Mr. Marcos Angelini mangelini@cnia.inta.gov.ar

Soil Institute National Institute of Agriculture Technology

DSM in Argentina: challenges to overcome

“Towards a Global Soil Partnership for Food Security and Climate Change Mitigation and Adaptation”

Day 3: Tools for Soil Mapping

Olmedo, G.; Rodriguez, D.; Moretti, L.; Zurita, J.; Lopez, A; Schulz, G; Pezzola, A.; Cruzate, G.; Morales, C.; Tenti, L.

Content

•Context

•National Project

• Study cases

• Soil map point based

• Polygon maps disaggregation

We need to give CLEAR soil information to

prevent environmental disasters.

…according to the user of information:

politicians, field technicians, farmers, etc.

DIGITAL SOIL INFORMATION Implementation & Validation New Methodologies for Up-dating the Soil Information

Objectives General

• Need of updated digital soil information that enables soil resource inventories and that identifies areas of opportunities & risks.

• To adapt and apply the appropriate methodology for each study area according to its requirements.

• To train researchers in the DSM methodology

• To generate soil inventories identifying areas of opportunities & risks

• To develop a Soil Data Base accessed via web.

Specific

35 researchers staff – 2009 to 2012

Study Areas

• Ing. Juárez: 500 km2. 80% of area is native vegetation. Slope < 0.5 %. Water Erosion. Soil Map 1:500 000.

• San Justo: 1600 km2. Crop lands. Water erosion and flood risk. Soil map scale 1:50 000.

• La Paz: Farm 50 Km2. Slopes> 5%. Partially deforest. Water Erosion. Soil Map 1:50 000 & 1:20 000

• Tupungato: 710 km2. Fruit and wine production area.

• Patagones: 1600 km2. Partially deforested. Severe wind erosion. Soil Map 1:250 000.

Tupungato, Mendoza.

121 Soil Profiles sampled 710 Km2.

Materials

Methodological Scheme

Multi-linear Regression

Properties to predict

Environmental Covariate

EC 0-30 cm ASPECT, CN_BL, LS, MRRTF, MRVBF, WTI

EC 30-60 cm SLOPE, ASPECT, CN_BL, MBI

pH 0-30 cm DEM, SLOPE, ASPECT, CN_BL, CI, MRRTF, WTI

pH 30-60 cm ASPECT, CN_BL, LS, MRRTF

Clay 0-30 cm SLOPE, ASPECT, Curv1KK, MRVBF, MBI

Silt 0-30 cm SLOPE, ASPECT, Curv1KK, MRVBF, MBI

Sand 0-30 cm SLOPE, ASPECT, Curv1KK, MRVBF, MBI

Clay 30-60 cm CI, MRVBF, MBI

Silt 30-60 cm CI, MRVBF, MBI

Sand 30-60 cm CI, MRVBF, MBI

Depth Prof. SLOPE, CN_BL, MRRTF, MRVBF

Covariates selected by stepway

Exclusion of outliers

70% of data for make models

30% of data for validation

RStudio

Modeling

Validation

Properties to predict

R2 adjusted SSE MPE RMSPE

EC 0-30 cm 0.27 128.06 0.81 3.04

EC 30-60 cm 0.19 469.82 1.03 2.09

pH 0-30 cm 0.27 14.12 -0.55 0.71

pH 30-60 cm 0.31 8.11 -0.24 0.46

Clay 0-30 cm 0.13 1263.39 3.66 5.21

Silt 0-30 cm 0.13 7948.79 9.24 13.08

Sand 0-30 cm 0.13 15567.76 -12.93 18.32

Clay 30-60 cm 0.13 728.43 0.96 3.64

Silt 30-60 cm 0.13 4582.95 2.39 9.12

Sand 30-60 cm 0.13 8974.72 -3.38 12.77

Depth Prof. 0.24 229074.57 46.09 64.90

(Sun et al., 2011; Hengl et al., 2004)

Results

(a) EC 0-30 cm (dS/m); (b) pH 0-30 cm; (c) Clay 0-30 cm en (%); (d) Silt 0-30 cm en (%); (e) Sand 0-30 cm (%); (f) Depth profile (cm.)

MLR prediction +

Kriging of residuals

Results

Disaggregation of polygon maps using decision tree models

Legacy data Legacy Data: Soil Map 1:50000 14000 km2

Legacy Data 288 Soil Profiles

Methodology

1. To get environmental covariates 2. Sampling soil map (Taxonomic Classes) 3. To make and apply decision tree model 4. To repeat loops of 2) and 3) 5. Cross-validation with soil profile points

Environmental covariates DEM Potassium_c ChNBL AAChN TWI Landsat Distance V_OFD

OFD Slope Environments NDVI01 NDVI09 Converg_I Stream_P LS

1/5

Sampling of taxonomic soil classes

Soil A: 60% Soil B: 40% A1 =

Soil B Soil A

Cartographic Unit Soil Map

A1

• Random sampling

2/5

Sampling of taxonomic soil classes

Merge environmental covariates and soil classes

2/5

Used Covariates: 100% DEM 100% Potassium_c 98% ChNBL 83% AAChN 69% TWI 41% Landsat 40% Distance 39% V_OFD 32% OFD 28% Slope 25% Environment 23% NDVI01 18% NDVI09 17% Converg_I 13% Stream_P 12% LS

Decision tree model

3/5

Applying model Map of A Soil Class

Map of B soil Class

x100 loops

3/5

Argiudoles vérticos

Applying model 4/5

Argiudoles típicos

Applying model 4/5

Applying model

4/5

Validation

5/5

Conclusions Recently developed tools for soil mapping have been combined to obtain soil maps point

based, what has shown promising results. The maps not only offer information about the edaphic variables, but also an estimation of point value and error. Other authors (Hengl et al, 2004; 2007a) have found some advantages using RK instead of ordinary kriging. Although this research do not analyze differences between the two methods, it can be seen that the maps obtain are of a higher quality.

When working on disaggregation polygons maps, the applied methodology allows the

use of information about the different soil classes that form the cartographic units. In this manner it can be obtained raster maps of soil classification associated to the probability of event occurrence.

It is suggested to continue applying these tools in order to find a solution to real

problems and state the advantages compared to other methodologies.

Mu ch a s Gr a ci a s