In the agroecological zone of the Biemso basin in the Ashanti Region of Ghana, soil erodibility
and rainfall erosivity patterns were estimated. The study aimed at investigating the temporal
variability of rainfall erosivity using the Fournier Index Method and assessing the soil
erodibility parameters of a Sawah site using the WEPP model. Four plots representing the
major land uses in the area for maize, oil palm, natural vegetation and plantain cultivation
were selected. Results showed that soil organic matter content ranged from 1.95 to 5.52%;
sand ranged from 14.34 to 31.86 %; silt ranged from 31.63 to 68.77%; clay ranged from 16.04
to 20.08% and very fine sand from 3.38 to 8.84%. The derived interrill erodibility (Ki) values
ranged from 44.26 to 51.70 kg s m-4 under all land uses considered at the study site and soils
in the study area were moderately resistant to erosion by raindrops. The derived rill erodibility
(Kr) values ranged from 0.005 to 0.012 s m-1 under all land uses considered at the study site.
Rill erodibility values were higher at the foot slopes under all land uses except under Oil Palm
land use. Rainfall values exceeded the 20-25 mm threshold value for erosive rains. Erosivity
values determined for the study site revealed a moderate erosion risk in the major rainy season
(April-July); low erosion risk in the minor rainy season (August-October ) and very low erosion
risk in the dry season (November-March). It is recommended that soil and land management
practices that would reduce water erosion during the major rainy season should be implemented
such as bunding, mulching and contour farming.
In the agroecological zone of the Biemso basin in the Ashanti Region of Ghana, soil erodibility
and rainfall erosivity patterns were estimated. The study aimed at investigating the temporal
variability of rainfall erosivity using the Fournier Index Method and assessing the soil
erodibility parameters of a Sawah site using the WEPP model. Four plots representing the
major land uses in the area for maize, oil palm, natural vegetation and plantain cultivation
were selected. Results showed that soil organic matter content ranged from 1.95 to 5.52%;
sand ranged from 14.34 to 31.86 %; silt ranged from 31.63 to 68.77%; clay ranged from 16.04
to 20.08% and very fine sand from 3.38 to 8.84%. The derived interrill erodibility (Ki) values
ranged from 44.26 to 51.70 kg s m-4 under all land uses considered at the study site and soils
in the study area were moderately resistant to erosion by raindrops. The derived rill erodibility
(Kr) values ranged from 0.005 to 0.012 s m-1 under all land uses considered at the study site.
Rill erodibility values were higher at the foot slopes under all land uses except under Oil Palm
land use. Rainfall values exceeded the 20-25 mm threshold value for erosive rains. Erosivity
values determined for the study site revealed a moderate erosion risk in the major rainy season
(April-July); low erosion risk in the minor rainy season (August-October ) and very low erosion
risk in the dry season (November-March). It is recommended that soil and land management
practices that would reduce water erosion during the major rainy season should be implemented
such as bunding, mulching and contour farming.
Soil erosion assessment using RUSLE and Projection Augmented Landscape Model ...ExternalEvents
Mr. José María León Villalobos, Centro de Investigación
en Ciencias de Información Geoespacia (CentroGeo),
Mexico. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.
SOIL LOSS ESTIMATION IN GIS FRAMEWORK: A CASE STUDY IN CHAMPABATI WATERSHEDAM Publications
RUSLE (Revised Universal Soil Loss Equation) is widely used to predict average annual rate of soil erosion. RUSLE parameters were assessed using Satellite Remote Sensing (RS) and GIS with a view to model soil erosion in CHAMPABATI watershed in Assam state. Assessment of soil erosion is useful in planning and conservation works in a watershed or basin. Modelling can provide a quantitative and consistent approach to estimate soil erosion under a wide range of conditions. The parameters of RUSLE model were estimated using remote sensing data and the erosion probability zones were deter-mined using GIS. The five major input parameters used in the study are Rainfall Erosivity Factor (R), Slope Length and Steepness Factor (LS), Soil Erodibility Factor (K), Cover and Management Factor (C) and Support Practice Factor (P). The R factor had been determined from monthly TRMM rainfall data of study area. The soil survey data from www.fao.org was used to develop the K factor and DEM of study area was used to generate topographic factor (LS). The value of P & C factor was obtained from land use land cover map & LANDSAT image respectively. Estimating C factor in this study involves the use of Normalized Difference Vegetation Index (NDVI), an indicator which shows vegetation cover, using the regression equation in Spatial Analyst tool of GIS Software. After generation of input parameters, analysis was performed for estimation of soil erosion using RUSLE model by spatial information analysis approach. The quantitative soil loss (t/ha/year) ranges were estimated and classified the watershed into different levels of soil erosion severity and also soil erosion index map was developed. The average annual soil losses of the study Watershed were then grouped into different severity classes based on the criteria of soil erosion risk classification suggested by FAO (2006). The estimated average annual soil loss for the study area is 5.8044 million t. yr-1.
