Investigating soil data with R - GitHub Pages

Investigating soil data with R Pierre Roudier1 and Dylan E. Beaudette2 1 2

Landcare Research – Manaaki Whenua, New Zealand Natural Resources Conservation Service, USDA, USA

Acknowledgement

Talk Outline

1 The soil resource

Soil matters! Soil science Soil data and its analysis 2 The aqp package Visualisation Classification Harmonisation Analysis and modelling 3 Recent and future developments Introduction of S4 classes Design 4 Conclusions and further work

Talk Outline 1 The soil resource

Soil matters! Soil science Soil data and its analysis 2 The aqp package

Visualisation Classification Harmonisation Analysis and modelling 3 Recent and future developments

Introduction of S4 classes Design 4 Conclusions and further work

Soil matters!

Soil a very thin layer between parent rock and atmosphere, a very complex body – physical, chemical and biological interactions, a support for almost all terrestrial ecosystems – and thus food production

Soil matters!

Soil is not dirt.

A threatened resource

How to feed 7 billion+ people? Growing tensions on arable land Urbanisation Erosion Etc.

,→ Important to provide soil information to a wide range of decision makers.

A threatened resource

“Man has only a thin layer of soil between himself and starvation.” –Bard of Cincinnati

Soils today

Long been only regarded as a producer of crops But soils are back on the global agenda New challenges through global projects

Soils today

Long been only regarded as a producer of crops But soils are back on the global agenda New challenges through global projects

GlobalSoilMap.net– a leading project International network of soil scientists Generate and provide a ≈ 100m grid of 9 key soils attributes globally R has been identified as a key platform for the various stages involved

Data courtesy of Tomislav Hengl.

GlobalSoilMap.net– a leading project International network of soil scientists Generate and provide a ≈ 100m grid of 9 key soils attributes globally R has been identified as a key platform for the various stages involved

Data courtesy of Tomislav Hengl.

,→ The way to do soil science is changing significantly (“pedometrics”)

The soil profile

The soil profile

Soil data: Highly multi-dimensional Point support Soft and hard data Importance of legacy data Most of the time associated with environmental covariates

Talk Outline 1 The soil resource

Soil matters! Soil science Soil data and its analysis 2 The aqp package

Visualisation Classification Harmonisation Analysis and modelling 3 Recent and future developments

Introduction of S4 classes Design 4 Conclusions and further work

The aqp package

aqp – Algorithms for Quantitative Pedology

Soil profile sketches

An example of profile sketches manually created from soil profile observations collected as part of the Pinnacles National Monument soil survey. Horizon designations, sequences, boundaries, and soil texture classes are usually sufficient for describing complex soil-landscape relationships.

(Digital) Soil profile sketches 08

14 A1 A2

13 A1

A/C Bt1

Bt2

12 A1

A2

Cr R

A2 R

09 A

03 A

Bw1 Bw2

Bw1

01 A AB

AB

15

07 A1 A2 Bw1

05 A AB1

Cr R

11

10

02

Oi Oe/A

A

A

Ad

AB1

AB

AB2

EC1

AB2

Oe A

Bt1 Bw2

Cr

Bw2 R

Bt Crt

Crt

Bw2

Bw1

Bt2

04

Oi/A A

AB1

Bw

Bw3

Cr R

Oi A

A AB

0 cm

Bt1

20 cm

Bt2 Bt1

Bt1 EC2

Bt3 AB2

Cr/Bw Cr

06

Bt2

??? BC Crt

60 cm

80 cm

Bt

Bt2

Cr

BC

40 cm

???

CB

100 cm

R

120 cm

140 cm

160 cm

180 cm

(Digital) Soil profile sketches 08

14

13 A1

A1

12 A1

A2

09 A

A2

01 A

15 Oi A

07 A1 A2

Bw1

A2

05 A

R

Bw1

Bw2

Cr R

AB

Bw1

10

02

A

A

Ad

AB1

AB

AB2

EC1

AB2

04

Bt1

Cr R

Bw2

Bw1

A AB1

Cr

Bw2 R

Cr

20 cm

40 cm Bt3

EC2

60 cm

AB2

Bt2

Cr/Bw

BC 80 cm

Cr Bt2

0 cm

Bt1

Bt1

Crt Crt

R

A AB

Bt2

Bt1

Bw2 Bt

06 Oi/A

Oe A

Bw

Bw3

Bt2

11 Oi Oe/A

AB1

AB

A/C Bt1

03 A

Bt

Bt2

CB

100 cm

R

120 cm

???

