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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

Danielle Resnick

Danielle Resnick is a Senior Research Fellow in the Markets, Trade, and Institutions Unit and a Non-Resident Fellow in the Global Economy and Development Program at the Brookings Institution. Her research focuses on the political economy of agricultural policy and food systems, governance, and democratization, drawing on extensive fieldwork and policy engagement across Africa and South Asia.

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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 480 employees working in over 70 countries with a wide range of local, national, and international partners.

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TOPIC

Artificial Intelligence (AI)

Artificial intelligence (AI) is poised to play a transformative role in advancing sustainable, resilient, and equitable food systems. AI refers broadly to models trained on data that can detect patterns, make predictions, generate insights, and support decision-making. This large umbrella includes methods ranging from machine learning and natural language processing to generative AI.

For agricultural research and policy, AI brings both opportunities and risks. It can expand the evidence base for decision-making, unlock new ways of analyzing complex systems, and improve how producers, researchers, and policymakers interact with data and models. Yet, without careful stewardship, AI can exacerbate inequalities, embed biases, or divert resources away from proven approaches.

As a global research organization, IFPRI has an obligation to improve access to data and research findings to emerging AI knowledge systems, while strengthening its research capacity and ensuring that these technologies are used responsibly to support inclusive and sustainable development.

IFPRI’s role is fivefold:

  • Building competencies and research skills. IFPRI applies AI across disciplines, from foresight modeling, econometrics, and natural language processing to remote sensing, data collection, and qualitative analysis. Building AI literacy among researchers and staff is key to strengthening institutional capacity.
  • Demonstrating use cases that center people. IFPRI explores AI applications across food systems—from farm-level decision support to policy analysis. We apply human-centered participatory design that augments rather than replaces trusted processes and institutional knowledge through transparent and explainable AI approaches.
  • Promoting responsible stewardship and investment. IFPRI analyzes how AI funding and investments shape food systems. Through strategic partnerships with governments, nongovernmental organizations, and the private sector, we help direct resources toward inclusive, evidence-based, and scalable applications.
  • Advancing digital public goods and open knowledge systems. IFPRI develops and shares FAIR (findable, accessible, interoperable, and reusable) datasets and open-access publications that serve as foundational resources for the global food policy research community. These digital public goods enable AI ecosystems to develop more relevant and context-appropriate AI applications for food systems.
  • Advancing ethics and responsible governance. IFPRI examines the ethical, legal, and social dimensions of AI in food systems. Our work informs policy frameworks and promotes innovation that is equitable, transparent, and accountable.

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topics

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Journal Article

Geospatial analysis enables combined poultry–fish farm monitoring in the fragile state of Myanmar

2025Belton, Ben; Fang, Peixun; Liu, Shuo; Zhang, Kaifeng; Zhang, Xiaobo
Details

Geospatial analysis enables combined poultry–fish farm monitoring in the fragile state of Myanmar

Food security is challenging to measure in fragile contexts. Here we combine data from previous field surveys with remotely sensed images and apply deep-learning techniques to estimate changes in the number and area of chicken houses on integrated chicken–fish farms and the supply of chicken meat and eggs from 2010 to 2023 in Yangon region, Myanmar. Yangon’s poultry sector grew ~10% annually from 2010 to 2020 but contracted ~8% annually from 2020 to 2023.

Year published

2025

Authors

Belton, Ben; Fang, Peixun; Liu, Shuo; Zhang, Kaifeng; Zhang, Xiaobo

Citation

Belton, Ben; Fang, Peixun; Liu, Shuo; Zhang, Kaifeng; and Zhang, Xiaobo. 2025. Geospatial analysis enables combined poultry–fish farm monitoring in the fragile state of Myanmar. Nature Food 6(July 2025): 664-667. https://doi.org/10.1038/s43016-025-01192-1

Country/Region

Myanmar

Keywords

Asia; South-eastern Asia; Fish Farms; Food Security; Poultry Farming; Spatial Data; Fish Culture; Fragility

