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Food and Earth Systems: Priorities for Climate Change Adaptation and Mitigation for Agriculture and Food Systems

CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Km. 17, Recta Cali-Palmira, Palmira 763537, Colombia
International Center for Tropical Agriculture (CIAT), Km. 17, Recta Cali-Palmira, Palmira 763537, Colombia
International Research Institute for Climate and Society (IRI), at Columbia University, 61 Route 9W, Monell Building, Palisades, NY 10964-1000, USA
World Agroforestry Centre (ICRAF), United Nations Avenue, Gigiri, P.O. Box 30677, Nairobi 00100, Kenya
Gund Institute for Ecological Economics, University of Vermont, Burlington, VT 05405, USA
Author to whom correspondence should be addressed.
Sustainability 2019, 11(5), 1372;
Original submission received: 8 September 2018 / Resubmission received: 11 February 2019 / Revised: 24 February 2019 / Accepted: 27 February 2019 / Published: 5 March 2019
(This article belongs to the Section Sustainable Agriculture)


Human activities and their relation with land, through agriculture and forestry, are significantly impacting Earth system functioning. Specifically, agriculture has increasingly become a key sector for adaptation and mitigation initiatives that address climate change and help ensure food security for a growing global population. Climate change and agricultural outcomes influence our ability to reach targets for at least seven of the 17 Sustainable Development Goals. By 2015, 103 nations had committed themselves to reduce greenhouse gas emissions from agriculture, while 102 countries had prioritized agriculture in their adaptation agenda. Adaptation and mitigation actions within agriculture still receive insufficient support across scales, from local to international level. This paper reviews a series of climate change adaptation and mitigation options that can support increased production, production efficiency and greater food security for 9 billion people by 2050. Climate-smart agriculture can help foster synergies between productivity, adaptation, and mitigation, although trade-offs may be equally apparent. This study highlights the importance of identifying and exploiting those synergies in the context of Nationally Determined Contributions. Finally, the paper points out that keeping global warming to 2 °C above pre-industrial levels by 2100 requires going beyond the agriculture sector and exploring possibilities with respect to reduced emissions from deforestation, food loss, and waste, as well as from rethinking human diets.

1. Introduction

During the 20th century, global populations rose from approximately 1.6 to 6.0 billion. During this same period, substantial increases in crop yields were achieved in many countries by adopting improved crop cultivars. In many instances, enhanced soil, crop, water, and nutrient management accompanied this, though environmental problems have also emerged. However, as we shall discuss later, adoption rates of more intensive and higher yielding agricultural practices have been substantially greater in developed nations compared to developing ones, where a range of constraints still limit widespread uptake of such practices. In spite of successes, by 2017 it was estimated that globally over 820 million of people were undernourished [1].
In the 21st century the challenges to achieve global food security will be immense. According to Alexandratos and Brunisma [2], by 2050 the world will need to respond to an increased demand due to population and income growth. Therefore, agricultural production will have to increase at least by 60% considering both food and nonfood products compared to 2005–2007. This challenge will be exacerbated by the fact that such ongoing population growth is inevitably and directly linked with growing constraints on land and water availability for crops and livestock and declining wild fishery stocks [3].
In addition, the latest report from the Intergovernmental Panel on Climate Change (IPCC) concludes that agriculture, and consequently food security, is already being affected by climate change [4]. At the same time, agriculture and food value chains, as significant emitters of CO2 and non-CO2 greenhouse gases (GHGs), are also very important in fueling climate change. With an estimated 2.5 billion people globally dependent on small-scale agriculture who are vulnerable to climate change, and with food systems contributing 19–29% of GHGs global emissions [5], the challenge related to agriculture and climate change in the 21st century is both urgent and multifaceted and is reflected in seven out of 17 of the Sustainable Development Goals (SDGs), namely, SDG1: no poverty; SDG2: zero hunger; SDG5: gender equality; SDG12: responsible consumption and production; SDG13: climate action; SDG14: life below water; and SDG15: life on land. As an imperative, it will require not only a major initiative to meet rapidly increasing food demands, but also a substantial investment in both adaptation and mitigation initiatives, within global agriculture itself and in the context of wider food systems (Figure 1) [6,7,8].

The Purpose of This Paper

This paper reviews the literature on priorities for adaptation and mitigation. While many of the adaptation and mitigation options are relevant globally, our focus is on small and family farms in developing countries, for it is that sector that will face the greatest challenges with respect to climate change. Some of the key mitigation options, e.g., reducing loss at the demand end of the supply chain and changes in consumption, are more relevant to developed countries, but will become global priorities as countries develop. In this paper, we use the terms small and family farms as used by Lowder et al. [9]. According to Lowder et al., “small farms (less than 2 ha) operate about 12% and family farms about 75% of the world’s agricultural land.” This paper used two sources of information to select the priorities for action on adaptation and mitigation: (i) the work implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security and (ii) a set of review papers that include priority setting analyses [10,11,12,13,14].
The paper starts by describing the evolving importance of agriculture and climate change within the United Nations Framework Convention on Climate Change (UNFCCC) and discusses some of the concerns that still remain to be addressed with regard to adaptation and mitigation actions by small and family farms. We then provide an overview of some potentially promising and more prevalent adaptation and mitigation options that can be adopted by such farmers but indicate where barriers to widescale uptake still need to be addressed.
In the case of mitigation, we show that such actions will need to go beyond agriculture alone and must be addressed across the whole global food system if the near-term goal for 2030 of 1 GtCO2 e yr−1 emission reduction is to be achieved. We then discuss the importance of identifying where potential synergies exist between improved food production, greater livelihood resilience (to climate change and other stresses), and mitigation. Finally, we distil important adaptation and mitigation messages that have emerged from the literature.

2. The Evolving Importance of Agriculture Within the UNFCCC

Within the UNFCCC, agriculture was first mentioned in Article 2 of the Earth summit in 1992 through a reference to food security [15] and, since then, has received an increasing amount of attention. However, it was not until the 17th Conference of Parties (COP 17) in Durban in 2011 that the conference requested that the Subsidiary Body for Scientific and Technological Advice (SBSTA) consider issues related to agriculture. However, actions agreed in Durban were in the mitigation track of UNFCCC negotiations, which are separate from adaptation discussions. This potentially obscured opportunities for agriculture which can deliver benefits for both, and has led to a concern that the focus on agricultural adaptation, a priority for developing countries, will be insufficient [16,17]. In addition, there are more specific concerns regarding agricultural mitigation that have emerged (see Box 1).
Box 1. Ref [16] Note the following concerns raised regarding agricultural mitigation being included in SBSTA text.
  • The inclusion of agriculture under the mitigation track could lead to mandatory commitments.
  • Possible mechanisms (e.g., carbon trading) will not benefit small-holder and family farmers.
  • Some countries do not welcome potential restrictions on conversion of land to agricultural use.
  • Export-focused producers worry that mitigation measures for agriculture could restrict trade from “high-emission” agriculture.
  • Some negotiators are concerned that technical challenges (e.g., carbon monitoring by millions of farmers and pastoralists) are too great to develop agriculture agreements.
Nevertheless, in 2015, in the lead up to the Paris Agreement, 160 states submitted Intended Nationally Determined Contributions (INDCs) for climate actions for the near term 2025–2030 [18] and, of these, 103 countries included agriculture as one of the sectors in which they intended to make emissions reductions towards their targets and 102 countries listed agriculture as a priority for adaptation. The implementation of INDCs in developing countries will be supported by, amongst others, the Green Climate Fund (GCF), to which industrialized nations have pledged $100 billion annually to support adaptation, GHG emissions abatements, climate-informed development, technology transfer, and capacity building. However, the GCF has not provided a specific funding window dedicated to the links between poverty, adaptation and mitigation [19]. Questions remain in relation to adaptation actions tending to be underrepresented in the current GCF portfolio of projects and to how to establish longer term emission targets post 2020 and how to enforce the Common but Differentiated Responsibility (CBDR) principle in executing INDCs [20].
However, in 2017, COP 23 addressed several of such concerns and marked a milestone for action in that during the closing plenary session, both the SBSTA and the Subsidiary Body for Implementation (SBI) were requested to jointly address issues related to agriculture which covered adaptation, mitigation, resilience, synergies and potential constraints to adoption [21]. Specific issues to be addressed include (i) methods and approaches for assessing adaptation, adaptation cobenefits, and resilience; (ii) improved soil carbon, soil health, and soil fertility under grassland and cropland as well as integrated systems, including water management; (iii) improved nutrient use and manure management towards sustainable and resilient agricultural systems; (iv) improved livestock management systems; and (v) socioeconomic and food security dimensions of climate change in the agricultural sector.

3. Adaptation Actions

There is growing recognition that adapting smallholder and family agriculture to climate change requires developing resilience to the risks associated with natural climate variability [22,23,24,25,26]. Because anthropogenic forcing interacts with natural climate variability, smallholder and family farmers experience climate change largely as shifts in the frequency and severity of extreme events. Increasing risk from extreme events, such as drought, flooding from extreme precipitation and coastal storm surge, and heat waves, is projected across much of the developing world [26,27]. Climate variability—through loss of productive assets and human capital resulting from extreme events [28,29,30,31], and the adverse effect that the resulting uncertainty has on investment in agricultural inputs and innovation [32,33,34,35,36]—frustrates the efforts of smallholder and family farmers in risk-prone environments to escape poverty and build a better life [37,38,39].
The actions required for adaptation to climate change within agriculture are wide-ranging. They span the continuum from incremental adaptation to transformative adaptation, where incremental adaptation is defined as actions where the central aim is to maintain the essence and integrity of a system or process at a given scale, whereas transformative adaptation involves changes in the fundamental attributes of a system in response to climate and its effects (pp. 15–58 [27]). It is important to mention that under some circumstances gradual changes and incremental steps can add up to a transformative change [40]. Both incremental and transformative adaptation can occur across scales from farm households to national agencies [41] (Figure 2). For example, with infrequent and/or minor climatic impacts, households can decide to change varieties and breeds, while with more frequent and/or more severe events they may need to shift to other crops or other livestock species. With even more extreme conditions, the only solution may be to transit out of agriculture, with migration being a valid, if sometimes risky, adaptation action. Examples of adaptation actions which span that continuum are shown in Figure 2. Equally important is to acknowledge the transition that has already taken place as people have shifted from agriculture to other sectors and the consequences that these transitions can have on adequate adaptation options.

3.1. New Stress Tolerant Crop/Livestock Varieties/Breeds

Climate change is already changing the frequency and severity of stresses such as high temperature, tropical storms, drought, and saline intrusion; the incidence and intensity of climate-sensitive pests and diseases of both crops and livestock is already occurring [42]. Initiatives to develop and promote new varieties, breeds, and populations that are adapted to such abiotic and biotic stresses are underway around the world [43,44,45]. In the context of the improvement of crop varieties, crop–climate models can be used as a tool to accelerate the development of crop germplasm adapted to future climates [46,47].
However, in spite of initiatives to promote the adoption of new crop varieties and animal breeds throughout the developing world, long recognized constraints to widespread adoption faced by resource poor farmers still remain in place and, in many instances, adoption rates are disappointingly low. Parallel initiatives that address easy and timely access to improved germplasm, information on complementary production inputs, more capable and better resourced extension services and the provision of climate information and rural financial services are all essential [48]. This was again confirmed by a recent study in Africa. Nine years after the Programme for Drought Tolerant Maize for Africa (DTMA) was launched, studies for adoption rates across six East and Southern African countries (Ethiopia, Tanzania, Uganda, Malawi, Zambia, and Zimbabwe) were made [45]. The results revealed considerable intercountry variation in farmer uptake of drought tolerant maize, from 9% of maize plots in Zimbabwe to 61% in Malawi and again identified lack of timely access to germplasm and lack of awareness as key constraints. However, whilst confirming the constraints identified by many previous studies, the results illustrate that where the right supportive measures are in place (as in Malawi), adoption rates can be considerably improved.

