a digitally integrated africa soil information service (afsis)
DESCRIPTION
Presentation by Peter Okoth for the CIAT KSW 2009TRANSCRIPT
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A Digitally Integrated Africa Soil Information Service (AfSIS)
Grantee institution:The International Centre for Tropical Agriculture (CIAT)
Budget: US$ 18.1 m
Implementing Institution:Tropical Soil Biology & Fertility (TSBF) Institute of (CIAT)
Partners:Columbia University’s Earth Institute
World Soil Information (ISRIC)World Agro-forestry Centre (ICRAF)
5 initial NARS in Africa (Kenya, Tanzania, Malawi, Mali & Nigeria)
22 other African countries
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Presentation Outline
• Background• Foreseen Impact• Project Activities• AfSIS Data Systems• Soil Surveys• Fertility trials• Capacity building, user outreach, policy &
dissemination
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Background
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Numbers
• About 500 million hectares of sub-Saharan Africa’s agricultural land are moderately or severely degraded
• African farmers are able to apply only 10 percent of the nutrients that farmers in the rest of the world return to the soil
• soils in southwestern Kenya, for example, lose an estimated 100 kilograms of nitrogen per hectare each year
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Nutrient depleted soils reason for poor crops & low productivity
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Large scale land degradation due to soil erosion: How to address?
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Impact
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Foreseen Impact• Provide accurate & spatially explicit soil database for 42
African countries
• Contribute to the reversal of soil degradation in Africa
• Contribute to increased crop yields & improved livelihoods for approximately 1 to 2 million poor African households
• Prepare material and evidence to guide policy and action that enhances Africa’s soil & crop productivity
• Contribute to the development of African institutions capacity to be able to map, disseminate and use soils information to plan natural resource management in their countries & in the continent
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The Sahelian DrylandsArea: 1.2 million km2
Population: 38 millionMillet & sorghum belt: 23 million ha
Humid Forest ZoneArea: 5.8 million km2
Population: 168 millionCassava belt: 18 million haNERICA potential: 2 million ha
Moist Savanna and Woodland Zones
Area: 4.4 million km2
Population: 157 millionMaize belt: 32 million haCA potential: 7 million ha
S
N
EW
0 1000 2000
kilometers
Initial Impact Initial Impact zones zones targeted by targeted by AfSISAfSIS
Nigeria
Kenya
Malawi
Tanzania
Mali
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Project Activities
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AfSIS Objectives
Objective 1: Global efforts to fund raise & prepare the GlobalSoilMap.net & collecting soil pedology legacy data for the African node (AfSIS)
Coordinated by ISRIC
Objective 2: Cyber Infrastructure & soils databaseCoordinated by CIESIN at Earth Institute
Objective 3: Soil survey covering 18.1 sq km using unbiased statistical sampling and soil properties prediction modeling from 60 sentinel sites in 27 sub-African countries
Coordinated by TROPAG at Earth Institute
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AfSIS ObjectivesObjective 4: • Implementing field management trials in 5 countries• Collecting & analyzing legacy experimental data
Coordinated by CIAT-TSBF
Objective 5: Capacity building, serving end users, defining use cases,
dissemination, articulating policy, & articulating impact pathways
Coordinated by CIAT-TSBF
Objective 6: M & E plan and overall project management
Project Director –Dr. Pedro Sanchez
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Some details about the AfSIS Activities
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AfSIS Data Systems
Objective 2: CIESIN
Sonya Ahamed, Tor V, Jubal, P. Okoth
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Dissemination
• Project Website
• http://www.AfricanSoils.net
• Map Servers
• Global plus AfSIS Databases
• Global soil data models
• Capability for on-line updates with walking sticks for off-line access & use
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Soil Surveys
Objective 3
Markus Walsh, Tor Vagen, Keith Shepherd, Jerome Tondoh, Luseged
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AfSIS Sentinel Sampling
• ~17.5 million km2 of continental SSA
• ~0.6 million km2 of Madagascar
• Spatially stratified random sampling approach consisting of 60 sites
• Each 100 km2 • 42 countries with 95% of
human population• ~9,600 new geo-referenced
soil profiles• 38,000 individual soil
samples
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One Sentinel Site
10 km
10 km
Total of 160 sampling Points per site
4 soil samples per point
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Example of a digital soil carbon condition map of Segou in Mali
Low resolution-wider coverage High resolution-less coverage
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Fertility Trials
Objective 4Jeroen Huising Cheryl Palm,
Generose Nguziheba, Shamie Zingore & P. Okoth
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The yield gap and the limitations that cause it
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Maize Soybean Cowpea
Gra
in y
ield
(t h
a-1
)
Farmer
Researcher
Derived Savanna
Potential yield
(Experimentation)
Actual yield
Yield gap
Biophysical limitations- Soil fertility- Water- Variety, etc
Socio economic and policy limitations- Knowledge- Credit availability-Inputs-Output Markets
Which inputs are lacking?
