Universal Block Kriging covariate definition for krige in gstat

Hello, I would like to rise a question on the use of predict {gstat},

I'm trying to perform the estimation of a spatially distributed variable at
the support scale of a particular area (Block kriging). I have access to an
additional variable, it is known that the variable of interest is
correlated to the new variable. So I would be interested on updating my
estimation by the use of this new information. This could be done by the
use of a kriging with external drift (KED), but with a block support
(Universal Block kriging). Theoretically this is included in the gstat
library as mentioned in the documentation.

The issue comes when I try to perform the prediction:

blockprediction <- predict(gstat(formula=Variabletopredict~additionalVariable,
data=Observed, model=vgm), newdata = shapefile)

The newdata argument should contain the prediction location. In a normal
KED we would include a dataframe with a grid (coordinates in which to
predict) and the values of the covariate (additionalVariable). As I'm
trying to use a universal block kriging, I understood the newdata should be
the region in which I'm interested to know the prediction, hence a polygon.
How could I include in newdata the values of the covariate if its
resolution is finer than my block?

As far as I know, what block kriging does is to predict point values inside
the region (which I could specified with the argument sps.args
discretization), and later average them. But I don't know how to attach the
covariate values to the block of interest (shapefile).

Thanks in advance,
I hope I could explain it properly, but I will give more details if
necessary.
Kind regards,
Antonio

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