Strengthening farmer‐led experiments through agronomic and causal inference frameworks

Abstract This study explores how scientists can support on‐farm experiments using analytical methods that align with farmers’ endogenous learning processes to inform their management decision. Four maize ( Zea mays L.) farmers across 10 site‐years in New York participated in this study to evaluate the effectiveness of a nitrogen‐fixing inoculant (NFI) applied with a reduced side‐dress nitrogen rate. Farmers designed and implemented their own experiments using a range of layouts, including side‐by‐side comparisons and strip trials. Two analytical approaches were compared: a quantitative yield analysis using spatial regression, and a causal pathway analysis based on mechanistic steps informed by field sampling (e.g., quantitative polymerase chain reaction detection of NFI organisms, nitrogen nutrition index, and yield). While yield data suggested positive or neutral treatment effects at all sites when simply comparing yield average, the spatial regression analysis and causal pathway analysis identified positive outcomes in only seven or four of 10 site‐years, respectively, reflecting a more conservative interpretation of efficacy. Both methods provided consistent conclusions at four out of 10 site‐years, demonstrating the contribution of metrics other than yield in the interpretation process. Findings suggest that simple causal diagrams can structure data collection and interpretation in ways aligned with farmers' goals. Supporting farmer experiments with digital agronomy, mechanistic reasoning, and site‐specific data enhances learning outcomes and scientific rigor without requiring formal replication. This work contributes to the development of collaborative, scalable methodologies that integrate farmer knowledge and scientific analysis in on‐farm experimentation.