Parameter Estimates needed from lmer output

Hello,
I am looking for some help on how I may be able to view estimated
values for 3 response variables with 1 fixed and 1 random effect using
lmer.
My data is proportional cover of three habitat variables (bare ground,
grass cover, shrub cover) that was collected during 3 years (1976,
2000, 2010) on 5 study plots, each plot divided into 50 m square
cells.
Portion of dataset (proportions were log transformed)
year	plot	cell_id bare_trans	grass_trans	shrub_trans
0	wh	whi1	-0.678240631	-0.892213913	-0.158328393
0	wh	whi2	-0.774640426	-0.745665597	-0.164722747
0	wh	whi3	-0.600670894	-0.545056465	-0.30835479
0	wh	whi4	-0.461018617	-0.704273962	-0.315083353
0	wh	whi5	-0.518221954	-0.643432282	-0.303575808
0	wh	whi6	-0.598043065	-0.588487184	-0.286051968
0	wh	whi7	-0.581336622	-0.356760604	-0.4880035
0	wh	whj1	-0.650114241	-0.706560469	-0.215255255

I am treating the group of response variables (bare_trans,
grass_trans, shrub_trans) as one multivariate response.
The year (0, 1, 2) is my fixed effect and cell_id (whi1 . . .) is my
random effect.

My model is:
m1 <- lmer(cbind(bare_trans,grass_trans,shrub_trans) ~ year +
(1|cell_id),data=whdata)

Summary output is:
Linear mixed model fit by REML
Formula: cbind(bare_trans, grass_trans, shrub_trans) ~ year + (1 | cell_id)
  Data: whdata
   AIC    BIC logLik deviance REMLdev
-97.86 -88.14  52.93   -119.1  -105.9
Random effects:
Groups   Name        Variance Std.Dev.
cell_id  (Intercept) 0.000000 0.00000
Residual             0.014523 0.12051
Number of obs: 84, groups: cell_id, 28

Fixed effects:
           Estimate Std. Error t value
(Intercept) -0.53781    0.02079  -25.87
year         0.24182    0.01610   15.02

Correlation of Fixed Effects:
    (Intr)
year -0.775

What is missing from this output that I need are estimated
coefficients of the 3 response variables (bare_trans, grass_trans,
shrub_trans) for each year (0, 1, 2), standard errors and p-values.

Any idea if lmer even generates these estimates? And if so, is there a
way of digging them out of the R blackbox?
If not, if anyone has suggestions on a more appropriate package to use
that would be great.
I essentially want to perform a MANOVA on my 3 response variables
while accounting for fixed and random effects.

Any help would be appreciated.

Thank you,
Stephen L. Peterson
Utah State University