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

== 5.6 Mitigation, adaptation, food security and land use: Synergies, trade-offs and co-benefits ==

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Food systems will need to adapt to changing climates and also reduce their GHG emissions and sequester carbon if Paris Agreement goals are to be met (Springmann et al. 2018a <sup>[[#fn:r946|946]]</sup> and van Vuuren et al. 2014 <sup>[[#fn:r947|947]]</sup> ). The synergies and trade-offs between the food system mitigation and adaptation options described in Sections 5.3 and 5.5 are of increasing importance in both scientific and policy communities because of the necessity to ensure food security, i.e., providing nutritious food for growing populations while responding to climate change (Rosenzweig and Hillel 2015 <sup>[[#fn:r948|948]]</sup> ). A special challenge involves interactions between land-based non-food system mitigation, such as negative emissions technologies, and food security. Response options for the food system have synergies and trade-offs between climate change mitigation and adaptation (Figure 5.13; Chapter 6).

Tirado et al. (2013) <sup>[[#fn:r949|949]]</sup> suggest an integrated approach to address the impacts of climate change to food security that considers a combination of nutrition-sensitive adaptation and mitigation measures, climate-resilient and nutrition-sensitive agricultural development, social protection, improved maternal and child care and health, nutrition-sensitive risk reduction and management, community development measures, nutrition-smart investments, increased policy coherence, and institutional and cross-sectoral collaboration. These measures are a means to achieve both short-term and long-term benefits in poor and marginalised groups.

This section assesses the synergies and trade-offs for land-based atmospheric carbon dioxide removal measures, effects of mitigation measures on food prices, and links between dietary choices and human health. It then evaluates a range of integrated agricultural systems and practices that combine mitigation and adaptation measures, including the role of agricultural intensification. The role urban agriculture is examined, as well as interactions between SDG 2 (zero hunger) and SDG 13 (climate action).

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Response options related to food system and their potential impacts on mitigation and adaptation. Many response options offer significant potential for both mitigation and adaptation.

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=== 5.6.1 Land-based carbon dioxide removal (CDR) and bioenergy ===

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Large-scale deployment of negative emission technologies (NETs) in emission scenarios has been identified as necessary for avoiding unacceptable climate change (IPCC 2018b <sup>[[#fn:r950|950]]</sup> ). Among the available NETs, carbon dioxide removal (CDR) technologies are receiving increasing attention. Land-based CDRs include afforestation and reforestation (AR), sustainable forest management, biomass energy with carbon capture and storage (BECCS), and biochar (BC) production (Minx et al. 2018 <sup>[[#fn:r951|951]]</sup> ). Most of the literature on global land-based mitigation potential relies on CDRs, particularly on BECCS, as a major mitigation action (Kraxner et al. 2014 <sup>[[#fn:r952|952]]</sup> ; Larkin et al. 2018 <sup>[[#fn:r953|953]]</sup> and Rogelj et al. 2018, 2015, 2011). BECCS is not yet deployable at a significant scale, as it faces challenges similar to fossil fuel carbon capture and storage (CCS) (Fuss et al. 2016 <sup>[[#fn:r954|954]]</sup> ; Vaughan and Gough 2016 <sup>[[#fn:r955|955]]</sup> ; Nemet et al. 2018 <sup>[[#fn:r956|956]]</sup> ). Regardless, the effectiveness of large-scale BECCS to meet Paris Agreement goals has been questioned and other pathways to mitigation have been proposed (Anderson and Peters 2016 <sup>[[#fn:r957|957]]</sup> ; van Vuuren et al. 2017, 2018; Grubler et al. 2018 <sup>[[#fn:r958|958]]</sup> ; Vaughan and Gough 2016 <sup>[[#fn:r959|959]]</sup> ).

Atmospheric CO <sub>2</sub> removal by storage in vegetation depends on achieving net organic carbon accumulation in plant biomass over decadal time scales (Kemper 2015 <sup>[[#fn:r960|960]]</sup> ) and, after plant tissue decay, in soil organic matter (Del Grosso et al. 2019 <sup>[[#fn:r961|961]]</sup> ). AR, BECCS and BC differ in the use and storage of plant biomass. In BECCS, biomass carbon from plants is used in industrial processes (e.g., for electricity, hydrogen, ethanol, and biogas generation), releasing CO <sub>2</sub> , which is then captured and geologically stored (Greenberg et al. 2017 <sup>[[#fn:r962|962]]</sup> ; Minx et al. 2018 <sup>[[#fn:r963|963]]</sup> ).

Afforestation and reforestation result in long-term carbon storage in above and belowground plant biomass on previously unforested areas, and is effective as a carbon sink during the AR establishment period, in contrast to thousands of years for geological carbon storage (Smith et al. 2016 <sup>[[#fn:r964|964]]</sup> ).

Biochar is produced from controlled thermal decomposition of biomass in absence of oxygen (pyrolysis), a process that also yields combustible oil and combustible gas in different proportions. Biochar is a very stable carbon form, with storage on centennial time scales (Lehmann et al. 2006 <sup>[[#fn:r965|965]]</sup> ) (Chapter 4). Incorporated in soils, some authors suggest it may lead to improved water-holding capacity, nutrient retention, and microbial processes (Lehmann et al. 2015 <sup>[[#fn:r966|966]]</sup> ). There is, however, uncertainty about the benefits and risks of this practice (The Royal Society 2018).

Land-based CDRs require high biomass-producing crops. Since not all plant biomass is harvested (e.g., roots and harvesting losses), it can produce co-benefits related to soil carbon sequestration, crop productivity, crop quality, as well improvements in air quality, but the overall benefits strongly depend on the previous land-use and soil management practices (Smith et al. 2016 <sup>[[#fn:r967|967]]</sup> ; Wood et al. 2018 <sup>[[#fn:r968|968]]</sup> ). In addition, CDR effectiveness varies widely depending on type of biomass, crop productivity, and emissions offset in the energy system. Importantly, its mitigation benefits can be easily lost due to land-use change interactions (Harper et al. 2018 <sup>[[#fn:r969|969]]</sup> ; Fuss et al. 2018 <sup>[[#fn:r970|970]]</sup> ; Daioglou et al. 2019 <sup>[[#fn:r971|971]]</sup> ).

