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

=== 5.7.5 Knowledge gaps and key research areas ===

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Knowledge gaps around options and solutions and their (co-)benefits and trade-offs are increasingly important now that implementation of mitigation and adaptation measures is scaling up.

Research is needed on how a changing climate and interventions to respond to it will affect all aspects of food security, including access, utilisation and stability, not just availability. Knowledge gaps across all the food security pillars are one of the barriers hindering mitigation and adaptation to climate change in the food system and its capacity to deliver food security. The key areas for climate change, food systems, and food security research are enlisted below.

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==== 5.7.5.1 Impacts and adaptation ====

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'''Climate Services (food availability).''' Agriculture and food security is a priority area for the Global Framework for Climate Services (GFCS) a programme of the World Meteorological Organization (WMO). The GFCS enables vulnerable sectors and populations to better manage climate variability and adapt to climate change (Hansen et al. 2018 <sup>[[#fn:r1294|1294]]</sup> ). Global precipitation datasets and remote sensing technologies can be used to detect local to regional anomalies in precipitation as a tool for devising early-warning systems for drought-related impacts, such as famine (Huntington et al. 2017 <sup>[[#fn:r1295|1295]]</sup> ).

'''Crop and livestock genetics (food availability, utilisation). ''' Advances in plant breeding are crucial for enhancing food security under changing climate for a wide variety of crops including fruits and vegetables as well as staples. Genetics improvement is needed in order to breed crops and livestock that can both reduce GHG emissions, increase drought and heat tolerance (e.g., rice), and enhance nutrition and food security (Nankishore and Farrell 2016 <sup>[[#fn:r1296|1296]]</sup> ; Kole et al. 2015 <sup>[[#fn:r1297|1297]]</sup> ). Many of these characteristics already exist in traditional varieties, including orphan crops and indigenous and local breeds, so research is needed to recuperate such varieties and evaluate their potential for adaptation and mitigation.

Phenomics-assisted breeding appears to be a promising tool for deciphering the stress responsiveness of crop and animal species (Papageorgiou 2017 <sup>[[#fn:r1298|1298]]</sup> ; Kole et al. 2015 <sup>[[#fn:r1299|1299]]</sup> ; Lopes et al. 2015 <sup>[[#fn:r1300|1300]]</sup> ; Boettcher et al. 2015 <sup>[[#fn:r1301|1301]]</sup> ). Initially discovered in bacteria and archaea, CRISPR–Cas9 is an adaptive immune system found in prokaryotes and since 2013 has been used as a genome editing tool in plants. The main use of CRISPR systems is to achieve improved yield performance, biofortification, biotic and abiotic stress tolerance, with rice (Oryza sativa) being the most studied crop (Gao 2018 <sup>[[#fn:r1302|1302]]</sup> and Ricroch et al. 2017 <sup>[[#fn:r1303|1303]]</sup> ).

'''Climate impact models (food availability).''' Understanding the full range of climate impacts on staple crops (especially those important in developing countries, such as fruits and vegetables) is missing in the current climate impact models. Further, the CO2 effects on nutrition quality of different crops are just beginning to be parameterised in the models (Müller et al. 2014 <sup>[[#fn:r1304|1304]]</sup> ). Bridging these gaps is essential for projecting future dietary diversity, healthy diets, and food security (Bisbis et al. 2018 <sup>[[#fn:r1305|1305]]</sup> ). Crop model improvements are needed for simulation of evapotranspiration to guide crop water management in future climate conditions (Cammarano et al. 2016 <sup>[[#fn:r1306|1306]]</sup> ). Similarly, mores studies are needed to understand the impacts of climate change on global rangelands, livestock and aquaculture, which have received comparatively less attention than the impacts on crop production.

'''Resilience to extreme events (food availability, access, utilisation, and stability).''' On the adaptation side, knowledge gaps include impacts of climate shocks (Rodríguez Osuna et al. 2014 <sup>[[#fn:r1307|1307]]</sup> ) as opposed to impacts of slow-onset climate change, how climate-related harvest failures in one continent may influence food security outcomes in others, impacts of climate change on fruits and vegetables and their nutrient contents.

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==== 5.7.5.2 Emissions and mitigation ====

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'''GHG emissions inventory techniques (food utilisation). ''' Knowledge gaps include food consumption-based emissions at national scales, embedded emissions (overseas footprints) of food systems, comparison of GHG emissions per type of food systems (e.g., smallholder and large-scale commercial food systems), and GHG emissions from land-based aquaculture. An additional knowledge gap is the need for more socio-economic assessments of the potential of various integrated practices to deliver the mitigation potential estimated from a biophysical perspective. This needs to be effectively monitored, verified, and implemented, once barriers and incentives to adoption of the techniques, practices, and technologies are considered. Thus, future research needs fill the gaps on evaluation of climate actions in the food system.

