Climate-informed priorities for One CGIAR Regional Integrated Initiatives Developed by Andy Jarvis (Alliance), Todd Rosenstock (CIFOR-ICRAF), Jawoo Koo (IFPRI), Phil Thornton (ILRI), Ana Maria Loboguerrero (Alliance), Bram Govaerts (CIMMYT), Julian Ramirez-Villegas (Alliance), Steven D. Prager (Alliance), Aniruddha Ghosh (Alliance) Climate-informed priorities for One CGIAR Regional Integrated Initiatives Developed by Andy Jarvis (Alliance), Todd Rosenstock (CIFOR-ICRAF), Jawoo Koo (IFPRI), Phil Thornton (ILRI), Ana Maria Loboguerrero (Alliance), Bram Govaerts (CIMMYT), Julian Ramirez-Villegas (Alliance), Steven D. Prager (Alliance), Aniruddha Ghosh (Alliance) Introduction The February 2021 RAFS-IAG meetings decided that the new One CGIAR’s Regional Integrated Initiatives need to define their initial focus with a climate lens. This report summarizes a rapid assessment of climate-related challenges to food-, land- and water- systems across the 6 CGIAR regions. Based on the evidence, priority geographies and systems are proposed which provide a starting point for the Regional Initiatives design process. It then assesses the readiness of the Two Degree Initiative (2DI) regional challenges for their fit to the challenges, and potential to form the basis for developing the first batch of Regional Integrated Initiatives. This rapid analysis consisted of the following1: 1. Identifying the primary climate hazards present in each CGIAR region 2. The exposure of people and agriculture to those climate hazards 3. The impact of future climate hazards to cropping and farming systems and regional assessment of agriculture contribution to poverty reduction and nutrition improvement 4. Disaggregated emissions data from the food system by region 5. Analyzed the performance of on-farm agricultural technologies and their barriers to adoption We present the key results as a series of figures and tables grouped by One CGIAR region. The Annex contains data and maps clipped to the specific region for ease of use. Support during the design phase can fill in gaps unable to be addressed due to time constraints (e.g., vulnerability). The data presented here can also be combined with integrated assessments and strategic foresight to assess likely impacts in each region, and also a methodology is provided to support an evidence based and inclusive design process to further develop each initiative. This report is organized in the following sections: Sect. 1 presents the key global adaptation and mitigation challenges; Sect. 2 explores the solution space; Sect. 3 dives into the region-specific results; Sect. 4 reviews the alignment of the two-degree initiative (2DI) with the region-specific results; Sect. 5 proposes a design process for the integrated initiatives; and Sect. 6 concludes with specific programmatic recommendations for initiative design. Methods associated with Sect. 1–3 are described in Annex 1. 1 Methods are described in Annex 1. 1 Section 1: Key adaptation and mitigation challenges We assessed the primary climate challenges facing each region using the IPCC Risk Framework (Box 1). The risk is determined by the interactions between hazards, vulnerability and exposure. In this definition, hazards encompass both extreme weather events (e.g. a one-day tropical storm) and slow climate trends (e.g. the annual average temperature increasing over decades). The analysis leverages off- the-shelf CCAFS datasets Box 1 – The IPCC Risk Framework and analytical capacity developed under the Bill and Melinda Gates Foundation funded Digital Atlas of Adaptation grant (hereafter ‘The Atlas’) and other CCAFS aligned projects. Though The Atlas is not formally released yet, the datasets are finalized and were made available for the analysis. Figure 1 | Climate hazards to agriculture, present and futures, based on Thornton (unpublished). Analysis describing rainfall variability (V), floods (F), drought (D), temperatures (T), growing season reductions (R), and combinations of the hazards under RCP8.5 clearly shows that climate hazards are both ubiquitous and place-based. Integrated regional program design should therefore take account of the diversity of hazards. However, as the geographic location and coverage of these hazards varies across regions, the RIIs should programmatically address those hazards which have the greatest effect on agricultural livelihoods. For example, much of Sub-Saharan Africa and South Asia will need to build resilience to high growing season temperature while Central America faces primarily high risks of floods and growing season reductions. 2 Figure 2 | Predominant climate related hazards and their relative distribution within each CGIAR region, expressed with respect to the level of exposure of value of production. Hazards presented in Figure 1 were overlaid with datasets from the MapSPAM2 on Value of Production (VoP) for both crops and for livestock, and for rural population. One important result related to Fig. 2 is that there is consistency in relation to the exposure of crops, livestock and people to these different hazards. Furthermore, a clear picture emerges with respect to the share of crop and livestock production value and population that are exposed to climate hazards (vs. not), as well as on the dominant climate factors that are likely to impact agricultural livelihoods. The region-specific sections presented later in this document provide a ranking of hazards for each region. 