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

=== 5.9.4 Aquaculture Adaptation ===

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==== 5.9.4.1 Adaptation planning ====

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Aquaculture is often viewed as an adaptation option for fisheries declines, thereby alleviating food security from losses of other climate change impacts ( [[#Sowman--2018|Sowman and Raemaekers, 2018]] ; [[#Johnson--2020|Johnson et al., 2020]] ) such as Pacific Islands freshwater aquaculture, Bangladesh crop-aquaculture systems or Viet Nam rice–fish cultivations ( [[#Soto--2018|Soto et al., 2018]] ). Many adaptations are specific to regions, countries or sectors, implemented on a regional to national scale ( [[#FAO--2018c|FAO, 2018c]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ). Adaptation likelihood (potential), effectiveness and risk of maladaptation was assessed per major FAO production region for inland, brackish and marine aquaculture (Figure 5.16) production systems. Potential adaptation measures to reduce production loss can be built upon existing adaptation planning and guidelines, to reduce the risk of maladaptation including feedback loops (e.g., [[#FAO--2015|FAO, 2015]] ; [[#Bueno--2017|Bueno and Soto, 2017]] ; [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Poulain--2018|Poulain et al., 2018]] ; [[#Brugère--2019|Brugère et al., 2019]] ; [[#Pham--2021|Pham et al., 2021]] ; [[#Soto--2021|Soto et al., 2021]] ). Large climate change adaptation strategies for the aquaculture sector exist, such as in the USA ( [[#Link--2015|Link et al., 2015]] ), Australia ( [[#Hobday--2017|Hobday et al., 2017]] ) and South Africa ( [[#Department%20of%20Environmental%20Affairs--2016|Department of Environmental Affairs, 2016]] ). Lower-income countries often lack financial, technical or institutional capacity for adaptation planning ( [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ), but examples include Bangladesh and Myanmar ( [[#FAO--2018c|FAO, 2018c]] ), with programmes offering adaptation funding ( [[#Dabbadie--2018|Dabbadie et al., 2018]] ). Early participation of stakeholders in adaptive planning has promoted action and ownership of results ( ''high confidence'' ), such as in India and the USA ( [[#Link--2015|Link et al., 2015]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Soto--2018|Soto et al., 2018]] ) Early outreach, education and knowledge gap assessments raise awareness, where utilisation of local knowledge and Indigenous knowledge and scientific involvement support informed adaptive planning and uptake for all stakeholders ( ''high confidence'' ) ( [[#Cooley--2016|Cooley et al., 2016]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Rybråten--2018|Rybråten et al., 2018]] ; [[#Soto--2018|Soto et al., 2018]] ; [[#McDonald--2019|McDonald et al., 2019]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ), as perceptions of climate risk and capacity will vary ( [[#Tiller--2018|Tiller and Richards, 2018]] ). Supporting the active involvement of women helps address gender inequity and perceived risk, particularly for smallholder farmers ( ''high confidence'' ) ( [[#Morgan--2015|Morgan et al., 2015]] ; [[#Barange--2018|Barange and Cochrane, 2018]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Avila-Forcada--2020|Avila-Forcada et al., 2020]] ). However, regional and national political influences, financial and technical capacity, governance planning and policy development will ultimately support or hinder adaptation for aquaculture ( ''high confidence'' ) ( [[#Cooley--2016|Cooley et al., 2016]] ; [[#FAO--2018c|FAO, 2018c]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; [[#Greenhill--2020|Greenhill et al., 2020]] ).

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[[File:38a48987bdb010dfd623e34bb7638ac3 IPCC_AR6_WGII_Figure_5_016.png]]

'''Figure 5.16 |''' '''Assessment of the likelihood and effectiveness of a range of adaptation options for potential implementation in the near term (next decade) for inland freshwater and brackish aquaculture''' ''(salinities of &lt;10 ppm and/or no connection to the marine environment)'' '''(a)''' ''and marine aquaculture systems'' '''(b)''' ''per major FAO production zone.'' See SM5.6 (Tables SM5.8, 5.12) for assessment methodologies.