'''Food supply chains (food availability).''' The expansion of the cold chain into developing economies means increased energy consumption and GHG emissions at the consumer stages of the food system, but its net impact on GHG emissions for food systems as a whole, is complex and uncertain (Heard and Miller 2016 <sup>[[#fn:r1308|1308]]</sup> ). Further understanding of negative side effects in intensive food processing systems is still needed.

Blockchains, as a distributed digital ledger technology which ensures transparency, traceability, and security, is showing promise for easing some global food supply chain management challenges, including the need for documentation of sustainability and the circular economy for stakeholders including governments, communities, and consumers to meet sustainability goals. Blockchain-led transformation of food supply chains is still in its early stages; research is needed on overcoming barriers to adoption (Tripoli and Schmidhuber 2018 <sup>[[#fn:r1309|1309]]</sup> ; Casado-Vara et al. 2018 <sup>[[#fn:r1310|1310]]</sup> ; Mao et al. 2018 <sup>[[#fn:r1311|1311]]</sup> ; Saberi et al. 2019 <sup>[[#fn:r1312|1312]]</sup> ).

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==== 5.7.5.3 Synergies and trade-offs ====

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'''Supply-side and demand-side mitigation and adaptation (food availability, utilisation).''' Knowledge gaps exist in characterising the potential and risks associated with novel mitigation technologies on the supply side (e.g., inhibitors, targeted breeding, cellular agriculture, etc.). Additionally, most integrated assessment models (IAMs) currently have limited regional data on BECCS projects because of little BECCS implementation (Lenzi et al. 2018 <sup>[[#fn:r1313|1313]]</sup> ). Hence, several BECCS scenarios rely on assumptions regarding regional climate, soils and infrastructure suitability (Köberle et al. 2019 <sup>[[#fn:r1314|1314]]</sup> ) as well as international trade (Lamers et al. 2011 <sup>[[#fn:r1315|1315]]</sup> ).

Areas for study include how to incentivise, regulate, and raise awareness of the co-benefits of healthy consumption patterns and climate change mitigation and adaptation; to improve access to healthy diets for vulnerable groups through food assistance programmes; and to implement policies and campaigns to reduce food loss and food waste. Knowledge gaps also exist on the role of different policies, and underlying uncertainties, to promote changes in food habits towards climate resilience and healthy diets.

'''Food systems, land-use change, and telecoupling (food availability, access, utilisation).''' The analytical framework of telecoupling has recently been proposed to address this complexity, particularly the connections, flows, and feedbacks characterising food systems (Friis et al. 2016 <sup>[[#fn:r1316|1316]]</sup> ; Easter et al. 2018 <sup>[[#fn:r1317|1317]]</sup> ). For example, how will climate-induced shifts in livestock and crop diseases affect food production and consumption in the future. Investigating the social and ecological consequences of these changes will contribute to decision-making under uncertainty in the future. Research areas include food systems and their boundaries, hierarchies, and scales through metabolism studies, political ecology and cultural anthropology.

'''Food-Energy-Water Nexus (food availability, utilisation, stability)''' . Emerging interdisciplinary science efforts are providing new understanding of the interdependence of food, energy, and water systems. These interdependencies are beginning to take into account climate change, food security, and AFOLU assessments (Scanlon et al. 2017 <sup>[[#fn:r1318|1318]]</sup> ; Liu et al. 2017 <sup>[[#fn:r1319|1319]]</sup> ). These science advances, in turn, provide critical information for coordinated management to improve the affordability, reliability, and environmental sustainability of food, energy, and water systems. Despite significant advances within the past decade, there are still many challenges for the scientific community. These include the need for interdisciplinary science related to the food-energy-water nexus; ground-based monitoring and modelling at local-to-regional scales (Van Gaelen et al. 2017); incorporating human and institutional behaviour in models; partnerships among universities, industry, and government to develop policy-relevant data; and systems modelling to evaluate trade-offs associated with food-energy-water decisions (Scanlon et al. 2017 <sup>[[#fn:r1320|1320]]</sup> ).

However, the nexus approach, as a conceptual framework, requires the recognition that, although land and the goods and services it provides is finite, potential demand for the goods and services may be greater than the ability to supply them sustainably (Benton et al. 2018 <sup>[[#fn:r1321|1321]]</sup> ). By addressing demand-side issues, as well as supply-side efficiencies, it provides a potential route for minimising trade-offs for different goods and services (Benton et al. 2018 <sup>[[#fn:r1322|1322]]</sup> ) (Section 5.6).

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