Blog / Partnership / Yield data and analytics in GeoPard


raw and processed yield data in GeoPard
6 mins read |

In this article:

  • Using yield analytics in precision agriculture
  • In-depth yield data analytics in GeoPard Agriculture 
  • Visualization of each attribute in Yield files
  • Correction of raw yield data 
  • 5 Practical examples of usage of yield maps
Raw and cleaned yield data in GeoPard
Raw and cleaned yield data in GeoPard

Yield data  allows you to make more informed decisions and improve growing efficiency.
Field management zones constructed from multiple years of yield data are suitable for an initial assessment of potential yield and soil nutrient variability to make future crop management decisions.

Analysis of yield data can be converted to a variable rate application map and used, for example, for fertilizer application. Its calibration is another topic you need to consider, we will cover it in a separate blog post.

The advanced analytics in GeoPard  is that you can perform multi-layer analysis by combining multiple layers of data into one map and looking for relationships between the data layers. 

Combined productivity zones can be generated based on vegetation indices from satellite imagery, topography, data from machinery such as yield, electrical conductivity, soil moisture, and others, as well as agrochemical analysis results.

Visualization of yield files  is done automatically after downloading the file, it’s automatic processing and cleaning. Two versions of maps are shown below – the original image with data from the equipment monitor as is, and the GeoPard visualization.

The raw data has been converted into a gradient continuous surface image, for an easier understanding of the field heterogeneity and for creating management zones.

Each of the attributes of the yield file is available for visualization, such as moisture, yield mass, yield volume wet and dry, downforce, fuel consumption, machine speed, and so on.

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Raw data correction  means that if a point in the field is unnatural, it will be smoothed (for example, working over not the full width of the combine header). When creating Zones-based yield data, you can correct individual zones and polygons. 

Let’s take a look at some practical examples of using yield maps and other GeoPard data layers.

1. Management zones based on yield data.  Management zones can be constructed based on either one year’s yield data or multiple years. It is important to note that you cannot directly stack yields from different years, as you will get a bias in favor of one of the years.

To reduce this effect, GeoPard applies several algorithms to make the weight of each year even.  You can set the importance of a single year through the Weight tool when you create a Multi-layer map.

Such field management zones can be used to build application/prescription/Rx (VRA) maps, calculating the potential yield in each zone.

Multi-year and multi-layer yield potential map
Multi-year and multi-layer yield potential map

2. Multi-layer zones with yield data and other data sources (topography, soil, sensor, satellite).  It can be added to multilayer analytics  and set the weight it will have on the final zones.

In this example, three layers of data are added to the map: Yield, Satellite imagery, and Topography. You can combine any data layers you consider relevant for analytics. The multi-layer map can be used for further yield analytics and for creating VRA maps. 

Yield, Topography and Satellite imagery
Multi-layer zones: Yield, Topography and Satellite imagery

3. Yield calculation on zone and field level.  To analyze different treatments, seed varieties, and agronomic practices you probably want to compare the average and total yield in each zone, strip, or between fields.

  GeoPard Field Potential maps vs Yield data

GeoPard automatically calculates this for you to make it easier to compare yield in absolute numbers. 

GeoPard calculates yield in abs numbers based on Yield files. Total and average for field and each zone
GeoPard calculates yield in abs numbers based on Yield files. Total and average for field and each zone

4. Dependency zones based on yield data. Zones based on yield data can be overlaid on other data zones and you can search for dependencies between data layers. This example shows the overlay of high yield and average protein (1) and low yield and high protein (2) of different wheat varieties in a field.

Other examples include the relationship between the influence of topography on yield, the intersection between low yields, and the lack of macro-and micronutrients in the soil, soil moisture, and electrical conductivity (EC) layers.

Intersections of different yield and protein levels
Intersections of different yield and protein levels

5. Variable Rate application (VRA) maps  based on yield data.  You can create prescription maps for different operations – fertilizing, seeding, spraying, irrigation, and planning of soil sampling. You can edit the number and shape of the zones.

You can also build a prescription map for a variable rate application by combining yield data with other data sources (soil, EC, satellite, topography). 

Variable rate Seeding rates per zone
Variable-rate Seeding rates per zone

Even if you do not have yield data, you can use GeoPard  multi-year zones (up to 33 years)  based on satellite imagery or  combine it with other data layers like topography  to start your precision agriculture journey. These analyses often correlate with yield analytics data, but this is another story.

Frequently Asked Questions

1. How to do yield analysis?

Yield analysis is a process used to assess the productivity and performance of a crop or agricultural system. Here are the steps to conduct a basic yield analysis:

  • Measure the total harvested yield : Collect all the harvested produce from a specific area and weigh it.
  • Determine the area : Measure or calculate the total area of land from which the yield was obtained.
  • Calculate the yield per unit area: Divide the total harvested yield by the area to get the yield per unit area (e.g., yield per hectare).
  • Compare and analyze: Compare the obtained yield with previous years’ data or regional averages to assess the performance and identify any variations or trends.
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Yield analysis helps farmers make informed decisions, monitor crop productivity, and identify areas for improvement in their farming practices.

2. What is yield data?

Yield data refers to the information collected and recorded about the amount of crop or agricultural produce obtained from a specific area of land. It includes measurements or estimates of the quantity of harvested yield, usually expressed in terms of weight or volume.

It provides valuable insights into the productivity and performance of crops, helping farmers make informed decisions about their farming practices, assess the effectiveness of different techniques or inputs, and monitor trends or variations in crop yields over time.

3. What is yield potential?

Yield potential refers to the maximum achievable yield or production level of a crop under ideal growing conditions. It represents the upper limit of what a specific crop variety or plant species can yield in terms of quantity and quality.

Yield potential is influenced by various factors such as genetics, environmental conditions, nutrient availability, and management practices. It serves as a benchmark or reference point for farmers and agronomists to evaluate the performance and productivity of different crop varieties and to identify areas where improvements can be made to optimize yield levels.

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