3.2. Climate Information Services

Climate Information Services (CIS) involve the production, translation (e.g., advisories, decision support), and communication and use of climate information. Information about past climate variability and trends and predictions at a range of lead times support the targeting and implementation of a range of adaptation actions. Appropriate information enables farmers to understand the role of climate vs. other drivers in perceived productivity changes [49,50], and to manage climate-related risks throughout the agricultural calendar. CIS supports incremental adaptation to climate change by allowing farmers to understand the trends, variability, and seasonality of their recent climate and anticipate and plan for the upcoming growing season.
Increasing interest and investment, supported in part by creation of the UN Global Framework for Climate Services, are expanding opportunities for CIS to contribute to agricultural adaptation. However, to be effective and adopted widely, such services must address the challenges of (i) ensuring that climate information and advisory services are relevant to the decisions of small-holder and family farmers (salience); (ii) providing timely climate services access to remote rural communities with marginal infrastructure (access); (iii) ensuring that farmers own climate services and shape their design and delivery (legitimacy); and (iv) ensuring that climate services are accessible by and useful to women and other socially and economically marginalized groups (equity). In addition, in order to enable effective management of climate-related agricultural risk, CIS should be integrated with other development interventions [51,52].
A recent Africa-focused review of published evidence on CIS for farmers found considerable variability in rates of access, but in most cases (with exceptions that include women farmers in Mali, and pastoralists) the majority of farmers who access CIS use it in their decision-making [53]. It highlighted challenges to reliably estimate how the use of CIS translates into agricultural productivity and livelihood benefits. Econometric studies highlight CIS as one of the most important factors influencing adaptation and transformation of farming systems. For example, an analysis across more than 5000 households in East and West Africa, South Asia, and Central America found access to CIS is a positive determinant of adaptation through agricultural diversification, and of agricultural intensification in Bangladesh and India [54]. The literature also highlights widespread gaps between farmers’ information needs and the information and services that are routinely available [51,55]. Good practice in CIS design and communication can reduce this gap, resulting in high rates of use and perceived benefits for farm decision-making, e.g. Clarkson et al., [56], but evaluation evidence is lagging for adoption of improved practice.

3.3. Index-Based Agricultural Insurance

Index insurance builds resilience and contributes to adaptation both by protecting farmers’ assets in the face of major climate shocks, by promoting access to credit, and adoption of improved farm technologies and practices [10,57,58,59]. Index insurance triggers payouts based on an index (e.g., rainfall, remote sensing) that is correlated with losses, rather than actual losses, eliminating costly farm visits to verify losses, reducing administration costs, lowering premiums, and providing more timely payments. “Basis risk”—farmers experiencing losses when a payout is not triggered or receiving a payout when losses are not experienced—is a particular challenge to adoption of index-based insurance [60,61]. The failure of many index insurance programs for farmers in the developing world has raised concerns about demand and variability [62,63]. However, evidence shows that demand for insurance is not fixed but can be enhanced by designing indexes that reduce basis risk [64] and that invest in farmers’ understanding and trust in insurance [65,66,67,68]. Index insurance is not a solution to all climate-related risk but appears to be scaling most successfully (without heavy premium subsidy) where it targets risk-related barriers to accessing improved production technologies and markets [69,70,71]. Developing the capacity of private insurers to address farmers’ insurance needs at scale may continue to depend on public support including: creating an enabling regulatory environment, investing in meteorological and agricultural data systems, educating farmers about the value of insurance, and facilitating international reinsurance. In some contexts, publicly subsidized index insurance is treated as an adaptive social protection intervention to address agricultural development or rural poverty reduction goals.

3.4. Productive Social Safety Nets (PSSN)

There is growing interest in protecting the livelihoods of chronically vulnerable and food-insecure populations from the increasing frequency and intensity of extreme climate events through PSSN, including cash and in-kind transfers [72,73]. PSSN have spread rapidly from their early prominence in the middle-income nations and, by 2015, over 130 developing countries made PSSN an important pillar of their development policies [74].
Well-designed PPSN programs have proven potential to reduce costly household coping strategies [75,76] and migration [77] in the face of climate shocks; and stimulate agricultural production by alleviating capital constraints [75,78,79,80,81]. Motivated in part by climate change, adaptive social protection extends social safety nets to promote improved livelihoods through, e.g., credit, production inputs, agricultural extension, and risk finance [82,83,84,85]; and introduces financial mechanisms to increase the responsiveness of PSSN programs to climate shocks [83,86,87,88]. For example, Ethiopia’s Livelihoods Early Assessment and Protection (LEAP) program adds a layer of contingent finance, triggering additional funds to scale up its Productive Safety Net Program when a climate index indicates emerging drought [88,89]. In cases where agricultural production may not offer a realistic near-term pathway out of poverty, PSSN may allow poor smallholder and family farmers to build up sufficient assets to move out of poverty through improvements to their farming, or provide a level of security until growth in other sectors expands off-farm employment opportunities [10]. Whilst the primary purpose of such PSSN is to protect the food security and livelihood resilience of chronically vulnerable households against severe climate shocks, it has been shown that Ethiopia’s PSSN, covering approximate 600,000 ha, has also resulted in a total reduction in net GHG emissions of 3.4 million Mg CO2 e y−1, which is approximately 1.5% of the emissions reductions in Ethiopia’s NDC for the Paris Agreement, even though it did not form an integral part of that NDC [90]. In order to maximize the potential of all such PSSN, the authors recommend that to enhance food security whilst at the same time maximizing mitigation, climate projections, and mitigation and adaptation responses should be mainstreamed into the future planning and implementation at all levels.

3.5. Agricultural Transformation and Migration

With progressive climate change, climatic suitability of cultivable areas will decline to a greater or lesser extent for most staple foods [91]. This means that farmers will need to adapt, either through access to improved crop germplasm with enhanced tolerance to the new climatic conditions (incremental adaption) or by switching crops or even activities (transformative adaptation). For example, in areas of sub-Saharan Africa already marginal for crop production, Jones et al. [92] suggest that farmers may be forced to switch to livestock as the primary source of livelihood. In Nicaragua, Bunn et al. [93] suggest that climate pressure might soon lead farmers to shift from coffee to other crops such as cocoa.
However, although there is an increasing recognition of the potential limitations of incremental adaptation, the literature available on transformational adaptation remains relatively scarce [94]. Nevertheless, from a recent review of 93 peer-reviewed papers which discussed transformational literature, it was noted that the literature on this topic has steadily increased from six articles in 2011 to 24 in 2016–2017 [95], and notes that transformative adaptation actions that are discussed can be broadly grouped in five types:
  • Adaptation actions adopted at a larger scale. For example, large scaling of rainwater harvesting in sub-Saharan Africa [96].
  • Shifting crops and changing agricultural systems. For example, shifting from rice to sugarcane production due to water scarcity and market access [97].
  • Changing business scale, structure, and location. For example, the change of Australian agricultural industries in structure and function [98].
  • Creating new croplands/irrigation. For example, large-scale development of irrigation schemes [99].
  • Forced farm abandonment and migration. For example, forced farm abandonment due to impacts of climate change in Central America [100].
However, through a study of irrigated agriculture in Canada [101], there are suggestions that there is a nuanced interplay between incremental and transformation adaptation and further developed the related concept of ‘transitional adaptation’, first defined as “…an intermediary form or adaptation. It can indicate an extension or resilient adaptation to include a greater focus on governance or an incomplete form of transformational adaptation that falls short of aiming for or triggering cultural or political regime change” [102]. Indications show that there are interactions between these three types of adaptation strategies in that (i) there are interdependencies between and among actions and actors across various scales and (ii) one type of adaptation can set boundaries for the other [101]. They also show that transitional adaptation, in their case study the development of water markets during a severe drought, was reversible to ‘incremental’ in that it has only occurred once in 2001 but is anticipated to become more frequent in the future in which case it would become ‘transformational’ [103].
In spite of the increasing attention being given to the complexities of transformational adaptation, some authors conclude that due to the current challenges facing agriculture in the developing world, coupled with the existing uncertainties of climate change projections, there seems to be little appetite for such approaches at the level of development practice [94]. We believe that this, as a priority, needs to change, especially with regard to climate-induced migration. Climate change impact on migration can happen through increases in the frequency and intensity of weather and climate risks. Such climate-related risks can be the actual or anticipated sudden onset events such as drought, floods, or tropical storms, but in the longer term is projected to become more associated with projected slow onset events such as sea-level rise, salinization, and desertification. Safe, orderly, and planned migration can contribute, through remittances, to agriculture development, economic growth, food security, and rural livelihoods. In contrast, poorly managed migration can increase vulnerability to climate risks, heighten pressure on scarce natural resources, and exacerbate tensions between migrants and host communities [104].
The fact remains however that, due to progressive climate change, farming is likely to become a nonviable enterprise in many areas, especially for coastal communities where sea-level rise will increasingly bring adverse impacts such as submergence, coastal flooding, and erosion [105]. In such cases, the only solution available to farmers might be transitioning out of agriculture and seeking alternative livelihoods through migration. Today the total number of international migrants, including those displaced by climate-related natural disasters, is 40% higher than in 2000, with numbers expected to exceed 400 million by 2050 [104].
The Sendai Framework for Disaster Risk Reduction [106], the Paris Agreement, and the 2030 Agenda for Sustainable Development [107] have all highlighted the need for urgent action to respond to climate change, and to address its role as a driver of migration. Given the complexity and projected scale of such a dramatic transformative adaptation action, we suggest that research for development needs to move from ‘recognition’ to ‘action’ and give urgent priority to identifying where such large-scale migration is likely to occur with the development of preplanned practical policy measures that will need to be put in place to support the future livelihoods of such migrants.

4. Actions Toward the Mitigation of GHG Emissions

With agriculture’s significant contribution to global emissions, the additional emissions in supply chains, the need for reductions in agriculture to meet global policy targets and the opportunity for reducing emissions across the food system (Table 1), reducing food system emissions, and sequestering carbon in the soil and biomass have received increasing attention [108]. Developing countries’ NDCs show they are ready to rise to the challenge, conditional on funding [18].