Major common challenges of implementing these large-scale CDR solutions, as needed to stabilise global temperature at ‘well-below’ 2°C by the end of the century, are the large investments and the associated significant changes in land use required. Most of the existing scenarios estimate the global area required for energy crops in the range of 109–990 Mha (IPCC 2018a <sup>[[#fn:r972|972]]</sup> ), most commonly around 380–700 Mha (Smith et al. 2016 <sup>[[#fn:r973|973]]</sup> ), reaching net area expansion rates of up to 23.7 Mha yr–1 (IPCC 2018b <sup>[[#fn:r974|974]]</sup> ). The upper limit implies unprecedented rates of area expansion for crops and forestry observed historically, for instance, as reported by FAO since 1961 (FAOSTAT 2018 <sup>[[#fn:r975|975]]</sup> ). By comparison, the sum of recent worldwide rates of expansion in the harvested area of soybean and sugarcane has not exceeded 3.5 Mha yr–1 on average. Even at this rate, they have been the source of major concerns for their possible negative environmental and food security impacts (Boerema et al. 2016 <sup>[[#fn:r976|976]]</sup> ; Popp et al. 2014 <sup>[[#fn:r977|977]]</sup> ).

Most land area available for CDR is currently pasture, estimated at 3300 Mha globally (FAOSTAT 2018 <sup>[[#fn:r978|978]]</sup> ). However, there is ''low confidence'' about how much low-productivity land is actually available for CDR (Lambin et al. 2013 <sup>[[#fn:r979|979]]</sup> and Gibbs and Salmon 2015). There is also ''low confidence'' as to whether the transition to BECCS will take place directly on low-productivity grasslands (Johansson and Azar 2007 <sup>[[#fn:r980|980]]</sup> ), and uncertainty on the governance mechanisms required to avoid unwanted spill-over effects, for instance causing additional deforestation (Keles et al. 2018 <sup>[[#fn:r981|981]]</sup> ).

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Further, grasslands and rangelands may often occur in marginal areas, in which case, they may be exposed to climate risks, including periodic flooding. Grasslands and especially rangelands and savannas tend to predominate in less-developed regions, often bordering areas of natural vegetation with little infrastructure available for transport and processing of large quantities of CDR-generated biomass (O’Mara 2012 <sup>[[#fn:r982|982]]</sup> ; Beringer et al. 2011 <sup>[[#fn:r983|983]]</sup> ; Haberl et al. 2010 <sup>[[#fn:r984|984]]</sup> ; Magdoff 2007 <sup>[[#fn:r985|985]]</sup> ).

CDR-driven reductions in the available pastureland area is a scenario of constant or increasing global animal protein output as proposed by Searchinger et al. (2018) <sup>[[#fn:r986|986]]</sup> . However, despite the recent reduction in meat consumption in western countries, this will require productivity improvements (Cohn et al. 2014 <sup>[[#fn:r987|987]]</sup> ; Strassburg et al. 2014 <sup>[[#fn:r988|988]]</sup> ). It would also result in lower emission intensities and create conditions for increased soil carbon stocks (de Oliveira Silva et al. 2016 <sup>[[#fn:r990|990]]</sup> ; Searchinger et al. 2018 <sup>[[#fn:r991|991]]</sup> ; Soussana et al. 2019 <sup>[[#fn:r992|992]]</sup> , 2013). At the same time, food security may be threatened if land-based mitigation displaced crops elsewhere, especially if to regions of lower productivity potential, higher climatic risk, and higher vulnerability.

There is low agreement about what are the more competitive regions of the world for CDRs. Smith et al. (2016) <sup>[[#fn:r993|993]]</sup> and Vaughan et al. (2018) <sup>[[#fn:r994|994]]</sup> identify as candidates relatively poor countries in Latin America, Africa and Asia (except China and India). Others indicate those regions may be more competitive for food production, placing Europe as a major BECCS exporter (Muratori et al. 2016 <sup>[[#fn:r995|995]]</sup> ). Economically feasible CDR investments are forecast to be directed to regions with high biomass production potential, demand for extra energy production, low leakage potential for deforestation and low competition for food production (Vaughan et al. 2018 <sup>[[#fn:r996|996]]</sup> ). Latin America and Africa, for instance, although having high biomass production potential, still have low domestic energy consumption (589 and 673 MTOE – 24.7 and 28.2 EJ, respectively), with about 30% of primary energy from renewable sources (reaching 50% in Brazil), mainly hydropower and traditional biomass.

There is ''high confidence'' that deployment of BECCS will require ambitious investments and policy interventions (Peters and Geden 2017 <sup>[[#fn:r997|997]]</sup> ) with strong regulation and governance of bioenergy production to ensure protection of forests, maintain food security and enhance climate benefits (Burns and Nicholson 2017 <sup>[[#fn:r998|998]]</sup> ; Vaughan et al. 2018 <sup>[[#fn:r999|999]]</sup> ; Muratori et al. 2016 <sup>[[#fn:r1000|1000]]</sup> ), and that such conditions may be challenging for developing countries. Increased value of bioenergy puts pressure on land, ecosystem services, and the prices of agricultural commodities, including food ( ''high confidence'' ).

There is ''medium confidence'' for the impact of CDR technologies on increased food prices and reduced food security, as these depend on several assumptions. Nevertheless, those impacts could be strong, with food prices doubling under certain scenario combinations (Popp et al. 2017 <sup>[[#fn:r1001|1001]]</sup> ). The impacts of land-mitigation policies on the reduction of dietary energy availability alone (without climate change impacts) is estimated at over 100 kcal per person per day by 2050, with highest regional impacts in South Asia and Sub-Saharan Africa (Hasegawa et al. 2018 <sup>[[#fn:r1002|1002]]</sup> ) (Section 5.2). However, only limited pilot BECCS projects have been implemented to date (Lenzi et al. 2018 <sup>[[#fn:r1003|1003]]</sup> ). Integrated assessment models (IAMs) use theoretical data based on high-level studies and limited regional data from the few on-the-ground BECCS projects.

Furthermore, it has been suggested that several BECCS IAM scenarios rely on unrealistic assumptions regarding regional climate, soils and infrastructure suitability (Anderson and Peters 2016 <sup>[[#fn:r1004|1004]]</sup> ), as well as international bioenergy trade (Lamers et al. 2011 <sup>[[#fn:r1005|1005]]</sup> ). Current global IAMs usually consider major trends in production potential and projected demand, overlooking major challenges for the development of a reliable international market. Such a market will have to be created from scratch and overcome a series of constraints, including trade barriers, logistics, and supply chains, as well as social, ecological and economic impacts (Matzenberger et al. 2015 <sup>[[#fn:r1006|1006]]</sup> ).