First meeting of the Editorial Board of the Soil Atlas of Asia, 12 - 15 March 2018, Quezon City, Philippines. The preparation of the Soil Atlas of Asia is sponsored by Joint Research Centre of the European Commission (JRC-EC).
Assessment of Spatial and Temporal Variations of Soil Salinity using Remote S...Hamdi Zurqani
“The aim of this paper is to identify the change in saline soils (Sebkha) using Remote Sensing (RS) and geographic information system (GIS) techniques”.
This presentation was presented during the 1 Parallel session on Theme 3.1, Managing SOC in: Soils with high SOC – peatlands, permafrost, and black soils, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Ivan Vasenev, from Timiryazev Academy – Russian Federation, in FAO Hq, Rome
Presentation by Tor-Gunnar Vagen at the session on sustainable soil management in Africa at the European Development Days 2017. https://eudevdays.eu/sessions/sustainable-soil-management-foundation-africas-future
Soil erosion assessment using RUSLE and Projection Augmented Landscape Model ...ExternalEvents
Mr. José María León Villalobos, Centro de Investigación
en Ciencias de Información Geoespacia (CentroGeo),
Mexico. Global Symposium on Soil Erosion (GSER19), 15 - 17 May 2019 at FAO HQ.
SOIL LOSS ESTIMATION IN GIS FRAMEWORK: A CASE STUDY IN CHAMPABATI WATERSHEDAM Publications
RUSLE (Revised Universal Soil Loss Equation) is widely used to predict average annual rate of soil erosion. RUSLE parameters were assessed using Satellite Remote Sensing (RS) and GIS with a view to model soil erosion in CHAMPABATI watershed in Assam state. Assessment of soil erosion is useful in planning and conservation works in a watershed or basin. Modelling can provide a quantitative and consistent approach to estimate soil erosion under a wide range of conditions. The parameters of RUSLE model were estimated using remote sensing data and the erosion probability zones were deter-mined using GIS. The five major input parameters used in the study are Rainfall Erosivity Factor (R), Slope Length and Steepness Factor (LS), Soil Erodibility Factor (K), Cover and Management Factor (C) and Support Practice Factor (P). The R factor had been determined from monthly TRMM rainfall data of study area. The soil survey data from www.fao.org was used to develop the K factor and DEM of study area was used to generate topographic factor (LS). The value of P & C factor was obtained from land use land cover map & LANDSAT image respectively. Estimating C factor in this study involves the use of Normalized Difference Vegetation Index (NDVI), an indicator which shows vegetation cover, using the regression equation in Spatial Analyst tool of GIS Software. After generation of input parameters, analysis was performed for estimation of soil erosion using RUSLE model by spatial information analysis approach. The quantitative soil loss (t/ha/year) ranges were estimated and classified the watershed into different levels of soil erosion severity and also soil erosion index map was developed. The average annual soil losses of the study Watershed were then grouped into different severity classes based on the criteria of soil erosion risk classification suggested by FAO (2006). The estimated average annual soil loss for the study area is 5.8044 million t. yr-1.
First meeting of the Editorial Board of the Soil Atlas of Asia, 12 - 15 March 2018, Quezon City, Philippines. The preparation of the Soil Atlas of Asia is sponsored by Joint Research Centre of the European Commission (JRC-EC).
Assessment of Spatial and Temporal Variations of Soil Salinity using Remote S...Hamdi Zurqani
“The aim of this paper is to identify the change in saline soils (Sebkha) using Remote Sensing (RS) and geographic information system (GIS) techniques”.