4.1. Direct Emission Targets

Reaching the reduction target for emissions from agriculture required to limit warming in 2100 to 2 °C above pre-industrial levels will be challenging. Indeed, achieving global food security in the light of projected increases in human populations without drastic increases in GHG emissions will necessitate policy measures to move from a business-as-usual (BAU) path and initiate a global transition to low-emission development (LED). From an analysis of 134 crop and livestock systems in 15 countries, it was reported that the adoption of improved management practices and technologies by small-holder farmers, notably alternate wetting and drying in paddy rice and agroforestry systems, would significantly reduce the GHG emission intensity of agricultural production, while either decreasing or only moderately increasing net GHG emissions per area [109]. However the authors concluded that whilst improvements in small-holder systems effectively reduce future GHG emissions compared to BAU development, these contributions are insufficient to significantly reduce net GHG emission in agriculture beyond current levels, particularly if future agricultural production grows at projected rates. Indeed, with a near-term goal for 2030 of 1 GtCO2e yr−1 for non-CO2 emissions reduction compared to the BAU scenario, and using optimistic assumptions with regard to the dissemination and adoption of currently available improved production practices, currently available mitigation practices in agriculture will only deliver 21–40% of the mitigation required, suggesting that more transformative technical options will be needed in the longer term [110].
Agricultural emissions (methane and nitrous oxide) vary significantly among countries, with only four countries contributing 39% of emissions: China, India, Brazil, and the USA [111]. The availability and suitability of mitigation measures varies both across countries and within. For example, additives to cattle feed and slow release fertilizer are still unaffordable in most low-income countries. Assessing the suitability of water management in paddy rice in Southeast Asia showed the areas that are biophysically eligible for this practice were more limited than previously thought [112].
Developing policies to achieve mitigation goals in the agricultural sector effectively is as important as developing technologies to deliver these reductions in emissions. This is reflected in the case of Costa Rica and its Coffee NAMA (Nationally Appropriate Mitigation Action). This national policy aims at improving resource use efficiency in the coffee sector and is part of a wider Costa Rican effort to attain carbon neutrality by 2021. The policy includes capacity building and awareness-raising to increase technical knowledge of low-carbon production, financial support and incentives, and market studies to promote access to markets for differentiated coffees [113].
Mitigation of non-CO2 gases emissions will be necessary given their significant contribution to global emissions and the opportunity for rapid reduction of these shorter lived GHGs in the atmosphere. Considering possible changes in technical options, changes in farming systems, and consumer responses to changes in food availability and price, Frank et al. [114] show that increasing the carbon price from $25/tCO2 eq to $100/tCO2 eq would provide non-CO2 reductions in agriculture of 2.6 GtCO2 eq/year by 2050. However, they stress that for such an optimistic target to be reached, several adoption barriers, such as lack of education and infrastructure and poor access to markets or land tenure insecurity, would have to be overcome. However, looking to the future mitigation potentials, promising transformative technical options for methane and nitrous oxide emission reductions are in the pipeline. Future options include (i) recently developed methane inhibitors that reduce dairy cow emissions by 30% while increasing body weight without affecting milk yields or composition [115], (ii) cattle breeds that produce less methane [116], and (iii) wheat and maize varieties that inhibit nitrous oxide production [117]. In addition, evidence suggests it may be possible to manage soil–plant microbial processes to increase the stability of soil organic matter and retain carbon longer [118].
Bioenergy crops play a potentially important role in the transition to renewable energy and can contribute to the mitigation of emissions from fossil fuels in fertilizer production, farm mechanization, and the food supply chain. However, issues related to competition with food crops, the role of bioenergy crops as a driver of land use change, the life cycle emissions and efficiency of biofuels, site-specific outcomes, differential impacts on the poor, and sustainable cultivation practices have made bioenergy controversial as a mitigation measure [119].
Additional mitigation in the food system is possible from land use change and food supply chains [120]. An integrated approach to addressing mitigation at all three scales—farm, landscape, and supply chain—will enable progress on multiple fronts to meet the 2030 target.

4.2. Reducing Deforestation and GHG Emission Whilst Enhancing Food Security through Agricultural Intensification

Despite growing awareness about their impacts, deforestation and forest degradation globally remain high with clearance of forests and trees for oil palm, cattle production, rubber, coffee and timber plantations, and for low-yielding agriculture. For example, in Africa, notably in West and East Africa, approximately 3.4 million ha−1 year−1 was lost between 2000 and 2010. In South America, almost 4 million ha−1 year−1 was lost during the same period. Independent global satellite imagery studies and ground-based inventories from 2000–2005 by several authors, show that emissions from gross deforestation in tropical regions reached 3.0 Gt CO2 yr−1 [121,122]. However, this value excludes emissions from the cultivation of mineral soils, peatland degradation, and forest degradation activities that could account for another 2.3 Gt CO 2 yr−1. Therefore, deforestation and forest degradation accounted for approximately 15% of total global GHG emissions per year for that period. It has been demonstrated that just 20 tropical countries with high emissions from agriculture-driven deforestation and with potential for forest-sparing interventions, have the potential to mitigate 1.3 Gt CO2 e yr−1 [123]. Given the urgency to cut these emissions, sustainable agricultural intensification (productivity increases per unit of land and other resources) and governance mechanisms for protecting the boundaries of high carbon stock areas are widely recognized as essential in making an important contribution to preserving forested land, grasslands, and wetlands and, consequently, reducing and capturing emissions whilst at the same time enhancing the food security of both current and future population [124,125,126].
Nevertheless, it is important to consider that having a more efficient agriculture could lead to higher profits, which could incentivize an expansion of the cultivated area. In the short-term, the magnitude of this direct rebound effect depends on the price elasticity and effective governance [127]. Also, it is important to highlight that in achieving the potential benefits of agricultural intensification on land preservation, food security, and GHG emission, it must be ensured that the required changes in land management practices are achieved without degrading soils and causing wider environmental damage [128]. For example, some authors have shown that once such soil degradation occurs, small-scale and family farmers in Africa are no longer able to benefit from key elements of intensification such as improved crop varieties and low level fertilizer inputs [129].
Earlier in this paper we referred to the disappointingly low adoption rates of many of the possible crop, soil, and water management innovations that can lead to successful agricultural intensification and have cited many authors who have stressed the need for a wide range of improved strategies and policies to provide an enabling environment for small-scale and family farmers in the developing world. Hence, investment in Agriculture Research for Development (AR4D) targeting improved farmer innovation, improving productivity, and enforcing forest boundaries, remains one way to reduce the pressure of deforestation by nearby farmers [130], although efforts to apply this approach widely have to deal with market price issues, unclear land rights, and frontier governance conditions. Some policy options include reducing overall agricultural rent and promoting economic development that can contribute to raise the opportunity cost of labor and, therefore, that can reduce agricultural rent and targeting intensive agriculture (where intensive means intensive in productive inputs other than land) [131].

4.3. Food Waste and Loss

It is estimated that globally one-third of human food produced annually—approximately 1.3 billion tones—gets lost or wasted [132]. To put this number in perspective, if just one-fourth of this waste could be saved, it would be enough to feed 870 million hungry people yearly. This is a major squandering of resources—water, land, energy, labor, and capital—and a source of needless GHG emissions. It is estimated that each year global food loss and waste generate 4.4 GtCO2 eq., or ~8%, of total anthropogenic GHG emissions [133], and that “if food wastage were a country, it would be the third largest GHG emitting country in the world behind only the USA and China.” The highest carbon footprint of wastage occurs at the consumption phase (37% of total) (Table 2).
Differentiating by regions (Table 2), it is clear that on a per capita basis annual food wastage (kg CO2 eq./year) is substantially greater in high income regions and that food volume wastage at the consumption stage is also greatest in these regions, largely due to factors such as aesthetic preferences and arbitrary ‘sell-by date’ labeling. Food wastage at the consumption stage is lower in low-income regions still experiencing food insecurity, but nevertheless, food wastage constitutes a resource for energy generation in these regions. In low-income regions, food losses tend to be more of a concern due to the lack of handling, storage, and transport infrastructure. This can often be exacerbated by unfavorable weather conditions which can accelerate food spoilage during storage [135]. A review of food waste and loss opportunities in USAID projects across 20 value chains in 12 countries and review of the literature suggests that food waste and loss opportunities vary more by specific localities than by region. One exception is dairy, where loss reductions have similar opportunities among regions because of the initial high emissions compared to other agricultural sectors [136]. One study of three value chains in sub-Saharan Africa showed food loss reductions ranging from 4.5% to 36%, with proportional GHG reductions [137].
The United Nations Sustainable Development Goal 12 (SDG 12) on “Ensuring sustainable consumption and production patterns” includes a specific food waste reduction target: “by 2030, to halve per capita global food waste at the retail and consumer levels.” Decreasing food waste is essential in any strategy for achieving global food security by 2050 whilst at the same time delivering the required GHG emission target [133].
There are many policy initiatives, especially in developed economies that are trying to address the issue of food waste and loss. Some of these include the United Kingdom campaign: Love Food Hate Waste under The Waste and Resources Action Programme (WRAP) launched in 2007 and that is currently running in Australia, New Zealand, and Canada; the May 2017 Resolution from the European Parliament to reduce food waste by setting binding reduction targets, updating the list of food exempt from “best before” labeling and establishing tax exemptions on food donations; and Japan’s regulatory efforts to reduce food waste by promoting recycling food waste into animal feed and fertilizers [138].

4.4. Changes in Food Consumption Patterns

Over the past 50 years, there has been an approximately 1.5-fold increase in the global numbers of cattle, sheep, and goats, with equivalent increases of 2.5- and 4.5-fold for pigs and chickens, respectively, largely due to human population growth and dietary changes associated with increased wealth (Table 3). The global average per capita meat consumption has increased approximately 15 kg/capita/year since 1973 with the average person consuming around 43 kg of meat/year in 2013, but with marked differences across regions.
Meat consumption is highest in regions dominated by high income countries such as North America (113 kg/capita/year), Europe (78 kg/capita/year), and Oceania (109 kg/capita/year). However, changes in consumption in high income countries have been much slower—with most stagnating or even decreasing over the period 1993–2013. Growth in per capita meat consumption since 1973 has been most marked in regions in which strong economic transition and associated changes in dietary preferences and aspirations have occurred such as Central and South America, Southern Europe, Southern Africa, and Eastern Asia. Notable examples of country level increases between 1973 and 2013, associated with economic transition, include Brazil, where consumption has increased from 32 to 98 kg/capita/year, and China, where it has increased from 10 to 61 kg/capita/year. The major exception to this pattern has been in India, where dominant lacto-vegetarian preferences mean that per capita meat consumption in 2013 was almost exactly the same as in 1973 at less than 4 kg per person. However, in the context of livestock related GHG emissions, this statistic masks the fact that cattle and buffalo numbers in India have increased from 240 million in 1973 to 298 million in 2013.
Projected human population increase (Table 3) across industrialized regions, as well as those in economic transition, will continue, which strongly suggests that GHG emissions from the livestock sector will remain a concern. As of 2000, the global livestock sector—including land use and land use change—is estimated to have contributed 18% of total anthropogenic GHG emissions, a figure projected to rise by approximately 40% by 2050 if current livestock production trends in response to changes in dietary preferences and human population projections continue. Given that the conversion efficiency of plant to animal matter is approximately 10%, and that about one-third of global cereal production is fed to animals, there is a strong case for a transition to lower meat diets, also for health reasons as recommended by several authors, especially in the developed world [125,141]. It is estimated that such dietary changes could free up to 2700 Mha of pasture and 100 Mha of cropland and reduce mitigation costs to achieve a 450 ppm CO2-eq. stabilization target in 2050 by about 50% [13]. However, given the robust projections of human population increases, especially in regions of emerging economic growth (Table 3) where such growth associates dietary changes to higher meat per capita consumption, such a potential is unlikely to be realized, unless meat and protein substitutes are found. Some reductions in GHG emissions will be possible in industrialized regions where per capita meat consumption is at its highest, but has leveled off or even decreased. However, such reductions in these regions will have to be substantial enough to offset projected human population increase for any net reduction in GHG emissions to have any significance.
Policy can play an important role in promoting the changes required in food consumption patterns. One interesting example is the New Nordic Diet, which has developed 24 Nordic policy solutions to change food consumption. These include meal initiatives, an agreement on facts for the Nordic Nutrition Recommendations, and capitalizing on new Nordic food culture, among others. Each solution represents a tangible step to address a specific issue, constituting a new and holistic approach to food policy [142,143].

