My maternal grandfather, Skip, was always a farmer in my eyes. Tragically, my mother passed away from leukemia just a month after my birth in 1988. Being the first grandchild in the family, Skip and I were very close. As a child, I spent my days riding on the arm rest of tractors and combines during annual wheat harvest — and when I became a teenager, I worked the farm as a summer job myself.
Yet, Skip’s earlier life was a far cry from the farming world I knew him for. Before I entered the scene, he had delved deep into academia, completing his PhD coursework in Statistics at Texas A&M, College Station, by 1972. Soon after, he embraced a professorship at the University of Maryland, finalizing his thesis in 1974. His groundbreaking research aimed at predicting and pinpointing safety and material risks in industrial settings. This monumental task demanded years of effort. He had to manually gather a decade’s worth of accident reports from diverse companies, process the statistics manually, and then convert these insights into punch card instructions for the university’s computer system. Securing time on that computer was not immediate; it required reservations made weeks or even months ahead. A single coding error could mean starting from scratch, potentially stalling his research by several months.
He left that life in the 1980s to take over the family farm back in East Texas and branch off into entrepreneurship. But the desire to leverage statistical inference was baked into everything he did as a farmer — I just didn’t realize it as a child. To my child’s mind, Skip was doing “office work” as he called it — but in reality he was leveraging IT to forecast and secure financing to meet his operational expenses, optimizing chemistry in fertilizers to increase crop yields, developing strategies to reduce uncertainty in his cash flows through trading futures on the Chicago Commodity Exchange all on a TRS 80 he bought at Radio Shack with 16 KB of RAM connected to a dot matrix printer. Agriculture can be a terribly low margin business — and Skip’s bet was he could use statistics to level the field a bit.
Over the years, the farm did not endure the test of time. Turns out inter-generational farming does not fare all too well when forced to skip a generation — and today’s input cost are more unforgiving than ever — economy of scale becomes the only way to compete profitably — and so most small to mid-size farmers from Skip’s generation were bought out and consolidated — but that happens gradually — little by little (at least it did for us).
I, of course, grew to appreciate the strong connection between statistics and agriculture. I still remember the annual visit from the U.S. Department of Agriculture meticulously sampling crop yields (on every farm, ours included) as part of their National Agricultural Statistics Service. Which provides, in my opinion, one of the great unsung and ongoing data projects in history — helping generations of farmers make “data-driven” decisions — before that was even a buzzword. But I found even more appreciation for all that Skip did decades later as I embarked on my own Analytics and Data Science career — my own second act after spending my 20’s and 30’s serving and globetrotting as a US Army Officer. I often reconnect with him over the phone asking him to recount how they used to run regressions or simulations or how they controlled for random sampling “back in the day”. And then occasionally telling him about how they are doing it now to gauge his excitement as I described to him concepts like machine learning, deep learning, reinforcement learning — — it is somewhat science fiction to him — but he loves hearing about it — even if it isn’t quite real to him this late in life.
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