This presentation was presented during the 1 Parallel session on Theme 3.1, Managing SOC in: Soils with high SOC – peatlands, permafrost, and black soils, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Ivan Vasenev, from Timiryazev Academy – Russian Federation, in FAO Hq, Rome
Presentation by Tor-Gunnar Vagen at the session on sustainable soil management in Africa at the European Development Days 2017. https://eudevdays.eu/sessions/sustainable-soil-management-foundation-africas-future
A Soil Erosion Indicator for Supporting Agricultural, Environmental and Clima...PANOS PANAGOS
Soil erosion is one of the eight threats in the Soil Thematic Strategy, the main policy
instrument dedicated to soil protection in the European Union (EU). During the last decade, soil
erosion indicators have been included in monitoring the performance of the Common Agricultural
Policy (CAP) and the progress towards the Sustainable Development Goals (SDGs). This study comes
five years after the assessment of soil loss by water erosion in the EU [Environmental science & policy 54,
438–447 (2015)], where a soil erosion modelling baseline for 2010 was developed. Here, we present an
update of the EU assessment of soil loss by water erosion for the year 2016. The estimated long-term
average erosion rate decreased by 0.4% between 2010 and 2016.
Climate Change and Biodiversity: Implications for Bay Area Conservation by Da...OpenSpaceCouncil
On November 10, 2010 the Bay Area Open Space Council convened a workshop at the Gordon & Betty Moore Foundation to discuss climate change and its impacts on land conservation.
Dr. David Ackerly from UC Berkeley presented on "Climate Change and Biodiversity: Implications for Bay Area Conservation."
Read more about the event here: http://openspacecouncil.org/blog/by-guest-blogger-kelly-cash-on-the-morning-of-the-day-that-the-san-francisco-giants-would-win-the-world-series-in-the-evenin/
See photos from the event here: https://www.flickr.com/photos/openspacecouncil/sets/72157625226473375/
المؤتمر الاول لإدارة الازمات و الكوارث و الحد م اخطارها نحو فعالية افضل للحد من اخطار الكوارث
Thursday, April 23, 2009
http://www.eip.gov.eg/crisisCD/Main.htm
Nitrogen deposition dose: Response relationships for habitats - Dr Chris FieldsIES / IAQM
Semi-natural habitats in Britain and, indeed, most highly populated world regions are threatened by aerial deposition of reactive nitrogen compounds, largely emitted from the processes of intensive agriculture, vehicles and power generation. We have been slow to understand the potential significance of the increased exposure and accumulation of nitrogen in the ecological system. Numerous research studies from varied habitats in different countries indicate that the composition and biodiversity of plant communities is adversely affected at even low levels of exposure and impacts may extend beyond the plant kingdom to other trophic levels such as butterflies and birds. Furthermore, the likelihood that negative change in habitats can be easily reversed is now being questioned due to the longevity of accumulated nitrogen in the ecosystem. This paper will discuss the main effects of nitrogen compounds and the nature of the dose response between nitrogen and ecological harm.
Similar to National Assessment of Soil Erosion in Canada from 1971 to 2016 (20)
Item 9: Soil mapping to support sustainable agricultureExternalEvents
SOIL ATLAS OF ASIA
2ND EDITORIAL BOARD MEETING
RURAL DEVELOPMENT ADMINISTRATION, NATIONAL INSTITUTE OF AGRICULTURAL SCIENCES,
JEONJU, REPUBLIC OF KOREA | 29 APRIL – 3 MAY 2019
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SOIL ATLAS OF ASIA
2ND EDITORIAL BOARD MEETING
RURAL DEVELOPMENT ADMINISTRATION, NATIONAL INSTITUTE OF AGRICULTURAL SCIENCES,
JEONJU, REPUBLIC OF KOREA | 29 APRIL – 3 MAY 2019
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SOIL ATLAS OF ASIA
2ND EDITORIAL BOARD MEETING
RURAL DEVELOPMENT ADMINISTRATION, NATIONAL INSTITUTE OF AGRICULTURAL SCIENCES,
JEONJU, REPUBLIC OF KOREA | 29 APRIL – 3 MAY 2019
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2ND EDITORIAL BOARD MEETING
RURAL DEVELOPMENT ADMINISTRATION, NATIONAL INSTITUTE OF AGRICULTURAL SCIENCES,
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This is a presentation by Dada Robert in a Your Skill Boost masterclass organised by the Excellence Foundation for South Sudan (EFSS) on Saturday, the 25th and Sunday, the 26th of May 2024.
He discussed the concept of quality improvement, emphasizing its applicability to various aspects of life, including personal, project, and program improvements. He defined quality as doing the right thing at the right time in the right way to achieve the best possible results and discussed the concept of the "gap" between what we know and what we do, and how this gap represents the areas we need to improve. He explained the scientific approach to quality improvement, which involves systematic performance analysis, testing and learning, and implementing change ideas. He also highlighted the importance of client focus and a team approach to quality improvement.
