If you have gridded observed data at close and regular spacing for the block, then why 
bother with a Gamma(V,V)? Just calculate the variance for the block directly.

On the one hand you need "a spatially dependent estimate of the whole plot/block 
variance" but on the other hand you want to dispense with modelling any correlation 
(whether variogram, or correlogram, etc) because it's "time consuming." Not sure how 
to tackle this one, something has to give. How about implementing an inverse distance 
weighting scheme to derive the within block variance instead of working with a 
variogram measure?

Cheers,

Syed

>> Note that gamma(V,V) is a variogram model property, not a data
>> property: in the figure you mention, the big dots are data points,
>> the small dots discretize the block but do not indicate observed
>> data.
>
>I know that you can discretize block estimates with a pretty good accuracy 
>with a limited amount of points. But, I was convinced that I could use 
>grided observed data and gamma(V,V) as an estimate for the variance within 
>my grided block.
>
>To clarify,
>
>I did measure the point values in a grid in a plot/block. I don't have to 
>estimate them, I just need a spacially depended estimate of the whole 
>plot/block variance.
>
>The problem is that I would like to automate the procedure of calculating 
>that within block variance because variogram models need to be fit 
>visually most of the time, wich is time consuming. When repeating 
>calculations on randomized data this isn't an option. So if gamma(V,V) 
>really is model property rather then a data property I have a problem...
>
>If you can see the trees for the forest, because I don't.
>Koen.
>
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