INDEPENDENT REVIEW

Assessment of outcomes based on the use of PIM-supported foresight modeling work, 2012–2018

Sarah K. Lowder and Anita RegmiNovember 2019

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Table of Contents

Acknowledgements ............................................................................................................... 3

Introduction .......................................................................................................................... 4

Previous evaluations ............................................................................................................. 5

Usage of PIM-supported foresight modeling and resulting outcomes ..................................... 5

Downloads of IMPACT datasets ......................................................................................................6

Citations analysis ...........................................................................................................................7

Electronic Survey ......................................................................................................................... 10

Interviews and correspondence .................................................................................................... 13 Summary of Outcomes ...................................................................................................................................... 13 Informing decision-making of multilateral organizations and donors ............................................................... 15 National-level outcomes .................................................................................................................................... 19 CGIAR system-level ............................................................................................................................................ 22 CGIAR center-level outcomes ............................................................................................................................ 23 Informing global debate and dialogue on sustainable diets .............................................................................. 27 Global partnerships in foresight analysis ........................................................................................................... 28 Trainings ............................................................................................................................................................. 29

Conclusions and areas for future work ................................................................................. 29

References cited in this report .............................................................................................. 32

Appendix A: Affiliation of individuals downloading datasets in IMPACT Dataverse ............... 38

Appendix B: References included in detailed citations analysis ............................................. 40

Appendix C: Additional references included in general citations analysis .............................. 43

Appendix D: Survey ............................................................................................................. 51

Appendix E: Key Stakeholders Interviewed ........................................................................... 55

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Acknowledgements This work was undertaken from October 2018 through November 2019. Although the review is independent, the assistance of IFPRI staff is gratefully acknowledged. The authors would like to thank Frank Place, Keith Wiebe, Nicola Cenacchi, Indira Yerramareddy, Nilam Prasai, Melissa Skees, Xinyuan Shang, and other IFPRI staff for suggestions and data for this paper. Also gratefully acknowledged are all interviewees and survey respondents for providing their time and insights through phone calls, questionnaires, and emails. Jimena Rotondi of American University provided invaluable research assistance.

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Introduction This report presents results of a study to assess the use of foresight modeling tools and outputs produced since 2012 and funded through Flagship 1, Cluster 1.1 of the CGIAR Research Program on Policies, Institutions, and Markets (PIM). The goal of this study is to examine how the tools and outputs of foresight modeling supported by PIM through Flagship 1 (hereafter “PIM-supported foresight modeling”) have been used by stakeholders. The study aims to identify as many uses of and outcomes from the PIM-supported foresight modeling as possible. It is by no means comprehensive, but it does cover usage by a wide range of stakeholders from across the CGIAR system, other international organizations, academia, and national governments.

PIM-supported foresight modeling has evolved considerably over time. The initial focus was on training, improvement, and application of the IMPACT system of water, crop, and economic models, led by IFPRI, in collaboration with a subset of other CGIAR centers, through the Global Futures and Strategic Foresight (GFSF) program. GFSF was initially jointly supported by the Bill & Melinda Gates Foundation (BMGF) and the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), with a major emphasis on the impacts of new crop varieties and climate change on agricultural productivity. With support from PIM and other sources, GFSF subsequently expanded to include all 15 CGIAR centers, using a wider set of foresight modeling tools to address a broader range of questions. The community of practice developed initially through GFSF is now evolving into a CGIAR foresight team, reflecting an even wider network of participants, partners, tools, and applications. PIM support provided through its Flagship 1 remains a key factor in this process. Much of the PIM-supported foresight modeling work included in this analysis has at its core the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) developed by IFPRI. The PIM-supported foresight modeling work assesses alternative scenarios of future climates, demographics, and other drivers to explore potential challenges and opportunities related to agricultural production, food demand, trade, diets, hunger, and natural resources. The modeling relies on linking a spatially explicit land allocation model with biophysical models (crop and hydrology models), global climate models, and economic models. PIM-supported foresight modeling can help weigh alternative strategies that farmers, policymakers and other decision-makers may use to address these challenges. For instance, it can help evaluate the potential of different agricultural technologies and management practices to make agriculture more resilient to stresses resulting from climate change. PIM-supported foresight modeling work can also help inform investment decisions of governments, the private sector, and other stakeholders to address other challenges. This report seeks to identify who has used PIM-supported foresight modeling research outputs and how. It also goes a step further and examines outcomes. An outcome may be defined as “a change in knowledge, skills, attitudes and/or relationships, manifest as a change in behavior, to

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which research outputs and related activities have contributed” (CGIAR MELCoP, 2018). In the PIM context, outcomes are often changes in strategies, policies, programs, or investments. The recognition or use of outputs by partners in decision-making or capacity strengthening can also be considered as outcomes. We may think of outcomes as early or mature. “Early outcomes” are defined by an initial use of PIM-supported foresight modeling work by decision-makers to consider the strategy or policies of their government or organization while a “mature outcome” is one that shows evidence of implementation of a change in strategy, policy, or other decision as a result of using PIM-supported foresight modeling work. This report first reviews previous assessments of PIM-supported foresight modeling work. It then uses a variety of methods, including analysis of downloads and citations as well as an electronic survey and interviews of stakeholders to examine usage of the PIM-supported foresight modeling work and resultant outcomes. It concludes with a summary of findings and areas for future work. Previous evaluations The last evaluation of PIM was commissioned by the Independent Evaluation Arrangement (IEA) of the CGIAR in 2014 and a report was released in 2015 (CGIAR-IEA, 2015). It included an evaluation of PIM’s Cluster 1.1, Foresight Modeling, and was largely limited to a review of the IMPACT model. The evaluation found little evidence that PIM’s foresight analysis activities undertaken by Cluster 1.1 were used to make decisions at that time. That was because of efforts to enhance the IMPACT model and build a wider community of practice, and because of lags in applications that could inform decision-making, according to the foresight research team. The evaluation team further stated that the direct outputs of foresight activities are large data sets that need careful analysis, interpretation, and dissemination to become useful for informing policy and public expenditure decisions on agricultural research. Outcomes are not easy to attribute since many are joint with other contributors, visible only over the long term, and global or regional in scope. Since the evaluation conducted in 2014, the PIM-supported foresight modeling team has strengthened its modeling capacity and conducted analyses that have gone beyond the use of the IMPACT model. Additionally, donors and other partners have become increasingly interested in foresight modeling to inform decision-making. For example, IFPRI was commissioned to undertake a study to inform the decision-making regarding the CGIAR research portfolio for 2017–2022, and PIM 1.1 funds helped support inputs from the other CGIAR centers. Therefore, it is now timely to assess the use of PIM-supported foresight modeling tools and outputs.

Usage of PIM-supported foresight modeling and resulting outcomes The study described in this report used various means to examine usage of and outcomes from PIM-supported foresight modeling work. The report proceeds as follows. First, we consider downloads of various IMPACT datasets as well as the institutional affiliation of individuals who downloaded the data. We summarize the results from Altmetric and Google Scholar citation searches for references describing PIM-supported foresight modeling. Next, we present results

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from an electronic survey of IMPACT data users, authors citing PIM-supported foresight modeling work, and other contacts familiar with the work. We then present usage of foresight and outcomes that were identified through follow-up emails to and phone interviews of electronic survey respondents as well as a round of interviews with 31 key stakeholders conducted prior to the electronic survey. Downloads of IMPACT datasets Through the PIM-supported foresight modeling work, seven datasets have been made available on Dataverse1 for download by the public; from 2016 through December 2018 these had been downloaded a total of 1,252 times (Table 1). The most frequently downloaded dataset (335 downloads) was IMPACT projections of food production, consumption, and hunger to 2050 with and without climate change. The second most frequently downloaded (210 downloads) was a similar dataset that looked at food production, consumption, and net trade. Table 1: Downloads of IMPACT datasets from 2016 – December 2018

Dataset title 2016 2017 2018

Total Number

of downloads

Extended Results from the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT version 3.2.1) for Sulser et al (2015)

3 192 195

IMPACT Projections of Change in Total Aggregate Cereal Demand, 2010-2050: Extended Country-level Results for 2017 GFPR Annex IMPACT Trend 2

34 45 79

IMPACT Projections of Demand for Agricultural Products: Extended Country-level Results for 2017 GFPR Annex IMPACT Trend 1

70 90 160

IMPACT Projections of Food Production, Consumption, and Hunger to 2050, With and Without Climate Change: Extended Country-level Results for 2017 GFPR Annex Table 6

128 207 335

IMPACT Projections of Food Production, Consumption, and Net Trade to 2050, With and Without Climate Change: Extended Country-level Results for 2017 GFPR Annex Table 7

93 117 210

IMPACT Projections of Share of Population at Risk of Hunger: Extended Country-level Results for 2017 GFPR Annex IMPACT Trend 3

25 53 78

Input Data for the IMPACT Model with Different Future Production Scenarios for Latin America

35 123 37 195

Total downloads including all IMPACT datasets 35 476 741 1252 Source: Institute for Quantitative Social Science (2018). 1 Dataverse is an open-source web application for the archiving and sharing of datasets developed by Harvard's Institute for Quantitative Social Science (IQSS), together with collaborators around the world.

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It is instructive to consider the institutional affiliation of those downloading the data (see Appendix A). The majority (53%) of the 214 institutional affiliations listed are universities or other institutions of higher learning. Thirteen are government entities; 4 are CGIAR centers; 6 are UN entities; and 6 are other international organizations. The remaining 71 affiliations are of another type. We have contact information for 320 of those downloading IMPACT data; this information was used for the electronic survey presented later in this report. Citations analysis In evaluating the PIM-supported foresight modeling program, its publications are a key output to consider. This section considers publications included in the CGIAR Research Program on Policies, Institutions, and Markets (PIM) that are related to foresight modeling. We consider all 89 foresight-related references that were funded by PIM between 2012 and 2018; these references are listed in Appendices B and C. A broad range of subjects are addressed by the 89 references. Seventy-eight of the 89 articles considered contain multiple keywords (an average of about nine per article) that allow us to gain an understanding of the subject matter addressed. The most common keywords used (appearing in the list of keywords for 59 of the 78 articles) were related to the theme of agricultural production, including “agricultural production”, “yields”, “productivity”, “agriculture”, “agricultural development” and “food supply”. Also quite common were keywords related to climate change; these appeared in 53 of the 78 articles. Forty-eight articles addressed farming methods and use keywords that include “technology”, “irrigation”, “fertilizer” and “zero till”. Forty-seven articles contained keywords related to the environment and natural resource use. A different set of 47 articles contained the keyword “food security” or a variation thereof. Thirty-one articles specified the type of agricultural activity considered, whether the production of wheat, rice, maize, or crops more generally or in a few cases livestock or fisheries. Other common themes specified by the keywords are as follows, with the number of articles using a keyword related to that theme in parentheses: model or methodology used for the article (24), economic development (26), nutrition (31), prices (29), commodities or trade (27), and agricultural policies or research (19). About 74 keywords (less than 10% of the total) were not easily grouped thematically. For 55 of the references, we have information on the country and/or region(s) considered by the study; 17 are global in scope. Sixteen references consider a single country (5 references examine issues in India, 3 references look at the Philippines, 2 references each cover Indonesia and Pakistan, and there is one reference each for the Republic of Korea, the United States, and Yemen). Of the remaining 22 references, 13 are focused on Africa or its subregions, 8 consider Africa in addition to another region, most commonly Asia or a subregion of Asia, and one is devoted to Southeast Asia. We may conclude that the publications including PIM-supported foresight modeling are mostly global in scope or focused on Africa; some PIM-supported foresight modeling work covers Asia or its subregions. Relatively little has been published on issues in Latin America and the Caribbean, Europe, and North America.