5. Climate-Smart Agriculture: An Attempt to Achieve the Necessary Synergies among Productivity, Adaptation, and Mitigation

According to the 2017 Sourcebook on CSA [144] “Climate-smart agriculture (CSA) is an approach for developing actions needed to transform and reorient agricultural systems to effectively support development and ensure food security under climate change. CSA aims to tackle three main objectives: sustainably increasing agricultural productivity and incomes; adapting and building resilience to climate change; and reducing and/or removing greenhouse gas emissions, where possible.”
CSA provides one framework to operationalize actions aimed at understanding synergies among productivity, adaptation and mitigation. Significant amount of evidence supports the potential for CSA technologies to produce such triple wins as are the examples of conservation agriculture in Tanzania and silvopastoral systems in Nicaragua (Table 4). Nevertheless, trade-offs amongst outcomes may be equally apparent. Some examples include (i) Rwanda´s “Girinka” program (One Cow per Poor Family), which sought to address malnutrition by increasing rural milk consumption. According to Paul et al. [145], with this program, food availability increased at the cost of increasing GHG emissions. (ii) Bellarby et al. [146] describe how Kenya and Ethiopia have increased maize yields while at the same time augmenting GHG emissions due to a rise in the use of fertilizers. (iii) Kurgat et al. [147] analyzed productivity and economic and climate trade-offs in soil fertility management strategies for smallholder farmers in Kiambu county, Kenya. According to this study, farmers are more likely to choose options with higher economic benefits when selecting soil management practices that improve productivity, but these are not necessarily the best options considering lower environmental impacts in terms of GHG emissions.
Although existing trade-offs can sometimes seem to outshine synergies, some global studies show otherwise. For example, a global study by Tilman et al. [153] suggests that agricultural intensification through technology adaptation and transfer and enhancement of soil fertility in the poorest countries could help reduce yield gaps, thus achieving a more equitable global food supply and contributing at the same time to decrease GHG emissions, amongst other benefits.
The orientation of CSA toward achieving outcomes versus just developing a technology and the diversity of potential solutions under the CSA umbrella provides farmers, project managers, program developers, and policy-makers significant opportunities to select the right intervention for the right place [154]. However, without (i) broad political will, such as expressed by the Global Alliance for Climate-Smart Agriculture (GACSA), aiming to help 500 million farmers practice CSA; (ii) good implementation capacity, as major global NGOs set up to be frontline workers; and (iii) emerging investment and commitments by the private sector and GCF, significant hurdles stand in the way of using CSA as a tool to promote the necessary changes to promote an adapted and sustainable agriculture.
Concerns have been raised about the fundamentals of CSA. Some authors suggest that although the bulk of discussion has focused on developing countries, there is a paucity of useful information on the policy and practical experienced gained in developed nations [155]. Despite the ability for CSA to provide holistic approaches to development, implementation is often constrained to market-oriented technical fixes, and thus does not live up to its stated aims [156]. These critiques and others suggest a disconnection between principles and practice. The way some have suggested to remedy this is thorough clearer definition of what is and what is not CSA [144]. That however goes against the fundamental definition of resilience, time, and location specificity which is core to CSA and the UNFCCC agenda which promotes continuous improvements. Therefore, what is CSA today may not be CSA tomorrow. Drawing boundaries would seemingly contradict part of what makes CSA attractive: its flexibility for different stakeholders with their own values to contribute to the same goals.
Whilst such discussion is likely to continue, the fact remains that CSA provides a framework within which synergies between adaptation, mitigation and improved food security for small-scale and family farmers can be identified, developed, and disseminated. Given that the majority of developing countries have identified adaptation to climate change as a priority for achieving future food security in their NDCs, but that many also have reservations with regard to mitigation commitments (see Box 1), identifying such synergies is essential. However, if CSA is to gain greater ability to achieve such transformative change it will need to tackle important issues including the generation of evidence in relation to progress towards increasing productivity and resilience and reducing GHG; and also demonstrating its added value as a development initiative.

6. One World, but Differentiated Challenges and Solutions

Priorities with respect to adaptation and mitigation are and should be varied by country specific conditions and by different types of farmers. Solutions need to be context specific. By combining production gaps with the severity of impacts of climate change on maize, wheat, and rice, Aggarwal et al. [157] demonstrate that different countries have different adaptation needs. Figure 3 illustrates the difference between countries such as Ethiopia and South Africa compared to India, Peru, and Pakistan. For the first set of countries, wheat shows moderate production gaps combined with small effects of climate change on production. The latter could imply a focus on incentivizing incremental adaptation actions at local scale and at the same time strengthening their food supply through trade. The story is different for India, Peru, and Pakistan where, most probably, technology growth will have to be bundled with transformative adaptation in relation to land use and the use of varieties that are tolerant to climate stress and high yielding. In these countries, the consumption of wheat is very large and important negative impacts of climate change are expected, implying the need to adapt to large production gaps.
A similar story can be told with respect to mitigation options. Just to give an example, Table 2 highlights the differentiated mitigation opportunities when considering reducing emission through reduction of food loss and waste. It is even emphasized that for this particular matter, opportunities vary more by local context than region. Figure 4 taken from IPCC’s Fourth Report, presents an overview of the differences between countries and regions when considering mitigation options [158]. As can be observed for all soil carbon options, mitigation potential goes all the way from 900 Mt CO2 eq/yr in China to around 50 Mt CO2 eq/yr in Central Asia.

7. Summary and Conclusions

In the coming decades leading up to 2050, the world faces an unprecedented challenge of having to raise food production by 60% in order to feed a projected global population of 9 billion people. Such a challenge, already daunting, is exacerbated by growing constraints on land and water availability for crops, pasture, and livestock production and the current and projected negative impacts of climate change. Recognition of the scale of the challenge is reflected in the increasing attention being given to food security, agriculture and climate change within the UNFCCC since the first Earth Summit in 1992. Most recently, in 2017, during the closing plenary session of COP 23, both the SBSTA and the SBI were requested to jointly address issues related to agriculture and climate change which covered adaptation, mitigation, resilience, opportunities for synergies, and potential constraints to adoption that are evident. Initiatives are needed to create a conducive enabling environment which encourages innovation, investment, and action. The right policies and incentives need to be in place so that the challenges imposed by climate change on food systems can be addressed. Based on the adaptation and mitigation literature we have discussed, the following important points have emerged.

7.1. Adaptation Actions

  • Potential adaptation actions are wide ranging and span the continuum from incremental to transformative, depending on the frequency and severity of extreme weather and climatic impacts. Both incremental and transformative adaptation can occur across scales from farm households to national agencies.
  • Breeding will play a crucial role in the development of crop varieties that are tolerant or resistant to both biotic and abiotic climate change impacts. Whilst the adoption of improved varieties has been disappointingly slow in some regions, evidence shows that where enabling dissemination and policy environments are in place, this is not the case.
  • Evidence is emerging that when CIS are constructed with famer input and are targeted in a timely and inclusive manner they are a positive determinant of adaptation through the adoption of more and/or different farm level practices. However, currently assessments of the economic impact of CIS are scarce; hence increased frequency of such studies is needed.
  • Index insurance schemes are often heavily subsidized. In addition, the failure of many index insurance programs for farmers in the developing world has raised concerns about demand. To overcome such constraints, there is a need to develop the capacity of private insurers to address farmers’ needs. For wide scale impact, creating an enabling regulatory environment, investing in meteorological and agricultural data systems, educating farmers about the value of insurance, and facilitating international reinsurance will all be essential.
  • PSSN have spread rapidly and are now in place in 130 developing countries. They have proven potential to reduce economic loss and migration in the face of climate shocks, especially when integrated with credit schemes, production inputs, extension, and risk finance. A recent analysis has shown that such schemes can result in substantial reductions of GHG emissions. More analyses on such mitigation potential are required.
  • Planned temporary or seasonal migration (incremental adaptation) can enhance household resilience and food security through remittances. However, longer term and large-scale migration out of agriculture due to the slow onset of projected climate change-induced events such as sea-level rise, salinization, and desertification (transformative adaptation) could prove catastrophic for hundreds of millions unless properly anticipated and planned. Urgent priority should be given to identifying where such large-scale migration is likely to occur with the development of practical policy measures that will need to be put in place to support the future livelihoods of such migrants.

7.2. Mitigation Actions

  • Analyses has shown that with a near term goal for 2030 of 1 Gt CO2 e yr−1 emission reduction compared to the BAU scenario, and using optimistic assumptions with regard to the dissemination and adoption of currently available improved production practices, direct emissions from agriculture will only deliver 21–40% of the mitigation required. Given the ongoing constraints that are associated with the implementation of LED pathways, additional mitigation will also be required from a more holistic approach which includes indirect emissions and which spans the entire food system (Table 1).
  • Reducing GHG emissions from forest clearance and degradation associated with agricultural expansion through agricultural intensification has enormous potential to reduce GHG emissions, with estimates of up to 1.3 Gt CO2 e yr−1 being possible. Investment in AR4D targeting improved adoption rates of intensification innovations remains one of the best ways to reduce pressure on increasingly scarce land resources.
  • Global food loss and waste amounts to 1.3 billion tones/year. Each year such loss and waste generate 4.4 Gt CO2 eq., or ~8% of total anthropogenic GHG emissions. Thirty-seven percent of this occurs at the consumption stage (Table 2). On a per capita basis food wastage (kg CO2 eq./year) is substantially greater in high income regions. In contrast, in low income regions, postharvest losses are highest due to the lack of infrastructure and of knowledge on proper storage and handling. Hence, actions to increase food security and reduce GHG emissions should target loss during the consumption stage in high income countries and postharvest handling and storage in low income countries.
  • As of 2000, the global livestock sector—including land use and land use change—is estimated to have contributed 18% of total anthropogenic GHG emissions, a figure projected to rise by approximately 40% by 2050 if current trends in per capita meat consumption and population growth continue. A transition to lower meat diets could reduce mitigation costs to achieve a 450 ppm CO2-eq. stabilization target in 2050 by ~50%. However, given the projections of human population increases, especially in regions of emerging economies where increased meat consumption is also occurring (Table 3), such potential reductions are unlikely to be realized. Reductions in GHG emissions should target industrialized regions where per capita consumption is highest but has leveled off or decreased in recent years.

7.3. Seeking Synergies

The Global Alliance for Climate-Smart Agriculture provides a framework within which synergies between adaptation, mitigation and improved food security for small-scale and family farmers can be identified, developed, and disseminated. Given that the majority of developing countries have identified adaptation to climate change as a priority for achieving future food security in their INDCs, but that many also have reservations with regard to mitigation commitments (Box 1), identifying such synergies is essential. Recent analyses of case studies have shown that synergies among productivity, resilience, and mitigation occur in many CSA innovations (Table 4). Where such opportunities exist and also meet the local aspirations of small-scale and family farmers, their successful scaling up is a priority, but will require transformative changes in current policies, institutional arrangements, and funding mechanisms. Such synergies are also important in large-scale agriculture where their realization will face fewer constraints.

Author Contributions

As this article contains a review of literature on priorities for adaptation and mitigation actions to respond to the climate change challenge, the authors participated in reviewing literature on these different actions. Specifically, the authors contributed as follows. New stress tolerant crop/livestock varieties/breeds: A.M.L., B.M.C., and P.J.M.C. Climate information services, index-based agricultural insurance, and productive social safety nets: A.M.L., P.J.M.C., and J.W.H. Agricultural transformation and migration: A.M.L., B.M.C., and P.J.M.C. Direct emission targets, reducing deforestation, food loss and waste, and changes in food consumption patterns: A.M.L., P.J.M.C., and E.W. Climate-smart agriculture: A.M.L., P.J.M.C., and T.R. In addition, A.M.L., B.M.C., and P.J.M.C. contributed towards the introduction, evolving importance of agriculture within the UNFCCC, as well as the summary and conclusions sections. We would also like to thank Mariam Awad and Fabian Verhage for excellent research assistance.


This work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), and as such the research and the APC were funded by the CGIAR Fund Donors and through bilateral funding agreements. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.

Conflicts of Interest

The authors declare no conflicts of interest.


