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Carbon Use Efficiency (v3.0c, 3.1g)

SOCSPOT 3.0c's Carbon Use Efficiency (CUE) estimates microbial carbon assimilation efficiency, respiration losses as CO₂, and microbial processing efficiency based on contemporary studies and geospatial data.

CUE is a measure of how efficiently soil microbes convert organic matter (i.e., carbon) into stable soil carbon instead of releasing it as carbon dioxide. A higher CUE means more carbon could be stored in the soil with sufficient organic matter inputs, which helps with long-term carbon sequestration and reducing greenhouse gases in the atmosphere. Carbon use efficiency scales with a combination of optimal moisture, temperature, and pH conditions within the soil. Generally, regions that exhibit more extreme conditions of temperature, moisture/aridity or pH will have lower CUE, while values towards moderate conditions will have higher (i.e., ‘better’) CUE values.

To access high-resolution data for your area(s) of interest, contact our team.

High-Level Description

Within SOCSPOT 3.0c and 3.1g, the carbon use efficiency (CUE) is a gridded data product that captures the fraction of carbon assimilated as microbial biomass, growth, or excretion relative to the total carbon intake/assimilation. As such, a value of 0.4 would indicate that 40% of the total carbon intake by microbes was assimilated as microbial biomass, growth, or excretion, while 60% of it was respired back into the atmosphere as CO2. Version 3.0c extends across rangelands and croplands within the Continental US (CONUS, 'c'), while 3.1g includes both across a global extent. This product leverages understanding from contemporary global studies of microbial CUE (Qiao et al., 2019; He et al., 2023) alongside high-quality geospatial data inputs and empirical measurements of CUE to generate a global map of annual CUE.

Use Cases

Knowing CUE would enable customers to:

  • Determine where carbon is more effectively assimilated into the soil matrix
  • Understand where more carbon is being respired back into the atmosphere as CO₂
  • Estimate where microbes are processing carbon inputs less efficiently

Technical Specifications

  • Native Resolution: 100-250m
  • Spatial extent: 3.0c = CONUS, 3.1g = Global
  • Domain: Croplands and/or rangelands
  • Units: Fractional (0-1)
  • Inputs: Mean annual precipitation, Mean annual temperature, soil pH, lat/long
  • Temporal frequency and range: Annual, 2018-2022

Algorithm Theoretical Basis

The algorithms for Carbon Use Efficiency (CUE) are based on empirical equations applied in peer-reviewed literature that utilize observations spanning forests, shrublands, grasslands, croplands and tundra across the globe. The formulations account for variability in precipitation, temperature, and soil pH. As such, CUE inputs to SOCSPOT vary temporally (annual frequency) and spatially as functions of temperature, precipitation, and soil pH rather than remaining static as in other biogeochemical modeling frameworks.

Model Assumptions & Constraints

A number of assumptions and methodological choices are important to consider when using CUE.

  • Substrate type and composition were not included in the calculations due to limited and/or no data available at a global scale. As such, the type of vegetation input into a given land parcel, which is known to impact CUE, was not incorporated. Future versions will work to incorporate substrate type into the calculation of CUE.

Known issues

  • Due to one data input, there is an anomalous shift and gap in CUE values in the Northeast corner of Russia along the international date line due to a gap in SoilGrids input data.
  • A few locations exhibited CUE values > 1 due to input values exceeding the range expected for the empirical equations used (Qiao et al., 2021). These anomalies were bounded to a value of 1.

What’s New?

Within the frameworks of SOCSPOT 3.0c and 3.1g, updates include:

  • SOCSPOT 3.0c and 3.1g are the first versions of SOCSPOT to incorporate CUE and the input data for land surface temperatures have changed.
  • Within SOCSPOT 3.1g, the extent has gone beyond CONUS to include global coverage.
  • From 3.0c to 3.1g, the data inputs for soil pH and precipitation have also changed.
  • Within SOCSPOT 3.1g, we have changed the data source inputs for long-term (30-year) mean annual precipitation (MAP) that are fed into the CUE calculations, which has a distinct temporal window compared to 3.0c.
  • With the transition from 3.0 to 3.1(c and g), mean annual temperatures are derived from ECMWF rather than NCEP.

Data Partners, Providers, and References

  • Oregon State University: PRISM Gridded Climate Data. 30-Year Normals (1991–2020).
  • University of California, Davis: Fick, S.E., and R.J. Hijmans. 2017. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. DOI: 10.1002/joc.5086
  • SoilGrids +: Poggio, L., de Sousa, L.M., Batjes, N.H., et al. 2021. SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. DOI: 10.5194/soil-7-217-2021
  • NCEP/NCAR + NOAA PSL (Physical Sciences Laboratory): Kalnay, E., Kanamitsu, M., Kistler, R., et al. 1996. The NCEP/NCAR 40-Year Reanalysis Project. Bulletin of the American Meteorological Society, 77(3), 437–471. DOI: 10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2
  • European Centre for Medium-Range Weather Forecasts: ECMWF (2019). IFS Documentation, Cycle 45r1. European Centre for Medium-Range Weather Forecasts. ECMWF IFS Documentation.
  • Hersbach, H., et al. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999-2049. DOI: 10.1002/qj.3803

Accessing Data

Contact our team to request high-resolution data for your area(s) of interest.