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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

Lilia Bliznashka

Lily Bliznashka is a Research Fellow in the Nutrition, Diets, and Health Unit. Her research focuses on assessing the effectiveness of multi-input nutrition-sensitive and nutrition-specific interventions and the mechanisms through which they work to improve maternal and child health and nutrition globally. She has worked in Burkina Faso, Burundi, Tanzania, and Uganda.

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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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Journal Article

Asking the right question: Toward a research agenda for responsible GAI in agricultural extension

2026Jones-Garcia, Eliot; Singaraju, Niyati; Davis, Kristin E.; Koo, Jawoo
Details

Asking the right question: Toward a research agenda for responsible GAI in agricultural extension

This study explores how generative AI (GAI) tools for agricultural extension can be designed and evaluated more responsibly. While current GAI systems offer scalable, personalized advice, they often ignore the lived realities of smallholder farmers—especially women—by relying on generic datasets and rigid evaluation metrics. We investigate three complementary methods: adversarial testing to expose gendered and contextual blind spots in model outputs; deliberative stakeholder engagement using the C-H-A-T framework, which focused on Collective knowledge, Human insight, Augmentation, and Trust, to surface value tensions and design trade-offs; and field-level insights from extension officers to uncover trust-building, diagnostic reasoning, and social intelligence absent from static GAI interactions. Together, these approaches reveal that responsible GAI requires more than technical accuracy. It demands participatory design processes that foreground user realities, surface stakeholder assumptions, and account for social and institutional context. We recommend developing gender-responsive benchmarks, embedding reflexive, participatory design methods, and modeling advisory reasoning based on real-world extension practice. The findings contribute to a growing agenda for responsible AI development—highlighting the importance of aligning GAI tools not only with technical goals, but with the social, cultural, and political contexts in which they operate.

Year published

2026

Authors

Jones-Garcia, Eliot; Singaraju, Niyati; Davis, Kristin E.; Koo, Jawoo

Citation

Jones-Garcia, Eliot; Singaraju, Niyati; Davis, Kristin E.; and Koo, Jawoo. 2026. Asking the right question: Toward a research agenda for responsible GAI in agricultural extension. Advancements in Agricultural Development 7(2): 35-49. https://doi.org/10.37433/aad.v7i2.633

Keywords

Gender; Women Farmers; Smallholders; Artificial Intelligence; Agricultural Extension; Evaluation

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.37433/aad.v7i2.633 Source record

Record type

Journal Article

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Journal Article

Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content

2026Keenan, Michael; Karachiwalla, Naureen; Koo, Jawoo; Mwangi, Christine Wamuyu; Breisinger, Clemens; Kim, MinAh
Details

Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content

Academic research is not always available in a form that is accessible or engaging to a non-academic audience, hindering readers’ engagement with it. Non-academics, even if highly educated and policy experts in their fields, tend to need research to be presented in a more accessible way than peer-reviewed articles — one example being non-technical blogs. However, writing these requires some effort from researchers. Artificial Intelligence (AI) tools can make academic research easier to understand by summarizing and simplifying academic papers much more quickly than researchers can, making it easier for researchers to produce such summaries. However, disclosure of AI use may lower readers’ perceived quality of and trust in the blog, generating a trade-off for the researcher. In this paper, we evaluate an 11-country experiment cross-randomizing a blog’s actual and reported author as AI or human. We find that research stakeholders rate the quality of AI-generated blogs marginally lower than human-written ones (p 0.1), but disclosure of AI use offsets the negative effect (p 0.1). The study sample consists of policy-relevant stakeholders who typically engage with academic research; they are highly educated and include thematic specialists. Indeed, findings indicate that this audience interprets “accessibility” differently, preferring slightly more technical summaries of research. The nature of the respondents may thus explain the particular findings in this study, suggesting that researchers should tailor their prompts for their intended audience. There are no effects on readers’ reported likelihood of engaging with the blog or on beliefs about others predicted engagement with it. Consequently, we hypothesize that researchers can leverage AI to communicate their research more easily without a penalty from disclosing its use.

