Bringing the Concept of Climate-Smart Agriculture to Life Insights from CSA Country Profiles across Africa, Asia, and Latin America © 2018 International Bank for Reconstruction and Development / The World Bank 1818 H Street NW Washington DC 20433 Telephone: 202-473-1000 Internet: www.worldbank.org This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. This document should be cited as: Sova, C. A., G. Grosjean, T. Baedeker, T. N. Nguyen, M. Wallner, A. Jarvis, A. Nowak, C. Corner-Dolloff, E. Girvetz, P. Laderach, and Lizarazo. M. 2018. “Bringing the Concept of Climate-Smart Agriculture to Life: Insights from CSA Country Profiles Across Africa, Asia, and Latin America.” World Bank, and the International Centre for Tropical Agriculture, Washington, DC. Rights and Permissions The material in this work is subject to copyright. Because The World Bank encourages dissemination of its knowledge, this work may be reproduced, in whole or in part, for noncommercial purposes as long as full attribution to this work is given. Any queries on rights and licenses, including subsidiary rights, should be addressed to World Bank Publications, The World Bank Group, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: pubrights@worldbank.org. Cover design: Fernanda Rubiano Acknowledgments This publication is the product of a collaborative effort by the International Center for Tropical Agriculture (CIAT), the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the World Bank, and the UK Government’s Department for International Development (DFID) under the co-leadership of Tobias Baedeker (World Bank), Godefroy Grosjean, and Evan Girvetz (CIAT). The working results contained in this report are drawn from Nowak et al. (forthcoming), a publication chronicling the CSA Country Profile methodology, and providing reflections on the state of CSA around the world.. Contributing authors: Chase Sova (Independent Consultant), Godefroy Grosjean (CIAT/CCAFS), Tobias Baedeker (World Bank), Tam Ninh Nguyen (CIAT/CCAFS), Martin Wallner (World Bank), Andy Jarvis (CIAT/CCAFS), Andreea Nowak (Independent Consultant), Caitlin Corner-Dolloff (USDA, formerly CIAT/CCAFS), Evan Girvetz (CIAT/ CCAFS), Peter Laderach (CIAT/CCAFS), and Miguel Lizarazo (CIAT/CCAFS).. Original graphics, design and layout: Fernanda Rubiano (Independent Consultant). The CSA Country Profile methodology was first prepared in 2014 under the leadership of Svetlana Edmeades and Ana Bucher (World Bank), Caitlin Corner-Dolloff and Andy Jarvis (CIAT/CCAFS), and Claudia Bouroncle (Tropical Agricultural Research and Higher Education Center - CATIE) and with a team comprised of Andreea Nowak (CIAT), Miguel Lizarazo (CIAT), Pablo Imbach (CATIE), Andrew Halliday (CATIE), Beatriz Zavariz-Romero (CIAT), Rauf Prasodjo (CIAT), María Baca (CIAT), Claudia Medellín (CATIE), Karolina Argote (CIAT), Chelsea Cervantes De Blois (CIAT), Juan Carlos Zamora (CATIE), and Bastiaan Louman (CATIE). It was subsequently revisited in 2015 and 2017 by Andreea Nowak, Caitlin Corner-Dolloff, Miguel Lizarazo, Andy Jarvis, Evan Girvetz, Godefroy Grosjean, Felicitas Roehrig, Jennifer Twyman, Julian Ramirez, Carlos Navarro, Jaime Tarapues, Steve Prager, Carlos Eduardo Gonzalez (CIAT/CCAFS), Tobias Baedeker, Minna Konnonen, Tim Searchinger (World Bank), Charles Spillane, Colm Duffy and Una Murray (National University Ireland Galway). Additional support for CSA Country Profile development was provided by the United States Agency for International Development Bureau for Food Security (USAID BFS) and the Food and Agriculture Organization (FAO) of the United Nations. Data coding and management for this report was also provided by Fridah Nyakundi and Rachel Mburu. The Climate-Smart Agriculture (CSA) Country Profile Series assesses climate change challenges and solutions in the agricultural sectors of more than 30 countries across Africa, Asia, and Latin America and the Caribbean (LAC). This report introduces the first analysis of a new dataset drawn from these profiles, aggregating bottom-up results from individual expert assessments of CSA technologies. It offers the most complete overview to date of technologies considered climate-smart around the world. The emerging insights shed light on technologies in different locations and farming systems, their strengths and weaknesses across different dimensions of climate-smartness, and their specific barriers to adoption. As a result, a more concrete and specific picture of CSA emerges that could help demystify the concept, reveal synergies between the three CSA pillars (productivity, adaptation, and mitigation), and allow for more targeted technology deployment and scale-up. Top 10 CSA Insights 1 Technologies considered climate-smart are highly diverse. The universe of potential CSA technologies presented here is vast, with more than 1,700 unique combinations of production systems, regions, and technologies assessed for their smartness across key indicators like yield, water use efficiency, impact on carbon stocks and others. This report covers almost 300 distinct production systems across 33 countries. There is considerable opportunity to tailor CSA to specific farmers’ needs. 2 A convergence is growing among stakeholders on where and how CSA can make the biggest difference. While CSA is diverse, just five technology clusters (water management, crop tolerance to stress, intercropping, organic inputs, and conservation agriculture) account for almost 50 percent of all CSA technologies identified by experts as climate-smart across the 33 countries. The top 15 technology clusters represent almost 80 percent of all identified technologies. 3 Technologies considered climate-smart by experts vary considerably across regions, reflecting the context-specificity of opportunities, constraints, vulnerabilities, and agricultural sector characteristics around the world. For example, Africa’s primary focus is land restoration strategies, while in Asia, agricultural diversification strategies are considered key. These differences suggest that CSA has already been heavily tested and adapted to local and regional settings. 4 The “smartness” of a given CSA technology is dependent on context, and can vary considerably between different production systems and locations. The spread between the highest and lowest smartness score for a given CSA technology in this global analysis is 6.34 points (on a -10 to +10 point scale). Key factors that influence success of CSA technologies in a system must be assessed before developing priorities. 5 CSA technologies with the highest smartness scores are not always widely prioritized by experts and widely identified technologies do not always hold the highest smartness scores. For example, within maize (corn) systems globally, experts identified boundary planting as climate-smart on just three occasions (representing 1.6 percent of all technologies for that crop), despite an average smartness score of 7.10. Crop tolerance to stress represented over 14 percent of all technologies, but received an average smartness score of only 3.31. Bringing the Concept of Climate-Smart Agriculture to Life 5 6 CSA appears to coincide with common-sense agriculture. Income and profit indicators were rated almost uniformly positive for all CSA technologies by experts. Technologies considered climate-smart generally scored highest in productivity and adaptation CSA pillars, emphasizing the importance of measures of yield and income in encouraging adoption of technologies and the general prioritization of productivity over other pillars. 