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With research staff from more than 70 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

Timothy Thomas

Timothy Thomas is a Senior Research Fellow in the Foresight and Policy Modeling Unit. His research focuses on the impact of climate change on agricultural productivity. Tim’s work involves developing innovative methods for simulated climate futures and model coordination between climate, crop, water, energy, and economics.

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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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experts

Agrifood Innovation and Resilience

Jawoo Koo

Senior Research Fellow

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Bio

Jawoo Koo is a Senior Research Fellow in the Agrifood Innovation and Resilience Unit, 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 co-led the CGIAR Platform for Big Data in Agriculture (2017–2021), led the CGIAR Initiative on Digital Innovation (2022–2024), and currently leads the Action Lab of the CGIAR Accelerator on Digital Transformation, coordinating the Accelerator’s activities to support AI-enabled research across CGIAR’s research portfolio. He manages several CGIAR-wide projects focusing on assessments of data ecosystems, co-design of AI and digital innovation, and enabling environments. 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 spoken: English, Korean

Google Scholar Citations

CV

Publications

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Journal Article

Where does decentralised green nitrogen fit in Africa’s fertiliser resilience strategy?

2026Nelson, Andrew; Byun, Jun Suk; Davidson, Eric A.; Dobermann, Achim; Dou, Yue; Fregene, Martin; Koo, Jawoo; Oberthür, Thomas; Rosa, Lorenzo; Thornton, Philip
Details

Where does decentralised green nitrogen fit in Africa’s fertiliser resilience strategy?

Africa faces recurrent fertiliser shocks because much of its nitrogen supply depends on imported products, fossil energy, and exposed trade routes, while production, product mix, logistics, and farm-gate demand remain poorly aligned. Decentralised green nitrogen production – defined as small-scale nitrogen fertilizer synthesis powered by renewable electricity – could make a major contribution to addressing these challenge, but to go to scale it will need to lower delivered costs, improve farm returns, reach farmers with responsive crops, and manage product, safety, resource, and governance risks. This opinion piece argues for an Africa-wide prioritisation assessment that compares potential decentralised production with imports, regional trade, larger low-emission plants, blending, logistics, and advisory support. The goal is to identify intervention bundles that deliver the right nitrogen product to the right farmers at the right cost for the coming decades.

Year published

2026

Authors

Nelson, Andrew; Byun, Jun Suk; Davidson, Eric A.; Dobermann, Achim; Dou, Yue; Fregene, Martin; Koo, Jawoo; Oberthür, Thomas; Rosa, Lorenzo; Thornton, Philip

Citation

Nelson, Andrew; Byun, Jun Suk; Davidson, Eric A.; Dobermann, Achim; Dou, Yue; et al. 2026. Where does decentralised green nitrogen fit in Africa’s fertiliser resilience strategy? Food Security 18(5): 1323-1331. https://doi.org/10.1007/s12571-026-01707-z

Keywords

Africa; Nitrogen Fertilizers; Fertilizers; Resilience; Decentralization

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.1007/s12571-026-01707-z Source record

Record type

Journal Article

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Journal Article

Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India

2026Gakhar, Shalini; Koo, Jawoo; Patnaik, Girija Prasad; Singh, Raj Kumar
Details

Bridging agronomic science and context specific farm-level advisory through generative AI for rice systems in India

Agriculture is increasingly characterized by a data paradox, while the sector generates massive volumes of genomic, climatic, remote sensing, and field data. Translating this information into actionable, farm-level insights remains a critical bottleneck. Traditional advisory mechanisms cannot operate at the spatial scales or provide the context-specificity needed for climate adaptation and food security. The work presents GenAI as a transformative interface that makes advanced agricultural science, including crop simulation models and remote sensing diagnostics, accessible to rice farmers in India through natural language, provided that challenges related to data sovereignty, infrastructure gaps, and the need for human oversight in advisory systems are effectively addressed. We trace the shift from static, rule-based expert systems to dynamic foundation models capable of complex reasoning and multimodal analysis. The paper critically reviewed the rise of crop-specific LLM agro-advisory model architectures, such as SeedLLM Rice and IPM-AgriGPT, which outperform general-purpose models by utilizing specialized scientific corpora to reduce hallucinations. Main attention is on the hybrid integration framework of LLMs with Knowledge Graphs (KGs) for factual grounding, and connecting with process-based simulators (e.g., DSSAT, APSIM) to make biophysical modelling more accessible through natural language. Furthermore, the manuscript examines the fusion of satellite, drone, and smart phone imagery with vision-language models, enabling real-time, context-aware diagnostics for smallholder farmers. The review concludes that while GenAI has the potential to support more equitable forms of precision agriculture, it can only do so under specific conditions. Its success depends on resolving infrastructure gaps, ensuring data sovereignty, and maintaining a “human-in-the-loop” architecture to guarantee scientific rigour and social inclusion.