  1. FAO; IFAD; UNICEF; WFP; WHO. The State of Food Security and Nutrition in the World 2018. In Building Climate Resilience for Food Security and Nutrition; FAO: Rome, Italy, 2018; Licence: CC BY-NC-SA 3.0 IGO. [Google Scholar]
  2. Alexandratos, N.; Bruinsma, J. World Agriculture towards 2030/2050: The 2012 Revision (ESA Working Paper 12-03); Food and Agriculture Organization of the United Nations: Rome, Italy, 2012. [Google Scholar]
  3. Wheeler, T.; von Braun, J. Climate change impacts on global food security. Science 2013, 341, 508–513. [Google Scholar] [CrossRef]
  4. Porter, J.R.; Xie, L.; Challinor, A.J.; Cochrane, K.; Howden, S.M.; Iqbal, M.M.; Lobell, D.B.; Travasso, M.I. Food security and food production systems. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014; pp. 485–533. [Google Scholar]
  5. Niles, M.T.; Ahuja, R.; Esquivel, J.; Mango, N.; Duncan, M.; Heller, M.; Tirado, C. Climate Change & Food Systems: Assessing Impacts and Opportunities. Meridian Institute, 2017. Available online: (accessed on 4 March 2019).
  6. Vermeulen, S.J.; Campbell, B.M.; Ingram, J.S.I. Climate Change and Food Systems. Annu. Rev. Environ. Resour. 2012, 37, 195–222. [Google Scholar] [CrossRef]
  7. Ericksen, P.J. Conceptualizing food systems for global environmental change research. Glob. Environ. Chang. 2008, 18, 234–245. [Google Scholar] [CrossRef]
  8. Lipper, L.; Thornton, P.; Campbell, B.M.; Baedeker, T.; Braimoh, A.; Bwalya, M.; Caron, P.; Cattaneo, A.; Garrity, D.; Henry, K.; et al. Climate-smart agriculture for food security. Nat. Clim. Chang. 2014, 4, 1068–1072. [Google Scholar] [CrossRef]
  9. Lowder, S.K.; Skoet, J.; Raney, T. The number, size and distribution of farms, smallholder farms and family farms worldwide. World Dev. 2016, 87, 16–29. [Google Scholar] [CrossRef]
  10. Hansen, J.; Hellin, J.; Rosenstock, T.; Fisher, E.; Cairns, J.; Stirling, C.; Lamanna, C.; van Etten, J.; Rose, A.; Campbell, B. Climate Risk Management and Rural Poverty Reduction. Agric. Syst. 2018. [Google Scholar] [CrossRef]
  11. West, P.C.; Gerber, J.S.; Engstrom, P.M.; Mueller, N.D.; Brauman, K.A.; Carlson, K.M.; Siebert, S. Leverage points for improving global food security and the environment. Science 2014, 345, 325–328. [Google Scholar] [CrossRef]
  12. Keating, B.A.; Herrero, M.; Carberry, P.S.; Gardner, J.; Cole, M.B. Food wedges: Framing the global food demand and supply challenge towards 2050. Glob. Food Secur. 2014, 3, 125–132. [Google Scholar] [CrossRef]
  13. Stehfest, E.; Bouwman, L.; Van Vuuren, D.P.; Den Elzen, M.G.J.; Eickhout, B.; Kabat, P. Climate benefits of changing diet. Clim. Chang. 2009, 95, 83–102. [Google Scholar] [CrossRef]
  14. Springmann, M.; Clark, M.; Mason-D’Croz, D.; Wiebe, K.; Bodirsky, B.L.; Lassaletta, L.; Willett, W. Options for keeping the food system within environmental limits. Nature 2018, 562, 519–525. [Google Scholar] [CrossRef]
  15. Muldowney, J.; Mounsey, J.; Kinsella, L. Agriculture in the climate change negotiations; ensuring that food production is not threatened. Anim. Int. J. Anim. Biosci. 2013, 7 (Suppl. 2), 206–211. [Google Scholar] [CrossRef] [PubMed]
  16. Beddington, J.R.; Asaduzzaman, M.; Clark, M.E.; Bremauntz, A.F.; Guillou, M.D.; Howlett, D.J.B.; Jahn, M.M.; Lin, E.; Mamo, T.; Negra, C.; et al. What Next for Agriculture after Durban? Science 2012, 335, 280–290. [Google Scholar] [CrossRef] [PubMed]
  17. Ogle, S.M.; Olander, L.; Wollenberg, L.; Rosenstock, T.; Tubiello, F.; Paustian, K.; Buendia, L.; Nihart, A.; Smith, P. Reducing greenhouse gas emissions and adapting agricultural management for climate change in developing countries: Providing the basis for action. Glob. Chang. Biol. 2014, 20, 1–6. [Google Scholar] [CrossRef] [PubMed]
  18. Richards, M.; Bruun, T.B.; Campbell, B.M.; Gregersen, L.E.; Huyer, S.; Kuntze, V.; Madsen, S.T.N.; Oldvig, M.B.; Vasileiou, I. How countries plan to address agricultural adaptation and mitigation: An analysis of Intended Nationally Determined Contributions; CCAFS Info Note; CGIAR Research Prog ram on Climate Change, Agriculture and Food Security (CCAFS): Copenhagen, Denmark, 2015; Available online: (accessed on 4 March 2019).
  19. Mathy, S.; Blanchard, O. Proposal for a poverty-adaptation-mitigation window within the Green Climate Fund. Clim. Policy 2016, 16, 752–767. [Google Scholar] [CrossRef]
  20. Ji, Z.; Sha, F. The challenges of the post-COP21 regime: Interpreting CBDR in the INDC context. Int. Environ. Agreem. Politics Law Econ. 2015, 15, 421–430. [Google Scholar] [CrossRef]
  21. UNFCCC. Koronivia Joint Work on Agriculture. Decision -/CP.23. 2017. Available online: (accessed on 4 March 2019).
  22. Thomas, D.S.G.; Twyman, C.; Osbahr, H.; Hewitson, B. Adaptation to climate change and variability: Farmer responses to intra-seasonal precipitation trends in South Africa. Clim. Chang. 2007, 83, 301–322. [Google Scholar] [CrossRef]
  23. Cooper, P.J.M.; Dimes, J.; Rao, K.P.C.; Shapiro, B.; Shiferaw, B.; Twomlow, S. Coping better with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: An essential first step in adapting to future climate change? Agric. Ecosyst. Environ. 2008, 126, 24–35. [Google Scholar] [CrossRef]
  24. Baethgen, W.E. Climate risk management for adaptation to climate variability and change. Crop Sci. 2010, 50, S-70–S-76. [Google Scholar] [CrossRef]
  25. Howden, S.M.; Soussana, J.; Tubiello, F.N.; Chhetri, N.; Dunlop, M.; Meinke, H. Adapting Agriculture to Climate Change. Proc. Natl. Acad. Sci. USA 2007, 104, 19691–19696. [Google Scholar] [CrossRef]
  26. IPCC. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change; Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2012; 582p. [Google Scholar]
  27. IPCC. Summary for policymakers. In Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014; pp. 1–32. [Google Scholar]
  28. Dercon, S. Growth and shocks: Evidence from rural Ethiopia. J. Dev. Econ. 2004. [Google Scholar] [CrossRef]
  29. Dercon, S.; Hoddinott, J. Health, Shocks, and Poverty Persistence. In Insurance against Poverty; Oxford Scholarship Online: Oxford, UK, 2005. [Google Scholar] [CrossRef]
  30. Hoddinott, J. Shocks and their consequences across and within households in rural Zimbabwe. J. Dev. Stud. 2006. [Google Scholar] [CrossRef]
  31. Wood, G. Staying secure, staying poor: The “Faustian bargain”. World Dev. 2003, 31, 455–471. [Google Scholar] [CrossRef]
  32. Dercon, S.; Christiaensen, L. Consumption Risk, Technology Adoption and Poverty Traps: Evidence from Ethiopia; World Bank: Washington, DC, USA, 2007; p. 41. [Google Scholar]
  33. Simtowe, F.; Mduma, J.; Phiri, A.; Thomas, A.; Zeller, M. Can risk-aversion towards fertilizer explain part of the non-adoption puzzle for hybrid maize? Empirical evidence from Malawi. J. Appl. Sci. 2006. [Google Scholar] [CrossRef]
  34. Alem, Y.; Bezabih, M.; Kassie, M.; Zikhali, P. Does fertilizer use respond to rainfall variability? Panel data evidence from Ethiopia. Agric. Econ. 2010, 41, 165–175. [Google Scholar] [CrossRef]
  35. Barrett, C.B.; Moser, C.M.; McHugh, O.V.; Barison, J. Better technology, better plots, or better farmers? Identifying changes in productivity and risk among Malagasy rice farmers. Am. J. Agric. Econ. 2004. [Google Scholar] [CrossRef]
  36. Maccini, Y.; Yang, D. Under the Weather: Health, Schooling, and Economic Consequences of Early-Life Rainfall. Am. Econ. Rev. 2009, 99, 1006–1026. [Google Scholar] [CrossRef] [PubMed]
  37. Barnett, B.J.; Barrett, C.B.; Skees, J.R. Poverty Traps and Index-Based Risk Transfer Products. World Dev. 2008, 36, 1766–1785. [Google Scholar] [CrossRef]
  38. Carter, M.R.; Barrett, C.B. The economics of poverty traps and persistent poverty: An asset-based approach. J. Dev. Stud. 2006. [Google Scholar] [CrossRef]
  39. Barrett, C.B.; Santos, P. The impact of changing rainfall variability on resource-dependent wealth dynamics. Ecol. Econ. 2014, 105, 48–54. [Google Scholar] [CrossRef]
  40. Connolly, D.J. Shifting Paradigms. J. Hosp. Leis. Mark. 2000, 7, 3–38. [Google Scholar] [CrossRef]
  41. Loboguerrero, A.; Birch, J.; Thornton, P.; Meza, L.; Sunga, I.; Bong, B.B.; Rabbinge, R.; Reddy, M.; Dinesh, D.; Korner, J.; et al. Feeding the World in a Changing Climate: An Adaptation Roadmap for Agriculture. Rotterdam and Washington, DC. 2018. Available online: (accessed on 3 March 2019).
  42. FAO. Climate Related Trans-Boundary Pests and Diseases. HLC/08/BAK/4; Technical Background Document from the High Level Consultation of Climate Change, Energy and Food. 25–27 February; FAO: Rome, Italy, 2008; Available online: (accessed on 3 March 2019).
  43. Tester, M.; Langridge, P. Breeding Technologies to Increase Crop Production in a Changing World. Science 2010, 327, 818–822. [Google Scholar] [CrossRef] [PubMed]
  44. Chapman, S.C.; Chakraborty, S.; Dreccer, M.F.; Howden, S.M. Plant adaptation to climate changeopportunities and priorities in breeding. In Crop and Pasture Science; CSIRO Publishing: Collingwood, Victoria, Australia, 2012; Volume 63, pp. 251–268. [Google Scholar] [CrossRef]
  45. Fisher, M.; Abate, T.; Lunduka, R.W.; Asnake, W.; Alemayehu, Y.; Madulu, R.B. Drought tolerant maize for farmer adaptation to drought in sub-Saharan Africa: Determinants of adoption in eastern and southern Africa. Clim. Chang. 2015, 133, 283–299. [Google Scholar] [CrossRef]
  46. Ramirez-Villegas, J.; Watson, J.; Challinor, A.J. Identifying traits for genotypic adaptation using crop models. J. Exp. Bot. 2015, 66, 3451–3462. [Google Scholar] [CrossRef] [PubMed]
  47. Challinor, A.J.; Müller, C.; Asseng, S.; Deva, C.; Nicklin, K.J.; Wallach, D.; Vanuytrecht, E.; Whitfield, S.; Ramirez-Villegas, J.; Koehler, A.K. Improving the use of crop models for risk assessment and climate change adaptation. Agric. Syst. 2018, 159, 296–306. [Google Scholar] [CrossRef] [PubMed]
  48. Tambo, J.A.; Abdoulaye, T. Climate change and agricultural technology adoption: The case of drought tolerant maize in rural Nigeria. Mitig. Adapt. Strateg. Glob. Chang. 2012, 17, 277–292. [Google Scholar] [CrossRef]
  49. Osbahr, H.; Dorward PStern, R.D.; Cooper, S.J. Supporting agricultural innovation in Uganda to climate risk: Linking climate change and variability with farmer perceptions. Exp. Agric. 2011, 47, 293–316. [Google Scholar] [CrossRef]
