How to create a prescription map for variable-rate seeding
And how OneSoil can help with it.
Usevalad Henin
Usevalad is an expert in GIS and agricultural chemistry. He has been developing precision farming tools since 2013. He is also the co-founder of OneSoil.
Here at OneSoil, we conducted a number of experiments on variable-rate seeding to find out how it can increase productivity in each hectare of a field. In this post, we'll share our insights into which data can be helpful when doing variable-rate seeding. More importantly, we'll look at how to put together prescription maps for equipment, fast and easy.
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Here at OneSoil, we conducted a number of experiments on variable-rate seeding to find out how it can increase productivity in each hectare of a field. In this post, we'll share our insights into which data can be helpful when doing variable-rate seeding. More importantly, we'll look at how to put together prescription maps for equipment, fast and easy.
Why you need variable-rate seeding
There're two ways you can increase a field's productivity: increasing yield or reducing the cost of seeds. Variable-rate seeding means we sow a different number of seeds in different parts of a field. We increase the rate in some parts of the field and decrease it in others. If we do everything right, we get a higher yield and maybe even reduce seed costs.
Variable-rate seeding is financially advantageous.
Variable-rate seeding is financially advantageous.
What information you need to create a seeding map
It's important to correctly prescribe seeding rates for different parts of a field. This prescription depends on the soil's fertility and, as a result, on the field's productivity. These are the main parameters we use when building seeding maps.
Here's the data we might use for this purpose:
Here's the data we might use for this purpose:
- soil nutrient test,
- soil brightness and relief,
- yield data for multiple years,
- vegetation data.
Soil nutrient test
A soil test gives us valuable information about the soil's properties. It tells us the soil's phosphorus, potassium, and other macronutrient and micronutrient content. It also gives insight into the soil's acidity level and organic matter content. We can then use this data to analyze the soil's fertility. But using soil test results has its downsides.
It's not precise. There's one clear methodology of how to conduct the soil test itself. However, scientific circles still dispute whether grid sampling or zone sampling is the better way to take samples. That means that relying on a soil test's data when assessing fertility can be risky.
It's expensive. It costs about USD $20–50 to test just one soil sample. On average, we need to take one soil sample from each 0.3 ha of a field to create a true map of its chemical properties. If we have a 100 ha field, we need to take 300 soil samples. The whole test will cost us $6,000–15,000 (and that's without counting the cost of taking the samples themselves). In our experience, these investments in soil testing don't provide a return, even if we reach a higher yield.
It's expensive. It costs about USD $20–50 to test just one soil sample. On average, we need to take one soil sample from each 0.3 ha of a field to create a true map of its chemical properties. If we have a 100 ha field, we need to take 300 soil samples. The whole test will cost us $6,000–15,000 (and that's without counting the cost of taking the samples themselves). In our experience, these investments in soil testing don't provide a return, even if we reach a higher yield.
It's not precise. There's one clear methodology of how to conduct the soil test itself. However, scientific circles still dispute whether grid sampling or zone sampling is the better way to take samples. That means that relying on a soil test's data when assessing fertility can be risky.
It's expensive. It costs about USD $20–50 to test just one soil sample. On average, we need to take one soil sample from each 0.3 ha of a field to create a true map of its chemical properties. If we have a 100 ha field, we need to take 300 soil samples. The whole test will cost us $6,000–15,000 (and that's without counting the cost of taking the samples themselves). In our experience, these investments in soil testing don't provide a return, even if we reach a higher yield.
It's expensive. It costs about USD $20–50 to test just one soil sample. On average, we need to take one soil sample from each 0.3 ha of a field to create a true map of its chemical properties. If we have a 100 ha field, we need to take 300 soil samples. The whole test will cost us $6,000–15,000 (and that's without counting the cost of taking the samples themselves). In our experience, these investments in soil testing don't provide a return, even if we reach a higher yield.
Soil brightness and relief
These are the two factors that most often tell us about the soil's organic nutrient content and moisture level. Organic nutrient content and moisture, in turn, influence the soil's fertility.
It's not accurate. Over the past two years, we've manually analyzed several hundred fields. In each case, the relief and organic nutrients' impact on the soil's fertility and productivity was different. Our experience has certainly shown that the relief had the biggest impact on the distribution of organic nutrients in the soil. That, in turn, affected the soil's fertility. However, in some fields, the organic nutrient content didn't depend on the relief at all. That could be because of a low acidity level, for example.
In other words, the relief and soil brightness don't always correctly reflect soil fertility and field zone productivity.
In other words, the relief and soil brightness don't always correctly reflect soil fertility and field zone productivity.
It's not accurate. Over the past two years, we've manually analyzed several hundred fields. In each case, the relief and organic nutrients' impact on the soil's fertility and productivity was different. Our experience has certainly shown that the relief had the biggest impact on the distribution of organic nutrients in the soil. That, in turn, affected the soil's fertility. However, in some fields, the organic nutrient content didn't depend on the relief at all. That could be because of a low acidity level, for example.
In other words, the relief and soil brightness don't always correctly reflect soil fertility and field zone productivity.
In other words, the relief and soil brightness don't always correctly reflect soil fertility and field zone productivity.
Yield data for multiple years
In this case, we need the yield data for at least the past 3 years. The perfect scenario is if the weather conditions and crops growing in the field varied during these 3 years. We need this information to make sure the productivity zones are stable and to avoid making mistakes when deciding sowing rates.