2 International Food Policy Research Institute, 2020, "Spatially-Disaggregated Crop Production Statistics Data in Africa South of the Sahara for 2017", https://doi.org/10.7910/DVN/FSSKBW, Harvard Dataverse, V3 3 Figure 3 | Relative contribution of different agriculture and other land use (AFOLU) sources of emissions disaggregated by region. Value at top of graph show the total gross emission per region (Tg CO2eq/year). These data show two dominant trends. One, Latin America and the Caribbean region are responsible for nearly double the gross annual emission than any other region, with deforestation accounting for nearly 50% of the emissions, followed by livestock emissions which account for another 20%. Two, livestock and rice production are the principal agricultural sources in Asia. 4 Section 2: Exploring solutions Figure 4 | Identifying best fits: interactions driving performance of on-farm agricultural technologies varies. The Evidence for Resilient Agriculture (ERA) database includes data derived from 2,000 scientific publications on the performance of 100s of management practices and technologies applied alone or in combination in SSA. The data show key trends that the impacts of changing technologies vary by farming systems context. For example, below, the show the impact of practice interactions on yield. Interactions are categorized based on if they are antagonistic (i.e., using both together yields less than either alone), averaging (i.e., using both is equal to using either alone), subadditive (i.e., using both yield more than either along but less than them both combine) and superaddivitive (i.e., using both together yield more than both added individually). 5 Figure 5 | The barriers to adoption of improved agriculture technologies in SSA. Colors indicate directional hypotheses of influence: dark green is positively related, light green is negatively related. Lines indicate confidence interval across 168 studies. If the confidence interval does not cross 0.5, then the factors can be considered to be associated with adoption more so than by chance. Values in parentheses are the number of times the determinant was included in the dataset. These types of data can be used to screen investments for major barriers to adoption and identify where complementary programming may help increase adoption. Data only available for SSA at this time (Arslan et al. in prep). 6 Figure 6 | Climate-informed investment design. Climate change presents risks to development investments; however, ex-ante cost-benefit analyses (CBA) rarely include climate risks. National-level CBA completed in collaboration with the World Bank integrated hazards, exposure, technology performance, adoption, and costs to estimate the most likely and extreme returns from agricultural investments. Note how the shape of the distributions of outcomes shift slightly when climate risks are included. 7 Section 3: Key regional results Central, West Asia and North Africa (CWANA) 8 CWANA Values and Value Shares of Commodity Production (average annual gross value of production 2017-2019) Central, West Asia & North Africa (CWANA) Commodity Value (million 2015 I$) Value Share (%) Total Poverty Stunting Total Poverty Stunting Cereal Grains Rice 3,590 180 78 1.5 0.9 2.0 Wheat 18,019 1,149 272 7.7 5.7 7.1 Maize 3,321 117 59 1.4 0.6 1.5 Millet 964 118 54 0.4 0.6 1.4 Sorghum 1,660 202 93 0.7 1.0 2.4 Barley 3,793 35 41 1.6 0.2 1.1 Roots, Tubers & Bananas Potato 8,079 593 130 3.4 2.9 3.4 Cassava 0 0 0 0.0 0.0 0.0 Yam 49 6 3 0.0 0.0 0.1 Sweet Potato 152 10 6 0.1 0.0 0.1 Taro (cocoyam) 2 0 1 0.0 0.0 0.0 Banana 1,276 67 39 0.5 0.3 1.0 Plantain 0 0 0 0.0 0.0 0.0 Oilseeds & Pulses Groundnut 2,181 235 107 0.9 1.2 2.8 Soybean 145 3 1 0.1 0.0 0.0 Sesame 1,294 149 68 0.6 0.7 1.8 Beans (phaseolus ) 507 8 7 0.2 0.0 0.2 Chickpea (cicer ) 744 17 9 0.3 0.1 0.2 Cowpea (vigna unguiculata ) 75 10 4 0.0 0.0 0.1 Pigeonpea (cajanus ) 0 0 0 0.0 0.0 0.0 Lentil (lens ) 431 3 5 0.2 0.0 0.1 Smallholder Cash Crops Cotton 6,456 1,775 83 2.7 8.7 2.1 Coffee 41 7 3 0.0 0.0 0.1 Coconut 0 0 0 0.0 0.0 0.0 Cocoa 0 0 0 0.0 0.0 0.0 Cashew 0 0 0 0.0 0.0 0.0 Vegetables & Melons Solanum (tomato, eggplant) 19,156 994 263 8.2 4.9 6.8 Allum (onion, shallot, garlic, leek) 6,882 574 145 2.9 2.8 3.8 Cucurbit (cucumber, pumpkin, melon) 6,975 468 102 3.0 2.3 2.6 Brassica (cabbage, cauliflower, broccoli) 999 126 13 0.4 0.6 0.3 Okra 526 42 21 0.2 0.2 0.6 Legume vegetables (green beans, peas) 905 14 13 0.4 0.1 0.3 Leafy vegetables (lettuce, spinach) 412 3 3 0.2 0.0 0.1 Vegetable, other 8,453 1,171 129 3.6 5.8 3.3 Tree Fruits Citrus (oranges, tangerines, lemons, other) 12,786 330 224 5.4 1.6 5.8 Mango 1,602 123 64 0.7 0.6 1.7 Pineapple 1 0 0 0.0 0.0 0.0 Papaya 10 1 1 0.0 0.0 0.0 Other tropical fruit 3,542 71 54 1.5 0.4 1.4 Other temperate fruit 13,192 989 164 5.6 4.9 4.3 Cultivated Forage Crops 238 5 6 0.1 0.0 0.2 Livestock Dairy (milk) 27,196 3,317 372 11.6 16.3 9.6 Cattle (meat) 21,208 3,201 364 9.0 15.8 9.4 Small ruminants (meat,milk, wool) 30,807 3,354 491 13.1 16.5 12.7 Poultry (meat & eggs) 25,521 717 321 10.9 3.5 8.3 Pigs 239 13 1 0.1 0.1 0.0 Aquaculture - Freshwater fish Carp 391 50 10 0.2 0.2 0.3 Tilapia 1,341 51 39 0.6 0.3 1.0 Misc freshwater fish 38 12 1 0.0 0.1 0.0 Cereal Grains 31,347 1,802 596 13.3 8.9 15.5 Roots, Tubers & Bananas 9,558 675 178 4.1 3.3 4.6 Oilseeds & Pulses 5,376 425 203 2.3 2.1 5.2 Smallholder Cash Crops 6,497 1,782 85 2.8 8.8 2.2 Vegetables & Melons 44,308 3,392 690 18.9 16.7 17.9 Tree Fruits 31,132 1,515 507 13.3 7.5 13.1 Livestock 104,971 10,603 1,549 44.7 52.2 40.2 Fish 1,771 113 50 0.8 0.6 1.3 ALL CROPS (x plantation) 128,219 9,591 2,260 54.6 47.2 58.6 ALL LIVESTOCK & FISH 106,742 10,716 1,599 45.4 52.8 41.4 ALL AGRICULTURE (x plantation) 234,961 20,307 3,859 100.0 100.0 100.0 Poverty = value weighted by $1.9/capita/day poverty headcount; stunting = value weighted by prevalence of child stunting. Value of forage crops included in value of livestock production. 