From S E Research To Modeling Impacts
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From Soil Erosion Research to Modeling Impacts of
Interventions: The Case of Ethiopia
By
Gete ZelekeGMP
UN expert group meeting on“Sustainable land management and agricultural practices in Africa:
Bridging the gap between research and farmers”April 16 – 17, 2009, University of Gothenburg, Sweden.
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Part I: Overview of Land Degradation
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- Huge gulley erosion- This process is still common in most parts of Ethiopia (mainly in potential areas)
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• Precious top soil that used to support life of different forms is washed out of the landscape and land is abandoned for any meaningful agricultural activity
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Gete, 2005
Common in the rainy season
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Land Coverin DembechaArea, Ethiopia,in 1957 and 1995
Gete Zeleke 2000
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Biological degradation: The invisible threat
• All soils in the country (except pastoralist areas) suffer from soil nutrient mining: Open nutrient cycle!– Use of cow dung and crop
residue as a source of energy in the country is equivalent to 700,000 tons of grain/year
– Deteriorates chemical and physical properties of the soil
• OM content• Soil moisture holding
capacity• Infiltration capacity• Soil structure
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Part II: Some of the major causes
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1. Poor Land Management
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• Free grazing– Deteriorates soil physical
properties– Destroy soil conservation
measures– Remove crop residue
from the soil– Detach soil particles
• Unbalanced carrying capacity resulted in overgrazing
2. Poor Livestock Management
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3. Energy sources for HH domestic use
Amare, 2008
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Slide from Zelalem, 2006
Population in Ethiopia from 1900 to 2006, with projections backward to 1600, and forward to 2100, respectively
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
1600 1700 1800 1900 2000 2100Year
Pop
ulat
ion
in m
illio
n
Hurni, 2007
4. High Population Pressure: it is contextual
– Limited capacity and access
– High level of illiteracy – weak awareness
– Dependant on subsistent farming – 85%
– Push people to marginal areas
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5. Lack of proper awareness on extent and impacts of LD & other EPs
• Very poor resource database and utilization– Eg. Soil database
• Lack of or incomplete empirical evidences on extent and impacts of LD and other EPs
• Poor way of communication – e.g.. tones of soil/ha/y, ha of forest/y, % siltation, climate change...etc *******
• No cost benefit analysis
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Part III: Experiences on SLM
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(4) Water tables and water supply(5) Biomass for livestock and bee keeping
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February 2004 October 2005
19
May 2003 October 2005
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Part IV: Mechanisms of capturing process to influence agricultural development positively
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What is need to make informed decisions on Agri. deve?
– Need to have resource database (at least soil, water, climate and genetic resources…)
– Need to know rate and magnitude of soil erosion/runoff under different AEZ, soil types, land use systems, etc
– Need to know what technology works, where, and under what condition
– Need to know negative impacts of Soil erosion (land degradation) and positive impacts of applying improved land management practices
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What are Possible approaches?Approach 1:• Measuring soil erosion in all watersheds• Measuring effects of all land management practices in
different parts (too expensive)
Approach 2:• Measure selective and representative watersheds• Measure selective and right combinations of LM
practices (Achievable)
Approach 3: • Extrapolate the result to ungauged areas using a
combination of GIS, biophysical and economic models (Achievable)
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Framework for Assessing Major LD Processes & Impacts (Approach 2)
LD
On-site
Off-site
Types of Impacts
Siltation/sedimentation
• Reservoirs/dams
• Lakes
• On farm lands
• Soil loss due to Sheet & rill erosion by water
• Loss of soil nutrients through DB, CRB & GR
Processes captured
• water supply
• irrigation water supply
• Power generation
• Biodiversity
• Destruction of productive lands
• Production loss
Impacts quantified
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What do we have on this?
LD
On-site
Off-site
• water supply
• irrigation water supply
• Power generation
• Biodiversity
• Destruction of productive lands
• Production loss
Siltation/sedimentation
• Reservoirs/dams
• Lakes
• On farm lands
• Soil loss due to Sheet & rill erosion by water
• Loss of soil nutrients through DB, CRB & GR
Types of Impacts Processes captured Impacts quantified
Very little
We know – SCRP & others
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Afdeyu
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Two key processes to be captured:
• Watershed status (hill slope Processes
• Stream gauging
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Database from SCRP
1. Watershed level• Catchment runoff - hydrographs• Sediment yield• Climate • Land use• Harvest• Soil depth• Socio-economic data
2. Plot level– Soil loss– Runoff– Yield– SWC measures impact/land use/design/type
With and without scenario}
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Framework for Assessing Impacts of LM Practices (Approach 2 cont..)