7 Most technologies considered climate-smart demonstrate synergies between productivity, adaptation, and mitigation pillars, revealing opportunities for co-benefits and potential “triple-wins.” Experts only identified trade-offs for a small number of technology clusters, whereas five technology clusters—tree management, improved pastures, silvopasture, conservation agriculture, and water management—are included in the top 10 smartest technologies for all three pillars. 8 CSA technologies in Country Profiles focus disproportionately on cropping systems, especially cereal crops. More than two-thirds of all technologies in this global synthesis apply to food crops such as maize, wheat, and rice or cash crops (perennials). Only 18 percent of technologies considered climate-smart were analyzed for livestock systems and just 2 percent for aquaculture systems. This is, in part, the product of experts consulted for Country Profiles specializing disproportionately on crop and cereal systems. 9 Capacity needs in the form of training and information was identified as the single largest barrier to CSA adoption across all regions, affecting almost 90 percent of all interventions. Investments in capacity building (for farmers, experts, and decision makers alike) and knowledge dissemination (through public extension services, universities and academia, or the private sector) are critical for ensuring the widespread adoption of CSA, particularly to enable the vital but complex implementation of integrated measures. 10 There is no CSA “silver bullet” and the smartness of a system depends on more than the technologies deployed at plot level. A key limitation of Country Profiles is their focus on singular interventions, rather than integrated packages of technologies. Country Profiles also focus on on-farm technologies as opposed to broader value chains or services. The next chapter in the CSA story will be to move beyond a “practice” lens, exploiting the potential for transformational change via locally appropriate bundles of technologies and services, and to integrate this information into the development of Climate Smart Investment Plans at the country level. 6 II. Introduction Agriculture is a risky business, even in the of settings around the world. The World best of conditions. This situation is especially Bank Group, the Food and Agriculture true in much of the developing world, where Organization (FAO) of the United Nations, and farmers rely heavily on rainfall, plant in the Consortium of International Agricultural degraded soils, and often lack access to high- Research Centers (CGIAR) are especially quality inputs or markets. These same places committed to scaling up CSA, helping farmers are where the impacts of climate change produce more, build resilience, and pursue will be the most hard felt. Climate change reduced GHG emissions. and associated extreme weather events will disrupt production and reduce agricultural Over the past decade, CSA has been a powerful yields across much of the developing world, organizing principle, bringing distinct placing new and greater stresses on natural stakeholders together in unprecedented resources required for food production. ways. CSA and its advocates have helped to place agriculture squarely on the negotiating Agriculture is also a major contributor to table at the Conference of the Parties (COP) climate change. Soil disruption, livestock to the United Nations Framework Convention methane emissions, deforestation, carbon- on Climate Change (UNFCCC). At COP23 heavy processing, and global supply chains in Bonn, a historic decision was made make agriculture one of the largest sectoral for the Subsidiary Body for Scientific and emitters of greenhouse gases (GHGs). Technological Advice and the Subsidiary Body These factors, combined with the effects of for Implementation to jointly address issues population growth, changing diets, rapid related to agriculture. This event represented urbanization, and other trends, will result in the first time that agriculture was identified as farmers, pastoralists, and fishers everywhere an implementation target in the Convention’s facing unprecedented challenges in feeding more-than 25-year history. themselves and the world. A central component of this new commitment Since the concept first emerged in 2009, CSA is pursuing methods and approaches for has helped to increase awareness of the two- assessing co-benefits among productivity, way relationship between agriculture and adaptation, and mitigation for agricultural climate change, drive the development of technologies. This report answers important more sophisticated tools for the assessment questions regarding CSA around the world of climate-smart technologies, and mobilize today, further demystifying CSA, and a coalition of organizations and institutions highlighting factors that may influence its dedicated to promoting CSA in a variety adoption and up-scaling. Climate-Smart Agriculture The CSA concept reflects an ambition to improve the integration of agricultural development and climate responsiveness. It aims to achieve food security and broader development goals under a changing climate and increasing food demand. CSA technologies sustainably increase productivity, enhance resilience, and reduce or remove GHGs. However, implementation of technologies requires planning to address trade-offs and synergies (co-benefits and “triple-wins”) between the three CSA pillars: productivity, adaptation, and mitigation. Bringing the Concept of Climate-Smart Agriculture to Life 7 III. CSA Country Profiles Case in Point—Country Ownership of CSA The global synthesis presented here is drawn Stakeholder consultation and country from CSA Country Profiles. The development ownership are central to linking CSA of these profiles is based on a participatory, Country Profiles to broader policy goals, rapid, and cost-effective approach to and scaling CSA deployment. In Tanzania, identifying entry points, opportunities, and for example, the government took strong challenges related to CSA at the country level. ownership over the profile development The first profiles were provided for 10 countries process, establishing a dedicated CSA in LAC in 2014 by the World Bank, International Profile Task Force. The task force, chaired Center for Tropical Agriculture, the Tropical by the Ministry of Agriculture, Fisheries, Agricultural Research and Higher Education and Livestock, helped to ensure that the Center, and the CGIAR research program profile was one of the key resources used by on Climate Change, Agriculture and Food the Government of Tanzania to inform the Security—together with national governments scaling up of climate-smart technologies and national experts. In the following years, in the country, while remaining in line with 23 subsequent profiles were developed