VRA Seeding: Endless Variables, One Goal
SST Software employee Patrick Sanders, when he’s not putting out fires for the Stillwater, OK-based ag tech outfit, helps raise corn and soybeans across 2,100 acres in Central Illinois with his father Greg Sanders, who is a fantastic source of information when it comes to collecting field data and using it in a variable-rate seeding (VRA) program.
This is mostly due to the fact that Sanders has been collecting yield data for the past decade and began making VRA planting decisions based off the data about two years ago. Sanders says that from his experience growers just getting started in VRA seeding should aim to have at least three years of normalized yield data at the ready, although five is the high-water mark.
“Prescriptions can be based on yield data from different crops, but I tend to gravitate toward using corn yield data for corn and soybean yield data for soybeans,” he says. “There’s enough difference between the two crops that I didn’t feel like we wanted to combine multiple crops together.”
Sanders points to a proprietary SST process as having been a big help in ensuring he and his father are using good, clean data, which of course makes for sounder decision making on where to place higher seed populations and where to pull back.
“SST has had a process developed over 15 years ago called multi-year yield analysis, that allows us to take yield data from different crops, and normalize each year independently,” he explains. “The process puts yield on a scale with 100 being the average for a given year, and then it scales it up or down based on a percentage below or above average.
“The process is repeated for each season of yield data, then averaged across all seasons to derive an average normalized yield value. This takes bushels per acre out of the process and leaves you with an index to average across multiple seasons.
“For example, a reading of 110 means you are typically 10% above average, therefore you can make the assumption that you can bump up your yield goal in that area by 10% — which means apply more seeds so you can get more bushels, add more nitrogen to feed that seed, and it ends up being something that you use across multiple decision making events.
For Sanders and the folks at SST, it’s all about historical yield data. “We have a saying at SST that nothing correlates to yield like historical yield,” he says. “Utilizing that approach we tend to bump up seeding rates in areas with a high index as we believe the investment in more seed will typically return more dollars per acre.”
In the historically-lower yielding areas of his fields, Sanders advocates a more conservative approach.
“So you might know that based on history that area doesn’t typically do well. There’s no reason to put more fertilizer down, there’s no reason to put more seed down because the rate of return from that area is not as significant, so you’re not really trying to level out the field as much as you’re trying to exaggerate the higher yielding areas and get more return out of those, and then save inputs on the lower yielding areas.”
The Next Step: Combining Data & History
Sanders and his dad then take everything one step further by combining the yield data with historical knowledge of the field to identify areas they deem unstable, where in a drought year the ground will perform admirably well, but if too much moisture comes into play the duo could be left with a complete loss.
“A temporal stability map (in SST Summit) shows you areas that are typically stable whether that be typically high, average or low yielding ,” he says. “What you are after with stability maps is finding those areas where production levels change significantly from season to season. The final step is combining the average normalized yield layer and the temporal stability layer to derive a layer we call ‘management units.’
“The management units map pulls out high, average, and low yielding areas, as well as unstable areas, which gives you the opportunity to treat an unstable area a little bit different,” Sanders continues. “If you feel like the upcoming season will be a little bit wetter or dryer than normal it allows for a change to our seeding practices to meet the level of risk we are willing to take in an unstable area.”
In closing, Sanders says that any grower can have the most accurate yield data, all perfectly normalized and they can still be missing a couple major components, one being the trusted advisor.
“In the end it all comes down to the local agronomist and the hybrid choice because you can make the best variable rate seeding recommendation, but if you’re not taking into account the hybrid that you’re going to plant and its response to changing the seeding rate, hybrid choice is a big part of making the right prescription for the field.