  50. Rao, K.P.C.; Ndegwa, W.G.; Kizito, K.; Oyoo, A. Climate variability and change: Farmer perceptions and understanding of intra-seasonal variability in rainfall and associated risk in semi-arid Kenya. Exp. Agric. 2011, 47, 267–291. [Google Scholar] [CrossRef]
  51. Hansen, J.W.; Mason, S.J.; Sun, L.; Tall, A. Review of Seasonal Climate Forecasting for Agriculture in Sub-Saharan Africa. Exp. Agric. 2011, 47, 205–240. [Google Scholar] [CrossRef]
  52. Tall, A.; Hansen, J.; Jay, A.; Campbell, B.; Kinyangi, J.; Aggarwal, P.K.; Zougmoré, R. Scaling up climate services for farmers: Mission possible. Learning from good practice in Africa and South Asia; CCAFS Report No. 13; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS): Copenhagen, Denmark, 2014; p. 44. [Google Scholar]
  53. Vaughan, C.; Hansen, J.; Roudier, P.; Watkiss, P.; Carr, E. Evaluating agricultural weather and climate services in Africa: Evidence, methodology, and a “learning agenda”. Wires Clim. Chang. 2018. Submitted. [Google Scholar]
  54. Chen, M.; Wichmann, B.; Luckert, M.; Winowiecki, L.; Förch, W.; Läderach, P. Diversification and intensification of agricultural adaptation from global to local scales. PLoS ONE 2018, 13, e0196392. [Google Scholar] [CrossRef]
  55. Lemos, M.C.; Kirchhoff, C.J.; Ramprasad, V. Narrowing the climate information usability gap. Nat. Clim. Chang. 2012, 2, 789–794. [Google Scholar] [CrossRef]
  56. Clarkson, G.; Dorward, P.; Kagabo, D.M.; Nsengiyumva, G. Climate Services for Agriculture in Rwanda: Initial Findings from PICSA Monitoring and Evaluation; CCAFS Info Note; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS): Wageningen, The Netherlands, 2017; Available online: (accessed on 3 March 2019).
  57. Carter, M.; de Janvry, A.; Sadoulet, E.; Sarris, A. Index-Based Weather Insurance for Developing Countries: A Review of Evidence and a Set of Propositions for Up-Scaling; Background document for the workshop: “Microfinance products for weather risk management in developing countries: State of the arts and perspectives”; FERDI: Paris, France, 2014; Available online: (accessed on 4 March 2019).
  58. Linnerooth-Bayer, J.; Hochrainer-Stigler, S. Financial instruments for disaster risk management and climate change adaptation. Clim. Chang. 2015, 133, 85–100. [Google Scholar] [CrossRef]
  59. Jensen, N.D.; Christopher, B. Agricultural Index Insurance for Development. Appl. Econ. Perspect. Policy 2017, 39, 199–219. [Google Scholar] [CrossRef]
  60. Miranda, M.J.; Farrin, K. Index Insurance for Developing Countries. Appl. Econ. Perspect. Policy 2012, 34, 391–427. [Google Scholar] [CrossRef]
  61. Jensen, N.D.; Christopher, B.B.; Andrew, G.M. Index Insurance Quality and Basis Risk: Evidence from Northern Kenya. Am. J. Agric. Econ. 2016, 98, 1450–1469. [Google Scholar] [CrossRef]
  62. Binswanger-Mkhize, H.P. Is there too much hype about index-based agricultural insurance? J. Dev. Stud. 2012, 48, 187–200. [Google Scholar] [CrossRef]
  63. Tadesse, M.A.; Shiferaw, B.A.; Erenstein, O. Weather index insurance for managing drought risk in smallholder agriculture: Lessons and policy implications for sub- Saharan Africa. Agric. Food Econ. 2015, 3, 26. [Google Scholar] [CrossRef]
  64. Elabed, G.; Carter, M.R. Compound-risk aversion, ambiguity and the willingness to pay for microinsurance. J. Econ. Behav. Organ. 2015, 118, 150–166. [Google Scholar] [CrossRef]
  65. Roncoli, C.; Jost, C.; Kirshen, P.; Sanon, M.; Ingram, K.; Woodin, M.; Leopold, S.; Ouattara, F.; Bienvenue, J.S.; Sia, C.; et al. From accessing to assessing forecasts: An end-to-end study of participatory climate forecast dissemination in Burkina Faso (West Africa). Clim. Chang. 2009, 92, 433–460. [Google Scholar] [CrossRef]
  66. Hill, R.V.; Viceisza, A. A field experiment on the impact of weather shocks and insurance on risky investment. Exp. Econ. 2012, 15, 341–371. [Google Scholar] [CrossRef]
  67. Karlan, D.; Osei, R.; Osei-Akoto, I.; Udry, C. Agricultural decisions after relaxing credit and risk constraints. Q. J. Econ. 2014, 129, 597–652. [Google Scholar] [CrossRef]
  68. Cai, J.; de Janvry, A.; Sadoulet, E. Social Networks and Insurance Take-Up: Evidence from a Randomized Experiment in China; ILO Micro Insurance Innovation Facility, Research Paper 8; International Labour Organization: Geneva, Switzerland, 2011. [Google Scholar]
  69. Greatrex, H.; Hansen, J.; Garvin, S.; Diro, R.; Blakeley, S.; Le Guen, M.; Rao, K.; Osgood, D. Scaling Up Index Insurance for Smallholder Farmers: Recent Evidence and Insights; CCAFS Report No. 14; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS): Copenhagen, Denmark, 2015. [Google Scholar]
  70. Carter, M.R.; Cheng, L.; Sarris, A. Where and how index insurance can boost the adoption of improved agricultural technologies. J. Dev. Econ. 2015, 118, 59–71. [Google Scholar] [CrossRef]
  71. Hazell, P.; Hess, U. Beyond Hype: Another Look at Index-Based Agricultural Insurance. In Agriculture and Rural Development in a Globalizing World; Pingali, P., Feder, G., Eds.; Earthscan Food and Agriculture Series; Routledge: London, UK, 2007; Chapter 11. [Google Scholar]
  72. Johnson, C.; Bansha Dulal, H.; Prowse, M.; Krishnamurthy, K.; Mitchell, T. Social protection and climate change: Emerging issues for research, policy and practice. Dev. Policy Rev. 2013, 31, 2–18. [Google Scholar] [CrossRef]
  73. Steinbach, D.; Wood, R.G.; Kaur, N.; D’Errico, S.; Choudhary, J.; Sharma, S.; Rahar, V.; Jhajharia, V. Aligning Social Protection and Climate Resilience; IIED: London, UK, 2016; Available online: (accessed on 3 March 2019).
  74. Honorati, M.; Gentilini, U.; Yemtsov, R.G. The State of Social Safety Nets 2015; World Bank Group: Washington, DC, USA, 2015; Available online: (accessed on 3 March 2019).
  75. Asfaw, S.; Carraro, A.; Davis, B.; Handa, S.; Seidenfeld, D. Cash Transfer Programmes for Managing Climate Risk: Evidence from a Randomized Experiment in Zambia; FAO: Rome, Italy, 2017; Available online: (accessed on 3 March 2019).
  76. Lawlor, K.; Handa, S.; Seidenfeld, D.; Zambia Cash Transfer Evaluation Team. Cash Transfers Enable Households to Cope with Agricultural Production and Price Shocks: Evidence from Zambia. J. Dev. Stud. 2017, 1–18. [Google Scholar] [CrossRef]
  77. Schwan, S.; Yu, X. Social protection as a strategy to address climate-induced migration. Int. J. Clim. Chang. Strat. Manag. 2018, 10, 43–64. [Google Scholar] [CrossRef]
  78. Fisher, E.; Attah, R.; Barca, V.; O’Brien, C.; Brook, S.; Holland, J.; Kardan, A.; Pozarny, P. The livelihood impacts of cash transfers in sub-Saharan Africa: Beneficiary perspectives from six countries. World Dev. 2017. [Google Scholar] [CrossRef]
  79. Tirivayi, N.; Knowles, M.; Davis, B. 2016. The interaction between social protection and agriculture: A review of evidence. Glob. Food Secur. 2016, 10, 52–62. [Google Scholar] [CrossRef]
  80. Todd, J.E.; Winters, P.C.; Hertz, T. Conditional cash transfers and agricultural production: Lessons from the Oportunidades experience in Mexico. J. Dev. Stud. 2010, 46, 39–67. [Google Scholar] [CrossRef]
  81. Kabeer, N.; Piza, C.; Taylor, L. What Are the Economic Impacts of Conditional Cash Transfer Programmes? A Systematic Review of the Evidence; Technical Report; EPPI- Centre, Social Science Research Unit, Institute of Education, University of London: London, UK, 2012; Available online: (accessed on 4 March 2019).
  82. Arnall, A.; Oswald, K.; Davies, M.; Mitchell, T.; Coirolo, C. Adaptive Social Protection: Mapping the Evidence and Policy Context in the Agriculture Sector in South Asia; IDS Working Paper; IDS: Wivenhoe Park, UK, 2010; pp. 1–92. [Google Scholar] [CrossRef]
  83. Davies, M.; Guenther, B.; Leavy, J.; Mitchell, T.; Tanner, T. ‘Adaptive social protection’: Synergies for poverty reduction. Ids Bull. 2008, 39, 105–112. [Google Scholar] [CrossRef]
  84. Davies, M.; Guenther, B.; Leavy, J. Climate Change Adaptation, Disaster Risk Reduction and Social Protection: Complementary Roles in Agriculture and Rural Growth? IDS Working Paper; IDS: Wivenhoe Park, UK, 2009; Volume 320, p. 39. [Google Scholar] [CrossRef]
  85. Davies, M.; Béné, C.; Arnall, A.; Tanner, T.; Newsham, A.; Coirolo, C. Promoting resilient livelihoods through adaptive social protection: Lessons from 124 programmes in South Asia. Dev. Policy Rev. 2013, 31, 27–58. [Google Scholar] [CrossRef]
  86. Gilligan, D.O.; Hoddinott, J.; Seyoum Taffesse, A. The Journal of Development Studies the Impact of Ethiopia’s Productive Safety Net Programme and its Linkages The Impact of Ethiopia’s Productive Safety Net Programme and its Linkages. J. Dev. Stud. 2009, 45, 1684–1706. [Google Scholar] [CrossRef]
  87. Hoddinott, J.; Berhane, G.; Gilligan, D.O.; Kumar, N.; Taffesse, A.S. The impact of Ethiopia’s productive safety net programme and related transfers on agricultural productivity. J. Afr. Econ. 2012, 21, 761–786. [Google Scholar] [CrossRef]
  88. Soares, F.V.; Knowles, M.; Daidone, S.; Tirivayi, N. Combined Effects and Synergies Between Agricultural and Social Protection Interventions: What Is the Evidence So Far; FAO: Rome, Italy, 2016. [Google Scholar] [CrossRef]
  89. Kuriakose, A.T.; Heltberg, R.; Wiseman, W.; Costella, C.; Cipryk, R.; Cornelius, S. Climate-Responsive Social Protection. Social Protection and Labor Discussion Paper, No. 1210. Background Paper for the World Bank 2012–2022 Social Protection and Labor Strategy. 2012. Available online: (accessed on 4 March 2019).
  90. Woolf, D.; Solomon, D.; Lehmann, J. Land restoration in food security programmes: Synergies with climate change mitigation. Clim. Policy 2017. [Google Scholar] [CrossRef]
  91. Jarvis, A.; Ramirez-Villegas, J.; Campo, B.V.H.; Navarro-Racines, C. Is Cassava the Answer to African Climate Change Adaptation? Trop. Plant Biol. 2012, 5, 9–29. [Google Scholar] [CrossRef]
  92. Jones, P.G.; Thornton, P.K. Croppers to livestock keepers: Livelihood transitions to 2050 in Africa due to climate change. Environ. Sci. Policy 2009, 12, 427–437. [Google Scholar] [CrossRef]
  93. Bunn, C.; Läderach, P.; Jimenez, J.G.P.; Montagnon, C.; Schilling, T. Multiclass classification of agro-ecological zones for arabica coffee: An improved understanding of the impacts of climate change. PLoS ONE 2015, 10, e0140490. [Google Scholar] [CrossRef]
  94. Brooks, N.; Rohrbach DChasi, V.; Cantrill, J. Transformational Adaptation: Concepts, Examples, and Their Relevance to Agriculture in Eastern and Southern Africa. 2017. Available online: (accessed on 4 March 2019).
