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With research staff from more than 60 countries, and offices across the globe, IFPRI provides research-based policy solutions to sustainably reduce poverty and end hunger and malnutrition in developing countries.

researcher spotlight

Emily Schmidt

Emily Schmidt is a Senior Research Fellow in the Development Strategies and Governance Unit. Her most recent research explores household livelihood strategies in Papua New Guinea, including linkages between agriculture, poverty, and nutrition outcomes among rural smallholder farmers.

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Since 1975, IFPRI’s research has been informing policies and development programs to improve food security, nutrition, and livelihoods around the world.

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IFPRI currently has more than 600 employees working in over 80 countries with a wide range of local, national, and international partners.

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Natural Resources and Resilience

Jawoo Koo

Senior Research Fellow

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Bio

Jawoo Koo is a Senior Research Fellow in the Natural Resources and Resilience Unit, part of the Transformation Strategies Department, based in Washington, DC. He has more than 20 years of experience in crop modeling, multidisciplinary geospatial analysis, and integrated modeling analyses for assessing climate and technology impacts on agrifood systems. He leads the CGIAR Research Initiative on Digital Innovation, researching pathways to bridge the digital divide, improve the quality of information systems, and strengthen local digital ecosystem capacities to realize the potential of digital technologies.

Jawoo holds a BA in agricultural biology from Korea University, Seoul, and an MS and a PhD in agricultural and biological engineering from the University of Florida.

Languages: English, Korean

Google Scholar Citations

CV

Publications

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Journal Article

Building Ethiopia’s food security resilience to climate and hydrological change

2025Murgatroyd, Anna; Thomas, Timothy S.; Koo, Jawoo; Strzepek, Kenneth M.; Hall, Jim
Details

Building Ethiopia’s food security resilience to climate and hydrological change

Historically Ethiopia’s food security has been sensitive to climatic variability, but changes in future weather and climate could lead to overall reductions and increased variability in agricultural production, without further adaptation. We present an integrated modelling assessment framework which combines climate, crop, and hydrological modelling to quantify future risks to Ethiopia’s food security. We explore the impacts of 2°C and higher climate change scenarios on water availability and crop yields and simulate how future climate shocks may impact Ethiopia’s food. We consider three adaptations to agricultural management practices (improved seed varieties, increased use of nitrogen fertilizer and supplementary irrigation) and quantify their effectiveness in enhancing the resilience of Ethiopia’s food system to climate and hydrological change by 2050. Results show that, without policy intervention, climate change creates a risk of declining Meher season crop yields across Ethiopia. Under the worst climate change scenario, teff (−12.0%), barley (−6.7%), and wheat (−4.4%) are projected to have the largest decline in average yields, whilst maize (−0.1%) and sorghum (+0.9%) yields are less impacted thanks to more favourable growing conditions. However, the results also indicate that the adaptation options have a bigger beneficial effect than the climate impact. Of the policies evaluated, improved seeds have a relatively greater effect than increased fertilizer use. Supplementary irrigation could help to mitigate increases in crop water requirements under warmer climate conditions and is most effective in drought prone basins and for drought-vulnerable crops. Overall, the results show that locally relevant agricultural policies are necessary to build Ethiopia’s food system resilience to climate and hydrological change by the mid-century.

Year published

2025

Authors

Murgatroyd, Anna; Thomas, Timothy S.; Koo, Jawoo; Strzepek, Kenneth M.; Hall, Jim

Citation

Murgatroyd, Anna; Thomas, Timothy S.; Koo, Jawoo; Strzepek, Kenneth M.; and Hall, Jim. 2025. Building Ethiopia’s food security resilience to climate and hydrological change. Environmental Research: Food Systems 2(1): 015008. https://doi.org/10.1088/2976-601X/ad99dd

Country/Region

Ethiopia

Keywords

Africa; Sub-saharan Africa; Eastern Africa; Food Security; Resilience; Climate Change; Climate Resilience; Hydrological Cycle

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.1088/2976-601x/ad99dd Source record

Project

Foresight

Record type

Journal Article

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Working Paper

Man vs. machine: Experimental evidence on the quality and perceptions of AI-generated research content