9 West and Central Africa (WCA) 10 WCA Values and Value Shares of Commodity Production (average annual gross value of production 2017-2019) West & Central Africa (WCA) Commodity Value (million 2015 I$) Value Share (%) Total Poverty Stunting Total Poverty Stunting Cereal Grains Rice 8,647 3,530 450 5.4 5.7 5.4 Wheat 27 12 2 0.0 0.0 0.0 Maize 6,227 2,610 335 3.9 4.2 4.0 Millet 4,782 2,078 344 3.0 3.4 4.1 Sorghum 3,523 1,453 227 2.2 2.4 2.7 Barley 1 0 0 0.0 0.0 0.0 Roots, Tubers & Bananas Potato 771 325 46 0.5 0.5 0.6 Cassava 22,128 10,106 1,277 13.7 16.4 15.2 Yam 19,303 6,933 1,042 12.0 11.2 12.4 Sweet Potato 1,814 780 110 1.1 1.3 1.3 Taro (cocoyam) 755 93 121 0.5 0.2 1.4 Banana 2,635 1,256 159 1.6 2.0 1.9 Plantain 7,670 2,901 379 4.7 4.7 4.5 Oilseeds & Pulses Groundnut 8,270 3,290 458 5.1 5.3 5.5 Soybean 488 186 27 0.3 0.3 0.3 Sesame 1,369 554 81 0.8 0.9 1.0 Beans (phaseolus ) 1,442 628 77 0.9 1.0 0.9 Chickpea (cicer ) 1 1 0 0.0 0.0 0.0 Cowpea (vigna unguiculata ) 2,465 1,010 169 1.5 1.6 2.0 Pigeonpea (cajanus ) 5 4 0 0.0 0.0 0.0 Lentil (lens ) 0 0 0 0.0 0.0 0.0 Smallholder Cash Crops Cotton 3,098 1,338 149 1.9 2.2 1.8 Coffee 472 202 23 0.3 0.3 0.3 Coconut 148 41 6 0.1 0.1 0.1 Cocoa 5,473 1,466 194 3.4 2.4 2.3 Cashew 1,511 588 61 0.9 1.0 0.7 Vegetables & Melons Solanum (tomato, eggplant) 2,999 1,080 166 1.9 1.8 2.0 Allum (onion, shallot, garlic, leek) 1,924 792 123 1.2 1.3 1.5 Cucurbit (cucumber, pumpkin, melon) 578 227 26 0.4 0.4 0.3 Brassica (cabbage, cauliflower, broccoli) 221 102 16 0.1 0.2 0.2 Okra 2,461 969 142 1.5 1.6 1.7 Legume vegetables (green beans, peas) 39 15 2 0.0 0.0 0.0 Leafy vegetables (lettuce, spinach) 81 37 6 0.1 0.1 0.1 Vegetable, other 5,731 2,305 335 3.5 3.7 4.0 Tree Fruits Citrus (oranges, tangerines, lemons, other) 5,784 2,334 328 3.6 3.8 3.9 Mango 1,774 789 100 1.1 1.3 1.2 Pineapple 1,731 667 94 1.1 1.1 1.1 Papaya 402 187 25 0.2 0.3 0.3 Other tropical fruit 180 75 9 0.1 0.1 0.1 Other temperate fruit 1,273 517 79 0.8 0.8 0.9 Cultivated Forage Crops 7 3 0 0.0 0.0 0.0 Livestock Dairy (milk) 1,428 576 86 0.9 0.9 1.0 Cattle (meat) 7,483 3,108 438 4.6 5.0 5.2 Small ruminants (meat,milk, wool) 19,990 4,759 465 12.4 7.7 5.5 Poultry (meat & eggs) 2,883 1,100 148 1.8 1.8 1.8 Pigs 1,321 570 75 0.8 0.9 0.9 Aquaculture - Freshwater fish Carp 35 14 0 0.0 0.0 0.0 Tilapia 124 28 4 0.1 0.0 0.1 Misc freshwater fish 4 1 1 0.0 0.0 0.0 Cereal Grains 23,206 9,683 1,359 14.4 15.7 16.2 Roots, Tubers & Bananas 55,075 22,395 3,135 34.1 36.3 37.3 Oilseeds & Pulses 14,041 5,672 813 8.7 9.2 9.7 Smallholder Cash Crops 10,702 3,635 434 6.6 5.9 5.2 Vegetables & Melons 14,035 5,528 817 8.7 9.0 9.7 Tree Fruits 11,144 4,571 635 6.9 7.4 7.6 Livestock 33,106 10,114 1,212 20.5 16.4 14.4 Fish 162 43 5 0.1 0.1 0.1 ALL CROPS (x plantation) 128,203 51,483 7,192 79.4 83.5 85.5 ALL LIVESTOCK & FISH 33,268 10,157 1,217 20.6 16.5 14.5 ALL AGRICULTURE (x plantation) 161,471 61,641 8,409 100.0 100.0 100.0 Poverty = value weighted by $1.9/capita/day poverty headcount; stunting = value weighted by prevalence of child stunting. Value of forage crops included in value of livestock production. 11 East and Southern Africa (ESA) 12 ESA Values and Value Shares of Commodity Production (average annual gross value of production 2017-2019) East & Southern Africa (ESA) Commodity Value (million 2015 I$) Value Share (%) Total Poverty Stunting Total Poverty Stunting Cereal Grains Rice 3,243 2,046 189 2.9 4.6 3.9 Wheat 1,715 524 83 1.5 1.2 1.7 Maize 9,336 3,509 428 8.3 7.9 8.7 Millet 483 182 24 0.4 0.4 0.5 Sorghum 1,145 457 61 1.0 1.0 1.3 Barley 510 159 26 0.5 0.4 0.5 Roots, Tubers & Bananas Potato 2,362 992 109 2.1 2.2 2.2 Cassava 4,489 2,652 271 4.0 6.0 5.5 Yam 50 23 3 0.0 0.1 0.1 Sweet Potato 3,757 2,116 215 3.3 4.8 4.4 Taro (cocoyam) 139 44 14 0.1 0.1 0.3 Banana 3,857 1,985 216 3.4 4.5 4.4 Plantain 2,189 1,080 124 2.0 2.4 2.5 Oilseeds & Pulses Groundnut 1,443 742 78 1.3 1.7 1.6 Soybean 796 251 30 0.7 0.6 0.6 Sesame 1,570 699 84 1.4 1.6 1.7 Beans (phaseolus ) 3,840 1,814 209 3.4 4.1 4.3 Chickpea (cicer ) 366 126 20 0.3 0.3 0.4 Cowpea (vigna unguiculata ) 294 126 16 0.3 0.3 0.3 Pigeonpea (cajanus ) 530 326 30 0.5 0.7 0.6 Lentil (lens ) 101 34 6 0.1 0.1 0.1 Smallholder Cash Crops Cotton 770 339 39 0.7 0.8 0.8 Coffee 2,033 835 111 1.8 1.9 2.3 Coconut 155 78 9 0.1 0.2 0.2 Cocoa 88 44 5 0.1 0.1 0.1 Cashew 619 377 45 0.6 0.9 0.9 Vegetables & Melons Solanum (tomato, eggplant) 1,583 762 79 1.4 1.7 1.6 Allum (onion, shallot, garlic, leek) 998 377 47 0.9 0.8 1.0 Cucurbit (cucumber, pumpkin, melon) 367 170 17 0.3 0.4 0.3 Brassica (cabbage, cauliflower, broccoli) 401 159 18 0.4 0.4 0.4 Okra 12 5 0 0.0 0.0 0.0 Legume vegetables (green beans, peas) 63 23 3 0.1 0.1 0.1 Leafy vegetables (lettuce, spinach) 45 15 2 0.0 0.0 0.0 Vegetable, other 3,383 1,572 174 3.0 3.5 3.5 Tree Fruits Citrus (oranges, tangerines, lemons, other) 4,055 1,833 190 3.6 4.1 3.9 Mango 2,136 1,253 115 1.9 2.8 2.3 Pineapple 569 290 28 0.5 0.7 0.6 Papaya 107 49 5 0.1 0.1 0.1 Other tropical fruit 426 222 22 0.4 0.5 0.4 Other temperate fruit 1,741 595 74 1.6 1.3 1.5 Cultivated Forage Crops 1,239 327 41 1.1 0.7 0.8 Livestock Dairy (milk) 8,681 3,270 395 7.7 7.4 8.1 Cattle (meat) 15,118 5,498 649 13.5 12.4 13.2 Small ruminants (meat,milk, wool) 18,260 3,848 317 16.3 8.7 6.5 Poultry (meat & eggs) 6,295 1,876 228 5.6 4.2 4.7 Pigs 1,818 890 91 1.6 2.0 1.9 Aquaculture - Freshwater fish Carp 4 2 0 0.0 0.0 0.0 Tilapia 176 83 4 0.2 0.2 0.1 Misc freshwater fish 64 27 1 0.1 0.1 0.0 Cereal Grains 16,432 6,876 811 14.6 15.5 16.6 Roots, Tubers & Bananas 16,843 8,892 951 15.0 20.0 19.4 Oilseeds & Pulses 8,942 4,119 472 8.0 9.3 9.6 Smallholder Cash Crops 3,665 1,673 208 3.3 3.8 4.2 Vegetables & Melons 6,853 3,083 339 6.1 6.9 6.9 Tree Fruits 9,033 4,242 433 8.1 9.6 8.8 Livestock 50,172 15,382 1,680 44.7 34.7 34.3 Fish 243 112 5 0.2 0.3 0.1 ALL CROPS (x plantation) 61,768 28,885 3,214 55.1 65.1 65.6 ALL LIVESTOCK & FISH 50,415 15,494 1,685 44.9 34.9 34.4 ALL AGRICULTURE (x plantation) 112,184 44,379 4,899 100.0 100.0 100.0 Poverty = value weighted by $1.9/capita/day poverty headcount; stunting = value weighted by prevalence of child stunting. Value of forage crops included in value of livestock production. 