Editing IPCC:AR6/WGII/Chapter-9 (section)

== 9.8 Food Systems ==

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Ideally, a systems approach ( [[#Ericksen--2008|Ericksen, 2008]] ; [[#Rosenzweig--2020|Rosenzweig et al., 2020]] ) could be used to assess how global environmental changes affect the food sector in Africa, emphasising the complex interactions that exist within the components of the food supply system, including its enabling socioeconomic and biophysical environment ( [[#Ingram--2011|Ingram, 2011]] ; [[#Foran--2014|Foran et al., 2014]] ; [[#Tendall--2015|Tendall et al., 2015]] ), and how food is connected to other critical systems such as energy, water and transportation ( [[#Albrecht--2018|Albrecht et al., 2018]] ; see Box 9.5). Production will not be the only aspect of food security that is impacted by climate change. Processing, storage, distribution and consumption will also be affected. Access to healthy and adequate food in the face of climate change requires resilience across these components of the food system ( [[#Adenle--2017|Adenle et al., 2017]] ). However, most studies on climate change impacts on food in Africa are heavily focused on production only. A significant knowledge gap, therefore, exists around the complex ways in which climate change will interact with broader components of African food systems, and strategies for making these systems more resilient, particularly in a context of rapid population growth and urbanisation across the continent ( [[#Adenle--2017|Adenle et al., 2017]] ; [[#Schmitt%20Olabisi--2018|Schmitt Olabisi et al., 2018]] ).

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<span id="vulnerability-to-observed-and-projected-impacts-from-climate-change"></span>
=== 9.8.1 Vulnerability to Observed and Projected Impacts from Climate Change ===

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Agricultural activities are mainly rainfed and subsistence across Africa. The dominant farming system is mixed cereal–livestock ( [[#Thornton--2015|Thornton and Herrero, 2015]] ; [[#Nematchoua--2019|Nematchoua et al., 2019]] ), with pastoral systems in east Africa, and commercial livestock and crop systems also representing a significant proportion of the food system in southern Africa ( [[#Thornton--2015|Thornton and Herrero, 2015]] ). Many African regions are vulnerable to food insecurity, facing dwindling food production, food access, stocks and income due to low adaptive capacity ( [[#Evariste--2018|Evariste et al., 2018]] ; [[#Fuller--2018|Fuller et al., 2018]] ; [[#Bang--2019|Bang et al., 2019]] ; [[#Gebre--2021|Gebre and Rahut, 2021]] ).

Across regions with food systems highly vulnerable to climate change, female farmers, cocoa farmers, pastoralists, plantain farmers, coastal zone communities, rural households and forest communities in central Africa indicate higher vulnerability ( [[#Chia--2016|Chia et al., 2016]] ; [[#Schut--2016|Schut et al., 2016]] ; [[#Nematchoua--2019|Nematchoua et al., 2019]] ). Their vulnerability is multi-dimensional and affected by low adaptive capacity, location, livelihood system, socioeconomic status, gender, age and ethnicity ( [[#Perez--2015|Perez et al., 2015]] ; [[#Weston--2015|Weston et al., 2015]] ; [[#Gebre--2021|Gebre and Rahut, 2021]] ; see also Box 9.1).

Across Africa, including west Africa, adverse climate conditions for agricultural and pastoral livelihoods have contributed to rural to urban migration patterns and migration among African regions (see Box 9.8; [[#Baudoin--2014|Baudoin et al., 2014]] ; Abbas, 2017; [[#Gemenne--2017b|Gemenne and Blocher, 2017b]] ). Rural to urban migration may increase vulnerability of migrants through exposure to additional risks, including food insecurity ( [[#Amadi--2015|Amadi and Ogonor, 2015]] ; Abbas, 2017). In general, west African countries are characterised by the poor adaptive capacity of rural households ( [[#Douxchamps--2015|Douxchamps et al., 2015]] ; [[#Dumenu--2016|Dumenu and Obeng, 2016]] ).

In north Africa, livelihoods and economies are strongly dependent on agriculture. Pressure on water demand due to climate change and variability is threatening income, development processes and food security in the region ( ''high confidence'' ) ( [[#Mohmmed--2018|Mohmmed et al., 2018]] ; [[#Khedr--2019|Khedr, 2019]] ). Increased temperatures and droughts have enhanced the vulnerability of the irrigation sector ( [[#Verner--2018|Verner et al., 2018]] ; [[#İlseven--2019|İlseven et al., 2019]] ), and the combined effect of these hazards negatively affects crop and animal production ( [[#Mohmmed--2018|Mohmmed et al., 2018]] ; [[#Verner--2018|Verner et al., 2018]] ). For example, dairy farms in Tunisia are experiencing warmer temperatures above the thermoneutral zone of cows for more than 5 months each year, reducing production efficiency and resulting in significant economic losses ( [[#Amamou--2018|Amamou et al., 2018]] ).

Non-climatic stressors aggravate food insecurity in many parts of the continent, including lack of access to production inputs and land, lack of education and limited income sources, with adverse climate impacts on agriculture reducing education attainment for children ( [[#9.11.1.2|Section 9.11.1.2]] ; [[#Evariste--2018|Evariste et al., 2018]] ; [[#Fuller--2018|Fuller et al., 2018]] ). Geographic and social isolation is another type of social vulnerability, especially for pastoralist communities in east and southern Africa ( [[#Sonwa--2017|Sonwa et al., 2017]] ; [[#Basupi--2019|Basupi et al., 2019]] ). Rural communities often have poor transport networks, limited access to markets or information and fewer livelihood alternatives, and are less able to be informed of risks or be assisted in the event of extreme climate events ( [[#Sonwa--2017|Sonwa et al., 2017]] ; [[#Basupi--2019|Basupi et al., 2019]] ).

Extreme climate events have been key drivers in rising acute food insecurity and malnutrition of millions of people requiring humanitarian assistance in Africa ( ''high confidence'' ). Between 2015 and 2019, an estimated 45.1 million people in the Horn of Africa and 62 million people in eastern and southern Africa required humanitarian assistance due to climate-related food emergencies. Children and pregnant women experience disproportionately greater adverse health and nutrition impacts ( ''very high confidence'' ) ( [[#Gebremeskel%20Haile--2019|Gebremeskel Haile et al., 2019]] ; see [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] [[IPCC:Wg2:Chapter:Chapter-7#7.2.4|Section 7.2.4]] ).

Future climate warming is projected to have a substantial adverse impact on food security in Africa and is anticipated to coincide with low adaptive capacity as climate change intensifies other anthropogenic stressors, as 85% of Africa’s poor live in rural areas and mostly depend on agriculture for their livelihoods ( [[#Adams--2018|Adams, 2018]] ; [[#Mahmood--2019|Mahmood et al., 2019]] ). This highlights the need to prioritise innovative measures for reducing vulnerabilities in African food systems ( [[#Fuller--2018|Fuller et al., 2018]] ; [[#Mahmood--2019|Mahmood et al., 2019]] ).

