Does Maize Respond
to an Increased Seeding Rate?
Estimated reading time — 15 minutes
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.
Last year, I conducted several field experiments to determine how effective variable-rate seeding is. I hypothesized that a crop's plasticity can affect the results of the seeding.
To test this hypothesis, I set up experiments with different corn hybrids in 23 fields. In this article, I'll talk about one of them, an experiment in two similar fields in Central Ukraine.
You can read conclusions and recommendations section to find out how the hybrids I selected responded to the different seeding rate, and whether I succeeded in increasing the corn yield.
To test this hypothesis, I set up experiments with different corn hybrids in 23 fields. In this article, I'll talk about one of them, an experiment in two similar fields in Central Ukraine.
You can read conclusions and recommendations section to find out how the hybrids I selected responded to the different seeding rate, and whether I succeeded in increasing the corn yield.
Experimental tasks
I conducted this experiment in Central Ukraine's chernozem, a humus-rich dark soil. Ivan Gumeniuk, innovations director at one of the local farms, helped me organize the project. The corn field experiment was conducted at this same farm last year, in which we tested variable-rate fertilizer application.
This experiment contained two tasks: determining whether variable-rate seeding would affect the corn yield, and testing how different corn hybrids would react to this same seeding method. I chose two fields with similar chemical and physical properties to keep other factors from affecting the results.
This experiment contained two tasks: determining whether variable-rate seeding would affect the corn yield, and testing how different corn hybrids would react to this same seeding method. I chose two fields with similar chemical and physical properties to keep other factors from affecting the results.
I conducted this experiment in Central Ukraine's chernozem, a humus-rich dark soil. Ivan Humenyuk, innovations director at one of the local farms, helped me organize the project. The corn field experiment was conducted at this same farm last year, in which we tested variable-rate fertilizer application.
This experiment contained two tasks: determining whether variable-rate seeding would affect the corn yield, and testing how different corn hybrids would react to this same seeding method. I chose two fields with similar chemical and physical properties to keep other factors from affecting the results.
This experiment contained two tasks: determining whether variable-rate seeding would affect the corn yield, and testing how different corn hybrids would react to this same seeding method. I chose two fields with similar chemical and physical properties to keep other factors from affecting the results.
Throughout the experiments, the OneSoil team helped farmers analyze and plan fieldwork. The farmers selected the seeds and equipment.
Experiment hypotheses
Hypothesis 1. High productivity zones can provide a larger quantity of plants with moisture and nutrients. Low productivity zones provide these resources to fewer plants.
To test the first hypothesis in high productivity areas, I increased the corn seeding rate. In low productivity areas, I decreased it. I can confirm the theory if I notice an increase in yield in the high productivity zones, but low productivity zones remain the same or all grow.
Hypothesis 2. Plants in high productivity zones compete for sunlight because they're not deprived of nutrients or moisture. If we decrease the corn seeding rate here, the competition drops, and a smaller number of seeds results in larger corn yield. Low productivity areas show the opposite: plants are evenly depressed. If we increase the seeding rate, the yield might increase.
If the corn yield increases in both zones, with variable-rate seeding, then that hypothesis is confirmed.
To test the first hypothesis in high productivity areas, I increased the corn seeding rate. In low productivity areas, I decreased it. I can confirm the theory if I notice an increase in yield in the high productivity zones, but low productivity zones remain the same or all grow.
Hypothesis 2. Plants in high productivity zones compete for sunlight because they're not deprived of nutrients or moisture. If we decrease the corn seeding rate here, the competition drops, and a smaller number of seeds results in larger corn yield. Low productivity areas show the opposite: plants are evenly depressed. If we increase the seeding rate, the yield might increase.
If the corn yield increases in both zones, with variable-rate seeding, then that hypothesis is confirmed.
Variable-rate planting experiment setup:
- Delimit field productivity zones
- Identify the yield limiting factor
- Configure variable-rate seeding prescription maps
- Assess the germination rate and number of ears of corn
- Analyze field yield maps
How the VRS experiment went in Delta Field
For convenience's sake, let's call the first field Delta.