In summary, there is high agreement that better assessment of BECCS mitigation potential would need to be based on increased regional, bottom-up studies of biomass potentials, socio-economic consequences (including on food security), and environmental impacts in order to develop more realistic estimates (IPCC 2018a <sup>[[#fn:r1007|1007]]</sup> ).

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=== 5.6.2 Mitigation, food prices, and food security ===

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Food prices are the result of supply, demand and trade relations. Earlier studies (e.g., Nelson et al. 2009 <sup>[[#fn:r1008|1008]]</sup> ) showed that recent climate impacts that reduced crop productivity led to higher prices and increased trade of commodities between regions, with asymmetric impacts on producers and consumers. In terms of published scenario analyses, the most affected regions tend to be Sub-Saharan Africa and parts of Asia, but there is significant heterogeneity in results between countries. Relocation of production to less affected areas buffers these impacts to a certain extent, and offers potential for improvements in food production technologies (Hasegawa et al. 2018 <sup>[[#fn:r1009|1009]]</sup> ; van Meijl et al. 2017 <sup>[[#fn:r1010|1010]]</sup> ; Wiebe et al. 2015 <sup>[[#fn:r1011|1011]]</sup> ; Lotze-Campen et al. 2014 <sup>[[#fn:r1012|1012]]</sup> ; Valin et al. 2014 <sup>[[#fn:r1013|1013]]</sup> ; Robinson et al. 2014 <sup>[[#fn:r1014|1014]]</sup> ).

A newer, less studied impact of climate change on prices and their impacts on food security is the level of land-based mitigation necessary to stabilise global temperature. Hasegawa et al. (2018) <sup>[[#fn:r1015|1015]]</sup> , using an ensemble of seven global economic models across a range of GHG emissions pathways and socioeconomic trajectories, suggested that the level of mitigation effort needed to reduce emissions can have a more significant impact on prices than the climate impacts themselves on reduced crop yields (Figure 5.14). This occurs because in the models, taxing GHG emissions leads to higher crop and livestock prices, while land-based mitigation leads to less land availability for food production, potentially lower food supply, and therefore food price increases.

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'''Regional impacts of climate change and mitigation on food price (top), population at risk of hunger or undernourishment (middle), GHG emissions (bottom) in 2050 under different socio-economic scenarios (SSP1, SSP2 and SSP3) based on AgMIP Global Economic Model analysis. Values indicate changes from no climate change and no climate change mitigation scenario. MAgPIE, a global […]'''
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Regional impacts of climate change and mitigation on food price (top), population at risk of hunger or undernourishment (middle), GHG emissions (bottom) in 2050 under different socio-economic scenarios (SSP1, SSP2 and SSP3) based on AgMIP Global Economic Model analysis. Values indicate changes from no climate change and no climate change mitigation scenario. MAgPIE, a global land-use allocation model, is excluded due to inelastic food demand. The value of India includes that of Other Asia in MAGNET, a global general equilibrium model (Hasegawa et al. 2018).

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Price increases in turn lead to reduced consumption, especially by vulnerable groups, or to shifts towards cheaper food, which are often less nutritious. This leads to significant increases in the number of malnourished people. Frank et al. (2017) <sup>[[#fn:r1016|1016]]</sup> and Fujimori et al. (2017) <sup>[[#fn:r1017|1017]]</sup> arrived at the same conclusions for the 1.5°C mitigation scenario using the IAM Globiom and ensembles of AgMIP global economic models. While the magnitude of the response differs between models, the results are consistent between them. In contrast, a study based on five global agroeconomic models highlights that the global food prices may not increase much when the required land for bioenergy is accessible on the margin of current cropland, or the feedstock does not have a direct completion with agricultural land (Lotze-Campen et al. 2014 <sup>[[#fn:r1018|1018]]</sup> ).

These studies highlight the need for careful design of emissions mitigation policies in upcoming decades – for example, targeted schemes encouraging more productive and resilient agricultural production systems and the importance of incorporating complementary policies (such as safety-net programmes for poverty alleviation) that compensate or counteract the impacts of climate change mitigation policies on vulnerable regions (Hasegawa et al. 2018 <sup>[[#fn:r1019|1019]]</sup> ). Fujimori et al. (2018) showed how an inclusive policy design can avoid adverse side effects on food security through international aid, bioenergy taxes, or domestic reallocation of income. These strategies can shield impoverished and vulnerable people from the additional risk of hunger that would be caused by the economic effects of policies narrowly focussing on climate objectives only.

In summary, food security will be threatened through increasing numbers of malnourished people if land-based mitigation raises prices, unless other policy mechanisms reduce its impact ( ''high confidence'' ). Inclusive policy design can avoid adverse side effects on food security by shielding vulnerable people from the additional risk of hunger that would be caused by the economic effects of policies narrowly focusing on climate objectives ( ''medium confidence'' ).

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=== 5.6.3 Environmental and health effects of adopting healthy and sustainable diets ===

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Two key questions arise from the potentially significant mitigation potential of dietary change: (i) Are ‘low-GHG emission diets’ likely to be beneficial for health? and (ii) Would changing diets at scale provide substantial benefits? In short, what are the likely synergies and trade-offs between low-GHG emissions diets and food security, health, and climate change? See Supplementary Material Section SM5.6 for further discussion.

Are ‘low GHG emission diets’ healthy? Consistent evidence indicates that, in general, a dietary pattern that is higher in plant-based foods, such as vegetables, fruits, whole grains, legumes, nuts, and seeds, and lower in animal-based foods, is more health-promoting and associated with lower environmental impact (GHG emissions and energy, land and water use) than either the current global average diets (Swinburn et al. 2019 <sup>[[#fn:r1021|1021]]</sup> ; Willett et al. 2019 <sup>[[#fn:r1022|1022]]</sup> ; Springmann et al. 2016b <sup>[[#fn:r1023|1023]]</sup> ), or the current average USA diet (Nelson et al. 2016 <sup>[[#fn:r1024|1024]]</sup> ). Another study (Van Mierlo et al. 2017 <sup>[[#fn:r1025|1025]]</sup> ) showed that nutritionally-equivalent diets can substitute plant-based foods for meat and provide reductions in GHG emissions.