Climate change impacts could increase the global number of people at risk of hunger in 2050 by 8 million under a scenario of sustainable development (SSP1) and 80 million under a scenario of reduced international cooperation and low environmental protection (SSP3), with populations concentrated in sub-Saharan Africa, south Asia and central America (see [[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] Sections 5.2.2; 5.4.2; 5.4.3). Global climate impacts on food availability are expected to lead to higher food prices, increasing the risk of hunger for people in African countries, and slowing progress towards eradicating child undernutrition and malnutrition in all its forms (see [[IPCC:Wg2:Chapter:Chapter-7|Chapter 7]] [[IPCC:Wg2:Chapter:Chapter-7#7.4|Section 7.4]] ).

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<span id="observed-impacts-and-projected-risks-to-crops-and-livestock"></span>
=== 9.8.2 Observed Impacts and Projected Risks to Crops and Livestock ===

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<span id="observed-impacts-and-projected-risks-for-staple-crops"></span>
==== 9.8.2.1 Observed Impacts and Projected Risks for Staple Crops ====

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Climate change is already negatively impacting crop production and slowing productivity growth in Africa ( ''high confidence'' ) ( [[#Iizumi--2018|Iizumi et al., 2018]] ; [[#Ray--2019|Ray et al., 2019]] ; [[#Sultan--2019|Sultan et al., 2019]] ; [[#Ortiz-Bobea--2021|Ortiz-Bobea et al., 2021]] ). Climate change has reduced total agricultural productivity growth in Africa by 34% since 1961, more than in any other region ( [[#Ortiz-Bobea--2021|Ortiz-Bobea et al., 2021]] ). Maize yields have decreased 5.8% and wheat yields 2.3%, on average, in sub-Saharan Africa due to climate change in the period 1974–2008 ( [[#Ray--2019|Ray et al., 2019]] ). Overall, climate change has decreased total food calories across all crops in sub-Saharan Africa by 1.4% on average compared to a no climate change counterfactual since 1970, with up to 10% reductions in Ghana and Zimbabwe ( [[#Ray--2019|Ray et al., 2019]] ).

Farmers perceive a wide variety of climate threats to crop production including droughts, precipitation variability, a delayed onset and overall reductions in early growing season rainfall and excess heat ( [[#Rankoana--2016a|Rankoana, 2016a]] ; [[#Elum--2017|Elum et al., 2017]] ; [[#Kichamu--2017|Kichamu et al., 2017]] ; [[#Alvar-Beltrán--2020|Alvar-Beltrán et al., 2020]] ). Farmers attribute these perceived changes as a major driver of yield losses ( [[#Ayanlade--2016|Ayanlade and Jegede, 2016]] ; see [[#9.4.5|Section 9.4.5]] ). Over half of surveyed farmers in west Africa perceive increases in crop pests and diseases as due to climate change as the range and seasonality of many pests and diseases change under warming ( [[#Callo-Concha--2018|Callo-Concha, 2018]] ). Pests and diseases contribute between 10–35% yield losses for wheat, rice, maize, potato and soybean in sub-Saharan Africa ( [[#Savary--2019|Savary et al., 2019]] ). Recent locust outbreaks in 2019 in east Africa have been linked to climate conditions caused in part by ocean warming ( [[#Wang--2020b|Wang et al., 2020b]] ; see Box 5.8).

Future climate change may increase insect pest-driven losses in Africa for maize, rice and wheat. Compared to 1950–2000, losses may increase by up to 50% at 2°C of global warming ( [[#Deutsch--2018|Deutsch et al., 2018]] ). However, many challenges remain in modelling pest and disease under climate change with additional research needed expanding the range of crops and diseases studied ( [[#Newbery--2016|Newbery et al., 2016]] ).

Agriculture in Africa is especially vulnerable to future climate change in part because 90–95% of African food production is rainfed ( [[#Adams--2018|Adams, 2018]] ). Maize, rice, wheat and soybean yields in tropical regions (20°S–20°N) are projected to decrease approximately 5% per degree Celsius of global warming in a multi-model ensemble ( [[#Rosenzweig--2014|Rosenzweig et al., 2014]] ; [[#Franke--2020|Franke et al., 2020]] ). Dryland agricultural areas are especially sensitive to changes in rainfall. For example, without adaptation, substantial yield declines are projected for staple crops in north Africa. A recent meta-analysis of 56 studies indicates that, compared to 1995–2005, economic welfare in the agriculture sector in north Africa is projected to decline 5% for 2°C global warming and 20% for 3°C global warming, and in sub-Saharan Africa by 5% (2°C) and 10% (3°C) ( [[#Moore--2017a|Moore et al., 2017a]] ), both more pessimistic than previous economic estimates.

A synthesis of projected staple crop impacts across 35 studies for nearly 1040 locations and cases shows, on average, decreases in crop yields with increasing global warming across staple crops in Africa, including when accounting for CO 2 increases and adaptation measures. For example, for maize in west Africa, compared to 2005 yield levels, median projected yields decrease 9% at 1.5°C global warming and 41% at 4°C, without adaptation (Figure 9.22). However, uncertainties in projected impacts across crops and regions are driven by uncertainties in crop responses to increasing CO 2 and adaptation response, especially for maize in east Africa and wheat in north Africa and east Africa (Figure 9.22; [[#Hasegawa--2021|Hasegawa et al., 2021]] ).

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[[File:0f438802e215429c53faa1822d1831d7 IPCC_AR6_WGII_Figure_9_022.png]]

'''Figure 9.22 |''' '''Projected yield changes for major staple crops in Africa due to climate change (compared to 2005 yield levels).''' Projected impacts are grouped by projected global warming levels. Boxplots show a synthesis of projected staple crop impacts, with and without adaptation measures (e.g., planting date, cultivar, tillage or irrigation). On average crop yields are projected to decrease with increasing global warming across staple crops in Africa. The overall adaptation potential to offset yield losses across Africa for rice, maize and wheat reduces with increasing global warming. On average, in projections including adaptation options, yield losses in the median case are reduced from −33% to −10% of 2005 levels at 2°C of global warming and from −46% to −23% at 4°C. Global warming levels were calculated using a baseline for pre-industrial global mean temperature of 1850–1900. Data are a synthesis across 35 studies for nearly 1040 locations and cases of projected impacts for regions of Africa for maize, rice and wheat ( [[#Hasegawa--2021|Hasegawa et al., 2021]] ; Table SM9.5).

There is also growing evidence that climate change is ''likely'' beginning to outpace adaptation in agricultural systems in parts of Africa ( [[#Rippke--2016|Rippke et al., 2016]] ). For example, despite the use of adjusted sowing dates and existing heat-tolerant varieties, Sudan’s domestic production share of wheat may decrease from 16.0% to 4.5–12.2% by 2050 under RCP8.5 (2.4°C global warming) ( [[#Iizumi--2021|Iizumi et al., 2021]] ).

Elevated CO 2 concentrations in the atmosphere might mitigate some or all climate-driven losses ( [[#Swann--2016|Swann et al., 2016]] ; [[#Durand--2018|Durand et al., 2018]] ), but there is considerable uncertainty around the CO 2 response ( [[#Deryng--2016|Deryng et al., 2016]] ; [[#Toreti--2020|Toreti et al., 2020]] ), especially when nutrients such as nitrogen and phosphorus are limiting crop growth. Additional Free-Air Carbon dioxide Enrichment (FACE) experiments are needed in the tropics, particularly on the African continent, to better understand the impacts of increased CO 2 concentrations on the productivity of crops and cultivars grown in Africa under additional temperature impacts and water and nutrient limitations ( [[#Ainsworth--2021|Ainsworth and Long, 2021]] ). Warming and elevated CO 2 may also change the nutritional content of some crops. By 2050 under RCP8.5 (2.4°C global warming), overall wheat yields and grain protein content may decrease by 10% and 15%, respectively, in north and east Africa, and by over 15% in southern Africa ( [[#Asseng--2019|Asseng et al., 2019]] ). See [[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] for more details on CO 2 impacts and uncertainties.

