Crop Yield Modeling Data with Multi-Source Geospatial Features

Code and supplementary data accompany a research paper on mitigating spatial non-stationarity in crop yield models. The dataset, published on figshare by dehao Zhao in May 2026, likely contains geospatial variables for agricultural analysis. Its 143.1 KB size indicates a limited scope, suitable for testing a process-based regionalization framework.

Use Cases

• Training models to predict crop yields using geospatial covariates (inferred from domain, verify after download)
• Developing methods to address spatial non-stationarity in environmental data (inferred from domain, verify after download)
• Benchmarking regionalization frameworks for agricultural machine learning (inferred from domain, verify after download)

Strengths

• Published on figshare under the CC-BY-4.0 license, ensuring open access and reuse.
• Includes multiple file formats (R, ZIP, CSV, XLSX) for flexibility in data loading and analysis.

Limitations

• Metadata is minimal; actual content requires verification after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

figshare

Freshness

Last updated 2026-05-12 12:11:04; freshness should be verified.