ANOVA contrast matrix vs. TukeyHSD?

Dear Help List,

Thanks in advance for reading...I hope my questions are not too ignorant.

I have an experiment looking at evolution of wing size [centroid] in 
fruitflies and the effect of 6 different experimental treatments 
[treatment]. I have five replicate populations [replic] in each 
treatment and have reared the flies in two different temperatures [cond] 
to assay the wing size, making measurements on males and females 
[gender]. My design can be summarized as follows:

This is my model (I think it's right, ignoring interaction terms for 
simplicity):

 > lm1 ~ aov (centroid ~ gender + cond + treatment/replic, data = parents)

The treatments are:

 > levels (parents$treatment)
[1] "c"  "h"  "mc" "mh" "s"  "t"

I only care about a few of the pairwise comparisons between the levels 
of "treatment", as only certain contrasts are scientifically interesting:

c vs. h
mh vs. mc
(c + h) vs. s   [I would like to compare the mean of c and h (my 
controls) to s and t)
(c + h) vs. t
s vs. t
h vs. mh
c vs. mc

These are two more than I can specify using "contrasts()" and they are 
not orthogonal. I can use the TukeyHSD and only look at the comparisons 
I care about, but I think this should give me much less power than 
specifying a few a priori contrasts (?). Also, I don't know how to 
combine my controls (c + h) into a single comparison using TukeyHSD.

My first problem is that when I specify the matrix shown below (the 
first 5 comparisons from above), I get a much higher p-value on some of 
the planned contrasts than I do on the TukeyHSD:

contrasts (parents$treatment) <- cbind 
(c(-1,1,0,0,0,0),c(-1,-1,0,0,2,0),c(-1,-1,0,0,0,2),c(0,0,-1,1,0,0),c(0,0,0,0,1,-1)) 

 > contrasts(parents$treatment)
  [,1] [,2] [,3] [,4] [,5]
c    -1   -1   -1    0    0
h     1   -1   -1    0    0
mc    0    0    0   -1    0
mh    0    0    0    1    0
s     0    2    0    0    1
t     0    0    2    0   -1

#### THE OUTPUT (truncated) ####

Call:
lm(formula = centroid ~ gender + cond + treatment/replic, data = parents)

Residuals:
     Min        1Q    Median        3Q       Max
-81.58846  -4.53540   0.00803   4.76568  39.84177

Coefficients: (1 not defined because of singularities)
                    Estimate Std. Error  t value Pr(>|t|)   
(Intercept)         328.73096    0.26303 1249.770  < 2e-16 ***
genders             -37.39069    0.19661 -190.179  < 2e-16 ***
condu               -37.47740    0.19693 -190.308  < 2e-16 ***
treatment1            0.51026    0.40084    1.273 0.203079   
treatment2           -0.17333    0.23175   -0.748 0.454541   
treatment3            0.07761    0.22535    0.344 0.730566   
treatment4           -1.96020    0.38524   -5.088 3.73e-07 ***
treatment5                 NA         NA       NA       NA  

###### The TukeyHSD output (truncated) #####

 Tukey multiple comparisons of means
   95% family-wise confidence level

Fit: aov(formula = centroid ~ gender + cond + treatment/replic, data = 
parents)
*
*
*
$treatment
            diff          lwr        upr     p adj
h-c   -1.38085941 -2.382732615 -0.3789862 0.0012123
mc-c  -2.22026936 -3.198423972 -1.2421147 0.0000000
mh-c  -2.27157901 -3.268013478 -1.2751445 0.0000000
s-c   -1.19540471 -2.170272952 -0.2205365 0.0063382
t-c   -0.39899955 -1.374954044  0.5769549 0.8533107
mc-h  -0.83940995 -1.813993060  0.1351732 0.1378366
mh-h  -0.89071960 -1.883648319  0.1022091 0.1081954
s-h    0.18545470 -0.785829956  1.1567394 0.9943136
t-h    0.98185986  0.009484949  1.9542348 0.0462121
mh-mc -0.05130965 -1.020300865  0.9176816 0.9999892
s-mc   1.02486465  0.078064558  1.9716647 0.0249356
t-mc   1.82126980  0.873351301  2.7691883 0.0000007
s-mh   1.07617430  0.110500644  2.0418480 0.0187007
t-mh   1.87257946  0.905809220  2.8393497 0.0000005
t-s    0.79640515 -0.148121782  1.7409321 0.1550137

When I specify the c vs. h comparison, I am getting a p-value of 
0.203079, but the TukeyHSD gives the same contrast a p-value of 
0.0012123. Also, the fifth comparison gives "NA"; I assume this is due 
to it being non-orthogonal? I feel like I am either misunderstanding the 
point of contrasts() completely or I have done something wrong, so I 
would really appreciate any help.

My other question is related...just wondering why I need to limit myself 
to only orthogonal comparisons using contrasts()? This eliminates 
comparisons of scientific interest, for example if c vs. h, mc vs. c, 
and mh vs. h are all different, I have no way of knowing if mc vs. mh is 
significantly different by examining the other contrasts.

Sorry if these questions are ignorant...I have spent a long time trying 
to figure it out and haven't found the answer in either the available 
books or the help list.

Many thanks,
Sam Yeaman