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<span id="observed-impacts-and-projected-risks-on-regional-cash-crops-and-food-crops"></span>
==== 9.8.2.2 Observed Impacts and Projected Risks on Regional Cash Crops and Food Crops ====

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Few studies have attributed changes in yields of cash crops and other regionally important food crops in Africa to human-caused climate change, but recent research suggests yields of cash crops in Africa have already been impacted by climate change, in both a negative and positive manner ( [[#Falco--2012|Falco et al., 2012]] ; [[#Traore--2013|Traore et al., 2013]] ; [[#Ray--2019|Ray et al., 2019]] ). For example, between the period 1974–2008, sugarcane yields decreased on average by 3.9% and 5.1% in sub-Saharan Africa and north Africa, respectively, due to climate change, while sorghum yields increased 0.7%, and cassava yield increased 1.7% in sub-Saharan Africa and 18% in north Africa ( [[#Ray--2019|Ray et al., 2019]] ).

There are also limited studies assessing projected climate change impacts on important cash crops and food crops other than maize, rice and wheat ( [[#Jarvis--2012|Jarvis et al., 2012]] ; [[#Schroth--2016|Schroth et al., 2016]] ; [[#Awoye--2017|Awoye et al., 2017]] ). These studies often represent changes at specific sites in a country or assess changes in the yield and/or suitability for cultivating a specific crop across a larger geographic area. Climate change is projected to have overall positive impacts on sugarcane and Bambara nuts in southern Africa, oil palm in Nigeria and chickpea in Ethiopia ( ''low confidence'' ) (Figure 9.23).

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[[File:4783493b4ddb04ea2e1d89f35619e11a IPCC_AR6_WGII_Figure_9_023.png]]

'''Figure 9.23 |''' '''Projected risks at increasing global warming levels for regionally important cash and food crops in Africa.''' Insufficient data indicates there were limited to no published studies that have quantified projected climate change impacts or adaptation options for specific crops under different warming levels (see Table SM9.6). Global warming levels were calculated using a baseline for pre-industrial global mean temperature of 1850–1900.

Climate change is projected to reduce sorghum yields in west Africa (Figure 9.23). For example, across the west African Sahel savanna sorghum yields are projected to decline on average 2% at 1.5°C and 5% at 2°C global warming ( [[#Faye--2018|Faye et al., 2018]] ). For coffee and tea in eastern Africa, olives in Algeria and sunflower in Botswana and Morocco, studies indicate mostly negative impacts on production systems. For example, in Kenya, compared to 2000, optimal habitat for tea production is projected to decrease in area by 27% with yields declining 10% for global warming of 1.8–1.9°C, although yield declines may be reduced at higher levels of warming ( [[#Beringer--2020|Beringer et al., 2020]] ; [[#Jayasinghe--2020|Jayasinghe and Kumar, 2020]] ; [[#Rigden--2020|Rigden et al., 2020]] ). Suitable area for tea production may reduce by half in Uganda ( [[#Eitzinger--2011|Eitzinger et al., 2011]] ; [[#Läderach--2013|Läderach et al., 2013]] ). In east Africa, the coffee-growing area is projected to shift up in elevation with suitability decreasing 10–30% between 1.5–2°C of global warming ( [[#Bunn--2015|Bunn et al., 2015]] ; [[#Ovalle-Rivera--2015|Ovalle-Rivera et al., 2015]] ).

For all other crops, there is at least one study that finds low to highly negative impacts for one or several warming levels (Figure 9.23). Mixed results on the direction of change often occur when several contrasting sites with varying baseline climates are studied, and when a study considers the full range of climate scenarios. For example, there are mixed results on the direction of change for impacts of 1.5°C global warming on cassava, cotton, cocoa and millet in west Africa ( ''low confidence'' ) (Figure 9.23). In general, there is limited evidence in the direction of change, due to single studies being available for most crop-country combinations ( [[#Knox--2010|Knox et al., 2010]] ; [[#Chemura--2013|Chemura et al., 2013]] ; [[#Asaminew--2017|Asaminew et al., 2017]] ; [[#Bouregaa--2019|Bouregaa, 2019]] ). Occasionally, two studies agree on the direction and magnitude of change, for example, for potatoes in east Africa, yields are projected to decrease by 11–17% with 3°C of warming ( [[#Fleisher--2010|Fleisher et al., 2010]] ; [[#Tatsumi--2011|Tatsumi et al., 2011]] ).

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<span id="observed-impacts-and-projected-risks-for-wild-harvested-food"></span>
==== 9.8.2.3 Observed Impacts and Projected Risks for Wild-Harvested Food ====

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Wild-harvested foods (e.g., fruits, vegetables and insects) provide dietary diversification and for many people in Africa, wild-harvested food plants may provide a livelihood and/or nutritional safety net when other sources of food fail, such as during drought ( [[#Sunderland--2013|Sunderland et al., 2013]] ; [[#Shumsky--2014|Shumsky et al., 2014]] ; [[#Wunder--2014|Wunder et al., 2014]] ; [[#Baudron--2019b|Baudron et al., 2019b]] ). In Zimbabwe, during lean times, consumption of wild fruits increases, as does their sale to generate income for additional food expenses in poor, rural households (Mithöfer and Waibel, 2004). In Mali, Tanzania and Zambia, household surveys indicate that forest products including wild foods can play an important role in reducing household vulnerability to climate shocks by providing alternative sources of food and income during droughts and floods ( [[#Robledo--2012|Robledo et al., 2012]] ). In the parklands of west Africa, wild trees are a significant source of wild foods and are thus a place where one might expect wild plant foods to make an important contribution to diets and nutrition ( [[#Boedecker--2014|Boedecker et al., 2014]] ; [[#Leßmeister--2015|Leßmeister et al., 2015]] ). Non-timber forest products are consumed by an estimated 43% of all households in Burkina Faso ( [[#FAO--2019|FAO, 2019]] ), and wild vegetables accounted for about 50% of total vegetable consumption in southeastern Burkina Faso ( [[#Mertz--2001|Mertz et al., 2001]] ).

The focus of projected climate change impacts has been almost exclusively on agricultural production, yet climate change could have substantial impacts on the distribution and availability of wild-harvested food plants in Africa ( [[#Wessels--2021|Wessels et al., 2021]] ). Non-cultivated species in Africa are vulnerable to current and future climate changes, with widespread changes in woody plant cover already observed (see [[#9.6.1.1|Section 9.6.1.1]] ). Evidence about the impacts of climate change on individual wild food species is less consistent. Communities in the Kalahari ( [[#Crate--2016|Crate and Nuttall, 2016]] ) and Zimbabwe ( [[#Sango--2015|Sango and Godwell, 2015]] ) report growing scarcity of wild foods (such as wild meat and fruit) perceived to be, at least in part, due to drought and climate change. Shea tree ( ''Vitellaria paradoxa'' ) nuts provide fats and oils for the diets of many rural populations in west Africa. In Burkina Faso, global warming of 3°C is projected to reduce area of suitable habitat for the shea tree by 14% ( [[#Dimobe--2020|Dimobe et al., 2020]] ). In southern Africa, 40% of native, wild-harvested food plant species are projected to decrease in geographic range extent at 1.7°C global warming with range reductions for 66% of species projected for 3.5°C ( [[#Wessels--2021|Wessels et al., 2021]] ).

