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Who we are

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.

David Spielman

David Spielman is the director of IFPRI’s Innovation Policy and Scaling Unit and has been with the institute since 2004. His research agenda covers a range of topics including agriculture and rural development policy; agricultural science, technology, and innovation; plant genetic resources and seed systems; agricultural extension and advisory services; and community-driven rural development.

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What we do

Since 1975, IFPRI’s research has been informing policies and development programs to improve food security, nutrition, and livelihoods around the world.

Where we work

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Where we work

IFPRI currently has more than 480 employees working in over 70 countries with a wide range of local, national, and international partners.

Overview

IFPRI has pioneered work on rigorous economic simulation modeling of food systems to inform decision making by national governments, funding partners, and other stakeholders. IFPRI-led models analyze impacts of policy and investment options on nutrition, poverty, social inclusion, climate change, and the environment under real-time shocks (such as COVID-19 and the conflict in Ukraine) and under alternative future scenarios (including different socioeconomic and climate change trajectories). Three complementary modeling systems focus on different geographic scales (subnational to global), time scales (near-term to several decades), and sectoral scales (agriculture sector to economywide).

IFPRI’s Modeling Systems

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RIAPA

RIAPA (The Rural Investment and Policy Analysis data and modeling system) is IFPRI’s primary tool for forward-looking, country-level analysis. RIAPA has features that make it ideal for tracking the economywide impacts of policies, investments, or economic shocks at national and subnational levels over the near-to-medium term. RIAPA tracks changes in growth and employment across and beyond the food system, as well as poverty and food security at the household level.

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MIRAGRODEP

MIRAGRODEP is a global Computable General Equilibrium (CGE) model that captures international economic linkages through the international trade of goods, as well as through the movement of people and capital. MIRAGRODEP provides a rich set of indicators for each region, which allows measurement of the impact of policy changes on both macroeconomic aggregates and inequality indicators over the near-to-medium term.

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IMPACT

IMPACT (the International Model for Policy Analysis of Agricultural Commodities and Trade) is a system of linked economic, water, and crop models for analysis of climate change and other long-term drivers of the global food system. IMPACT focuses on the agriculture sector at subnational to global scales (including 60 commodities in 158 countries) over the medium-to-longer term (several decades).

Other modeling frameworks supported by IFPRI

DREAMpy (Dynamic Research EvaluAtion for Management, python version)

Open source, user-friendly software for evaluating the economic impacts of agricultural research and development projects.

MINK

A global-scale, systematically geographically gridded, process-based crop simulation modeling system.

SPAM (Spatial Production Allocation Model)

Open source, user-friendly software for evaluating the economic impacts of agricultural research and development projects.

  • 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, Soonho Kim (Senior Data Manager, IFPRI) will introduce how Agentic AI…

  • Leveraging Automatic Speech Recognition and Farmer-Generated Data for Insight, Inclusion, and Impact

    As speech recognition and mobile data collection tools mature, increasing attention is turning to a critical next question: how can farmer-generated data be meaningfully used to inform research, programs, and policy? This session builds directly on earlier discussions of Automatic Speech Recognition (ASR) for agriculture by focusing on the applications, utility, and downstream impacts of…

  • Agricultural Insurance: Innovations, Policies, and Pathways to Scale

    Also streaming on Please type your questions into the chat box with name, affiliation, and country. The event video, presenter slides, and podcast will be available in the days following the event. Farm households face numerous risks that can discourage investments and trap them in poverty. Insurance should be a useful tool to reduce these…

  • 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…