Once the fields are added to the account, choose the ones you want to start working with right now from the list. To initiate the analysis, click the 'Activate fields' button.
The app will start processing an enormous amount of data – that's why processing can take several hours. Meanwhile, you can go about your business. Even if you close the browser window, the processing will keep running.
What data we process
1️⃣ NDVI data from 2016
This data is needed to build productivity zones. Productivity zones are areas in a field with different yield potentials. The area with the highest yield for several seasons is considered the high-productivity zone. The areas that produced medium and low yield for multiple seasons, are considered medium- and low-productivity zones respectively.
How do we know which zones produced more yield, and which ones produced less? We know that in a certain growth stage, NDVI data of certain crops has a strong correlation with actual yield. Our algorithms analyze 6 years' worth of satellite data. In each season, they select one image that represents the crop development in this stage. Then we look for patterns between these images: we look for those where high and low NDVI zones are constant from season to season and use these images to build the productivity zones.
The main role productivity zones play is to determine the productive potential of each zones and to predict the distribution of yield for the next season. The accuracy of zones affects the results of using VRA technology. If the platform classifies a field as 'suitable for VRA', this means we can predict the productive potential of each zone with sufficient accuracy.
2️⃣ Soil brightness
Soil brightness in satellite imagery can tell us about organic matter content and soil moisture. Soil brightness correlates best with organic matter in the near-infrared spectrum. If the soil is dark, it contains a lot of organic matter and moisture. If it's light, it means the opposite.
To create a soil brightness map, we use satellite images from the Sentinel-2 satellite. Our algorithm selects images in which the soil is plowed and has no crop residues.
3️⃣ Elevation data
Elevation impacts most physical and chemical processes in a field. Analyzing elevation data helps find the limiting factors associated with moisture or the presence of silty particles in the soil.
Elevation data can be found in open sources published by national governments or intergovernmental organizations. In future versions of the app, we'll use data from your machinery to make the elevation map more accurate.
Now you know how we analyze your field data.
👨🌾 After processing is complete, we'll show whether it's possible to create productivity zones in this field. If not, we'll explain why.
Sections and icons in the list of fields will help you quickly understand which fields have already been analyzed, where specific prescription maps were created, and where they haven't been created yet.
Using the filter in the top-right corner of the screen, you can easily sort fields by area, crops, field stability percentage, and more.
If you need to find a field with a specific name, use the search bar in the right upper corner of the map.
👌 A field is suitable for variable-rate application by productivity zones if the productivity zones in it are stable. They should meet the following conditions: productive potential of each zone is constant for more than two seasons, or the area of stable zones in the field is over 40% for two seasons in a row. This allows us to predict the yield distribution in the next season. Pay attention to the elevation and soil brightness because these data points will help explain the productivity zones and increase the level of confidence in the zones built by the app.
For fields that are suitable for variable-rate application by productivity zones, we'll build productivity zones and create a soil brightness map and elevation map. For such fields, you can create a VRA map for planting, fertilization, and plant protection products and set up a VRA test with control strips.
🚫 If a field isn’t suitable for variable-rate application by productivity zones, there may be three reasons for that:
The field was divided into two or more fields season after season with different crops growing in them at the same time. Try updating field boundaries so that they are accurate for each season.
Zones are not constant from season to season in terms of their productive potential because of unpredictable weather conditions. We can't predict the distribution of productivity zones for the next season.
From year to year, the NDVI in the field is homogeneous. So it's no use trying VRA there.
If productivity zones can't be built in a field, we'll explain the reason and create a soil brightness map and elevation map.
For fields like this, you can create a VRA map for fertilizer or plant protection application based on the recent NDVI image (or any other image available for the last six months). If you see the message about the "divided field" issue (i.e. if a field was divided into two or more fields with different crops), you can adjust its shape so that its boundaries are accurate for each season.
Time to check out the field report! Click a field on the map or in the list of fields and find out how the field's productivity zones correlate to the elevation and soil brightness.
Already using OneSoil Pro?
Ready to study your field inside and out?
Here's what you'll learn from the field report