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Wheat is an important food crop of the country. Its productivity lies in a very wide
range due to diverse bio-physical and socio-economic conditions in the growing regions.
Crop cutting and sample surveys are time consuming as well tedious, and procedure of
forecast is delayed. CAPE methodology, which uses remote sensing, ground truth and
prevailing weather, has been very successful, but empirical in nature. In a joint IARI-SAC
Research Programme, possibility of linking the dynamic wheat growth model with the
remote sensing input and other relational database layers was tried. Use of WTGROWS, a
wheat growth model developed at IARI, with the remote sensing and relational databases
is dynamic and can be updated whenever weather, acreage and fertilizer and other inputs
are received. National wheat yield forecast was done for three seasons on meteorological
sub-division scale by using WTGROWS, relational database layers and satellite image.
WTGROWS was run for historic weather dataset (last 25 years), with the relational
database inputs through their associated growth rates and compared with the productivity
trends of the met-subdivision. Calibration factor, for each met-subdivision, were obtained
to capture the other biotic and abiotic stresses and subsequently used to bring down the
yields at each sub-division to realistic scale. The satellite image was used to compute the
acreage with wheat in each sub-division. Meteorological data for each-subdivision was
obtained from IMD (weekly basis). WTGROWS was run with actual weather data obtained upto a given time, and weather normals use for subsequent period, and the forecast was
prepared. This was updated on weekly basis, and the methodology could forecast the
wheat yield well in advance with a great accuracy. This procedure shows the pathway for
Crop Growth Monitoring System (CGMS) for the country, to be used for land use planning
and agri-production estimates, which although looks difficult for diverse agro-ecologies and
wide range of bio-physical and socio-economic characters contributing to differential
productivity trends.
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