Sorting Through Big Ag Data Part 2

  • Many are concerned with the privacy issues associated with precision agriculture data. These are real concerns.  I have worked with private farm data all my career, but precision maps are a particularly difficult kind of data to protect as geo-referenced maps can be quickly matched with other geo-located files and identities revealed.  Because of the size of data and for security reasons, I think we will see this data storied securely and rather than sharing data sets, and analysts will come to the data, run analysis, and walk away with results only.
  • Many producers will interact/share data with various machinery companies, input suppliers, consultants and others. Some will want to lock you into their system, and many producers are faced with the challenge of moving information such as a field boundary map or fertility prescription to various users.  Producers need a safe place to store this data, and seamless means to move this information to fertilizer applicators, USDA/FSA.  I am proud that Mississippi State University is a founding member of the Ag Data Coalition that involves farm organizations, agribusiness firms, and universities committed to helping producers control and utilize their data.  I encourage you to check it out at
  • Getting advice from a black box. It seems that almost every input supplier now has an app or software that is designed to help farmers make decisions.  In some cases, these are going to be really useful farm management tools.  There are some that impress me a lot. I also believe that some are way to entice farmers to give data to service provider or to make it difficult to leave a service provider.  I am also struck by the similarity of the sales pitches made by some big data based decision aids and that of the market advisory services that have a great sales pitch but when evaluated, have no better results than flipping a coin.
  • I just helped organize a conference on big ag data at the Agricultural and Applied Economics meetings. We had big name speakers from many disciplines speak.  In the end, there are limits on what one can learn from unstructured ag data that may not include some really important variables.  Using machine learning to find correlations among millions of records can lead to new discovery, but it can also find spurious correlations that may lead to bad decisions.  We will still need well designed statistical studies to answer some big questions about causation.
  • Big data begs several policy questions such as rural broadband access, funding for research on the topic, and the role of USDA data collection. I once worked at USDA and saw the hard work that goes into collecting agricultural data that is unbiased and statistically sound.  However, over my entire career I have watched as hundreds of ‘experts’ make a living claiming to know better than the USDA reports.   What you can’t find is a multitude of studies assessing the accuracy of these experts like one can find for the USDA reports.  I think we are beginning to see a new wave of ‘we know better than USDA’ business models that charge a fee and have not been verified to really be better.  Let the buyer beware, and let’s remember why USDA was asked to do these reports – to insure everybody in the ag sector had equal access to market information.

Sorting Through Big Ag Data Part 1

Today, one of the biggest buzz phrases in agriculture is ‘big ag data.’  While precision agriculture has been around for some time, it’s important to recognize that the data produced by agricultural equipment, field sensors, and various other technologies may pose a variety of opportunities but may also disrupt some long-standing relationships as well.  I have had the opportunity to engage this topic in recent years by writing a soon to be released white paper as well as being a Board member of the Ag Data Coalition, which is bringing together industry, farm groups, and Universities, to develop producer controlled data storage.

  • Your farm data has value to you, but don’t buy all the hype. Your data has value to you if it helps you make decisions that make you more money. But remember, this will be true only if it really is better than what you already knew.  For example, does a variable rate technology map make a difference is a very uniform field?  Probably not.  Another possibility is if you get information faster. But again, is there a critical decision that makes use of the timelier data?
  • Many talk of the 4 V’s of big data – volume, velocity, variety, and veracity. Volume because of the sheer quantity of data being generated by our farms.  Velocity, reflecting the speed at which data arrives. Variety reflecting the wide array of data – images, sensor readings, numerical data etc.  Finally, veracity reflecting that much of the data produced is of poor quality due to a lack of calibration or some other reason.  Bad data= bad decisions.
  • Your farm data has value to others. Machinery companies can learn how to build a better combine, a seed dealer can learn how a variety performs in actual field conditions, or a company can use your neighbor’s data to benchmark your farm against peers.  However, Terry Griffin of Kansas State points out that data is not like most goods.  If I use a gallon of gas, you can’t have it.  It’s gone.  But I can use my farm data and I can give it or sell it to others and they can use it too.  I think we are beginning to see that it is often not individual data but aggregated data that may be what has the greatest value anyway.
  • The question of data ownership is a murky legal territory. What is paramount for producers to understand is that data contract and the associated fine print are important.  In many ways this is what determines who has access to your farms data.  I recommend you check out the Ag data transparency evaluator at  Rather than ownership perhaps the useful concern is who has access to my data.