How a Variable-Rate Strategy Reduced Seed and Fertilizer Costs by $64/ha While Increasing Corn Yield
Reading time - 10 minutes
A case study from Ukraine showing that precision agriculture delivers the greatest value when every decision is based on field data
Olha Matsera
OneSoil Agronomist
Precision agriculture is often associated with individual technologies - variable-rate application maps, satellite imagery, soil analysis, or precision seeding.
In reality, the biggest gains come not from using these tools independently, but from combining them into a structured decision-making process.
This case study follows a farm in Ukraine's Vinnytsia region that used OneSoil Productivity Zones to guide soil sampling, identify yield-limiting factors, and implement variable-rate fertilizer application in corn.
During the very first season, the farm reduced seed and fertilizer costs while increasing average yield. More importantly, it established a repeatable workflow for making future agronomic decisions based on data rather than assumptions.
In reality, the biggest gains come not from using these tools independently, but from combining them into a structured decision-making process.
This case study follows a farm in Ukraine's Vinnytsia region that used OneSoil Productivity Zones to guide soil sampling, identify yield-limiting factors, and implement variable-rate fertilizer application in corn.
During the very first season, the farm reduced seed and fertilizer costs while increasing average yield. More importantly, it established a repeatable workflow for making future agronomic decisions based on data rather than assumptions.
Crop: Grain corn
Field size: 34.9 ha
Location:
Vinnytsia region, Ukraine
Field size: 34.9 ha
Location:
Vinnytsia region, Ukraine
Results After the First Season
$64/ha
1.37 t/ha
$2,234
Total savings reached
The yield gain reached
Reduction in seed and fertilizer costs across the entire field
Step 1. Identify Stable Productivity Zones
The project started with understanding how the field had performed over time.
OneSoil analyzed six growing seasons of satellite imagery, automatically excluding low-quality images, to generate a Productivity Map.
The analysis showed that 64% of the field consistently remained within the same productivity class over multiple seasons. This indicated that yield variability was stable rather than random, making the field a good candidate for precision agriculture.
OneSoil analyzed six growing seasons of satellite imagery, automatically excluding low-quality images, to generate a Productivity Map.
The analysis showed that 64% of the field consistently remained within the same productivity class over multiple seasons. This indicated that yield variability was stable rather than random, making the field a good candidate for precision agriculture.
Step 2. Validate the Productivity Map
Before using the map for management decisions, the agronomy team needed to verify that it reflected real field conditions.
The productivity map was compared with topography and, later, soil analysis results. The comparison produced a 0.95–0.98 correlation, confirming that the identified productivity zones closely matched the field's underlying characteristics.
This validation gave confidence that the map could serve as the basis for future agronomic decisions.
The productivity map was compared with topography and, later, soil analysis results. The comparison produced a 0.95–0.98 correlation, confirming that the identified productivity zones closely matched the field's underlying characteristics.
This validation gave confidence that the map could serve as the basis for future agronomic decisions.
Step 3. Collect Soil Samples by Productivity Zone
Zone-based sampling provides a much clearer picture of field variability and helps improve nutrient management decisions.
The next step after creating the Productivity Zones map was to generate a soil sampling map for agrochemical analysis.
A common approach to soil sampling is to use a grid-based sampling map. However, this method has a significant limitation, especially in heterogeneous fields: a single sample may include both high- and low-productivity areas.
As a result, the laboratory reports an average value that does not accurately represent either zone.
That's why we use zone-based soil sampling. This approach not only reduces the number of samples and lowers analysis costs, but also provides results that accurately reflect the characteristics of each individual area of the field.
A common approach to soil sampling is to use a grid-based sampling map. However, this method has a significant limitation, especially in heterogeneous fields: a single sample may include both high- and low-productivity areas.
As a result, the laboratory reports an average value that does not accurately represent either zone.
That's why we use zone-based soil sampling. This approach not only reduces the number of samples and lowers analysis costs, but also provides results that accurately reflect the characteristics of each individual area of the field.
Step 4. Identify Yield-Limiting Factors
The laboratory results were then compared with the Productivity Map.