There are several studies that estimate health adequacy and sustainability and conclude that healthy sustainable diets are possible. These include global studies (e.g., Willett et al. 2019 <sup>[[#fn:r1026|1026]]</sup> ; Swinburn et al. 2019 <sup>[[#fn:r1027|1027]]</sup> ), as well as localised studies (e.g., Van Dooren et al. 2014). For example, halving consumption of meat, dairy products and eggs in the European Union would achieve a 40% reduction in ammonia emissions, 25–40% reduction in non-CO <sub>2</sub> GHG emissions (primarily from agriculture) and 23% per capita less use of cropland for food production, with dietary changes lowering health risks (Westhoek et al. 2014 <sup>[[#fn:r1028|1028]]</sup> ). In China, diets were designed that could meet dietary guidelines while creating significant reductions in GHG emissions (between 5% and 28%, depending on scenario) (Song et al. 2017 <sup>[[#fn:r1029|1029]]</sup> ). Changing diets can also reduce non-dietary related health issues caused by emissions of air pollutants. For example, specific changes in diets were assessed for their potential to mitigate PM 2.5 in China (Zhao et al. 2017b <sup>[[#fn:r1030|1030]]</sup> ).

Some studies are starting to estimate both health and environmental benefits from dietary shifts. For example, Farchi et al. (2017) estimate health (colorectal cancer, cardiovascular disease) and GHG outcomes of ‘Mediterranean’ diets in Italy, and found the potential to reduce deaths from colorectal cancer of 7–10% and CVD from 9–10%, as well as potential savings of up to 263 CO <sub>2</sub> -eq per person per year. In the USA, Hallström et al. (2017) found that adoption of healthier diets (consistent with dietary guidelines, and reducing amounts of red and processed meats) could reduce relative risk of coronary heart disease, colorectal cancer, and type 2 diabetes by 20–45%, USA healthcare costs by 77–93 billion USD per year, and direct GHG emissions by 222–826 kg CO <sub>2</sub> -eq per person per year (69–84 kg from the healthcare system, 153–742 kg from the food system). Broadly similar conclusions were found for the Netherlands (Biesbroek et al. 2014 <sup>[[#fn:r1031|1031]]</sup> ); and the UK (Friel et al. 2009 <sup>[[#fn:r1032|1032]]</sup> and Milner et al. 2015 <sup>[[#fn:r1033|1033]]</sup> ).

Whilst for any given disease, there are a range of factors, including diet, that can affect it, and evidence is stronger for some diseases than others, a recent review found that an overall trend toward increased cancer risk was associated with unhealthy dietary patterns, suggesting that diet-related choices could significantly affect the risk of cancer (Grosso et al. 2017 <sup>[[#fn:r1034|1034]]</sup> ). Tilman and Clark (2014) <sup>[[#fn:r1035|1035]]</sup> found significant benefits in terms of reductions in relative risk of key diseases: type 2 diabetes, cancer, coronary mortality and all causes of mortality (Figure 5.15).

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'''Diet and health effects of different consumption scenarios (Tilman and Clark 2014) (*reflects data from a single study, hence no error bars).'''
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Diet and health effects of different consumption scenarios (Tilman and Clark 2014 <sup>[[#fn:r1466|1466]]</sup> ) (*reflects data from a single study, hence no error bars).

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==== 5.6.3.1 Can dietary shifts provide significant benefits? ====

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Many studies now indicate that dietary shifts can significantly reduce GHG emissions. For instance, several studies highlight that if current dietary trends are maintained, this could lead to emissions from agriculture of approximately 20 GtCO <sub>2</sub> -eq yr <sup>–1</sup> by 2050, creating significant mitigation potential (Pradhan et al. 2013b <sup>[[#fn:r1036|1036]]</sup> ; Bajželj et al. 2014 <sup>[[#fn:r1037|1037]]</sup> ; Hedenus et al. 2014 <sup>[[#fn:r1038|1038]]</sup> ; Bryngelsson et al. 2017 <sup>[[#fn:r1039|1039]]</sup> ). Additionally in the USA, a shift in consumption towards a broadly healthier diet, combined with meeting the USDA and Environmental Protection Agency’s 2030 food loss and waste reduction goals, could increase per capita food-related energy use by 12%, decrease blue water consumption by 4%, decrease green water use by 23%, decrease GHG emissions from food production by 11%, decrease GHG emissions from landfills by 20%, decrease land use by 32%, and increase fertiliser use by 12% (Birney et al. 2017 <sup>[[#fn:r1040|1040]]</sup> ). This study, however, does not account for all potential routes to emissions, ignoring, for example, fertiliser use in feed production. Similar studies have been conducted, for China (Li et al. 2016 <sup>[[#fn:r1041|1041]]</sup> ), where adoption of healthier diets and technology improvements have the potential to reduce food systems GHG emissions by &gt;40% relative to those in 2010; and India (Green et al. 2017 <sup>[[#fn:r1042|1042]]</sup> ; Vetter et al. 2017 <sup>[[#fn:r1043|1043]]</sup> ), where alternative diet scenarios can affect emissions from the food system by –20 to +15%.

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Springmann et al.(2018a) modelled the role of technology, waste reduction and dietary change in living within planetary boundaries (Rockström et al. 2009 <sup>[[#fn:r1044|1044]]</sup> ), with the climate change boundary being a 66% chance of limiting warming to less than 2°C. They found that all are necessary for the achievement of a sustainable food system. Their principal conclusion is that only by adopting a ‘flexitarian diet’, as a global average, would climate change be limited to under two degrees. Their definition of a flexitarian diet is fruits and vegetables, plant-based proteins, modest amounts of animal-based proteins, and limited amounts of red meat, refined sugar, saturated fats, and starchy foods.

Healthy and sustainable diets address both health and environmental concerns (Springmann et al. 2018b <sup>[[#fn:r1045|1045]]</sup> ). There is high agreement that there are significant opportunities to achieve both objectives simultaneously. Contrasting results of marginal GHG emissions, that is, variations in emissions as a result of variation in one or more dietary components, are found when comparing low to high emissions in self-selected diets (diets freely chosen by consumers). Vieux et al. (2013) <sup>[[#fn:r1046|1046]]</sup> found self-selected healthier diets with higher amounts of plant-based food products did not result in lower emissions, while (Rose et al. 2019) <sup>[[#fn:r1047|1047]]</sup> found that the lowest emission diets analysed were lower in meat but higher in oil, refined grains and added sugar. Vieux et al. (2018) <sup>[[#fn:r1048|1048]]</sup> concluded that setting nutritional goals with no consideration for the environment may increase GHG emissions.