2024Keenan, Michael; Koo, Jawoo; Mwangi, Christine Wamuyu; Karachiwalla, Naureen; Breisinger, Clemens; Kim, MinAh
Details

Man vs. machine: Experimental evidence on the quality and perceptions of AI-generated research content

Academic researchers want their research to be understood and used by non-technical audiences, but that requires communication that is more accessible in the form of non-technical and shorter summaries. The researcher must both signal the quality of the research and ensure that the content is salient by making it more readable. AI tools can improve salience; however, they can also lead to ambiguity in the signal since true effort is then difficult to observe. We implement an online factorial experiment providing non-technical audiences with a blog on an academic paper and vary the actual author of the blog from the same paper (human or ChatGPT) and whether respondents are told the blog is written by a human or AI tool. Even though AI-generated blogs are objectively of higher quality, they are rated lower, but not if the author is disclosed as AI, indicating that signaling is important and can be distorted by AI. Use of the blog does not vary by experimental arm. The findings suggest that, provided disclosure statements are included, researchers can potentially use AI to reduce effort costs without compromising signaling or salience. Academic researchers want their research to be understood and used by non-technical audiences, but that requires communication that is more accessible in the form of non-technical and shorter summaries. The researcher must both signal the quality of the research and ensure that the content is salient by making it more readable. AI tools can improve salience; however, they can also lead to ambiguity in the signal since true effort is then difficult to observe. We implement an online factorial experiment providing non-technical audiences with a blog on an academic paper and vary the actual author of the blog from the same paper (human or ChatGPT) and whether respondents are told the blog is written by a human or AI tool. Even though AI-generated blogs are objectively of higher quality, they are rated lower, but not if the author is disclosed as AI, indicating that signaling is important and can be distorted by AI. Use of the blog does not vary by experimental arm. The findings suggest that, provided disclosure statements are included, researchers can potentially use AI to reduce effort costs without compromising signaling or salience.

Year published

2024

Authors

Keenan, Michael; Koo, Jawoo; Mwangi, Christine Wamuyu; Karachiwalla, Naureen; Breisinger, Clemens; Kim, MinAh

Citation

Keenan, Michael; Koo, Jawoo; Mwangi, Christine; Karachiwalla, Naureen; Breisinger, Clemens; and Kim, MinAh. 2024. Man vs. machine: Experimental evidence on the quality and perceptions of AI-generated research content. IFPRI Discussion Paper 2321. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/169363

Keywords

Southern Asia; Artificial Intelligence; Communication; Research

Language

English

Access/Licence

Open Access

Source

Source record

Project

Digital Innovation

Record type

Working Paper

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Other

Digital Twin for management of water resources in the Limpopo River Basin: a concept

2024Garcia Andarcia, Mariangel; Dickens, Chris; Silva, Paulo; Matheswaran, Karthikeyan; Koo, Jawoo
Details

Digital Twin for management of water resources in the Limpopo River Basin: a concept

A Digital Twin is a virtual representation of an object or system that spans its lifecycle, is updated from real-time data, and uses simulation, machine learning and reasoning to help decision making. The use of Digital Twins to aid decision-makers to make realtime decisions in complex systems is a growing field, with large potential for water system management. The CGIAR Initiative on Digital Innovation is developing a prototype Digital Twin for the Limpopo River Basin in close combination with stakeholders, to enable better management and conservation of this imperilled natural resource upon which millions of people depend. The Digital Twin will provide an attractive and easy-to-use interface for users to intuitively understand large volumes of data and modelling results for timely management decisions, and to simulate the impact of these decisions on the complex river basin ecosystem before they are put into action. This includes an artificial intelligence (AI) tool to interrogate and visualize key actionable data and forecasts. It will be developed in a phased approach according to stakeholder priorities.

Year published

2024

Authors

Garcia Andarcia, Mariangel; Dickens, Chris; Silva, Paulo; Matheswaran, Karthikeyan; Koo, Jawoo

Citation

Garcia Andarcia, Mariangel; Dickens, Chris; Silva, Paulo; Matheswaran, Karthikeyan; Koo, Jawoo. 2024. Digital Twin for management of water resources in the Limpopo River Basin: a concept. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 4p.

