Assessment of outcomes based on the use of PIM-supported foresight modeling work, 2012-2018 - INDEPENDENT REVIEW
←
→
Page content transcription
If your browser does not render page correctly, please read the page content below
INDEPENDENT REVIEW Assessment of outcomes based on the use of PIM-supported foresight modeling work, 2012–2018 Sarah K. Lowder and Anita Regmi November 2019
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
2Acknowledgements
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.
3Introduction
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
4which 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
5from 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
Total
Number
of
Dataset title 2016 2017 2018 downloads
3 192 195
Extended Results from the International Model for Policy
Analysis of Agricultural Commodities and Trade (IMPACT
version 3.2.1) for Sulser et al (2015)
IMPACT Projections of Change in Total Aggregate Cereal 34 45 79
Demand, 2010-2050: Extended Country-level Results for 2017
GFPR Annex IMPACT Trend 2
IMPACT Projections of Demand for Agricultural Products: 70 90 160
Extended Country-level Results for 2017 GFPR Annex IMPACT
Trend 1
128 207 335
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
93 117 210
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
IMPACT Projections of Share of Population at Risk of Hunger: 25 53 78
Extended Country-level Results for 2017 GFPR Annex IMPACT
Trend 3
35 123 37 195
Input Data for the IMPACT Model with Different Future
Production Scenarios for Latin America
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.
6It 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.
7We 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.
8Table 1: Altmetric score and Google Scholar citations for PIM-funded foresight related
references, by publisher
information
not
mean median min max count available
All references
funded by PIM and Altmetric score 55.1 2 0 1,396 60 29
related to
foresight modeling Google Scholar
(89) citations 31.6 9 0 311 89 0
Altmetric score 1.4 0 0 16 20 1
Published Google Scholar
internally (21) citations 17.0 2 0 111 21 0
Altmetric score 82.0 6.5 0 1,396 40 28
Published Google Scholar
externally (68) 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
9Africa; 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
10and 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.
11In 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
12answered 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
13PIM-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
14are 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”)
15Food 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”)
16The 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
17countries, 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
18management 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
19will 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
20You can also read