Tukker et al. (2011) <sup>[[#fn:r1049|1049]]</sup> also found a slight increase in emissions by shifting diets towards the European dietary guidelines, even with lower meat consumption. Heller and Keoleian (2015) <sup>[[#fn:r1050|1050]]</sup> found a 12% increase in GHG emissons when shifting to iso-caloric diets, defined as diets with the same caloric intake of diets currently consumed, following the USA guidelines and a 1% decrease in GHG emissions when adjusting caloric intake to recommended levels for moderate activity. There is scarce information on the marginal GHG emissions that would be associated with following dietary guidelines in developing countries.

Some studies have found a modest mitigation potential of diet shifts when economic and biophysical systems effects are taken into account in association with current dietary guidelines. Tukker et al. (2011) <sup>[[#fn:r1051|1051]]</sup> , considering economic rebound effects of diet shifts (i.e., part of the gains would be lost due to increased use at lower prices), found maximum changes in emissions of the EU food system of 8% (less than 2% of total EU emissions) when reducing meat consumption by 40 to 58%. Using an economic optimisation model for studying carbon taxation in food but with adjustments of agricultural production systems and commodity markets in Europe, Zech and Schneider (2019) found a reduction of 0.41% in GHG emissions at a tax level of 50 USD per tCO <sub>2</sub> -eq. They estimate a leakage of 43% of the GHG emissions reduced by domestic consumption, (i.e., although reducing emissions due to reducing consumption, around 43% of the emissions would not be reduced because part of the production would be directed to exports).

Studying optimised beef production systems intensification technologies in a scenario of no grasslands area expansion de Oliveira Silva et al. (2016) <sup>[[#fn:r1052|1052]]</sup> found marginal GHG emissions to be negligible in response to beef demand in the Brazilian Cerrado. This was because reducing productivity would lead to increased emission intensities, cancelling out the effect of reduced consumption.

In summary, there is significant potential mitigation ( ''high confidence'' ) arising from the adoption of diets in line with dietary recommendations made on the basis of health. These are broadly similar across most countries. These are typically capped at the number of calories and higher in plant-based foods, such as vegetables, fruits, whole grains, legumes, nuts and seeds, and lower in animal-sourced foods, fats and sugar. Such diets have the potential to be both more sustainable and healthier than alternative diets (but healthy diets are not necessarily sustainable and vice versa). The extent to which the mitigation potential of dietary choices can be realised requires both climate change and health being considered together. Socio-economic (prices, rebound effects), political, and cultural contexts would require significant consideration to enable this mitigation potential to be realised.

<span id="sustainable-integrated-agricultural-systems"></span>
=== 5.6.4 Sustainable integrated agricultural systems ===

<div id="section-5-6-4-sustainable-integrated-agricultural-systems-block-1"></div>

A range of integrated agricultural systems are being tested to evaluate synergies between mitigation and adaptation and lead to low-carbon and climate-resilient pathways for sustainable food security and ecosystem health (robust evidence, medium agreement). Integration refers to the use of practices that enhance an agroecosystem’s mitigation, resilience, and sustainability functions. These systems follow holistic approaches with the objective of achieving biophysical, socio-cultural, and economic benefits from land management systems (Sanz et al. 2017 <sup>[[#fn:r1053|1053]]</sup> ). These integrated systems may include agroecology (FAO et al. 2018 <sup>[[#fn:r1054|1054]]</sup> ; Altieri et al. 2015 <sup>[[#fn:r1055|1055]]</sup> ), climate smart agriculture (FAO 2011c <sup>[[#fn:r1056|1056]]</sup> ; Lipper et al. 2014 <sup>[[#fn:r1057|1057]]</sup> ; Aggarwal et al. 2018 <sup>[[#fn:r1058|1058]]</sup> ), conservation agriculture (Aryal et al. 2016 <sup>[[#fn:r1059|1059]]</sup> ; Sapkota et al. 2015 <sup>[[#fn:r1060|1060]]</sup> ), and sustainable intensification (FAO 2011d <sup>[[#fn:r1061|1061]]</sup> ; Godfray 2015 <sup>[[#fn:r1062|1062]]</sup> ), amongst others.

Many of these systems are complementary in some of their practices, although they tend to be based on different narratives (Wezel et al. 2015 <sup>[[#fn:r1063|1063]]</sup> ; Lampkin et al. 2015 <sup>[[#fn:r1064|1064]]</sup> ; Pimbert 2015 <sup>[[#fn:r1065|1065]]</sup> ). They have been tested in various production systems around the world (Dinesh et al. 2017 <sup>[[#fn:r1066|1066]]</sup> ; Jat et al. 2016 <sup>[[#fn:r1067|1067]]</sup> ; Sapkota et al. 2015 <sup>[[#fn:r1068|1068]]</sup> and Neufeldt et al. 2013 <sup>[[#fn:r1069|1069]]</sup> ). Many technical innovations, for example, precision nutrient management (Sapkota et al. 2014 <sup>[[#fn:r1070|1070]]</sup> ) and precision water management (Jat et al. 2015 <sup>[[#fn:r1071|1071]]</sup> ), can lead to both adaptation and mitigation outcomes and even synergies; although negative adaptation and mitigation outcomes (i.e., trade-offs) are often overlooked. Adaptation potential of ecologically intensive systems includes crop diversification, maintaining local genetic diversity, animal integration, soil organic management, water conservation and harvesting the role of microbial assemblages (Section 5.3). Technical innovations may encompass not only inputs reduction, but complete redesign of agricultural systems (Altieri et al. 2017 <sup>[[#fn:r1072|1072]]</sup> ) and how knowledge is generated (Levidow et al. 2014 <sup>[[#fn:r1073|1073]]</sup> ), including social and political transformations.