  95. Panda, A. Transformational adaptation of agricultural systems to climate change. Wiley Interdiscip. Rev. Clim. Chang. 2018. [Google Scholar] [CrossRef]
  96. Karpouzoglou, T.; Barron, J. A global and regional perspective of rainwater harvesting in sub-Saharan Africa’s rainfed farming systems. Phys. Chem. Earth 2014, 72–75, 43–53. [Google Scholar] [CrossRef]
  97. Warner, B.P.; Kuzdas, C.; Yglesias, M.G.; Childers, D.L. Limits to adaptation to interacting global change risks among smallholder rice farmers in Northwest Costa Rica. Glob. Environ. Chang. Part A Hum. Policy Dimens. 2015, 30, 101–112. [Google Scholar] [CrossRef]
  98. Dowd, A.; Marshall, N.; Fleming, A.; Jakku, E.; Gaillard, E.; Howden, M. The role of networks in transforming Australian agriculture. Nat. Clim. Chang. 2014, 4, 558–563. [Google Scholar] [CrossRef]
  99. Leclère, D.; Havlík, P.; Fuss, S.; Schmid, E.; Mosnier, A.; Walsh, B.; Obersteiner, M. Climate change induced transformations of agricultural systems: Insights from a global model. Environ. Res. Lett. 2014, 9, 124018. [Google Scholar] [CrossRef]
  100. Eakin, H.; York, A.; Aggarwal, R.; Waters, S.; Welch, J.; Rubiños, C.; Anderies, J.M. Cognitive and institutional influences on farmers’ adaptive capacity: Insights into barriers and opportunities for transformative change in Central Arizona. Reg. Environ. Chang. 2015, 16, 801–814. [Google Scholar] [CrossRef]
  101. Hadarits, M.; Pittman, J.; Corkal, D.; Hill, H.; Bruce, K.; Howard, A. The interplay between incremental, transitional, and transformational adaptation: A case study of Canadian agriculture. Reg. Environ. Chang. 2017, 17, 1515–1525. [Google Scholar] [CrossRef]
  102. Pelling, M. Resilience and transformation. In Climate Change and the Crisis of Capitalism: A Chance to Reclaim Self, Society and Nature; Pelling, M., Manuel-Navarette, D., Redclift, M., Eds.; Routledge: New York, NY, USA, 2012; pp. 51–65. [Google Scholar]
  103. Nicol, L.A.; Klein, K.K. Water Market Characteristics: Results from a Survey of Southern Alberta Irrigators. Can. Water Resour. J./Revue Canadienne Des Ressources Hydriques 2006, 31, 91–104. [Google Scholar] [CrossRef]
  104. FAO Migration. Agriculture and Climate Change; FAO: Rome, Italy, 2017; Available online: (accessed on 4 March 2019).
  105. Summary for Policymakers. Available online: (accessed on 4 March 2019).
  106. UNISDR (United Nations Office for Disaster Reduction). Sendai Framework for Disaster Risk Reduction (2015–2030); UNISDR: Geneva, Switzerland, 2015; Available online: (accessed on 4 March 2019).
  107. UN. Transforming Our World: The 2030 Agenda for Sustainable Development. 2015. Available online: (accessed on 4 March 2019).
  108. Soussana, J.F.; Lutfalla, S.; Ehrhardt, F.; Rosenstock, T.; Lamanna, C.; Havlík, P.; Lal, R. Matching policy and science: Rationale for the “4 per 1000—Soils for food security and climate” initiative. Soil Tillage Res. 2017. [Google Scholar] [CrossRef]
  109. Grewer, U.; Nash, J.; Gurwick, N.; Bockel, L.; Galford, G.; Richards, M.; Costa, C.; White, J.; Pirolli, G.; Wollenberg, E. Analyzing the greenhouse gas impact potential of smallholder development actions across a global food security program. Environ. Res. Lett. 2018, 13. Available online: (accessed on 4 March 2019). [CrossRef]
  110. Wollenberg, E.; Richards, M.; Smith, P.; Havlík, P.; Obersteiner, M.; Tubiello, F.N.; Herold, M.; Gerber, P.; Carter, S.; Reisinger, A.; et al. Reducing emissions from agriculture to meet the 2 °C target. Glob. Chang. Biol. 2016, 22, 3859–3864. [Google Scholar] [CrossRef] [PubMed]
  111. Richards, M.B.; Wollenberg, E.; Buglion-Gluck, S. Agriculture’s Contributions to National Emissions; CCAFS Info Brief; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS): Copenhagen, Denmark, 2015; Available online: (accessed on 4 March 2019).
  112. Sander, B.O.; Wassmann, R.; Palao, L.K.; Nelson, A. Climate-based suitability assessment for alternate wetting and drying water management in the Philippines: A novel approach for mapping methane mitigation potential in rice production. Carbon Manag. 2017, 8, 331–342. [Google Scholar] [CrossRef]
  113. Nieters, A.; Grabs, J.; Jimenez, G.; Alpizar, W. NAMA Café Costa Rica: A Tool for Low Carbon Development. NAMA Facility Technical Support Unit on behalf of German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB)/UK Department for Energy and Climate Change (DECC), 2015. Available online: (accessed on 4 March 2019).
  114. Frank, S.; Beach, R.; Havlík, P.; Valin, H.; Herrero, M.; Mosnier, A.; Hasegawa, T.; Creason, J.; Ragnauth, S.; Obersteiner, M. Structural change as a key component for agricultural non-CO2 mitigation efforts. Nat. Commun. 2018, 9. [Google Scholar] [CrossRef]
  115. Hristov, A.N.; Oh, J.; Giallongo, F.; Frederick, T.W.; Harper, M.T.; Weeks, H.L.; Branco, A.F.; Moate, P.J.; Deighton, M.H.; Williams, S.R.; et al. An inhibitor persistently decreased enteric methane emission from dairy cows with no negative effect on milk production. Proc. Natl. Acad. Sci. USA 2015, 112, 10663–10668. [Google Scholar] [CrossRef]
  116. Herd, R.M.; Bird, S.H.; Donoghue, K.A.; Arthur, P.F.; Hegarty, R.F. Phenotypic associations between methane production traits, volatile fatty acids and animal breeding traits. Proc. Assoc. Adv. Anim. Breed. Genet. 2013, 20, 286–289. [Google Scholar]
  117. Subbarao, G.V.; Yoshihashi, T.; Worthington, M.; Nakahara, K.; Andoa, Y.; Sahrawat, K.L.; Madhusudhan, I.; Lata, R.J.; Kishii, M.; Braune, H. Suppression of soil nitrification by plants. Plant Sci. 2015, 233, 155–164. [Google Scholar] [CrossRef] [PubMed]
  118. Paustian, K.; Lehmann, J.; Ogle, S.; Reay, D.; Robertson, G.P.; Smith, P. Climate-smart soils. Nature 2016. [Google Scholar] [CrossRef] [PubMed]
  119. Smith, P.; Bustamante, M.; Ahammad, H.; Clark, H.; Dong, H.; Elsiddig, E.A.; Haberl, H.; Harper, R.; House, J.; Jafari, M.; et al. Agriculture, Forestry and Other Land Use (AFOLU). In Climate Change 2014: Mitigation of Climate Change. Contribution of Working Group III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change; Edenhofer, O., Pichs-Madruga, R., Sokona, Y., Farahani, E., Kadner, S., Seyboth, K., Adler, A., Baum, I., Brunner, S., Eickemeier, P., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014. [Google Scholar]
  120. Niles, M.T.; Ahuja, R.; Barker, T.; Esquivel, J. Climate change mitigation beyond agriculture: A review of food system opportunities and implications. Renew. Agric. Food Syst. 2018, 33, 297–308. [Google Scholar] [CrossRef]
  121. Baccini, A.; Goetz, S.J.; Walker, W.S.; Laporte, N.T.; Sun, M.; Sulla-Menashe, D.; Hackler, J.; Beck, P.S.A.; Dubayah, R.; Friedl, M.A.; Samanta, S.; Houghton, R.A. Estimated carbon dioxide emissions from tropical deforestation improved by carbon density maps. Nat. Clim. Chang. 2012, 2, 182–185. [Google Scholar] [CrossRef]
  122. Harris, N.L.; Brown, S.; Hagen, S.C.; Saatchi, S.S.; Petrova, S.; Salas, M.; Hansen, M.C.; Potapov, P.V.; Lotsch, A. Baseline map of carbon emissions from deforestation in tropical regions. Science 2012, 336, 1573–1575. [Google Scholar] [CrossRef] [PubMed]
  123. Carter, S.; Herold, M.; Rufino, M.C.; Neumann, K.; Kooistra, L.; Verchot, L. Mitigation of agriculture emissions in the tropics: Comparing forest land-sparing options at the national level. Biogeosci. Discuss. 2015, 12, 5435–5475. [Google Scholar] [CrossRef]
  124. Mueller, N.D.; Gerber, J.S.; Johnston, M.; Ray, D.K.; Ramankutty, N.; Foley, J.A. Closing yield gaps through nutrient and water management. Nature 2012, 490, 254–257. [Google Scholar] [CrossRef]
  125. Godfray, H.C.J.; Beddington, J.R.; Crute, I.R.; Haddad, L.; Lawrence, D.; Muir, J.F.; Pretty, J.; Robinson, S.; Thomas, S.M.; Toulmin, C. Food Security: The Challenge of Feeding 9 Billion People. Science 2010, 327, 812–818. [Google Scholar] [CrossRef]
  126. Godfray, H.C.J.; Garnett, T. Food security and sustainable intensification. Philos. Trans. R. Soc. B Biol. Sci. 2014, 369, 20120273. [Google Scholar] [CrossRef]
  127. Lambin, E.F.; Meyfroidt, P. Global land use change, economic globalization, and the looming land scarcity. Proc. Natl. Acad. Sci. USA 2011, 108, 3465–3472. [Google Scholar] [CrossRef] [PubMed]
  128. Vanwalleghem, T.; Gomez, J.A.; Amate, J.I.; Gonzalez de Molina, M.; Vanderlinden, K.; Guzman, G.; Laguna, A.; Giraldez, J.V. Impact of historical land use and soil management change on soil erosion and agricultural sustainability during the Anthropocene. Anthropocene 2017, 17, 13–29. [Google Scholar] [CrossRef]
  129. Tittonell, P.; Giller, K.E. When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Field Crop. Res. 2013, 143, 76–90. [Google Scholar] [CrossRef]
  130. Byerlee, D.; Stevenson, J.; Villoria, N. Does intensification slow crop land expansion or encourage deforestation? Glob. Food Secur. 2014, 3, 92–98. [Google Scholar] [CrossRef]
  131. Angelsen, A. Policies for reduced deforestation and their impact on agricultural production. Proc. Natl. Acad. Sci. USA 2010, 107, 19639–19644. [Google Scholar] [CrossRef] [PubMed]
  132. FAO. SAVE FOOD: Global Initiative on Food Loss and Waste Reduction. In Key Facts on Food Loss and Waste You Should Know! Food and Agriculture Organization of the United Nations: Rome, Italy, 2016; pp. 1–2. Available online: (accessed on 4 March 2019).
  133. FAO. Food Wastage Footprint & Climate Change; FAO: Rome, Italy, 2015; pp. 1–4. Available online: (accessed on 4 March 2019).
  134. FAO. Food Wastage Footprint—Impacts on Natural Resources—Summary Report; FAO: Rome, Italy, 2013; Available online: (accessed on 4 March 2019).
  135. Bajželj, B.; Richards, K.S.; Allwood, J.M.; Smith, P.; Dennis, J.S.; Curmi, E.; Gilligan, C.A. Importance of food-demand management for climate mitigation. Nat. Clim. Chang. 2014, 4, 924–929. [Google Scholar] [CrossRef]
  136. Nash, J.; Peña, O.; Galford, G.; Gurwick, N.; Pirolli, G.; White, J.; Wollenberg, E. Reducing Food Loss in Agricultural Development Projects through Value Chain Efficiency; CCAFS Working Paper no. 204; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS): Wageningen, The Netherlands, 2017; Available online: (accessed on 4 March 2019).
  137. Gromko, D. Climate Change Mitigation and Food Loss and Waste Reduction: Exploring the Business Case; CCAFS working paper; CCAFS): Wageningen, The Netherlands, 2017; in press. [Google Scholar]