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<span id="observed-impacts-and-projected-risks-on-livestock"></span>
==== 9.8.2.4 Observed Impacts and Projected Risks on Livestock ====

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Livestock systems in Africa are already being affected by changes in climate through increased precipitation variability leading to decreasing fodder availability ( [[#Sloat--2018|Sloat et al., 2018]] ; [[#Stanimirova--2019|Stanimirova et al., 2019]] ). More than twice as many countries in Africa have experienced increases in precipitation variability in the last century than decreases ( [[#Sloat--2018|Sloat et al., 2018]] ). Fodder availability is also being impacted by woody plant encroachment—the increase in shrub and tree cover—which has increased by 10% on subsistence grazing lands and 20% on economically important grazing lands in south Africa in the last 60 years ( [[#Stevens--2016|Stevens et al., 2016]] ), and is driven in part by climatic factors (see [[#9.6.1.1|Section 9.6.1.1]] ). Increased temperature and precipitation have contributed to the expanding range, especially in east and southern Africa, of several ixodid tick species which carry economically important livestock diseases ( [[#Nyangiwe--2018|Nyangiwe et al., 2018]] ).

Pastoralists in Africa perceive the climate as already changing and report more erratic and reduced rainfall, prolonged and more frequent droughts and a rise in temperature ( [[#Sanogo--2017|Sanogo et al., 2017]] ; [[#Kimaro--2018|Kimaro et al., 2018]] ). They also report reduced milk production, increased deaths and disease outbreaks in their herds due to malnutrition and starvation resulting from the shortages in forage and water ( [[#Kimaro--2018|Kimaro et al., 2018]] ). Additional research is required to attribute precipitation variability to human-induced climate change (see [[#9.5|Section 9.5]] ), and to evaluate the relative contributions of climate change and management to disease vector extent.

Future climate change will have compounding impacts on livestock, including negative impacts on fodder availability and quality, availability of drinking water, direct heat stress and the prevalence of livestock diseases ( [[#Nardone--2010|Nardone et al., 2010]] ; [[#Rojas-Downing--2017|Rojas-Downing et al., 2017]] ; [[#Godde--2021|Godde et al., 2021]] ). Climate change is projected to negatively affect fodder availability ( [[#Briske--2017|Briske, 2017]] ) because overall rangeland net primary productivity (NPP) by 2050 is projected to decrease 42% under RCP4.5 (2°C global warming) and 46% under RCP8.5 (2.4°C global warming) for western sub-Saharan Africa, compared to a 2000 baseline ( [[#Boone--2018|Boone et al., 2018]] ). NPP is also projected to decline by 37% in southern Africa, 32% in north Africa and 5% in both east Africa and central Africa by 2050 under RCP8.5 (2.4°C global warming) ( [[#Boone--2018|Boone et al., 2018]] ). For example, in Zimbabwe by 2040–2070, net revenues from livestock production, compared to a 2011 survey, are projected to decline by 8–32% under RCP4.5 for 2°C and 11–43% under RCP8.5 for 2.7°C global warming due to a decline in fodder availability ( [[#Descheemaeker--2018|Descheemaeker et al., 2018]] ). The available literature does not comprehensively capture the economic implications of climate-related impacts on livestock production across Africa.

Fodder quality, critical for animal health and weight gain, is at risk from climate change as increases in temperature, elevated CO 2 and water stress have been shown to reduce dry matter digestibility and nitrogen content for C 3 grasses ( [[#Augustine--2018|Augustine et al., 2018]] ), tropical C 4 grasses ( [[#Habermann--2019|Habermann et al., 2019]] ) and fodder crops such as Lucerne/Alfalfa ( [[#Polley--2013|Polley et al., 2013]] ; [[#Thivierge--2016|Thivierge et al., 2016]] ).

Climate change is projected to threaten water availability for livestock. Droughts in Africa have become more intense, frequent and widespread in the last 50 years ( [[#Masih--2014|Masih et al., 2014]] ), and progressive increase in droughts between 3- and 20-fold under climate change up to 3°C of warming are projected for most of Africa ( [[#9.5|Section 9.5]] ). In the Klela basin in Mali by 2050, groundwater recharge is projected to decline by 49% and groundwater storage by 24% under RCP8.5 (2.4°C global warming) compared to the 2006 baseline ( [[#Toure--2017|Toure et al., 2017]] ). Water availability for livestock during drought is a major concern for many African pastoralists including but not limited to those in Zimbabwe ( [[#Dzavo--2019|Dzavo et al., 2019]] ) and Nigeria ( [[#Ayanlade--2019|Ayanlade and Ojebisi, 2019]] ). Increased livestock mortality and livestock price shocks have been associated with droughts in Africa, as well as being a potential pathway for climate-related conflict ( [[#Catley--2014|Catley et al., 2014]] ; see Box 9.9; [[#Maystadt--2014|Maystadt and Ecker, 2014]] ).

Heat stress may already be the largest factor impacting livestock production in many regions in Africa ( [[#El-Tarabany--2017|El-Tarabany et al., 2017]] ; [[#Pragna--2018|Pragna et al., 2018]] ), as the combination of high temperatures and high relative humidity can be dangerous for livestock and has already decreased dairy production in Tunisia ( [[#Amamou--2018|Amamou et al., 2018]] ). Climate change is projected to increase heat stress for all types of livestock, especially in the tropics (Figure 9.24; [[#Lallo--2018|Lallo et al., 2018]] ). More studies quantifying the impact of heat stress on other types of livestock production loss are needed in Africa ( [[#Rahimi--2021|Rahimi et al., 2021]] ).

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[[File:f05a4c8929a6a6a995c432f95aaae706 IPCC_AR6_WGII_Figure_9_024.png]]

'''Figure 9.24 |''' '''Severe heat stress duration for cattle in Africa is projected to increase with increasing global warming.'''

'''(a)''' Number of days per year with severe heat stress in the historical climate (1985–2014).

'''(b)''' Historical cattle exposure to severe heat. Cattle density data from [[#Gilbert--2018|Gilbert et al. (2018)]] .

'''(c, d)''' Projected increase in the number of days per year with severe heat stress for a global warming level of 1.5°C and 3.75°C. Severe heat stress for cattle is projected to become much more extensive in the future in Africa at increased global warming levels. Strong mitigation would substantially limit the spatial extent and the duration of cattle heat stress across Africa. Heat stress is estimated using the Temperature Humidity Index with a value greater than 79 considered the onset of severe heat stress (Livestock Weather Safety Index) ( [[#Lallo--2018|Lallo et al., 2018]] ). Global warming of 1.5°C used scenario SSP1–2.6 and global warming of 3.75°C used SSP5-8.5, both for 2070–2099 (12 climate models from [[#O’Neill--2016|O’Neill et al., 2016]] ; [[#Tebaldi--2021|Tebaldi et al., 2021]] ). Global warming levels were calculated using a baseline for pre-industrial global mean temperature of 1850–1900.

