Digital summaries of legacy pedon descriptions

NRCS soil databases

National Soil Information System (NASIS) (SQL Server)
- SSURGO and Soil Data Access
- STASTGO2
Soil Characterization Database (Access)
Ecological Site Descriptions (Text)
Official Series Descriptions (Text)

* sorted by database sophistication

Legacy pedon data within the US

library(aqp)
library(soilDB)
library(plyr)
library(ggplot2)
library(reshape2)
library(stringr)
library(knitr)


pedons <- c(577, 6152, 9517, 19058, 42587, 112182, 231609, 184913)
year <- c("<1950s", "1950s", "1960s", "1970s", "1980s", "1990s", "2000s", "2010s")

cat("# pedons = ", formatC(sum(pedons), big.mark = ",", format = "fg"), "\n", "# lab pedons = ~64,000", sep = "")

# pedons = 606,595
# lab pedons = ~64,000

ggplot(data.frame(pedons, year), aes(x=year, y=pedons)) + geom_bar(stat="identity")

plot of chunk unnamed-chunk-1

# There has been lots of talk about the number or Soil Series, Components, Map units, etc... but little focus on the point data resource.
# Lots of talk about collecting new data, but little appreciation for existing data.

NASIS data structure

alt text

Released in 1994, custom Microsft SQL Server
Tables for: field pedons, lab pedons, component data, map units, legends, and projects
Functions for: queries, tables, reports, interpretations, calculations/validations, and exports

NASIS data structure

Horizon data

Site (Covariate) data

slope
landform
precipitation
etc…

Tools for interacting with soil data

Tabular analysis

1. Pencil and paper
2. Excel
3. PedonPC and AnalysisPC (Microsoft Access template) 
4. NASIS
5. R

Spatial analysis

1. SoilWeb
2. Web Soil Survey
3. Soil Data Viewer
4. SSURGO file geodatabases
5. R

* sorted by user sophistication

Objective

Problems

1. Data is underutilized
2. Inefficient tools
3. Fluid series concepts
4. Vaguely defined uncertainty metrics
5. Data isn't digitized
6. Tools are difficulat (especially R?)

Solution ?

1. standardized R reports

Why hasn't this been done already

alt text

description styles
legacy nomenclature
varying depths

Methods

Setup ODBC connection and install additional packages
Develop and assign a generic horizonation
Generate report and evaluate

→ extended tutorial for horizon generalization
→ extended tutorial for R reports (Region 11 SharePoint)

Assumptions

horizonation is exists and is accurate
a subset of pedons (sample) should represent a component (population or aggregate)
some semi-automated process is necessary to efficiently summarize soil data
null hypthesis - pedons are assumed similar unless significantly(?) different
low-rv-high values should approximate the bulk of the distribution

Develop a typical horization

Look up the series RIC if available

alt text

sort by frequency

	A	Bt1	Bt2	Cr	Bt3	R	Oi	Crt	BA	2Bt3
d	62	59	58	35	27	22	21	18	11	5

graphically examine

Assign generic horizonation

pattern matching via regular expression (REGEX)
- this is where most micro-correlation decisions are defined
GHL and rules for our sample dataset:
- A: ^A$|Ad|Ap
- Bt1: Bt1$
- Bt2: ^Bt2$
- Bt3: ^Bt3|^Bt4|CBt$|BCt$|2Bt|2CB$|^C$
- Cr: Cr
- R: R
special characters in REGEX rules:
- | = “or”
- ^ = anchor to left-side
- $ = anchor to right-side

Evaluate typical horizonation

	2BCt	2Bt2	2Bt3	2Bt4	2CB	2Cr	2Crt	2R	A	AB
A	0	0	0	0	0	0	0	0	62	0
Bt1	0	0	0	0	0	0	0	0	0	0
Bt2	0	0	0	0	0	0	0	0	0	0
Bt3	1	1	5	4	1	0	0	0	0	0
Cr	0	0	0	0	0	3	1	0	0	0
R	0	0	0	0	0	0	0	2	0	0
not-used	0	0	0	0	0	0	0	0	0	1
Sum	1	1	5	4	1	3	1	2	62	1

Evaluate typical horizonation

plot of chunk plot-ghl-1

Demonstrate Reports

open existing reports

→ examples of R reports

Closing thoughts

we have a wealth of existing data
data on soil series should be viewed in aggregate
“we shouldn't let the perfect be the enemy of the good”
reproducible research is good
Soil scientists are great at collecting data, but we have to just as good at analyzing it.

Thank you, any questions...?

Links to Reports and supporting material

Additional AQP Contributors:

Pierre Roudier (Landcare Research)

Acknowledgements

Alena Stephens, John Hammerly, Jennifer Outcalt, Henry Ferguson, Paul Finnell, and others…