Probably, but this depends on the kinds of variables that will be included in your tests, and how your data are distributed. See http://www.ats.ucla.edu/stat/mult_pkg/whatstat/.

Dear Stephen, Thank you very much.

Please provide more information about how you performed the experiment: how many experimental units (EUs) per treatment?  How many samples per EU?  How many different treatments?  How close/far were your EUs from each other?  Were your EUs blocked or completely random?  

Dear David, there were 3 different treatments, 3 experimental units per treatment and 10 samples per EU. here, I used completely randomized design. distance among EU were 25 cm. Thanks.

So it sounds like you need mixed ANOVA. If each treatment (condition) had three leaves (subjects) and each leaf contributed 10 samples (repeated measures) then that means you have a between-subjects factor (treatment) and repeated measures within subjects (repeated samples for each leaf). That's a straightforward mixed ANOVA or linear mixed effect design.

Yes, I think ANOVA is the correct method with treatment as the fixed effect.  Random effect would be reps nested within treatment.  Variation among samples is the residual term.  

Alternatively, you could average the samples first and then the model would only include the fixed treatment effect.

In either case remember to verify the assumptions of normality and homogeneity of residuals before making any conclusions about the treatment effect.

Just wondering, how large was each EU?  And the distance between EUs was 25 cm? That doesn't seen very far apart.  Why 10 samples per unit?  Were you expecting a lot of variation within each unit?  You many want to consider using more reps and fewer samples in the future.  With only 3 reps the inference space is rather limited.  Were the samples taken all at the same time or over time?  If over time, then you have a repeated measures ANOVA which is a bit more complex to analyze.