GeoPard's Synthetic Yield Map Module enables farmers to generate accurate retrospective yield maps for past years, enhancing data-driven farm management.
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The GeoPard Synthetic Yield Map Module addresses a common challenge in agriculture: the lack of detailed historical yield data for many farmers. While farmers often have average or total yield values per field, detailed yield maps for past years are frequently unavailable. GeoPard's module offers a solution by enabling the retrospective creation of yield maps with high accuracy (over 90%).
This functionality is particularly valuable for farmers who want to understand and analyze the yield performance of their fields over the years. The module uses available average or total yield data and applies GeoPard's analytical methods to generate detailed yield maps for any past year.
By creating these synthetic yield maps, farmers gain insights into yield distribution and variability within their fields. This information is crucial for making informed decisions about crop management, resource allocation, and planning future agricultural strategies.
In summary, the GeoPard Synthetic Yield Map Module provides a practical tool for farmers to unlock historical yield insights, allowing for a more data-driven approach to farming even when detailed past records are not available.
Understanding the geospatial distribution of yield is crucial for fine-tuning agronomic decisions year after year, and Yield Maps (or Yield Datasets) play a key role in this process.
However, necessary equipment is not always installed at harvesters to record yield mapping data, especially for older models. Consequently, Yield Maps often cover only portions of fields that are harvested using machines with yield mapping capabilities.
Addressing these challenges, GeoPard is on the finalization release of its Synthetic Yield Map, a solution designed to provide comprehensive yield insights regardless of equipment limitations.
Synthetic Yield Maps are generated by leveraging GeoPard's extensive experience in yield dataset management, focusing on identifying yield-limiting factors, evaluating historical , and analyzing . This methodology allows for the generation of yield distribution maps even in the absence of direct yield data from harvesting machines.
Remote sensing data forms the basis for the geospatial distribution, while the calibration to actual yield values is achieved by aligning with the field's reported average or total yield. This approach ensures each area of the field is accurately represented, offering a complete and reliable picture of yield distribution across the entire field.
The verification of the Synthetic Yield Map was conducted using an original Yield Map of Corn Silage. This process highlighted the necessity of Yield Calibration as an intermediate step to eliminate data outliers and correct yield values.
The statistical and geospatial data distributions of Corn Yield as Mass (t/ha) for Calibrated Yield and Synthetic Yield datasets are available in the following screenshot.
The following screenshot presents the statistical and geospatial data distributions for Soybean Yield mass in both Calibrated and Synthetic Yield datasets. It's important to note that while the Original Yield Data was provided in mg/m2, the Synthetic Yield Data has been converted and is presented in t/ha.
The Yield Calibration step is still required to eliminate anomalies in yield data distribution.
The comparison of the known parts of the Yield Map and the restored Yield Map is in the following screenshot.
The geospatial pattern of the Synthetic Yield Map is derived from GeoPard's advanced understanding of and their expertise in . Then it was calibrated using the average value of Corn Silage Yield, ensuring that the geospatial pattern aligns closely with actual yield figures. The statistical and geospatial data distributions of Yield as Mass (t/ha) are available in the following screenshot.
In many instances, only a partial Yield Map is available. There are many factors, among the most popular: older machinery lacks the necessary equipment to log harvesting data (1), the human factor (2), and the collected data turns out to be unusable (3). GeoPard addresses these challenges by restoring incomplete Yield Maps. The process involves a comprehensive assessment of and , utilizing GeoPard's refined methods in yield data analysis to enhance the completeness of the map.
