Space-time Statistical Modelling of Soil Organic Carbon

A ploughed field of a soil rich in organic matter (Luvic Chernozem).  Location: Agricultural Research Station in Turda, Romania.
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To evaluate soil carbon sequestration and land degradation neutrality policies and measures there is a need for a global web-based platform to inform on the status and trends of soil organic carbon (SOC). Among others, such a platform requires a statistical methodology that allows to predict SOC in space and time from SOC point observations and spatial and spatio-temporal maps of environmental covariates.


This project develops, implements and applies a statistical space-time SOC mapping methodology, using Argentina as a (first) pilot area. Web-based visualization of the resulting time series of SOC maps is done in a parallel project.


  1. Assemble soil profile data and covariates for the pilot area, covering a 50 year time period.
  2. Develop a space-time statistical model and calibrate it using the available data.
  3. Make space-time predictions of SOC concentration and SOC stocks for the pilot area and chosen time period.
  4. Quantify the uncertainties associated with the predictions and predicted temporal trends.
  5. Run scenarios to make future SOC predictions.
  6. Publish the project outcome in a peer-reviewed scientific journal.

Interim deliverables (not freely accessible)

Project report 1: Description and summary statistics of available soil data for Argentina.

Project report 2: Quality-assessed, geo-referenced soil data for Argentina with metadata.

Project report 3: Covariates for selected pilot area and time period.



Partners within the project consortium are ISRIC, the National Institute of Agricultural Technology (Argentina), Woods Hole Research Center (USA) and Cornell University (USA). The technical implementation of the platform as done in the parallel project is executed by Vizzuality (Spain).


The project is commissioned by The Nature Conservancy (USA).

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