Repeated Measures, groupedData and lme

Hello

I am trying to fit a REML to some soil mineral data which has been
collected over the time period 1999 - 2004. I want to know if the 19
different treatments imposed, differ in terms of their soil mineral
content. A tree model of the data has shown differences between the
treatments can be attributed to the Magnesium, Potassium and organic
matter content of the soil, with Magnesium being the primary separating
variable.

I am looking at soil mineral data were collected : 99, 02, 04. 

In the experiment, there are 19 different treatments (treatmentcontrol,
treatment6TFYM, treatment 12TFYM etc),  which are replicated in 3
blocks.

For the magnesium soil data, I have created the following groupedData
object: 

magnesium<-groupedData(Mg~year|treatment, inner=~block) 
Where mg=magnesium Kg/ha

As it is a repeated measures I was going to use an lme.  I have looked
at Pinherio and Bates : Mixed-Effects models in S and S-plus and I am
getting slightly confused.  In order to fit the lme, should I specify
the data to use in the model as the grouped structure model?

If so is the following command correct:

Model1<-lme(mg~treatment, random=block|year, data=magnesium)? 

I am slightly worried that it isn't, because in model summary, instead
of listing the 19 different treatments in the fixed effects section, it
writes intercept (as normal), then treatment^1, treatment^2 etc.

However if I don't specify the groupedData object in the model, then in
the fixed effects section, it names the treatments (i.e. intercept,
treatmentcontrol, treatment6TFYM.

Should I be fitting the model using the whole data set rather than the
groupedData object?