with existing national priorities. additional support from the United Kingdom’s Department for International Development, the United States Agency for International Development Bureau for Food Security, and the Food and Agriculture Organization of the United Nations. “Pakistan is a developing nation and [among the] most vulnerable communities While Country Profiles are holistic in their to Climate Change in the region. I believe scope (figure 1), at their core is an empirical active participation from the public sector assessment of potential CSA technologies. in developing the CSA Country Profile has These assessments are built on structured shown commitment at the policy level and it expert consultation rather than on direct will influence policy and planning [from the observation or monitoring and evaluation of beginning].” technology implementation. Approximately 40–50 Country-level experts assign scores —Aamer Hayat Bhandara, (assessments) to CSA indicators related to Farmer, District Council Member (Pakpattan), productivity, adaptation, and mitigation Pakistan for select technologies in key production systems and agro-ecological zones. Experts were identified by national consultants and partner organizations familiar with the “The CSA Profiles are innovative in how they landscape of agricultural actors in each present highly complex information on CSA country, with an effort to diversify expertise in an easily digestible and visually appealing across a broad range of production systems. format and allow for benchmarking and These assessments indicate the change that comparison across countries. The CSA experts expect in a specific production system Profiles have already underpinned several if an identified technology were to be applied. World Bank investments and have paved the The assessments provided in the profiles are way for future initiatives linking agriculture built on expert perception, based on intimate and climate change agendas!” knowledge of local production systems, agricultural practices, and challenges and —Svetlana Edmeades, Senior Agriculture opportunities for implementing and scaling Economist, World Bank CSA interventions. Still, the findings presented here rely heavily on the composition of the expert group assembled and may not always be fully reflective of the broader agricultural system. 8 Figure 1. CSA Country Profiles at a Glance Bringing the Concept of Climate-Smart Agriculture to Life 9 IV. Global Synthesis and CSA Trends: Demystifying CSA The standard approach within CSA Country sociopolitical contexts. To date, across all Profiles allows the combined data to be used profiles, more than 1,700 combinations of to examine trends on a global or regional CSA technologies, cropping systems, and scale. Country Profiles and the smartness geographic locations have been analyzed and scores enable practitioners and decision assigned a “smartness” score by local experts. makers to compare very complex processes This analysis covers approximately 17 percent across countries and better understand the of all GHG emissions from agricultural systems, “baseline” state of climate sustainability in and represents 16 percent of international agricultural systems. While the 33 countries livestock production and almost 20 percent of for which profiles have been developed are global cereal production in dollar terms. This neither exhaustive nor representative, the global evidence base covers 290 production aggregated data between profiles represent systems and has involved more than 1,500 an unprecedented snapshot of the state expert consultations. Here we document 10 of CSA around the world, across a diverse insights about the state of CSA emerging range of geographies, agro-ecologies, and from this global synthesis. Insight Technologies considered climate-smart are highly 1 diverse. Considerable opportunity exists for tailoring CSA to specific farmers’ needs. The universe of potential CSA technologies rainwater harvesting. Meanwhile, improved is expansive. Across this global analysis, pastures and grazing management includes 44 separate clusters (see Annex 1) of CSA shifts to “zero-grazing” systems, the use of technologies were identified, ranging from feed supplements, changing stocking rates, water management to crop rotation, improved planting fodder crops, rotational grazing, and pastures to tree boundary planting, and many improved management of pasture fertilizers, others (figure 2). among other strategies. In total, CSA Country Profiles capture hundreds of unique CSA Each technology cluster hosts a variety opportunities being implemented across of sub-technologies. For example, water diverse production systems and regions, management includes drip irrigation, demonstrating extraordinary potential for contouring, and terracing (for example, bunds, technology transfer, adaptation, and lessons furrows, ditches, ridges, and half-moons), and learned. “The CSA profile has helped agriculture stakeholders in the country to identify business opportunities in the different agricultural value chains. This is demonstrated through increase of involvement of partners such CSA value chains in the SAGCOT Region.” —Ms. Shakwaanande Natai, Head of Environment Management Unit, Tanzania Ministry of Agriculture 10 Figure 2. Frequency of Technologies Considered Climate-Smart by Cluster (global) There is a growing convergence on where and Insight how CSA can make the biggest difference. Several 2 technologies are frequently identified as climate- smart and are highly scored globally. While CSA is diverse in the sheer number within crops and production systems as well of technologies, crops, and regions that (figure 4). This situation is especially true it spans, experts consistently identify for global rice production, where just three several technology clusters as climate- technology clusters account for more than 50 smart or highly scored. Just five technology percent of all CSA technologies applied to that clusters account for nearly 50 percent of all crop: crop tolerance to stress, integrated rice technologies considered climate-smart in this management, and water management. global synthesis: water management, crop tolerance to stress, intercropping, organic Globally, the clusters with the highest inputs, and conservation agriculture (figure smartness scores are silvopasture (the use 3). The top 15 technology clusters, which of trees in pasture land) (6.49), conservation include technologies such as integrated pest agriculture (5.56), cover crops (5.43), water management, reduced or no-tillage, and management (4.95), improved pastures (4.91), diversification, represent almost 80 percent and biogas (4.80) (figure 5). These global of all CSA technologies globally. This may be trends hold several regional variations. For the product of experts being influenced by example, while silvopasture and conservation the growing body of CSA literature related to agriculture are considered highly climate- these technologies. smart across all regions, aquasilviculture ranks highly only in Asia. Mulching is a CSA This same concentration of technology technology cluster ranked highly in Africa and clusters considered climate-smart