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National Assessment of Soil Erosion in Canada from 1971 to 2016
1. National Assessment of Soil
Erosion in Canada
from 1971 to 2016
David Lobb1, Sheng Li2, Brian
McConkey2, Nasem Badreldin1,3
1University of Manitoba,2Agriculture and Agri-Food Canada,
3University of Guelph
1
2. Saskatchewan (1980s)
SOIL EROSION IN CANADA
Crop production over the last 100 to 200 years in Canada has
resulted in significant degradation of soil.
3. Saskatchewan (1930s)
The Dirty Thirties
Crop production over the last 100 to 200 years in Canada has
resulted in significant degradation of soil.
SOIL EROSION IN CANADA
4. The Heavy Seventies
SOIL EROSION IN CANADA
Crop production over the last 100 to 200 years in Canada has
resulted in significant degradation of soil.
5. Assessment and Prediction Models:
Agriculture and Agri-Food Canada’s
Agri-Environmental Indicators program
Soil Erosion Risk Indicator models
ASSESSMENT OF SOIL EROSION
2000 20102005 2016
6. 849
89
162 Ecoregions
Ecodistricts
Ecozones
SLC Polygons
(#89 = 0.13 million ha)
Canada
(#849 = 0.9 million ha)
(#162 = 3.3 million ha)
(Prairie = 6.5 million ha)
Figure1: Map of the province of Manitoba illustrating the spatial framework of biophysical data
in Canada. Source: Agriculture and Agri-food Canada.
Agriculture and Agri-Food Canada
Agriculture et Agroalimentaire Canada
Manitoba
0 250 500 1000
ProjectionAzimutaldeEqui-airedeLambert
0 500250 750 1000 1500
LambertAzimuthalEqualAreaProjection
ECHELLE ESCALA SCALE
1:45000000
miles
Kms.
ProyeccionAzimultaldeEqui-areadeLambert
15
1
1
1
1
2
2
2
2
2
2
2
7
7
3
6
3
3
5
55
5
5
5
5
8
9
9
6
10
6
7
7
11
10
10
6
13
12
13
12
14
13
14
15
14
14
4
COMMISSION DE
CO
COMISIONPARALA
COOPERACIONAMBIENTAL
COMMISSIONFOR
ENVIRONMENTALCOOPERATION
CCE
CCA
CEC
OP RATIONENVIRONNEMENTAL
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
NIVEAU I. NIVELI. LEVELI
CORDILLEREARCTIQUE
CORDILLERAARTICA
ARCTICCORDILLERA
TOUNDRA
TUNDRA
TUNDRA
TAIGA
TAIGA
TAIGA
PLAINED'HUDSON
PLANICIEDEHUDSON
HUDSONPLAIN
FORETSSEPTENTRIONALES
BOSQUESSEPTENTRIONALES
NORTHERNFORESTS
MONTAGNESFORESTEESDUNORD-QUEST
MONTANASBOSCOSASNOROCCIDENTALES
NORTHWESTERNFORESTEDMOUNTAINS
FORETMARITIMEDELACOTEOCCIDENTALE
BOSQUECOSTEROOCCIDENTAL
MARINEWESTCOASTFOREST
FORETSTEMPEREESDEL'EST
BOSQUESTEMPLADOSDELESTE
EASTERNTEMPERATEFORESTS
GRANDESPLAINES
GRANDESPLANICIES
GREATPLAINS
DESERTSDEL'AMERIQUEDUNORD
DESIERTOSDENORTEAMERICA
NORTHAMERICANDESERTS
CALIFORNIEMEDITERRANEENNE
CALIFORNIAMEDITERRANEA
MEDITERRANEANCALIFORNIA
HAUTESTERRESSEMI-ARIDESMERIDIONALES
ELEVACIONESSEMIARIDASMERIDIONALES
SOUTHERNSEMI-ARIDHIGHLANDS
SIERRASTEMPEREES
SIERRASTEMPLADAS
TEMPERATESIERRAS
FORETSTROPICALESSECHES
SELVASCALIDO-SECAS
TROPICALDRYFORESTS
FORETSTROPICALESHUMIDES
SELVASCALIDO-HUMEDAS
TROPICALWETFORESTS
Limiteinternationale
Limiteinternacional
Internationalboundary
LimitederegionsNiveauI
LimitederegionesNivelI
RegionboundaryLevelI
Figure 2: Ecozones of North America. Source: Commission for Environmental Cooperation.
