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We\\u2019ve spoken a lot lately about how agriculture is making use of Big Data to help improve its operations. Everyone from big companies like Monsanto and John Deere to little ones like Trimble are busy collecting and analyzing data to improve their operations. In and of itself, this is a very good thing. Anything that will help agriculture companies work more efficiently will be beneficial to everyone. After all, more efficient farming means more crops, which means being able to feed more people at hopefully a reduced cost. At least, that is what it should mean. What it winds up meaning in practice is often something different entirely.\\xa0

A recent article on the Agriculture Analytics Market report attempts to analyze all the current conditions and make predictions on what is to come. Right away, you should notice something, namely the presence of that pesky little \\u2018p\\u2019 word, \\u2018predictions\\u2019.\\xa0 As long-time readers (or listeners of our podcast) will know, predictions are another word for \\u2018guessing\\u2019. At best, they are an educated guess. You can gather all the data you want about what happened in the past and what is happening right now and predicting what will happen in five years based on that is not likely to be reliable. To some extent, of course, any organization has to plan for the future and that means making your best guess on what that future will be. So that isn\\u2019t the problem, the problem is that too many call their guesses \\u2018predictions\\u2019 and treat those \\u2018predictions\\u2019 as though they are prophecies. Projecting more than a year out has always been dicey but especially in the wake of COVID even a year seems like a distant, unknown land.\\xa0

As annoying as the drive to try to predict the future can be, that isn\\u2019t the main problem we\\u2019ve noticed in the article. We identified a running theme, and one that we see far too often \\u2013 how do we get more? How can we get 20% year-over-year growth every year forever? We touched on an example of this recently in the way that John Deere has been operating and interacting with data. They\\u2019re selling expensive farm machines loaded with sensors to gather information to help improve farming efficiency. All well and good, right? Wrong. Why? Because they sell that to other parties without rewarding the farmers for generating all that data in the first place. Rather than constantly looking for ways to double-dip, why not be content with making money, even if it\\u2019s only the same amount as last year? Why does it always have to be more? Why not, once the profit is made, turn some of those leftover resources to more directly helping people? Not just as a tax write-off but because helping people is the right thing to do.

Rather than taking all the data and selling it, John Deere could offer it back to the farmer, or at least it could do both. As an alternative to finding new mechanical means of squeezing every possible crop out of the soil, why not look for more natural solutions?\\xa0

With TARTLE being in over 70 countries, it is possible to gather data on any number of small farms in a variety of climates. While there are averages to crop yields, it\\u2019s important to remember that those are averages. Those who do unusually well or unusually poor could be studied to find out that they are doing right or wrong. Such knowledge could then be duplicated elsewhere and possibly scaled up to work on the massive farms that inhabit the American Heartland. That is the real genius of TARTLE, we provide a data marketplace that allows the vast resources of the corporate world to support and amplify the knowledge and skill of the smallest farmer in a nearly forgotten valley, while still allowing that farmer to benefit from the exchange. That\\u2019s what we are about, that\\u2019s the TARTLE way.

What\\u2019s your data worth? www.tartle.co