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We may consider how the references were used by the media through a service known as Altmetric. Of the 89 references, 21 were published internally (by IFPRI) and 68 were published externally (Table 1). The Altmetric attention score2 for the period from late 2013 through January 2019 is higher for externally published references (averaging 82.0) than for internally published references (averaging 1.4). This suggests that the process of publishing work externally is an effective way to reach a wider readership. A reference by Springmann et al. (2016) had the highest Altmetric score (1396) as of January 2019; it is a journal article appearing in the Lancet. The article is a global study of the impacts of climate change on agriculture and health. The next highest Altmetric score (546) was for a reference authored by Hasegawa et al. (2018). It was published in the journal Nature Climate Change and is a global assessment of the impact of climate change policy on food security. Of the 60 references for which we have Altmetric attention scores, 24 can be ranked among the top 25% of all research outputs scored by Altmetric and, of those, 11 may be considered among the top 5%. Altmetric reports that the references are used by a wide range of media outlets, including: AllAfrica, the Bangkok Post, BBC News, Bloomberg, CNN News, China Post, Daily Mail, Daily Nation (Kenya), Forbes, Fortune, Huffington Post, L’Express, Le Monde, New Delhi News, New Kerala, Newsweek, Radio New Zealand, The Australian, The Express Tribune (Pakistan), The Financial Express (IND), The Guardian, The Japan Times, The Malay Mail Online, The Myanmar Times, The Toronto Star, Thomson Reuters Foundation, TIME Magazine, Times of India, Voice of America, and Yahoo! News. While Altmetric helps us understand use of the references by the media, Google Scholar helps us understand use of the references by the academic community by tracking citations of the reference by scientific journals. The number of citations is highest (averaging 36.2 citations) for externally published references. Among such references, the most highly cited (with 311 citations) is a journal article by Nelson et al. (2014) that examines the effect of various economic models on the results of modeling the impact of climate change on agriculture worldwide. Internally published references average 17 citations. Among these references, the most frequently cited (with 111 citations) is a book by Rosegrant et al. (2014) that uses DSSAT and IMPACT modeling to examine the impact of agricultural technology on yields and natural resources in various regions of the world. References with non-IFPRI staff as author are the least frequently cited (averaging 14.9 citations). These results suggest that studies with a global scope are cited more frequently.

2 Altmetric publishes an attention score that helps us track media use of references. The attention score is the weighted sum of numerous components. For instance, 8 points are assigned every time a reference appears in the news; 5 points for each time it is used in a blog; 3 points each for its use in policy documents, patents, or Wikipedia and 1 point for each time it is used in Twitter. Eleven additional outlets are considered and between 0.25 and 1 point are assigned for each appearance in those outlets.

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Table 1: Altmetric score and Google Scholar citations for PIM-funded foresight related references, by publisher

mean median min max count

information not

available All references funded by PIM and related to foresight modeling (89)

Altmetric score 55.1 2 0 1,396 60 29

Google Scholar citations 31.6 9 0 311 89 0

Published internally (21)

Altmetric score 1.4 0 0 16 20 1

Google Scholar citations 17.0 2 0 111 21 0

Published externally (68)

Altmetric score 82.0 6.5 0 1,396 40 28

Google Scholar citations 36.2 11.5 0 311 68 0

Note: The Google Scholar and Altmetric searches were performed in January 2019. For a sample of 18 of the 89 PIM-supported foresight modeling references we use Google Scholar to examine what articles cite the references (see Appendix B for the list of 18 references included). These 18 references were chosen based on the frequency with which they were cited in Google Scholar and the magnitude of their Altmetric score as well as suggestions made by IFPRI staff working on PIM-supported foresight modeling. The references are primarily journal articles and book chapters. Internally published references include a report and a book; no discussion or working papers were considered. With the combined number of Google Scholar citations for the 18 references totaling more than 1,200, we are unable to examine each citation. For those references registering fewer than 10 citing articles on Google Scholar, we examine each citing article. For those with 10–199 citing articles we consider 10 articles, and for those with 200 or more citing articles we consider 20 citing articles. We describe the region of focus of the article citing the PIM-supported foresight modeling reference, frequency with which the reference is cited within the article, location of citation within the citing article, and the way in which the reference was used by the citing article. We consider 192 articles that cite one or more of the 18 PIM-supported foresight modeling references. We have information on the regional or country coverage for 138 of these articles. Twenty-two of the articles are global studies (we consider a study global when it covers Asia, Africa, Latin America and the Caribbean, and at least one other region). Twenty-seven of the studies focus exclusively on Africa or its subregions, 6 focus on Asia or parts thereof, 2 consider Latin America and the Caribbean or its subregions, and 7 are focused on other regions (typically Europe). The remaining 41 studies cover two or three regions; of these, 37 studies include

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Africa; 37 include Asia; and 6 include Latin America and the Caribbean. Thirty-one studies consider a single country; the most frequently studied are China (6 articles), the United States (3 articles), and Australia, Brazil, Indonesia and Russia (2 articles each). We have information regarding the number and placement of citations of the PIM-supported foresight modeling references for 97 of the 192 articles considered. The average number of times one of the references was cited in an article was 1.5; 45% of the articles cite the reference in the introductory section of the article; 66% of articles include the citation in the body of the article; and 6% include a citation in the conclusions. For 95 of the 192 citing articles, we are able to describe how the reference was used. In 20 instances the citation referred to both the model and the results; in 43 instances the results were presented without mentioning the model; and in 18 articles the model was mentioned without presenting results. For 11 of the articles, results included use of a graphical figure, table, or chart that appeared in the PIM-supported foresight modeling reference. In addition to analyzing the citations in an effort to better understand how PIM-supported foresight modeling references are used, we harvested the emails of authors of the 192 articles that cite such references. Those authors were included in an electronic survey we sent to various contacts in an effort to understand what outcomes have resulted from the usage of PIM-supported foresight modeling work. Electronic Survey A survey instrument (see Appendix D) was developed to help identify outcomes attributable to use of the CGIAR Foresight Modeling outputs. It was developed based on the terms of reference for this assignment and in extensive consultation with IFPRI staff. It is important to note that while this report is focused on PIM-supported foresight modeling, the wording used for the survey was “CGIAR Foresight Modeling” which encompasses a broader set of foresight tools. The survey is divided into five parts. First, we asked the respondent questions about his or her professional affiliation and some other background information. In the second section, we asked about their use of the CGIAR Foresight Modeling outputs, whether such outputs have affected their decision-making, and what impacts may have resulted from the decision. The third section asks about the respondent’s awareness of national governments, international organizations, or other groups using Foresight Modeling outputs and how their decisions may have been affected by such work. The fourth section asks for an additional example of use of Foresight Modeling outputs by national governments, international organizations or other groups. The final section asks for the respondent’s contact information should we wish to follow up. The survey was sent on August 12, 2019, via survey monkey software to 177 email addresses and successfully delivered to 166 recipients. Most of the emails were from a list of 120 people who were suggested in interviews with key stakeholders; the list included at least 2 recipients for each of the 15 CGIAR centers. Fifty-seven of the 320 individuals had downloaded IMPACT data

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and also agreed to be surveyed; they were included in the August 12 survey collector.3 The survey collector was closed on September 16 and the number of responses totaled 57 for a response rate of 34.3%. The same survey was sent on August 19 to 377 emails of authors and co-authors of articles that cite PIM-supported foresight references, and was successfully delivered to 354 recipients. The survey collector was closed on September 16 and 53 responses were received for a response rate of 15%. The combined response rate was 21.2%, which is on par with the response rate obtained for another similar exercise by CCAFS (2017). It is useful to note that the response rate was higher for the respondents suggested by key stakeholders and those who had downloaded IMPACT data than it was for those who had authored studies citing PIM-supported foresight references. This is indicative of the dedication of those interacting with the foresight modeling team. The survey helped identify who uses CGIAR Foresight Modeling4 work. Many of respondents (37%) were affiliated with an academic institution. Thirty percent were from a CG center and 16% from a research institution or think tank. Five percent of respondents were from other international organizations and 5% from government. Only one respondent identified as being from an NGO and one from the private sector. The survey also indicates how respondents learned of the modeling work. Most (60%) learned of CGIAR Foresight Modeling by collaborating in a project/study or program; 37% learned of it through a publication that references Foresight; 28% learned about it at a conference or workshop; and 28% were contacted directly by members of the Foresight team. Other ways in which respondents learned about the modeling work included: recommendations of colleagues (20%); web search (19%); IFPRI newsletter/blog (14%); and published media (14%). Most respondents (80%) have used CGIAR Foresight Modeling products and 21% have used other foresight modeling products. The other types of foresight modeling include: CSA for CCAFS country profiles, World Bank studies, FAO work including Scenarios to 2050, FAO-OECD 2018–2028, WEF, Oxford Food Systems, products from RTI, IIASA work, MAGNET, DICE, GLOBIOM, DREAM, IPCC Projections, Shell Scenarios, and Transmango Scenarios. Fifteen percent had not used either CG or other foresight modeling products. Forty-two percent of respondents have used CGIAR Foresight Modeling work to contribute to their own foresight work. Thirty-three percent of respondents say they have used CGIAR Foresight Modeling products to inform decision-making. The publications that were especially useful included those on the model (IMPACT or other models) (61% of respondents chose this) and those on climate change (72% chose this). Forty-four percent of respondents found products about agricultural technologies among the most helpful; 28% found those on crop production the most useful; and finally products related to agricultural research were deemed among the most useful by 14% of respondents. 3 In accordance with the General Data Protection Regulation, all downloaders of IMPACT data were contacted and asked whether they would like to participate in our survey and be contacted at the email address they provided when downloading the data. 4 Again, we note that while this report is focused on PIM-supported Foresight Modeling, the wording used for the survey was “CGIAR Foresight Modeling,” which encompasses a broader set of foresight tools.

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In terms of information that would be helpful but is not yet available, suggestions related to data and methodology included, in the respondents’ own words:

• link the different models, including IMPACT, to a more general equilibrium model • provide subnational data • provide total factor productivity elasticities that underly their studies on investment

prioritization • improve web-based, user-friendly version of IMPACT • provide more disaggregated data • update IMPACT results

Regarding topics that are not currently addressed but would be useful, respondents suggested including more information on the following:

• livestock products (mentioned twice) • nutritional and environmental outcomes (mentioned twice) • fisheries module • human migration and employment • costs and benefits • extent to which policies and public investments are being implemented in practice • impacts on farmers' income • specific crop types • complete household models that extend beyond production and deal with economic and

social aspects that allow or constrain adoption of technologies • crop management practices in a gridded format on a global scale • wild legume germplasm • land use change • soil nutrient dynamics • circular economy innovations • biotechnologies

A majority (68%) of those responding (50 people responded, while 60 skipped this question), replied that CGIAR Foresight Modeling had been used by themselves or colleagues at their workplace to inform activities or decisions. Twelve respondents noted that there had been outcomes (changes in behavior, policy, or investment decisions) resulting from such use. Such outcomes include the following:

• Changes were made at the International Potato Center regarding resource allocation (including funding and staff time).

• A climate-smart agriculture project in Cambodia was formulated and funded. • In Uzbekistan, the government decided to implement drip irrigation in agriculture more

widely.

Respondents were also asked whether CGIAR Foresight Modeling had been used by other organizations to inform activities or decisions. Half of those responding (74 respondents

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answered and 36 skipped this question) confirmed their knowledge of others using CGIAR Foresight Modeling to inform decisions. Eleven respondents noted that there had been outcomes (changes in behavior, policy, or investment decisions) resulting from such use. Such outcomes include the following:

• A study and workshops led by the African Union used CGIAR Foresight Modeling and resulted in increased support by African governments for research on wheat.

• The Philippines National Economic Development Authority changed its policy on rice imports.

• The Cambodian Ministry of Agriculture, Forestry and Fisheries reformulated elements of its National Agricultural Plan partly in response to CGIAR Foresight Modeling work.

• The national climate change adaptation strategy for the agrifood sector (ENACCSA) of Honduras and the regional climate-smart agriculture strategy (estrategia ASAC) for the SICA region were developed with support of CCAFS and UCI.

A series of phone interviews and follow-up emails were undertaken in order to gather additional information regarding the more promising outcomes identified through the electronic survey. Outcomes from and use of PIM-supported foresight modeling work are described in the next section of this report together with interviews conducted prior to the survey. Interviews and correspondence From November 2018 through January of 2019, interviews of 31 key stakeholders were conducted in an effort to gain an overview of use of and outcomes resulting from PIM-supported foresight modeling (see Appendix E for a list of interviewees). Use and outcomes were also assessed through follow-up phone interviews and correspondence with survey respondents who reported outcomes that had not been identified through the initial phone interviews (see Appendix E). The subsequent sections provide a summary of outcomes identified, followed by detailed description of each organization’s and stakeholder’s use of PIM-supported foresight modeling. Summary of Outcomes Through interviews and correspondence we have identified 54 outcomes (21 of which are mature) resulting from use by international organizations, national stakeholders, or CG centers other than IFPRI (Table 2). Table 2: Early and Mature Outcomes Identified by User Type User Type Early Mature International Organizations 17 4 National Stakeholders 5 9 CG Centers other than IFPRI 11 8

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PIM-supported foresight work has been used to inform the decision-making of multilateral organizations and donors; these include the ADB, CAC, FAO, IADB, IFAD, Bill & Melinda Gates Foundation, OECD, UNEP, and the World Bank. Mature outcomes resulting from the work of such organizations include:

• The CAC and CIAT used foresight to help develop a climate-smart agriculture policy for the region of Central America.