Cr

BC

???

Crt

140 cm

Backslope

Summit

Swale 160 cm

98

79

summit

67

40

30

−5 −5 −9 −13 −30 −51 Relative Topographic Position (50 meter radius)

−54

−81

backslope swale

−86

−97

180 cm

Numerical soil classification evaluate dissimilarity metric for each depth increment

dissimilarity matrices for all depth increments

apply depth-weighting and sum dissimilarities

final between-profile dissimilarity matrix

Numerical soil classification 30 25 20 15 10 5 0 01

09 Oi A

05 A

04 Oi Oe/A Ad

AB

07

15

Oi/A

A

A

AB1

AB2 Bw

13 Oe A

14 A1

12 A1 A2

A2

A2

Bw2 Cr/Bw

Bw1 Bt1

AB1

Bt1 Bw1 Bt2

Crt Crt

02 A1

19Tollhs A Bw1

Bw2

Bt2 AB2

Bw2 R

R Cr R

03

10 A

18Chualr A Ap1

A1

AB

Bw2

EC1

A2

Cr R

C Cr

Bt1

Ap2

Bt2

Bt3 EC2

A1

A1 A2

A3

11

17Ahwahn A

A11

AB1

A12

AB2

A2

50 cm

Bt1 B1

0 cm

B1

BC B2

A2

Cr Bt R

Bt2

Bt2

??? ???

Cr

Bt1 BC

CB

B2

C1

100 cm

R Bt21

Sampled Soils Soil Survey Legend

16 Vista A AB

Bw3

Cr

Cr

Group 1 Group 2 Group 3 Group 4

06

Bt1

AB

A/C

Bw1

Bt

08 A1 A2

Crt C2

C

150 cm

Bt22

200 cm

Soil data harmonisation

Harmonisation of heterogeneous data sets

Legacy data Diversity of measurement methods Etc.

Soil data harmonisation Harmonisation of the depth support 0

20

Depth (cm)

40

60

80

100

120

0.0

0.5

1.0

Organic carbon (%)

1.5

Soil data harmonisation

0

0

20

20

40

40

60

60

Depth (cm)

Depth (cm)

Harmonisation of the depth support

80

80

100

100

120

120

0.0

0.5

1.0

Organic carbon (%)

1.5

0.0

0.5

1.0

Organic carbon (%)

1.5

Soil data harmonisation

0

0

20

20

20

40

40

40

60

60

60

Depth (cm)

0

Depth (cm)

Depth (cm)

Harmonisation of the depth support

80

80

80

100

100

100

120

120

120

0.0

0.5

1.0

Organic carbon (%)

1.5

0.0

0.5

1.0

Organic carbon (%)

1.5

0.0

0.5

1.0

Organic carbon (%)

1.5

Aggregation of soil properties

compute summary statistics by segment

segment each profile

collect profiles

representative depth function and characteristic variability

Aggregation of soil properties Meta-basalt Sand (%)

Clay (%)

Granodiorite

Total Carbon (%)

CIE Redness

pH

CEC (cmol [+] / kg)

0

Fe Crystallinity

0

Mn Crystallinity

Total Ca (%)

Total Fe (%)

Total Zr (ppm)

Total Si (%)

30

25

20

5 10 15

7. 5

7. 0

6. 5

6. 0

16

14

8 10 12

6

3

2

1

0

40

30

100 20

80

100 10

60

80

80

40

60

60 70

40

40 50

20

20 30

20

0

40

35

14 0 30

12 0

80

25th Percentile, Median, 75th Percentile

10 0

60

8 10 12

6

4

2

1. 4 0. 5 1. 0 1. 5 2. 0 2. 5 3. 0

1.

1.

0.

0.

0.

0.

2

100 0

80

100 8

60

80

4

40

60

3

40

2

20

1

20

0.

Depth (cm)

0

Pedotransfer functions

”Pedotransfer functions” are models predicting a soil attribute from other(s) soil attribute(s) and environmental covariates.

From simple approach with multivariate linear models... ... to state-of-the-art machine learning algorithms (ANN, random forests, etc.)

,→ R is of course a natural platform for these tasks.

