Yara is the world’s leading fertilizer company, is headquartered in Oslo, Norway, and has more than 16,000 employees worldwide.
Our mission is to responsibly feed the world while respecting the planet. To this end, the digital transformation in agriculture will allow a drastically more precise use of crop nutrition products, conceivably down to the single plant. Yara Digital Labs is shaping these future tools to help farmers achieve their most ambitious goals reliably and with ease. Tapping into Yara’s extensive agronomic knowledge, we collect, transform and integrate current and historical datasets while building new tools from them.
Our vision is a world free from hunger – a difficult task in the face of steady population growth and changing environmental conditions. Saving water - especially in farming - will be one of the huge challenges to overcome in the near future: In areas which have struggled with this in the past years, the conditions have become increasingly severe, and even in areas where irrigation has not been of such concern in the past, the farmers are often not well prepared for dealing with extensive droughts.
In his talk, Richard will present the Yara Water Solution, a hardware based system for irrigation monitoring and -optimization in tree orchards, as an example for operating an analytics back end based on R. On the development side, the focus is on close collaboration with agronomists and maintaining continuously high data quality. On the operations side, the main emphasis is on compatibility and stability, as well as using the new resources within the context of Yara Digital Labs.
Richard is a Data Scientist / Data Engineer at Yara Digital Labs and he strives to make a difference by contributing to the urgent task of worldwide sustainable agriculture. He is currently working for the Yara Water Solution in analytics model development and operations as well as dataset unification/harmonization to maintain its high data quality.
He holds a PhD in physics. His professional interests include asking the right question and answering it with data, building a model out of it (preferably with Tensorflow), and translating this knowledge into new insights.