13 South East Asia (SEA) 14 SEA Values and Value Shares of Commodity Production (average annual gross value of production 2017-2019) Southeast Asia Commodity Value (million 2015 I$) Value Share (%) Total Poverty Stunting Total Poverty Stunting Cereal Grains Rice 76,436 2,041 1,741 29.7 20.8 30.1 Wheat 28 0 1 0.0 0.0 0.0 Maize 10,381 381 267 4.0 3.9 4.6 Millet 56 1 1 0.0 0.0 0.0 Sorghum 44 1 1 0.0 0.0 0.0 Barley 27 0 0 0.0 0.0 0.0 Roots, Tubers & Bananas Potato 583 17 15 0.2 0.2 0.3 Cassava 10,967 219 214 4.3 2.2 3.7 Yam 120 41 7 0.0 0.4 0.1 Sweet Potato 1,008 94 32 0.4 1.0 0.5 Taro (cocoyam) 55 6 10 0.0 0.1 0.2 Banana 6,953 498 204 2.7 5.1 3.5 Plantain 1,680 78 49 0.7 0.8 0.9 Oilseeds & Pulses Groundnut 1,834 42 45 0.7 0.4 0.8 Soybean 484 13 13 0.2 0.1 0.2 Sesame 956 16 23 0.4 0.2 0.4 Beans (phaseolus ) 4,705 72 116 1.8 0.7 2.0 Chickpea (cicer ) 363 5 9 0.1 0.1 0.2 Cowpea (vigna unguiculata ) 37 1 1 0.0 0.0 0.0 Pigeonpea (cajanus ) 312 4 8 0.1 0.0 0.1 Lentil (lens ) 1 0 0 0.0 0.0 0.0 Smallholder Cash Crops Cotton 312 5 8 0.1 0.1 0.1 Coffee 5,638 254 137 2.2 2.6 2.4 Coconut 6,088 339 177 2.4 3.5 3.1 Cocoa 1,141 66 34 0.4 0.7 0.6 Cashew 616 22 15 0.2 0.2 0.3 Vegetables & Melons Solanum (tomato, eggplant) 1,117 40 29 0.4 0.4 0.5 Allum (onion, shallot, garlic, leek) 1,763 47 44 0.7 0.5 0.8 Cucurbit (cucumber, pumpkin, melon) 950 34 22 0.4 0.3 0.4 Brassica (cabbage, cauliflower, broccoli) 725 20 17 0.3 0.2 0.3 Okra 84 2 2 0.0 0.0 0.0 Legume vegetables (green beans, peas) 399 11 9 0.2 0.1 0.2 Leafy vegetables (lettuce, spinach) 71 1 2 0.0 0.0 0.0 Vegetable, other 13,332 544 324 5.2 5.5 5.6 Tree Fruits Citrus (oranges, tangerines, lemons, other) 12,677 497 290 4.9 5.1 5.0 Mango 4,408 109 90 1.7 1.1 1.5 Pineapple 3,175 106 70 1.2 1.1 1.2 Papaya 466 17 12 0.2 0.2 0.2 Other tropical fruit 2,930 101 66 1.1 1.0 1.1 Other temperate fruit 4,135 295 111 1.6 3.0 1.9 Cultivated Forage Crops 238 5 6 0.1 0.1 0.1 Livestock Dairy (milk) 2,262 36 46 0.9 0.4 0.8 Cattle (meat) 7,460 223 181 2.9 2.3 3.1 Small ruminants (meat,milk, wool) 14,780 1,995 62 5.7 20.3 1.1 Poultry (meat & eggs) 30,868 773 698 12.0 7.9 12.1 Pigs 16,488 512 368 6.4 5.2 6.4 Aquaculture - Freshwater fish Carp 2,249 46 58 0.9 0.5 1.0 Tilapia 2,415 83 62 0.9 0.8 1.1 Misc freshwater fish 3,783 113 100 1.5 1.2 1.7 Cereal Grains 86,972 2,424 2,012 33.8 24.7 34.7 Roots, Tubers & Bananas 21,365 953 531 8.3 9.7 9.2 Oilseeds & Pulses 8,691 153 215 3.4 1.6 3.7 Smallholder Cash Crops 13,796 687 371 5.4 7.0 6.4 Vegetables & Melons 18,440 700 448 7.2 7.1 7.7 Tree Fruits 27,791 1,125 638 10.8 11.4 11.0 Livestock 71,857 3,539 1,356 27.9 36.0 23.4 Fish 8,446 242 220 3.3 2.5 3.8 ALL CROPS (x plantation) 177,054 6,041 4,215 68.8 61.5 72.8 ALL LIVESTOCK & FISH 80,304 3,782 1,575 31.2 38.5 27.2 ALL AGRICULTURE (x plantation) 257,358 9,823 5,790 100.0 100.0 100.0 Poverty = value weighted by $1.9/capita/day poverty headcount; stunting = value weighted by prevalence of child stunting. Value of forage crops included in value of livestock production. 15 South Asia (SA) 16 SA Values and Value Shares of Commodity Production (average annual gross value of production 2017-2019) South Asia Commodity Value (million 2015 I$) Value Share (%) Total Poverty Stunting Total Poverty Stunting Cereal Grains Rice 97,012 18,859 2,918 21.5 21.5 20.8 Wheat 30,578 5,712 1,026 6.8 6.5 7.3 Maize 8,070 1,465 266 1.8 1.7 1.9 Millet 2,906 621 90 0.6 0.7 0.6 Sorghum 1,118 242 34 0.2 0.3 0.2 Barley 352 77 11 0.1 0.1 0.1 Roots, Tubers & Bananas Potato 16,879 3,332 522 3.7 3.8 3.7 Cassava 727 154 21 0.2 0.2 0.1 Yam 0 0 0 0.0 0.0 0.0 Sweet Potato 346 71 10 0.1 0.1 0.1 Taro (cocoyam) 0 0 0 0.0 0.0 0.0 Banana 11,307 2,505 338 2.5 2.9 2.4 Plantain 298 11 5 0.1 0.0 0.0 Oilseeds & Pulses Groundnut 5,788 1,282 174 1.3 1.5 1.2 Soybean 4,816 1,079 144 1.1 1.2 1.0 Sesame 924 194 28 0.2 0.2 0.2 Beans (phaseolus ) 4,717 1,036 143 1.0 1.2 1.0 Chickpea (cicer ) 7,396 1,616 226 1.6 1.8 1.6 Cowpea (vigna unguiculata ) 3 0 0 0.0 0.0 0.0 Pigeonpea (cajanus ) 3,092 695 92 0.7 0.8 0.7 Lentil (lens ) 1,222 252 37 0.3 0.3 0.3 Smallholder Cash Crops Cotton 19,862 3,618 677 4.4 4.1 4.8 Coffee 673 149 20 0.1 0.2 0.1 Coconut 2,727 531 76 0.6 0.6 0.5 Cocoa 33 7 1 0.0 0.0 0.0 Cashew 745 161 22 0.2 0.2 0.2 Vegetables & Melons Solanum (tomato, eggplant) 16,062 3,520 483 3.6 4.0 3.4 Allum (onion, shallot, garlic, leek) 12,265 2,509 379 2.7 2.9 2.7 Cucurbit (cucumber, pumpkin, melon) 1,775 335 56 0.4 0.4 0.4 Brassica (cabbage, cauliflower, broccoli) 4,275 932 128 0.9 1.1 0.9 Okra 5,877 1,301 178 1.3 1.5 1.3 Legume vegetables (green beans, peas) 2,074 447 62 0.5 0.5 0.4 Leafy vegetables (lettuce, spinach) 330 68 10 0.1 0.1 0.1 Vegetable, other 19,108 3,993 587 4.2 4.6 4.2 Tree Fruits Citrus (oranges, tangerines, lemons, other) 24,359 4,858 773 5.4 5.5 5.5 Mango 18,138 3,709 567 4.0 4.2 4.0 Pineapple 821 173 24 0.2 0.2 0.2 Papaya 2,158 481 64 0.5 0.5 0.5 Other tropical fruit 2,561 494 81 0.6 0.6 0.6 Other temperate fruit 9,077 1,680 302 2.0 1.9 2.1 Cultivated Forage Crops 4,158 934 124 0.9 1.1 0.9 Livestock Dairy (milk) 45,698 8,653 1,515 10.1 9.9 10.8 Cattle (meat) 9,735 1,213 376 2.2 1.4 2.7 Small ruminants (meat,milk, wool) 22,835 3,327 527 5.1 3.8 3.8 Poultry (meat & eggs) 19,880 3,568 653 4.4 4.1 4.7 Pigs 797 174 24 0.2 0.2 0.2 Aquaculture - Freshwater fish Carp 8,264 1,677 248 1.8 1.9 1.8 Tilapia 462 64 13 0.1 0.1 0.1 Misc freshwater fish 3,515 708 103 0.8 0.8 0.7 Cereal Grains 140,036 26,976 4,345 31.0 30.8 31.0 Roots, Tubers & Bananas 29,558 6,073 897 6.5 6.9 6.4 Oilseeds & Pulses 27,959 6,154 843 6.2 7.0 6.0 Smallholder Cash Crops 24,040 4,465 795 5.3 5.1 5.7 Vegetables & Melons 61,766 13,106 1,884 13.7 15.0 13.4 Tree Fruits 57,113 11,395 1,811 12.6 13.0 12.9 Livestock 98,946 16,935 3,095 21.9 19.3 22.1 Fish 12,240 2,449 363 2.7 2.8 2.6 ALL CROPS (x plantation) 340,472 68,169 10,575 75.4 77.9 75.4 ALL LIVESTOCK & FISH 111,186 19,384 3,458 24.6 22.1 24.6 ALL AGRICULTURE (x plantation) 451,658 87,553 14,033 100.0 100.0 100.0 Poverty = value weighted by $1.9/capita/day poverty headcount; stunting = value weighted by prevalence of child stunting. Value of forage crops included in value of livestock production. 