Abstract

Sustainable agriculture prioritizes food production conserving and rehabilitating the land. Data is a key material to understand the impact of agriculture on lands and promote resilient and sustainable production. The presence of open data offers educational material to explore specific agronomic practices to local ecosystems and climates. However, the competence to handle this data is not sufficiently addressed across formal education in the agricultural sector. The problem analyzed in this work is how to tailor data literacy to the dynamic developments of digital agriculture, open data and data science. Aiming to asses required competences to work with agroecosystems, we investigated course descriptions, educational material, interviews to students and other actors regarding education on open data and statistical thinking. All formal higher education programs offered at the university level in Córdoba, Argentina were characterized analyzing variable correlations by cuali-and-quantitative methods. Results suggest that regular statical courses teaching design and analysis of agronomic experiments that were useful to industrial agriculture, are not enough to handle ecosystems were complex interactions occurs. To ensure statistical literate professionals for sustainable agriculture, statistical thinking should be taught from the earliest educational programs across various disciplines that then converge in multidisciplinary approaches at the ecosystem level. Current educational programs would benefit from boosting statistical thinking with new data analytic tools and open data embedded in project-based activities. Guidelines regarding statistical literacy for graduate and postgraduate level in agriculture will be discussed.