How to Interpret a Top Ten Report

A Field, Grower, or Group Top Ten Report can be a little overwhelming. Here's how to break it down and understand what the data is telling you.

It's important to understand that a "Top Ten Report" is going to show you which attributes are either directly or inversely correlated MORE THAN .10 with yield. 

Correlation Table

• A quick way to dive into the data and know where to look for correlations.
• Red is an INVERSE correlation, not a negative correlation.
• If you do not understand Correlation, check out the Understanding Correlation Premier Process.
• Check: The wider the rate range the bigger the correlation factor.

Let's look at this example:

1. It is important to know that Total P Rate (or any "Total X Rate") is applied nutrients.
   
   
2. Whereas, Phosphorus (P) (or any "Nutrient (N)") is referencing soil test values.
   
   

Time to dig into the rest of the report!

Premier 10-20 Classification

• Designed to pull out and show variability across a field.
• Does a great job of showing unevenness across different attributes to combat: "My fields don't have variability.  They are pretty much the same."
  
  

• For the 270.5 acres (bottom 10% yielding 142 bu/ac, with a range of 17.14 - 163.25 bu/ac) in this Top Ten Report, have an average Soil Test Potassium value of 241 ppm, whereas, the top 10% yielding (257 bu/ac), has an average Soil Test Potassium value of 300 ppm.  Although it bounces around in values in the bottom 50%, clearly the top 50% of these acres has higher Soil Test Potassium values.
• Notice how as yield increases that Avg K (Potassium) also increases.
• Conversely, look at Avg CEC (Cation Exchange Capacity):  As yields increased, the average CEC decreased.

• Notice that the rate of applied nutrients also directly correlated to yields increasing.
• You can see that the nitrogen usage or efficiency varies, as well as the Ndex™ value.
• Manure applications can make for large applied nutrient differences across a grower's operation!

Inverse Correlation Questions:

• Why would you have inverse correlations to applied fertilizer? (N Rate P Rate, K Rate)
  • High soil test fertility to begin with?
  • Zones not classified correctly?
  • Not using Management Zone Recs with the latest soil tests and calibrated yield files?
  • Drowned out areas of fields?
  • Sometimes it’s inevitable.
• Why would you have inverse correlations to soil test fertility?
  • Flat rate fertilizer spreading for years will cause this.
  • The high soil test areas are lower productivity and the low soil test areas have been mined and are of high productivity.
  • Not a long term recipe for success.
  • Drowned out areas?
  • Manure applications?
  • High soil test values to begin with?