Keywords

Southern Africa; Water Resources; Water Management; Decision-support Systems; Modelling; Digital Innovation; Artificial Intelligence; River Basins; Ecosystem Services

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Project

Digital Innovation

Record type

Other

Blogs

Trust the messenger? The role of AI transparency in policy research communication
IFPRI Blog
 | 
December 31, 2024

Trust the messenger? The role of AI transparency in policy research communication

By Michael Keenan, Jawoo Koo, Christine Mwangi, Naureen Karachiwalla, Clemens Breisinger, and MinAh Kim

Testing reader responses to chatbot- vs. human-written blog posts.

Can we trust AI to generate agricultural extension advisories?
IFPRI Blog
 | 
December 05, 2023

Can we trust AI to generate agricultural extension advisories?

By Jawoo Koo, Medha Devare, and Brian King

A chatbot experiment.

How can artificial intelligence-powered chatbots help policymakers? A roadmap for Kenya
IFPRI Blog
 | 
June 01, 2023

How can artificial intelligence-powered chatbots help policymakers? A roadmap for Kenya

By Boniface Akuku, Clemens Breisinger, Joseph Karugia, Jawoo Koo, Michael Keenan, and Richard Ndegwa

Integrating AI into the policy process.

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Events

IFPRI @ CGIAR Science Week
IFPRI at External Event

 | 

April 7 to 12, 2025

IFPRI @ CGIAR Science Week

When humanity has faced its greatest challenges, science and innovation have provided solutions. Today, major and connected global challenges threaten the sustainability of food, land, and water systems, with the most vulnerable people and communities at greatest risk. Recognizing the urgency of these challenges, CGIAR and the Kenyan Agricultural and Livestock Research Organization (KALRO) are […]

How Can We Improve Food Security Monitoring in Conflict-Affected Regions? Machine Learning for Spatially Granular Food Security Mapping
IFPRI Program/Country Event

 | 

March 25, 2025, 9:30 – 10:30 am

How Can We Improve Food Security Monitoring in Conflict-Affected Regions? Machine Learning for Spatially Granular Food Security Mapping

Machine learning is transforming agricultural and food security research, enabling more accurate and timely insights. The International Food Policy Research Institute (IFPRI) is advancing data-driven approaches in various domains, including crop-type mapping, maize yield estimation, and boat detection. These innovations demonstrate the potential of machine learning in addressing complex challenges and informing policy decisions. A […]

How can we improve global crop mapping? IFPRI’s Spatial Production Allocation Model (SPAM)
IFPRI Program/Country Event

 | 

November 21, 2024, 11:00 – 12:00 pm

How can we improve global crop mapping? IFPRI’s Spatial Production Allocation Model (SPAM)

Accessibility to consistent, subnational, spatial information on crops globally will be hugely beneficial to  researchers and policy makers. Researchers need this data to evaluate the benefits and costs of adopting new crop and livestock technologies, estimate the impact of climate change on agriculture calculate yield gaps, and analyze the historical evolution of farming systems. Policymakers, […]

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News

September 24, 2020 | In the News

Hewlett Packard Enterprise Advances the Global Food System Through Memory-Driven Computing With CGIAR (Digital Journal) 

Digital Journal republished a press release from Hewlett Packard announcing a partnership with the CGIAR. Researchers need to generate a timely, high-frequency picture of what is happening in “food basket” locations – or areas of significant food production – around the world. A complete picture often requires data from multiple sources including crop performance, weather records, economic activity, […]

September 12, 2020 | In the News

ECONOMÍA. Saocom: satellite data fill the void of dwindling crop tours (El Intransigente)

El Intransigente (Argentina) published an article stating that the pandemic is helping to usher in a new era of food-production forecasts that rely more on satellite data and artificial intelligence and less on information gathered by people. Rather than sending people to visit things, the market is looking for remote sensing. Still, there are shortcomings in some satellite […]

June 15, 2018 | In the News

Is There an Algorithm to End World Hunger? (Food Tank)

Food Tank published a report recognizing the use of Big Data and innovative technology as “the next agricultural revolution.”

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