Gates Foundation

Scaling biofortified wheat production for agrifood and nutritional security in eastern India: Evidence from five years of multi-location field evaluations in Bihar

Systematic risk profiling: Assessing compounding economic risks in developing countries

This paper presents a systematic risk profiling (SRP) framework to identify the most critical economic risks facing developing countries. Integrating computable general equilibrium (CGE) models with historical shock data and machine-learning tools, we examine how compound shocks affect development outcomes. We apply this method to Kenya, Rwanda, and Malawi, simulating thousands of plausible combinations of world price, capital flow, and productivity exogenous shocks and their impacts on countries’ GDP, household consumption, poverty, and undernourishment. The results reveal distinct risk profiles driven by structural differences: Kenya’s primary vulnerability is the volatility in global beverage crop prices, whereas Rwanda and Malawi face the highest risks from domestic root crop and cereal yields, respectively. These findings underscore that vulnerability is not just a function of shock magnitude, but of the specific structure of each economy. Specifically, the high economic volatility in Malawi and Rwanda is driven by the larger role of subsistence agriculture and more volatile domestic yields, whereas Kenya’s agricultural sector is more export-oriented. Unlike standard ad hoc scenario analysis, SRP quantifies both the likelihood of compound events and the relative importance of their drivers. This transparent, scalable framework provides policymakers a new tool to move beyond reactive measures and design targeted, country-specific resilience strategies for an increasingly volatile world.

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.