• The OECD has undertaken more long-term projections work as a result of collaboration on foresight modeling.

• Results from foresight have helped design large regional World Bank operations in the livestock and irrigation sectors as well as some of the Bank’s natural resource management programs.

PIM-supported foresight work has also been used by national governments, including in Cambodia, Colombia, Dominican Republic, Indonesia, Philippines, South Africa, United Kingdom, the United States, Uzbekistan, and Viet Nam. Mature outcomes at the national level include the following:

• In Cambodia, a climate-smart agriculture project was funded. • Colombia included AFOLU in its NDC for the UNFCCC. • The national agriculture research service in the Dominican Republic has proposed to

include climate change as a line item in its budget. • Work by ICRAF together with the Government of Indonesia has helped shape the

country’s Medium Term Development Plan. • In the Philippines, foresight analysis led to reform of rice trade policy and informed the

Philippine Development Plan of 2017–2022. • In the United States, foresight modeling informed USAID’s Global Food Security

Research Strategy and helped to safeguard resources allocated to agricultural research and development within the Feed the Future programming.

PIM-supported foresight modeling has informed the 2017–2022 CGIAR Research Program portfolio, with the modeling included in proposals for the CRPs on Livestock; Roots, Tubers and Bananas; Wheat; Grain Legumes; Dryland Cereals; and Fish. Mature outcomes achieved by individual CGIAR centers other than or in collaboration with IFPRI include:

• CIAT’s use of foresight tools has helped the World Bank identify the best option for investing bank funds in climate-smart agriculture in Burkina Faso, Cote d’Ivoire, Ghana, and Mali.

• CIMMYT’s work has helped establish wheat as a priority crop in Africa. • ILRI’s foresight work helped to identify priority countries for the livestock CRP.

The next sections provide detailed descriptions of the use of and outcomes from the foresight work. They begin with a description of the use of PIM-supported foresight modeling to inform decision-making of multilateral organizations and donors, followed by discussion of national-level use of the work. These sections then describe how this modeling has informed the CGIAR portfolio as well as how individual CGIAR centers have been involved in foresight work. Examples of how foresight has informed the global debate on sustainable diets are presented as

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are global partnerships that have developed for conducting foresight analysis. Lastly, some of the training initiatives are described. Informing decision-making of multilateral organizations and donors PIM-supported foresight modeling has been commissioned or used by numerous multilateral donors and international organizations. The work feeds into the production of reports that involve numerous policymakers and that are presented to many stakeholders; although the outcomes are rarely traceable, the work is often used in decision-making. Asian Development Bank (ADB): As part of several technical assistance projects (TA), the Asian Development Bank has commissioned work from IFPRI that makes use of IMPACT modeling work. As a part of each project, the results of such work have been disseminated through workshops, intergovernmental meetings, and publication launches. As such, the work influences the design of ADB projects as well as decision-making by national policymakers and other stakeholders.

• As early as 2008, ADB engaged IFPRI to analyze climate change impacts on agriculture; for this analysis IFPRI incorporated DSSAT crop models in IMPACT modeling for the first time and has been using this approach ever since (ADB, 2009). (“early outcome”)

• From 2010–2012, ADB engaged IFPRI through TA 7394 entitled “Climate Change, Food Security, and Socioeconomic Livelihood in Pacific Islands.” As a part of this project, IFPRI and ADB published a report using the IMPACT model to consider the impact of climate change on food security and livelihoods in Pacific Islands (see Rosegrant et al., 2015). In addition to this report and other knowledge products, the project included capacity building workshops which trained national partners in analyzing the impacts of climate change (ADB, 2014). (“early outcome”)

• A recent and ongoing collaboration is taking place through a TA aimed at increasing investments by governments and ADB in agriculture and natural resources. The TA 9218 entitled “Investment Requirements to Achieve Food Security in Asia and the Pacific in 2030” includes a regional report and a report on Indonesia using the IMPACT model linked with CGE (ADB, 2016). The reports assess the total investment required in the agriculture sector (agricultural R&D, irrigation, rural infrastructure) to achieve food security by 2030 in the Asia-Pacific region and in Indonesia. The publications were presented to stakeholders during the ADB Rural Development and Food Security Forum in October 2019 (ADB, 2019a and ADB, 2019b). (“early outcome”)

Central American Agricultural Council (CAC): PIM-supported foresight modeling was used as part of the process for revising the draft Climate Smart Agriculture Strategy for the Central American Integration System (SICA) region. The draft strategy was formulated as a participatory and consultative process led by the Central American Agricultural Council (CAC), CIAT, and the University for International Cooperation (UCI). A workshop was then held to consider the strategy in light of four possible future scenarios using PIM-supported foresight modelling. Next, a period of online consultation was held and revisions were made to the policy. The policy was officially launched in October 2017. (“mature outcome”)

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Food and Agriculture Organization of the United Nations: The Food and Agriculture Organization of the United Nations (FAO) has engaged IFPRI on several occasions by commissioning work using its IMPACT model.

• In 2016, FAO commissioned a report from IFPRI estimating the investments in agriculture required to end hunger in Africa. Results of that report were presented at the 22nd Conference of the Parties (COP22) at the United Nations Climate Change Conference in Marrakesh in 2016. (“early outcome”)

• Also in 2016, FAO commissioned two background papers from IFPRI on climate change and agriculture for its flagship publication The State of Food and Agriculture 2016: Climate Change, Food Security and Agriculture (FAO, 2016). The SOFA 2016 received extensive media coverage. It was launched at a press conference at FAO; presented at an event at the National Press Club in Washington, DC; and discussed at the 2017 session of the FAO Conference. Specific reference was made to it in United Nations General Assembly Resolution A/RES/71/245 (United Nations, 2017). (“early outcome”)

• The FAO Regional Office for Asia and the Pacific invited IFPRI to provide inputs to a report on future food systems in Asia and the Pacific. The work was published in 2018 as Dynamic Development, Shifting Demographics and Changing Diets (FAO, 2018). (“early outcome”)

Inter-American Development Bank (IADB): IADB commissioned CIAT to undertake foresight analysis of five core commodities (dry bean, maize, rice, soy, and wheat) and some country-specific commodities for most countries in Latin America and the Caribbean. It produced a series of policy briefs for 14 countries in the region. The briefs communicate the potential impacts of climate change on various crops as well as options for various levels of intervention; they are intended to help governments as well as the IADB understand how their investments might be adjusted to better respond to climate change impacts. The findings of such work have been presented to decision-makers through workshops. Resulting outcomes include one in the Dominican Republic (see section on national outcomes) (“mature outcome”) International Fund for Agricultural Development (IFAD): Quantitative foresight modeling led by PIM in collaboration with IFPRI was commissioned by IFAD to provide key input to IFAD’s chapter entitled "Climate Change Is a Youth Issue" in its 2019 Rural Development Report (IFAD, 2019). IFAD’s Rural Development Report, which is released every three years, informs allocation of resources for IFAD projects. (“early outcome”) Bill & Melinda Gates Foundation (BMGF): Much of the IMPACT modeling during the period of this assessment was funded by the BMGF through the GFSF project, which sought to improve the IMPACT model and extend its use in collaboration with other CGIAR centers. The modeling has now become a resource upon which BMGF draws. BMGF has requested specific scenario analyses and results to inform questions from Senior Management at BMGF about the foundation’s policy and investment decisions in several areas including inclusive market strategy, the future of agricultural trade, inclusive market strategies, and interventions around micronutrient deficiencies. (“early outcome”)

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The Program for Biosafety (PBS), hosted by IFPRI and funded by BMGF includes a project known as the Biotechnology and Biosafety Rapid Assessment and Policy Platform (BioRAPP), an economic modeling tool for evidence-based policy reform on biotechnology and biosafety. BioRAPP is focused on developing and implementing ex ante assessments of genetically engineered crops in five African countries (Ethiopia, Ghana, Nigeria, Tanzania, and Uganda) using secondary data. The work uses data from the IMPACT database. The analysis has generated interest from the President and the Vice President of the foundation. (“early outcome”) Organization for Economic Co-operation and Development: The Organisation for Economic Co-operation and Development (OECD) has collaborated with IFPRI on various pieces of work using IFPRI’s IMPACT model. Through a training IFPRI provided on foresight modeling, collaboration with the OECD was identified as mutually promising. Foresight modeling was used as an input to a joint paper with the OECD entitled Modeling Adaption to Climate Change in Agriculture, published in 2014 (Ignaciuk and Mason-D'Croz, 2014). As a result of this joint work, foresight modeling was improved and used as inputs to higher profile OECD reports. (“early outcome”) IFPRI contributed scenario analyses to an OECD report on Alternative Futures for Global Food and Agriculture, which was published in 2016 (OECD, 2016a). The following results may be attributed to the report:

• A key background note to the 2016 OECD Agricultural Ministerial Meeting built on the Alternative Futures report (OECD, 2016b). The note led to OECD Ministers and those from key partner countries giving increased attention to longer-term developments in discussing a “vision for 21st century agricultural policy.” (“early outcome”)

• The foresight work also influenced OECD work going forward. The OECD has engaged in new work on modeling long-term market developments in order to better understand implications of longer-term challenges (e.g., from resource constraints or limits to productivity growth) on the OECD/FAO Agricultural Outlook (its medium-term market projections) (Saunders, Adenauer and Brooks, 2019). (“mature outcome”)

• The OECD, its members, and partner countries have agreed to make additional efforts to better understand the linkages between agricultural policies and the sector’s environmental footprint both at the national/subnational and the global level (Henderson and Lankoski, 2019). (“early outcome”)

IMPACT modeling was used for inputs to an OECD report on Water in Agriculture (OECD, 2017). The report went through three rounds of peer review by agriculture officials and economic experts from 35 OECD countries (such as the USDA Economic Research Service), international consensus was reached (all countries agreed with the findings of the report), and publication was approved in August 2017. The report was then presented at the OECD Committee for Agriculture and to the Food and Agriculture Committee of Business at the OECD in May 2017. The report was selected internally as being of high potential and disseminated via a number of communication efforts, including a live webinar, with 118 participants from 22

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countries, including representatives from Bayer, Cargill, USDA, NOAA, NASA, FAO, WRI, IWA, IFPRI, IISD, Chatham House, and universities in the US, UK, and Italy. It was also presented at external meetings including the first meeting of Water Resource Authorities organized by the Asia Pacific Economic Cooperation (APEC) in Can Tho, Viet Nam (with 60 participants from 14 countries), and during individual seminars at the Dutch Ministry of Economic Affairs in the Hague, Netherlands, the USDA Economic Research Service in Washington, DC, and the French Ministry of Agriculture and Forestry in Paris. As a result of these efforts, the report, published on September 25, 2017, had been downloaded 1,876 times by users from 45 countries as of January 23, 2018. (“early outcome”) OECD and IFPRI collaboration on the foresight modeling has been mutually beneficial. It has helped the OECD support evidence-based policy discussions. It has also helped IFPRI refine its model and gain visibility. When IMPACT was initially proposed as a model to analyze climate change adaptation, a few countries were not convinced that it was representative of their domestic agriculture. After some work and refinement (such as of early assumptions in IFPRI’s production or irrigation data), all members agreed to declassify and publish the work. (“early outcome”) United Nations Environment Programme (UNEP): UNEP-WCMC, FAO, and CCAFS collaborated in 2015 to develop regional socioeconomic scenarios (using Globiom and IMPACT models) on agriculture, food security, and climate change in Southeast Asia to identify future threats from land use and climate change. A workshop convened national policymakers from Cambodia, Laos, and Viet Nam who reviewed scenarios and existing national agricultural policies. (“early outcome”) World Bank Group: A team from the World Bank and FAO led a major initiative on the African Drylands. CGIAR-PIM supported the effort, which involved several CG centers including IFPRI, ILRI, ICRISAT, and ICRAF. The IMPACT model was used in several of the background papers. The study assessed vulnerability and resilience in drylands, identified cost-effective interventions, and provided an evidence-based framework to enhance decision-making on strategies for enhancing resilience. (“early outcome”) PIM-supported foresight modeling tools and outputs provided the input for the “umbrella model” used in Confronting Drought in Africa’s Drylands (Cervigni and Morris, 2019). Preliminary insights influenced the development of several components of the World Bank’s “Africa Climate Business Plan” launched in 2015, which is a platform for action on climate change in Africa that finances 176 projects totaling $17 billion dollars (World Bank, 2019). The study has informed the design of more than 40 World Bank projects (under preparation or implementation); examples include the Indonesian Landscape Program, the Nicaraguan Drylands Corridor Program, and Madagascar’s Forest Landscape Program. (“early outcome”) PIM-supported foresight modeling also informed “deep dives” on Dryland Classification (Morris et al, 2016); Livestock (de Haan, 2016); Water Management (Ward, Torquebiau and Xie, 2016); Irrigation Development; Agriculture (Walker et al, 2016); and Tree-based Systems (Place et al, 2016). Results emerging from the “deep dive” background studies were used to help design large regional World Bank operations in the livestock and irrigation sectors as well as natural resource