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==== 5.9.4.2 Species selections and selective breeding ====

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Adaptation options at the operational level include species selections, such as cultivation of brackish species (shrimp, crabs) during dry seasons, and rice-finfish in wetter seasons in Thailand ( [[#Chiayarak--2019|Chiayarak et al., 2019]] ), use of salt-tolerant plants in Viet Nam ( [[#Nhung--2019|Nhung et al., 2019]] ; [[#Paik--2020|Paik et al., 2020]] ), converting inundated rice paddies into aquaculture, rotating shrimp, and rice culture ( ''high confidence'' ) ( [[#Chiayarak--2019|Chiayarak et al., 2019]] ). Species diversification through co-culture, integrated aquaculture–agriculture (e.g., rice–fish) or integrated multi-trophic culture (e.g., shrimp–tilapia–seaweed or finfish–bivalve–seaweed) may maintain farm long-term performance and viability by: creating new aquaculture opportunities; promoting societal and environmental stability; reducing GHG emissions through reduced feed usage and waste; and carbon sequestration ( ''medium confidence'' ) (see [[#5.10|Section 5.10]] , [[#Ahmed--2017|Ahmed et al., 2017]] ; [[#Bunting--2017|Bunting et al., 2017]] ; [[#Gasco--2018|Gasco et al., 2018]] , [[#Soto--2018|Soto et al., 2018]] ; [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Dubois--2019|Dubois et al., 2019]] ; [[#FAO--2019c|FAO, 2019c]] ; [[#Li--2019|Li et al., 2019]] ; [[#Freed--2020|Freed et al., 2020]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; Prasko et al., 2020; [[#Tran--2020|Tran et al., 2020]] ). In practice, most aquaculture operations concentrate on single-species systems ( [[#Metian--2020|Metian et al., 2020]] ), and barriers such as land availability, freshwater resources and lack of credit access may limit the uptake and success of integrated adaptation approaches to climate change ( [[#Ahmed--2019|Ahmed et al., 2019]] ; [[#Tran--2020|Tran et al., 2020]] ; [[#Kais--2021|Kais and Islam, 2021]] ).

Selective breeding can promote climate resilience ( ''medium confidence'' ) ( [[#Klinger--2017|Klinger et al., 2017]] ; [[#Fitzer--2019|Fitzer et al., 2019]] ), and operations have already intentionally, or unintentionally, selected for production traits for changing conditions ( [[#de%20Melo--2016|de Melo et al., 2016]] ; [[#Tan--2020|Tan and Zheng, 2020]] ). Exposure of broodstock to future climate conditions may or may not confer advantages to offspring ( ''moderate evidence'' , ''low agreement'' ) ( [[#Parker--2015|Parker et al., 2015]] ; [[#Griffith--2017|Griffith and Gobler, 2017]] ; [[#Thomsen--2017|Thomsen et al., 2017]] ; [[#Durland--2019|Durland et al., 2019]] ). Traditional pedigree developments require extensive phenotypic data, but genomic selections can rapidly select for robust climate-associated traits ( [[#Sae-Lim--2017|Sae-Lim et al., 2017]] ; [[#Gutierrez--2018|Gutierrez et al., 2018]] ; [[#Zenger--2018|Zenger et al., 2018]] ; [[#Houston--2020|Houston et al., 2020]] ; [[#Tan--2020|Tan and Zheng, 2020]] ). Genomic resources are available for salmon, rainbow trout, coho, carp, tilapia, seabass, bream, turbot, flounder, catfish, yellow drum, scallops, oysters and shrimp, but have been developed for disease and growth selections rather than climate resistance ( [[#Dégremont--2015a|Dégremont et al., 2015a]] ; [[#Dégremont--2015b|Dégremont et al., 2015b]] ; [[#Abdelrahman--2017|Abdelrahman et al., 2017]] ; [[#Gjedrem--2018|Gjedrem and Rye, 2018]] ; [[#Gutierrez--2018|Gutierrez et al., 2018]] ; [[#Guo--2018|Guo et al., 2018]] ; [[#Liu--2018a|Liu et al., 2018a]] ; [[#FAO--2019d|FAO, 2019d]] ; [[#Houston--2020|Houston et al., 2020]] ), although bivalve selections for ocean acidification and warming resiliency are underway ( [[#Tan--2020|Tan and Zheng, 2020]] ). Targeted genome editing could modify phenotypes of major aquaculture species ( [[#Li--2014|Li et al., 2014]] a; [[#Elaswad--2018|Elaswad et al., 2018]] ; [[#Yu--2019|Yu et al., 2019]] ; [[#Houston--2020|Houston et al., 2020]] ), but uptake is dependent upon national regulatory and public approvals. Local adaptations within species with higher climate resiliencies may assist in selections ( [[#Thomsen--2017|Thomsen et al., 2017]] ; [[#Falkenberg--2019|Falkenberg et al., 2019]] ; [[#Scanes--2020|Scanes et al., 2020]] ; [[#Toomey--2020|Toomey et al., 2020]] ), but highlight the need to consider specific farming environments for selective processes ( [[#Houston--2020|Houston et al., 2020]] ). Projections of climate on aquaculture production traits are not well understood ( [[#Lhorente--2019|Lhorente et al., 2019]] ); therefore, genetic diversity needs to be maintained to ensure population fitness ( ''high confidence'' ) ( [[#Bitter--2019|Bitter et al., 2019]] ; [[#Lhorente--2019|Lhorente et al., 2019]] ; [[#Visch--2019|Visch et al., 2019]] ; [[#Houston--2020|Houston et al., 2020]] ; [[#Mantri--2020|Mantri et al., 2020]] ).

