Yield Monitoring And Mapping In Precision Farming
Many decisions in farming still rely on assumptions — that all parts of a field will respond the same, that last year’s approach will work again. But yield monitoring and mapping show something different: how uneven fields really are and where input use doesn’t pay off. Two areas next to each other can produce very different results, even if treated the same. These differences aren’t random. They reflect long-term issues like soil health, drainage, and input history. When managing large areas, overlooking this variability leads to higher costs and missed opportunities. This article breaks down how to use crop yield monitoring and mapping to avoid costly mistakes in the field.
What is yield monitoring and mapping?
Yield monitoring is the process of measuring how much crop, like corn, soybeans, or wheat, is collected by a combine harvester during harvest. As the agricultural machine works, it uses built-in sensors to record both the amount harvested and other key characteristics (e.g., the moisture content of the grain). This data is tagged with GPS coordinates for yield mapping — creating a detailed map that shows how crop productivity varies across the field.
Crop monitoring and mapping tools are becoming standard on many farms. In fact, over 68% of large U.S. farms were already using yield monitoring and mapping systems by 2023, and this number is constantly increasing . With historical productivity mappings, farmers can clearly see which field areas are performing well and which need improvement, helping them plan more precise actions for the growing season.
Yield monitoring and mapping in precision agriculture
Precision agriculture depends on knowing how different areas of a field behave, and crop yield mapping is key to that. Instead of assuming uniform conditions, it shows which parts of the field consistently under- or outperform others. A study in wheat fields in India illustrated this well: yield monitoring revealed variability with coefficients of 33% and 28% from average . These differences often point to deeper causes — compacted soil, drainage issues, or past input use.
Yield monitoring and mapping data allows farmers to rethink their strategy: perhaps applying different tillage practices, changing crop rotation scheme, or skipping costly inputs where crop productivity doesn’t justify them. Over time, these monitoring and mapping insights support more stable production and smarter investments in every part of the field.
How does yield monitoring and mapping work?
Traditional ground-based yield monitoring relies on tools installed directly on harvesting machines. Ground-based monitoring systems typically include a sensor to measure the grain flow, a GPS unit to record the harvester’s location, and a display that shows the data in real time.
Newer methods allow yield monitoring and estimation not only with ground equipment but also using satellite data. A recent market report valued the agricultural mapping services sector at $5.7 billion in 2024, with satellite-based yield monitoring among the key drivers of an expected 4.4% annual growth through 2032 . Here’s how satellite-based systems work:
- Satellite images provide a wide view of fields, capturing plant growth and health.
- Vegetation indices, which, for example, reflect plant biomass and photosynthesis activity, are computed from the images.
- Monitoring and mapping satellite-based vegetation indices during the growing season helps estimate how crop productivity varies. Studies on maize farms have shown that using Sentinel-2-powered vegetation indices allows for accurate estimates, even for large, varied fields .
- A detailed yield map is then generated, showing where the field is expected to produce more or less.
Let’s compare these two approaches to monitoring and mapping in the table below.
| Method | Satellite-based monitoring | Ground-based monitoring |
|---|---|---|
| Data coverage | Covers the entire field and even multiple fields at once | Limited to areas where the equipment is operating |
| Measurement method | Estimation based on crop reflectance and model correlations | Manual data collection through machine-based sensors |
| Historical data | Easy access to past seasons’ data through archived satellite imagery | Only available if precision yield monitors were used in previous years |
| Frequency | Regular image acquisition during the growing season | Only during harvest |
| Setup requirements | No need for on-field hardware; only software and satellite data | Requires specialized machinery, sensors, and calibration |
| Costs | Cost-effective for large-scale monitoring across multiple fields | High initial investment and maintenance costs |
| Field variability | Detects spatial variability in crop performance early and consistently | Provides accurate data, but only after harvest |
| Scalability | Easily scalable to monitor tens of thousands of hectares remotely | Limited by the number of equipped machines and operators |
| Use cases | Scalable harvest estimation, early detection of growth trends, strategic decision-making across regions | Detailed tracking at harvest for individual field optimization |
Farmers don’t need to choose one method over the other — satellite multispectral yield mapping works great alongside ground-based sensors, adding spatial context and helping detect issues early. It’s especially useful when consistent field-wide insights are needed between harvests.
Getting started with yield tracking and mapping using EOSDA Crop Monitoring
Starting with monitoring crop growth and harvests doesn’t have to be difficult. With our digital crop monitoring system, EOSDA Crop Monitoring, you can take a smart and easy approach to yield tracking, estimation, and mapping. The platform offers:
- Vegetation indices (NDVI, NDRE, MSAVI, and more). Satellite-based vegetation index mapping visualizes crop growth and health during the season. These maps help spot areas that are likely to produce more or less.
- Historical data. You can access past satellite images and vegetation trends to track how different parts of your field performed over the years. Comparing this with the current season helps catch potential problems early.
