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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.

Erick Boy

researcher spotlight

Erick Boy

Erick Boy is the Chief Nutritionist in the HarvestPlus section of the Innovation Policy and Scaling Unit. As head of nutrition for the HarvestPlus Program since 2008, he has led research that has generated scientific evidence on biofortified staple crops as efficacious and effective interventions to help address iron, vitamin A, and zinc deficiency in sub-Saharan Africa, Latin America, 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 600 employees working in over 80 countries with a wide range of local, national, and international partners.

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experts

Markets, Trade, and Institutions

Yanyan Liu

Senior Research Fellow

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Bio

Yanyan Liu is a Senior Research Fellow in the Markets, Trade, and Institutions Unit. Yanyan’s research has focused on program impact evaluation, microfinance, microinsurance, and economic transformation. Her work has been published in journals such as Economic Journal, European Economic Review, Journal of Applied Econometrics, Journal of Risk and Insurance, American Journal of Agricultural Economics, World Bank Economic Review, and World Development.

Prior to joining IFPRI in 2009, Yanyan worked for RTI International and for the Development Research Group at the World Bank. She received a joint PhD in Economics and Agricultural Economics from Michigan State University.

RePEc Author Profile

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Publications

Resource thumbnail

Journal Article

High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning

2025Constenla-Villoslada, Susana; Liu, Yanyan; McBride, Linden; Ouma, Clinton; Mutanda, Nelson; Barrett, Christopher B.
Details

High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning

The number of acutely food insecure people worldwide has doubled since 2017, increasing demand for early warning systems (EWS) that can predict food emergencies. Advances in computational methods, and the growing availability of near-real time remote sensing data, suggest that big data approaches might help meet this need. But such models have thus far exhibited low predictive skill with respect to subpopulation-level acute malnutrition indicators. We explore whether updating training data with high frequency monitoring of the predictand can help improve machine learning models’ predictive performance with respect to child acute malnutrition by directly learning the dynamic determinants of rapidly evolving acute malnutrition crises. We combine supervised machine learning methods and remotely sensed feature sets with time series child anthropometric data from EWS’ sentinel sites to generate accurate forecasts of acute malnutrition at operationally meaningful time horizons. These advances can enhance intertemporal and geographic targeting of humanitarian response to impending food emergencies that otherwise have unacceptably high case fatality rates.

Year published

2025

Authors

Constenla-Villoslada, Susana; Liu, Yanyan; McBride, Linden; Ouma, Clinton; Mutanda, Nelson; Barrett, Christopher B.

Citation

Constenla-Villoslada, Susana; Liu, Yanyan; McBride, Linden; Ouma, Clinton; Mutanda, Nelson; and Barrett, Christopher B. 2025. High-frequency monitoring enables machine learning–based forecasting of acute child malnutrition for early warning. Proceedings of the National Academy of Sciences of the United States of America (PNAS) 122(23): e2416161122. https://doi.org/10.1073/pnas.2416161122

Keywords

Monitoring; Machine Learning; Children; Malnutrition; Food Security; Early Warning Systems

Language

English

Access/Licence

Open AccessCC-BY-NC-ND-4.0

Links

https://doi.org/10.1073/pnas.2416161122 Source record

Record type

Journal Article

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Report

Solar drying technology for post-harvest loss management of horticulture products: Findings from baseline survey in Nigeria

2024Balana, Bedru; Popoola, Olufemi; Yamauchi, Futoshi; Olanipekun, Caleb; Totin, Edmond; Salaudeen, Kamaldeen Oladimeji; Muhammad, Aminu; Shi, Weilun; Liu, Yanyan
Details

Solar drying technology for post-harvest loss management of horticulture products: Findings from baseline survey in Nigeria

Creating a sustainable food system requires addressing the critical challenges of food waste and loss. This is particularly crucial for small-scale farmers who supply local markets but lack access to modern preservation technologies, leading to significant product losses between harvesting and selling. Research indicates that in low-and middle-income countries, approximately 38 percent of harvested perishable agricultural goods are lost before consumption. Globally, about 22 percent of fruits and vegetables are lost in the supply chain before reaching retailers (FAO, 2019). These postharvest losses have significant impacts to low economic return and household food and nutrition security. Post-harvest losses also contribute significantly to environmental concerns, accounting for roughly 8 percent of yearly global greenhouse gas emissions. Among all food categories, fruits and vegetables experience the highest losses by weight.