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==== 5.9.4.3 Farm site selection, infrastructure and husbandry ====

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Land-based aquaculture systems including hatcheries may reduce exposure to climatic extremes (due to better control of the culture environment), limit water usage, reduce juvenile reliance and buffer climate effects using optimal diets ( ''high confidence'' ) ( [[#Barton--2015|Barton et al., 2015]] ; [[#Reid--2019|Reid et al., 2019]] ; [[#Cominassi--2020|Cominassi et al., 2020]] ). However, land-based aquaculture requires large capital and operational costs and use of land, increasing conflicts between land and water use, have increased energy demands (increasing GHG if fossil fuels are the primary energy source), require necessary expertise and will not reduce outgrowing exposures ( ''high confidence'' ) (see [[#5.13|Section 5.13]] , [[#Beveridge--2018b|Beveridge et al., 2018b]] ; [[#Soto--2018|Soto et al., 2018]] ; [[#Tillotson--2019|Tillotson et al., 2019]] ; [[#Costello--2020|Costello et al., 2020]] ; Prakoso et al., 2020).

Geographical selection of marine farm sites may prevent climate productivity declines ( ''medium confidence'' ) ( [[#Froehlich--2018a|Froehlich et al., 2018a]] ; [[#Sainz--2019|Sainz et al., 2019]] ; [[#Oyinlola--2020|Oyinlola et al., 2020]] ), particularly for temperature-related mortality hotspots ( [[#Garrabou--2019|Garrabou et al., 2019]] ), HAB occurrences ( [[#Dabbadie--2018|Dabbadie et al., 2018]] ) or extreme events ( [[#Liu--2020|Liu et al., 2020]] ; [[#Wu--2020|Wu et al., 2020]] ). However, while downscaled climate forecasts facilitate localised adaptation planning ( [[#Falconer--2020a|Falconer et al., 2020a]] ), such projections are rare ( [[#Whitney--2020|Whitney et al., 2020]] ). GIS can be used for climate adaptive planning along with routine site assessments ( [[#Falconer--2020b|Falconer et al., 2020b]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; [[#Jayanthi--2020|Jayanthi et al., 2020]] ). Building coastal protection, stronger cages and mooring systems, and deeper ponds and using sheltered bays can reduce escapees and mortalities related to flooding, increased storms and extreme events ( ''medium confidence'' ) ( [[#Dabbadie--2018|Dabbadie et al., 2018]] ; [[#Bricknell--2021|Bricknell et al., 2021]] ; [[#Kais--2021|Kais and Islam, 2021]] ). Inshore aquaculture in low-lying areas prone to sea level salinity intrusion (e.g., Mekong delta and Viet Nam) have already implemented adaptation measures, such as conversion of land to mixed plant–animal systems ( [[#Nguyen--2019a|Nguyen et al., 2019a]] ), conversion of freshwater ponds to brackish or saline aquaculture ( [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ), building of dams and dykes ( [[#Renaud--2015|Renaud et al., 2015]] ) and intensification of shrimp or fish pond culture to reduce water and land usage ( [[#Nguyen--2019b|Nguyen et al., 2019b]] ; [[#Johnson--2020|Johnson et al., 2020]] ). Other adaptation options for limited water supply are government equitable water allocations and water storage ( ''high confidence'' ) ( [[#Bunting--2017|Bunting et al., 2017]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ).

