It happens on every farm, and we all know it well. That time after seeding when not only do you not want to see anybody from work, but the thought of getting back into a tractor cab has you claustrophobic. It reminds me of the movie The Hangover, where one of the best quotes was “you are literally too stupid to insult”. I believe this sums up the attitude of many post-seeding.
Lucky for me, I get to avoid this hangover. Whether they don’t trust me on a drill anymore, or I am too important to be at the farm in the spring (I am fully aware it is the first one), I get to sit at my desk and debrief the seeding window while the rest of the team recovers.
This is one of the missing data pieces on most operations, the review of operational and financial efficiencies that make up the small seeding window. For me, as both a consultant and an accountant, this is one of the more exciting times in terms of learning and providing value. What do the machines and the analytics show us went right, and what went wrong?
The Human Factor
Although many would believe that the machinery data is the most fascinating and telling, it is actually the human factor that we focus on first. Equipment is short-term, and people are long-term. If you cannot keep employees around, it does not matter how large or new the equipment is, the work will not get done.
Rather than focus on efficiencies in humans and total time worked, we focus on lowering the hours worked per employee, per day, and most importantly how well we limited the 16-hour parameters. Burn-out is our largest concern at seeding time because retention is our most important annual metric.
During seeding we review hours worked per employee (in comparison to prior years), overtime hours, and days over 16 hours. These metrics are done weekly because they identify how hard we are pushing the crew and most importantly how safe the operation is.
Studies have shown that the 16-hour window is the cut-off for safety, mistakes, and all-around breakdown of employees. It is for this reason that we run 24 hours during the seeding window. We have the ability to keep all of our employees below 16 hours per day and it limits the total overtime hours required for keeping on budget.
Our 2023 seeding was difficult to compare to the past year. The spring of 2022 was one of the most unique in the last decade – we were three weeks behind on starting, had excess moisture before, during, and after the seeding window, and used the Valmar on close to 4,000 acres due to moisture. The metrics that we found the most beneficial were that our total median hours per day fell around 14.5 (up from the prior year’s 12.5 due to more sustained days of seeding due to fewer rain delays), but still well below the 16-hour threshold. Our total hours to complete seeding was around 3,850 over 19 days while in the prior year, we were 4,650 over 33 days (even though acres were a few thousand higher this year). Lastly, days worked over 16 hours by our crew were reduced over the seeding weeks by 29 days.
What metrics do you use to evaluate time worked by family or employees?
The Equipment Factor
As most of you know the Hebert Grain Ventures farm bleeds green paint these days. To be fully transparent I had not actually sat in a Deere until I joined the team in 2019, we grew up red (which is why I spend my days sitting in a Case IH chair). Until John Deere gets me a chair, I will continue this trend even as parts of the chair are starting to fall apart. We are green primarily because the data and analytics John Deere provides are priceless.
The Ops Centre application from Deere provides me access to every machine on the farm (from combines to sprayers, to tractors, to the tanks, and finally to the service trucks and secondary tractors). This allows me to pull a number of metrics that we not only can benchmark to our historical operations, but also to other farms.
I was able to identify the variance in fuel usage, machinery efficiency, and acres per day on the drill with worn tips versus the new drill in the prior year. Did it do a better job seeding, probably not, but the learning curve out of the machines is what gets me out of bed some mornings.
So, what did we find this year?
One of the most interesting factors was identifying what the inefficiencies in the prior year were. Due to the excess moisture, we spent a significant amount of time searching out dry ground last year. This had our transport hours percentage on the machines closer to 17% of total hours (when joined with idle time for filling and fueling this was the non-working percentage of 41%).
In the current year with better conditions, we lowered the transport down to 4% of total hours (now 34% of total non-working). The other interesting item was that even though seeding was 50% fewer days in 2023, the difference in machinery hours was only around 100 hours (showing the difficulty in the prior year).
The other interesting item was that one of our goals was to slow the machines down this year to see the variance in yield results. Overall, we dropped the machines’ average speed across the farm from 5.2 down to 4.8 across all commodities. The data on potential yield will be interesting once we complete the analysis. This, in combination with an easier pull in dryer conditions, also led to a drop in fuel consumption drastically from the prior year which lowered our cost per acre for the drills.
What metrics do you use to evaluate machinery optimization and utilization?
As farm operations continue to size and the margins continue to tighten, certain efficiencies will become important. Whether it be the labour force being stronger and more efficient, or the machinery being able to cover more acres per day with fewer costs or requirements, these will add up in the long run. I believe that the “pen is mightier than the sword” and for this, I will continue to stare at computer screens, not fields.