Global Rice Paddy Methane Emissions and Irrigation Trial Data, 2001–2020

Spatial data at 0.5° × 0.5° resolution shows total CH₄ emissions from global rice paddies from 2001 to 2020. JINGTING ZHANG created this dataset using machine learning methods, which also includes Excel data from field trials on irrigation practices. The supporting code provides workflows for applying trained Random Forest models to generate regional predictions of soil GHG emissions and crop yield.

Use Cases

• Analyze spatial trends and geographical variability of methane emissions based on the 0.5° resolution global grid.
• Evaluate the impact of irrigation regimes like AWD on GHG emissions based on the field trial meta-analysis data.
• Model future emissions scenarios using the provided Random Forest code workflows for regional predictions.
• Assess the relationship between management practices (fertilizer, residue, biochar) and emissions based on site-level experimental data.

Strengths

• Covers a 20-year time range from 2001 to 2020.
• Provides global-scale data with a spatial resolution of 0.5° × 0.5°.
• Includes experimental data from field trials used for meta-analysis.
• Comes with code for applying trained Random Forest models to generate predictions.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect geographic or source bias inherent to the compilation methods.

Provenance

Source

JINGTING ZHANG

Collection Method

Generated using machine learning methods; field trial data used for meta-analysis.

Time Range

2001–2020

Freshness

Last updated 2026-05-23 07:02:20; freshness should be verified.

Geography

Global