Year published

2026

Authors

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

Citation

Keenan, Michael; Karachiwalla, Naureen; Koo, Jawoo; Mwangi, Christine Wamuyu; Breisinger, Clemens; and Kim, MinAh. 2026. Man vs. machine: Multi-country experimental evidence on the quality and perceptions of AI-generated research blog content. PLoS One 21(3): e0342852. https://doi.org/10.1371/journal.pone.0342852

Keywords

Artificial Intelligence; Generative Artificial Intelligence; Research; Quality

Language

English

Access/Licence

Open AccessCC-BY-4.0

Links

https://doi.org/10.1371/journal.pone.0342852 Source record

Project

Digital Innovation

Record type

Journal Article

Resource thumbnail

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

Blogs

Two forms of critical AI literacy and why they matter for farming communities
Issue Post
 | 
May 29, 2026

Two forms of critical AI literacy and why they matter for farming communities

By Chioma Chigozie-Okwum, Ameen Jauhar, and Eliot Jones-Garcia

Contributing to app development requires an understanding of AI.

Beyond the model: Evaluating AI agricultural advisory systems so they work in the field
Issue Post
 | 
May 18, 2026

Beyond the model: Evaluating AI agricultural advisory systems so they work in the field

By Josué Kpodo, Jagannath R, Michael Minkoff, and Niyati Singaraju

Tailoring benchmarks for local reliability.

As AI reshapes development research, investing in data must be a key priority
Issue Post
 | 
May 13, 2026

As AI reshapes development research, investing in data must be a key priority

By Kalle Hirvonen and Jessica Leight

Making models more robust and useful.

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Events

AI Workflows for Food Systems Research: A Demonstration of AutoDiscovery with the Ai2 Asta Team
IFPRI Program/Country Event

 | 

June 4, 2026, 9:30 – 10:30 am

AI Workflows for Food Systems Research: A Demonstration of AutoDiscovery with the Ai2 Asta Team

As the volume of scientific literature and data in food, land, and water systems continues to grow, researchers face increasing challenges in identifying relevant evidence, synthesizing insights, and translating them into actionable research questions. New AI tools are emerging to support this process—but what does this look like in practice? This webinar features the Asta […]

IFPRI @ ICT4D Conference 2026
IFPRI at External Event

 | 

May 20 to 22, 2026

IFPRI @ ICT4D Conference 2026

IFPRI is participating in the ICT4D Conference 2026 in Nairobi, Kenya, on May 20–22, 2026, bringing together global leaders, practitioners, and innovators to explore the future of digital transformation. The ICT4D Conference is a leading global platform exploring how digital innovation and data-driven solutions can transform humanitarian relief and development. Founded in 2010 by Catholic […]

When Data Is Everywhere: Digital Research Methods Transforming Food Systems Science
IFPRI Program/Country Event

 | 

May 14, 2026, 9:00 – 10:00 am

When Data Is Everywhere: Digital Research Methods Transforming Food Systems Science

In an era of data abundance, novel digital research methods are reshaping how we study and improve food systems. Building on earlier sessions focused on speech-based AI and farmer-generated data, this discussion broadens the lens, bringing together two researchers who are applying cutting-edge digital tools to address complex questions in the food domain.  First, Bia […]

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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.

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.

View all News

Projects

Fairgrounds: The Gateway to FAIR, Collaborative Agricultural Research

Fairgrounds: The Gateway to FAIR, Collaborative Agricultural Research

Fairgrounds is a collaborative, research platform redefining how agricultural data are accessed, shared, and applied. Built on FAIR principles (findable, accessible, interoperable, reusable), it connects……

Generative AI for Agriculture (GAIA) – Phase I & II

Generative AI for Agriculture (GAIA) – Phase I & II

Traditional agricultural advisory services face significant limitations in reaching smallholder farmers with timely, accurate information. Advancements in Large Language Models (LLMs) show potential for empowering……

AI For Food Systems Research

AI For Food Systems Research

The AI For Food Systems Research initiative is bringing together researchers and practitioners across CGIAR and its partners to explore how artificial intelligence (AI) can……

Nudging for Good: A.I.-driven diagnostics and behavior change to improve diets and nutrition

Nudging for Good: A.I.-driven diagnostics and behavior change to improve diets and nutrition

…

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