Lower-productivity areas consistently showed lower levels of available sulfur and zinc. While these nutrients were not assumed to be the only factors affecting yield, the analysis suggested they were among the key constraints limiting crop performance in those zones.
These findings became the basis for planning fertilizer application.
Lower-productivity areas consistently showed lower levels of available sulfur and zinc. While these nutrients were not assumed to be the only factors affecting yield, the analysis suggested they were among the key constraints limiting crop performance in those zones.
These findings became the basis for planning fertilizer application.
Step 5. Create a Variable-Rate Fertilizer Map and Establish Field Trials
Using both the Productivity Map and the soil analysis results, the agronomy team created a variable-rate fertilizer prescription map.
At the same time, fertilizer rate trials were established within the field (control stripes).
Rather than assuming that different application rates would deliver better results, the farm chose to test those assumptions under real field conditions.
This approach allows fertilizer strategies to improve over time based on measured performance rather than fixed recommendations.
At the same time, fertilizer rate trials were established within the field (control stripes).
Rather than assuming that different application rates would deliver better results, the farm chose to test those assumptions under real field conditions.
This approach allows fertilizer strategies to improve over time based on measured performance rather than fixed recommendations.
Step 6. Evaluate Different Fertilizer Rates
The main conclusion was straightforward: profitability came not from applying less fertilizer everywhere, but from applying the right amount where it could actually produce a return.
After harvest, the results of the field trials were analyzed. In high-productivity areas, the highest fertilizer rate produced the greatest yield - 9.89 t/ha.
However, the difference compared with the medium fertilizer rate was not statistically significant. This indicated that the medium rate could achieve nearly the same yield while reducing fertilizer costs.
In low-productivity zones, increasing fertilizer rates produced virtually no yield response. Applying additional fertilizer in those areas simply did not generate sufficient economic return.
However, the difference compared with the medium fertilizer rate was not statistically significant. This indicated that the medium rate could achieve nearly the same yield while reducing fertilizer costs.
In low-productivity zones, increasing fertilizer rates produced virtually no yield response. Applying additional fertilizer in those areas simply did not generate sufficient economic return.
Step 7. Validate the Strategy After Harvest
The final step was to compare the combine yield map with the original productivity map.
The resulting 0.8 correlation confirmed that the Productivity Zones accurately represented the field's production potential. This also means that the accumulated data can be used with greater confidence when planning future seasons.
The resulting 0.8 correlation confirmed that the Productivity Zones accurately represented the field's production potential. This also means that the accumulated data can be used with greater confidence when planning future seasons.
Economic Results
This represented a 15.7% reduction in combined seed and fertilizer costs.
At the same time, average corn yield increased by 1.37 t/ha, from 8.01 to 9.38 t/ha. That is a 17.1% increase in the first season of variable-rate application.
At the same time, average corn yield increased by 1.37 t/ha, from 8.01 to 9.38 t/ha. That is a 17.1% increase in the first season of variable-rate application.
Why the result was not simply about using fewer inputs
This project demonstrates that the value of precision agriculture does not come from any single technology.
The farm first identified stable productivity zones, validated them against field characteristics, used them to guide soil sampling, identified potential yield-limiting factors, implemented variable-rate fertilizer application, tested different fertilizer rates through field trials, and finally evaluated the results using harvest data.
The outcome was a $64/ha reduction in seed and fertilizer costs, together with a 17.1% increase in average corn yield during the first season.
Perhaps the most valuable result, however, is that the farm now has a reliable, data-driven foundation for future decisions. Each new season adds another layer of knowledge, making fertilizer strategies progressively more precise, more efficient, and more profitable.
The farm first identified stable productivity zones, validated them against field characteristics, used them to guide soil sampling, identified potential yield-limiting factors, implemented variable-rate fertilizer application, tested different fertilizer rates through field trials, and finally evaluated the results using harvest data.
The outcome was a $64/ha reduction in seed and fertilizer costs, together with a 17.1% increase in average corn yield during the first season.
Perhaps the most valuable result, however, is that the farm now has a reliable, data-driven foundation for future decisions. Each new season adds another layer of knowledge, making fertilizer strategies progressively more precise, more efficient, and more profitable.
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