Pattern analysis, involving the joint use of classification and ordina
tion, has been applied in plant breeding trials to find relationships
among environments according to their discrimination among test genoty
pes (clones). However, rationalisation of the number of test environme
nts from such analyses has often been subjective. A method which allev
iates this subjectivity involves considering the blocks (experimental
replicates) within environments as different environments and conducti
ng a pattern analysis of blocks. The method is demonstrated using thre
e sugarcane (Saccharum spp. hybrids) genotype X environment data sets,
which contained clones representative of specific selection stages in
the breeding program. The first data set contained 54 relatively unse
lected clones evaluated in two randomised complete blocks (replicates)
at each of nine environments in southeastern Queensland (Qld.) and no
rth-eastern New South Wales (N.S.W.), Australia. The second data set c
ontained 52 moderately selected clones, evaluated in two replicates at
each of six environments in south-eastern Qld. The last data set cont
ained information on six highly selected clones present in the same tr
ials from which the second data set was collected. The two blocks with
in each environment were considered to be different environments and a
pattern analysis of the blocks present in each data set was conducted
. Results confirmed that an appropriate group number following hierarc
hical classification could be found for the first data set by truncati
ng the hierarchy when both blocks from each environment were grouped t
ogether. For the other two data sets an appropriate group number was f
ound when the blocks from five of the six environments grouped togethe
r. Reasons for the non-grouping of the blocks from that environment ar
e discussed. Similarly, an indication of the proximity among blocks ne
cessary for them to be considered similar was made by examining the di
stance between blocks from the same environment on the first three vec
tors from a principal component analysis.