Data = Understanding
Jan 21, 2018
“Most of the world will make decisions by either guessing or using their gut. They will be either lucky or wrong.”- Suhail Doshi, CEO, Mixpanel
Your success starts with seed choices you make. And while you don’t have a crystal ball to see what next year’s conditions will be, you won’t make a bad decision if you rely on data. When you spend hundreds of dollars on just one bag of seed why would you gamble on that investment? You deserve to know how that variety will perform in your particular environment before you open the bag.
Leverage Technology to Help Select Corn Seed
You weigh many factors before deciding what seed to purchase, including soil type, management practices and past performance. And, because we are human, all seed selection decisions contain some degree of “this is what my gut is telling me to pick.” But as technology evolves, it becomes increasingly important to use quality data to enable solid seed decisions. One of the places to access this type of information is through the Character Hybridization Charts (CHT Tool).
The CHT Tool lets you use data to make detailed comparisons of seed from a variety of companies. These comparisons take into account soil type, crop rotation, plant population and management practices such as the level of fertility and type of tillage. And, because the hybrids have undergone numerous side-by-side comparisons, field variability has been virtually eliminated in the data. This level of specificity is something no other ag company can offer. Included within the R7® Tool by WinField® United, the CHT Tool uses data from the Answer Plot® Program to compare CROPLAN® seed products as well as seed products from other major companies to see how they are projected to perform on fields just like yours. In many cases, you can purchase multiple seed brands with top-performing genetics, but sorting through the top genetic suppliers and the top-performing hybrids in the CHT Tool helps you choose the ones that best align with the conditions on your farm and your yield goals. The best way to maximize your hybrid placement is by understanding its response to the environment.
Don’t Compare Apples to Oranges
- Response to Continuous Corn (RTCC)
RTCC (or response to rotation) indicates how well each hybrid performs when planted in a continuous corn rotation. See Seed Characterizations below.
- Response to Fungicide (RTF)
RTF designates a hybrid’s expected level of response to fungicide treatment. See Seed Characterizations below.
- Response to Nitrogen (RTN)
Corn hybrids are evaluated in varying nitrogen environments to determine each hybrid’s ability to tolerate or respond to varying levels of nitrogen management. See Seed Characterizations below.
- Response to Population (RTP)
RTP indicates each corn hybrid’s ability to tolerate low plant population or respond to aggressive population management in different crop rotations. See Seed Characterizations below.
Why Trial Error Matters for Your Data Quality
Trial error calculations helps us understand variation within replicated trials that cannot be accounted for by controlled treatments. In a nutshell, trial error comes from factors that we cannot see or anticipate that affect our outcomes.
For example, if you planted the same corn hybrid in two different locations, it may yield differently due to disparities in nutrient profile, soil moisture, pest pressure, etc. Now let’s say we implemented a trial design that accounted for all of these factors and created a scenario where both fields were, in theory, identical. What if they still yielded differently? It is this unexplained variation that cannot be easily accounted for that we call “trial error.”
How would you explain a veteran baseball player who averages 10 home runs a year suddenly hitting 50 homers one season? First, you’d look at the different variables. Are the baseballs wound tighter? Are the stadiums smaller? Are pitchers less capable? If everything remained the same, is it safe to assume that player will hit 50 home runs again next year? Probably not. But over time and repeated trials (seasons), we’ll be able to tell with certainty if the breakout season was an anomaly or a true indicator of performance.
In short, replication and low trial error are necessary to be confident in your data.