such as grapes, warming-induced changes in sugar composition affect both colour and aroma (Mira de Orduña 2010 <sup>[[#fn:r429|429]]</sup> ). Changing heat stress in poultry can affect yield as well as meat quality (by altering fat deposition and chemical constituents), shell quality of eggs, and immune systems (Lara and Rostagno 2013 <sup>[[#fn:r430|430]]</sup> ).

'''Effects of rising CO <sub>2</sub> concentrations''' . Climate change is being driven by rising concentrations of carbon dioxide and other GHG’s in the atmosphere. As plants use CO <sub>2</sub> in photosynthesis to form sugar, rising CO <sub>2</sub> levels, all things being equal, enhances the process unless limited by water or nitrogen availability. This is known as ‘CO <sub>2</sub> fertilisation’. Furthermore, increasing CO <sub>2</sub> allows stomata to partially close during gas exchange, reducing water loss through transpiration. These two factors affect the metabolism of plants, and, as with changing temperatures, affects plant growth rates, yields and their nutritional quality. Studies of these effects include meta-analyses, modelling, and small-scale experiments (Franzaring et al. 2013 <sup>[[#fn:r431|431]]</sup> ; Mishra and Agrawal 2014 <sup>[[#fn:r432|432]]</sup> ; Myers et al. 2014 <sup>[[#fn:r433|433]]</sup> ; Ishigooka et al. 2017 <sup>[[#fn:r434|434]]</sup> ; Zhu et al. 2018 <sup>[[#fn:r435|435]]</sup> ; Loladze 2014 and Yu et al. 2014 <sup>[[#fn:r436|436]]</sup> ).

With regard to nutrient quality, a meta-analysis from seven Free-Air Carbon dioxide Enrichment (FACE), (with elevated atmospheric CO <sub>2</sub> concentration of 546–586 ppm) experiments (Myers et al. 2014), found that wheat grains had 9.3% lower zinc (CI 5.9–12.7%), 5.1% lower iron (CI 3.7–6.5%) and 6.3% lower protein (CI 5.2–7.5%), and rice grains had 7.8% lower protein content (CI 6.8–8.9%). Changes in nutrient concentration in field pea, soybean and C4 crops such as sorghum and maize were small or insignificant. Zhu et al. (2018) <sup>[[#fn:r437|437]]</sup> report a meta-analysis of FACE trials on a range of rice cultivars. They show that protein declines by an average of 10% under elevated CO <sub>2</sub> , iron and zinc decline by 8% and 5% respectively. Furthermore, a range of vitamins show large declines across all rice cultivars, including B1 (–17%), B2 (–17%), B5 (–13%) and B9 (–30%), whereas vitamin E increased. As rice underpins the diets of many of the world’s poorest people in low-income countries, especially in Asia, Zhu et al. (2018) estimate that these changes under high CO <sub>2</sub> may affect the nutrient status of about 600 million people.

Decreases in protein concentration with elevated CO <sub>2</sub> are related to reduced nitrogen concentration possibly caused by nitrogen uptake not keeping up with biomass growth, an effect called ‘carbohydrate dilution’ or ‘growth dilution’, and by inhibition of photorespiration which can provide much of the energy used for assimilating nitrate into proteins (Bahrami et al. 2017 <sup>[[#fn:r438|438]]</sup> ). Other mechanisms have also been postulated (Feng et al. 2015 <sup>[[#fn:r439|439]]</sup> ; Bloom et al. 2014 <sup>[[#fn:r440|440]]</sup> ; Taub and Wang 2008 <sup>[[#fn:r441|441]]</sup> ). Together, the impacts on protein availability may take as many as 150 million people into protein deficiency by 2050 (Medek et al. 2017 <sup>[[#fn:r442|442]]</sup> ). Legume and vegetable yields increased with elevated CO <sub>2</sub> concentration of 250 ppm above ambient by 22% (CI 11.6–32.5%), with a stronger effect on leafy vegetables than on legumes and no impact for changes in iron, vitamin C or flavonoid concentration (Scheelbeek et al. 2018 <sup>[[#fn:r443|443]]</sup> ).

Increasing concentrations of atmospheric CO <sub>2</sub> lower the content of zinc and other nutrients in important food crops. Dietary deficiencies of zinc and iron are a substantial global public health problem (Myers et al. 2014 <sup>[[#fn:r444|444]]</sup> ). An estimated two billion people suffer these deficiencies (FAO 2013a <sup>[[#fn:r445|445]]</sup> ), causing a loss of 63million life-years annually (Myers et al. 2014 <sup>[[#fn:r446|446]]</sup> ). Most of these people depend on C3 grain legumes as their primary dietary source of zinc and iron. Zinc deficiency is currently responsible for large burdens of disease globally, and the populations who are at highest risk of zinc deficiency receive most of their dietary zinc from crops (Myers et al. 2015 <sup>[[#fn:r447|447]]</sup> ). The total number of people estimated to be placed at new risk of zinc deficiency by 2050 is 138 million. The people likely to be most affected live in Africa and South Asia, with nearly 48 million residing in India alone. Differences between cultivars of a single crop suggest that breeding for decreased sensitivity to atmospheric CO <sub>2</sub> concentration could partly address these new challenges to global health (Myers et al. 2014 <sup>[[#fn:r448|448]]</sup> ).