Year published

2024

Authors

Balana, Bedru; Popoola, Olufemi; Yamauchi, Futoshi; Olanipekun, Caleb; Totin, Edmond; Salaudeen, Kamaldeen Oladimeji; Muhammad, Aminu; Shi, Weilun; Liu, Yanyan

Citation

Balana, Bedru; Popoola, Olufemi; Yamauchi, Futoshi; Olanipekun, Caleb; Totin, Edmond; et al. 2024. Solar drying technology for post-harvest loss management of horticulture products: Findings from baseline survey in Nigeria. CGIAR Initiative on Rethinking Food Markets Technical Report. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/169151

Country/Region

Nigeria

Keywords

Africa; Western Africa; Capacity Development; Food Security; Horticulture; Households; Nutrition; Solar Drying; Sustainability

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Project

Fruit and Vegetables for Sustainable Healthy Diets

Record type

Report

Resource thumbnail

Report

Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases

2024Kim, Soonho; Song, Xingyi; Park, Boyeong; Ko, Daeun; Liu, Yanyan
Details

Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases

This report presents a comprehensive overview of the real-time media analysis system developed to assess risks associated with the top five prioritized pests and diseases affecting crops. The activity, under Work Package 2 of the CGIAR Research Initiative on Plant Health, utilizes advanced text mining and machine learning techniques, including a Large Language Model (LLM), to process and analyze media articles. Key achievements include the development of an automated media analysis pipeline to monitor pests and diseases globally, the integration of GPT-4 to classify and extract detailed information from news articles, the creation of a public, interactive Crop Disease Dashboard providing real-time insights, the implementation of a cloud-based interface and REST API for user-friendly interaction and integration, and the ongoing refinement of the system based on human verification and feedback. This innovative approach aims to strengthen crop health monitoring and support policymakers and researchers in mitigating the risks posed by crop diseases and pests.

Year published

2024

Authors

Kim, Soonho; Song, Xingyi; Park, Boyeong; Ko, Daeun; Liu, Yanyan

Citation

Kim, Soonho; Song, Xingyi; Park, Boyeong; Ko, Daeun; and Liu, Yanyan. 2024. Real-time media analysis using large language model (LLM) for the top 5 prioritized pests and diseases. Washington, DC: International Food Policy Research Institute. https://hdl.handle.net/10568/172706

Keywords

Artificial Intelligence; Large Language Models; Postharvest Control; Plant Diseases; Plant Disease Control

Language

English

Access/Licence

Open AccessCC-BY-4.0

Source

Source record

Project

Plant Health

Record type

Report

Blogs

Farmers’ responses to warming early in growing seasons can affect yields
IFPRI Blog
 | 
August 03, 2020

Farmers’ responses to warming early in growing seasons can affect yields

By Yanyan Liu and Emily Wu

Decisions on fertilizer and pesticide use impact crops and finances.

Harnessing telecommunications network data for rainfall monitoring in developing countries
IFPRI Blog
 | 
February 24, 2020

Harnessing telecommunications network data for rainfall monitoring in developing countries

By Noam David, Oliver Gao, and Yanyan Liu

Filling a key information gap can make it easier to provide insurance to smallholders.

Model offers new approach to account for intangible factors driving microfinance group loan performance
IFPRI Blog
 | 
October 08, 2018

Model offers new approach to account for intangible factors driving microfinance group loan performance

By Manuel A. Hernandez, Yanyan Liu, and Sara Gustafson

A paper offers important new data points on group loans.

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Events

Worsening food insecurity, waning response capacity: Options for a better way forward
IFPRI Policy Seminar

 | 

July 2, 2025, 9:30 – 11:00 am

Worsening food insecurity, waning response capacity: Options for a better way forward

Food crises arise from the complex interplay of conflict, poverty, climate variability, and economic shocks, resulting in acute food insecurity among vulnerable populations. According to the latest Global Report on Food Crises (2025), the number of people facing severe levels of acute food insecurity has tripled over the past decade, rising from around 100 million […]

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 […]

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

April 17, 2025 | In the News

As famine data dries up, can AI step in? (Devex)

Devex quoted Yanyan Liu in this article on how researchers are developing AI tools to predict famine more accurately and affordably.

September 14, 2020 | In the News

Pork is too expensive! Mainland banking trial implementation of live pig mortgage loan (Epoch Times)

Epoch Times published an article that states China’s pork prices remain high and the consumer price index increases, and the CCP government hopes to restore the number of live pigs and encourage pig farmers to replenish the amount of pigs to be slaughtered, but small family pig farms are already in debt. There are no assets that can […]

September 14, 2020 | In the News

China serves up pig-backed loans for its hogtied farmers (Fox Business News)

Fox Business News published an article on how China’s is so desperate to rebuild pig herds after swine fever that its banks are experimenting with loans to farmers backed by a novel asset: their hogs. Senior Research Fellow Yanyan Liu says banks would face challenges both in valuing pigs and enforcing their rights if loans went bad. Hog prices are […]

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