The actual crop yield data, in this case, is the best source to look at when assessing the field's productivity.
The actual crop yield data, in this case, is the best source to look at when assessing the field's productivity.
Limited access to historical yield data. Productivity zones are not always stable. If they change every year because of the weather or crop's sensitivity to growing conditions, we need to look at yield data for the past 5 or even 7 years. Oftentimes, we simply don't have this data.
Calibration issues with combines. If you have one combine working in the field, we can collect all its data and calibrate it in the office. It gets more complicated when there are several combines. With several combines, we have to calibrate all the equipment perfectly to get the real yield data. If we don't track the actual data that each combine collected in the field, we won't be able to calibrate it well in the office.
Calibration issues with combines. If you have one combine working in the field, we can collect all its data and calibrate it in the office. It gets more complicated when there are several combines. With several combines, we have to calibrate all the equipment perfectly to get the real yield data. If we don't track the actual data that each combine collected in the field, we won't be able to calibrate it well in the office.
Limited access to historical yield data. Productivity zones are not always stable. If they change every year because of the weather or crop's sensitivity to growing conditions, we need to look at yield data for the past 5 or even 7 years. Oftentimes, we simply don't have this data.
Calibration issues with combines. If you have one combine working in the field, we can collect all its data and calibrate it in the office. It gets more complicated when there are several combines. With several combines, we have to calibrate all the equipment perfectly to get the real yield data. If we don't track the actual data that each combine collected in the field, we won't be able to calibrate it well in the office.
Calibration issues with combines. If you have one combine working in the field, we can collect all its data and calibrate it in the office. It gets more complicated when there are several combines. With several combines, we have to calibrate all the equipment perfectly to get the real yield data. If we don't track the actual data that each combine collected in the field, we won't be able to calibrate it well in the office.
Vegetation data
In 2019, we conducted experiments on variable-rate seeding in 40 fields with three spring crops: soybeans, sunflowers and corn. In each experiment, we used 4 to 5 years of vegetation relative data values to build productivity maps. What does that mean? We didn't know the actual grain weight that we got at each point of the field, but we could see that Part A of a particular field was 17% more productive than Part B of that same field.
Yield map and vegetation map of a cornfield in the milk stage
This is the fastest and easiest method to use. In just a few clicks, the OneSoil platform lets you get the last 5 years of aggregate vegetation data for each field. We'll automatically choose images for days when the correlation between the modeled crop yield and vegetation was strongest. Then we'll take that data to build a prescription map.
How to create a seeding map in the OneSoil Yield application
1
Go to the 'Create VRA map' section on the report page. Select 'Planting'.
Select the type of prescription: based on historical productivity zones or on recent NDVI image. Check out our recommendations about which type to choose in this section.
Select the type of prescription: based on historical productivity zones or on recent NDVI image. Check out our recommendations about which type to choose in this section.
2
In the next window, specify a crop and variety/hybrid.
3
Input the standard seeding rate and select your preferred unit system (kg/ha or seeds/ha). The seed manufacturer has a standard seeding rate recommendation, but you can also rely on your prior experience.
4
Select the number of zones. Note that it's more convenient to use 3 zones to build control strips.
5
Specify the rates for each zone. If you know how yield varies within this field, follow this rule of thumb: the more heterogeneous the field is, the bigger the gap should be in the low- and high-productivity (or low and high NDVI) zones.
6
Go to the 'Trials' tab to add control strips. This step is necessary to set a trial and interpret its results properly. If you don't want to set a trial, you can download a VRA map by clicking the 'Export' button. If you use the John Deere Operations Center, see how to transfer a VRA map to machinery in a few clicks.
All you need to do now is upload this file on the onboard computer and start sowing.
Keep this in mind when using variable-rate seeding
Our team's experiments in previous years showed us that it's not only important to correctly assess a field's productivity zones, but also to choose the right hybrid.
In 23 cornfields in central Ukraine, high productivity zones saw a yield increase for the majority of hybrids planted in them when the seeding rate was increased. The yield didn't change in low productivity zones when the rate was decreased.
In soybean fields, we noticed a slight yield increase when applying the same approach to seeding rates. However, in the 14 sunflower fields with different soil types and located in various climatic conditions, the yield didn't depend on the sowing rate at all.
In soybean fields, we noticed a slight yield increase when applying the same approach to seeding rates. However, in the 14 sunflower fields with different soil types and located in various climatic conditions, the yield didn't depend on the sowing rate at all.
In 23 cornfields in central Ukraine, high productivity zones saw a yield increase for the majority of hybrids planted in them when the seeding rate was increased. The yield didn't change in low productivity zones when the rate was decreased.
In soybean fields, we noticed a slight yield increase when applying the same approach to seeding rates. However, in the 14 sunflower fields with different soil types and located in various climatic conditions, the yield didn't depend on the sowing rate at all.
In soybean fields, we noticed a slight yield increase when applying the same approach to seeding rates. However, in the 14 sunflower fields with different soil types and located in various climatic conditions, the yield didn't depend on the sowing rate at all.
Variable-rate seeding explained by Usevalad Henin
Illustrations created by Vanya Uvarov and Dasha Sazanovich
Text edited by Tanya Kavalchuk
Layout by Anton Sidorov
Illustrations created by Vanya Uvarov and Dasha Sazanovich
Text edited by Tanya Kavalchuk
Layout by Anton Sidorov
Tell us how you create seeding maps:
Usevalad Henin
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