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management programs, including the Regional Sahel Pastoralism Support Project (P147674) and the Regional Pastoral Livelihood Resilience in East Africa (P150006). (“mature outcome”) National-level outcomes Cambodia: As a result of a joint UNEP, FAO, and CCAFS workshop, Cambodia included a climate change component for the first time in its National Agricultural Policy for 2014–2018. CCAFS collaborated with the Group for the Environment, Renewable Energy and Solidarity (GERES), an NGO, and the Ministry of Agriculture, Forestry and Fisheries (MAFF) in 2016 to formulate a project called “Increasing Resilience to Climate Change for Farmers in Rural Cambodia through Climate Smart Agriculture Practices (IR- CSA).” The project was funded by the Cambodia Climate Change Alliance (CCCA) for a three-year period and includes foresight modeling as well as adaptation strategies, involving stakeholders as diverse as smallholder farmers and national policymakers. (“mature outcome”) Colombia: In Colombia, the Ministry of the Environment and Sustainable Development (Ministerio de Ambiente y Desarrollo Sostenible, MADS) was charged with leading development of the country’s nationally determined contribution (NDC). NDCs are plans for reductions in greenhouse gas emissions that show how a country plans to contribute to goals set under the United Nations Framework Convention on Climate Change. In order to produce evidence-based scenarios of possible reductions in emissions through the agriculture, forestry, and other land use sector (AFOLU) sector, MADS decided to create a partnership with the Universidad de los Andes, IFPRI, and CCAFS (De Pinto, et al., 2018). The objective of the collaboration was to produce ex ante analyses of viable emissions reduction commitments. The research carried out by IFPRI included the use of IMPACT data and land use and crop models; the AFOLU sector was found to offer significant mitigation potential. As a result, the country’s NDC included measures to be taken in the AFOLU as well as other sectors. (“mature outcome”) Dominican Republic: In the Dominican Republic, the IDIAF (national agricultural research service) used IADB-supported foresight analysis by CIAT to reformulate their strategic plan to include a climate component; the plan is awaiting approval from the board of directors and, once approved, will include a line item on climate change. (“mature outcome”)

Indonesia: As a knowledge service, foresight modeling work has been instrumental and very useful in the process of mainstreaming green growth scenarios and interventions into medium-term development planning of the government of Indonesia to reach sustainable development. ICRAF staff and partners, supported by the National Development Planning Department, brought this work to the attention of the government and as a result the planning process was more inclusive, integrative, and informed. The Indonesian government made three key decisions based on the foresight modeling: (1) establishing a green growth target that includes economic, environmental, and social indicators; (2) selection of interventions; (3) decision on land allocation/spatial planning. In particular, one of the government regulations explicitly cited the use of the model. A resulting impact is that, as part of the Medium Term Plan, the interventions

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will get an annual budget allocation, and the targeted indicators will be monitored, so the government can be held accountable. (“mature outcome”) Philippines: PIM-supported foresight modeling by IFPRI has had a significant impact in shaping the agriculture and climate change policy of the Philippines. Input from the work influenced the May 21, 2018, adoption of the Amendment of the Agricultural Tariffication Act of 1996. This new legislation removed quantitative restrictions on rice. PIM-supported foresight modeling also informed the Philippine Development Plan of 2017–2022. Details follow. In 2014, with support from CCAFS and PIM, an IFPRI research project on “Addressing the Impacts of Climate Change in the Agriculture Sector of the Philippines” was initiated in collaboration with the country’s National Economic and Development Authority (NEDA) and leading researchers in the Philippines. The analytical framework analysis links (1) general circulation models (GCMs) that generate climate change scenarios; (2) biophysical crop modeling; (3) partial equilibrium economic modeling of the agriculture sector incorporating a new module for the Philippines within IFPRI’s IMPACT model; and (4) economywide analysis using a dynamic computable general equilibrium model of the Philippines (Phil-DCGE), which was developed under this project. In 2016, two technical training sessions, dialogue with NEDA partners, publication of two policy notes, and preparation of a book manuscript were completed. (“early outcome”) At the Global Landscapes Forum in Marrakech in November 2016, a talk by the director of NEDA noted that IFPRI’s IMPACT model and the Phil-DCGE model developed in this project provided valuable and significant inputs to policy formulation and development planning (Sombilla, 2016). The Phil-DCGE model demonstrated adverse impacts of the country’s current rice trade policy, which would increase with climate change. These results, together with those from other studies, have been used by NEDA, the Department of Finance, the Department of Budget and Management (DBM), and the Department of Trade and Industry to decide not to extend or renew the Quantitative Restriction aspect of the current rice trade policy (Government of the Philippines, 2018). (“mature outcome”) Furthermore, several recommendations from the NEDA-IFPRI study were applied in the formulation of the strategies to facilitate rapid expansion of opportunities of economic sectors, including agriculture, in the Philippine Development Plan 2017–2022. A number of adaptation strategies identified and analyzed in the study are considered in the update of the Agriculture and Fisheries Modernization Program (2018–2023) (Government of the Philippines, 2017). (“mature outcome”) South Africa: The National Treasury of South Africa has engaged IFPRI to assess the implications of climate change for agriculture in South and Southern Africa. IFPRI has developed IMPACT-SIMM, which is a newly developed country-level version of the full IMPACT model. This application uses a crop model emulator to examine 1,200 future climate scenarios for Southern Africa; the scenarios were developed jointly by IFPRI and the Massachusetts Institute of Technology Joint Program on the Science and Policy of Global

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Change. The standard IMPACT water model has been replaced with a more detailed model focused on the Zambezi River Basin. This work is underway and, once complete, will feed into policymaking. (“early outcome”) United Kingdom: The IMPACT team under the Global Futures and Strategic Foresight (GFSF) program provided technical expertise and content in the form of model simulation results and analysis, presented in a report (see GLOPAN, 2016), for the Foresight Report of the Global Panel on Agriculture and Food Systems for Nutrition. The report was also presented and officially communicated to the UK Department for International Development (DFID) and the UK Parliament. Charlotte Watts, Chief Scientific Advisor and Director of Research and Evidence for DFID, helped to present the results of the report with the All-Party Parliamentary Group on Agriculture and Food for Development at the UK House of Commons on November 2, 2016. (“early outcome”) United States: Quantitative foresight modeling that helped inform the 2017–2022 CRP approval process also contributed to the white paper that informed the US Government’s Global Food Security Research Strategy (USAID, 2017). This Research Strategy indicates USAID’s intentions in directing Feed the Future resources and programing for 2017–2021 toward achieving three strategic objectives: promoting inclusive, sustainable agriculture-led economic growth; building resilience among vulnerable populations and households; and improving nutritional outcomes, especially among women and children. (“mature outcome”) Feed the Future funding for agricultural research and development was compared in recent years with other programmatic areas within the Feed the Future program. A foresight report (Rosegrant et al., 2017) showing returns to different types of investment (in agricultural research and development, roads, and irrigation) provided USAID leadership with an evidence base that returns to investments in agricultural research and development are quite high. This resulted in the safeguarding of resources allocated to agricultural research and development within USAID’s Feed the Future programming and also ensured that Feed the Future continued to fund agricultural research centers in universities as well as the CG system and other research entities. (“mature outcome”) USAID commissioned quantitative foresight modeling by IFPRI in collaboration with USDA (drawing on PIM-supported foresight modeling work) to help inform the establishment of the multi-donor Crops to End Hunger Initiative, which seeks to strengthen the crop improvement programs of the CGIAR system together with national agricultural research systems (NARs) (CGIAR, 2018). This multi-donor initiative is led by USAID and includes BMGF, DFID, GiZ, and ACIAR. It aims to reinvigorate plant breeding for the staple crops important in the developing world by seeking to modernize and prioritize public plant breeding programs in the developing world. (“mature outcome”) Uzbekistan: Research was undertaken on sub-basin planning of irrigation technology adoptions, primarily as part of a ZEF/UNESCO project in Uzbekistan that was active from 2001–2012. The research used a basin-scale hydro-economic model with equations similar to those of the

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IMPACT–WATER model. The model was used to assess the adoption scope and investment costs of various water conservation technologies across the provinces in Central Asia. Based on the interdisciplinary research outcomes, the project prepared books and journal articles and delivered several workshops to policymakers (ministry officials) and the research community about the potential impacts of agrarian change and technologies for improved livelihoods. As a result, the government of Uzbekistan decided to implement drip irrigation in agriculture more widely (Government of Uzbekistan, 2018). A presidential decree indicates the government’s plan to adopt drip irrigation in Uzbek provinces (Government of Uzbekistan, 2019). (“mature outcome”) Viet Nam: In Viet Nam, the long-term strategy on agriculture to 2100 is reviewed annually. Modeling work from the 2015 collaboration among UNEP-WCMC, FAO, and CCAFS (see description under UNEP in preceding section on multilateral organizations) was used in the annual review to inform what crops should be emphasized by policymakers, including coffee in the north of the country. (“early outcome”) CGIAR system-level Quantitative foresight modeling funded by PIM (Rosegrant et al., 2017), led by IFPRI and undertaken in partnership with all 15 CGIAR centers, informed the decision-making process for the 2017–2022 CGIAR Research Program portfolio. The Livestock CRP has incorporated the results from PIM foresight analysis in its decision-making at the CRP management level. The proposal submitted for 2017–2022 funding includes results from analysis of the IMPACT model (CGIAR, 2016a). The Roots, Tubers and Bananas (RTB) CRP conducted a priority-setting exercise to inform its 2017–2022 proposal submitted to the CGIAR. As part of this process, RTB included outputs from existing foresight modeling work supported by PIM, including those using IMPACT (CGIAR, 2016b). The Wheat CRP proposal submitted for 2017–2022 funding cites foresight work undertaken by IFPRI that was published in 2010 (CGIAR, 2016c). The Grain Legumes and Dryland Cereals 2017–2022 CRP proposal incorporated analyses that used the IMPACT model. Additionally, analysts engaged in preparing this material had benefited from participation and training in the Global Futures and Strategic Foresight project (CGIAR, 2016d). The FISH CRP proposal incorporates the involvement of a quantitative and qualitative priority-setting exercise, including PIM’s foresight analysis (CGIAR, 2016e). The examples above illustrate the engagement and co-investment of other CRPs in joining with PIM to support foresight modeling work in the CGIAR. That co-investment is also reflected in the CGIAR center-level outcomes described next. (“mature outcomes”)

