CGIAR Trust Fund

Trade-offs and synergies in agroecosystem services with organic and integrated nutrient management in South Asian agri-food systems: Evidence from a meta-analysis

The effects of cash and group therapy in the context of conflict: Evidence from a randomized evaluation in Ethiopia

The prevalence of depression remains high in low-income contexts, particularly those affected by conflict. This paper reports on a randomized controlled trial conducted in rural Ethiopia assessing the effects of a psychological (group therapy) intervention delivered by non-specialist health staff, as well as a large one-time cash transfer delivered post-therapy. The trial includes three arms comparing group therapy, cash, and both jointly to a status quo control within a sample of individuals reporting some depressive symptoms or functional impairment at baseline. The study occurred between 2022 and 2024, during a period of active armed conflict. Findings show that sixteen months post-baseline, there are no persistent positive effects of group therapy alone; cash alone improves time use and economic outcomes. Group therapy and cash jointly improve psychosocial skills, time use, and economic outcomes, and in areas not affected by conflict, the joint intervention also improves mental health.

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.