Climate change will impact livestock disease prevalence primarily through changes in vector dynamics or range ( [[#Abdela--2016|Abdela and Jilo, 2016]] ; [[#Semenza--2018|Semenza and Suk, 2018]] ). African Rift Valley Fever (RVF) and trypanosomiasis are positively associated with extreme climate events (droughts and ENSO) ( [[#Bett--2017|Bett et al., 2017]] ) and are projected to expand in range under climate change ( [[#Kimaro--2017|Kimaro et al., 2017]] ; [[#Mweya--2017|Mweya et al., 2017]] ). More quantitative estimates of projected risk from diseases are needed.

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<span id="adapting-to-climate-variability-and-change-in-agriculture"></span>
=== 9.8.3 Adapting to Climate Variability and Change in Agriculture ===

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Agricultural and livelihood diversification are strategies used by African households to cope with climate change, enabling them to spread risks and adjust to shifting climate conditions ( [[#Thierfelder--2017|Thierfelder et al., 2017]] ; [[#Thornton--2018|Thornton et al., 2018]] ). This includes adjusting cropping choices, planting times, or size, type and location of planted areas ( [[#Altieri--2015|Altieri et al., 2015]] ; [[#Nyagumbo--2017|Nyagumbo et al., 2017]] ; [[#Dayamba--2018|Dayamba et al., 2018]] ). In southern Africa, changes in planting dates provide farmers with greater yield stability in uncertain climate conditions ( [[#Nyagumbo--2017|Nyagumbo et al., 2017]] ). In Ghana, farmers are changing planting schedules and using early maturing varieties to cope with late-onset and early cessation of the rainy season ( [[#Antwi-Agyei--2014|Antwi-Agyei et al., 2014]] ; [[#Bawakyillenuo--2016|Bawakyillenuo et al., 2016]] ).

The use of drought-tolerant crop varieties is another adaptation available to African farmers ( [[#Hove--2018|Hove and Gweme, 2018]] ; [[#Choko--2019|Choko et al., 2019]] ). Adoption, however, is hindered by lack of information and training, availability or affordability of seed, inadequate labelling and packaging size for seed supplies and financial constraints ( [[#Fisher--2015|Fisher et al., 2015]] ). Moreover, drought-tolerant varieties do not address changing temperature regimes ( [[#Guan--2017|Guan et al., 2017]] ).

Crop diversification enhances crop productivity and resilience and reduces vulnerability in smallholder farming systems ( [[#McCord--2015|McCord et al., 2015]] ; [[#Mulwa--2020|Mulwa and Visser, 2020]] ). In Tanzania, diversified crop portfolios are associated with greater food security and dietary quality ( [[#Brüssow--2017|Brüssow et al., 2017]] ). In Kenya, levels of crop diversity are higher in villages affected by frequent droughts, which are the main cause of crop failure ( [[#Bozzola--2020|Bozzola and Smale, 2020]] ). Crop diversification also helps control pest outbreaks, which may become more frequent and severe under increased climate variability and extreme events ( [[#Schroth--2014|Schroth and Ruf, 2014]] ). High farming diversity enables households to better meet food needs, but only up to a certain level of diversity ( [[#Waha--2018|Waha et al., 2018]] ), and the viability of and benefits from mixed farming are highly context dependent ( [[#Thornton--2015|Thornton and Herrero, 2015]] ; [[#Weindl--2015|Weindl et al., 2015]] ).

Agroecological and conservation agriculture practices, such as intercropping, integration of legumes, mulching and incorporation of crop residues, are associated with household food security and improved health status ( [[#Nyantakyi-Frimpong--2017|Nyantakyi-Frimpong et al., 2017]] ; [[#Shikuku--2017|Shikuku et al., 2017]] ). These practices can enhance the benefits of other adaptations, such as planting drought- and heat-tolerant or improved varieties, although effects vary across soil types, geographical zones and social groups ( [[#Makate--2019|Makate et al., 2019]] ; [[#Mutenje--2019|Mutenje et al., 2019]] ). Non-climatic variables, such as financial resources, access to information and technology, level of education, land security and gender dynamics affect feasibility and adoption ( [[#Makate--2019|Makate et al., 2019]] ; [[#Mutenje--2019|Mutenje et al., 2019]] ).

To mitigate growing water stress, countries like Ethiopia, Rwanda, Tanzania and Uganda are striving to improve irrigation efficiency ( [[#McCarl--2015|McCarl et al., 2015]] ; [[#Connolly-Boutin--2016|Connolly-Boutin and Smit, 2016]] ; [[#Herrero--2016|Herrero et al., 2016]] ). The feasibility and effectiveness of this adaptation depend on biophysical and socioeconomic conditions ( [[#Amamou--2018|Amamou et al., 2018]] ; [[#Harmanny--2019|Harmanny and Malek, 2019]] ; [[#Schilling--2020|Schilling et al., 2020]] ). Irrigation is unaffordable for many smallholder farmers and only covers a negligible proportion of the total cultivated area. Nonetheless, in some regions of west Africa, small-scale irrigation, including the digging of ditches, holes and depressions to collect rainwater ( [[#Makondo--2018|Makondo and Thomas, 2018]] ), is widely adopted and promoted to support national food security ( [[#Dowd-Uribe--2018|Dowd-Uribe et al., 2018]] ).

African farmers are also diversifying their income sources to offset reduced yields or crop losses by shifting labour resources to off-farm work, or by migrating seasonally or longer term ( [[#Kangalawe--2017|Kangalawe et al., 2017]] ; [[#Hove--2018|Hove and Gweme, 2018]] ). Off-farm activities provide financial resources that rural households need to cope with extreme climate variability ( [[#Hamed--2018|Hamed et al., 2018]] ; [[#Rouabhi--2019|Rouabhi et al., 2019]] ). However, in some cases, these off-farm activities can be maladaptive at larger scales, such as when households turn to charcoal production, which contributes to deforestation ( [[#Egeru--2016|Egeru, 2016]] ). Whether off-farm activities constitute maladaptation depends on whether resources are available to upgrade skills or support investments that make a new business more lucrative. Without such resources, this option may lead to impoverishment (see Box 5.8).

Smallholder farmers’ responses tend to address short-term shocks or stresses by deploying coping responses (e.g., selling labour, reducing consumption and temporary migration), rather than longer-term sustainable adaptations ( [[#Ziervogel--2014|Ziervogel and Parnell, 2014]] ; [[#Jiri--2017|Jiri et al., 2017]] ). This is partly due to institutional barriers (e.g., markets, credit, infrastructure and information) and resource requirements that are unaffordable to smallholder farmers ( [[#Pauline--2017|Pauline et al., 2017]] ). There is a need for policies that strengthen natural, financial, human and social capitals, the latter being key to household and community resilience, especially where government services may be limited ( [[#Mutabazi--2015|Mutabazi et al., 2015]] ; [[#Alemayehu--2017|Alemayehu and Bewket, 2017]] ). There is evidence that collective action, local organisations and climate information are associated with higher food security, and that institutional interventions are needed to ensure scaling up of adaptations ( [[#Thornton--2018|Thornton et al., 2018]] ).

