Best Practices for Variable Rate Seeding Pt. 1 - Building Scripts
There are multiple ways to build out scripts for your seeding populations and understanding your fields is essential to starting the process. But first things first, what data are you going to use to understand the field spatially?
Yield data is the standard when it comes to understanding the final performance of the field. To count on your data you want to make sure your equipment has been calibrated and the maps have been cleaned up. Some of the benefits to yield data include that it is your own, trusted data, it easily shows you the performance across your field, and it ties actual yield information to the field spatially.
A downside is that a lot can happen from the field's potential yield to final harvest. It's unfortunate when it happens but late fall weather issues like hurricanes or hybrid issues, like poor stalks, can influence yield without it being a true representation of the field’s ability. Keep these outside impacts in mind when using multiple years of yield data.
USDA has provided SSURGO (Soil Survey Geographic database) maps for almost any area of interest through web soil service. This information can be very valuable for understanding the soil variability across the field and is often a starting point for growers who do not have access to other types of trusted data. This information’s accuracy varies from geography to geography. If you plan on using it, be sure to ground truth it by examining the field in person to verify soil changes spatially.
Satellite imagery has come a long way from Google aerial images to detailed fine-resolution actionable maps with normalized difference vegetative indexes (NDVI). It is more and more accessible to growers across the United States. Finding imagery at peak vegetation for your field is often a good indicator of yield potential. It eliminates some of the noise environmental impacts can have on your crop later in the season and usually highlights variability across the field clearly. Like yield data, you can combine multiple years of NDVI images together to make a composite spatial map of your field basing the areas off of vegetation rather than actual yield. Bare soil images are also available and useful when understanding soil variability specifically when you notice discrepancies with SSURGO data. These bare images often indicate areas of higher and lower organic matter and may help you identify areas where drainage may be an issue.
Understanding the elevation differences or “relief” in your field can definitely impact the rate changes you plan on making in your VR seeding script. Soil survey data will supply you with general slope information but access to LiDAR (light detection and ranging) maps can give you a precise spatial representation of your fields elevation and relief.
A combination of yield, soil maps, satellite, and elevation maps is helpful since they all provide value, but all can still have holes. Together, they can provide a more complete look at the field. Now that you have your field broken into zones, it’s time to start thinking about how you will populate them. We will talk more about how to work through that process in our next blog.