17 Latin America and the Caribbean (LAC) 18 LAC Values and Value Shares of Commodity Production (average annual gross value of production 2017-2019) Latin America & Caribbean (LAC) [excluding Brazil & Southern Cone] Commodity Value (million 2015 I$) Value Share (%) Total Poverty Stunting Total Poverty Stunting Cereal Grains Rice 5,028 238 60 3.1 3.1 3.2 Wheat 869 17 8 0.5 0.2 0.4 Maize 7,849 243 98 4.8 3.2 5.2 Millet 0 0 0 0.0 0.0 0.0 Sorghum 433 16 5 0.3 0.2 0.3 Barley 248 5 2 0.2 0.1 0.1 Roots, Tubers & Bananas Potato 3,279 121 42 2.0 1.6 2.2 Cassava 733 40 8 0.4 0.5 0.4 Yam 252 19 3 0.2 0.3 0.2 Sweet Potato 244 10 2 0.1 0.1 0.1 Taro (cocoyam) 5 0 2 0.0 0.0 0.1 Banana 7,870 376 170 4.8 5.0 9.0 Plantain 3,436 148 45 2.1 2.0 2.4 Oilseeds & Pulses Groundnut 296 12 5 0.2 0.2 0.3 Soybean 1,300 56 21 0.8 0.7 1.1 Sesame 165 10 4 0.1 0.1 0.2 Beans (phaseolus ) 1,850 87 30 1.1 1.1 1.6 Chickpea (cicer ) 174 3 2 0.1 0.0 0.1 Cowpea (vigna unguiculata ) 17 3 0 0.0 0.0 0.0 Pigeonpea (cajanus ) 58 9 1 0.0 0.1 0.1 Lentil (lens ) 11 0 0 0.0 0.0 0.0 Smallholder Cash Crops Cotton 1,124 25 11 0.7 0.3 0.6 Coffee 5,218 353 88 3.2 4.6 4.7 Coconut 433 14 4 0.3 0.2 0.2 Cocoa 967 37 15 0.6 0.5 0.8 Cashew 12 1 0 0.0 0.0 0.0 Vegetables & Melons Solanum (tomato, eggplant) 3,412 91 37 2.1 1.2 2.0 Allum (onion, shallot, garlic, leek) 1,792 60 21 1.1 0.8 1.1 Cucurbit (cucumber, pumpkin, melon) 1,800 71 25 1.1 0.9 1.3 Brassica (cabbage, cauliflower, broccoli) 472 19 7 0.3 0.3 0.4 Okra 64 3 1 0.0 0.0 0.0 Legume vegetables (green beans, peas) 211 8 4 0.1 0.1 0.2 Leafy vegetables (lettuce, spinach) 778 31 8 0.5 0.4 0.4 Vegetable, other 4,247 121 45 2.6 1.6 2.4 Tree Fruits Citrus (oranges, tangerines, lemons, other) 12,742 436 137 7.8 5.7 7.3 Mango 2,880 137 34 1.8 1.8 1.8 Pineapple 3,098 95 30 1.9 1.2 1.6 Papaya 1,068 24 10 0.7 0.3 0.6 Other tropical fruit 1,942 68 21 1.2 0.9 1.1 Other temperate fruit 2,413 76 27 1.5 1.0 1.4 Cultivated Forage Crops 12,838 386 156 7.8 5.1 8.3 Livestock Dairy (milk) 19,005 708 204 11.6 9.3 10.9 Cattle (meat) 19,941 789 253 12.2 10.4 13.4 Small ruminants (meat,milk, wool) 14,068 1,977 18 8.6 26.0 1.0 Poultry (meat & eggs) 25,317 842 299 15.5 11.1 15.9 Pigs 6,171 181 69 3.8 2.4 3.7 Aquaculture - Freshwater fish Carp 28 0 0 0.0 0.0 0.0 Tilapia 294 16 4 0.2 0.2 0.2 Misc freshwater fish 68 2 1 0.0 0.0 0.0 Cereal Grains 14,428 518 173 8.8 6.8 9.2 Roots, Tubers & Bananas 15,820 714 272 9.7 9.4 14.5 Oilseeds & Pulses 3,872 179 63 2.4 2.4 3.3 Smallholder Cash Crops 7,754 428 119 4.7 5.6 6.3 Vegetables & Melons 12,775 403 148 7.8 5.3 7.8 Tree Fruits 24,143 835 259 14.8 11.0 13.8 Livestock 84,501 4,496 843 51.6 59.2 44.8 Fish 390 18 5 0.2 0.2 0.3 ALL CROPS (x plantation) 78,792 3,078 1,034 48.1 40.5 54.9 ALL LIVESTOCK & FISH 84,892 4,514 848 51.9 59.5 45.1 ALL AGRICULTURE (x plantation) 163,683 7,592 1,882 100.0 100.0 100.0 Poverty = value weighted by $1.9/capita/day poverty headcount; stunting = value weighted by prevalence of child stunting. Value of forage crops included in value of livestock production. 19 Section 4: The Two Degree Initiative Regional Challenges vis-à-vis Emerging Priorities The ‘Two Degree Initiative’ (2DI) formed as a coalition of hundreds of like-minded partner organizations from around the world, brought together with a single unifying vision – to transform the global food system for a climate-smart future. 2DI’s ambition is to reach 200 million farmers in ten participating Regional Challenges that include low- and middle-income countries. It organizes its projects around a set of six emerging themes that align with a rigorous theory of change. Over a thousand stakeholders were consulted through more than 50 meetings (Listening Sessions) held between May – October 2020. These were led by CCAFS and the World Resources Institute (WRI), which co-manages the Global Commission on Adaptation and is a leading partner in the 2DI coalition. The goal of the Listening Sessions was to develop a common vision of climate resilient agriculture and food systems for each of ten participating Regional Challenges. The sessions (held virtually because of the COVID-19 situation) resulted in a roadmap of actions and partnerships required to achieve the common vision of each region. In addition, they helped to identify (preliminarily at least) global topics for rigorous Research for Development (R4D) programs to be led by the CGIAR and its regional partners, all of which are locally-informed and designed to lead to transformational change on the ground. These listening sessions provide an interesting starting point for considering climate-leading Regional Integrated Initiatives for CGIAR. Six emerging findings came from the listening sessions which are of relevance for the design process of Regional Initiatives: 1. Forging a new partnership model for CGIAR, where CGIAR is a key knowledge partner in coalitions of change agents, led by change agents 2. Research and action on sustainable finance for small-scale agricultural producers, bringing in USD 100s of millions to foster climate action, implying a strong private sector focus 3. Research and action to empower small-scale agricultural producers, women, youth and marginalized communities as groups that are most vulnerable to climate change 4. Climate-informed digital advisories, services and decision support to ensure that small-scale agricultural producers and their service providers and value chain actors are getting the appropriate information and services to manage risks, grow farmer incomes and expand employment opportunities 5. Attention to research and action on policy and institutional reforms, as the policy and institutional context is crucial to achieve scale and transformation 6. Mainstreaming low emissions value chains, including attention to the necessary changes in consumption and food loss and waste (while much of the work will be on adaptation, the intention is to put development on a low emissions pathway) In the below analysis, four criteria were used to assess the goodness of fit for the 2DI regional challenges to form a basis for developing One