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