Talk Outline 1 The soil resource

Soil matters! Soil science Soil data and its analysis 2 The aqp package

Visualisation Classification Harmonisation Analysis and modelling 3 Recent and future developments

Introduction of S4 classes Design 4 Conclusions and further work

Structuring soil data Site Data

50 - 200 cm

100 meters x, y, z

slope, erosion, drainage, geology, ...

species, abundance

xxxxx

xxxx xxxxxxxx xxxxx xxxx xxxxx

xxxxxx xxxxxxxx xxxxxxxx xxxxxx xxx xxxxxxxxxxx

Horizon Morphology Data name, depths, roots, color, structure, texture, ...

feature, abundance

xxxx xxxxxx xxxxxxxxx xxxxxx

xxxxxx xxxxxxxxx xxxxxxxxx xxxxxx xxxxxx xxxxxxxxx

xxxxxxx

Laboratory Data available {Na, K, Mg, Ca}, total {Si, Al, Fe}, CEC, ph, PSD, EC, SAR, ... xxxxxxxxxxxx xxxxxxxxxxxxxxx xxxxxxx xxxxxxxxxx xxxxxxxx xxxxx

Spatial analysis

Spatial analyis is an example where we need bindings to other packages.

Slice the soil collection on any depth interval Cast it as a sp object (SpatialPointsDataFrame) Apply sp/raster methods for spatial data analysis

Spatial analysis

0.15 0.10 0.05

10 cm depth 20 cm depth 30 cm depth 40 cm depth 50 cm depth 60 cm depth

0.00

semivariance

0.20

0.25

0.30

Si:Al Variogram Models

50

100

150 distance

200

250

Spatial analysis

S4 Classes and Methods for Soil Information Formal class 'SoilProfileCollection' [package ”.GlobalEnv”] with 4 slots ..@ id : Named chr ”P001” .. ..− attr(*, ”names”)= chr ”id” ..@ depths : int [1:6, 1:2] 0 2 14 49 57 89 2 14 49 57 ... .. ..− attr(*, ”dimnames”)=List of 2 .. .. ..$ : chr [1:6] ”1” ”2” ”3” ”4” ... .. .. ..$ : chr [1:2] ”top” ”bottom” ..@ units : chr ”cm” ..@ horizons:'data.frame': 6 obs. of 4 variables: .. ..$ texture : Factor w/ 14 levels ”C”,”CBVSCL”,”FSL”,..: 8 8 8 8 9 NA .. ..$ stickiness : Factor w/ 4 levels ”MS”,”SO”,”SS”,..: NA NA NA NA NA NA .. ..$ plasticity : Factor w/ 4 levels ”MP”,”PO”,”SP”,..: NA NA NA NA NA NA .. ..$ field ph : num [1:6] 7.9 7.7 8 7.9 7.4 NA ..@ site:'data.frame': 6 obs. of 2 variables: .. ..$ bound topography: Factor w/ 3 levels ”B”,”S”,”W”: 2 2 2 2 2 NA .. ..$ elevation: int [1:6] 13 7 9 14 21 NA

S4 Classes and Methods for Soil Information Formal class 'SoilProfileCollection' [package ”.GlobalEnv”] with 4 slots ..@ id : Named chr ”P001” .. ..− attr(*, ”names”)= chr ”id” ..@ depths : int [1:6, 1:2] 0 2 14 49 57 89 2 14 49 57 ... .. ..− attr(*, ”dimnames”)=List of 2 .. .. ..$ : chr [1:6] ”1” ”2” ”3” ”4” ... .. .. ..$ : chr [1:2] ”top” ”bottom” ..@ units : chr ”cm” ..@ horizons:'data.frame': 6 obs. of 4 variables: .. ..$ texture : Factor w/ 14 levels ”C”,”CBVSCL”,”FSL”,..: 8 8 8 8 9 NA .. ..$ stickiness : Factor w/ 4 levels ”MS”,”SO”,”SS”,..: NA NA NA NA NA NA .. ..$ plasticity : Factor w/ 4 levels ”MP”,”PO”,”SP”,..: NA NA NA NA NA NA .. ..$ field ph : num [1:6] 7.9 7.7 8 7.9 7.4 NA ..@ site:'data.frame': 6 obs. of 2 variables: .. ..$ bound topography: Factor w/ 3 levels ”B”,”S”,”W”: 2 2 2 2 2 NA .. ..$ elevation: int [1:6] 13 7 9 14 21 NA ..@ spatial:'SpatialPoints' ..@ metadata:'data.frame'

S4 Classes and Methods for Soil Information # Initialization depths(spc)