Comparing Yield Datasets
Compare yield datasets to gain deeper insights into crop performance across seasons, covering proper Yield Cleaning and Calibration and Synthetic Yield usage.
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Compare yield datasets to gain deeper insights into crop performance across seasons, covering proper Yield Cleaning and Calibration and Synthetic Yield usage.
Last updated
Was this helpful?
Modern agricultural decision-making heavily relies on Yield Datasets, which represent collected yield and reflect a major portion of grower income. These datasets must be accurate and of high quality to inform decisions on input management and optimize future planting and fertilization strategies.
Yield Data is typically collected by harvesting equipment, yet raw readings are often incomplete, contain errors, or require calibration to address sensor inconsistencies and variable field conditions. To overcome these challenges, professionals commonly employ cleaning, calibration, and synthetic dataset generation techniques to produce reliable, comparable Yield Data.
Both and are supported by GeoPard.
Comparing Yield Datasets from different crop years provides valuable insights, helping validate management practices, confirm sensor accuracy, and improve strategies for upcoming seasons. These comparisons also guide the refinement of fertility and seeding prescriptions, ensuring each decision is based on trustworthy information.
To quantitatively compare Yield Datasets, we utilize a pre-saved Equation named Spatial Correlation Analysis (Data Layers Similarity) that measures the similarity between yield-associated attributes from Yield Datasets on a spatial basis.
This equation assigns a similarity score, indicating how closely one dataset matches another in spatial pattern and value distribution.
The similarity values range from 0 to 1, where 0 indicates no match and 1 signifies 100% value-spatial match. In other words, the closer the similarity score is to 1, the more similar yield attributes are.
In this case, we begin with raw Yield Data collected during two different growing seasons 2015 and 2018 with the same crop soybean. The initial datasets contain abnormally high and low locations, especially at the start/end of harvester passes, and the data requires slight recalibration.
After applying GeoPard’s cleaning and calibration tools, the resulting dataset is more uniform, consistent, and easier to interpret.
The Similarity Equation execution map is below as a screenshot.
From a statistical perspective, it shows a high average (0.869) and median (0.876), indicating that the 2018 soybean yield patterns strongly resemble those from 2015. While some areas dip to 0.599, the low variation (0.005) and modest standard deviation (0.073) confirm overall consistency.
From an agronomic standpoint, this stability suggests the field’s underlying conditions and responses to management practices have remained largely unchanged.
In this scenario, we start with raw Yield Data from two corn seasons — 2022 and 2024. The initial datasets contain anomalies like abnormally high or low readings, cross passes, and curved trajectories, and indicating the need for sensor recalibration.
After applying GeoPard’s cleaning and calibration tools, the datasets become more reliable, enabling automated analysis and informed decision-making.
The Similarity Equation execution map is below as a screenshot.
From a statistical perspective, an average of 0.791 and a median of 0.799 show 2024 corn yields largely resemble 2022, though areas as low as 0.413 indicate variability. A standard deviation of 0.115 confirms some differences across the field.
From an agronomic standpoint, consistent patterns suggest stable conditions and effective management over time. However, localized lower-similarity zones may require targeted adjustments to improve future yield performance.
In this scenario, we begin with raw Yield Dataset from the 2023 oilseed rape season and a Synthetically generated Yield Dataset for the same crop and the same year 2023. The goal is to assess the spatial accuracy of Real versus Synthetic Yield, providing a pathway to fill unlogged data, address gaps in yield data, and correct anomalies using synthetic values. The Real Yield Dataset contains issues such as abnormally high or low readings, cross passes, curved trajectories, and zero passes, all indicating a need for sensor recalibration.
The Similarity Equation execution map is below as a screenshot.
From a statistical perspective, the high average (0.889) and median (0.904) scores indicate that, overall, the Synthetic Yield Dataset closely matches the Real 2023 Oilseed Rape Yield spatial patterns. While one area dips as low as 0.291, the low variation (0.006) and modest standard deviation (0.08) suggest that most parts of the field align closely between the Real and Synthetic Datasets, with very few outliers.
From an agronomic standpoint, this strong similarity implies that the Synthetic Yield Data can serve as a reliable proxy for real field conditions, reinforcing confidence in using modeled scenarios to guide decisions. The agronomic practices reflected in the Real Yield Data are well captured by the Synthetic Yield model, enabling more informed and consistent planning for future management strategies.
After applying GeoPard’s to the Real Yield Data and generating for oilseed rape, we can initiate a meaningful comparison of their similarity.