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.1038/s43016-025-01192-1 Source record

Project

Digital Innovation

Record type

Journal Article

Publications

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Brief

PROBES snapshot: Longa, automated speech recognition for Bantu languages

2025Jones-Garcia, Eliot; Benavente, Grecia; Jimenez, Daniel Ricardo
Details

PROBES snapshot: Longa, automated speech recognition for Bantu languages

This one-page brief, developed by the Digital Transformation Accelerator (DTA) team at the Alliance of Bioversity International and CIAT, is based on the Longa probe led by Eliot Jones-Garcia, Senior Research Analyst at IFPRI. The probe explores the use of automatic speech recognition (ASR) for Bantu languages to enable voice-based interaction with digital agricultural services. Longa tests whether low-resource ASR models can accurately capture farmers’ spoken input in real-world conditions and convert it into usable data for digital extension and advisory systems. Implemented as a safe-to-fail, early-stage experiment, the probe focuses on learning how voice-first technologies can reduce literacy and language barriers, improve inclusivity, and strengthen farmer engagement. The brief synthesizes key insights on accuracy, usability, and scalability to inform future development and potential integration of speech-based tools within CGIAR digital pathways.

Year published

2025

Authors

Jones-Garcia, Eliot; Benavente, Grecia; Jimenez, Daniel Ricardo

Citation

Jones-Garcia, E.; Benavente, G.; Jimenez, D.R. (2025) PROBES snapshot: Longa, automated speech recognition for Bantu languages. 1 p.

Keywords

Information and Communication Technologies; Artificial Intelligence; Digital Inclusion; Speech Recognition; Decision Support Systems

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Record type

Brief

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Opinion Piece

Beyond the human touch: Can generative AI transform Uganda’s agricultural extension and advisory services?

2025Asiimwe, Elizabeth; Davis, Kristin E.
Details

Beyond the human touch: Can generative AI transform Uganda’s agricultural extension and advisory services?

Generative Artificial Intelligence (Gen AI) is a type of artificial intelligence that can create new content such as text, images, audio, video, or even computer code, based on the data it has been trained on. Generative AI is trained on huge amounts of data (like books, images, and conversations), and it learns patterns. When prompted, it uses the learned patterns to generate something new that fits the request.

Year published

2025

Authors

Asiimwe, Elizabeth; Davis, Kristin E.

Citation

Asiimwe, Elizabeth; and Davis, Kristin E. 2025. Beyond the human touch: Can generative AI transform Uganda’s agricultural extension and advisory services? UFAAS Blog Post. https://ufaas-ugandacf.org/beyond-the-human-touch-can-generative-ai-transform-ugandas-agricultural-extension-and-advisory-services/

Country/Region

Uganda

Keywords

Africa; Sub-saharan Africa; Eastern Africa; Artificial Intelligence; Agricultural Extension; Advisory Services; Digital Technology

Language

English

Access/Licence

Open Access

Source

Source record

Record type

Opinion Piece

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Training Material

Ethical use of artificial intelligence in food, land and water systems research: A guide for equity and inclusion

2025Jones-Garcia, Eliot; Malapit, Hazel J.; Magalhaes, Marilia; Go, Ara; Bryan, Elizabeth
Details

Ethical use of artificial intelligence in food, land and water systems research: A guide for equity and inclusion

This document aims to support CGIAR researchers and their partners by providing guidance, recommendations and resources on ethical considerations in using artificial intelligence (AI) in food, land and water systems (FLWS) research and practice with attention to equity and inclusion dimensions. This toolkit complements the GENDER Impact Platform Ethics and Standards Toolkit and aligns with CGIAR’s core ethical values outlined in the CGIAR Ethics Framework and the CGIAR Research Ethics Code. In response to the rapid development and adoption of emerging AI technologies, the toolkit presents key issues and practical recommendations for researchers to consider across the research cycle, drawing on a comprehensive literature review and interviews with experts in AI ethics and social inclusion. AI systems, as used in this context, refer to technologies that mimic aspects of human cognition and behavior—interpreting real-world data, generating predictions, and adapting through continuous learning (Benefo et al. 2022; Fu 2022; Manning et al. 2022). These systems are increasingly central to a suite of AI and agricultural technologies poised to transform FLWS research: not only in how we conduct research and generate insights, but also in how we define best practice for gender-related research and understand the downstream impacts of our research outputs.

