Unpacking the Yield Data in the Test Plots

Over the last few years we have been tracking latitude and longitude coordinates for our test plots. From these coordinates we are using Climate FieldView™ to extract soil type and rainfall amounts to use in our own database system with the hope of gaining a better understanding of how our varieties respond to different environmental conditions. So, what are we finding?

To be honest, in my initial foray looking at the data I’m finding more questions than answers are arising. A big part of the problem is I’m still viewing the data at a very high level. Many variables determine final yield. Even if we were to ignore storm damage, insect feeding and disease damage, we still have sunlight, temperature, nutrients, and water to consider since a shortage or overabundance of these can drastically alter yield. Also, with these conditions, timing is everything. To do this data justice, you need to track all four on a daily basis to look for days when one or several of these factors are going to limit yield and by how much. We have not gotten there yet. 

Instead, we are starting at a much more limited scope to see what patterns emerge.

I have taken each variety and determined its average yield. Results that are 75% above the average yield are classified as ‘High’; plots 75% below average are considered ‘Sub-average’; and everything else is considered ‘Medium’. This produces three yield levels in which we can look for patterns from other variables. See below for what this looks like in table form.

Next, we tracked rainfall at the end of each month and cross referenced that amount with the three yield levels. We can now look for gross response, variety by variety, in relationship to the amount of water. As I strongly implied earlier, this is far from the best way to go about it because we ignore factors such as nutrient uptake, sunlight and temperature. Still, we can pick out a few rough gems from the data. Here are two:

The Tale of Two Years

Here is one question I have been asking: In 2017 RK642SSTX was the product. However, in 2018 it wasn’t quite as dominant. What happened? 2017 saw significantly wetter conditions than 2018 and yield tracked strongly with moisture. 2018 was a drier year and too much moisture seemed to hold back yields, which I must confess is a bit of a head scratcher. My interpretation is that in 2018 we had a lot of late humid weather that let in diseases which we normally don’t see. The drier areas avoided this problem and generated more yield because they were not losing yield to disease. 

So how did 2018’s stud RK579DGVT2P look? It too loved the drier conditions. This should come as little surprise being a Droughtgard® corn, but it is good to see it shown by the data.

Late Soybeans Need Water to Start

One pattern which emerged from that group III soybeans was this: if they hadn’t received more than 6 inches of rain before the end of May, they tended to have sub-average yields. This may be a function of southwestern soils being dry as a rule and not being capable of making up for the moisture throughout the growing season.  

Wrap up

As I have stated, just looking at rainfall does not tell the complete story. It is necessary to roll in the other factors as well. My vision is to look for conditions that will limit yield and evaluate varieties under these conditions. This will require more expanded data tracking and analysis. Stay tuned…

 

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