<div id="section-5-6-4-1-agroecology"></div>

<span id="agroecology"></span>
==== 5.6.4.1 Agroecology ====

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Agroecology (see Glossary) (Francis et al. 2003 <sup>[[#fn:r1074|1074]]</sup> ; Gliessman and Engles 2014 <sup>[[#fn:r1075|1075]]</sup> ; Gliessman 2018 <sup>[[#fn:r1076|1076]]</sup> ), provides knowledge for their design and management, including social, economic, political, and cultural dimensions (Dumont et al. 2016 <sup>[[#fn:r1077|1077]]</sup> ). It started with a focus at the farm level but has expanded to include the range of food system activities (Benkeblia 2018 <sup>[[#fn:r1078|1078]]</sup> ). Agroecology builds systems resilience through knowledge-intensive practices relying on traditional farming systems and co-generation of new insights and information with stakeholders through participatory action research (Menéndez et al. 2013 <sup>[[#fn:r1079|1079]]</sup> ). It provides a multidimensional view of food systems within ecosystems, building on ILK and co-evolving with the experiences of local people, available natural resources, access to these resources, and ability to share and pass on knowledge among communities and generations, emphasising the inter-relatedness of all agroecosystem components and the complex dynamics of ecological processes (Vandermeer 1995 <sup>[[#fn:r1080|1080]]</sup> ).

At the farm level, agroecological practices recycle biomass and regenerate soil biotic activities. They strive to attain balance in nutrient flows to secure favourable soil and plant growth conditions, minimise loss of water and nutrients, and improve use of solar radiation. Practices include efficient microclimate management, soil cover, appropriate planting time and genetic diversity. They seek to promote ecological processes and services such as nutrient cycling, balanced predator/prey interactions, competition, symbiosis, and successional changes. The overall goal is to benefit human and non-human communities in the ecological sphere, with fewer negative environmental or social impacts and fewer external inputs (Vandermeer et al. 1998 <sup>[[#fn:r1081|1081]]</sup> ; Altieri et al. 1998 <sup>[[#fn:r1082|1082]]</sup> ). From a food system focus, agroecology provides management options in terms of commercialisation and consumption through the promotion of short food chains and healthy diets (Pimbert and Lemke 2018 <sup>[[#fn:r1083|1083]]</sup> ; Loconto et al. 2018 <sup>[[#fn:r1084|1084]]</sup> ).

Agroecology has been proposed as a key set of practices in building climate resilience (FAO et al. 2018 <sup>[[#fn:r1085|1085]]</sup> ; Altieri et al. 2015 <sup>[[#fn:r1086|1086]]</sup> ). These can enhance on-farm diversity (of genes, species, and ecosystems) through a landscape approach (FAO 2018g <sup>[[#fn:r1087|1087]]</sup> ). Outcomes include soil conservation and restoration and thus soil carbon sequestration, reduction of the use of mineral and chemical fertilisers, watershed protection, promotion of local food systems, waste reduction, and fair access to healthy food through nutritious and diversified diets (Pimbert and Lemke 2018 <sup>[[#fn:r1088|1088]]</sup> ; Kremen et al. 2012 <sup>[[#fn:r1089|1089]]</sup> ; Goh 2011 <sup>[[#fn:r1090|1090]]</sup> ; Gliessman and Engles 2014 <sup>[[#fn:r1091|1091]]</sup> ).

A principle in agroecology is to contribute to food production by smallholder farmers (Altieri 2002 <sup>[[#fn:r1092|1092]]</sup> ). Since climatic events can severely impact smallholder farmers, there is a need to better understand the heterogeneity of small-scale agriculture in order to consider the diversity of strategies that traditional farmers have used and still use to deal with climatic variability. In Africa, many smallholder farmers cope with and even prepare for climate extremes, minimising crop failure through a series of agroecological practices (e.g., biodiversification, soil management, and water harvesting) (Mbow et al. 2014a <sup>[[#fn:r1093|1093]]</sup> ). Resilience to extreme climate events is also linked to on-farm biodiversity, a typical feature of traditional farming systems (Altieri and Nicholls 2017 <sup>[[#fn:r1094|1094]]</sup> ).

Critiques of agroecology refer to its explicit exclusion of modern biotechnology (Kershen 2013 <sup>[[#fn:r1095|1095]]</sup> ) and the assumption that smallholder farmers are a uniform unit with no heterogeneity in power (and thus gender) relationships (Neira and Montiel 2013 <sup>[[#fn:r1096|1096]]</sup> ; Siliprandi and Zuluaga Sánchez 2014 <sup>[[#fn:r1097|1097]]</sup> ).

<div id="section-5-6-4-2-climate-smart-agriculture"></div>

<span id="climate-smart-agriculture"></span>
==== 5.6.4.2 Climate-smart agriculture ====

<div id="section-5-6-4-2-climate-smart-agriculture-block-1"></div>

‘Climate-smart agriculture’ (CSA) is an approach developed to tackle current food security and climate change challenges in a joint and synergistic fashion (Lipper et al. 2014 <sup>[[#fn:r1098|1098]]</sup> ; Aggarwal et al. 2018 <sup>[[#fn:r1099|1099]]</sup> ; FAO 2013c <sup>[[#fn:r1100|1100]]</sup> ). CSA is designed to be a pathway towards development and food security built on three pillars: increasing productivity and incomes, enhancing resilience of livelihoods and ecosystems and reducing, and removing GHG emissions from the atmosphere (FAO 2013c <sup>[[#fn:r1101|1101]]</sup> ). Climate-smart agricultural systems are integrated approaches to the closely linked challenges of food security, development, and climate change adaptation/mitigation to enable countries to identify options with maximum benefits and those where trade-offs need management.

Many agricultural practices and technologies already provide proven benefits to farmers’ food security, resilience and productivity (Dhanush and Vermeulen 2016 <sup>[[#fn:r1102|1102]]</sup> ). In many cases, these can be implemented by changing the suites of management practices. For example, enhancing soil organic matter to improve the water-holding capacity of agricultural landscapes also sequesters carbon. In annual cropping systems, changes from conventional tillage practices to minimum tillage can convert the system from one that either provides adaptation or mitigation benefits or neither to one that provides both adaptation and mitigation benefits (Sapkota et al. 2017a <sup>[[#fn:r1103|1103]]</sup> ; Harvey et al. 2014a <sup>[[#fn:r1104|1104]]</sup> ).

Increasing food production by using more fertilisers in agricultural fields could maintain crop yield in the face of climate change, but may result in greater overall GHG emissions. But increasing or maintaining the same level of yield by increasing nutrient-use- efficiency through adoption of better fertiliser management practices could contribute to both food security and climate change mitigation (Sapkota et al. 2017a <sup>[[#fn:r1105|1105]]</sup> ).