exists LAC, but is not frequently identified in Asia. Bringing the Concept of Climate-Smart Agriculture to Life 11 Figure 3. Frequency of Technology Clusters Considered Climate-Smart (global) Figure 4. Frequency of Technology Clusters Considered Climate-Smart by Select Crops (global) 12 Figure 5. Top 10 Climate-Smart Technology Clusters (global) Technologies considered climate-smart vary Insight considerably across regions, reflecting the context- 3 specificity of opportunities, constraints, and agricultural sector characteristics around the world. Two of the top 10 smartest technology • Disease management and livestock clusters—conservation agriculture and water genetic improvement are most management—are common across the commonly identified in African nations. Africa, Asia, and LAC regions. Still, technologies considered climate-smart vary considerably • Risk management technologies between regions (figure 6). For example, (representing less than 3 percent of CSA Country Profiles reveal the following all technologies considered climate- characteristics for Africa: smart globally) are more commonly identified—especially climate services. • Contouring, agroforestry, and other land management or land reclamation technologies are more common than in other regions. Bringing the Concept of Climate-Smart Agriculture to Life 13 Asia, meanwhile, hosts a high number of Case in Point— agricultural diversification strategies as well Aquasilviculture in Asia as many interventions that are focused on aquaculture and fisheries. Diversification as a CSA strategy was commonly identified Aquasilviculture is a technology cluster only in Asia, and less so in Africa and LAC. considered climate-smart almost Technologies considered climate-smart in exclusively in Asia. In southern Bangladesh, LAC and Asia focus more heavily on cash many rural families are adopting small crops than in Africa. Energy switching and ponds, or “ghers”, for prawn and fish improved cookstoves are nearly exclusive production. These ghers are dug with to LAC, while land restoration practices are wide embankments offering resilience largely absent in this region. In short, CSA is against flood and cyclone damage, and highly sensitive to local conditions, and the providing an elevated platform on which concept is being thoroughly verified by the to grow vegetables and other crops. This prevailing conditions in each region (see aquasilviculture technology has expanded regional dashboards). greatly in the region thanks to training programs and rigorous documentation of investment returns. Private and government banks have begun investing widely in gher excavations given their relatively low risk and profitable returns. Figure 6. Top 10 Smartest CSA Technology Clusters by Region (n>5) 14 Bringing the Concept of Climate-Smart Agriculture to Life 15 16 Bringing the Concept of Climate-Smart Agriculture to Life 17 Climate-smartness is a function of context, not Insight an innate property. The smartness of a given CSA 4 technology can vary considerably between different production systems and locations. Climate-smartness is highly context- largest standard deviation of any technology dependent (figure 7). The average difference cluster). In Rwanda, stakeholders scored the between minimum and maximum smartness technology 9.04 with strong marks for water scores for all technologies considered climate- and nitrogen smartness when applied in smart globally is 6.34 points1 (on a -10 to +10 maize systems. A similar boundary planting point scale). For example, the smartness score intervention, however, scored just 0.06 in for improved fertilizer management varies Senegal among vegetable farmers, with by up to nine points between contexts, the only positive carbon smartness identified largest single variation of any CSA technology. by experts. For other technologies such as Similarly, roughly two-thirds of the smartness integrated nutrient management (min-max scores for boundary planting—identified 19 difference of ~2.3 points) and multi-strata times globally by local experts—fall between agroforestry (~3.0), scores are less dispersed. 0.89 and 6.19, a considerable range (the Figure 7. Minimum, Maximum, and Average Smartness Scores of Technology Clusters Considered Climate-Smart (global) (n>10) 1 Note that for the 10 Latin American countries, climate-smartness was measured on an entirely positive scale (0-10). When normalized across scales, this min-max distance will be greater still. 18 The climate-smartness of a given technology applied to pulse crops where it achieved its cluster can also vary considerably across lowest average smartness score from experts. production systems. Figure 8 shows the This result is also true of crop tolerance to smartness scores of technology clusters most stress when applied to rice systems, and frequently considered climate-smart globally intercropping when applied to pulses. (water management, crop tolerance to stress, Ultimately, climate-smart agriculture is an intercropping, and use of organic inputs) approach, not an assessed list of interventions. across different agricultural production The results of this global synthesis support the systems. Some of the clusters most commonly CSA community’s reluctance to overdefine selected by experts for a given cropping or produce a static list of climate-smart system yield the lowest average perceived technologies across contexts. smartness scores. For example, the organic inputs technology cluster was most often Figure 8. Smartness Scores for Technology Clusters Frequently Identified as Climate- Smart Across Production Systems (global) (n>5) Bringing the Concept of Climate-Smart Agriculture to Life 19 Insight CSA technologies with the highest smartness scores are not always widely prioritized by experts 5 and widely identified technologies do not always hold the highest smartness scores. CSA technologies with the highest perceived recognition of that technology’s climate- smartness scores are not always widely smartness. In other words, the smartness identified by experts as climate-smart. For of a technology depends on the system example, silvopasture is the highest ranked under analysis. Yet even within the same CSA technology in Africa, with an average production system—maize, for example— score of 5.78. Yet stakeholders identified boundary planting was identified as climate- this technology on just six occasions across smart on just three occasions (representing 17 countries (representing 0.6 percent of all just 1.6 percent of all technologies), despite technologies). Similarly, silvopasture holds an average smartness score of 7.10. Crop an average CSA smartness score of 7.62 in tolerance to stress represents 14 percent of LAC, but was also identified just six times (2 all maize technologies (n=25) but scored, on percent of all technologies). To a large extent, average, just 3.31. While always subject to local the frequency that a technology is identified contexts, the relationship between frequency as climate-smart in this global dataset is a and smartness provides useful insights into function of applicability and is not a universal key technology groupings (table 1). Table 1. Emerging