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Playing Small Data = Agronomic Success
Based on home runs, the 2014 Kansas City Royals should have finished in the cellar of the American League Central division.
They whiffed, finishing dead last in the league in taters.
Instead, they played “small ball,” a fast-paced style that featured singles, bunts, and stolen bases. This gave the Royals the moxie needed to win the 2014 American League pennant.
Think of the Royals the next time you feel overwhelmed by “Big Data.” It’s easy to be blitzkrieged by the dizzying array of field maps, sensors, monitors, data warehouses, and all other tools this concept encompasses.
“Big data is diverse and complex,” says Matt Darr, an Iowa State University (ISU) Extension agricultural engineer. “Its scale, diversity, and complexity require new architecture, techniques, algorithms, and analytics to manage and extract value and hidden knowledge from it.”
In turn, big data is fueling a new industry, says Darr. Digital agriculture teams large data sources with advanced crop and environmental models that key decisions. Components include:
• Data generation and capture. These include yield maps, aerial imagery, unmanned aerial systems, and wireless data transfer.
• Data warehouses. These store data in the cloud.
• Prescription agriculture. Tools include variable-rate technology and multihybrid planting.
• Probabilistic decision management. These include nitrogen (N) modeling, weather, and soil suitability.
Intimidated? Don’t be.
“Big data,” muses Scott Shearer, Ohio State University (OSU) Extension agricultural engineer. “Maybe it’s not so big. Maybe it’s just small data that wants to be big.”
Break it down with a choice you make each year, such as picking seed.
Ron Wood, Salix, Iowa, began combining satellite imagery with boots-on-the-ground agronomic expertise in his seed-selection strategy three years ago. He uses the R7 tool from WinField Solutions. He consults with his seed rep as he pulls up multiyear layered maps containing vegetation, soils, and yield data while making his seed decisions.
“What I like about it is, between the R7 tool and my seed rep, we can take all the data and layer it together and come up with the right hybrid or variety,” Wood says.
The seemingly small-ball decision of picking the right seed looms large.
ISU research shows two hybrids grown in the same field with identical inputs may differ in yield up to 50 bushels per acre. If a big data tool can help you pick the right hybrid for your field, the investment is worth it.
How It Started
Twenty years ago, cost-cutting precision agriculture tools like autosteer and swath control were just a gleam in even the most tech-savvy farmer’s eye. Today, they’re popular tools, says ISU’s Matt Darr. Typical benefits gleaned by Iowa farmers include:
- An overlap reduction of 3.3% at planting that fuels a $7.89 per-acre return on investment.
- An overlap reduction of 7% for each tillage pass that garners 96¢ per acre.
Based on 180 bushel-per-acre corn yields and a $4.50 per-bushel price, these savings tally $8.10 per acre. This roughly equals a 1% yield gain.
If information gleaned by big data can be put in practice by digital agriculture, these amounts can be compounded into yield gains (or cost savings) equivalent to:
3% that = $24.30 per acre
5% that = $40.50 per acre
7% that = $56.70 per acre
“That’s what could happen if you could make continually better decisions year after year,” says Darr. “Part of these gains can be due to genetics, but they can also be due to the decisions you make from the (Digital Agriculture) tools that you have.”