Expert meeting on the application of climate forecasts for agriculture 1 The application of climate forecasts and agrometeorological information for agriculture, food security, forestry, livestock and fisheries G. Maracchi, F. Meneguzzo, M. Paganini Banjul, Gambia, 9-13 December, 2002
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Expert meeting on the application of climate forecasts for agriculture 1 The application of climate forecasts and agrometeorological information for agriculture,
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Expert meeting on the application of climate forecasts for agriculture
1
The application of climate forecasts and
agrometeorological information for agriculture,
food security, forestry, livestock and fisheries
G. Maracchi, F. Meneguzzo, M. PaganiniBanjul, Gambia, 9-13 December, 2002
Expert meeting on the application of climate forecasts for agriculture
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Information needs ofFOOD SECURITY
Availability of input data
Appropriate location
Appropriate spatial resolution
Timely information
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Existing Food Security Systems
Name ParameterAGRHYMET/SISP Prediction of the current crop yieldUSAID/FEWS Identification of vulnerable groupsAGRHYMET/DHC Prediction of the current crop yieldWFP/VAM Mapping disaster in order to mitigate itFAO/GIEWS Warnings on food shortages
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Existing Food Security Systems - AGRHYMET SISP
Base parameters•statistical analysis procedures on rainfall for ecological zoning;
•a millet simulation model to estimate millet crop conditions and the effect of rainfall distribution;
•statistical analysis of the yields.
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Existing Food Security Systems - AGRHYMET SISP
Varieties Phases length InitialKc
Growth at five days interval
Growing FloweringGrain filling75 days 50 15 15 0.2 0.0890 days 65 15 15 0.15 0.06538120 days 80 15 20 0.1 0.0562120 daysphot.
JFL - JSEM 15 20 0.1 (1-0.1)/(IPFL-IPSEM)
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Existing Food Security Systems - AGRHYMET SISP
-12 -10 -8 -6 -4 -2 0 2 410
12
14
16
18
20
22
24
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
0.55
0.60
0.65
0.70
Variability of SISP yield index
-12 -10 -8 -6 -4 -2 0 2 410
12
14
16
18
20
22
24
5101520253035404550556065707580859095
Average yield index 1961-90
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Expert meeting on the application of climate forecasts for agriculture
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Existing Food Security Systems - USAID FEWS
The analysis is organised in three sections:
• Vulnerability/Baseline Information
• Hazard/Shock Information
• Risk/Outcome Analysis
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Expert meeting on the application of climate forecasts for agriculture
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Existing Food Security Systems - DHC-Champs pluviaux
The CCD are used in the crop water diagnostic (DHC) in order to produce:
• maps of the crop water satisfaction• maps of the crop water needs• maps of crop yields
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Existing Food Security Systems - DHC-Champs pluviaux
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Existing Food Security Systems - World Food Programme -Vulnerability Analysis & MappingWFP has produced vulnerability assessment maps in 3 stages:
identifying the income sources for each relevant groupanalysing the causal structure of vulnerabilityreconciling the analysis of risk and coping capacity
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Expert meeting on the application of climate forecasts for agriculture
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Existing Food Security Systems - FAO GIEWS
-monitors food supply and demand
-analyses information on production stocks, trade and food aid
-monitors export prices
-reacts to natural disasters
-issues Special Alerts and up-to-date reports
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Existing Food Security Systems - FAO GIEWS
-web pages on the Internet
-develops new approaches for early warning
-cultivates and maintains information-sharing between governmental and private actors
-depends on the free exchange of information
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Existing Food Security Systems - FAO GIEWS
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Solving the problem
• Information available on Internet• More appropriate to the decision
makers information needs
• Improved survey methods and operations for monitoring actual and potential outbreak areas
• Create interaction between producers of information
FOOD SECURITY INFORMATION
CLIMATE PREDICTION INFORMATION
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The local CLIMATE
• Decreasing annual pluviometry S-N
• Alternation of dry season (9-5 months) and rainy season
• The monsoon is the main defining factor
• Unimodal distribution of the rain
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Link between climate and teleconnections
The average of the weather over periods
The effects of changes in sea surface temperatures in the Pacific Ocean on temperature and rainfall patterns in regions that are far away from the Pacific
CLIMATE DEFINITION
TELECONNECTIONS DEFINITION
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Teleconne-ctions in Sahel
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Sim
ulta
neou
s C
orre
latio
n o
f S
ah
el R
ain
fall w
ith S
ST (Ju
ne,
July
)
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Sim
ulta
neou
s C
orre
latio
n o
f S
ah
el R
ain
fall w
ith S
ST (A
ug
ust,
Sep
tem
ber)
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Corre
latio
n o
f Sah
el R
ain
fall in
Ju
ne a
nd
July
with