Year published

2026

Authors

Gakhar, Shalini; Koo, Jawoo; Patnaik, Girija Prasad; Singh, Raj Kumar

Citation

Gakhar, Shalini, Jawoo Koo, Girija Prasad Patnaik, and Raj Kumar Singh. 2026. "Bridging Agronomic Science and Context Specific Farm-Level Advisory through Generative AI for Rice Systems in India." Frontiers in Artificial Intelligence 9: 1808028. https://doi.org/10.3389/frai.2026.1808028

Country/Region

India

Keywords

Southern Asia; Asia; Digital Agriculture; Generative Artificial Intelligence; Large Language Models; Artificial Intelligence; Decision-support Systems; Agricultural Extension; Advisory Services; Rice

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.3389/frai.2026.1808028 Source record

Record type

Journal Article

Resource thumbnail

Journal Article

Integrating Earth observation and digital tools for agricultural mapping and monitoring: The Mozambique case study

2026
Paris, Claudia; de By, Rolf A.; Ohuru, Robert; Çelik, Mehmet Furkan; Ellsäßer, Florian J.; Tseng, Gabriel; Zhang, Yawen; Mananze, Sosdito Estevão; Manuel, Lourenço; Koo, Jawoo
…more Nelson, Andrew
Details

Integrating Earth observation and digital tools for agricultural mapping and monitoring: The Mozambique case study

Accurate and timely agricultural data are vital in low- and middle-income countries for effective national planning, informed food policy decisions, and progress toward achieving sustainable development goal (SDG) 2 (zero hunger). Annual field surveys are costly, time-consuming, hampered by weak infrastructure, and vulnerable to extreme weather. This article presents the architecture developed in the statistics from space project, which integrates Earth observation (EO) data, digital tools for field-data collection, and a cloud-based aggregation and visualization platform to propose a cost-effective solution for efficient agricultural monitoring. The workflow was tested in Mozambique and includes: first, an EO-based sampling-frame methodology for heterogeneous agro-landscapes, second, an open-source smartphone application for efficient, geo-referenced field data collection, third, a data-aggregation and visualization system for real-time quality monitoring and national-level dissemination, and fourth, automated methods for EO-based crop type mapping. The project delivered a comprehensive framework for integrating EO and digital technologies into efficient and scalable workflows. High-resolution crop type maps (openly accessible) and statistics were generated for the main crops in three pilot provinces, Gaza, Manica, and Zambezia, for the 2024/25 season.

Year published

2026

Authors

Paris, Claudia; de By, Rolf A.; Ohuru, Robert; Çelik, Mehmet Furkan; Ellsäßer, Florian J.; Tseng, Gabriel; Zhang, Yawen; Mananze, Sosdito Estevão; Manuel, Lourenço; Koo, Jawoo; Nelson, Andrew

Citation

Paris, Claudia; de By, Rolf A.; Ohuru, Robert; Çelik, Mehmet Furkan; Ellsäßer, Florian J.; et al. 2026. Integrating Earth observation and digital tools for agricultural mapping and monitoring: The Mozambique case study. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 19: 20072-20091. https://doi.org/10.1109/JSTARS.2026.3698556

Country/Region

Mozambique

Keywords

Africa; Sub-saharan Africa; Eastern Africa; Capacity Building; Earth Observation Satellites; Digital Technology; Cartography; Satellite Observation

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.1109/jstars.2026.3698556 Source record

Record type

Journal Article

Events

AI Policy Considerations for Food, Land, and Water Systems
IFPRI Policy Seminar

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September 15, 2026, 9:30 – 11:00 am

AI Policy Considerations for Food, Land, and Water Systems

Amid rapid advances in artificial intelligence (AI), there is growing interest in using AI as a tool to advance food systems transformation. AI has significant potential to transform policy research in food, land, and water systems (FLWS) by enhancing accessibility, efficiency, and responsiveness in policy analysis, but only if tools are rigorously tested, made transparent, […]

Africa Food Systems Forum (AFSF) 2026
IFPRI at External Event

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August 31 to September 4, 2026

Africa Food Systems Forum (AFSF) 2026

The Africa Food Systems Forum (AFSF) will host its 20th Annual Summit in Kigali, Rwanda from 31 August to 4 September 2026, under the theme “Investing in Africa’s Agri-Food Systems: Nourishing Nations, Growing Jobs, Building Resilience.” The Summit will bring together stakeholders from across the agri-food sector for a programme of plenary sessions, technical discussions, […]

AI for Food Security Forum
IFPRI at External Event

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April 30, 2026, 8:30 – 6:30 pm

AI for Food Security Forum

The Artificial Intelligence (AI) for Food Security Forum, brings together U.S. and global policymakers, technology developers, financers, researchers, and implementing partners working at the forefront of AI for food security. Join CSIS, and the member organizations of the AI Collaborative: Food Security, for cross-cutting discussions that explore the promises and risks of AI-enabled technologies for food […]

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News

February 17, 2026 | Press Release

IFPRI and Farm Radio International collaborate to give a voice to the most vulnerable farmers by advancing AI tools for African languages

Introducing Longa, an AI-powered speech recognition tool designed to strengthen agricultural communication and inclusivity across sub-Saharan Africa.

November 12, 2025 | Press Release

IFPRI and Digital Green expand collaboration to test AI innovations for smallholder farmers

Building on years of joint research on digital extension, the collaboration will now focus on user testing of Digital Green’s FarmerChat application as part of IFPRI’s Generative AI for Agriculture (GAIA) initiative.

August 29, 2025 | Press Release

IFPRI at Africa Food Systems Forum 2025

IFPRI’s delegation at AFSF 2025—including Director General and senior researchers—will participate in plenary sessions, high-level panels, side and special events as part of the wider CGIAR delegation at AFSF.

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