applications of fao agrometeorological software in response farming rené gommes environment and...
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Applications ofFAO agrometeorological software in response farming
René Gommes
Environment and Natural Resources Service, SDRN
Expert meeting on Weather, Climate and Farmers
Geneva, 15-18 November 2004
The (simple) message:
The inter-annual “chronic” variability of weather is the major cause of food insecurity
Simple methods can help reducing it’s impact (generalized “response farming”, RF)
RF can be modernized!
Trend in total rice yields in Bangladesh
R2 = 0.9566
0
0.5
1
1.5
2
2.5
3
3.5
1970 1975 1980 1985 1990 1995 2000 2005
Yie
ld (
To
ns/
Ha)
Trends in main rice crops in Bangladesh
R2 = 0.8577
R2 = 0.8258
R2 = 0.7938
0
0.5
1
1.5
2
2.5
3
3.5
1970 1975 1980 1985 1990 1995 2000 2005
Yie
ld (
To
ns/
Ha) Aus
Aman
Boro
Poly. (Boro)
Poly. (Aman)
Poly. (Aus)
Rajshahi T-Aman Yields
R2 = 0.3476
R2 = 0.1162
400
500
600
700
800
900
1000
1980 1985 1990 1995 2000 2005
Kg
per
acr
e Local
HYV
Poly. (HYV)
Linear (Local)
Cereal “losses” in Thailand
Source: based on FAO data
Defining Response Farming (RF) RF aims at improving tactical
decision making at farm level based on the quantitative observation of local environ-mental factors (I. Stewart, Univ. Davis, 1980s)
Proposal: improve approach by the inclusion of modern sources of data, tools of analysis and communications
World Hunger Alleviation through Response Farming
Typical flag diagram Niamey, 1954-83
0
100
200
300
400
500
600
700
800
900
110 120 130 140 150 160 170 180 190 200 210
Number of day(1-365) when season starts
To
tal s
easo
nal
rai
nfa
ll (m
m)
Operational aspects of RF RF is based on decision support tools (from
decision tables to models) which derive from the analysis of past environmental impacts
RF does include economic constraints Advice is relayed to farmers through
agricultural extension officers
Options to modernise RF Central storage of reference data Automatic collection of weather data Real-time modelling of crops Use of satellite imagery: rainfall estimation,
model input, spatialisation, and rapid post-disaster impact assessment, if necessary
Electronic transmission from and to villages
A growing software family:
WinDispFAOCLIM & GeoContext
ADDATI & ADDAPIXAgroMetShell (AMS)
LocClim, New_LocClim, Web_LocClim
AgroMetShell AMS
Some AMS functions
AMS: water balance
AMS: risk analysis
LocClim, New_LocClim, Web_LocClim
LOCLIM
Estimation of local climatology based on FAOCLIM2 or user provided data
Altitude, geographic gradient shadow correction, etc.
8 spatial interpolation techniques Important note: Point Vs Pixel estimates
LocClim
New_LocClim
New_LocClim
New_LocClim
New_LocClim
New_LocClim
ADDATI/ADDAPIX
Zimbabwe: some rainfall profiles
0
50
100
150
200
250
300
350
July Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June
Rai
nfa
ll a
mo
un
t m
m
1991-921982-831994-951986-871972-731981-821979-801990-912000-011988-891992-931976-771999-001970-711984-85Class 1Class 4Class 10
Yield = -1.80 StD
Yield = 0.21 StD
Yield = 1.19 StD
Clustering method
0
50
100
150
200
250
300
350
July Aug. Sep. Oct. Nov. Dec. Jan. Feb. Mar. Apr. May June
Rai
nfa
ll
Class 1
Class 3
Class 8Class 9
Class 11
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
2.5
-1.5 -1 -0.5 0 0.5 1 1.5
Yield estimated
Yiel
d ob
serv
ed
C-1C-2C-3C-4C-5C-6C-7C-8C-9C-10C-11C-12
Zimbabwe clustering method (12 classes)
Comparison of methods
TotalMethodTrend
0.73940.5692Clustering
0.70130.5311Threshold
0.73550.56530.1702 +
Water Balance
0.62650.4563AverageRainfall
R2
Method
Conclusions The inter-annual “chronic” variability
of weather is a major factor in food insecurity
Generalized/modernized “response farming”, can help reducing it’s impact
Main difficulty is understanding why RP does not interest donors
Thank you!
Source of farmers: 1634 etching by Rembrandt (Het Rembrandthuis Museum, Amsterdam)
FTP://FTP.FAO.ORG/ext-ftp/SD/Upload/AgroMet