CSA Technology Clusters: frequency and smartness High The highest performing CSA technology clusters globally. Water Management (n=230; Frequency, These suites of interventions are both frequently identified by s=4.95) Intercropping (n=160; local experts as climate-smart and hold higher-than-median High s=4.74) Organic Inputs (n=150; climate-smartness scores. These are high-scoring, well- Smartness s=4.13) known clusters. Medium Conservation Agriculture (n=83; Clusters that perform above the global average for smartness Frequency, s=5.57). and are selected at a moderate rate (n>50). Collectively, these Crop Rotation (n=80; s=4.14) High constitute technologies that show high potential but could Fertilizer Management (n=71; Smartness benefit from continued scaling and awareness-building. s=4.34) Clusters with high smartness scores—especially in the mitigation pillar—but identified as climate-smart only a Low small number of times. These are high-potential strategies Frequency, Green Manure (n=18; s=5.43) that would benefit from continued scaling and promotion Silvopasture (n=13; s=6.49) High or increased research into potential adoption barriers. These Energy Switching (n=9; s=5.59) Smartness clusters have high mitigation potential that could benefit society more broadly. Farmers may require additional incentive mechanisms to adopt them. High This cluster is an outlier, frequently selected but with a lower- Frequency, than-average global smartness score. This outcome may be Crop tolerance to stress (n=214; the result of the composition of the expert groups consulted Low s=3.66) in Country Profile development, privileging expertise in crop Smartness and cereal systems and improved crop varieties. Note: n=frequency, s=average smartness score. 20 Crop Tolerance to Stress: A Deeper Dive Each technology cluster shown in this global analysis is composed of a host of higher-resolution CSA technologies. Crop tolerance to stress is among the most commonly identified CSA technology clusters globally. This group accounts for almost 13 percent (n=214) of all technologies considered climate-smart by experts across the three regions, despite its lower-than-average smartness score. It includes the use of crop varieties with improved tolerance to variations in temperature and moisture conditions as well as the use of certified seeds. Global: Following multiple stress-tolerance combinations (for example, drought tolerant and early maturing; saline tolerant and high yielding), drought tolerance was the most commonly identified sub-category, accounting for 16 percent of all selections. This sub-category was followed by pest and disease tolerance and early maturing varieties. As with CSA technologies more broadly, the most commonly selected tolerance sub-categories are not necessarily the highest scoring. Tolerance to multiple stressors (3.05) was frequently identified as climate-smart. However, its score was below the global average smartness score (3.66) for all crop tolerance to stress sub-categories. The “other” category, which includes the use of certified seeds and heat- and cold-tolerant crop varieties, was selected just 11 percent of the time, but scored 4.29—well above the global smartness average for crop tolerance to stress. In general, less research has been undertaken to develop heat-tolerant varieties in comparison to the tolerance of other stressors like drought. While crop tolerance to stress technologies score close to the global mean in regard to productivity (4.37), this cluster’s average smartness for both adaptation (3.57) and mitigation (3.08) pillars falls well below global mean. The adoption of improved crop varieties, despite their potential to adapt to new and changing climatic conditions, may be driven predominantly by considerations relating to productivity rather than adaptation or mitigation, or may simply reflect experts’ preference for productivity outcomes, a bias evident across most technologies in this synthesis. Regional: These global results hold several regional variations. While “multiple” and drought tolerance are the largest single crop tolerance sub-categories globally, pest and disease tolerance are the most common tolerance or resistance trait selected by experts in LAC. Saline- and flood- tolerant varieties, meanwhile, are almost entirely unique to Asia while high yielding varieties were not widely selected in that same region. In LAC, “other varieties”, which includes certified and heat-tolerant seeds, was the highest scoring crop tolerance to stress sub-category (7.67). In Asia, early maturing varieties was the highest scoring (4.12). Pest and disease tolerance is considered a highly climate-smart sub-category across all regions. In comparing stress-tolerant varieties of grain crops and cash crops, grain crops seem to be more climate-smart, especially in LAC and Asia. CSA appears to coincide with common-sense Insight agriculture. Income and profit indicators were rated 6 almost uniformly positive for all CSA technologies by experts. Any intervention identified by experts that This potentially reflects the relative ease of contributed to at least two of the three identifying yield and income impacts for pillars of CSA—productivity, adaptation, and these strategies; experts have long been mitigation—was considered for inclusion in trained to pursue yield and on-farm income CSA Country Profiles. Across all regions and improvements over other considerations. It technologies, experts scored productivity may also reflect the prioritization of these measures (composed of yield, income, and benefits over adaptation or mitigation. profit indicators) the highest (an average of Productivity biases, however, are not 4.48, compared to 4.3 and 3.9 for adaptation universally present. Some low frequency, and mitigation pillars, respectively) (see table high smartness technologies such as green 2). Almost all technologies considered climate- manure, energy switching, and silvopasture smart for each region showed positive income did receive high mitigation pillar scores by or profit smartness (see trade-offs discussion experts, reflecting the importance of climate in next section). mitigation in the choice of CSA practices. Still, 22 the data presented here demonstrate that constraint in identifying climate-smart measures of yield and income are important technologies, while adaptation and mitigation in encouraging adoption of CSA technologies. are more likely considered as co-benefits. Higher productivity remains a binding Table 2. Mean and Median Smartness Scores (all technologies) (global) Productivity Adaptation Mitigation Smartness Total Smartness (T) Smartness (P) Smartness (A) (M) Median Scorea 4.0 4.94 3.70 3.94 Mean Score 4.48 4.30 3.91 4.31 a The median climate-smartness score varies considerably between regions (Asia 3.34, Af rica 3.64, Latin America 6.64). This result is due, in part, to methodological changes during the course of the CSA Country Profile development process, especially in LAC where smartness scores were produced on an entirely positive scale (0-10). Case in Point—Productivity Driving CSA Uptake Improving farmer income is central to CSA adoption and sustainability. In Lower