CGIAR Regional Integrated Initiatives. Two initiatives emerge as being highly relevant as the starting point for the Design Process (Asian Mega Deltas and Southern Africa Drylands) having been developed with considerable stakeholder consultation, and being a close match to CGIAR strategy and the analysis of climate hazards. Two LAC regional challenges exist, and some combination of these two would also form the basis for a solid Regional Integrated Initiative which 20 responds to demands from the region, aligns to CGIAR strategy and matches the evidence of the principal challenges. Table 1: Criteria for 2DI regional challenges prioritization Alignment with major Consistency with Region in Stakeholder risks in relation to Consolidated 2DI Regional Challenge OneCGIAR OneCGIAR engagement1 2 vulnerability and assessmentapproach mitigation potential Southern African Drylands: Climate ESA H H H H resilient and Water-Secure Livelihoods Latin America: Transitioning to Low- Emissions Sustainable Meat and Dairy LAC M M M M Production Landscapes Securing the Asian Mega-Deltas against Sea-Level Rise, Flooding, and Salinization SEA, SA H H H H Building Food System Resilience to Climate WCA M M M M Shocks in the Sahel Resilient Households in Central and LAC H H M H Tropical Andes and Central America Horn of Africa ESA L M M M MENA Grand Challenge CWANA M H M M West Africa: One-Health WCA M M L M Blue Challenge: Resilient Fisheries and SA, SEA, Aquaculture M M M MESA H=high; M=medium; L=low Table 2: Alignment of 2 Degrees Initiative Regional Challenges to One CGIAR ways of working 2DI Regional Challenge Systems Strategic Multiple Risk and Innovative Digital Transformation Alliances Pathways Resilience Finance Revolution Final Southern African Drylands: Climate resilient and H H H M H H H Water-Secure Livelihoods Latin America: Transitioning to Low-Emissions M H M M H L M Sustainable Meat and Dairy Production Landscapes Securing the Asian Mega-Deltas against Sea- H H M H H H H Level Rise, Flooding, and Salinization Building Food System Resilience to Climate M H L M L H M Shocks in the Sahel Resilient Households in Central and Tropical H H M H H H H Andes and Central America Horn of Africa H M M M H L M MENA Grand Challenge H H L M H H H West Africa: One-Health H H L H L M H Blue Challenge: Resilient Fisheries and M M L M H L M Aquaculture H=high; M=medium; L=low 21 Table 3: Alignment of 2 Degrees Initiative Regional Challenges to One CGIAR impact areas Gender Nutrition, Poverty Equality, Climate 2DI Regional Challenge Health Reduction, Youth Adaptation Environmental and Food Livelihoods and and Health and Final Security and Jobs Social Mitigation Biodiversity Inclusion Southern African Drylands: Climate resilient and Water- L H H H M H Secure Livelihoods Latin America: Transitioning to Low-Emissions Sustainable M M L M H M Meat and Dairy Production Landscapes Securing the Asian Mega-Deltas against Sea-Level Rise, M M H H M M Flooding, and Salinization Building Food System Resilience to Climate Shocks in the H M H M M M Sahel Resilient Households in Central and Tropical Andes and H H H H H H Central America Horn of Africa H M H M H H MENA Grand Challenge H M H H L H West Africa: One-Health H M M M M M Blue Challenge: Resilient Fisheries and Aquaculture M M H H M M H=high; M=medium; L=low 22 Section 5: Proposed priority setting process during Initiative Design Integrated programs should follow fundamental steps as they move from strategic planning to tactical implementation3: • Strategic multi-stakeholder planning process • Tactical plan development based on the integration of production and demand (private sector and public purchasing), where appropriate taking a mixed menu-approach based on and connected with the global and regional science domains. • Implementation of multi-stakeholder innovation hubs for the generation of impact and that serve as on the ground real life ‘experiments’ • Feedback loop of data and information to the science domains Integrated regional programs are governed by multi-stakeholder arrangement and are designed to engage diverse partners and to undertake: • Baseline assessments (e.g., agricultural value chains; household dietary patterns; gender norms; local markets and institutions) and gap analysis for the ability of local agri-food systems to deliver sustainable livelihoods and health diets; • Business-as-usual and preferred production-consumption scenarios; • Regional risk and opportunity assessment with robust, well-structured stakeholder engagement; • Evaluation of potential pathways (including value chain and public policy interventions); • Selection and testing of household, farm, landscape and value chain strategies to ensure diversified nutrition and income; • Applied research and innovation • Big data generation • Review and communication of results, development and refinement of innovation models / prototypes; • Policy engagement (e.g. through inter-ministerial platforms). • Training of NARS scientists (especially women and other underrepresented groups) and value chain actors (farmers, public / private sector farmer advisors) 3 Methodology based on: 1. Bram Govaerts, Christine Negra, Tania Carolina Camacho Villa, Xiomara Chavez Suarez, Anabell Diaz Espinosa, Simon Fonteyne, Andrea Gardeazabal, Gabriela Gonzalez, Ravi Gopal Singh, Molly Jahn, Victor Kommerell, Wietske Kropff, Victor Lopez Saavedra, Georgina Mena Lopez, Sylvanus Odjo, Natalia Palacios Rojas, Julian Ramirez-Villegas, Jelle Van Loon, Daniela Vega, Nele Verhulst, Lennart Woltering, Martin Kropff. 2021. The Integrated Agri-food Systems Initiative: from short-termism to transformation within the OneCGIAR and beyond?! Under revision. 