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CGIAR center-level outcomes Much of the CGIAR center-based foresight work during the reporting period was initiated through or related to participation in the Global Futures and Strategic Foresight project, led by IFPRI with major funding support from PIM. While several of the centers currently run independent foresight analysis and priority-setting activities, a significant part of their work has been inspired by the initial PIM work and often uses IFPRI’s IMPACT model, the core efforts for which have been supported by PIM. The PIM-supported foresight team has engaged with crop and livestock breeders and other non-economists and made them aware of the potential impact of their research. These scientists in different CGIAR centers are some of the users of PIM-supported foresight modeling work. Often, they use PIM-supported foresight reports and analyses to bolster their research proposals. An important outcome of PIM-supported foresight collaboration with other CGIAR centers and CRPs has been the creation of research synergies through global partnerships, such as with the Agricultural Model Intercomparison and Improvement Project (AgMIP). This collaboration has resulted in strengthening research capacity and generating a new pipeline of researchers (by engaging students). Center-led Foresight work seeks to increase the relevance of modeling outputs by targeting analysis such as in identification of priority breeding traits. A clear success of the PIM-supported foresight modeling work is the dedication of resources at the center-level for foresight work for the 2017–2025 planning period. The Africa Rice Center (AfricaRice) joined the PIM-supported foresight modeling community of practice relatively late in the period covered by this report, but has contributed to several of the joint foresight modeling activities described in this report. (“early outcome”) Bioversity International has engaged in the Foresight4Food Initiative and the EAT-Lancet Commission (see later sections). It has used foresight modeling to understand how RTB crops will compare with other important foods (cereals and meats) in the next 40 years at regional and global levels (i.e., their respective proportions in the regions and on the world market). The approach and methodology have since been adapted to work that Bioversity is undertaking on vitamin A–rich bananas in Uganda. (“mature outcomes”) The Center for International Forestry Research (CIFOR) joined the PIM-supported foresight modeling community of practice relatively late in the period covered by this report, but has contributed to several of the joint foresight modeling activities described in this report. (“early outcome”) The International Center for Tropical Agriculture (CIAT) has used foresight analysis in each of the mandate commodity programs (beans, cassava, and forages) and worked with partner organizations (Latin America Fund for Irrigated Rice, FLAR). Key advances include the incorporation of foresight into the budget for a large bean breeding program (BMGF sponsored)

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and, at an institutional level, the raising of foresight into the organizational MELIA program (monitoring, evaluation, learning, impact assessment), which is now MELIAF (with the addition of foresight). CIAT has undertaken analysis for IADB regarding climate change impacts on different crops in numerous countries in Latin America and the Caribbean (see section on the IADB and national results for the Dominican Republic). (“mature outcome”) CIAT has been invited by numerous stakeholders to share foresight modeling results. For instance, the INIA (the NAR in Argentina) invited CIAT to share foresight results with various ministries and the academic community in order to illustrate the potential impacts of climate change on agriculture. The Agence Française de Coopération Internationale requested access to CIAT’s data on the impacts of climate change. The agency used the data to prioritize its research, interventions, and calls for funding as well as to select the crops on which they should focus their actions in the Dominican Republic. (“mature outcome”) CIAT’s engagements in Honduras have included the Secretary of Agriculture and Livestock (SAG, a public institution), IFAD, and other local organizations working and residing in the dry corridor. With these groups, CIAT is jointly developing strategies for disseminating the foresight results, validating data, and calibrating model parameters, among various activities. The effort aims to involve not only national organizations but also institutions with local presence, providing information related to crops, the municipalities, potential impacts, etc., in order to better equip the local actors to adjust their interventions and action plans. CIAT has also used foresight modeling tools to develop Climate Smart Agricultural Investment Plans for Burkina Faso, Côte d’Ivoire, Ghana, and Mali. These plans have been used by countries and the World Bank in identifying the best options for investment of bank funds. (“mature outcomes”) The International Maize and Wheat Improvement Center (CIMMYT) was an early participant in the Global Futures and Strategic Foresight project, which contributed to the establishment of a foresight unit in CIMMYT. The PIM program was instrumental in mainstreaming the use of foresight by CIMMYT, where the PIM work is being leveraged by researchers. The research conducted by the CIMMYT foresight, ex ante impact assessment, and targeting team, with PIM funding, has a primary focus of informing R4D priority-setting, especially with regard to climate-change-related abiotic stress and, increasingly, emerging biotic stress, which is often but not exclusively linked to climatic change. The work has helped focus research programs on heat, drought, and heat- and drought-tolerant maize and wheat varieties for both Africa south of the Sahara and South Asia. With the objective of creating a critical mass of African agricultural researchers and development actors, CIMMYT has held numerous capacity building workshops in Africa (see section on training). (“mature outcome”) CIMMYT has investigated the impact of climate change on wheat production in Mexico. CIMMYT used the MINK system (Robertson, 2017) to run wheat and maize gridded simulations. The modeling was used to inform growers, researchers, and government agents about climate change impacts, new adaptation technologies, and the need for investments in the

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wheat production sector. As a result of this work, more attention has been dedicated to releasing heat- and drought-tolerant varieties for Mexico. (“early outcome”) CIMMYT and partners conducted a foresight study on wheat in Africa (Negassa et al., 2013). The Ethiopian Government hosted the Wheat Conference for Africa with representation of various member countries of the African Union. Nigeria, Zambia, Ethiopia, Kenya, Rwanda, and other African countries supported increasing self-sufficiency in wheat as well as several wheat-related R4D initiatives in their respective countries. Prior to 2010, wheat was not considered as a strategic crop in Africa. After the foresight work and its impact on policy, however, the “Feed Africa Strategy for Agricultural Transformation in Africa, 2016–2025,” developed by the AU, established wheat as one of the priority crops in the region (AfDB, 2016). (“early outcome”) The International Potato Center (CIP) received initial training in foresight analysis through participation in PIM’s Global Futures and Strategic Foresight program. The center continues to conduct foresight analysis using a simpler center-based partial equilibrium model that enables a consistent analysis across different commodities with varying levels of data availability. Foresight modeling was used for setting priorities for the research portfolios of both the CIP and the CRP on RTB. The result was a change in resource allocation across the programs, including funding and staff time. Islam et al. (2016) showed that breeding for drought resistance and heat tolerance in potatoes helps to address climate change and therefore this line of research continued to be prioritized by CIP. (“early outcome”) The International Center for Agricultural Research in the Dry Areas (ICARDA) was a partner in the work on Africa’s drylands undertaken with the World Bank and FAO (see section on the World Bank). (“early outcome”) The World Agroforestry Centre (ICRAF) has worked with the government of Indonesia on foresight modeling work that has led to mature outcomes (see section on National outcomes). (“mature outcome”) The International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and other CGIAR centers have been involved in the strengthening of national and regional capacity with the establishment of new AgMIP activities in South Asia and Africa south of the Sahara. (“early outcome”) The International Food Policy Research Institute (IFPRI) has played a critical role in leading the Global Futures and Strategic Foresight Program, which (with support from PIM) built a community of practice for foresight modeling that grew to include all 15 CGIAR centers during the period covered by this report, and subsequently evolved into the CGIAR foresight team. Building on its development of the IMPACT system of models in the 1990s, IFPRI has produced or contributed to many of the PIM-supported foresight activities and outcomes described throughout this report. The International Institute of Tropical Agriculture (IITA), based on IMPACT training and capacity developed through participation in PIM-supported foresight activities, contributed to

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foresight modeling analyses to the development of the Grain Legumes and Dryland Cereals 2017–2022 CRP proposal. (“mature outcome”) The International Livestock Research Institute (ILRI) has routinely used IMPACT model projections in communications and advocacy that ILRI management conducts with donors, government officials, and other key stakeholders. It has also used IMPACT model projections to inform its own programming. In 2018, the Livestock CRP management requested relevant foresight model projections as input into decision-making regarding country prioritization of ILRI’s research program. IMPACT model projections to 2030/50 of demand, supply, and trade of livestock food products were provided to aid assessments of the potential future demand for these commodities. While other assessments were produced that used different tools and approaches, it was thought that the IMPACT projections added a standard approach that could be compared across countries, i.e., using the same definitions, data sources, and estimation processes. The global focus of the underlying economic model was also considered helpful for looking at socioeconomic impacts beyond local borders. After this exercise, four (of the original seven) countries were selected as priority Livestock CRP countries that could be the primary focus of Window1/2 funding for the remainder of the CRP funding cycle (2019–2021). The selection represented a consolidation of effort in Africa to East Africa (three countries), and in Asia to the Southeast region (one country). It is important to note, however, that other analyses and considerations went into the final decision-making, including business case–type assessments. As such, the decision was not fully determined by the foresight projections, but they were considered as one of several key inputs as is often advised. (“mature outcome”) The International Rice Research Institute (IRRI) participated in the initial Global Futures and Strategic Foresight project. Foresight modeling has been adopted even more strongly since the adoption of IRRI’s new strategic plan for 2017–2025 (IRRI, 2017). A new Agri-Food Policy unit has been established with the goal of focusing on economic modeling and social science research. This unit will conduct market and situation analyses as well as multi-scale scenario analysis and foresight modeling. The goal of this unit is to help identify research needs and provide information for policymakers. The unit is expected to work closely with PIM researchers and use partial equilibrium (PE) and computable general equilibrium (CGE) modeling tools. While IRRI has long been a member of the AgMIP community, this partnership is expected to be reenergized under its new strategy and the newly established Agri-Policy unit. (“early outcome”) The International Water Management Institute (IWMI) joined the PIM-supported foresight modeling community of practice relatively late in the period covered by this report but has contributed to several of the joint foresight modeling activities described in this report. (“early outcome”) WorldFish has been engaged with Foresight Modeling since 2013. This partnership has been instrumental in prioritizing research by the Center and by the FISH CRP. With donor buy-in and stakeholder input, scenarios were developed for Zambia, Indonesia, Egypt, Tanzania, and Bangladesh. Work on Nigeria will start in 2019 and Myanmar is also being considered. The tools used are IMPACT for regional ASEAN and Africa analysis and country-level tools that have been developed by the Center. (“early outcome”)

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Informing global debate and dialogue on sustainable diets PIM’s core outputs have contributed to numerous articles, many of which are considered in the preceding section on citations analysis. One such article—the 2018 Nature article coauthored by Marco Springmann and others, including staff of IFPRI and Bioversity—considers various options (reduction of food waste, use of technologies, and shifts to more plant-based diets) for reducing the environmental impacts of the food system, based on projections produced by the IFPRI team using the IMPACT model. The article was published in October 2018; this was toward the end of the period for which we ran a citations analysis5 and therefore we reported relatively few citations for it in the section on citations analysis. As of October 2019 (one year after publication), the article had 179 citations and an Altmetric score of 2271; this indicates a greater level of visibility for PIM-supported foresight modeling than had been achieved by previous articles. The article has contributed greatly to the current global debate and dialogue on sustainable diets. The PIM-supported foresight modeling results from the Springmann et al. (2018) article were used in a report by the EAT-Lancet Commission on Healthy Diets from Sustainable Food Systems (Willett et al., 2019). The EAT-Lancet report was published in February 2019 and as of mid-October 2019 it had 339 citations according to Google Scholar. (“early outcome”) The EAT-Lancet Commission convenes leading global researchers from diverse scientific disciplines. Their mission is to advance the development of scientific targets for healthy diets from environmentally sustainable food production. The Stockholm Resilience Centre houses the EAT-Lancet Commission secretariat and co-leads the Commission’s research activities with EAT. As part of the EAT-Lancet Commission, work is being conducted on targets for healthy and sustainable food systems. These targets define a safe operating space—the upper and lower limits for adequate diets and food production—that ensures human health and environmental sustainability. These upper and lower limits include the amount of individual foods that should be consumed, the amount of land use, biodiversity loss, water use, greenhouse gas emissions, plus nitrogen and phosphorous pollution that can stem from food production. The report provides scientific targets to guide actors in the development of actions in line with achieving their SDGs and Paris Agreement goals. (“early outcome”)

As part of the EAT-Lancet Commission work, a 20-country team is building independent national pathways to healthy and sustainable food systems by 2050. The pathways are integrated into a global model used to assess whether the sum of national efforts yields global targets. The national pathways are iteratively adjusted in an attempt to achieve global goals. This work is being influenced by the Food and Land Use Coalition, particularly the Food, Agriculture, Biodiversity, Land and Energy (FABLE) team. While many of the Commission’s findings and recommendations have been controversial, they have helped to generate a high level of visibility and attention to questions relating to healthy and sustainable diets. (“early outcome”)

