Steve Mathews

The Digital Revolution in Agriculture

7-8 August 2017, Canberra

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Steve Mathews

Steve Mathews is the Head of Strategy at Gro Intelligence, a software company focused on the global food and agriculture markets. Before joining Gro, Steve worked as a portfolio manager and the head of commodities research at Tudor Investment Corporation. During his tenure there, he developed extensive commodities analysis software and conducted practical study of agriculture, energy, and metals. He is a crop scout with the Pro Farmer Crop Tour each summer, and teaches agricultural hedging at the University of Memphis. Prior to finance, Steve commanded a tank company in the U.S. Army.

Steve holds a B.S. in Operations Research from USMA (West Point) and an M.B.A. in Statistics from the Stern School of Business at NYU. He’s currently about halfway through an M.S. degree in Agronomy from Iowa State University. He’s also a holder of the Chartered Financial Analyst designation.

Steve Mathews Paper Crawford Fund Conference 2017

Steve Mathews Presentation

Local applications for global data and AI


Big data has great unrealized potential in most parts of the agricultural value chain. We can divide that data up into several categories, each with their own good and bad points. Starting in 1972, the US Landsat program collected the original “big data,” but the capability to perform meaningful analysis of the photos remained very expensive until recently. Other flows have begun in the past few decades, from private satellites, point-of-sale systems, land-based sensors, and aerial drones. Unlike Landsat, the various newer sources have different ownership statuses. Globally, most smallholders don’t generate the revenue to pay for any of the various proprietary data sources or analysis. But we see significant value in the application of machine learning/big data techniques to publicly available satellite and other sources. Advances in information technology allow us to disseminate quality yield, drought, and other analyses at a much lower cost than previously. As a result, relatively small external contributions can bring the established benefits of modern modeling expertise to a hugely broader and more diverse audience.