Delta Field's area is 112 hectares. The relief has a difference in elevation of 56 meters.
Determining productivity zones. I identified productivity zones based on multiple years of vegetation data that I acquired from satellite images. From year to year, the vegetation data for different parts of the maize field hadn't changed. That's why the productivity zones in this field were pretty stable.
Determining productivity zones. I identified productivity zones based on multiple years of vegetation data that I acquired from satellite images. From year to year, the vegetation data for different parts of the maize field hadn't changed. That's why the productivity zones in this field were pretty stable.
Delta Field's productivity zones
Finding the limiting factor. To determine the reason why the corn field was heterogenous, I studied the relief map, soil brightness data, and soil test results. The farm hired a third-party company to do the soil test.
I came to the conclusion that the primary factor affecting corn yield is the field's terrain; in this case, the length and steepness of its slopes. A long, steep slope retains snow and water poorly. Consequently, during the growing season, these areas see insufficient moisture and, as a result, low yield values.
I came to the conclusion that the primary factor affecting corn yield is the field's terrain; in this case, the length and steepness of its slopes. A long, steep slope retains snow and water poorly. Consequently, during the growing season, these areas see insufficient moisture and, as a result, low yield values.
Delta Field's relief map
We can confirm this if we compare the relief map with the productivity zones map. High productivity zones are situated in flat areas of the field and in drainage hollows. These parts of the field retain much more moisture than the slopes during the growing season.
Creating a prescription map. I then put together a prescription map based on the productivity zone. I defined three corn seeding rates for each productivity zone to get more insight into the relationship between yield and the seeding rate. The first zone had 72,000 maize seeds per hectare. This represented the farm's usual application rate. I increased this number to 75,000 per hectare in high productivity zones. Conversely, I decreased this number to 50,000 per hectare in low productivity zones.
To test the second hypothesis, I laid down two test strips. One with reverse logic for the seeding rate and one with the farm's standard maize seeding rate (72,000 seeds per hectare).
To test the second hypothesis, I laid down two test strips. One with reverse logic for the seeding rate and one with the farm's standard maize seeding rate (72,000 seeds per hectare).
Hypothesis 2. Plants in high productivity zones compete for sunlight. If we decrease the seeding rate here, a smaller number of seeds results in larger crop yield. In low productivity areas it’s the opposite.
Delta Field's prescription map
Evaluating the maize seed germination rate. One cornstalk can grow several ears of corn. That's why the farm's workers and I counted both the number of sprouts and ears of corn.
I marked two to three points in zones with variable-rate seeding. Farmworkers walked to the designated point, manually counting the number of sprouts and ears of corn in an approximately 10-sq. meter area.
I marked two to three points in zones with variable-rate seeding. Farmworkers walked to the designated point, manually counting the number of sprouts and ears of corn in an approximately 10-sq. meter area.
A map of sprouts and ears of corn in Delta Field
After analyzing the manual counts, I noticed that, unlike the case for sunflowers, corn's germination rate didn't depend on the seeding rate. Wherever I sowed 75 plants, there were about 75 sprouts. Wherever I sowed 50 plants, just about as many sprouted.
It's interesting that we saw an average of 61 ears of corn per 10 square meters in a low productivity zone with a low seeding rate (50,000). In a zone with a low seeding rate but high productivity, we saw 73 ears of corn. This means that plants in more fertile soil grow more ears of corn.
It's interesting that we saw an average of 61 ears of corn per 10 square meters in a low productivity zone with a low seeding rate (50,000). In a zone with a low seeding rate but high productivity, we saw 73 ears of corn. This means that plants in more fertile soil grow more ears of corn.
Analyzing the corn yield. I analyzed the yield map in homogeneous zones. Homogeneous zones are small areas of the field where I applied the same seeding rate at each point and got about the same yield. I delineated these zones manually. I couldn't do it automatically due to the statistical noise. Noise, in this case, refers to anomalous yield values that an onboard computer recorded when equipment passed through.
Noise can appear when unloading grain, for example, or when equipment moves along a tramline.