- VRA map builder. Upload your harvester’s data and use it along with satellite imagery to create variable rate application (VRA) maps. You can also overlay soil or elevation maps based on the platform’s historical data to better understand what’s affecting productivity.
- Agriculture machinery data integration. Seamlessly integrate data from your equipment (e.g., John Deere Operations Center) for a full view of what’s happening in your fields — from machine records to satellite yield monitoring insights.
- Field activity log. Plan and record your work in the field, including planting, fertilizing, watering, and compare it to vegetation patterns and harvest results. This helps see what worked as expected and what didn’t.
- Yield prediction. Available as a custom service, this feature gives early estimates of total harvest to support and improve planning.
Rather than relying on ground-based yield maps alone, EOSDA Crop Monitoring connects harvest results with what led up to them: crop growth patterns, weather shifts, field practices, and soil conditions. These monitoring and mapping features make it possible to see not just where crop productivity varies, but why. And once you know the “why,” you can adjust your strategy during the season, when it still matters.
Real-life example: How satellite yield mapping system helped a U.S. Midwest corn farmer reduce crop variability
When traditional tools weren’t enough to explain why some zones yielded less, an Iowa farmer turned to satellite-based monitoring techniques. This real case shows how EOSDA Crop Monitoring helped identify, analyze, and solve the problem of field variability using smart, targeted input management.
Challenge: Uneven crop productivity and inefficient fertilizer use
A large-scale farmer in Iowa manages several thousand acres of corn fields and has been tracking crop productivity for years using precision combine yield monitors. These data showed that some zones consistently fell behind. The farmer wanted to figure out why and adjust fertilizer use to improve overall productivity without wasting inputs.
Solution: Targeted nitrogen management based on satellite-based yield mapping
To get a better view of what’s happening in the fields, this Iowan farmer signed up for EOSDA Crop Monitoring, an agri-focused platform delivering high-quality satellite images for agriculture during the entire growing season. The platform offers site-specific data from Sentinel-2 (for regular monitoring and mapping) and PlanetScope (for more detailed analysis and mapping).
Using our precision agriculture monitoring and mapping software, the farmer calculated key vegetation indices from the satellite images:
- NDVI images allowed for monitoring and mapping general plant health and biomass;
- NDRE gave a clearer picture of chlorophyll levels, especially later in the season when the plants were fully grown.
With several years of yield monitor data from the combine, the farmer used our agricultural platform to compare satellite index maps from key growth stages with the past vegetation maps. There was a clear match: areas with high NDVI and NDRE values during the season produced better at harvest, while low index readings matched lower-productivity areas.
The farmer now had a map showing where the crops were underperforming. These areas had:
- consistently low NDVI and NDRE readings;
- matching low harvests in previous years.
To find out why, they collected soil samples and studied topography. The tests showed lower nitrogen levels in lower-productivity areas. Some areas were also in slight dips where water often collected, leading to waterlogging.
Using these combined yield monitoring and mapping tools — vegetation indices, and terrain data — the farmer created a VRA map for nitrogen fertilizer. The idea was simple:
- apply less nitrogen where productivity was already high;
- apply more nitrogen where productivity was low to boost growth and even out vegetation across the field.
Next season, the farmer applied nitrogen based on VRA mapping and monitored the field again with satellite data. At harvest, a new crop yield map showed:
- less variability across the field;
- better fertilizer allocation and efficiency;
- higher average harvest quantity and quality.
Outcome: More uniform crop productivity and improved nitrogen efficiency
This new monitoring and mapping approach, guided by vegetation indices, VRA maps, and other complementary EOSDA Crop Monitoring features, helped the Iowan corn farmer not just identify problem areas, but actually fix them. After applying nitrogen variably based on satellite analysis and past yield monitoring data, they saw clear benefits:
- pinpointing which areas needed attention;
- understanding the causes behind low performance;
- adjusting fertilizer rates using VRA mapping for targeted impact.
As a result, the farmer improved overall yield and how efficiently inputs were used. It proved that data-driven field management can lead to measurable improvements without extra input costs.
Monitor yields consistently to make better field decisions
Yield monitoring is most useful in agriculture when it’s part of an ongoing process that enables growers to step back and see the full picture: the shifts, trends, and signals that single-year results often miss. With satellite imagery, farmers can track productivity field by field and year by year, even across crop rotations. Long-term monitoring and mapping help identify trends, such as persistent low-yield zones or gradual improvements after a change in management. Reliable satellite data and mapping models empower farmers to plan and adjust field strategies confidently.
About the author:
Vasyl Cherlinka is a Doctor of Biosciences specializing in pedology (soil science), with 30 years of experience in the field. He attended the engineering college in Ukraine and received his degree in agrochemistry, agronomy and soil science in the Chernivtsi National University. Since 2018, Dr. Cherlinka has been advising EOSDA on problems in soil science, agronomy, and agrochemistry.
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