A range of options is considered potentially effective in reducing future climate change risk, including plant breeding, crop diversification alongside livestock, mixed planting, intercrops, crop rotation and integrated crop–livestock systems (see [[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] Sections 5.4.4; 5.14.1; [[#Thornton--2014|Thornton and Herrero, 2014]] ; [[#Cunningham--2015|Cunningham et al., 2015]] ; [[#Himanen--2016|Himanen et al., 2016]] ; [[#Farrell--2018|Farrell et al., 2018]] ; [[#Snowdon--2021|Snowdon et al., 2021]] ). However, adaptation limits for crops in Africa are increasingly reached for global warming above 2°C ( ''high confidence'' ), and in tropical Africa may already be reached at current levels of global warming ( ''low confidence'' ).

Global warming beyond 2°C will place nearly all of sub-Saharan Africa cropland substantially outside of its historical safe climate zone ( [[#Kummu--2021|Kummu et al., 2021]] ) and may exponentially increase the cost of adaptation and residual damage for major crops ( [[#Iizumi--2020|Iizumi et al., 2020]] ). Without accounting for CO 2 increases, global-scale studies employing ensembles of gridded crop models for 2°C of global warming find that for adaptation using genetic cultivar change in most of Africa net losses are projected, even with adaptation up to 2°C of global warming for rice, maize, soybean and wheat ( [[#Minoli--2019|Minoli et al., 2019]] ; [[#Zabel--2021|Zabel et al., 2021]] ), although model uncertainty is still high ( [[#Müller--2021|Müller et al., 2021]] ). In contrast, when accounting for CO 2 increases, applying new genetics for rice under warming is projected to fully counteract all climate change-induced losses in Africa up to 3.5°C of global warming, except in west Africa ( [[#van%20Oort--2018|van Oort and Zwart, 2018]] ).

However, compared to temperate regions, risks of adaptation shortfalls—that is climate change impacts even after adaptation—are generally greater for current agricultural conditions across much of Africa (tropical, arid and semi-arid) ( [[#Sun--2019|Sun et al., 2019]] ). The overall adaptation potential to offset yield losses across Africa for rice, maize and wheat reduces with increasing global warming. On average, in projections including adaptation options, yield losses, in the median case, are reduced from −33% to −10% of 2005 levels at 2°C of global warming and from −46% to −23% at 4°C, but estimates vary widely (Figure 9.22; [[#Hasegawa--2021|Hasegawa et al., 2021]] ). Across Africa, the risks of no available genetic varieties of maize for growing season adaptation are higher for east Africa and southern Africa than for central or west Africa ( [[#Zabel--2021|Zabel et al., 2021]] ). To keep pace with expected rates of climate change, crop breeding, development and adoption must accelerate to meet the challenge ( [[#Challinor--2016|Challinor et al., 2016]] ). Regional modelling has shown very little efficacy for late sowing, intensification of seeding density and fertilizers, water harvesting and other measures for cereals in west Africa at 2°C of global warming ( [[#Sultan--2016|Sultan and Gaetani, 2016]] ; [[#Guan--2017|Guan et al., 2017]] ). Historical climate change adaptation by crop migration has been shown in some cases ( [[#Sloat--2020|Sloat et al., 2020]] ) but poses risks to biodiversity and water resources, and this option may be limited for maize in Africa by suitable climate shifting completely across national borders and available land at the edges of the continent ( [[#Franke--2021|Franke et al., 2021]] ). More research is required to evaluate the potential effectiveness and limits of adaptation options in African agriculture under future climate change (see [[IPCC:Wg2:Chapter:Chapter-5|Chapter 5]] [[IPCC:Wg2:Chapter:Chapter-5#5.4.4|Section 5.4.4]] for more details).

<div id="9.8.4" class="h2-container"></div>

<span id="climate-information-services-and-insurance-for-agriculture-adaptation"></span>
=== 9.8.4 Climate Information Services and Insurance for Agriculture Adaptation ===

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In addition to adaptation in crop, soil and water management, the combination of (a) Climate Information Services, (b) institutional capacity building and (c) strategic financial investment can help African food producers adapt to projected climate risks ( [[#Carter--2015|Carter et al., 2015]] ; [[#Surminski--2016|Surminski et al., 2016]] ; [[#Scott--2017|Scott et al., 2017]] ; [[#Cinner--2018|Cinner et al., 2018]] ; [[#Diouf--2019|Diouf et al., 2019]] ; [[#Hansen--2019a|Hansen et al., 2019a]] ). There is growing evidence of farmers’ use of weather and climate information, especially at the short- and medium-time horizon ( [[#Carr--2016|Carr et al., 2016]] ; [[#Singh--2018|Singh et al., 2018]] ). Digital services can contribute to the sustainable intensification of food production globally ( [[#Duncombe--2018|Duncombe, 2018]] ; [[#Klerkx--2019|Klerkx et al., 2019]] ). This points to the need for the scientific and development communities to better understand the conditions that enable widespread adoption in Africa.

Although climate information services have the potential to strengthen farmers’ resilience, barriers to accessibility, affordability and utilisation remain ( [[#Krell--2021|Krell et al., 2021]] ). Often the information offered is not consistent with what farmers need to know and how they access and process information ( [[#Meadow--2015|Meadow et al., 2015]] ; [[#Singh--2018|Singh et al., 2018]] ). Production of salient and credible climate information is hindered by the limited availability of and access to weather and climate data ( [[#Coulibaly--2017|Coulibaly et al., 2017]] ; [[#Hansen--2019a|Hansen et al., 2019a]] ). The existing weather infrastructure remains suboptimal to enable the development of reliable early warning systems ( [[#Africa%20Adaptation%20Initiative--2018|Africa Adaptation Initiative, 2018]] ; [[#Krell--2021|Krell et al., 2021]] ). Of the 1017 land-based observational networks in the world, only 10% are in Africa, and 54% of Africa’s surface weather stations cannot capture data accurately ( [[#Africa%20Adaptation%20Initiative--2018|Africa Adaptation Initiative, 2018]] ; [[#World%20Bank--2020d|World Bank, 2020d]] ).

Advances in remote sensing and climate analysis tools have allowed the development of weather index insurance products as a potential adaptation option, with Malawi and Ethiopia being early testbeds ( [[#Tadesse--2015|Tadesse et al., 2015]] , [[#9.11.4|Section 9.11.4]] ). These pilot projects were initially sponsored by NGOs, but in the last decade, the private sector has become more active in this sector. The Ghana Agricultural Insurance Pool and Agriculture and Climate Risk Enterprise (ACRE) in Kenya, Tanzania and Rwanda are examples. Despite the potential for weather index insurance, uptake by smallholder farmers in Africa remains constrained by several factors. These include the failure to capture actual crop loss as in traditional crop insurance products, as well as the inability of poor farmers to pay premiums ( [[#Elum--2017|Elum et al., 2017]] ; [[#Weber--2019|Weber, 2019]] ). Weather index insurance could be part of a wider portfolio of risk mitigation services offered to farmers ( [[#Tadesse--2015|Tadesse et al., 2015]] ; [[#Weber--2019|Weber, 2019]] ). Strategic partnerships between key players (e.g., credit institutions, policymakers, meteorologists, farmer associations, extension services, NGOs) are needed to develop better products and build capacity among smallholder farmers to engage more beneficially with weather index insurance ( [[#Singh--2018|Singh et al., 2018]] ; [[#Tesfaye--2019|Tesfaye et al., 2019]] ).