  138. UNEP. Prevention and Reduction of Food and Drink Waste in Businesses and Households—Guidance for Governments, Local Authorities, Businesses and Other Organisations, Version 1.0; UNEP: Athens, Greece, 2014. [Google Scholar]
  139. Ritchie, H.; Roser, M. Meat and Seafood Production & Consumption. Our World in Data, 2018. Available online: (accessed on 4 March 2019).
  140. Roser, M. Future Population Growth. Our World in Data, 2018. Available online: (accessed on 4 March 2019).
  141. Pelletier, N.; Tyedmers, P. Forecasting potential global environmental costs of livestock production 2000-2050. Proc. Natl. Acad. Sci. USA 2010, 107, 18371–18374. [Google Scholar] [CrossRef]
  142. Halloran, A.; Fischer-Møller, M.F.; Persson, M.; Skylare, E. Solutions Menu: Nordic Guide to Sustainable Food; Nordic Council of Ministers: Copenhagen, Denmark, 2008. [Google Scholar] [CrossRef]
  143. Nordic Cooperation. (n.d.) On the Menu: 24 Nordic Policy Solutions to Change Food Consumption. Available online: (accessed on 14 December 2019).
  144. FAO. Climate Smart Agriculture Sourcebook | Food and Agriculture Organization of the United Nations. Available online: (accessed on 6 September 2019).
  145. Paul, B.K.; Frelat, R.; Birnholz, C.; Ebong, C.; Gahigi, A.; Groot, J.C.J.; van Wijk, M.T. Agricultural intensification scenarios, household food availability and greenhouse gas emissions in Rwanda: Ex-ante impacts and trade-offs. Agric. Syst. 2018, 163, 16–26. [Google Scholar] [CrossRef]
  146. Bellarby, J.; Stirling, C.; Vetter, S.H.; Kassie, M.; Kanampiu, F.; Sonder, K.; Hillier, J. Identifying secure and low carbon food production practices: A case study in Kenya and Ethiopia. Agric. Ecosyst. Environ. 2014, 197, 137–146. [Google Scholar] [CrossRef]
  147. Kurgat, B.K.; Stöber, S.; Mwonga, S.; Lotze-Campen, H.; Rosenstock, T.S. Livelihood and climate trade-offs in Kenyan peri-urban vegetable production. Agric. Syst. 2017, 160, 79–86. [Google Scholar] [CrossRef]
  148. Sileshi, G.W.; Debusho, L.K.; Akinnifesi, F.K. Can Integration of Legume Trees Increase Yield Stability in Rainfed Maize Cropping Systems in Southern. Agron. J. 2012, 104, 1392–1398. [Google Scholar] [CrossRef]
  149. Liang, K.; Zhong, X.; Huang, N.; Lampayan, R.M.; Pan, J.; Tian, K.; Liu, Y. Grain yield, water productivity and CH4 emission of irrigated rice in response to water management in south China. Agric. Water Manag. 2016, 163, 319–331. [Google Scholar] [CrossRef]
  150. Kimaro Aa Mpanda, M.; Rioux, J.; Aynekulu, E.; Shaba, S.; Thiong’o, M.; Mutuo, P.; Abwanda, S.; Shepherd, K.; Neufeldt, H.; Rosenstock, T.S. Is conservation agriculture “climate-smart” for maize farmers in the highlands of Tanzania? Nutr. Cycl. Agroecosyst. 2015. [Google Scholar] [CrossRef]
  151. Kariuki, J.N.; Gitau, G.K.; Gachuiri, C.K.; Tamminga, S.; Muia, J.M.K. Effect of supplementing napier grass with desmodium and lucerne on DM, CP and NDF intake and weight gains in dairy heifers. Livest. Prod. Sci. 1999, 60, 81–88. [Google Scholar] [CrossRef]
  152. Gaitán, L.; Läderach, P.; Graefe, S.; Rao, I.; Van Der Hoek, R. Climate-smart livestock systems: An assessment of carbon stocks and GHG emissions in Nicaragua. PLoS ONE 2016, 11, e0167949. [Google Scholar] [CrossRef] [PubMed]
  153. Tilman, D.; Balzer, C.; Hill, J.; Befort, B.L. Global food demand and the sustainable intensification of agriculture. Proc. Natl. Acad. Sci. USA 2011, 108, 20260–20264. [Google Scholar] [CrossRef]
  154. Bell, P.; Namoi, N.; Lamanna, C.; Corner-Dollof, C.; Girvetz, E.; Thierfelder, C.; Rosenstock, T.S. A Practical Guide to Climate-Smart Agricultural Technologies in Africa; CCAFS Working Paper no. 224; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS): Wageningen, The Netherlands, 2018; Available online: (accessed on 4 March 2019).
  155. Chandra, A.; McNamara, K.E.; Dargusch, P. Climate-smart agriculture: Perspectives and framings. Clim. Policy 2017, 18, 526–541. [Google Scholar] [CrossRef]
  156. Taylor, M. Climate-smart agriculture: What is it good for? J. Peasant Stud. 2018, 45, 89–107. [Google Scholar] [CrossRef]
  157. Aggarwal, P.; Vyas, S.; Thornton, P.; Campbell, B.M. How much does climate change add to the challenge of feeding the planet this century? Environ. Res. Lett. 2019, in press. [Google Scholar] [CrossRef]
  158. Smith, P.; Martino, D.; Cai, Z.; Gwary, D.; Janzen, H.; Kumar, P.; McCarl, B.; Ogle, S.; O’Mara, F.; Rice, C.; et al. Agriculture. In Climate Change 2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change; Metz, B., Davidson, O.R., Bosch, P.R., Dave, R., Meyer, L.A., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007. [Google Scholar]
Figure 1. The food system concept [6,7,8].
Figure 1. The food system concept [6,7,8].
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Figure 2. Climate adaptation requires a diversity of actions [41].
Figure 2. Climate adaptation requires a diversity of actions [41].
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Figure 3. Hotspots of climate change based on assessments of impacts after adaptation on crop yield at country scale for the 2050s and the production gap (the difference between estimated cereal demand in 2050 and current cereal supply). Countries with a large cereal gap and high impacts of climate change are most vulnerable. Countries included only if the cropped area >10,000 ha [157].
Figure 3. Hotspots of climate change based on assessments of impacts after adaptation on crop yield at country scale for the 2050s and the production gap (the difference between estimated cereal demand in 2050 and current cereal supply). Countries with a large cereal gap and high impacts of climate change are most vulnerable. Countries included only if the cropped area >10,000 ha [157].
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Figure 4. Regional estimates of technical mitigation potential by 2030 [158].
Figure 4. Regional estimates of technical mitigation potential by 2030 [158].
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Table 1. Estimates of the relative contributions of different stages of the food system to global greenhouse gas (GHG) emissions (Adapted from [6]).
Table 1. Estimates of the relative contributions of different stages of the food system to global greenhouse gas (GHG) emissions (Adapted from [6]).
Stage of Food SystemEmissions (MtCO2 e)
PreproductionFertilizer manufacture282–575
Energy use in animal feed production60
Pesticide production3–140
ProductionDirect emissions from agriculture5.120–6.116
Indirect emissions from agriculture2.198–6.567
PostproductionPrimary and secondary processing192
Storage, packaging, and transport396
Retail activities224
Catering and domestic food management160
Waste disposal72
Table 2. Percent relative food wastage by volume per region 1 and by phase of food supply chain (Source: Abstracted from FAO [134]) and contribution of each phase of the food supply chain to food wastage and carbon footprint (Source: Abstracted from FAO [133]).
Table 2. Percent relative food wastage by volume per region 1 and by phase of food supply chain (Source: Abstracted from FAO [134]) and contribution of each phase of the food supply chain to food wastage and carbon footprint (Source: Abstracted from FAO [133]).
Stage of Food Supply Chain% by Volume% of C-footprintEUNAMiASSSANASALAM
Agricultural production321737322635303240
Postharvest handling and storage231710102035243621
Per capita food wastage footprint (kg CO2 eq./year)680860810210350350540
1 EU = Europe; NAM = N. America & Oceania; iAS = Industrialized Asia; SSA = sub-Saharan Africa; NA = N. Africa + W. & C. Asia; AS = S & SE. Asia; LAM = L. America.
Table 3. Trends in per capita meat consumption for the period 1973–2013 [139] and projected human population increases for the period 2013–2050 [140].
Table 3. Trends in per capita meat consumption for the period 1973–2013 [139] and projected human population increases for the period 2013–2050 [140].
Regions & Selected CountriesMeat Consumption kg/Capita/YearMeat Consumption Increase kg/Capita/YearProjected Human Population Increase (Millions)
Northern America100.3114.0112.713.7−1.384
Central America22.535.854.913.319.164
South America37.452.881.515.428.792
Northern Europe65.270.480.25.29.816
Western Europe82.490.485.38.0−3.28
Eastern Europe58.861.868.83.07.0−35
Southern Europe54.382.381.928.0−0.4−13
Northern Africa12.317.428.95.111.5143
Middle Africa13.016.723.93.77.2240
Eastern Africa12.810.210.8−2.60.6510
Western Africa9.410.512.81.12.3476
Southern Africa34.537.760.03.222.324
Western Asia17.
Eastern Asia11.931.560.219.628.7−30
South East Asia9.416.629.47.212.8180
Southern Asia4.
Central Asian/a36.545.2n/a8.742.1
Table 4. Select examples of synergies among productivity, resilience, and mitigation in agriculture.
Table 4. Select examples of synergies among productivity, resilience, and mitigation in agriculture.
Management PracticeLocationProduct% ChangeIndicator% ChangeIndicator% changeIndicatorSource
Mixed inorganic–organic fertilizerKenyaVegetables159Benefit-Cost Ratio--−75N2OiKurgat et al., [147]
Intercropped leguminous agroforestryZambiaMaize102Yield23Yield stability Sileshi et al., [148]
Alternate wetting and dryingChinaRice0Yield43Water prod.−59CH4Liang et al., [149]
Conservation agricultureTanzaniaMaize40Yield21Rainfall use intensity−32GHGiKimaro et al., [150]
Improved feedingKenyaMeat13Live weight gain5Feed efficiency--Kariuki et al., [151]
Silvopastoral systems 1NicaraguaMilk82Yield--−36CH4Gaitán et al., [152]
1 Based on models estimates of GHGs.

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Loboguerrero, A.M.; Campbell, B.M.; Cooper, P.J.M.; Hansen, J.W.; Rosenstock, T.; Wollenberg, E. Food and Earth Systems: Priorities for Climate Change Adaptation and Mitigation for Agriculture and Food Systems. Sustainability 2019, 11, 1372.

AMA Style

Loboguerrero AM, Campbell BM, Cooper PJM, Hansen JW, Rosenstock T, Wollenberg E. Food and Earth Systems: Priorities for Climate Change Adaptation and Mitigation for Agriculture and Food Systems. Sustainability. 2019; 11(5):1372.

Chicago/Turabian Style

Loboguerrero, Ana Maria, Bruce M. Campbell, Peter J. M. Cooper, James W. Hansen, Todd Rosenstock, and Eva Wollenberg. 2019. "Food and Earth Systems: Priorities for Climate Change Adaptation and Mitigation for Agriculture and Food Systems" Sustainability 11, no. 5: 1372.

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