2. Gardeazabal, A., Lunt, T., Jahn, M.M., Verhulst, N., Hellin, J., Govaerts, B., 2021. Knowledge management for innovation in agri-food systems: a conceptual framework. Knowledge Management Research & Practice, DOI: 10.1080/14778238.2021.1884010 23 Analysis of • Modeled BAU (ocio-economic, political, sectoral) current • Strategies (e.g. policies; value chain status interventions; technologies)• Expert evaluation • Neutral facilitator Sectoral • Alternative scenarios• Drivers of change consultation • Short / medium / long-term actions • Shared commitment to implementation • Validation through high-level Validation / consultations • Measurable, time-bound targets targets • Concrete actions based on desired scenario Expanded • Traditional agricultural sector stakeholders partnership • National development banks space • Fintech providers • Structured stakeholder Progress / engagement course • Indicators of progress correction dashboard• Investable opportunities Past examples demonstrate that the integrated regional approach can deliver impact at scale. Common success factors have been noted: • A deliberate mix of technological, institutional, and socio-economic analysis • Sufficient time for multi-objective demand mapping undertaken by a multi-disciplinary team; • An inclusive, iterative consultative process with all prospective innovation and scaling partners resulting in a robust theory of change and concrete commitments and co-investments by partners for an agreed research program; • Multiple R4D and scaling pathways and impacts (e.g. capacity building; policy; value chains); • Program and budget continuity to sustain productive innovation and scaling partnerships and influence enabling institutions; • Well-resourced, professionally-staffed support functions that reduce management complexity (e.g. knowledge management, M&E, communications). Future enhancements can include: • More deeply engaging women and men in place-based, collaborative improvement of their agricultural productivity, income, nutrition, equity, and agency including multi-scale interactions. • Developing additional integrative skillsets and trans-disciplinary competencies. • Eliciting nutrition-sensitive agrifood innovation that meaningfully increases food-related income, diversifies local markets and diets, and builds resilience into agricultural value chains. • Monitoring performance of long-term organizational partnerships that deliver regional integrated programs. Integrated programs – methodology for operation Knowledge is a critical enabling factor for healthy agri-food innovation systems (AIS). AIS and related knowledge management frameworks face significant implementation challenges. The proposed 24 framework addresses systemic interactions favouring innovation outcomes by formalizing flows and management of information and knowledge between diverse sets of stakeholders; and explicitly considering previously unresolved practical and relational barriers aiming to facilitate more equitable, rapidly evolving, and actionable knowledge generation and management for innovation and transformational change. Figure 7 represents the primary processes within the proposed framework, integrating the eight principles with key concepts required to facilitate innovation in agri-food systems. The process elements of the framework are explained below. Figure 7 – Proposed operational framework to facilitate innovation in agri-food systems The innovation work is organized in distinct hubs (Fig. 8); each hub has a physical infrastructure, including research platforms, modules, extension and impact areas, which are used for networking, knowledge exchange and co-creation. In the research platforms, local partners evaluate technologies resulting from the global initiatives and local tacit knowledge to develop research-based recommendations for farmers. In modules, farmers are connected to peers, farm advisors and other value chain actors. Together they implement and adapt best practices from research platforms and compare them with conventional practices. Extension areas are agricultural fields where farmers test new technologies in connection with modules or research platforms, whereas in impact areas farmers have adapted and adopted similar knowledge, technologies and innovations on their own. This infrastructure is used to build a network of stakeholders – farmers, farm advisors, scientists, research centres, private initiative, and government actors, among others – that collaborate around a common objective: innovation in the agri-food system to make it more sustainable, productive, profitable and resilient. The hub model considers farmers important change agents and central to the approach. Since inception, hubs have been allowed to evolve independently in order to match their 25 divergent agricultural, stakeholder, and technological contexts, and to reflect the landscape of relationships between different actors in the agri-food system (Camacho-Villa et al., 2016). Figure 8 – Operational innovation network of an integrated agri-food system initiative Integrated programs – Way forward Existing regional integrated programs span multiple science domains: • Policy, value chains and markets • Nutrition and one health • Sustainable agricultural production systems • Land, soil and water systems • Genetic resources and breeding With support from ABC, CIMMYT has tested and implemented a methodology to develop the • Strategic planning based on scenarios for a better future • Tactical planning • Operational methodologies for implementation 26 Section 6: Conclusions and proposed ways forward Region Potential way forward based on analysis CWANA Develop a Regional Integrated Initiative with a strong focus on drought and climate variability as the primary climate hazards affecting the region. WCA Consider am initial focus on, or on root and tuber crop, forest-based, and cereal- root-crop mixed systems in more humid southern regions of WA. Further disaggregated analysis of climate hazards for humid regions should help further prioritise, but initially consider growing season high temperatures, and growing season length reductions as key climate hazards for focus. Another RII for WCA may concentrate on agro-pastoral dry