Agriculture Reference
In-Depth Information
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E mpirical Techniques
With the purpose of developing a forecasting technique for predicting the
national wheat production for the leading U.S. states using the 11-year
smoothed mean anomaly, η 11 , the correlation graphs between η 11 and the
S 11 index and between η 11 and the HTC 11 index have been analyzed (figure
34.6).
The calculations have shown that in each specific case both the 11-year
smoothed means, S 11 , and HTC 11 , can be used as a predictor of this empir-
ical forecasting technique; the choice between them should be determined
by the value of the associated correlation coefficient. For wheat production
in Colorado State shown in figure 34.6a, the correlation coefficient was .69.
These graphs can be used to calculate the changes in the number of agricul-
turally abnormal years using the forecast of the regional climate changes
in any future decade, from which it is possible to obtain the estimate for
the change in the S 11 index. The corresponding estimated change in η 11 can
then be determined from the straight line in figure 34.6a. For example, if in
the state of Colorado the S 11 index becomes equal to -0.1, it corresponds
to a change in the decade-mean anomaly of the relative productivity from
0 to -0.12. Using historical data on η values in this area (figure 34.1), it
can be concluded that the probability of drought (that is, when the wheat
production loss will exceed the standard deviation of the η -indicator, which
is 23% for Colorado) can be more than double in the decade. In the case
of any other area for which detailed information is not available, we could
use the generalized graphs by combining the empirical data of the climat-
ically analogous regions for forecasting purposes. An example of such a
correlation graph is shown in figure 34.6b, which generalizes the empirical
data of the three selected U.S. states.
[443
Line
——
0.0
——
Norm
PgEn
[443
A -Indicator
We also find it convenient to introduce an additional parameter, A-indica-
tor, which is the standard deviation of the relative crop yield. We deter-
mined A-indicator for wheat producing regions of North America and
the former USSR (Menzhulin et al., 1987; Gleik et al., 1990; Menzhulin,
1992). It is commonly assumed that up to the middle of the 1980s global
climate changes were insignificant; therefore, the estimates obtained for A-
indicator using the data on crop productivity for the previous years can be
assumed to correspond to the anthropogenically undisturbed climate. For
this analysis the data on annual wheat yields for separate small areas of the
two regions for the period 1945-82 have been used.
In analyzing the spatial distribution of the A-parameter for wheat crops
over the grain zone of the former USSR, it is important to note that the areas
of winter wheat cultivation are mainly located in European Russia. The
climate in eastern Russian territories is too severe. For the European part
of the former USSR the annual variability of winter wheat production is
 
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