7. Soil Landscape Canada (SLC) Polygon,
10,000 – 1,000,000 ha
TillERI
Erosion Models
Landform
National Soil
Database
(NSDB)
Census of
Agriculture
1981- 2016
Soil,
Topography
Climate
Stations
Long Term
Climate Data
Crop type,
Tillage
Up
Mid
Low
Dep
WaterERI WindERI
SoilERI
Province
Canada
Data inputs
Aggregation
Aggregation
Aggregation
ShengLietal.,WCSS,2010
8. • 19 landform types:
• 7 surface forms
• 1 – 4 slope classes
• 4 segments (some only have 3):
• Up, Mid, Low and Depression
• length and slope gradient
• Calculation unit:
• Landform (hill slope)
• Multiple hill slopes in one SLC polygon
• Allocation:
• Soil, crop type, tillage system
• Each segment in each landform
Landform
Up
Mid
Low
Dep
ShengLietal.,WCSS,2010
9. • Equation (WindERI)
• Wind Erosion Equation (WEQ)
• AWd = f(I,K,C,L,V)
• Individual factors
• C-, L- and V-factor for the hillslope
• I- and K-factor for each segment
• Adjustments
• Expert knowledge
○ Processes and conditions in Canada
Landform
Up
Mid
Low
Dep
TillERI
Erosion Models
WaterERI WindERI
SoilERI
ShengLietal.,WCSS,2010
10. • Equation (WaterERI)
• Universal Soil Loss Equation (USLE)
• ATi = R • K • LS • C • P
• Individual factors -- RUSLE
• R-, C- and P-factor for the hillslope
• K- and LS-factor for each segment
• Adjustments -- RUSLE2
• Interactions between factors
• Soil accumulation rates
• Regression equations
• Intensive test runs in RUSLE2
Landform
Up
Mid
Low
Dep
TillERI
Erosion Models
WaterERI WindERI
SoilERI
ShengLietal.,WCSS,2010
11. • Equation (TillERI)
• ATi = ET • EL
• Erosivity of tillage (ET)
• Crop type and tillage system
○ Tillage equipment
○ Number of passes per year
• Field experiment data
• Erodibility of Landform (EL)
• Slope gradient and slope length
Landform
Up
Mid
Low
Dep
TillERI
Erosion Models
WaterERI WindERI
SoilERI
ShengLietal.,WCSS,2010
12. soil loss
soil
accumulation
soil
accumulation
Tillage erosion is the net redistribution (losses and gains) of soil resulting from the
variability in the movement of soil by tillage.
Cropping and tillage systems that employ intensive tillage (frequent, deep, fast)
can cause severe tillage erosion.
ET
13. • SoilERI model (SoilERI)
• ASoil = ATi + AWt + AWd
• For each segment in each landform (hillslope)
• Aggregation
• Area-weighted across
○ Landform
○ Crop type
○ Tillage system
• Value for each segment
○ SLC polygon
○ Province
○ Canada
Landform
Up
Mid
Low
Dep
Soil Landscape Canada (SLC) Polygon,
10,000 – 1,000,000 ha
TillERI
Erosion Models
WaterERI WindERI
SoilERI
Province
Canada
Aggregation
Aggregation
Aggregation
ShengLietal.,WCSS,2010
14. Assessment and Prediction Models:
Classes of Soil Erosion / Degree of Soil Loss:
Extremely Low / Negligible0 – 3 t ha-1 yr-1
3 – 6 t ha-1 yr-1
6 – 11 t ha-1 yr-1
11 – 22 t ha-1 yr-1
22 – 33 t ha-1 yr-1
>33 t ha-1 yr-1
}Sustainable
ASSESSMENT OF SOIL EROSION
15. * Upper slopes
ASSESSMENT OF SOIL EROSION
Distribution of Soil Loss Rates for 1971 and 2011: Wind Erosion
Soil Erosion Risk Classes
1971
2011
16. * Upper + Mid slopes
ASSESSMENT OF SOIL EROSION
Distribution of Soil Loss Rates for 1971 and 2011: Water Erosion
Soil Erosion Risk Classes
1971
2011
17. * Upper slopes
ASSESSMENT OF SOIL EROSION
Distribution of Soil Loss Rates for 1971 and 2011: Tillage Erosion
Soil Erosion Risk Classes
1971
2011
18. Distribution of Soil Loss Rates for 1971 and 2011: Soil Erosion
Soil Erosion Risk Classes
1971
2011
ASSESSMENT OF SOIL EROSION
19. CONCLUSIONS
• The annual rates of soil erosion by wind, water and tillage, and
their combination, have declined over the past 45 years in
response to the decline in use of intensive tillage practices.