5 The citations search was conducted in January of 2019.

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Global partnerships in foresight analysis Agricultural Model Intercomparison and Improvement Project (AgMIP): AgMIP is an international effort that links the climate, crop, and economic modeling communities to produce improved crop and economic models as well as climate impact projections for the agriculture sector. IFPRI and other centers engaging in the PIM-funded foresight modeling provide intellectual leadership and expertise in the integration of economic modeling and crop modeling as part of the AgMIP community. PIM-supported foresight modeling has been instrumental in linking crop models and economic models to examine social welfare at the national as well as at the household level using household/community-scale economic models. This partnership has provided an analytic platform that enables analysis under future uncertainties associated with climate change. By doing so, PIM-supported foresight modeling has enabled better communication of outcomes of the crop modeling community. In other words, the PIM-supported foresight team has helped communicate uncertainties associated with yield variations into economic terms. This activity has highlighted modeling limitations and differences across models. Thus, the PIM-supported foresight group has not only helped link crop models to economic models and improved communication of the outcomes of the AgMIP community, but they have also provided an incentive to continuously seek to improve forecasts, crop models, and model interpretation. PIM-supported foresight modeling has also played a role in getting global economic modeling groups to collaborate and compare different economic models. Improved global connections have emerged out of partnerships between AgMIP and PIM-supported foresight modelers. Decisions were made by cooperating global economic modelers to improve their respective models after better understanding the implications of different assumptions in their models. In addition, decisions were made to improve existing crop models and engage in new crop models such as vegetables. A recent success story is the establishment of dedicated national and regional AgMIP activities, such as in India and Pakistan. (“mature outcome”) The impact of PIM-supported foresight modeling and AgMIP collaboration is evident from the changes made to existing crop models and the modeling efforts that have been started for new crops. The latter include a USDA-NIFA grant through which IFPRI is collaborating with crop modelers at the University of Florida and elsewhere to incorporate specialty crops in the crop modeling package (DSSAT) for use in the economic models under climate uncertainties, including lifecycle assessment of these crops. (“early outcome”) Foresight 4 Food: Bioversity, IFPRI, and other CGIAR centers are actively involved in the Foresight4Food initiative. The initiative was established as the result of a scoping meeting in Oxford in 2017; participants included key international organizations, leading research institutes, development agencies, and business representatives. The Secretariat is housed at the Food Systems Group at the University of Oxford's Environmental Change Institute (ECI). It aims to provide a mechanism for better analysis and synthesis of key trends and possible futures in global food systems and to support more informed and strategic dialogue between the private sector, government, science, and civil society. (“early outcome”)

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Oxford University: IFPRI’s foresight modeling group and the Martin School at Oxford University began an informal collaboration in 2011. This partnership was subsequently formalized with support from the Wellcome Trust. The partnership focuses on analyzing options for sustainably meeting food and nutritional needs, while keeping economic and agricultural development within planetary boundaries. The partnership uses IMPACT analysis for simulating scenarios under different SSPs and food consumption patterns. The Oxford team’s contribution has been in linking the different food consumption patterns to health outcomes. The partnership uses IMPACT for scenario analyses and information on agricultural commodities, the Global Dietary Database from Tufts University for dietary information, and Oxford University’s expertise for connecting diets to health outcomes. An Oxford-IFPRI workshop convened in November 2018 focused on intervention options (including but not limited to reductions of meat consumption) for achieving desired health outcomes and keeping within planetary boundaries. (“early outcome”) University of Florida: In order to scale up spatial crop modeling work, simpler versions of DSSAT models have been developed by the University of Florida in collaboration with IFPRI and PIM. Analysis has been conducted to compare the simpler DSSAT models with the more complex original models for rice, maize, wheat, and potato, and a paper on this work is currently under review. Prototypes for simple DSSAT models for potatoes and tomatoes have been developed and plans are underway for other fruit and vegetables. (“mature outcome”) Trainings Another contribution of the IFPRI-led work on foresight modeling has been in providing training for researchers who have subsequently become key partners at other CGIAR centers, academic institutions, and other national institutions. Numerous training initiatives have taken place in Africa south of the Sahara, Asia (East, South, Southeast, and Central), and Latin America and the Caribbean. With the objective of creating a critical mass of African agricultural researchers and development actors, CIMMYT has designed a series of capacity building workshops on decision support tools that assist in making appropriate agricultural decisions. As part of its work under PIM and the GFSF project, CIMMYT organized training workshops titled “Crop and Bioeconomic Modeling under Uncertain Climate” for three consecutive years (2014, 2015, and 2016). In 2016, 16 participants (4 women) from seven African countries attended the training workshop. (“early outcome”)

Conclusions and areas for future work This report provides evidence of the wide use of and outcomes from PIM-supported foresight modeling work. Key findings are as follow.

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The examination of downloads shows that the more frequently downloaded IMPACT datasets are those providing foresight related to climate change outcomes. Most of those downloading such data are affiliated with a university. Only four CGIAR centers are among the 214 institutions downloading foresight data. References produced by PIM-supported foresight most often address at least one of the following five topics: agricultural production, climate change, farming methods, natural resource use, or food security. They cover mostly Africa and Asia or the entire world with little regional coverage of Europe, Latin America and the Caribbean, or North America. Articles citing foresight references are more likely to be global or cover Africa, Asia, or Latin America and the Caribbean. On average citing articles mention the reference 1.5 times and most often that mention is made in the introduction or body of the article, not in the conclusion. Google Scholar citations and Altmetric attention scores are highest for global studies and for externally published references that contain at least one IFPRI author. The survey results show a higher response rate for those who have interacted with the foresight team and thus indicate the dedication of the modeling community to foresight. Survey respondents were likely affiliated with a university (37% of respondents) or a CGIAR center (30%). Most respondents (60%) learned of foresight by participating in a foresight project, study, or program. A third of respondents used foresight products to inform decision-making. Publication topics that were especially useful included descriptions of the IMPACT or other foresight models (61% of respondents) and climate change (72%). Most respondents (68%) had used CGIAR Foresight Modeling or knew of colleagues at their workplace who had used it to inform activities or decisions, and 12 respondents noted that outcomes had resulted. Fifty percent of respondents were aware of CG foresight modeling being used to inform decisions of other organizations. Eleven respondents noted that outcomes had resulted. Interviews and email correspondence were undertaken with such respondents in order to gain a better description of the outcomes. This was combined with interviews that had been conducted one year previously. PIM-supported foresight work has been used to inform the decision-making of multilateral organizations and donors; these include the ADB, CAC, FAO, IADB, IFAD, Bill & Melinda Gates Foundation, OECD, and the World Bank. It has also been used by national governments including in Cambodia, Colombia, Dominican Republic, Indonesia, Philippines, South Africa, United Kingdom, United States, Uzbekistan, and Viet Nam. The work has informed the 2017–2022 CGIAR Research Program portfolio, with the modeling included in proposals for the CRPs on Livestock; Roots, Tubers and Bananas; Wheat; Grain Legumes and Dryland Cereals; and FISH. PIM-supported foresight work has informed the global debate on sustainable diets and global partnerships have been established for conducting foresight analysis. Finally, numerous training initiatives have also been undertaken, expanding the community of practice of foresight modelers. Mature outcomes resulting from PIM-supported foresight work include:

• CAC and CIAT used foresight to help develop a climate-smart agricultural policy for the Central America region.

• The OECD has undertaken more long-term projections work as a result of collaboration on foresight modeling.

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• Results from foresight have helped design large regional World Bank operations in the livestock and irrigation sectors as well as some of the Bank’s natural resource management programs.

• In Cambodia, a climate-smart agriculture project was funded. • Colombia included AFOLU in its NDC for the UNFCCC. • The national agriculture research service in the Dominican Republic has proposed

including climate change as a line item in its budget. • The government of Indonesia worked with ICRAF and used foresight modeling to help

shape its Medium Term Development Plan. • In the Philippines, foresight analysis led to reform of rice trade policy and informed the

Philippine Development Plan of 2017–2022. • In the United States, foresight modeling informed USAID’s Global Food Security

Research Strategy and helped to safeguard resources allocated to agricultural research and development within Feed the Future programming.

• CIAT’s use of foresight tools has helped the World Bank identify the best option for investing bank funds in climate smart agriculture in Burkina Faso, Côte d’Ivoire, Ghana, and Mali.

• CIMMYT’s work has helped establish wheat as a priority crop in Africa. • ILRI’s foresight work helped to identify priority countries for the livestock CRP.

This assessment has identified numerous uses of and outcomes from PIM-supported foresight modeling. Going forward, it could be useful to undertake analysis to better understand what factors helped to ensure that outcomes resulted from the use of PIM-supported foresight modeling. Factors contributing to outcomes may include the involvement of national policymakers or other decision-makers in trainings or the use of results in high profile publications of international organizations.

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CGIAR, 2016a. Proposal Livestock Agri-Food Systems. CGIAR Research Program. Overall and flagship narratives. Available at: https://cgspace.cgiar.org/bitstream/handle/10947/4398/2.Livestock%20-%20CRP%20and%20FP%20Narratives%20Proposal%202017-2022.pdf?sequence=1&isAllowed=y CGIAR, 2016b. Roots, Tubers and Bananas. Proposal 2017 – 2022. Volume 1. Available at: https://cgspace.cgiar.org/handle/10947/4286 CGIAR, 2016c. Wheat Agri-food systems proposal. 2017 – 2022. Available at: https://cgspace.cgiar.org/bitstream/handle/10947/4296/2-WHEAT%20Full%20Proposal.pdf?sequence=1&isAllowed=y CGIAR, 2016d. CGIAR research program on grain legumes and dryland cereals 2017 – 2022. Available at: https://cgspace.cgiar.org/bitstream/handle/10947/4383/2.%20GLDC%20-%20CRP%20and%20FP%20Narratives%20Proposal%202017-2022.pdf?sequence=1&isAllowed=y CGIAR, 2016e. CGIAR Research program on fish agri-food systems. Available at: https://cgspace.cgiar.org/bitstream/handle/10947/4240/1-FISH%20Full%20Proposal.pdf?sequence=1&isAllowed=y CGIAR, 2018. CGIAR System 3-Year Business Plan (2019-2021) Companion Document Initiative on “Crops to End Hunger” Strategy and Options for CGIAR Support to Plant Breeding. SC7 Meeting Agenda Item 4. Available at: https://storage.googleapis.com/cgiarorg/2018/11/SC7-B_Breeding-Initiative-1.pdf de Haan, Cees. 2016. Prospects for Livestock-Based Livelihoods in Africa's Drylands. World Bank Studies. Washington, DC: World Bank. Available at: https://openknowledge.worldbank.org/handle/10986/24815

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Appendix A: Affiliation of individuals downloading datasets in IMPACT Dataverse CGIAR centers (4): AfricaRice; CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS); International Center for Tropical Agriculture (CIAT); International Food Policy Research Institute (IFPRI) United Nations (6): FAO Saudi Arabia Riyadh; UN–FAO; United Nations Population Fund (UNFPA); United Nations (UN); United Nations Framework Convention on Climate Change (UNFCC); World Food Program (WFP) Other international organizations (6): African Development Bank; Centre population et développement (CEPED); International Institute for Applied Systems Analysis (IIASA); Organisation for Economic Co-operation and Development (OECD); World Bank Group; World Trade Organization (WTO) Government entities (13): Chinese Academy of Agriculture Sciences; Die Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH; Embrapa; Fisheries and Oceans Canada; High Commission for Planning Morocco; Indian Council of Agricultural Research; Indian Council of Agricultural Research (ICAR); Indian Institute of Oilseeds Research; Instituto Tecnológico Autónomo de México (ITAM); Japan International Cooperation Agency (JICA); Ministry of Agricultural Development, Government of Nepal; Ministry of Agriculture; State Office for Health and Social Affairs, Berlin; Swiss Agency for Development and Cooperation (SDC) Universities and institutions of higher learning (114): Accadis University of Applied Sciences; Addis Adaba Science and Technology University; Aix-Marseille University; American University (AU); Anadolu University; Andhra University; Antioch University, New England; Beijing Normal University; Boston University; Brawijaya University; British University Egypt; Cairo University; Centurion University of Technology and Management; Chuo University; Copenhagen University; Deakin University in Melbourne, Australia; Duke University; Facultad Latinoamericana de Ciencias Sociales (FLACSO); George Mason University; Georgetown University; Gothenburg University; Higher School of Economics; Howard University; Imperial College London; Indian Instittue of Technology-IIT Delhi; Indian Institute of Technology-IIT Indore; Johns Hopkins University; KULeuven; Kyoto University; Lady Irwin College Delhi University; Laval University Quebec City, Canada; London School of Economics (LSE); Massey University, New Zealand; Meisei University, Tokyo Japan; Mekelle Universit; Middle East Technical University; MIT- Massachusetts Institute of Technology; Murray State University; Nanjing Agricultural University; Nanjing University of Information Science and Technology

