SUMMARY: Changes in donor priorities have meant that agronomists working in the tropics find themselves in a fundamentally new operational space, one that demands rapid improvements in farmers' livelihoods resulting from the large-scale adoption of new technologies and crop management practices. As a result, on-farm trials in contemporary Agricultural Research for Development (AR4D) are increasingly implemented both to collect data and to spur farmer adoption. We examine the different interpretations and organisational practices of AR4D organisations in this new operational space, and reflect on the usefulness of on-farm trials for agricultural technology scaling. Three case studies are presented to address these questions – two in sub-Saharan Africa and one in South Asia. Each study is considered in light of Science and Technology Studies theory and locates science as a politically situated practice, recognising the tension that scientists face between providing evidence and persuading selected audiences. The case studies show that this tension results in the introduction of several biases that limit the scalability of the technologies under investigation. These include biases at the level of the trial location, host-farmer selection, trial design, management and evaluation. We conclude by discussing how the contemporary political and institutional environment of AR4D produces project beneficiaries and research outcomes on selected farms, but not necessarily impacts at scale.

This paper examines how professionals (N = 32) working on artificial intelligence (AI) view structural AI ethics challenges like injustices and inequalities beyond individual agents' direct intention and control. This paper answers the research question: What are professionals’ perceptions of the structural challenges of AI (in the agri-food sector)? This empirical paper shows that it is essential to broaden the scope of ethics of AI beyond micro- and meso-levels. While ethics guidelines and AI ethics often focus on the responsibility of designers and the competencies and skills of designers to take this responsibility, our results show that many structural challenges are beyond their reach. This result means that while ethics guidelines and AI ethics frameworks are helpful, there is a risk that they overlook more complicated, nuanced, and intersected structural challenges. In addition, it highlights the need to include diverse stakeholders, such as quadruple helix (QH) participants, in discussions around AI ethics rather than solely focusing on the obligations of AI developers and companies. Overall, this paper demonstrates that addressing structural challenges in AI is challenging and requires an approach that considers four requirements: (1) multi-level, (2) multi-faceted, (3) interdisciplinary, and (4) polycentric governance.

Agricultural extension services in poor countries often identify opinion leaders based on criteria such as wealth and social status. We explore the effectiveness of this top-down approach by analys...