Nyando, Kenya, communities are participating in Climate-Smart Villages designed to test and deploy CSA strategies. The increased adoption of agroforestry and alley cropping has helped to spur investments in 22 private tree nurseries in the surrounding area, half of which are women- owned. These investments will help to sustain momentum around agroforestry practices while simultaneously strengthening the rural economy, and provide alternative livelihood opportunities for entrepreneurs in Lower Nyando. While trade-offs exist, most CSA technologies Insight demonstrate synergies between the productivity, 7 adaptation, and mitigation pillars. There are ample opportunities for co-benefits and triple-wins. The identification of trade-offs and synergies with considerable synergies between CSA between the productivity, adaptation, pillars, rather than profound trade-offs. Figure and mitigation pillars is among the core 9 shows the relative weight of each pillar in contributions of CSA Country Profiles. Experts contributing to a technology’s total smartness across the three regions overwhelmingly score. Almost 90 percent of technology clusters identified technologies as climate-smart (40 of 44) have perceived benefits that span all Bringing the Concept of Climate-Smart Agriculture to Life 23 three CSA pillars. Biological control of vectors, considered climate-smart2. In Africa and Asia, heat management, improved cookstoves, and the CSA indicator with the largest number risk insurance are the four exceptions. Even of negative values is soil health (adaptation these clusters, however, show co-benefits pillar), followed by energy (mitigation) and with another CSA pillar. Technology clusters nitrogen indicators (mitigation). Experts with especially high synergies (and high were least likely to identify trade-offs (that frequency) include conservation agriculture, is, undesirable effects or negative values) for crop rotation, use of organic inputs, mulching, productivity, especially income, and more diversification, and reduced/no-tillage. With likely to allow trade-offs for adaptation and the exception of diversification, each of these mitigation pillars, especially soil health, energy, highly synergistic technology clusters hold and nitrogen indicators. Technologies with smartness scores above the global average. trade-offs in this global synthesis have slightly lower than average productivity benefits while Still, trade-offs are apparent in the data. CSA adaptation and mitigation scores are far below Country Profile experts assessed climate- the mean (table 3). In other words, experts smart technologies on a scale from -10 to +10, believe that farmers may tolerate reduced with negative values representing undesirable soil health or reduced nutrient-use efficiency impacts on an indicator. Negative values for gains in other indicators, but they will not signal potential trade-offs for technologies tolerate income losses. Table 3. Average Smartness of Technologies with Trade-Offs (with at least one negative indicator) Productivity Adaptation Mitigation Technologies with trade-offs 4.15 1.94 2.02 All technologies 4.48 4.30 3.91 The highly synergistic nature of many and productivity pillars. Among the smartest technologies is clearly visible when each CSA technologies globally, interventions considered pillar is mapped separately (figure 10). Five highly climate-smart in one pillar are often technology clusters—tree management, considered highly climate-smart in the others. improved pastures, silvopasture, conservation The notable exception is biogas which scores agriculture, and water management— high in both mitigation and adaptation are included in the top 10 technologies for smartness but low in productivity. smartness for the mitigation, adaptation, 2 Numerous trade-off models have been developed and there are additional methods to assess the more intimate conflicts between indicators for each practice (beyond the use of negative values applied here). We did not attempt a high-resolu- tion comparison here, but a full tradeoff assessment of any CSA priority would be needed prior to investment. Additionally, there are other transaction costs not captured here that may affect technology adoption—high productivity assessments do not translate automatically into high rates of technology adoption. 24 Figure 9. Productivity, Adaptation, and Mitigation Contributions to Overall Smartness Scores of Technologies Considered Climate-Smart (global) Figure 10. Smartness Scores of Climate-Smart Technologies by Pillar (global) Bringing the Concept of Climate-Smart Agriculture to Life 25 Insight Technologies considered climate-smart in CSA 8 Country Profiles focus disproportionately on cropping systems, especially cereal crops. Across all regions, CSA technologies were It is also, in part, the product of experts most commonly identified by experts for consulted for Country Profiles specializing food crops such as rice, maize, and wheat. In disproportionately on crop and cereal systems. fact, 43 percent of all technologies analyzed Yet demand for animal protein is growing at globally relate to these and other food crops a rapid pace as more people enter the global (figure 11). Another 24 percent of technologies middle class. CSA advocates and practitioners considered climate-smart pertain to cash must work to close the knowledge gap with crops like coffee, cocoa, and rubber.3 However, regard to key climate change adaptation and only 20 percent of all interventions relate to coping strategies for livestock and aquaculture either livestock or aquaculture, despite the production systems. fact that livestock production alone represents almost 35 percent of all agricultural production The focus on food crops is not universal across (US$) in the countries included in this analysis regions. In LAC, for example, the largest single (FAOSTAT 2016). Cereal crops like maize, wheat, commodity-type grouping is cash crops. and rice are important food security crops While in Asia, commodity-type trends are and are widely grown. This bias, then, stems more balanced across food crops, cash crops, from a deliberate prioritizing of food security livestock, and fisheries. considerations in the profile methodology. Figure 11. Technologies Considered Climate-Smart by Commodity Type and CSA Category (global) 3 Cash crops include perennial crops (coffee, cocoa, rubber, cashew nuts), sugar, vegetables, and fruits. 