Every year, various natural and management-induced factors influence your crop yields. You probably use raw combine data and properly-calibrated yield monitor data to find out what’s happening at the field level.
However, the large amount of data you have to collect (plus the fact the quality varies) can make it challenging to interpret the exact cause(s) of your variable yields.
If you know how to process those factors and give proper weight to data, you can make more educated decisions about future management plans.
Here’s what you need to know about using combine and monitor data to determine yield variability.
Yield Data Has Potential and Limitations
If you’re like most farmers, you have access to a lot of data regarding which of your fields are more productive. You may even have an idea of how much yield variability exists within a specific field.
While yield data always has the potential to be useful, here’s what you should know:
- Raw combine data is an estimate of yield for a given location and time. It can’t tell us why the yield is high or low, and it won’t reflect why or how yields were affected by inputs, management, pest pressure, topography, or other factors.
- While yield monitors are reasonably accurate, they’re not perfect. What records as high or low yield in one location may be due to various hiccups that occurred during the estimation process.
- Yield monitor data gathered after accurate calibration is much more reliable. It helps put a quantifiable number on the yield variability and it allows you to discover information about the underlying causes of this variability within each field.
- Yield monitor data can also give insight into the effects of various management practices, which guides economically sound management decisions, such as hybrid selection or fertility inputs and rates.
So, can you trust data points in a yield map?
Accurate calibration of a combine ensures the majority of data collected within a strip or field is a good representation of actual yield. It also allows you to identify trends in the data and create future management plans.
However, while it provides an overall indication of yield, it can’t ensure every single point in every location is accurate.
For example, in both yield maps below, some locations appear distinctly different from their neighbors.
Are these yields actually higher or lower than the nearby points? Probably not. They’re more likely due to error from combine measurements.
Also, if you average the data, these points could drastically influence measurements of the final yield for the entire field.
Where do you go from here?
While you’re attempting to derive useful information from the data to make better-informed decisions, analyzing of combine and yield data is not a straightforward process.
Remember, the ultimate goal is to come up with a reasonable estimate of how variable yields are within a field and what management practices can be used to improve productivity in the future.
Key Points to Remember When Reviewing Combine and Monitor Data
Correctly calibrating equipment before and during harvest is crucial if you want to rely on the yield data gathered. See our detailed steps for calibrating a yield monitor.
When reviewing combine and yield monitor data, look for larger trends, and don’t get hung up on each yield point. These can be attributable to hiccups or glitches in the data gathering process.
Always consider how or why yield trends occur in the field by comparing yield data to various management practices, inputs, or pest pressure.
Why You Need Clean Data
All data collected has a role to play, but the reliability varies. Raw combine data is good. Data from a properly-calibrated yield monitor is better.
Clean data is best.
For example, a 150-acre field will generate 30,000-60,000 data points, which can be overwhelming. To ensure the data is correct, clean the data to remove obvious and not-so-obvious sources of error.
(And this is where we come in.)
We help farmers interpret their data and determine actual yield and the effects of natural and management-induced factors.
We analyze data with a layered approach, using various statistical methods to determine yield drives, and then derive recommendations based on this analysis process.
We can also help farmers quickly and easily analyze their data to make decisions they can be confident about year after year.