This is a guest blog post from Monsanto, a Community sponsor of the StampedeCon Artificial Intelligence Conference 2017 in St. Louis on October 17.

What comes to mind when you think of ‘modern agriculture’? If Monsanto were to answer this question, the words ‘Data Science’ would certainly come up. Over the past few years, Monsanto has made significant progress pairing data with insightful products in the field, in the lab, and, quite literally, in the hands of our customers and growers.

Although historically not a data-driven industry, today’s world of growing demand for food and limited resources have led agriculture to become ever-more reliant on data-driven insights.  The challenge is keeping farmland sustainable, and increasing food production, while simultaneously battling the constraints and limitations posed by nature.

At Monsanto, we meet challenges with innovation. Collectively. Our community of research scientists, data scientists, engineers, developers and many others, work hand-in-hand to build a data-driven eco-system that has brought Monsanto to the forefront of emerging data science companies. This eco-system houses various pillars that support our innovation including technical elements such as cloud-analytics platform,IoT platform, and enterprise data assets, as-well-as technical development programs that are together enabling us to develop, deliver and sustain data-driven capabilities and engage and excite top-notch data science talent.

The Monsanto Data Science Community develops better products through the judicious use of genomic data. We reduce our environmental footprint by building models that leverage soil, weather and remote sensing data. We better understand and serve our growers’ needs through real-time data collected straight from the fields. Our data science community shapes data into insights and actions by combining domain-specific knowledge with operations research and machine learning. Then, our engineering and data platforms deliver digital innovations in a scalable and impactful manner.

As an example, we’ve used 15 years’ worth of data from our corn R&D pipeline and developed a machine learning-based product that is helping Monsanto researchers more accurately predict how thousands of seeds will perform in their first year in the field. This lets us evaluate about five times more corn varieties than in the past, and saves a year of research time.

Rendering of Monsanto’s future R&D Center of Excellence at its Chesterfield Village research site in St. Louis.

Another example is how we have used data science to optimize seed production in our supply chain. By taking into account factors such as customer demand variability, supply forecast, placement of crops, environmental variations, etc., we created a standardized, automated and robust field production forecast from pre-planting to harvest. This has enabled improved planting decisions and supply planning, which strengthens our ability to deliver a great customer experience.

Year after year, we see growth in the importance of data science as it connects the ingredients of every decision we make as a company. The work we are doing is truly exciting, and data science is not only relevant to agriculture, it is in fact feeding the world.