26 Case in Point—Value Chain Case in Point— Climate Risk Profiles Climate Risk Management Acknowledging the on-farm technology While risk management technologies do bias present in CSA Country Profiles, not feature centrally in this analysis, efforts the International Centre for Tropical to provide smallholder farmers with Agriculture (CIAT); Kenya Ministry of improved climate information services Agriculture, Livestock and Fisheries; and are being widely undertaken globally. In the World Bank have developed 31 Climate Benin, for example, researchers, farmers, Risk Profiles at the subnational (county) and local decision makers have come level in Kenya. Risk Profiles examine key together in farmer field schools to discuss commodities, map vulnerabilities and and disseminate climate information prior risks across the value chain from input to and during the planting season. With provision to marketing, and identify the help of key partners in the Ministry adaptation strategies and off-farm of Agriculture (MAEP), the National services for combating these risks. Climate Agricultural Research System (INRAB), Risk Profiles are being developed in the national universities, and the National Philippines for the three Island groups Meteorological Service (Meteo Benin), (Luzon, Visayas, and Mindanao). CIAT is more than 300 farmers in 60 field schools preparing these profiles for the Philippine interact with meteorologists and climate Department of Agriculture and FAO, and researchers to make informed choices they will be used with others to prepare a about when to sow and harvest crops. proposal to the Green Climate Fund. These same schools serve as platforms to disseminate innovative agricultural practices and improved crop varieties. An extension of this cereal crop bias is The primary focus of CSA Country Profiles is on- that almost 70 percent of all technologies farm technologies, leaving considerable space considered climate-smart fall into the for the continued investigation of downstream broad CSA category of agronomy (that is, or enabling environment-type services. For soil management and crop production). example, technologies related to post-harvest Technologies related to energy, risk processing and risk management (n=82) management, and post-harvest categories (for instance, climate services, risk insurance, were not widely identified in any region. In and so on) account for less than five percent fact, energy technologies such as biogas, of all technologies considered by experts to improved cookstoves, and alternative on- be climate-smart. Off-farm, downstream or farm energy sources represent just over 1.5 enabling environment technologies represent percent of all technologies identified globally just 3 percent of interventions prioritized by as climate-smart. Among the small number of experts globally. mentions, these energy technologies are most common in LAC. Bringing the Concept of Climate-Smart Agriculture to Life 27 Case in Point—Strengthening Livestock in CSA Stakeholders–public as well as private-aiming to develop climate-smart livestock investments often lack adequate information and tools to support them. The “Guide to Investing in Sustainable Livestock: Environment,” a joint web resource being developed by the World Bank and the FAO, aims to close this gap. The tool simplifies sustainability in livestock production and processing by identifying seven core principles, the application of which is explained through practical guidance for six specific contexts, covering different climatic conditions and livestock farming systems. The tool will be completed in early 2019 and launched through a joint World Bank-FAO event. Guide to Investing in Sustainable Livestock: Six Contexts Livestock Farm size Climate Livestock system species Ruminants Small Dry to temperate Mixed crop/livestock Pastoral Ruminants Small to Medium Dry (mobile grazing) Semi-dry to Ruminants Small to Medium Grazing humid Ruminants Medium to Large Temperate/cold Grazing Intermediate and Monogastrics Medium Various Industrial Monogastrics Small to Large Humid tropics Mixed crop/livestock Insight Training and information were identified as the single largest barrier category to CSA adoption 9 across all regions, affecting almost 90 percent of all interventions. Cost-benefit analysis is often used to predict experts as the single largest barrier category technology adoption. But contrary to this across all regions, affecting almost 90 percent conventional wisdom, the most commonly of all interventions (figure 12). The prominence identified barriers to CSA technology adoption of training and information barriers points in this global synthesis were not economic to the critical importance of research and in nature. Instead, training and information academia in this quickly evolving area. Many barriers were identified by CSA Country Profile CSA technologies are knowledge-intensive. 28 Case in Point—Overcoming Information Barriers Increasing the adoption rate of potential CSA strategies such as biogas requires improved information dissemination, and engagement with the private sector. In Tanzania, construction of biogas digesters to produce fuel for household and farm needs was undertaken with funding from the Netherlands Development Organization. This effort was supported by Sokoine University to encourage practice dissemination. Key to the project moving forward is the private sector’s engagement in the design, manufacture, and repair of biodigesters. This CSA technology spans sectors, from agriculture to health, and sanitation to energy, making it a priority intervention for the country’s poverty alleviation goals and its efforts to support small and medium enterprises. Of particular importance will be to further While these trends were similar between develop and test location, and system-specific regions, socio-cultural barriers were found to knowledge on CSA technologies as well as be stronger in Asia. Policy and institutional delivery mechanisms and required policy barriers were more frequently identified in and enabling environments. Training and African countries, characterized by poorly information barriers were followed by policy functioning agricultural extension systems, and institutional barriers (affecting 36 percent limited access to input and output markets, of interventions), economic barriers (31 inefficient risk management systems, and percent), socio-cultural barriers (25 percent), scarce social safety net systems. and environmental barriers (9 percent). Figure 12. Barriers to CSA Technology Adoption (global) High, smartness technologies do not appear and crop tolerance to stress (which often to be disproportionately plagued by a higher requires research, seed multiplication, and number of barriers to implementation distribution). Conversely, technologies that can (figure 13). Instead, and perhaps intuitively, be implemented more easily by the farmers technologies with an especially high number themselves tend to be perceived as having of barriers tend to be those requiring a lower number of barriers (for example, the involvement of multiple actors in the crop rotation, conservation agriculture, implementation process, and those that intercropping, reduced/no-tillage). Many are knowledge- and capital-intensive of these technologies have above-average such as multi-strata agroforestry, biogas, smartness scores. genetic improvement, disease management, Bringing the Concept of Climate-Smart Agriculture to Life 29 Figure 13. Climate-Smartness with Barriers to Adoption (global) While more research is required to determine For example, the identification of training/ the specific magnitude of these barriers, there information and social/cultural barriers is an early indication that specific barriers reduced climate-smartness scores between 5 have a disproportionately strong impact and and 10 percent across all practices, speaking to can produce a bias in the scoring of certain the potential weight of these barriers. (figure interventions identified as climate-smart. 