Mixed farming systems integrating crops, livestock, fisheries and agroforestry could maintain crop yield in the face of climate change, help the system to adapt to climatic risk, and minimise GHG emissions by increasingly improving the nutrient flow in the system (Mbow et al. 2014a <sup>[[#fn:r1106|1106]]</sup> ; Newaj et al. 2016 <sup>[[#fn:r1107|1107]]</sup> ; Bioversity International 2016 <sup>[[#fn:r1108|1108]]</sup> ). Such systems can help diversify production and/or incomes and support efficient and timely use of inputs, thus contributing to increased resilience, but they require local seed and input systems and extension services. Recent whole farm modelling exercises have shown the economic and environmental (reduced GH emissions, reduced land use) benefits of integrated crop-livestock systems (Gil et al. 2018 <sup>[[#fn:r1109|1109]]</sup> ) compared different soy-livestock systems across multiple economic and environmental indicators, including climate resilience. However, it is important to note that potential benefits are very context specific.

Although climate-smart agriculture involves a holistic approach, some argue that it narrowly focuses on technical aspects at the production level (Taylor 2018 <sup>[[#fn:r1110|1110]]</sup> ; Newell and Taylor 2018 <sup>[[#fn:r1111|1111]]</sup> ). Studying barriers to the adoption and diffusion of technological innovations for climate-smart agriculture in Europe, Long et al. (2016) <sup>[[#fn:r1112|1112]]</sup> found that there was incompatibility between existing policies and climate-smart agriculture objectives, including barriers to the adoption of technological innovations.

Climate-smart agricultural systems recognise that the implementation of the potential options will be shaped by specific country contexts and capacities, as well as enabled by access to better information, aligned policies, coordinated institutional arrangements and flexible incentives and financing mechanisms (Aggarwal et al. 2018 <sup>[[#fn:r1113|1113]]</sup> ). Attention to underlying socio-economic factors that affect adoption of practices and access to technologies is crucial for enhancing biophysical processes, increasing productivity, and reducing GHG emissions at scale. The Government of India, for example, has started a programme of climate resilient villages (CRV) as a learning platform to design, implement, evaluate and promote various climate-smart agricultural interventions, with the goal of ensuring enabling mechanisms at the community level (Srinivasa Rao et al. 2016 <sup>[[#fn:r1114|1114]]</sup> ).

<div id="section-5-6-4-3-conservation-agriculture"></div>

<span id="conservation-agriculture"></span>
==== 5.6.4.3 Conservation agriculture ====

<div id="section-5-6-4-3-conservation-agriculture-block-1"></div>

Conservation agriculture (CA) is based on the principles of minimum soil disturbance and permanent soil cover, combined with appropriate crop rotation (Jat et al. 2014 <sup>[[#fn:r1115|1115]]</sup> ; FAO 2011e <sup>[[#fn:r1116|1116]]</sup> ). CA has been shown to respond with positive benefits to smallholder farmers under both economic and environmental pressures (Sapkota et al. 2017a, 2015 <sup>[[#fn:r1117|1117]]</sup> ). This agricultural production system uses a body of soil and residues management practices that control erosion (Blanco Sepúlveda <sup>[[#fn:r1118|1118]]</sup> and Aguilar Carrillo 2016) and at the same time improve soil quality, by increasing organic matter content and improving porosity, structural stability, infiltration and water retention (Sapkota et al. 2017a, 2015 <sup>[[#fn:r1119|1119]]</sup> and Govaerts et al. 2009).

Intensive agriculture during the second half of the 20th century led to soil degradation and loss of natural resources and contributed to climate change. Sustainable soil management practices can address both food security and climate change challenges faced by these agricultural systems. For example, sequestration of soil organic carbon (SOC) is an important strategy to improve soil quality and to mitigation of climate change (Lal 2004 <sup>[[#fn:r1120|1120]]</sup> ). CA has been reported to increase farm productivity by reducing costs of production (Aryal et al. 2015 <sup>[[#fn:r1121|1121]]</sup> ; Sapkota et al. 2015 <sup>[[#fn:r1122|1122]]</sup> ; Indoria et al. 2017 <sup>[[#fn:r1123|1123]]</sup> ) as well as to reduce GHG emission (Pratibha et al. 2016 <sup>[[#fn:r1124|1124]]</sup> ).

Conservation agriculture brings favourable changes in soil properties that affect the delivery of nature’s contribution to people (NCPs) or ecosystem services, including climate regulation through carbon sequestration and GHG emissions (Palm et al. 2013 <sup>[[#fn:r1125|1125]]</sup> ; Sapkota et al. 2017a <sup>[[#fn:r1126|1126]]</sup> ). However, by analysing datasets for soil carbon in the tropics, Powlson et al. (2014, 2016) argued that the rate of SOC increase and resulting GHG mitigation in CA systems, from zero-tillage in particular, has been overstated (Chapter 2).

However, there is unanimous agreement that the gain in SOC and its contribution to GHG mitigation by CA in any given soil is largely determined by the quantity of organic matter returned to the soil (Giller et al. 2009 <sup>[[#fn:r1127|1127]]</sup> ; Virto et al. 2011 <sup>[[#fn:r1128|1128]]</sup> ; Sapkota et al. 2017b <sup>[[#fn:r1129|1129]]</sup> ). Thus, a careful analysis of the production system is necessary to minimise the trade-offs among the multiple use of residues, especially where residues remain an integral part of livestock feeding (Sapkota et al. 2017b <sup>[[#fn:r1130|1130]]</sup> ). Similarly, replacing mono-cropping systems with more diversified cropping systems and agroforestry, as well as afforestation and deforestation, can buffer temperatures as well as increase carbon storage (Mbow et al. 2014a <sup>[[#fn:r1131|1131]]</sup> ; Bioversity International 2016 <sup>[[#fn:r1132|1132]]</sup> ), and provide diversified and healthy diets in the face of climate change.

Adoption of conservation agriculture in Africa has been low despite more than three decades of implementation (Giller et al. 2009 <sup>[[#fn:r1133|1133]]</sup> ), although there is promising uptake recently in east and southern Africa. This calls for a better understanding of the social and institutional aspects around CA adoption. Brown et al. (2017a) <sup>[[#fn:r1134|1134]]</sup> found that institutional and community constraints hampered the use of financial, physical, human and informational resources to implement CA programmes.

