Abstract:
Crop yield forecasting models are needed to help farmers and decision makers cheaply
detect crop condition early enough to assess and mitigate its impacts on grain
production. A precise estimate of crop production requires an accurate measure of the
total cultivated area and well-established knowledge of crop yield. The first
requirement is no longer a problem as is technically solved through various techniques
such as area frame sampling. With respect to the second, great efforts have been made
to find an accurate definition of the crop yield with respect to the actual factors that
shape its growth through out the season. Agrometeorological models have found a
wide range of applications in agricultural research and technology and are playing an
increasing role in translating information about climate variability into assessments,
predictions and recommendations tailored to the needs of agricultural decision makers.
However these models have generally been developed and tested for application at the
scale of a homogeneous plot. They are criticized for their inability to address large-scale
yield estimates at regional or even national levels in addition to their high cost of
application. This is because field conditions during the period of crop establishment at
the regional scale may be quite variable and poorly represented by standard parameter
values of the crop model.