The Case for Transdisciplinary Data Science Education in Agricultural Economics

In agricultural fields, automation is rapidly increasing, and the needs of employers are increasingly shifting toward integrating skills in data analysis and effectively collaborating and communicating with colleagues and stakeholders. Preparation for an increasingly integrated world must challenge graduates of agricultural programs beyond their traditional silos to gain an integrated understanding of skills and techniques for the twenty-first-century workforce. Higher education is slowly shifting toward an integrative model where students gain experiences and professors facilitate their education more than talking at them for three hours per week. Here, we call for a transdisciplinary approach to education in agricultural economics where students are presented with opportunities to develop technical skills in data science and analytics and experiences to develop soft skills to ask questions and effectively communicate results. These skills will make graduates more competitive in the workforce as more data become available and more production will be needed while increasingly minimizing the ecological impact.