International Symposium on Black Soils (ISBS18): Protect Black Soils, Invest in the Future, 10 - 12 September 2018, Harbin, China

1. Black Soils and Digital Soil Mapping in Canada
Xiaoyuan Geng, Brian McConkey and Juanxia He
Head/Chef, Soil Scientist/chercheur en pédologie
Canadian Soil Information Service (CanSIS)
Science and Technology Branch / Analyse et applications de paysage
Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada
Telephone/Téléphone: 613-759-1895
Room 1136, Neatby Bld., 960 Carling Av. Ottawa, ON K1A0C6
http://sis.agr.gc.ca/
Network of Black Soils, FAO, Harbin, P. R. China, September, 2018

2. 2
 National Ecological Framework (ECO)
 Soil Landscapes of Canada (SLC)
 Canada Land Inventory (CLI)
 Detailed Soil Surveys (DSS)
 Site (pedon) data
 Soil Classification System for Canada
 National peatland and soil carbon database
 http://sis.agr.gc.ca/cansis
National soil database contents

3. 3
Soil Landscape
of Canada
Hierarchy of Canadian
Land Resource Data
Ecoregion
Ecozone
1:1,000,000
1:7,500,000
Ecodistrict
Scaling-up local results for
regional assessment
Hierarchical land and soil framework

4. 4
Soil landscape of Canada framework
Version 1 and version 2 cover all of Canada, version 3.x covers
agricultural region. Scale of 1:1,000,000

5. 5
Black soils and agriculture in Canada
Mollisols are extensive in sub-humid to semiarid areas on the plains of North America, Europe,
Asia, and South America.
According to the Soil Taxonomy (1999), Mollisols should have Mollic Epipedon.
The thickness of the Mollic Epipedon depends on the depth and texture of the soil is at least
equal or above 10cm deep.
According to the Canadian Soil Classification System(1988), Chernozemic soils are dominant in
the grassland regions of Canada including the great expanse of the Canadian Prairies. Ah or Ap
horizon of Chenozemic soils should be above 10cm while meets other criteria.

6. 6
Characteristics of black soils in Canada
Huffman, T., Coote, D. R. and Green, M. 2012. Twenty-five years of changes in soil cover on Canadian Chernozemic (Mollisol) soils, and the impact on the
risk of soil degradation. Can. J. Soil Sci. 92: 471479.

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• Evolving from the former National Agri-Environmental Health and Reporting
Program (NAHARP), the goal of Sustainability Metrics* is to provide
scientifically credible, timely and relevant measurements of the environmental
sustainability of Canadian agriculture to support AAFC’s mission and mandate.
The best known of these measures are the Agri-Environmental Indicators (AEIs)
• The AEIs are grouped into four themes: Biodiversity,
Soil Quality, Water Quality and Air Quality and use models
to integrate information on soils, climate and topography
with statistics on land use and crop and livestock
management practices (to date these data are mostly derived
from the Census of Agriculture).
• The AEI report - Environmental Sustainability of Canadian
Agriculture: Agri-Environmental Indicator Report Series – Report #4
was published in July 2016 on Publications Canada, and twelve complementary
web pages were published on AAFC online. Associated data and metadata are
publically available on Open Data.
7
Monitor black soil health using sustainable metrics

8. 8
Soil Erosion – improvements in the Prairies due to soil improvement
measures, slight decline in Eastern Canada related to shift to annual
crops
8
Reduced soil erosion due to BMPs

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Water quality indicators - Phosphorus
Phosphorus – large increase in risk between 1981 and 2011, due to increased
fertilizer use and wet weather

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 No-till now predominant
 Main opportunity now to convert more full- to reduced-till
-300 -100 -10 10 100 300
Map of SOC change across Canada
0
5
10
15
20
25
30
1971 1975 1980 1985 1990 1995 2000 2005 2010 2015
Tillagearea(Millionha)
Full
Reduced
No-till

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Climate change and beyond
Heaton E., and Kulshreshth, S. 2017. Environmental Sustainability of Agriculture Stressed by Changing Extremes
of Drought and Excess Moisture: A Conceptual. Sustainability 2017, 9, 970; doi:10.3390/su9060970

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Data issues: overly generalized
Single Scale
Single vintage
Out-of-date
Incompatible
Costly to update
Lack of resources

13. 13

14. 14
Towards Inference based DSM

15. 15
Technical framework of DSM
New
point
data
New
point
data

16. 16
a b
c d
Feature reduction
using Best GLM or RF
Geospatial covariant and feature reduction

17. 17
Training data from legacy soil survey
Hypothesis
Any location within each of the
single component polygons of the
detailed soil survey can be used to
represent a spatial location of the
associated soil component or type
for that polygon.
This can be further stratified using
landscape facets such as slope
position and/or surficial geological
material data.

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New data collection using optimized cLHS
1. Specify range of sampling numbers e.g. 10
to 500
2. Calculate the spatial distribution of each
draw using conditional Latin Hypercube
Sampling (cLHS)
3. Extract covariant values of each cLHS
sampling points
4. Increase sampling size by 10
5. Repeat step 2 to 4 until reaching the
maximum e.g. 500
1. Using one or all of the optimization
solutions:
1) Proportion of points within convex
hull
2) Kullback-Leibler (KL) divergence
(Kullback and Leibler 1951)
This method is generic and could be used
in other research questions! For more
reading: Minasny and McBratney, 2006. A conditioned Latin
hypercube method for sampling in the presence of ancillary information,
Computers & Geosciences 32:1378–1388

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Methods of soil property mapping

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Global and National data development
Hengl, T., J. M. Jesus, G. B . M. Heuvelink, M. R. Gonzalez, M. Kilibarda, A. Blagoti, W. Shangguan, M. N. Wright, X. Geng, B.
Bauer-Marschallinger, M. A. Guevara, R. Vargas, R. A. MacMillan, N.H. Batjes, J.G.B. Leenaars, I. Wheeler, S. Mantel, B. Kempen,
2016. SoilGrids250m: global gridded soil information based on Machine Learning

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Soil association and soil type map
 New soil association
groups need to be renamed
accordingly
 With uncertainty data of the
prediction, soil properties
will be derived using
averaged or central values
of soil groups.
 The framework of training
data-inference-soil type
map-soil property map
provides effective way of
soil property renewal.

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From soil type map to attribute map update

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Aggregate
ETL
Interoperable
services
OLAP
Inference
Model
Query
Geo-BI
Report
Statistics
ETL-Extract, Transform and Load
OLAP-Online Analytical Processing
Geo-BI – Geospatial Business Intelligence
Source: Statistics Canada (2015)
Census Agricultural Region 2413
(Montérégie, Québec)
Integrated use of soil information

24. 24
LS CKR
X X X X
=
P
Crop Cover &
Management Factor
Slope Length FactorSoil Erodibility
Factor
Conservation Practice
Factor
E
Expected Soil Loss
(T/Ha)
 E = R x K x LS x C x P
 where
 R = Rainfall-runoff erosivity
factor
 K = Soil erodibility factor
 LS = Length-slope factor
 C = Cover management
factor
 P = Support practice factor
Annual, commodity and forecast based….
(Forecasted)
Rainfall
Factor

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General soil sample processing for soil biome work
Source: Lori Phillips, 2018

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Source:
http://www.sciencedirect.com/science/article/pii/S0012825216300113
Spectral library for cost-effective measurements

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Thank you!