Danny Vandecoevering wrote with this interesting email. I thought it was worth sharing here:
A couple weeks ago I asked you a question on twitter [his question was in response to this post] regarding what variation in day to day clipping volume would be considered statistically relevant. The reason I asked is because when I first heard about the concept of measuring clipping volume, my first thought was that there’s no way that could be an accurate or consistent measure of growth. My thought at the time was that wet clippings would aggregate more than dry clippings, leading to higher volumes, or that a number of other factors would lead to information that wasn’t consistent or useful. I don’t think I was alone in thinking that at the time, and I’d bet many people still think along these lines. (For the record, I’ve made a complete 180 in terms of how I feel about collecting this data)
What I find most interesting when I reflect on this early introduction to clip volume is not how I attempted to discredit its usefulness, but simply that I did. We apply water, in general, based on ET or what our moisture meters tell us. We apply pesticides and PGRs based on GDD models or other various environmental thresholds. Certainly, the same should apply to how we fertilize turf.
I’ve been reading through a book called Thinking, Fast and Slow by a psychologist named Daniel Kahneman. The book discusses the tendencies of the conscious and subconscious human mind across many applications, and while dense, has been a fascinating read. The most recent chapter discussed the use of algorithms and formulas to make decisions versus the use of expert opinion. He discusses research done by Paul Meehl (specifically “Clinical vs. Statistical Prediction: A Theoretical Analysis and Review of the Evidence”) that has shown that 60% of the time algorithms or formulas are a more accurate method of predicting an outcome than “experts”. He then goes on to say that the remaining 40% of the studies ended in a tie between experts and formulas, there were no examples of a human being a more accurate predictor than formulas and algorithms.
He then goes on to say that this research is unsurprisingly unpopular among experts and specialists, and that the aversion to using algorithms or formulas to make decisions comes from humanity’s deep - rooted desire for natural over synthetic.
The topic of clipping volume kept coming to mind as I was reading this chapter, and I couldn’t help but think that one of the greatest challenges to the widespread adoption of this method of greenkeeping has nothing to do with whether or not it works, but rather is contingent on one’s ability to overcome their human nature. Meehl’s research suggests that if one were to utilize clipping volume as tool to apply fertilizer then the worse case scenario is that the accuracy of their fertilizer applications doesn’t improve or get better, but it is more likely that it improves.
If you read all of this waiting for a question, sorry to disappoint, I simply thought the correlation between the research discussed in this book and the real life example of utilizing clipping volume to apply fertilizer was very interesting. I wouldn’t be surprised to be sharing information you’re already well aware of, but if not then I hope you find it interesting and / or helpful.