Applications of FAO agrometeorological software in response farming René Gommes Environment and Natural Resources Service, SDRN Expert meeting on Weather, Climate and Farmers Geneva, 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

Trends in main rice crops in Bangladesh

Rajshahi T-Aman Yields

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,

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: WinDisp FAOCLIM & GeoContext ADDATI & ADDAPIX AgroMetShell (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

ADDATI/ADDAPIX

Zimbabwe: some rainfall profiles

Clustering method

Zimbabwe clustering method (12 classes)

Comparison of methods TotalMethodTrend Clustering Threshold Water Balance Average Rainfall R 2 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