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(NUIST); Newcastle University; North Carolina State University; Northeastern University; Oakland Community College; Oregon State University; Oxford; Politecnico di Milano; Princeton University; Rutgers University; S. Rajaratnam School of International Studies (RSIS); Sciences Po; Shahjalal University of Science & Technology (SUST); Shahjalal University of Science and Technology Sylhet Bangladesh; Shenzhen University; Sher-e-Bangla Agricultural University; Solent University Southampton; Southwestern University of Finance and Economics (SWUFE); SRUC - Scotland's Rural College; Tamil Nadu Agricultural University (TNAU); Technical University Munich; Technical University of Denmark (DTU); The University of Tokyo; The University of Western Australia; Trinity College Dublin Ireland; Tsinghua University; Universidad de Occidente; Universidad EAFIT; Universidad Politecnica de Madrid; Universidade Federal do Rio Grande do Sul (UFRGS); Università degli Studi di Firenze; Universität Hohenheim; Universität Wien; Université d'Avignon (France); Université Le Havre; Universiteit Antwerpen; University Carlos III; University College London; University of Aberdeen; University of Arkansas; University of Bath; University of Birmingham; University of British Columbia; University of Calgary; University of California, Berkeley; University of California, Santa Barbara; University of Cape Town; University of Chicago; University of Cocody, Abidjan; University of Cologne; University of Denver; University of Dschang; University of Dundee; University of Durham; University of Fort Hare; University of Ghana; University of Gothenburg; University of Haifa; University of Illinois, Urbana-Champaign; University of Melbourne; University of Notre Dame; University of Oxford; University of Reading; University of Saint Joseph (USJ); University of Saskatchewan; University of Sydney (USYD); University of Toronto; University of Washington; University of Waterloo; University of Wisconsin-Madison; Vijayanagara Sri Krishnadevaraya University (VSKU); Vrije Universiteit Amsterdam; Wageningen University; Western Governors University (WGU); Winthrop University Other (71): Abt Associates Inc.; ATA; Autonomy Capital; Beyond Ratings; BHP Billiton; BTU; Cargill; Centre for Energy study; CES; China National Cereals, Oils and Foodstuffs Corporation (COFCO Group); CNA; Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET); Council for Scientific and Industrial Research (CSIR)- Science and Technology Policy Research Institute (STEPRI); DebreTabor; Denver Seminary; Douglas Kazibwe; Energy Innovation; Environmental Resources Management (ERM); Erasmus; Estado de la Nacion; FEEM; Finance in Motion; Fine Films; Gates Foundation; GBS Finance; GRI; Gro Intelligence; HBAU; Honey Inco; HSE; IDSC; IEAD; IFO; iis; IMEMO RASN; Institute of Water Engineering and Environment (IIAMA); International Reference Centre for the Life Cycle of Products, Processes and Services (CIRAIG); Ipsos; Iranian Fisheries Organization; Josic Media LLC; Krishi Uddhyamshala Pvt Ltd; Laboratoire d'Analyse et de Modelisation des Politiques Economiques du Centre de Recherche pour le Developpement; Mars Incorporated; MCL; Metropol; Minerva Schools at KGI; MU; Natural Resources and Environmental Research Center (NRERC); Nomura Research Institute; oss; Paliz Agriculture; Plenty International; Podium Data Inc; Potsdam Institute for Climate Impact Research (PIK); Regional Collaboration Centre (RCC) - BOAD; Rise Against Hunger; SISCA; Socio; South Pole; STEMI; Sun institute of agricultural sciences; The Breakthrough Institute; The Economist; Tohmatsu Innovation Co. Ltd; United States Institute of Peace (USIP); Vizry Group; VMI; Voloridge Investments; Western Asset; World Agricultural Economic and Environmental Services (WAEES) and WSP.

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Appendix B: References included in detailed citations analysis

Full citation

Altmetric Score

Google Scholar Citations

Externally published references

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Springmann, Marco; Mason-D’Croz, Daniel; Robinson, Sherman; Wiebe, Keith D.; and Scarborough, Peter. 2016. The health co-benefits of a global greenhouse-gas tax on food. Presented at the 19th Annual Conference on Global Economic Analysis, Washington DC, USA. https://www.gtap.agecon.purdue.edu/resources/download/8059.pdf NA 0

Springmann, Marco; Mason-D’Croz, Daniel; Robinson, Sherman; Wiebe, Keith D.; Godfray, Charles; Rayner, Mike; and Scarborough, Peter. 2017. Health-motivated taxes on red and processed meat: a modelling study on optimal tax levels and health and climate-change co-benefits. Global Trade Analysis Project Resource 5407: https://www.gtap.agecon.purdue.edu/resources/res_display.asp?RecordID=5407 NA 0 Supplement to: Springmann, Marco; Mason-D’Croz, Daniel; Robinson, Sherman; Garnett, Tara; Godfray, H. Charles J.; Gollin, Douglas; Rayner, Mike; Ballon, Paola; and Scarborough, Peter. Global and regional health effects of future food production under climate change: A modelling study. The Lancet 387(10031), 7 - 13 May 2106: 1937 - 1946. http://dx.doi.org/10.1016/S0140-6736(15)01156-3 NA 119

van der Velde, Marijn; See, Linda; You, Liangzhi; Balkovič, Juraj; Fritz, Steffen; Khabarov, Nikolay; Obersteiner, Michael; Wood, Stanley. 2013. Affordable nutrient solutions for improved food security as evidenced by crop trials. PLoS ONE 8(4): e60075. http://dx.doi.org/10.1371/journal.pone.0060075 9 22 von Lampe, Martin; Willenbockel, Dirk; Ahammad, Helal; Blanc, Elodie; Cai, Yongxia; Calvin, Katherine; Fujimori, Shinichiro; Hasegawa, Tomoko; Havlik, Petr; Heyhoe, Edwina; Kyle, Page; Lotze-Campen, Hermann; Mason d'Croz, Daniel; Nelson, Gerald C.; Sands, Ronald D.; Schmitz, Christoph; Tabeau, Andrzej; Valin, Hugo; van der Mensbrugghe, Dominique; and van Meijl, Hans. 2014. Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison. Agricultural Economics 45(1): 3-20. http://dx.doi.org/10.1111/agec.12086 15 152

WorldFish. 2015. Envisioning possible futures for fish production in Indonesia. Colombo, Sri Lanka: WorldFish. http://www.worldfishcenter.org/content/envisioning-possible-futures-fish-production-indonesia NA 0 Zhu, Tingju; Ringler, Claudia; Iqbal, M. Mohsin; Sulser, Timothy B.; and Goheer, M. Arif. 2013. Climate change impacts and adaptation options for water and food in Pakistan: Scenario analysis using an integrated global water and food projections model. Water International 38(5): 651-669. Special Issue on Water for food security: Challenges for Pakistan. http://dx.doi.org/10.1080/02508060.2013.830682 1 20

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Internally published references Anderson, Weston; You, Liangzhi; Wood, Stanley; Wood-Sichra, Ulrike and Wu, Wenbin. 2014. A comparative analysis of global cropping systems models and maps. IFPRI Discussion Paper 1327. Washington, D.C.: International Food Policy Research Institute (IFPRI). 0 9 Cenacchi, Nicola; Lim, Youngah; Sulser, Timothy B.; Islam, Shahnila; Mason-D’Croz, Daniel; Robertson, Richard D.; Kim, Chang-Gil; and Wiebe, Keith D. 2016. Climate change, agriculture, and adaptation in the Republic of Korea to 2050: An integrated assessment. IFPRI Discussion Paper 1586. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/130999 0 0 Koo, Jawoo; Cox, Cindy M. 2014. Effects of rainfall variability on maize yields. In Atlas of African agriculture research and development: Revealing agriculture's place in Africa. Sebastian, Kate, Ed. Pp. 44-45. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/128740 http://dx.doi.org/10.2499/9780896298460_19 0 6 Koo, Jawoo; Cox, Cindy M. 2014. Rainfall data comparison. In Atlas of African agriculture research and development: Revealing agriculture's place in Africa. Sebastian, Kate, Ed. Pp. 50-51. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/128751 http://dx.doi.org/10.2499/9780896298460_22 0 0 Koo, Jawoo. 2014. Maize yield potential. In Atlas of African agriculture research and development: Revealing agriculture's place in Africa. Sebastian, Kate, Ed. Pp. 58-59. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/128747 http://dx.doi.org/10.2499/9780896298460_25 0 0 Nelson, Gerald C. and van der Mensbrugghe, Dominique. 2014. Public sector agricultural research priorities for sustainable food security: Perspectives from plausible scenarios. IFPRI Discussion Paper 1339. Washington, D.C.: International Food Policy Research Institute (IFPRI) and Food and Agriculture Organization (FAO). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/128123 0 6 Robinson, Sherman and Gueneau, Arthur. 2013. Economic evaluation of the Diamer-Basha dam: Analysis with an integrated economic/water simulation model of Pakistan. PSSP Working Paper 14. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/128002 0 2 Robinson, Sherman et al. 2015. Climate change adaptation in agriculture: Ex ante analysis of promising and alternative crop technologies using DSSAT and IMPACT. IFPRI Discussion Paper 1469. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/129694 0 12 Robinson, Sherman; Mason d'Croz, Daniel; Islam, Shahnila; Sulser, Timothy B.; Robertson, Richard D.; Zhu, Tingju; Gueneau, Arthur; Pitois, Gauthier; and Rosegrant, Mark W. 2015. The International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT): Model description for version 3. IFPRI Discussion Paper 1483. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/129825 3 67

Rosegrant, Mark W.; Koo, Jawoo; Cenacchi, Nicola; Ringler, Claudia; Robertson, Richard D.; Fisher, Myles; Cox, Cindy M.; Garret, Karen; Perez, Nicostrato D.; Sabbagh, Pascale. 2013. Food security in a world of growing natural resource scarcity: The role of agricultural technologies. http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/127896 0 111

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Full citation

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Google Scholar Citations

Internally published references, continued

Rosegrant, Mark W.; Koo, Jawoo; Cenacchi, Nicola; Ringler, Claudia; Robertson, Richard D.; Fisher, Myles; Cox, Cindy M.; Garret, Karen; Perez, Nicostrato D.; Sabbagh, Pascale. 2013. Segurança alimentar em um mundo em crescente processo de escassez de recursos naturais. http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/128119 0 2

Rosegrant, Mark W.; Koo, Jawoo; Cenacchi, Nicola; Ringler, Claudia; Robertson, Richard D.; Fisher, Myles; Cox, Cindy M.; Garrett, Karen; Perez, Nicostrato D.; Sabbagh, Pascale. 2014. Synopsis of Food security in a world of natural resource scarcity: The role of agricultural technologies. http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/128023 NA 0

Rosegrant, Mark W.; Perez, Nicostrato D.; Pradesha, Angga; Thomas, Timothy S. 2015. The economywide impacts of climate change on Philippine agriculture. Policy Note 1. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/129544 0 2

Thomas, Timothy S. US maize data reveals adaptation to heat and water stress. 2015. IFPRI Discussion Paper 1485. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/129841 0 1

Thomas, Timothy S.; Pradesha, Angga; and Perez, Nicostrato. 2016. Agricultural growth, climate resilience, and food security in the Philippines: Subnational impacts of selected investment strategies and policies. Climate Change Policy Note 2. Washington, D.C.: International Food Policy Research Institute (IFPRI). https://doi.org/10.2499/9780896292468 0 1

Thomas, Timothy S.; Pradesha, Angga; Perez, Nicostrato D. 2015. Agricultural growth and climate resilience in the Philippines: Subnational impacts of selected investment strategies and policies. Policy Note 2. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/129543 2 4

Wiebe, Keith D.; Stads, Gert-Jan; Beintema, Nienke M.; Brooks, Karen; Cenacchi, Nicola; Dunston, Shahnila; Mason-D’Croz, Daniel; Sulser, Timothy B.; and Thomas, Timothy S. 2017. West African agriculture for jobs, nutrition, growth, and climate resilience. IFPRI Discussion Paper 1680. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://ebrary.ifpri.org/cdm/ref/collection/p15738coll2/id/131462 0 2

