The Data-Driven Weekly is kicking off 2016 by exploring how big data and analytics is powering data-driven business in different industries. First off is the world of agriculture. While data has always played a prominent role in agriculture and ranching, the explosion of cheap sensors and data storage means that every aspect of agriculture can now be measured and optimized.
According to AGCO (machinery manufacturer), there are “two separate data ‘pipelines’ for [their] customers’ data to flow t...” John Deere has a similar vision that focuses on “sensors added to their equipment to help farmers manage their fleet...” Apparently they combine the sensor data with real-time weather and agronomic data on their MyJohnDeere.com portal. While all this sounds interesting, the vision appears a bit anachronistic, relying on dashboards and human drivers. We can see this in their “imagined future” video, where the farmer sits at his desk sipping coffee instead of checking the crops by hand.
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I’m assuming that John Deere and the other big manufacturers don’t actually believe that with all this kit humans will still be at the wheel of tractors and combines, but they don’t want to scare their customers into thinking their jobs will be automated away. So baby steps. Human drivers aside, too much of John Deere’s vision (if we take the video at face value) is predicated on human decision making and intervention. One thing going for John Deere is that they use R for their models.
Monsanto, on the other hand, sees a slightly different future. Their Climate Corp subsidiary focuses “data prediction models that draw on a range of field and climate va...” Judging from the simulation description, they are doing a Monte Carlo analysis to optimize crop performance.
For those that shudder at the thought of Monsanto having an even tighter grip on the food supply , fear not. The International Centre for Tropical Agriculture (CIAT) offers an alternative via the WorldClim dataset, which provides an open/free “set of global climate layers (climate grids) with a spatial resolution of about 1 square kilometer”. This enables farmers to “optimize crop yields by adjusting their management practices to sub...”
Speaking of all this data, the natural question of ownership arises. According to AGCO, they believe that “the farmer owns his or her data, and it is up to us leaders in the industry to help them access, process and utilize it.” For others, it’s not so clear. In November, John Deere announced a partnership with the Climate Corp to automatically collect and share agronomic data with the Climate cloud. Touted as a convenient way to get data-driven insights, it’s unclear who owns the data once it is pushed to Climate Corp’s cloud. At a congressional hearing on big data in agriculture last October, the President of the Missouri Farm Bureau said that “farmers should understand what will become of the data collected fr...“, including who has access to it and for what purposes it can be used. From the farmer’s perspective, they “must do everything we can to ensure producers own and control their data, can transparently ascertain what happens to the data, and have the ability to store the data in a safe and secure location.” It will certainly be interesting to see how this plays out, particularly between developed and developing nations.
Those interested in exploring this area can get started with some of the following datasets. In addition to the WorldClim dataset, the SPADE dataset provides soil property data for Europe. For machinery compatibility, there is the AEF Database, provided by the Agricultural Industry Electronics Foundation.
Feel free to add more resources in the comments.
Brian Lee Yung Rowe is Founder and Chief Pez Head of Pez.AI // Zato Novo, a conversational AI platform for guided data analysis and Q&A. Learn more at Pez.AI.