LM
On-site
Off-site
Types of Impacts
Effect in reducing Siltation/sedimentation
• Reservoirs/dams
• Lakes
• On farm lands
• Effectiveness in reducing soil loss
• Effect in improving soil fertility and depth
• Effect on moisture conservation and soil water holding capacity
Processes captured
• water supply
• irrigation water supply
• Power generation
• Biodiversity
• Protection of productive lands
• Production gains
Impacts quantified
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What do we have?
LM
On-site
Off-site
• water supply
• irrigation water supply
• Power generation
• Biodiversity
• Protection of productive lands
• Production gains
Effect in reducing Siltation/sedimentation
• Reservoirs/dams
• Lakes
• On farm lands
• Effectiveness in reducing soil loss
• Effect in improving soil fertility and depth
• Effect on moisture conservation and soil water holding capacity
Types of Impacts Processes captured Impacts quantified
No measured data
We have some data (SCRP/HLI/EIAR but need to be organized)
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What is missing or less addressed?
• Offsite processes and impacts of LD
• Info on impacts of LM practices (only few)
• Info on impacts of integrated land management practices
• Socio-economic aspects of SLM and LD (need more work)
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Approach 3: Methods of extrapolation
1. Selection of appropriate models and tools– Modeling
• Model – Choice of appropriate model
» USLE – On-site processes (soil loss)» SWAT- Off-site process (siltation)
• Gauged values to calibrate and validate model» SCRP watersheds
– GIS• To identify recommendation domains
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Methods of extrapolation cont…
2. Steps to be followed• Characterize each station
– Biophysical parameters– Socio-economic parameters
• Reclassify country coverages of bio-physical and SE parameters
• Identify recommendation domains• Test model on station• Calibrate and validate model using gauged
station data• Apply model on recommendation domains
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Processes of Identifying Recommendation Domains using GIS Environment (soil loss, Nutrient loss & SLM)
- Altitude
- Rainfall
- LGP
- Slope
- Soil
-Farming system
-Land Use …etc
Reclassify country coverage and develop layers for each parameter
Recommend. Domains
Develop Ranges of parameters
Overlay
(model)
GIS Modelling
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slope
< 2%
2% - 5%
5% - 8%
8% - 12%
> 12%
m.a.s.l.
High : 4378
Low : -152
ann.precip. (mm)
< 400
401 - 800
801 - 1,200
1,201 - 1,600
> 1,600
growing days
0 - 60
61 - 120
121 - 180
181 - 240
241 - 365
Examples of National coverage for key parameters
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Anjeni station dominant cereal
Barley
Maize
Millet
Sorghum
Teff
Wheat
Pastoral
farming systemFarming system: Ethiopia
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extrapolation of station data
based on similarity of environmental conditions
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farming system
Farming system:teff, wheat, maize & pulses
Farming system – Similar to Anjeni
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soilsSoils – Similar to Anjeni
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Anjeni station m.a.s.l.
-152 - 1,700
1,701 - 2,600
2,601 - 4,378
altitudeAltitudinal belts – Similar to Anjeni
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Anjeni station annual rainfall (mm)
95 - 1,400
1,401 - 1,800
1,801 - 2,231
rainfallAnnual rainfall – Similar to Anjeni
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Anjeni station LGP (days)
0 - 180
181 - 250
251 - 365
LGP – Similar to Anjeni
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Anjeni station slope
0% - 2%
3% - 8%
9% - 62%
slopeSlope – Similar to Anjeni
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Anjeni station Annual Mean Temperature
degrees Celsius
< 15
15 - 22
> 22
temperatureAnnual Meant Temp – Similar to Anjeni
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Anjeni station similarity of environmental conditions
most similar
least similar
GIS Modelling and Overlays: Areas that can be represented by Anjeni
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Anjeni station
representative areas
Areas that can be represented by Anjeni- by woreda (best fit areas only)
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Part V: Extrapolation
A: Model application (Eg. USLE) to extrapolate on-site processes
and impacts
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LD
On-site
• Production loss
• Soil loss due to Sheet & rill erosion by water
• Loss of soil nutrients through DB, CRB & GR
Types of Impacts Processes captured Impacts quantified
• Effectiveness in reducing soil loss
• Effect in improving soil fertility and depth
• Effect on moisture conservation and soil water holding capacity
SLM• Production gains
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Phase I: Model validation at station level
1. Quantifying Soil Loss: • Parameter generation
– R, K, S, L, C, P
• Model calibration• Parameter adjustment including methods of
generation• Validate model• Recommend options for:
– Model response and interpretation requirements– procedures parameter generation– Options including tables, equations
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Parameter generation options• Eg. K factor
1.