In summary, while increased CO <sub>2</sub> is projected to be beneficial for crop productivity at lower temperature increases, it is projected to lower nutritional quality (e.g., less protein, zinc, and iron) ( ''high confidence'' ).

<span id="climate-change-impacts-on-food-stability"></span>
=== 5.2.5 Climate change impacts on food stability ===

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Food stability is related to people’s ability to access and use food in a steady way, so that there are not intervening periods of hunger. Increasing extreme events associated with climate change can disrupt food stability (see Section 5.8.1 for assessment of food price spikes).

<div id="section-5-2-5-1-impacts-of-extreme-events"></div>

<span id="impacts-of-extreme-events"></span>
==== 5.2.5.1 Impacts of extreme events ====

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FAO et al. (2018) <sup>[[#fn:r449|449]]</sup> conducted an analysis of the prevalence of undernourishment (PoU) and found that in 2017, the average of the PoU was 15.4% for all countries exposed to climate extremes (Supplementary Material Figure SM5.2). At the same time, the PoU was 20% for countries that additionally show high vulnerability of agriculture production/yields to climate variability, or 22.4% for countries with high PoU vulnerability to severe drought. When there is both high vulnerability of agriculture production/yields and high PoU sensitivity to severe drought, the PoU is 9.8 points higher (25.2%). These vulnerabilities were found to be higher when countries had a high dependence on agriculture as measured by the number of people employed in the sector. Bangkok experienced severe flooding in 2011–2012 with large-scale disruption of the national food supply chains since they were centrally organised in the capital city (Allen et al. 2017 <sup>[[#fn:r450|450]]</sup> ).

The IPCC projects that frequency, duration, and intensity of some extreme events will increase in the coming decades (IPCC 2018a <sup>[[#fn:r451|451]]</sup> , 2012 <sup>[[#fn:r452|452]]</sup> ). To test these effects on food security, Tigchelaar et al. (2018) <sup>[[#fn:r453|453]]</sup> showed rising instability in global grain trade and international grain prices, affecting especially the about 800 million people living in extreme poverty who are most vulnerable to food price spikes (Section 5.8.1). They used global datasets of maize production and climate variability combined with future temperature projections to quantify how yield variability will change in the world’s major maize-producing and exporting countries under 2°C and 4°C of global warming.

Tesfaye et al. (2017) <sup>[[#fn:r454|454]]</sup> projected that the extent of heat-stressed areas in South Asia could increase by up to 12% in 2030 and 21% in 2050 relative to the baseline (1950–2000). Another recent study found that drier regions are projected to dry earlier, more severely and to a greater extent than humid regions, with the population of Sub-Saharan Africa most vulnerable (Lickley and Solomon 2018 <sup>[[#fn:r455|455]]</sup> ).

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==== 5.2.5.2 Food aid ====

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Food aid plays an important role in providing food security and saving lives after climate disasters. In 2015, 14.5 million people were assisted through disaster-risk reduction, climate change and/ or resilience building activities (WFP 2018 <sup>[[#fn:r456|456]]</sup> ). However, there is no agreement on how to better use emergency food aid, since it can come with unintended consequences for individuals, groups, regions, and countries (Barrett 2006 <sup>[[#fn:r457|457]]</sup> ). These may include negative dependency of food recipients (Lentz et al. 2005 <sup>[[#fn:r458|458]]</sup> ) or price increases, among others.

Some authors state that tied food aid provided as ‘in kind’ by the donor country hampers local food production (Clay 2006 <sup>[[#fn:r459|459]]</sup> ), although others found no evidence of this (Ferrière and Suwa-Eisenmann 2015 <sup>[[#fn:r460|460]]</sup> ). Untied cash aid can be used to buy food locally or in neighbouring countries, which is cheaper and can contribute to improving the livelihoods of local farmers (Clay 2006 <sup>[[#fn:r461|461]]</sup> ).

Ahlgren et al. (2014) <sup>[[#fn:r462|462]]</sup> found that food aid dependence of Marshall Islands due to climate change impacts can result in poor health outcomes due to the poor nutritional quality of food aid, which may result in future increases of chronic diseases. In this regard, Mary et al. (2018) <sup>[[#fn:r463|463]]</sup> showed that nutrition-sensitive aid can reduce the prevalence of undernourishment.

In summary, based on AR5 and SR15 assessments that the likelihood of extreme weather events will increase, (e.g., increases in heatwaves, droughts, inland flooding, and coastal flooding due to rising sea levels, depending on region) in both frequency and magnitude, decreases in food stability and thus increases in food insecurity will likely rise as well ( ''medium evidence, high agreement'' ).

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