Year published

2025

Authors

Jones-Garcia, Eliot; Malapit, Hazel J.; Magalhaes, Marilia; Go, Ara; Bryan, Elizabeth

Citation

Jones-Garcia, E., Malapit, H., Magalhaes, M., Go, A. and Bryan, E. 2025. Ethical use of artificial intelligence in food, land and water systems research: a guide for equity and inclusion. Nairobi, Kenya: CGIAR Gender Equality and Inclusion.

Keywords

Artificial Intelligence; Agrifood Systems; Research

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Record type

Training Material

Blogs

How natural language processing and AI can help policymakers address global food insecurity
Research Post
 | 
February 12, 2026

How natural language processing and AI can help policymakers address global food insecurity

By Marieke Meeske

Scaling up data analysis to meet SDG2.

AI, copyright, and content licensing in digital agriculture
Issue Post
 | 
January 22, 2026

AI, copyright, and content licensing in digital agriculture

By Ameen Jauhar

Addressing the problem of massive datasets.

From principles to practice: Why ethical AI starts with data
Research Post
 | 
December 16, 2025

From principles to practice: Why ethical AI starts with data

By Carolina Martins and Berta Ortiz-Crespo

Fostering fairness, transparency, accountability, and privacy.

View all Blogs

Events

Farmer-Centric AI for Livestock Systems: Design, Explainability, and Responsible Innovation
IFPRI Program/Country Event

 | 

March 26, 2026, 9:30 – 10:30 am

Farmer-Centric AI for Livestock Systems: Design, Explainability, and Responsible Innovation

As AI tools begin to influence livestock production, health management, and advisory services, ensuring these technologies are farmer-centric, explainable, and context-appropriate is essential. This webinar brings together researchers working at the intersection of livestock systems, digital innovation, and responsible AI to explore how AI can be designed and deployed in ways that genuinely serve livestock keepers.  We begin with Karen […]

Large Language Models for Policy Makers: Exploring RIAPA-AI and PEPA-AI
IFPRI Program/Country Event

 | 

March 12, 2026, 9:00 – 10:00 am

Large Language Models for Policy Makers: Exploring RIAPA-AI and PEPA-AI

As large language models (LLMs) begin to influence how evidence is generated, synthesized, and communicated, new opportunities are emerging for policymakers and analysts working in food systems. This webinar explores how generative AI tools can support policy design, analysis, and dialogue—bridging technical modeling and real-world decision-making.  Askar Mukashov, Research Fellow at IFPRI, will introduce RIAPA-AI, an extension of the Rural Investment and […]

From Lab to Field: Designing and Testing Agricultural LLMs
IFPRI Program/Country Event

 | 

February 12, 2026, 9:30 – 10:30 am

From Lab to Field: Designing and Testing Agricultural LLMs

As interest in large language models (LLMs) for agricultural advisory grows, developers face two critical questions: How should these tools be built to meet the specific needs of farmers? and How should they be tested?  This webinar brings together two teams building agricultural LLMs through different but complementary approaches—offering a behind-the-scenes look at how AI systems are co-designed, refined, and validated with domain experts, farmers and extensionists.  […]

View all Events

News

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.

October 06, 2025 | In the News

Agriculture Ministry and LIFAAS host generative AI workshop to strengthen extension services (Ministry of Agriculture, Liberia)

With support from IFPRI, the workshop brought together diverse stakeholders to explore how generative AI can close extension gaps and enhance the reach and impact of agricultural advisory services in Liberia.

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