SS
T in
May
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INTERTROPICAL CONVERGENCE ZONE - location
Drought years are associated with the ITCZ being south of its normal position, while wet years are associated with the ITCZ north of normal
Warmer SST in Guinea Gulf lead to higher precipitation over Guinea coast (increased moisture) and lesser over
Sahel (northerly flow, sinking at low levels)
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INTERTROPICAL CONVERGENCE ZONE - location
Rapidly increasing SST in May over Guinea cause
delayed monsoon in
Sahel (June and July)
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Synthetic descriptions of atmospheric teleconnection patterns
Can be found at following addresses:
The Climate Diagnostics Center (NOAA)http://www.cdc.noaa.gov/TeleconnectionsClimate Precition Center (NOAA): http://www.cpc.noaa.gov/data/teledoc/telecontents.html
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Existing climate predictions
Amount of rainfall• IRI Net assessments• PRESAO outlook• CLIMAG WA enhanced methodology
Onset of the growing season• IBIMET methodology (Maracchi/Pini)• Omotosho method• CLIMAG WA enhanced methodology
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Applications for 2001 & 2002
•Comparison of results per single zone for each year
BUTBUT
•Each methodology has its own spatial resolution
•Each methodology has its own temporal resolution
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Data formats
Spatial scale Time scale Methodology
Amount of rainfall
IRI Net assessments 2,8° of resolution Three months Coupled dynamicmodels andstatistical models
PRESAO outlook Regional Once a year, inMay
Dynamical andstatistical models
CLIMAG WAenhancedmethodology
Punctual -spatialised
Once a year, inApril
Statistical
Onset of the growingseason
Maracchi methodology Punctual -spatialised
Five days SISPmethodology
Omotosho method Punctual -spatialised
Three weeks Omotosho fieldmethodology
CLIMAG WAenhancedmethodology
Punctual -spatialised
Once a year, inApril
AP3Amethodology
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The IRI Forecast Process (1)
• Forecasting the tropical SST anomalies using dynamical and statistical models
• Using the predicted SST for atmospheric general circulation models (GCMs)
• Estimating the expected skill
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• Statistical postprocessing of model output
• Putting all the indications together a final IRI forecast called net assessment
• issued in the form of maps that show regions having homogeneous forecast probabilities for the below, near and above normal terciles
The IRI Forecast Process (2)
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Examples of Net Assessments
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Omotosho methodology
Onset of the growing seasonThe method is empirical/dynamical and uses the following requirements:
•Difference between the U-component of the wind at 3000 m and at the surface must be between –20 m/s and –5 m/s Difference between the U-component of the wind at 7500 m and at 3000 m must be between 0 and 10 m/s
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Omotosho methodology
Onset of the growing season
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• Predict the seeding decades for the different zones in order to produce advises to peasants
• The philosophy is to utilise the information already available on INTERNET (NOAA, IGES COLA, ADDS)
Onset of the growing seasonIBIMET method
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1 – Rainfall Forecasting sectionNOAA - Climate Prediction Center, Prediction of the rainfall quantity at 24-96 hours2 – Rainfall Estimation sectionADDS - Africa Data Dissemination Service, Decadal rainfall estimation images3 – Field data sectionReal sowing dates in different areas in Mali collected by local institutions
Exercise for the agricultural season 2001
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Forecasting Section
Total rainfall of the decade
Daily forecast images =
Through the daily images it is possible to forecast the amount of rainfall expected in the decade and give the advise of the sowing date to farmers
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Estimation Section
Precipitation Estimate based on
GPI, SSM/I, AMSU and GTS
The image has been utilised to validate the information prepared by the forecasting information
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Field Observation Data Section
Field observation areas
Data collected by local institutions
The collected information are related to the real sowing date
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The information of the different three sections has been compared in order to evaluate the process
REAL SOWING FORECASTED ESTIMATED DATE
DEPARTMENT DATE 2001 SOWING DATE SOWING DATE
Gao-Central 5/7 15/7 25/6
Gossi 5/7 5/7 25/6
Nara-Central 25/6 25/6 25/6
Douentzan-Centr 5/7 25/6 15/6
Kayes-Central 5/6 15/6 5/6
Ambidedi 5/6 15/6 5/6
Same 5/6 15/6 5/6
Kolokani-Centr 5/6 5/6 5/6
Mahina 5/6 5/6 5/6
Djidian 5/6 5/6 5/6
Kita-Central 5/6 5/6 5/6
Koutiala-Centr 15/5 15/5
Bougouni-Centr 15/5 15/5
Sikasso-Central 15/5 15/5
relation between forecasted and real sowing date
15/7
5/7
25/6
5/65/65/65/6
25/615/6
R2 = 0.7948
real sowing decade 2001
fore
cast
ed s
ow
ing
dat
e 20
01
Results-2001relation between forecasted and real sowing date
R2 = 0.7948
6/5
16/5
26/5
5/6
15/6
25/6
5/7
15/7
25/7
6/5 16/5 26/5 5/6 15/6 25/6 5/7 15/7 25/7
real sowing decade 2001
fore
cast
ed s
ow
ing
dat
e 20
01
relation between estimated and real sowing date
R2 = 0.9162
6/5
16/5
26/5
5/6
15/6
25/6
5/7
15/7
25/7
6/5 16/5 26/5 5/6 15/6 25/6 5/7 15/7 25/7
real sowing decade 2001
esti
mat
ed s
ow
ing
dat
e 20
01
.
42A
Expert meeting on the application of climate forecasts for agriculture