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Cover crop data sources

Regrow's cover crop adoption geo-database provides activity input for on-field emissions quantification (both DNDC modeling when available and IPCC calculations), as well as cover crop seed production emissions.  Cover crop activity data comes from a combination of Monitor (when available), and scientific studies, literature and global datasets. When field-level Monitor residue data is not available, default data are used at the crop × country level based on best available sources and geographic coverage.

Data sources

Primary data source

Monitor field-level cover crop determinations

In regions where Monitor is available, Regrow uses remote-sensing based field-level cover crop detections. This provides field and cultivation cycle specific cover crop practice information. Current Monitor coverage includes parts of CONUS & Canada, Europe, and Australia, with availability varying by crop, geography, and year. 

See Where is Monitor available? for current coverage details.

Secondary data source

In regions where Monitor is not available, default data is used. Because publicly available data on cover crop adoption is limited in many regions, Regrow uses a tiered approach to build default cover crop adoption rates by geography.

Europe

For European countries, Regrow uses a remote sensing-derived cover crop dataset based on Sentinel-1 observations. The source study maps cover crop presence across the European Union and the United Kingdom at 100-meter spatial resolution, providing a gridded estimate of where cover crops are present.

Source attribution: From regional to parcel scale: A high-resolution map of cover crops across Europe combining satellite data with statistical surveys (Fendrich et. al 2022)

United States and Canada

In the United States and Canada, Regrow uses government and public statistical sources to define default cover crop adoption rates. In the U.S., these include USDA Economic Research Service materials on cover crop adoption by cash crop and region. For example, USDA reports adoption rates of about 5% for corn-for-grain, 8% for soybean, 13% for cotton, and 25% for corn silage in the survey years cited. In Canada, Regrow uses Statistics Canada sources on cover, companion, and green manure crop use.

Source attribution:

Argentina, China, and India

For Argentina, China, and India, Regrow uses country-specific reports and publications, as no stronger global default source was available. These these values are used as practical defaults rather than as precise measures of national adoption.

Source attribution:

Remaining countries

For remaining countries, Regrow uses a coarse fallback approach based on published country-level conservation agriculture (CA) adoption estimates. The underlying CA source defines conservation agriculture using three linked principles: minimal soil disturbance, permanent soil cover, and crop diversification. Regrow converts those country-level CA area estimates into a cover crop default by comparing them with FAOSTAT harvested area and then applying an additional assumption that only a share of CA area includes cover cropping.

Global fallback

If no usable country-specific source is available after the steps above, Regrow applies a 2% cover crop adoption default. This ensures that every geography has a usable value, while acknowledging that the estimate is coarse and intended only as a fallback.

For field-level defaulting applications, the cover crop system representing the highest area x crop is selected.

Assumptions and limitations

  • Publicly available data on cover crop adoption is scarce and uneven across countries, so some defaults are based on stronger sources than others.

  • For many countries, Regrow relies on conservation agriculture adoption as a proxy for cover crop use. This is a coarse assumption because conservation agriculture includes several practices, not all of which require cover cropping.

  • In the broad fallback method, Regrow assumes that only one-third of conservation agriculture area uses cover cropping. This is a pragmatic assumption rather than a strongly supported global statistic.