For the majority of our agronomic history—definitely since land grant universities began their correlations and calibrations—our lives have been defined by determinate math, the notion that if you do this, then this will happen. This is how most science has gone since the 1970s.
There is a problem with determinate math in agronomy and agriculture, though. It is a poor way to predict and measure a biological system.
Under the paradigm of determinate math, our shoulds, coulds, and whats are based on means and stats. Researchers do replication after replication to be able to say, “This is the mean.” The problem with this is however carefully a mean is established, you know very well that you can do the exact same things two years in a row and still find a 30 bushel difference.
So enter chaos theory. Enter the butterfly effect. The study of the behavior of a dynamic system that is highly sensitive to initial conditions. Or, less formally stated, the study of surprises of the nonlinear and unpredictable. Which is, you know, what we face every day.
The reality is that we cannot accurately predict production in one season. When I’ve shared such a sentiment in the past, I’ve chalked that shortcoming up to the markets, the weather, or the government, but truly, it amounts to much more than those three. We can’t ever pinpoint here is where it went right or here is where it went wrong because there are simply too many variables to allow us to do so.
Which is why chaos is the better approach to what we do. It has only been in the last decade that we’ve truly begun to apply the idea of chaos to agronomy. It has driven the development of precision technologies including things like capacitance probes, which lend a higher degree of accuracy to our understanding of crop water use and have made us better irrigators; and our suite of imagery, which allows us to see the whys of our problems more narrowly. It has likewise driven the advances in our ability to control planting operations.
Overall, the introduction of chaos math to agronomy and agriculture was revolutionary. It has shed light on what we don’t know about the biological system we work with every day, but more importantly, it has introduced the precision to narrow the possible outcomes we may face from over 1000 (which is about what we face right now) to somewhere around 10 to 30.
Nothing will ever bring us to the point of perfect prediction, but chaos theory will meet the internet of things, and we will progress. Right now, I expect that within ten years, we’ll be able to set forth a plan for a season and predict with 90% confidence what the outcome will be. Which is a huge improvement in our ability to measure and predict.
And such advancement is based in the introduction and application of chaos theory and math into our science and discipline. We’re only ten years into the future of our industry, and in another ten, we’ll have honed our ability to accurately predict outcomes by tenfold. It might all seem a bit chaotic, friends, but it’s a good time to be along for the ride.