Why inputs are not used?
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Our aim: prevention, restoration & productivity
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AfSIS Impact Target Zones
• Soil fertility recommendation trials installed in 5 countries marked in yellow
• Soil management recommendation framework developed from past experimental legacy data and from new management recommendation trials
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Capacity building, user outreach & dissemination
Objective 5
Peter Okoth, Policy Economist, Extension, Market, other PIs, etc
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Activities under Objective 5
• Capacity building
• Use Cases
• Impact pathways
• Policy formulation
• Partnerships
• Dissemination
• Monitoring & Evaluation
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Training & Capacity building
• Training for capacity building in:– Soil Survey (numbers trained on the job)– NIR/MIR scanning, analysis & interpretations
(numbers trained on the job)– Management & recommendation trials
(numbers trained on the job)– ISFM and targeting recommendations
(numbers trained on the job)– Formal training ( numbers of graduate level
training at MSc & PhD)
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Dissemination
Peter Okoth, Extension (Public, Private, NGOs), Agro-dealers,
Media
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Questions
• Dissemination: Which tools are currently available for this? How do we maximize out-scaling of
knowledge and data? Which are the incentives to ensure adoption
and sustenance of technology use once disseminated?
Who does what? How do they do it? Who pays for it?
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Steps towards dissemination
soil fertility ISFMmanagement
trialspossible spatial
interventiondomains
geo-referencedspatial soils data
& information
cyberspace
ana
lysi
s &
inte
rpre
tatio
n
diag
nost
ic d
educ
tions
other dissemination
media
analysis & interpretation
diagnostic deductions
managementrecommendationsPolicy guidelines
& Briefs
1
23
45
6
7
8
9
10
F
F
Obj 3
Obj 4Obj 4
Objs 4 & 5
F
Objs 2 & 5 legacydata
analysis
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AfSIS dissemination construction & user
concerns
object location
geometricattributes
soilattributes
HA
RD
WA
RE
SOFTWARE
KN
OW
LED
GE
WH
O?
HOW
? WH
ERE?
WH
AT?