Wiebe, Keith D.; Sulser, Timothy B.; Mason-D’Croz, Daniel; and Rosegrant, Mark W. 2017. The effects of climate change on agriculture and food security in Africa. In A thriving agricultural sector in a changing climate: Meeting Malabo Declaration goals through climate-smart agriculture, eds. Alessandro De Pinto and John M. Ulimwengu. Chapter 2, pp. 5-21. Washington, D.C.: International Food Policy Research Institute (IFPRI). http://dx.doi.org/10.2499/9780896292949_02 0 2

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Appendix D: Survey SURVEY of experts assessing the use and outcomes of CGIAR Foresight Modeling Introduction 1. How would you describe your organization? Part of the CGIAR Other international organization/ development agency Non-governmental organization National government National association or civil society organization Academic institution Research institution/ think tank For-profit/ company/ private sector Independent Other 2. How would you describe your position in the organization? Management Staff Consultant Professor Researcher Student Intern Other 3. Have you heard of CGIAR Foresight Modeling products? Yes No 4. How did the CGIAR Foresight Modeling project, tools or products come to your attention? Check all that apply. Recommended by a colleague IFPRI newsletter/ blog PIM or other CRP newsletter/ blog Through a research publication that references CGIAR Foresight Modeling Through published media that discussed CGIAR Foresight Modeling Learned about CGIAR Foresight Modeling at a conference or workshop Web search Was contacted directly by members of the CGIAR Foresight Modeling team Through collaboration in a project/ study/ program Other 5. Have you used CGIAR Foresight Modeling products (e.g. articles, discussion papers, reports, datasets, etc.) or other foresight modeling products in your work since 2012? Check all that apply. No. I have neither used CGIAR Foresight Modeling products nor any other foresight modeling products. I have used CGIAR Foresight Modeling products (e.g., articles, discussion papers, reports, datasets, etc). I have used other foresight modeling products as specified:

6. Have you personally contributed to the CGIAR Foresight Modeling work? If yes, please check all that apply. If no, please skip to the next question.

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Yes, as a modeler working on some of the tools Used tools to design scenarios and perform my own foresight analyses Participated as a foresight modeling trainer in a workshop Participated as a foresight modeling trainee in a workshop Organized/ participated in other events or presentations related to foresight modelling Other (please specify) 7. Have you used CGIAR Foresight Modeling project and its products to contribute to your own foresight analysis work? If no, skip to the next question. If yes, check all of the following ways in which you used CGIAR Foresight Modeling outputs (data and/ or reports). To inform specific policy-making. To prepare reports and publications. As a source of information regarding future trends in agriculture As a source of information regarding specific promising agricultural technologies. To inform decisions made by my organization, including priority setting. To perform research prioritization for my institution or other clients. To learn methodologies that may help strengthen the modeling capacity of my institution. To develop contacts with a global network of modelers with similar goals. Other (please specify)

Impact of CGIAR Foresight Modeling on your decision-making 8. Have you used CGIAR Foresight modeling products to inform decision making? If no, skip to the next question. If yes, check any of the following types of CGIAR Foresight Modeling products that have been especially valuable to you. CGIAR Foresight modeling publications on tools like IMPACT and other models CGIAR Foresight modeling publications on the impact of climate change on agriculture and food security CGIAR Foresight modeling publications on promising future agricultural technologies CGIAR Foresight modeling of crop production CGIAR Foresight modeling publications that prioritize different types of agricultural research Other (please specify) 9. In what other ways has CGIAR Foresight Modeling been valuable? That is, what does it offer (in addition to the information listed in questions 6 or 7) that has been useful for your work? 10. What other tool or information would be most valuable to you, but is not available through current CGIAR Foresight Modeling approaches and outputs? 11. Have you or your colleagues used CGIAR Foresight Modeling to inform your own work activities (eg. research, projects or advocacy) or decisions regarding policies, programs, strategies and/ or expenditures? Yes No 12. Please describe which activities or decisions were affected by CGIAR Foresight Modeling by providing (i) a clear description of the activity or decision (ii) identification of the government or organization where you work (iii) identification of the decision maker and (iv) a description of how the CGIAR Foresight Modeling output was used. (i) a clear description of the activity or decision (ii) identification of the government or organization where you work as well as the decision maker (iii) identification of the decision maker (iv) a description of how the CGIAR Foresight Modeling output was used.

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13. Have there been any visible/ tangible impacts (e.g. changes in behavior, policy or investment decision) as a result of such activities or decisions? Yes No 14. Please describe the visible/ tangible impacts resulting from such activities or decisions and provide any available written evidence such as a study, citation, interview, etc. visible tangible impact(s) written evidence (eg. citation) 15. Why do you think there have been no visible/ tangible impacts as a result of such activities or decisions? What future impacts might be anticipated as a result of such activities or decisions? Please describe. why no visible impacts what future impacts anticipated 16. Is there someone other than yourself (a colleague or collaborator of yours) with whom we should follow up to get further information on this? If so, please provide name, email and if available, phone number with country code. name email phone number with country code Use of CGIAR Foresight Modeling by national governments, international organizations or other groups 17. Are you aware of decision makers from a national government(s), international organization(s) or other group(s) (other than your own place of employment) using or being exposed to CGIAR Foresight Modeling products? Yes No 18. Please specify which part(s) of the government of what country(s) or please name the organization(s) or other group(s). 19. Are you aware of any of the governments, organizations or groups that you listed in question 17 using CGIAR Foresight modeling work as a basis for changing their activities or decisions regarding their policies, programs, strategies or expenditures/ investments? Yes No 20. Please describe one of the more impactful changes in activity or decisions made by the government, organization or other group as a result of their using CGIAR Foresight Modeling, including (i) a description of the activity or decision (ii) identification of the government, organization or other group (iii) identification of the decision maker and (iv) a description of how the CGIAR Foresight Modeling output was used. (i) a description of the activity or decision (ii) identification of the government, organization or other group (iii) identification of the decision maker (iv) a description of how the CGIAR Foresight Modeling output was used

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21. Have there been any visible/ tangible impacts (e.g. changes in behavior, policy or investment decision) as a result of the change in activity or decision? If yes, please describe the impact(s) and provide any available written evidence such as a study, citation, interview, etc. If no, move to the next question. describe the impact(s) provide any written evidence 22. Is there a contact person at the government, international organization or other with whom we might follow up if clarification is needed? If yes, please provide contact information below. This information will only be seen by the researcher and a few IFPRI staff members. Name email phone number with country code 23. Are there additional examples of the governments, organizations or groups that you listed in question 17 using CGIAR Foresight modeling work as a basis for changing their activities or decisions? Yes No 24. Please describe this change in activity or decision made by a government, organization or other group as a result of their using CGIAR Foresight Modeling, including (i) a clear description of the activity or decision (ii) identification of the government, organization or other group (iii) identification of the decision maker and (iv) a description of how the CGIAR Foresight Modeling output was used. (i) a clear description of the activity or decision (ii) identification of the government, organization or other group (iii) identification of the decision maker (iv) a description of how the CGIAR Foresight Modeling output was used 25. Have there been any visible/ tangible impacts (eg. changes in behavior, policy or investment decisions) as a result of the change in activity or decision? If yes, please describe the impact(s) and provide any available written evidence such as a study, citation, interview, etc. If no, move to the next question. describe the impact(s) provide any written evidence 26. Is there a contact person at the government, international organization or other with whom we might follow up to query further? If yes, please provide contact information below. This information will only be seen by the researcher and a few IFPRI staff members. name email phone number with country code Final Section 27. May we call you with some follow up questions about the impact of CGIAR Foresight Modeling? If yes, please provide your full name, e-mail and if available, skype contact and a phone number with country code. This information will be anonymous to all but the researcher and a few IFPRI staff members. Your full name Your email Your skype ID Your phone number with country code

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Appendix E: Key Stakeholders Interviewed Interviews of key stakeholders Contact Affiliations email Interview Jim Jones NSF & University of Florida jwjones@nsf.gov 10/31/18 Michael Morris World Bank mmorris3@worldbank.org 11/1/18 Tim Thomas IFPRI tim.thomas@cgiar.org 11/2/18 Prager, Steven CIAT S.Prager@cgiar.org 11/2/18 Mark Rosegrant IFPRI M.ROSEGRANT@cgiar.org 11/9/18 Claudia Ringler IFPRI c.ringler@cgiar.org 11/12/18 Arega Alene IITA A.ALENE@cgiar.org 11/14/18 Chin Yee Chan WorldFish C.Chan@cgiar.org 11/14/18 Sika Gbegbelegbe IITA S.Gbegbelegbe@cgiar.org 11/14/18 Elisabetta Gotor Bioversity e.gotor@cgiar.org 11/14/18 Gideon Kruseman CIMMYT G.KRUSEMAN@cgiar.org 11/14/18 Swamikannu Nedumaran ICRISAT S.Nedumaran@cgiar.org 11/14/18 Nhuong Tran WorldFish N.Tran@cgiar.org 11/14/18 Alan Rennison BMGF alan.rennison@gatesfoundation.org 11/15/18 Aslihan Arslan IFAD a.arslan@ifad.org 11/27/18 Eric Witte USAID ewitte@usaid.gov 11/27/18 Jennifer (Vern) Long USAID

jlong@usaid.gov 11/27/18 Guy Hareau CIP G.Hareau@cgiar.org 11/29/18 Dolapo Enahoro ILRI D.Enahoro@cgiar.org 11/30/18 David Hegwood USAID dhegwood@usaid.gov 12/4/18 James Thurlow IFPRI J.Thurlow@cgiar.org 12/4/18 Kamiljon Akramov IFPRI K.AKRAMOV@CGIAR.ORG 12/7/18 Timothy Sulser IFPRI T.Sulser@cgiar.org 12/7/18 David Spielman IFPRI d.spielman@cgiar.org 12/13/18 Rowena Valmonte-Santos IFPRI R.VALMONTE-SANTOS@CGIAR.ORG 12/13/18 Patricia Zambrano IFPRI p.zambrano@cgiar.org 12/13/18 Peter Scarborough Oxford University peter.scarborough@dph.ox.ac.uk 12/19/18 Fabrice De Clerck Bioversity, EAT Forum f.declerck@cgiar.org 1/11/19 Jarilkasin Ilyasov IFPRI j.ilyasov@cgiar.org 1/15/19 Jean Balie IRRI j.balie@irri.org 1/15/19 Ron Sands ERS, USDA rsands@ers.usda.gov 12/20/19

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Appendix E, continued

Follow up phone interviews of and correspondence with survey respondents

Contact Affiliations email Date complete

Maksud Bekchanov University of Bonn mbekchan@uni-bonn.de 10/9/19 Vern Long former USAID vern@worldcoffeeresearch.org 10/11/19 Dolapo Enahoro ILRI d.enahoro@cgiar.org 10/11/19 Alessandro de Pinto IFPRI a.depinto@cgiar.org 10/15/19 Channing Arndt IFPRI c.arndt@cgiar.org 10/16/19 Keith Wiebe IFPRI k.wiebe@cgiar.org 10/17/19 Mark Rosegrant IFPRI m.rosegrant@cgiar.org 10/18/19 Steven Prager CIAT S.Prager@cgiar.org 10/21/19 Carlos Eduardo Gonzalez CIAT c.e.gonzalez@cgiar.org 10/21/19 Stanley Wood BMGF Stanley.Wood@gatesfoundation.org 10/22/19 Guy Hareau CIP g.Hareau@cgiar.org 10/22/19 Diego Pequeno CIMMYT d.pequeno@cgiar.org 10/23/19 Jakob Skoet FAO jakob.skoet@fao.org 10/23/19 Daniel Mason d'Croz former IFPRI Daniel.Mason-D'Croz@csiro.au 10/23/19 Guillaume Gruere OECD Guillaume.GRUERE@oecd.org 10/24/19 Martin von Lampe OECD Martin.VONLAMPE@oecd.org 10/24/19

Marieke Veeger

University for International Cooperation mveeger@uci.ac.cr 10/24/19

Rathana Peou Utrecht University rathana.pvdh@gmail.com 10/24/19 Enoch Kikulwe Bioversity e.kikulwe@cgiar.org 10/24/19