The agricultural sector is becoming more digitalised, where data is crucial for decision-making to improve food security and sustainability. Thus, making successful decisions dependent on high data quality (DQ). However, the quality of agricultural data (agri-data) faces numerous challenges that stem from the complex interaction between social and technical approaches. This study presents an integrated socio-technical view of challenges affecting the quality of agri-data. We identified the following social challenges: (1) digital and data literacy limitations; (2) trust and data sharing, (3) knowledge and cultural resistance, (4) organisational challenges; and technical challenges: (5) systems interoperability and integration, (6) infrastructure and scalability, (7) temporal and spatial data validation complexity, (8) data heterogeneity and standardisation, (9) data sovereignty. The findings reveal that socio-technical challenges are interconnected. In order to create effective solutions, it is required to address DQ of agri-data through a holistic view that considers people, organisational and technical aspects equally, with regard to their data collection, validation, sharing and use. This integrated perspective offers valuable insights for researchers, policymakers and technology providers working to enhance the DQ and trustworthiness of data ecosystems in agriculture.