14). Figure 14. Smartness Scores of Technologies Considered Climate-Smart, with and without Select Barriers (global) 30 Barriers to CSA adoption are influenced by the however, international development partners composition of institutions and organizations represent over one-third of all institutional supporting farmers in implementing and actors. These partners include multilateral scaling CSA technologies. Organizations play and bilateral development agencies (serving distinct roles in promoting and implementing as the largest single source of funding for CSA, such as: developing enabling policies, CSA implementation), international non- undertaking research, providing funding, governmental organizations, networks, and and sharing knowledge and expertise, alliances. Research, training, and academic among others. Globally, the single largest institutions will play a central role in the CSA-related institutional category is success of CSA scaling. These organizations government, which often includes ministries represent between 15 and 25 percent of CSA- of agriculture, environment, finance, and related institutions, depending on the region. planning (figure 15). Government actors Private sector organizations represent just five represent the largest institutional grouping percent of CSA-related institutions globally. in both Asia and Latin America. In Africa, Figure 15. Involvement of CSA-Related Institutions (global) Bringing the Concept of Climate-Smart Agriculture to Life 31 Insight There is no CSA “silver bullet” and the smartness of 10 a system depends on more than the technologies deployed at plot level. CSA Country Profiles include a wide array important tools for building technical and of stakeholders, and take into account political momentum and have fostered a complex socio-economic, environmental, and strong group of CSA advocates poised to political landscapes around climate-smart continue development and implementation agriculture. These profiles have been critically of practices in each country. Case in Point—Climate Smart Investment Plans The Climate Smart Investment Plan (CSIP) approach developed by the World Bank in collaboration with CIAT, the International Institute for Applied Systems Analysis, CEA, and others builds on the highly successful CSA Profile Series. Where CSA Profiles provided a stocktaking and overview, CSIPs use this information, applying participatory analytical tools to identify sets of transformative CSA investment and policy opportunities in support of countries’ climate commitments. The analytical tools are tailored to each country context but include visioning exercises, robust decision making under uncertainty, and quantitative modeling—and are all deployed collaboratively with the client team and multiple in-depth stakeholder consultations. As a result, clients are able to identify investment and policy opportunities in the agriculture sector that increase productivity and incomes, strengthen the sector’s resilience to climate-change impacts, and reduce emissions. The proposed opportunities will be contextualized within existing client policies and targets and be presented in different formats depending on context. CSIP pilots are under way in Bangladesh and Zambia, and CSIPs are being developed using similar methodologies tailored to local conditions in Côte d’Ivoire, Lesotho, Mali, and Zimbabwe. Pilots have shown encouraging results, providing a practical avenue toward integration and implementation of Nationally Determined Contributions and agriculture sector strategies. The combination of participatory processes with quantitative elements to inform prioritization of investments has proven effective. This strategy has resulted in a shortlist of interventions with specified parameters across the “what and how” of technical content, implementation, and financing mechanisms, as well as accompanying policy interventions. Each CSIP identifies financing needs for the prioritized CSA opportunities and discusses potential sources to cover these needs, including by following the Maximizing Finance for Development (MFD) approach to leverage private sector finance and exploring opportunities to attract climate finance. Lessons learned and critical success factors identified during the pilot phase include the following: • Client ownership is imperative; CSIPs should be programmed well in advance to allow sufficient time to engage with clients to build ownership. 32 • Local knowledge is key to understanding the specific context and interlinkages between climate change and agriculture. • Reverse-engineering approaches are useful to define the CSIP methodology. • Selectivity of interventions, while challenging, is necessary as it sharpens the focus of analysis and increases stakeholder engagement. • Building local capacity in modeling is necessary to ensure clients can use the tools after CSIP development is completed. Going forward, the Country Profile competing sectoral priorities, the cumulative methodology will further evolve including to effect of combined suites of CSA technologies, better reflect system level aspects (including and the potential for transformational change the landscape level) and to integrate newly in global food systems. The next step is to available evidence on impacts from CSA move beyond the “practice” lens of CSA alone. projects, pooling all scientific evidence into The smartness of a system depends on more one large dataset, the CSA Compendium. than the technologies deployed at plot level. Integrated landscape level approaches are Even the best technologies would not make best suited to manage synergies and tradeoffs an agricultural system climate-smart where across different land use intensities and slash and burn agriculture, extensive soil agriculture production systems, facilitating erosion and the expansion of degraded lands multidimensional decision making. Beyond prevail, for example. By using CSA Country continued outreach and expansion of the Profiles, CSIPs, and subnational Climate Country Profiles’ global footprint (both in Risk Profiles, countries can better prepare terms of countries and continents), the next their food systems for the impacts of climate chapter in CSA’s story will be embedding change in an integrated way that reduces Country Profiles into a broader suite of the sector’s contribution to the underlying decision-making tools—from Climate problem, thus addressing one of the most Smart Investment Profiles to subnational pressing challenges of our time. Climate Risk Profiles—that take into account “Concise as it is, [the CSA Country Profile] is a helpful reference for policy makers, technical people and agri-fishery practitioners in crafting policies, programs and projects for the sector. DA-SWCCO used the relevant information in the CSA Country Profile in presenting the Adaptation and Mitigation Initiative in Agriculture (AMIA) program, serving as the backgrounder and recommending ways forward.” —U-Nichols Manalo, Director, Systems-Wide Climate Change Office, Philippines Department of Agriculture Bringing the Concept of Climate-Smart Agriculture to Life 33 34