Delta Field's yield map. It was created based on data from an onboard computer
Average yield in the homogeneous plots in Delta Field
I delineated 156 homogeneous plots. I established the experiment's results based on the average yield value in the homogeneous plots.
Average yield, t/ha
Analyzing homogeneous zones in the Delta Field
High productivity zone. In all homogeneous plots with seeding rates of 75,000 seeds per hectare, I noticed a pattern of 2−4 more quintals (1 quintal = 100 kg) in yield compared to plots with a seeding rate of 72,000 seeds per hectare. According to my calculations, the average growth when increasing the seeding rate by 3,000 seeds was 2.5 quintals.
I noted the opposite in areas with a seeding rate of 50,000 seeds per hectare, where the yield fell by 2−7 quintals.
Moderate productivity zone. I did not detect consistent yield growth when increasing the seeding rate from 60,000 to 72,000 seeds per hectare. In some areas, the yield decreased by 3 quintals, while in others, it grew by 2. Another factor most likely affected the yield.
Low productivity zone. I did not notice any pattern of yield change here with seeding rates of 50,000 and 75,000 seeds per hectare. In the area with a seeding rate of 72,000 seeds per hectare, the yield increased by 1.3 quintals. But this area was in the top part of the low productivity zone. This part of the field can be considered the border area between the low and moderate productivity zones. I did not factor these results into my conclusion. As it turns out, the yield in low productivity zones also failed to increase with a higher seeding rate.
I noted the opposite in areas with a seeding rate of 50,000 seeds per hectare, where the yield fell by 2−7 quintals.
Moderate productivity zone. I did not detect consistent yield growth when increasing the seeding rate from 60,000 to 72,000 seeds per hectare. In some areas, the yield decreased by 3 quintals, while in others, it grew by 2. Another factor most likely affected the yield.
Low productivity zone. I did not notice any pattern of yield change here with seeding rates of 50,000 and 75,000 seeds per hectare. In the area with a seeding rate of 72,000 seeds per hectare, the yield increased by 1.3 quintals. But this area was in the top part of the low productivity zone. This part of the field can be considered the border area between the low and moderate productivity zones. I did not factor these results into my conclusion. As it turns out, the yield in low productivity zones also failed to increase with a higher seeding rate.
Experiment results for Delta Field
The first hypothesis was confirmed. When increasing the seeding rate in high productivity zones, the corn yield grew. When decreasing the seeding rate in low productivity areas, the yield didn’t change. Monsanto’s DKC 4014 hybrid corn responded positively to variable-rate seeding.
How the VRS experiment went in Eta Field
Let's call the second field Eta.
Eta Field's area is 78 hectares. Its relief, soil brightness, and soil test results are similar to Delta Field's. Delta and Eta Fields are next to each other. Both fields also had the same limiting factor, their relief. The only difference between the two fields was the hybrid that I planted. In every other regard, the reasoning behind the experiment for Eta Field was the same.
Eta Field's productivity zones
Eta Field's relief
Eta Field's most productive part was the drainage hollow and areas with slopes of up to 2 degrees. For the most part, low productivity zones are areas with a slope of over 3 degrees.
Creating a prescription map. Just as with the first field, I created the prescription map based on productivity zones.
Creating a prescription map. Just as with the first field, I created the prescription map based on productivity zones.
Eta Field's prescription map
In the second field, I used my previous approach to the seeding rate, i.e., I increased the seeding rate in high productivity zones and decreased it in low productivity zones. I laid down two test strips once again, this time, to test the alternative hypothesis. One with reverse logic for the seeding rate and one with the farm's standard seeding rate (72,000 seeds per hectare).
Evaluating the germination rate. The hand count of sprouts and ears of corn in the second field yielded the same results. The seeding rate didn't affect the corn's germination rate.
Analyzing the crop yield. After harvesting Eta Field, I began to analyze the yield map based on homogeneous plots.
Evaluating the germination rate. The hand count of sprouts and ears of corn in the second field yielded the same results. The seeding rate didn't affect the corn's germination rate.