However, a considerable amount of cropland remains at
moderate to very high rates of soil erosion.
22. CONCLUSIONS
• Tillage erosion is a major cause for moderate to high rates of soil
erosion, and should be the focus of future soil conservation
efforts.
Conservation tillage must focus on the amount of soil movement
during tillage as well as the amount of crop residue left on the
soil surface.
23. CONCLUSIONS
• An integrated approach to managing all forms of soil erosion is
necessary to minimize soil loss and restore eroded soils.
This can be challenging. Some soil conservation practices will
reduce one form of erosion while exacerbating another form. In
particular, tillage practices that are effective in reducing wind and
water erosion are not necessarily effective against tillage erosion.
For example: the chisel plough leaves more crop residues on the
soil surface than the moldboard plough, providing more
protection against wind and water. At the same time, the chisel
plough can move soil over a much greater distance and cause
more tillage erosion.
24. There are tillage operations that are
more erosive than the mouldboard
plough
We must consider how far soil is moved
during tillage as well as how much crop
residue is left on the soil surface
• We must redefine and redesign conservation tillage, and foster
its implementation.
25. • We must redefine and redesign conservation tillage, and foster
its implementation.
The current trend is towards higher speed
tillage, throwing soil much further
There are tillage operations that are
more erosive than the mouldboard
plough
26. Even seeding operations
move a lot of soil and cause tillage erosion
• We must redefine and redesign conservation tillage, and foster
its implementation.
There are tillage operations that are
more erosive than the mouldboard
plough
27. Even crop management operations
move a lot of soil and cause tillage erosion
• We must redefine and redesign conservation tillage, and foster
its implementation.
There are tillage operations that are
more erosive than the mouldboard
plough
28. CONCLUSIONS
• The assessment of soil erosion must consider both the annual
rates and cumulative total of soil loss. It is necessary to know the
historical impact of soil erosion to target effective management
practices to sustain or enhance soil and crop productivity and
profitability.
29. Soil Loss and Yield Loss Relationship:
0
10
20
30
40
50
60
70
80
90
100
0102030405060708090100
Amount of original topsoil remaining (%SOC)
Cropyield(%)
2xDTS
1xDTS
* non-linear response
ASSESSMENT OF SOIL EROSION
31. • Conservation tillage simply reduces the loss of soil organic
carbon and productivity, we must focus on good crop
management to increase organic carbon inputs into the soil
and increase soil and crop productivity.
32. Assessment of the Cost of Soil
Erosion to Crop Production
in Canada
Nasem Badreldin1,2, David Lobb1
1University of Manitoba, 2University of Guelph
32
33.
34. A
B
C
Soil-landscape variability in a hilly landscape
unbroken, uncultivated
negligible soil erosion
variability caused by pedogenic processes
THE COST OF DOING NOTHING
35. A
B
C
Soil-landscape variability in a hilly landscape
few decades of cultivation (~1940)
juvenile state of erosion
THE COST OF DOING NOTHING
36. • There is growing urgency to take effective action.
• Increasing variability in climate and the increasing severity
and frequency of weather extremes can only amplify the
losses in crop production and threaten farm and food
security.
THE COST OF DOING NOTHING
37. • There is growing urgency to take effective action.
• Increasing variability in climate and the increasing severity
and frequency of weather extremes can only amplify the
losses in crop production and threaten farm and food
security.
• The degradation of soil landscapes is increasing in areal
extent, more of farm fields are suffering the loss of topsoil.
This is a result of progressive tillage erosion.
THE COST OF DOING NOTHING
38. A
B
C
Soil-landscape variability in a hilly landscape
several decades of cultivation (~1990)
mature state of erosion
THE COST OF DOING NOTHING
39. A
B
C
Soil-landscape variability in a hilly landscape
continued cultivation (~2010)
advanced state of erosion
entire soil-landscape degraded
There is a need for effective and
preventative and corrective action!
THE COST OF DOING NOTHING