2.
3.
4. Or use table developed based on Ethiopian experience
100
35.2225.3120002114.1
SSMK
perstrOM
ffffK hisandorgsiclcsand
2
7101.0
659.1log5.0exp0405.00034.0594.7
DK
g
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2. Quantifying Nutrient Loss
• We choose only three important ways of nutrient loss from the soil– Burning of dung– Burning of crop residue– Removal of grain
• We consider three possibilities of nutrient addition to the soil– Fixation – Weathering – Artificial fertilizer or manuring
• We only address two nutrients– N– P
Remark: the method can be used to include others
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Procedures
• Determine nutrient content of dung, crop residue and major crops
• Set conversion coefficient - % of energy consumption covered by dung and crop residue per person, per year
• Convert this to dung and CR used
• Convert this to N & P loss from dung and CR
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Procedures cont…
• Estimate production of major crops for each zone and
• Convert this into N & P loss
• Calculate Nutrient addition by different means
• Calculate net nutrient loss:grdb cb AN N N NN grdb cb AP P P PP
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3. Quantifying impacts of SLM practices
1. Scenario 1. Only Physical SWC measures– Use SCRP values – Adjust P factor
2. Scenario 2. Physical SWC with SF/SMM– Extrapolate from existing values or measure– Adjust P factor and may be C factor
3. Scenario 3. All best LMP– Extrapolate of measure– Adjust P factor and C factor
4. Area closure and forestry – – Extrapolate or measure– adjust P factor and C factor
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Phase II: Procedures of extrapolation to Recommendation Domains (Eg)
K- Map
LS - Map
C/P - Map
R – Map
Soil Loss (Map and Data)
Soil Map
DEM
Land Use Map
Climate
Overlay
i i iiaezC SLPKiA R
1
n
ii
g AA
fn g DSA A *0.01nAS
B
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B: Model application (Eg. SWAT) to extrapolate off-site processes
and impacts
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Off-site
• water supply
• irrigation water supply
• Power generation
• Biodiversity loss
• Destruction of productive lands
Siltation/sedimentation
• Reservoirs/dams
• Lakes
• On farm lands
Types of Impacts Processes captured Impacts quantified
Improved:
• water supply
• irrigation water supply
• Power generation
• Biodiversity
• Protection of productive lands
Effect in reducing Siltation/sedimentation
• Reservoirs/dams
• Lakes
• On farm lands
LD
SLM
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1. Quantifying siltation using SWAT
• SWAT is a dynamic watershed/basin model• Developed by USDA-ARS• It simulates:
– Crop growth– Hydrology– Soil erosion– Climate – Updates model parameters on a daily basis– It takes point sources as input– Impacts of land management practices– Point and non point pollutant
• Output: – Sediment yield– Runoff – Nutrient and pollutant balance– Etc……
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1. Quantifying siltation using SWAT cont….
• SWAT simulates at three levels– HRU
• Homogenous units in terms of soil and land use
– Sub-basin (smaller watershed)• Holds a number of HRU
– Basin (bigger watershed)• Holds a number of sub-basins
• Has GIS interface and the model can run through GIS
• Has weather generator
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!(!(
!(!(
!(!(
!(
!(!(
!(!(
!(!(!(!(
!(!(!(!(!(!(
!(!(
!(
!( drainage outlets
drainage lines
microbasins
HRUs
Sub- basin/sub-watershed
!(
!(
!(!(
!(!(!(!(
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!(!(!(!(
!( !(!(!(!(
!( !(!(!(!(!( !(!( !(!(
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watersheds& drainage
drainage lines
!( drainage outlets
Basin/Big watershed
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Procedures of using SAWT
1. Validate model using SCRP station data• Generate parameter• Conduct sensitivity analysis• Calibrate model• Adjust model parameters• Validate model
2. Apply model on selected basins• Prepare soil map and data• Prepare and use map and data• Prepare DEM• Map climate variables• Indicate point sources• Produce parameter – built model database• Run the model
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a. Build required resource databaseb. Develop information on SLM scenarios
• Develop generic values based on some observation
• If absolutely needed establish learning sites
c. Build capacity• On modelling• Parameterization
d. Develop packages of recommendatione. Develop feedback system among
research, extension and policy makers
Conclusion