Analyzing the crop yield. After harvesting Eta Field, I began to analyze the yield map based on homogeneous plots.
Eta Field's yield map. It was created based on data from an onboard computer
Delta Field's homogeneous parts in the moderate productivity zone
Average yield, t/ha
I accidentally sowed a different kind of hybrid in the second test strip with the farm’s standard seeding rate (72,000 seeds per hectare), so I disregarded that data. When analyzing the effect of the 72,000-per-hectare seeding rate in the low productivity zone, I based myself on the low productivity area in the first test strip, where I seeded at the same rate.
Analyzing homogeneous zones in the Eta Field
High productivity zones. There were two such zones in this field: an area with a low slope on its left side and the drainage hollow in the central area. The area on the left side of the field was a moderate productivity zone in 2019. The central area continued to be a high productivity zone. When the seeding rate was increased from 50,000 to 75,000 seeds per hectare, the field’s yield fell by 1 quintal.
Moderate productivity zones. The plots in this zone saw their yield grow by an average of 2.4 quintals when the seeding rate was increased from 50,000 to 75,000 seeds per hectare.
Low productivity zones. I did not detect yield growth when increasing the seeding rate from 60,000 to 72,000 seeds per hectare in these areas.
Moderate productivity zones. The plots in this zone saw their yield grow by an average of 2.4 quintals when the seeding rate was increased from 50,000 to 75,000 seeds per hectare.
Low productivity zones. I did not detect yield growth when increasing the seeding rate from 60,000 to 72,000 seeds per hectare in these areas.
Experiment results for Eta Field
Neither hypothesis was confirmed. The yield fell by 1 quintal in high productivity zones, while remaining the same in low productivity zones. I only noted an increase in yield in the moderate productivity zone. This most likely occurred due to the particular qualities of the hybrid corn that I sowed.
Conclusions and recommendations
In this experiment, I confirmed the first hypothesis regarding the increase in yield through variable-rate seeding. When variable-rate seeding was applied in Delta Field, the corn yield increased. Both the hybrid selected for the experiment and the system for determining seeding rates affected the yield's growth. I had the same result in several other corn fields with the same hybrid. This indicates that I also proved my hypothesis about hybrids' effect on the results of variable-rate seeding in the conditions experienced in 2019.
What can we do with this information?
1. Study crop hybrids before seeding. Different hybrids from the same crop respond to variable-rate seeding in different ways. What's more, the same hybrid behaves in the same way during variable-rate seeding, despite other conditions. Over here at OneSoil, we conducted experiments in 23 cornfields last year. We confirmed this conclusion.
2. Test seeding rates for each productivity zone to understand which rate is optimal for the specific field and hybrid. Productivity zones can be created using the OneSoil app's phosphorus and potassium calculator (we'll discuss how to do this in our next article).
2. Test seeding rates for each productivity zone to understand which rate is optimal for the specific field and hybrid. Productivity zones can be created using the OneSoil app's phosphorus and potassium calculator (we'll discuss how to do this in our next article).
What can we do with this information?
1. Study crop hybrids before seeding. Different hybrids from the same crop respond to variable-rate seeding in different ways. What's more, the same hybrid behaves in the same way during variable-rate seeding, despite other conditions. Over here at OneSoil, we conducted experiments in 23 cornfields last year. We confirmed this conclusion.
2. Test seeding rates for each productivity zone to understand which rate is optimal for the specific field and hybrid. Productivity zones can be created using the OneSoil app's phosphorus and potassium calculator (we'll discuss how to do this in our next article).
2. Test seeding rates for each productivity zone to understand which rate is optimal for the specific field and hybrid. Productivity zones can be created using the OneSoil app's phosphorus and potassium calculator (we'll discuss how to do this in our next article).
Experiment conducted by Usevalad Henin
Illustrations created by Nastia Zenovich and Dasha Sazanovich
Text edited by Tanya Kavalchuk
Article composed by Anton Sidorov
Illustrations created by Nastia Zenovich and Dasha Sazanovich
Text edited by Tanya Kavalchuk
Article composed by Anton Sidorov
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