Digital transformation in Canadian crop production: A scoping review of technologies, adoption drivers, and systemic barriers

• Canada’s agricultural productivity has declined since 2010, increasing the need for sustainable innovation in crop production. • Digital agricultural technologies (DATs) show promise, but adoption in Canadian field crops remains limited. • This scoping review synthesized evidence from 57 technical studies and 7 stakeholder-focused studies. • Most research emphasizes soil, crop, and yield prediction using sensing and machine learning, with limited cross-regional validation and economic evidence. • Adoption is primarily shaped by cost–benefit considerations, with key barriers including uncertain returns, data governance concerns, and limited advisory support. • Using sectoral innovation systems and responsible research and innovation lenses, the study identifies structural constraints and policy gaps affecting DAT scale-up in Canada. Digital agricultural technologies are widely promoted as pathways to improve productivity, profitability, and environmental performance in crop production. Yet their development, validation, and adoption vary significantly across contexts. This scoping review synthesizes evidence from 64 Canadian and international studies published since 2013 to (i) identify DATs developed, piloted, or validated in major Canadian field crops, and (ii) examine stakeholder perspectives on their adoption. Following PRISMA-ScR guidelines, we reviewed peer-reviewed and grey literature on soil sensing, thematic soil mapping, soil moisture estimation, crop and yield prediction, fertilization optimization, pest and weed detection, and precision planting. Most technical studies focused on sensing-based and analytics-driven applications, often using machine learning with proximal or remote sensing data. These technologies demonstrated strong predictive performance under localized conditions, particularly for soil properties, soil moisture, and yield estimation, but frequently lacked cross-regional calibration, long-term validation, and integration into decision-support systems. The evidence base is dominated by studies on oilseed and grain systems, reflecting Prairie and Ontario field crop production, with comparatively limited attention to specialty crops and robotics-intensive operations. Adoption studies identified cost–benefit considerations, data governance and privacy concerns, interoperability challenges, and uneven advisory capacity as key determinants of uptake. Interpreting the evidence through a sectoral innovation systems lens reveals structural constraints shaping Canada’s digital agriculture trajectory, including concentrated public funding, limited commercialization pathways, regional imbalances in testing environments, and underdeveloped data governance frameworks. These findings underscore the need for a Responsible Research and Innovation approach for coordinated and regionally distributed validation, stronger advisory and training systems, clearer data governance, and responsible innovation practices. The review provides an integrated evidence base to inform policy and investment strategies that support an equitable and effective digital transformation of Canadian crop production.