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Welcome to Algorithms for Quantitative Pedology project!

A collection of algorithms related to the modeling of soil resources, soil classification, soil profile aggregation, and visualization.

Algorithms for Quantitative Pedology (AQP) is a collection of code, ideas, documentation, and examples wrapped-up into several R packages. The theory behind much of the code can be found in this Computers & Geosciences paper. Links to project member contacts, code, and other information hosted by R-Forge can be found here. Worked examples along with discussion and application to soil survey work can be found on the CA Soil Resource Lab website. AQP is very much a work in progress! If you are interested in contributing code, documentation, bug reports, or even scathing criticism, feel free to contact Dylan at debeaudette [at] ucdavis [dot] edu.

AQP is a collaborative effort, funded in part by the Kearney Foundation of Soil Science (2009-2011) and USDA-NRCS (2011-current).


  1. 2015 Digital Soil Morphometrics - Aggregate representation of genetic soil horizons via proportional-odds logistic regression
  2. 2015 Digital Soil Morphometrics - Algorithms for Quantitative Pedology: a toolkit for digital soil morphometrics
  3. Soil Data Aggregation
  4. AQP, DSM, and ESD
  5. Numerical Classification with AQP
  6. AQP and SoilDB Demo
  7. 2011 UseR AQP Talk
  8. 2011 Pedometrics


  1. SoilProfileCollection object introduction
  2. dealing with bad data
  3. aggregate properties by taxon name
  4. aggregate properties by bedrock kind
  5. soil profile dissimilarity
  6. vertical vs. perpendicular horizon measurements
  7. getting, plotting, saving detailed soil series extent data (US-only)
  8. Component Relation Graphs
  9. A Novel Display of Categorical Data
  10. OSD Dendrogram
  11. Horizon Transition Probabilities
  12. USDA-NRCS Data Sources
    1. getting soils data from USDA-NCSS databases
    2. Querying the Soil Data Access web service
    3. Component interpretation comparison via SDA
    4. getting/comparing KSSL data
    5. Loading NASIS pedon data
    6. Loading NASIS component data
    7. Export NASIS pedon data to SHP file
    8. Export NASIS pedon data to Google Earth
    9. gridded SSURRGO (gSSURGO) and SDA
  13. Pedon Data Aggregation
    1. Assignment of generalized horizon labels
    2. Computing range in characteristics by generalized horizon label
    3. Soil color aggregation ideas
    4. Estimation of most-likely horizonation
    5. Sample Reports
  14. Spatial Data Aggregation
    1. Sampling Raster Data Sources

Manual Pages c/o knitr:

  1. aqp manual pages with figures
  2. soilDB manual pages with figures
  3. sharpshootR manual pages with figures

Sample Figures