| Abstrakt: |
When forming core subsets, accessions from a collection are classified into clusters, and then samples are drawn from the clusters with the aim of maintaining the diversity of the collection. In a stratified sampling strategy, the allocation method provides a criterion for determining the number of accessions to be selected from each cluster. This paper proposes an allocation method (D method) and compares it with three other allocation methods (L, LD, and NY methods). In these allocation methods, the number of accessions sampled per cluster is proportional to (i) the mean of the Gower's distance between accessions within thc cluster (D method), (ii) the logarithm of the chister size (L method), (iii) the product of the cluster size times the mean Gower distance (NY method), and (iv) the product of the logarithm of the cluster size times the mean Gower distance (LD method). Five hundred independent stratified random samples with two sampling intensities (10 and 20%) were obtained from four datasets. The allocation methods were compared on the basis of three criteria: diversity of the samples, recovery of the range of variables in the sample, and variances of the samples. Results showed that the D method produced samples (i) with significantly more diversity than the other allocation methods, (ii) that recovered more of the range of the variables, (iii) with higher variances for the continuous variables than the other three methods, and (iv) with variances higher than the variance among accessions of the collection. A sampling intensity of 10% preserves the same or more variability than a sampling intensity of 20%. [ABSTRACT FROM AUTHOR] |
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