<div id="9.8.5" class="h2-container"></div>

<span id="marine-and-inland-fisheries"></span>
=== 9.8.5 Marine and Inland Fisheries ===

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<div id="9.8.5.1" class="h3-container"></div>

<span id="observed-impacts-of-climate-variability-and-change-on-marine-and-inland-fisheries"></span>
==== 9.8.5.1 Observed Impacts of Climate Variability and Change on Marine and Inland Fisheries ====

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Marine and freshwater fisheries provide 19.3% of animal protein intake ( [[#Chan--2019|Chan et al., 2019]] ) and support the livelihoods of 12.3 million people ( [[#de%20Graaf--2015|de Graaf and Garibaldi, 2015]] ) across Africa. Estimates suggest that fish provides approximately 200 million people in Africa with their main source of animal protein and key micronutrients ( [[#Obiero--2019|Obiero et al., 2019]] ). Although marine fisheries account for &gt;50% of total capture fishery production ( [[#Obiero--2019|Obiero et al., 2019]] ), 2.9 million tonnes of fish are harvested annually from inland water bodies constituting the highest per capita inland fishery production of any continent (2.56 kg per person per year) ( [[#Harrod--2018a|Harrod et al., 2018a]] ; [[#Funge-Smith--2019|Funge-Smith and Bennett, 2019]] ).

Climate change already poses a significant threat to marine and freshwater fisheries and aquaculture in Africa ( [[#Blasiak--2017|Blasiak et al., 2017]] ; [[#Harrod--2018a|Harrod et al., 2018a]] ). Severe (&gt;30%) coral bleaching has impacted ~80% of major reef areas in the western Indian Ocean and Red Sea along Africa’s eastern coast ( [[#Hughes--2018|Hughes et al., 2018]] ). Biological effects (e.g., changes in primary production, fish distribution) have also occurred ( [[#Hidalgo--2018|Hidalgo et al., 2018]] ). Range shifts in marine fish species can exacerbate boundary conflicts among fisher communities ( [[#Penney--2017|Penney et al., 2017]] ; [[#Belhabib--2019|Belhabib et al., 2019]] ). Changes in fish distribution and reductions in catch across inland fisheries are associated with climatic variability by fishing communities ( [[#Okpara--2017b|Okpara et al., 2017b]] ; [[#Lowe--2019|Lowe et al., 2019]] ; [[#Muringai--2019b|Muringai et al., 2019b]] ). Floods and reduced river flow reduces fish catches ( [[#Kolding--2019|Kolding et al., 2019]] ), which scale positively with discharge rates in rivers across Africa ( [[#McIntyre--2016|McIntyre et al., 2016]] ). Warming air and water temperatures have altered water stratification patterns in African lakes causing reductions in or redistributions of primary productivity and leading to reduced fish biomass ( [[#9.6.1.3|Section 9.6.1.3]] ). Such changes, partially explain reduced fish catches in Lake Tanganyika ( [[#Cohen--2016|Cohen et al., 2016]] ). In some regions, water scarcity has resulted in conflict within and among food production sectors (pastoralists, fishers and farmers) in this region ( [[#Okpara--2017b|Okpara et al., 2017b]] ). Small-scale and artisanal fisher communities are ill-equipped to adapt to climate impacts because there are few financially accessible alternative livelihoods ( [[#Belhabib--2016|Belhabib et al., 2016]] ; [[#Ndhlovu--2017|Ndhlovu and Saito, 2017]] ).

<div id="9.8.5.2" class="h3-container"></div>

<span id="projected-risks-of-climate-change-to-fisheries"></span>
==== 9.8.5.2 Projected Risks of Climate Change to Fisheries ====

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At 4.3°C global warming, maximum catch potential (MCP) from marine fisheries in African Exclusive Economic Zones (EEZs) would decrease by 12–69% by the end of the 21st century relative to recent decades (1986–2005), whereas global warming of 1.6°C would limit the MCP decrease to 3–41% ( [[#Cheung%20William--2016|Cheung William et al., 2016]] ; [[#IPCC--2019c|IPCC, 2019c]] ). By mid-century under 2°C global warming, MCP would decrease by 10 to &gt;30% on the western coast of South Africa, the Horn of Africa and west Africa, indicating these regions could be at risk to declines in MCP earlier in the century than other parts of Africa ( [[#Cheung--2016|Cheung et al., 2016]] ). Declining fish harvests due to sea temperature rise could leave 1.2–70 (median 11.1) million people in Africa vulnerable to deficiencies in iron, and up to 188 million to vitamin A and 285 million to vitamin B 12 and omega-3 fatty acids by mid-century under 1.7°C global warming ( [[#Golden--2016|Golden et al., 2016]] ). [[#Maire--2021|Maire et al. (2021)]] assessed the nutritional vulnerabilities of African countries to climate change and overfishing, and found that the four most vulnerable countries ranked on a scale from 0 (low vulnerability) to 100 (high vulnerability) were Mozambique (87), Madagascar (76), Tanzania (61) and Sierra Leone (58). Coral reef habitat in east Africa is projected to decrease, resulting in negative impacts on demersal fish stocks and invertebrates ( [[#Hoegh-Guldberg--2018|Hoegh-Guldberg et al., 2018]] ). Central, west and east Africa are projected to be at the greatest nutritional risk from sea temperature rise, leading to reduced catch in coastal waters (Figure 9.25; [[#Golden--2016|Golden et al., 2016]] ). In north Africa, a rise in water temperatures is expected to impact the phenology and migratory patterns of large pelagic species (e.g., bluefin tuna, ''Thunnus thynnus'' ) ( [[#Hidalgo--2018|Hidalgo et al., 2018]] ). Increased sea surface temperatures have been associated with increases in spring and summer upwelling intensity reducing the abundance and larval survival of small pelagic fishes and shellfish in west Africa ( [[#Bakun--2015|Bakun et al., 2015]] ; [[#Tiedemann--2017|Tiedemann et al., 2017]] ; [[#Atindana--2020|Atindana et al., 2020]] ). Ocean warming, acidification and hypoxia are predicted to affect the early life history stages of several marine food species, including fish and crustaceans ( [[#Kifani--2018|Kifani et al., 2018]] ). Climate warming is projected to impact water temperature and horizontal and vertical mixing on the southern Benguela ecosystem, with marked negative effects on the biomass of several important fishery resources by 2050 amplified under 2.5°C compared to 1.7°C global warming ( [[#Ortega-Cisneros--2018|Ortega-Cisneros et al., 2018]] ).

<div id="_idContainer077" class="Figure"></div>

[[File:dd7f7df3b9c3499f3c2fa8ab68a87c41 IPCC_AR6_WGII_Figure_9_025.png]]

'''Figure 9.25 |''' '''Climate change increases risks to the catch potential and nutrition from marine fisheries.'''

'''(a)''' The percentage of animal source foods consumed that originate from a marine environment. Countries with higher dependence are indicated by darker shades of green ( [[#Golden--2016|Golden et al., 2016]] ).