USERS
USERS
USER
S
USE
RS
GIS system interface
User concerns & project objectives response interface
3,4
3,4,5
2,5
2,5
object
Specific objectiveattention domains
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Who?International National Local
HarvestChoice Agro-dealers Researchers
HarvestPlus Fertilizer buyers Local public extension service providers
Fertilizer manufacturers Fertilizer blenders CBOs/NGOs
AGRA Public extension service providers
Agri-business
African CGIAR scientists Seed companies Media
Regional networks Private advisory services Local policy makers
Un agencies Policy makers & regulators NUANCES-farmers
NEPAD Agricultural investors Breeders
Development partners Researchers & students Watershed managers
Global climate modelers Ecosystem modelers & land use planners
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WhatThemes Measured attributes Predicted attributes
Keith obj3 soil samples spectral signatures
inherent soil properties
Markus obj3 Field and sampled soil properties
Functional soil properties
Jeroen obj4 yield responses to different management treatments
Decision framework to arrive at predicted responses for different management scenarios
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CovariatesCovariates
Legacy & Legacy & new datanew data
1. Data Input1. Data Input
2. Soil properties2. Soil properties
Soil MapSoil Map
ClayClay
OCOC
pHpH
BD Water Water storagestorage
Al toxicityAl toxicity
FertilizerFertilizer responseresponse
ErosionErosion
CarbonCarbonManagementManagement
3. Soil functions3. Soil functionsSpatial Spatial
inferencinferenc
ee
modelsmodels Pedotransfer
Pedotransferfunctionsfunctions
Social Social CovariatesCovariates
Legacy dataLegacy data
4. Management4. Management InputsInputs
5. Management5. Management recommendations recommendations
FertilizerFertilizerapplicationapplication
ErosionErosion controlcontrol
ClimateClimate mitigationmitigation
Crop Crop selectionselection
IrrigationIrrigation
6. End Users6. End Users
Policy-makersPolicy-makers
ExtensionExtension
ScientistsScientists
Farmers
Agro-business
Decision Decision
analysisanalysis
Dec
isio
nan
alys
is
TrainingTrainingDisseminationDisseminationCyber-Cyber-
infrastructureinfrastructure
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What?
• Structured content (tables, narratives, figures, pictures, video clips, trends) from objectives 3 & 4
• Decisions on what to avail for access by the different levels of users
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Where
• Geo-referenced geometric as well as thematic attributes of the soil properties
• Political boundaries (national, district, location, sub-location, etc)
• Geomorphologic boundaries and the corresponding landform units
• Road networks with centers and towns
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Dissemination tools in AfSIS
Tools Target Audience
Project Website & Blogs Global
Google Earth et al. Global
Publications Global Scientists
Workshops International, national, district, local
Extension (public, private, NGOs, etc)
Farmers indirectly, Local & National
Wireless and mobile phones Extension & farmers
CDs, DVDs, Mailing list
TV, Radio, Print Media Local, national
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AFSISPROJECT
DATABASE
WEATHERCHANNEL
AGRO DEALERSDIGITAL MAP
COMMODITYTRADERS
FARMERS, AGRO-DEALERS
REGISTER &DATABASE
NU
AN
CE
S
EX
TE
NS
ION
SE
RV
ICE
NA
TIO
NA
LP
OL
ICY
MA
KE
RS
DE
VE
LO
PM
EN
T
PA
RT
NE
RS
INV
ES
TO
RS
GSM NETWORKS
PRIVATE IT COMPANY
FARMERS & AGRO-DEALERS
FARMER’S & AGRODEALERS
REQUESTS
SOILWEATHER
PRICESMARKETSE-CREDIT
COMPANIESNGOSCBOS
SC
IEN
TIF
IC C
OM
MU
NIT
Y
GOOGLE EARTH
IMPACT ORIENTED DISSEMINATION
POSSIBLE END USERS
Impact!-How?
FARMER SUPPORTSERVICES
COMPANY RESPONSE
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Achievements so far• Launch on 13th January 2009• Promotion in over 50 media houses including print, audio and blogs• Visited 3 node countries to install necessary infrastructure• Purchased vehicles • Purchased computer hardware• Purchased 20 GPSs• Recruited 1 MSc student already in Aberdeen University• Hired Scientist for West & Southern Africa (Objective 3)• Hired Socio-economist• Conducted interviews for an M&E position• Taken on board Southern Africa TSBF scientists• Worked on 2009 project workplan• Initiated experimental work in Mali• Process of getting the Arusha office operational• Ordered Spectrometers & GPS equipment• Implemented www.Africasoils.net• Initiated discussions with AGcommons on Use Case definition
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Thank you!Ahsante sana!
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Use cases
• Which uses of soil data and information can we think of?
• How do we capture the variety of potential users?
• Which tools are available to do this?• Which soil variables are most relevant?• Which decisions can be made from AfSIS
soil information • Which kind of decisions can be made?
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Impact Pathways
• Partnerships: Which kind of partnerships do we need to
have for maximum impact? How do we structure the partnerships? How do we sustain the partnerships?