With the release of the Equation-based analytics module, the GeoPard team has taken a big step forward in empowering farmers, agronomists, and spatial data analysts with actionable insights for each square meter. The module includes a catalog of over 50 predefined GeoPard precision formulas that cover a wide range of agriculture-related analytics.
The precision formulas have been developed based on multi-year independent agronomic university and industry research and have been rigorously tested to ensure their accuracy and usefulness. They can be easily configured to be executed automatically for any field, providing users with powerful and reliable insights that can help them to optimize their crop yields and reduce input costs.
The Equation-based analytics module is a core feature of the GeoPard platform, providing users with a powerful tool to gain a deeper understanding of their operations and make data-driven decisions about their farming practices. With the ever-growing catalog of formulas and the ability to customize formulas for different field scenarios. The GeoPard can meet the specific needs of any farming operation.
Potassium Removal based on Yield data
Use Cases (see examples below):
- Nitrogen Uptake in absolute numbers using Yield and Protein data
- Nitrogen Use Efficiency (NUE) and Excess calculations with Yield and Protein data layers
- Lime recommendations based on pH data from soil sampling or soil scanners
- Sub-field (zones or pixel-level ROI maps)
- Micro and Macro nutrients fertilization recommendations based on Soil sampling, Field Potential, Topography, and Yield data
- Carbon modeling
- Change detection and alerting (calculate difference between Sentinel-2, Landsat8-9 or Planet imagery)
- Soil and grain moisture modeling
- Calculation of dry yield out of wet yield datasets
- Target Rx vs As-applied maps difference calculation
Potassium Recommendations based on Two Yield Targets (Productivity Zones)
Fertilizer: Recommendations Guide (South Dakota State University): Potassium / Corn. Review and Revision: Jason Clark | Assistant Professor & SDSU Extension Soil Fertility Specialist
Potassium Use Efficiency in Kg/Ha
Nitrogen Use Efficiency in percentage. Calculation is based on Yield, Protein and Grain Moisture data layers
Nitrogen: Target Rx vs As-Applied
Chlorophyll difference between two satellite images
A user of GeoPard can adjust existing and create their private formulas based on Imagery, Soil, Yield, Topography, or any other data layers GeoPard supports.
Examples of the template GeoPard Equations
Formula-based analytics helps farmers, agronomists, and data scientists to automate their workflows and make decisions based on multiple data and scientific research to enable easier implementation of sustainable and precision agriculture.
What is Equation-based Analytics in Precision Agriculture? The Use of Precision Formula
Equation-based analytics in precision agriculture refers to the use of mathematical models, equations, precision formula, and algorithms to analyze agricultural data and derive insights that can help farmers make better decisions about crop management.
These analytics methods incorporate various factors such as weather conditions, soil properties, crop growth, and nutrient requirements to optimize agricultural practices and improve crop yields, while minimizing resource waste and environmental impact.
Some of the key components of equation-based analytics in precision agriculture include:
- Crop growth models: These models describe the relationship between various factors such as weather, soil properties, and crop management practices, to predict crop growth and yield. Examples of such models include the CERES (Crop Environment Resource Synthesis) and APSIM (Agricultural Production Systems sIMulator) models. These models can help farmers make informed decisions about planting dates, crop varieties, and irrigation scheduling.
- Soil water models: These models estimate the water content in the soil profile based on factors such as rainfall, evaporation, and crop water use. They can help farmers optimize irrigation practices, ensuring that water is applied efficiently and at the right time to maximize crop yields.
- Nutrient management models: These models predict nutrient requirements for crops and help farmers determine the optimal rates and timing of fertilizer application. By using these models, farmers can ensure that crops receive the right amount of nutrients, while minimizing the risk of nutrient runoff and environmental pollution.
- Pest and disease models: These models predict the likelihood of pest and disease outbreaks based on factors such as weather conditions, crop growth stages, and management practices. By using these models, farmers can make proactive decisions about pest and disease management, such as adjusting planting dates or applying pesticides at the right time.
- Remote sensing-based models: These models use satellite imagery and other remote sensing data to monitor crop health, detect stress factors, and estimate yield. By integrating this information with other data sources, farmers can make better decisions about crop management and optimize resource use.
In summary, equation-based analytics in precision agriculture uses mathematical models and algorithms to analyze complex interactions between various factors that affect crop growth and management. By leveraging these analytics, farmers can make data-driven decisions to optimize agricultural practices, improve crop yields, and minimize environmental impact.
Frequently Asked Questions
1. How can precision agriculture help address resource use and pollution issues in agriculture?
It can help address resource use and pollution issues in agriculture through targeted resource application, efficient resource management, enhanced monitoring, and the adoption of conservation practices. By applying inputs such as fertilizers and pesticides only where needed, farmers can reduce waste and minimize pollution.
Data-driven decision-making enables optimal resource management, while real-time monitoring allows for timely interventions to prevent pollution incidents. Additionally, the implementation of conservation practices promotes sustainable agriculture and reduces environmental impacts.
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