By Matt Bogard
I just came back from the SAS Global Forum conference where 'big data' was an ongoing theme, which reminded me I needed to post the second installment of my Big Ag and Big Data series. Previously I discussed the role of social media in producing ‘big data’ and tools that may be used to get the most from this data in the ag industry. In this second installment I’m going to discuss other sources of ‘big data.’
I recall once about 10 years ago attending a UK College of Agriculture field day in Princeton Ky, and someone made the comment that went something like this:
"these events are good because on the farm we don’t have time to set up experiments, collect data, and analyze to figure out best practices. We can’t stop and measure and record and report about everything we do."
It’s certainly true that extension services will continue to conduct valuable research and it will probably remain a fact that producers aren’t going to necessarily have the time and resources to reduce their operation to a collection of well-crafted scientific experiments. However, every decision made on the farm is a trial of sorts, and with modern technology it is much easier to collect and log data about your operation, and some companies are now figuring out ways to take this farm level data and turn it into powerful analytical tools that can boost productivity and efficiency. In a recent article ‘Building Big Data: Farming Big Data Goes To The Cows’ the following statement is made:
"The major problem we keep on seeing — especially in bigger, modern farms — is that there's a lot of data being created and not being used, on how they're performing, what they're doing."
How is this data being generated? Lots if it is generated via your equipment including GPS:
"Next generation farm equipment like combines and tillers are going to be able to take soil samples as they move along, perform analysis on those samples, and feed the results of the analysis back to the manufacturer for crunching on a macro scale. This will result in a better understanding of what is happening in that entire area and make it possible to adjust things like the amount or types of fertilizer and chemicals that should be applied. If the farm equipment manufacturers figure out how to harness all this information, this kind of big-picture analysis could change the commodity trading markets forever." – from 4 Examples of Big Data Trends. Spetember 27,2012. VmwareBlogs.
And how might we use this data? Well some seed companies are already combining farm level data, public data, and their own proprietary data to develop some pretty powerful analytical tools. As discussed recently in an AgWeb technology article Steyer seeds offers a great example with its ACRES tool which is based on a complex form of decision tree
"After they sign up, customers start by selecting their fields from Google Earth maps. Back-end programming then pulls up a wealth of information – everything from soil type to yield potential. As farmers enter in additional information about their farm, such as crop rotation, traits used, etc., the ACRES algorithm spits out recommendations, which users can accept or tweak as needed." AgWeb - Unlock Your Farm Data
Another company, Climate Corporation is also taking advantage of massive amounts of data useful in agricultural applications:
"We took 60 years of crop yield data, and 14 terabytes of information on soil types, every two square miles for the United States, from the Department of Agriculture," says David Friedberg, chief executive of the Climate Corporation, …We match that with the weather information for one million points the government scans with Doppler radar — this huge national infrastructure for storm warnings — and make predictions for the effect on corn, soybeans and winter wheat." –New York Times
We’ve seen lots of efficiency, environmental, and productivity gains in agriculture related to GPS/GIS and biotechnology
. But with every trip across the field more and more data is being generated. Combining these technologies with ‘big data’ definitely will have its benefits, if not continue to revolutionize the industry.
Big Data Goes to the Cows
Big Data in the Dirt (and the Cloud) October 11,2011. NYT. Quentin Hardy.
4 Examples of Big Data Trends. Spetember 27,2012. Vmware|Blogs.
Data analysis, biotech are key in agriculture's future sustainability
By Sarah Gonzalez
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Unlock Your Farm Data
February 15, 2013