Existing Crop Yield Prediction Models Utilizing Historical Weather & Soil Data - With Website Links

Existing Crop Yield Prediction Models Utilizing Historical Weather & Soil Data - With Website Links

Several established models and platforms are currently used for crop yield prediction based on historical weather and soil data. Here's a breakdown of some prominent examples, including website links:

1. DSSAT (Decision Support System for Agrotechnology Transfer):

• Description: A widely used, process-based crop modeling software developed by the International Crop Modeling Program. DSSAT simulates crop growth and development based on weather, soil, crop management, and genetic coefficients. It requires significant input data but offers detailed simulations.
• Website: https://dssat.net/

2. APSIM (Agricultural Production Systems sIMulator):

• Description: Another powerful process-based modeling framework, APSIM simulates biophysical processes in agricultural systems. It's highly flexible and can model a wide range of crops and farming systems. Like DSSAT, it requires detailed data input.
• Website: https://www.apsim.info/

3. Climate FieldView (Bayer):

• Description: A commercially available digital farming platform that integrates weather data, soil maps, and field observations to provide yield predictions and insights. It utilizes machine learning algorithms to analyze data and offer recommendations. (Subscription-based)
• Website: https://www.climatefieldview.com/

4. Granular (Corteva Agriscience):

• Description: Similar to Climate FieldView, Granular is a digital agriculture platform offering yield forecasting, field profitability analysis, and farm management tools. It leverages data analytics and machine learning. (Subscription-based)
• Website: https://www.granular.ag/

5. NASA GEEC (Global Ecosystem Ecology Consortium):

• Description: NASA's GEEC provides publicly available yield forecasts for major crops globally, based on satellite imagery, weather data, and crop models. It's a valuable resource for monitoring food security.
• Website: https://geec.gsfc.nasa.gov/

6. Predicta (formerly Ceres Imaging):

• Description: Predicta uses aerial imagery and machine learning to provide insights into crop health and yield potential. They focus on providing actionable data to optimize irrigation and fertilization.
• Website: https://predicta.com/

7. OneAgri (formerly Farmers Edge):

• Description: Offers a suite of digital farming tools, including yield prediction models based on weather, soil, and field data. They emphasize data-driven decision-making. (Subscription-based)
• Website: https://www.oneagri.com/

Important Considerations:

• Data Requirements: Process-based models (DSSAT, APSIM) require extensive data input.
• Cost: Commercial platforms (Climate FieldView, Granular) typically require subscriptions.
• Accuracy: Model accuracy varies depending on the crop, region, and data quality.
• Complexity: Some models are more complex to use than others.

This list provides a starting point for exploring available yield prediction models. The best choice will depend on your specific needs, resources, and technical expertise.