Using model predictions of soil carbon in farm-scale auditing - A software tool

J.J. de Gruijter*, I. Wheeler, B.P. Malone

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


We introduce a software tool for optimal sampling design in the context of farm-scale soil carbon auditing, where the amount of sequestered soil carbon will be estimated from a random sample. Existing tools do not use available ancillary information, or do not have the functionality needed for farm-scale soil carbon auditing. Using a grid of predicted carbon content with associated uncertainty, the software optimises a stratified random sampling design, such that the profit is maximised on the basis of sequestered carbon price, sampling costs, and a trading parameter that balances farmer's and buyer's risks due to uncertainty of the estimated amount of sequestered carbon. As the algorithm is computationally intensive, the package is written in Julia for speed. From a case study we conclude that our software is an effective tool for farm-scale soil carbon auditing, and that it outperforms the existing tools in terms of efficiency and functionality.

Original languageEnglish
Pages (from-to)24-30
JournalAgricultural Systems
Publication statusPublished - 1 Feb 2019


  • Julia
  • Map uncertainty
  • Prediction error
  • Soil carbon auditing
  • Stratified random sampling
  • Value of information

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