Synthetic Climate Impact on Agriculture with Regional and Crop Variables

A synthetic dataset simulating the impact of climate variables on agricultural productivity. It includes modeled data for temperature, precipitation, crop yields, and extreme weather events across various geographic regions. The dataset was created by Waqar Ali for academic and machine learning applications.

Use Cases

• Build predictive models for crop yield based on simulated temperature and precipitation data.
• Conduct climate risk assessment for agriculture using modeled extreme weather event counts.
• Analyze regional differences in climate-agriculture interactions across the simulated geographic regions.
• Support food security and sustainability research with synthetic yield data for key crops like wheat, rice, and corn.

Strengths

• Includes multiple simulated climate and agricultural variables such as temperature, precipitation, crop yield, and extreme weather events.
• Designed for specific use cases like predictive modeling, climate risk assessment, and regional analysis.
• Dataset description explicitly lists key features and column semantics.

Limitations

• Dataset is completely synthetic and should not be used for real-world policy or forecasting.
• Row count, file formats, and last update date are unknown, limiting suitability assessment.
• Actual data quality and distributions require manual inspection after download.

Provenance

Source

Waqar Ali via Kaggle/OpenML.

Collection Method

Synthetically generated for simulation purposes.

Time Range

Modeled year of data collection is included, but specific temporal coverage is not stated.

Freshness

Last updated date is unknown; freshness unverified.

Geography

Includes various simulated geographic regions.