Feed formulations and improved feed conversion can reduce climate-associated stress for freshwater species, significantly reducing waste and increase sustainability ( ''medium confidence'' ) ( [[#FAO--2018c|FAO, 2018c]] ; [[#Gasco--2018|Gasco et al., 2018]] ; [[#Chen--2019|Chen and Villoria, 2019]] ). Projected decreases in fish meal and global targets of limiting warming to under 2°C may increase the ratio of plant-based diets but reduce fish nutritional content (see Sections 5.10 and 5.13, [[#Hasan--2017|Hasan and Soto, 2017]] ; [[#Johnson--2020|Johnson et al., 2020]] ). Companies provide insurance in major production areas, but aquaculture is considered high risk with large levels of small claims ( [[#Secretan--2007|Secretan et al., 2007]] ). Insurance covers natural disasters and disease, helping to reduce and cope with climate-induced risk, enabling faster livelihood recoveries and preventing poverty ( ''high agreement'' , ''limited evidence'' ) ( [[#Xinhua--2017|Xinhua et al., 2017]] ; [[#Kalikoski--2018|Kalikoski et al., 2018]] ; [[#Soto--2018|Soto et al., 2018]] ). For example, small-scale shrimp farmers were willing to pay higher premiums to manage risk, after participation in government pilot insurance schemes, ensuring greater pay-outs if a mortality event occurred ( [[#Nyguyen--2016|Nyguyen and Pongthanapanic, 2016]] ; [[#Pongthanapanic--2019|Pongthanapanic et al., 2019]] ). Technological innovations are more widely implemented in larger operations, with Internet access promoting adoption at the farm site ( [[#Joffre--2017|Joffre et al., 2017]] ; [[#Salazar--2018|Salazar et al., 2018]] ). Improved farm management is a key opportunity ( ''high confidence'' ) to reduce climate risks on aquaculture, where Best Management Practices can increase resiliency ( [[#Soto--2018|Soto et al., 2018]] ) and lower additional risk from non-climatic stressors ( [[#Gattuso--2018|Gattuso et al., 2018]] ; [[#Smith--2020|Smith and Bernard, 2020]] ), and decision-tree frameworks can provide adaptation choices when events occur ( [[#Nguyen--2016|Nguyen et al., 2016]] ).

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==== 5.9.4.4 Early-warning and monitoring systems ====

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Globally, monitoring is increasing to fill scientific uncertainties ( [[#Goldsmith--2019|Goldsmith et al., 2019]] ) but is not often at spatial scales which facilitate farm or regional adaptation management ( [[#Whitney--2020|Whitney et al., 2020]] ) or data complexities prevent direct uptake by operators, resource managers and policymakers ( ''medium confidence'' ) ( [[#Soto--2018|Soto et al., 2018]] ; [[#Gallo--2019|Gallo et al., 2019]] ). Specialised industry portals (Pacific shellfish) and government-established monitoring programmes (Chilean salmon) and other observational networks (e.g., Global Ocean Acidification Observing Network (GOA-ON)) can provide real-time monitoring and early-warning event alerts and facilitate aquaculture decision making ( ''medium confidence'' ) ( [[#Cross--2019|]] [[#Cross--2019|Cross et al., 2019]] ; [[#Farcy--2019|Farcy et al., 2019]] ; [[#Soto--2019|Soto et al., 2019]] ; [[#Tilbrook--2019|Tilbrook et al., 2019]] ; [[#Bresnahan--2020|Bresnahan et al., 2020]] ; [[#Peck--2020|Peck et al., 2020]] ). Seasonal forecasting, downscaled models and early-warning systems provide valuable regional or farm site risk information ( [[#Hobday--2018|Hobday et al., 2018]] ; [[#Galappaththi--2020b|Galappaththi et al., 2020b]] ; [[#Whitney--2020|Whitney et al., 2020]] ), but monitoring will need to be useful for farmers, involve farmers, and be accurate, timely, cost-effective, reviewed and maintained in order to ensure uptake ( ''high confidence'' ) ( [[#Soto--2018|Soto et al., 2018]] ). Early-warning systems for HABs enable rapid decision making and risk mitigation ( ''medium confidence'' ), such as ocean colour monitoring in South Africa ( [[#Smith--2020|Smith and Bernard, 2020]] ), where early harvesting and additional husbandry were used to minimise production and economic losses ( [[#Pitcher--2019|Pitcher et al., 2019]] ). New tools, strategies and observations are needed to predict HAB occurrences and range shifts with changing climate ( ''high confidence'' ) ( [[#Schaefer--2019|Schaefer et al., 2019]] ; [[#Tester--2020|Tester et al., 2020]] ), as there is uncertainty on drivers of incidence and toxicity ( [[#Wells--2020|Wells et al., 2020]] ).

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