'''(b–c)''' Projected percentage change in maximum catch potential of marine fisheries compared to the recent past (1986–2005) under 1.6°C global warming and &gt;4°C global warming by end of 21st century (2081–2100) in countries’ Exclusive Economic Zones (EEZs) ( [[#Cheung%20William--2016|Cheung William et al., 2016]] ). Darker red indicates greater percentage reduction (negative values).

'''(d–e)''' Countries (in purple) that have overlap between high nutritional dependence on marine fisheries and high risk of reduction in maximum catch potential under the two global warming scenarios. Global warming levels were calculated using a baseline for pre-industrial global mean temperature of 1850–1900.

For inland fisheries, 55–68% of commercially harvested fish species will be vulnerable to extinction under 2.5°C global warming by the end of the 21st century (2071–2100) compared to 77–97% under 4.4°C global warming (Figure 9.26). This will increase the number of countries that are at food security risk due to fishery species declines from 10 to 13 (Figure 9.26). Other recent analyses suggest that African countries with the highest inland fisheries production have low- to mid-range projected climate risk (2.4°C–2.6°C local temperature increase compared to other regions with 2.7°C–3.3°C increase by end of century) based on a 3.9°C global warming scenario ( [[#Harrod--2018b|Harrod et al., 2018b]] ). In regions where inland fishery production is derived primarily from lakes, there is a lower likelihood of reduced catch, especially where precipitation is projected to increase (e.g., African Great Lakes region) ( [[#Harrod--2018b|Harrod et al., 2018b]] ). Regions reliant on rivers and floodplains (e.g., Zambezi and Niger basins) are more ''likely'' to experience downturns in catch, as hydrological dynamics may be altered ( [[#Harrod--2018b|Harrod et al., 2018b]] ). Projections suggest that opportunistic species that do well in modified systems ( [[#Escalera-vázquez--2017|Escalera-vázquez et al., 2017]] ) and small pelagic fishes will remain important components of inland fishery food systems ( [[#Kolding--2016|Kolding et al., 2016]] ; [[#Gownaris--2018|Gownaris et al., 2018]] ; 2019). Climate adaptation responses that rely on freshwater resources (e.g., hydroelectric power generation, agricultural irrigation) represent threats to inland fisheries ( [[#Cowx--2018|Cowx et al., 2018]] ; [[#Harrod--2018c|Harrod et al., 2018c]] ), by changing flow regimes, reducing water levels, and increasing runoff of pesticides and nutrients ( [[#Harrod--2018c|Harrod et al., 2018c]] ).

<div id="_idContainer079" class="Figure"></div>

[[File:16a1a5a6a362d2da1a05b2693b7ca404 IPCC_AR6_WGII_Figure_9_026.png]]

'''Figure 9.26 |''' '''Climate change risk to''' '''freshwater fisheries.'''

'''(a)''' Countries’ dependence on inland fisheries for nutrition; darker green shows higher dependence on inland fisheries.

'''(b–c)''' Projected numbers of freshwater fishery species vulnerable to climate change within freshwater ecoregions under &gt;2°C global warming and &gt;4°C global warming estimated by the end of the 21st century (2071 to 2100). Numbers of vulnerable fish species translate to an average of 55–68% vulnerable at &gt;2°C and 77–97% vulnerable at &gt;4°C global warming. Darker reds indicate higher concentrations of vulnerable fish species.

'''(d–e)''' Countries (in purple) that have an overlap between high dependence on freshwater fish and high concentrations of fishery species that are vulnerable to climate change under two warming scenarios. Countries’ dependence on inland fisheries for nutrition was estimated by catch (total, tonnes) ( [[#FAO--2018b|FAO, 2018b]] ; [[#Fluet-Chouinard--2018|Fluet-Chouinard et al., 2018]] ), per capita catch (kg per person per year) ( [[#FAO--2018b|FAO, 2018b]] ), percentage reliance on fish for micronutrients, and percentage consumption per household ( [[#Golden--2016|Golden et al., 2016]] ). Z-scores of each metric were averaged for each country to create a composite index describing ‘current dependence on freshwater fish’ for each country with darker blue colours indicating higher dependence. Data on vulnerable fish species was from ( [[#Nyboer--2019|Nyboer et al., 2019]] ).

For both marine and freshwater fisheries, climate-related extreme weather events and flooding may drive the loss of fishing days, cause damage and loss to fishing gear, endanger the lives of fishers and block transportation from damaged roads ( [[#Muringai--2021|Muringai et al., 2021]] ). Fish processing via weather-dependent techniques such as sun drying may be hampered, causing post-harvest losses ( [[#Akintola--2017|Akintola and Fakoya, 2017]] ; [[#Chan--2019|Chan et al., 2019]] ).

<div id="9.8.5.3" class="h3-container"></div>

<span id="current-and-future-adaptation-responses-for-fisheries"></span>
==== 9.8.5.3 Current and Future Adaptation Responses for Fisheries ====

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Patterns of vulnerability and adaptive capacity are highly context dependent and vary within and among fishing communities in coastal and riparian areas ( [[#Ndhlovu--2017|Ndhlovu and Saito, 2017]] ; [[#Lowe--2019|Lowe et al., 2019]] ; [[#D’agata--2020|D’agata et al., 2020]] ). Interventions that integrate scientific knowledge and fishers’ local knowledge while focusing on vulnerable groups are expected to be more successful ( [[#Musinguzi--2018|Musinguzi et al., 2018]] ; [[#Muringai--2019b|Muringai et al., 2019b]] ). Infrastructure improvements (e.g., storage facilities, processing technologies, transport systems) could reduce post-harvest losses and improve food safety ( [[#Chan--2019|Chan et al., 2019]] ). Fisher safety can be aided by early warning of severe weather conditions ( [[#Thiery--2017|Thiery et al., 2017]] ), enhanced through communication via mass media and mobile phones ( [[#Thiery--2017|Thiery et al., 2017]] ; [[#Kiwanuka-Tondo--2019|Kiwanuka-Tondo et al., 2019]] ). Although changing fishing gears and shifting target species are important adaptation options for artisanal fishers, many have instead expanded their fishing range or increased effort ( [[#Musinguzi--2015|Musinguzi et al., 2015]] ; [[#Belhabib--2016|Belhabib et al., 2016]] ). Adapting to the impacts of climate change on marine fisheries productivity requires management reforms accounting for shifting productivity and species distributions, such as increasing marine protected areas, strengthening regional trade networks, and increasing the investment and innovation in climate-resilient aquaculture production ( [[#Golden--2021|Golden et al., 2021]] ). This could yield higher catch and profits in the future relative to today in 50% of African countries with marine territories under 2°C global warming and in 35% under 4.3°C global warming ( [[#Free--2020|Free et al., 2020]] ). For inland fisheries, opportunities for adaptation include better integration of inland fisheries into management plans from other sectors (e.g., hydropower and irrigation) ( [[#Harrod--2018c|Harrod et al., 2018c]] ; [[#Cowx--2019|Cowx and Ogutu-Ohwayo, 2019]] ; [[#McCartney--2019|McCartney et al., 2019]] ). There is growing interest in enhancing the supply of freshwater fishery production from small water bodies and reservoirs in dryland regions of sub-Saharan Africa ( [[#Kolding--2016|Kolding et al., 2016]] ).

<div id="9.9" class="h1-container"></div>

<span id="human-settlements-and-infrastructure"></span>