AbstractAgricultural and environmental literacy are essential public goods, but associated education efforts struggle to reach broad audiences. Understanding learner backgrounds and lived experiences can help address this challenge. We assessed the relative importance of demographics, parent views of agriculture, interactions with farmers and parents, and learning setting in predicting agricultural literacy among 525 elementary school children in North Carolina, USA. We used classification and regression trees and random forest models, which account for non-linear and interacting relationships. Knowing a farmer and engagement with parents were more predictive of children agricultural literacy than demographics, countering historically held deficit-based assumptions around agricultural and environmental literacy.Keywords: agricultural literacyenvironmental literacyclassification and regression tree analysisculturally responsive programming AcknowledgementsWe would like to thank the participating families who took time out of their farm visits or busy days to share their thoughts and feelings on local foods. We also thank the teachers who partnered with us on this project, particularly those who continued as COVID-19 posed a myriad of challenges.Disclosure statementNo potential competing interest was reported by the author(s).