systems in the Sahel as an additional priority within the region. ESA Design a Regional Initiative which builds off of the Two Degree Initiative on southern African drylands, considering the geographic priorities to potentially extend to high hazard regions in East Africa, and/or paying special attention to maize mixed systems and pastoral systems. Asia (SEA + SA) Develop a regional integrated initiative which builds off of the 2DI regional challenge on mega deltas, focusing on flooding and salinization as the primary climate hazards. Consider the extent to which mixed upland intensive systems are relevant, given the significant area impacted by climate stemming from this analysis. LAC Develop Regional Integrated Initiative building off of the 2DI regional challenge on building household resilience that addresses both subsistence and commercial farming through innovations that equip farmers with better on-farm technologies, information, and access to finance. Bring in an additional component integrating mitigation action also into the initiative given the significant share of emissions coming from Latin America. The latter should likely explore land-use change at the forest-agriculture frontier, as well as livestock- based farming systems. This can potentially come from the other 2DI regional challenge on meat and dairy in LAC. Forest-based and extensive-mixed farming systems should be considered in the design process. 27 Annex 1 – Methods Figure 1: The base layer for analysis of climate hazards was developed by Phil Thornton (unpublished), which uses three indicators for current climate hazards: • Areas where the coefficient of variation of annual rainfall (the standard deviation divided by the mean, expressed as a percentage) is currently greater than the median value for the global tropics (24%). In lower latitudes, climate change is projected to increase this variability, making both cropping and rangeland production more risky. Because we have little information on the nature of this variability change, we used current variability as a proxy for future variability. • For relative flood risk, grid cells in the top two deciles (most risky) of the data set of Dilley et al. (2005) were defined as high-risk flooding areas. • For relative drought risk, grid cells in the top two deciles (most risky) of the data set of Dilley et al. (2005) were defined as high-risk drought areas. For future climate hazards, we used downscaled climate projections from 17 CMIP5 global climate models using the methods in Jones and Thornton (2009; 2013; 2015) and climatologies for the 2050s as projected in response to RCP 8.5. Two climate hazard thresholds were defined in relation to climate and how changes to the 2050s might affect different facets of agriculture and food security: • Reduction in the number of reliable crop growing days per year below 90, a critical threshold (Nachtergaele et al., 2002), mostly due to changes in rainfall distributions and amounts; • Areas in which the average maximum daily temperature during the primary growing season moves above 30 ⁰C by the 2050s, a critical threshold for several major crops (Boote et al., 1998; Prasad et al., 2008). • The intersection of the various climate hazards, exposure and vulnerability data layers were analysed with respect to their areas, agricultural land use (i.e., whether crop land, pastureland, or land with both crops and pasture present), and human population. We used estimates of human population for the year 2020 from CIESIN (2018). The resultant map is shown below in Figure 1. Figure 2: These hazards in Figure 1 were then overlaid with datasets from the Digital Atlas of Adaptation on Value of Production for both crops and for livestock, and for rural population. The value of production was calculated based on spatial layers of crop distribution, and economic data on productivity and prices. The dataset is disaggregated for livestock and for crops. Furthermore, rural population was also calculated for each region, and overlaid with the hazards. Figure 3: Emissions data was taken from Roman-Cuestra et al. (2016)4, reporting for each region the gross emissions (Figure 3a) and the relative source of emissions from different sources, including deforestation, fire, wood harvest, livestock, rice, and crop (essentially representing soil based emissions, e.g. fertilizer use, soil management) (Figure 3b). Unfortunately, in this dataset the data is not available 4 Rosa Maria Roman-Cuesta, Mariana C Rufino, Martin Herold, Klaus Butterbach-Bahl, Todd S Rosenstock, Mario Herrero, Stephen Ogle, Changsheng Li, Benjamin Poulter, Louis Verchot, Christopher Martius, John Stuiver, Sytze De Bruin, 2016, Hotspots of gross emissions from the land use sector: patterns, uncertainties, and leading emission sources for the period 2000–2005 in the tropics, Biogeosciences. https://bg.copernicus.org/preprints/bg-2016-99/bg-2016-99-manuscript-version4.pdf 28 for CWANA, but can be calculated relatively easily and feed into the design process over the coming months. Figure 4: This figure is derived from the Evidence for Resilient Agriculture (ERA) meta-data project.5 ERA has screened, harmonized and extracted data from 2,000 journal articles that describe the impacts of changing agronomic, agroforestry or livestock technologies on more than 75 indicators of productivity, resilience or emission. ERA uses meta-analysis to synthesize results across studies providing general conclusions on what works where. Figure 5: This figure represents data from the largest meta-analysis of adoption literature. It includes information derived from 168 separate studies analyzing the same management practices included in ERA. The analysis uses a quantitative sign-test to evaluate the likelihood of a statistically significant association in the dataset. Preliminary results can be found in Arslan et al.6 5 http://era.ccafs.cgiar.org 6 https://www.ifad.org/en/web/knowledge/publication/asset/42041675 29