MSc Digital Technology for Sustainable Agriculture

The world’s population is expected to grow to approx. 10 billion by 2050. This growth will result in increased demand for resources, raw materials and food. Furthermore, the world faces intersecting challenges like climate change, exploitation of natural capital and an aging and declining rural population.

To produce a “sustainable food future,” the world must increase food production while cutting GHG emissions and maintaining (or reducing) the land used in agriculture.

Digital agriculture could address these challenges by making the agri-food value chain more efficient, equitable, and environmentally sustainable - before, during, and after on-farm production.

The programme is aimed at students who wish to build their knowledge and skills-base to address the complexities of developing, deploying and managing digital technology in the agriculture sector. With a focus on design, numeracy, and hardware software technology, our students will be deeply engaged agricultural with production, and specifically technology to enhance efficiency, sustainability, resilience and reliability.

The programme is delivered by a highly research-intensive and multi-disciplinary school – Ireland’s premier agri-food related research entity with excellent networks into the agri-food industry and with a particular focus on working with industry to provide sustainable technical solutions. The school’s academic staff have a major global research impact, with three professors who are among the top 1% of the most cited researchers in the world according to the latest Highly Cited Researchers list published by Clarivate.

Delivery Mode & Themes

This Programme is Primarily Delivered Face to Face, but Will Also Include Some Fully Online Modules and Blended Delivery Models. All Modules Are Optional and Students Will Be Able to Take Themed Clusters of Modules (E.g. Three Modules of Precision Farming, Three Modules of Sensing Technology, Three Modules of Computers and Electronics, Three Modules of Data Science) to Reflect Specific Technical Interests or Needs for Upskilling.