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Who we are

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

Liangzhi You

Liangzhi You is a Senior Research Fellow and theme leader in the Foresight and Policy Modeling Unit, based in Washington, DC. His research focuses on climate resilience, spatial data and analytics, agroecosystems, and agricultural science policy. Gridded crop production data of the world (SPAM) and the agricultural technology evaluation model (DREAM) are among his research contributions. 

Where we work

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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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experts

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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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

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Working Paper

Can we trust large language models to summarize food policy research papers and generate research briefs?

2023Kim, MinAh; Koo, Jawoo; Jung, Yunchul
Details

Can we trust large language models to summarize food policy research papers and generate research briefs?

Generative large language models (LLMs), while widely accessible and capable of simulating policy recommendations, pose challenges in the assessment of their accuracy. Users, including policy analysts and decision-makers, bear the responsibility of evaluating the outcomes from these models. A significant limitation of LLMs is their potential to overlook critical, context-specific factors. For example, in formulating food policies, it is vital to consider regional climate and environmental variables that influence water and resource availability. Nonetheless, due to their reliance on word sequencing probabilities from training datasets, LLMs might propose similar policies for distinct regions. Despite these limitations, LLMs offer considerable advantages for rapid policy analysis, particularly when resources are constrained. They serve as quick, accessible, and cost-effective tools for policy research and development, requiring minimal training and infrastructure. In our study, we assessed the efficacy of LLMs in generating policy briefs by inputting an IFPRI discussion paper into three different LLM-based approaches: a standard chatbot without extra data, a Retrieval Augmented Generation model integrating semantic search with LLM, and a custom-developed Brief Generator designed to create policy summaries from AI-analyzed paper structures. Our findings revealed that none of the LLM-generated briefs fully captured the original paper’s intent, underscoring the need for further research. Future investigations should focus on gathering more empirical data with diverse text types and volumes to better understand these outcomes.

Year published

2023

Authors

Kim, MinAh; Koo, Jawoo; Jung, Yunchul

Citation

Kim, MinAh; Koo, Jawoo; and Jung, Yunchul. 2023. Can we trust large language models to summarize food policy research papers and generate research briefs? Digital Innovation Working Paper December 2023. Washington, DC: International Food Policy Research Institute (IFPRI).

Keywords

Policies; Artificial Intelligence; Technological Changes; Data

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Project

Digital Innovation

Record type

Working Paper

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Report

Longa: An automated speech recognition tool for Bantu languages

2023Mganga, Nelson; Jones-Garcia, Eliot; Monsalue, Andrea Gardeazabal; Koo, Jawoo
Details

Longa: An automated speech recognition tool for Bantu languages

Farm Radio International (FRI) and the CGIAR Research Initiative on Digital Innovation have col laborated on the development of an end-to-end, automatic speech recognition pipeline for the tran scription, translation, and analysis of Swahili and Luganda. This task is particularly challenging due to the number of languages used by FRI’s clients and the limited training data available for speech recognition in African languages. The tool is named ‘Longa’, or ‘Let’s chat’ in Swahili. Longa will be used to answer the surplus of phone calls currently being received from smallholder farmers asking questions about radio programs which FRI does not presently have the capacity to address. When fully implemented, Longa should allow FRI to design their broadcasts more intricately in line with the needs of farmers and better deliver insights to those most in need, such as female and youth farmers. Key results from the collaboration include a series of design principles iteratively and col laboratively developed to reflect the common values and goals of FRI and the CGIAR, a proof of concept for Longa, building on open-source models and open access corpora, to be shared with the developer community upon completion of the final tool, a 10% improvement upon the state-of-the art automatic speech recognition in Luganda radio-speech performance and accuracy, some im provement in performance with audio enhancement processes using real-world data, and proof that fine-tuning is an effective approach to expanding Longa to new languages. The next steps of the collaboration will focus on the analysis and interpretation of an aggregation of farmer phone calls and integration with the existing FRI workflow and software.

Year published

2023

Authors

Mganga, Nelson; Jones-Garcia, Eliot; Monsalue, Andrea Gardeazabal; Koo, Jawoo

Citation

Mganga, Nelson; Jones-Garcia, Eliot; Monsalue, Andrea Gardeazabal; and Koo, Jawoo. 2023. Longa: An automated speech recognition tool for Bantu languages. Digital Innovation Technical Report December 2023. Washington, DC: International Food Policy Research Institute (IFPRI).

Keywords

Africa; Artificial Intelligence; Innovation Adoption; Languages; Farmers

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Project

Digital Innovation

Record type

Report

Blogs

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.

Rural populations face heightened COVID-19 risks
IFPRI Blog
 | 
May 12, 2021

Rural populations face heightened COVID-19 risks

By Jawoo Koo, Claudia Ringler, Aniruddha Ghosh, and Carlo Azzarri

An analysis of 12 low- and middle-income countries shows potential pandemic hotspots.

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Events

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, […]

IFPRI @ 29th UN Climate Change Conference of the Parties (COP29)
IFPRI at External Event

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November 11 to 22, 2024

IFPRI @ 29th UN Climate Change Conference of the Parties (COP29)

IFPRI is pleased to participate in the 29th UN Climate Change Conference of the Parties (COP29) being held in Baku, Azerbaijan from November 11 to November 22, 2024. COP29 is a pivotal opportunity to accelerate action to tackle the climate crisis. With global temperatures hitting record highs, and extreme weather events affecting people around the globe. […]

ICTforAg 2024: Localizing impact
IFPRI at External Event

 | 

May 28, 2024, 9:00 – 5:00 pm

ICTforAg 2024: Localizing impact

Event: May 28, 2024 – 9:00am to 5:00pm EDT. This event explores the localization of ICT, responsible AI in agriculture, the potential for AI to increase farm profitability, empowering citizen science, and harnessing digital solutions for sustainable and regenerative farming practices.

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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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