Gender plays an important role at the intra-household level in regard to decision-making and distributing benefits. Conservation agriculture interventions have implications for labour requirements, labour allocation, and investment decisions, all of which impact the roles of men and women (Farnworth et al. 2016 <sup>[[#fn:r1135|1135]]</sup> ) (Section 5.1.3). For example, in the Global South, CA generally reduces labour and production costs and generally leads to increased returns to family labour (Aryal et al. 2015 <sup>[[#fn:r1136|1136]]</sup> ) although a gender shift of the labour burden to women have also been described (Giller et al. 2009 <sup>[[#fn:r1137|1137]]</sup> ).

<div id="section-5-6-4-4-sustainable-intensification"></div>

<span id="sustainable-intensification"></span>
==== 5.6.4.4 Sustainable intensification ====

<div id="section-5-6-4-4-sustainable-intensification-block-1"></div>

The need to produce about 50% more food by 2050, required to feed the increasing world population (FAO 2018a <sup>[[#fn:r1138|1138]]</sup> ), may come at the price of significant increases in GHG emissions and environmental impacts, including loss of biodiversity. For instance, land conversion for agriculture is responsible for an estimated 8–10% of all anthropogenic GHG emissions currently (Section 5.4). Recent calls for sustainable intensification (SI) are based on the premise that damage to the environment through extensification outweighs benefits of extra food produced on new lands (Godfray 2015 <sup>[[#fn:r1139|1139]]</sup> ). However, increasing the net production area by restoring already degraded land may contribute to increased production on the one hand and increased carbon sequestration on the other (Jat et al. 2016 <sup>[[#fn:r1140|1140]]</sup> ), thereby contributing to both increased agricultural production and improved natural capital outcomes (Pretty et al. 2018 <sup>[[#fn:r1141|1141]]</sup> ).

Sustainable intensification is a goal but does not specify ''a priori'' how it could be attained, for example, which agricultural techniques to deploy (Garnett et al. 2013 <sup>[[#fn:r1142|1142]]</sup> ). It can be combined with selected other improved management practices, for example, conservation agriculture (see above), or agroforestry, with additional economic, ecosystem services, and carbon benefits. Sustainable intensification, by improving nutrient, water, and other input-use efficiency, not only helps to close yield gaps and contribute to food security (Garnett et al. 2013 <sup>[[#fn:r1143|1143]]</sup> ), but also reduces the loss of such production inputs and associated emissions (Sapkota et al. 2017c <sup>[[#fn:r1144|1144]]</sup> ; Wollenberg et al. 2016 <sup>[[#fn:r1145|1145]]</sup> ). Closing yield gaps is a way to become more efficient in use of land per unit production. Currently, most regions in Africa and South Asia have attained less than 40% of their potential crop production (Pradhan et al. 2015 <sup>[[#fn:r1146|1146]]</sup> ). Integrated farming systems (e.g., mixed crop/livestock, crop/aquaculture) are strategies to produce more products per unit land, which in regard to food security, becomes highly relevant.

Sustainable intensification acknowledges that enhanced productivity needs to be accompanied by maintenance of other ecosystem services and enhanced resilience to shocks (Vanlauwe et al. 2014 <sup>[[#fn:r1147|1147]]</sup> ). SI in intensively farmed areas may require a reduction in production in favour of increasing sustainability in the broad sense (Buckwell et al. 2014 <sup>[[#fn:r1148|1148]]</sup> ) (Cross-Chapter Box 6 in Chapter 5). Hence, moving towards sustainability may imply lower yield growth rates than those maximally attainable in such situations. For areas that contain valuable natural ecosystems, such as the primary forest in the Congo basin, intensification of agriculture is one of the pillars of the strategy to conserve forest (Vanlauwe et al. 2014 <sup>[[#fn:r1149|1149]]</sup> ). Intensification in agriculture is recognised as one of the pathways to meet food security and climate change adaptation and mitigation goals (Sapkota et al. 2017c <sup>[[#fn:r1150|1150]]</sup> ).

However, SI does not always confer co-benefits in terms of food security and climate change adaption/mitigation. For example, in the case of Vietnam, intensified production of rice and pigs reduced GHG emissions in the short term through land sparing, but after two decades, the emissions associated with higher inputs were likely to outweigh the savings from land sparing (Thu Thuy et al. 2009). Intensification needs to be sustainable in all components of food system by curbing agricultural sprawl, rebuilding soils, restoring degraded lands, reducing agricultural pollution, increasing water use efficiency, and decreasing the use of external inputs (Cook et al. 2015 <sup>[[#fn:r1151|1151]]</sup> ).

A study conducted by Palm et al. (2010) <sup>[[#fn:r1152|1152]]</sup> in Sub-Saharan Africa, reported that, at low population densities and high land availability, food security and climate mitigation goals can be met with intensification scenarios, resulting in surplus crop area for reforestation. In contrast, for high population density and small farm sizes, attaining food security and reducing GHG emissions require the use of more mineral fertilisers to make land available for reforestation. However, some forms of intensification in drylands can increase rather than reduce vulnerability due to adverse effects such as environmental degradation and increased social inequity (Robinson et al. 2015 <sup>[[#fn:r1153|1153]]</sup> ).

Sustainable intensification has been critiqued for considering food security only from the supply side, whereas global food security requires attention to all aspects of food system, including access, utilisation, and stability (Godfray 2015 <sup>[[#fn:r1154|1154]]</sup> ). Further, adoption of high-input forms of agriculture under the guise of simultaneously improving yields and environmental performance will attract more investment leading to higher rate of adoption but with the environmental component of SI quickly abandoned (Godfray 2015 <sup>[[#fn:r1155|1155]]</sup> ). Where adopted, SI needs to engage with the sustainable development agenda to (i) identify SI agricultural practices that strengthen rural communities, improve smallholder livelihoods and employment, and avoid negative social and cultural impacts, including loss of land tenure and forced migration; (ii) invest in the social, financial, natural, and physical capital needed to facilitate SI implementation; and (iii) develop mechanisms to pay poor farmers for undertaking sustainability measures (e.g., GHG emissions mitigation or biodiversity protection) that may carry economic costs (Garnett et al. 2013 <sup>[[#fn:r1156|1156]]</sup> ).

In summary, integrated agricultural systems and practices can enhance food system resilience to climate change and reduce GHG emissions, while helping to achieve sustainability ( ''high confidence'' ).

<div id="section-5-6-4-4-sustainable-intensification-block-2" class="box"></div>

<span id="ccb6-agricultural-intensification-land-sparing-land-sharing-and-sustainability"></span>