Cluster analysis for fruit yield components in grapes
For any plant improvement programme, different genotypes are to be classified into clusters based on genetic diversity. Further the extent of genetic divergence between them needs to be estimated. D2 statistics is one of the powerful tools to assess the relative contribution of different component t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Indian Society of Plant Breeders
2017-03-01
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| Series: | Electronic Journal of Plant Breeding |
| Subjects: | |
| Online Access: | http://ejplantbreeding.org/index.php/EJPB/article/view/1163 |
| _version_ | 1828750380616384512 |
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| author | Navjot Gupta MIS Gill and N.K. Arora |
| author_facet | Navjot Gupta MIS Gill and N.K. Arora |
| author_sort | Navjot Gupta |
| collection | DOAJ |
| description | For any plant improvement programme, different genotypes are to be classified into clusters based on genetic diversity. Further the extent of genetic divergence between them needs to be estimated. D2 statistics is one of the powerful tools to assess the relative contribution of different component traits to the total diversity, to quantify the degree of divergence and to choose genetically diverse parents for obtaining desirable recombinants. The present study was conducted at Punjab Agricultural University, Regional Research Station, Bathinda from 2011-13 on 20 genotypes of grapes for genetic divergence with respect to nine yield contributing traits viz., bunch length, bunch breadth, bunch weight, berry length, berry breadth, berry weight, TSS (%), acidity (%) and fruit yield per vine. The analysis of variance exhibited significant differences among genotypes for all the nine characters studied. Twenty genotypes were grouped into five clusters. The cluster size varied from single genotype (cluster V) to seven genotypes (cluster I). Cluster II, III, IV had 4, 5 and 3 genotypes respectively. No relationship between geographic and genetic diversity was revealed as genotypes from same geographic area fell in different clusters and vice versa. The intercluster distances were more than intracluster distances. The highest intercluster distance was observed between cluster I and cluster IV followed by clusters I and V. The other intercluster distances were of low magnitude. Hence crossing between genotypes of cluster I with those from cluster IV and V will be rewarding. Based on intercluster distances and cluster mean for different characters, parents were identified which upon crossing may yield desirable recombinants. |
| first_indexed | 2024-12-10T20:37:18Z |
| format | Article |
| id | doaj.art-846ea119ab314243905129ad3774f96f |
| institution | Directory Open Access Journal |
| issn | 0975-928X |
| language | English |
| last_indexed | 2024-12-10T20:37:18Z |
| publishDate | 2017-03-01 |
| publisher | Indian Society of Plant Breeders |
| record_format | Article |
| series | Electronic Journal of Plant Breeding |
| spelling | doaj.art-846ea119ab314243905129ad3774f96f2022-12-22T01:34:29ZengIndian Society of Plant BreedersElectronic Journal of Plant Breeding0975-928X2017-03-018130631010.5958/0975-928X.2017.00044.8Cluster analysis for fruit yield components in grapesNavjot Gupta MIS Gill andN.K. AroraFor any plant improvement programme, different genotypes are to be classified into clusters based on genetic diversity. Further the extent of genetic divergence between them needs to be estimated. D2 statistics is one of the powerful tools to assess the relative contribution of different component traits to the total diversity, to quantify the degree of divergence and to choose genetically diverse parents for obtaining desirable recombinants. The present study was conducted at Punjab Agricultural University, Regional Research Station, Bathinda from 2011-13 on 20 genotypes of grapes for genetic divergence with respect to nine yield contributing traits viz., bunch length, bunch breadth, bunch weight, berry length, berry breadth, berry weight, TSS (%), acidity (%) and fruit yield per vine. The analysis of variance exhibited significant differences among genotypes for all the nine characters studied. Twenty genotypes were grouped into five clusters. The cluster size varied from single genotype (cluster V) to seven genotypes (cluster I). Cluster II, III, IV had 4, 5 and 3 genotypes respectively. No relationship between geographic and genetic diversity was revealed as genotypes from same geographic area fell in different clusters and vice versa. The intercluster distances were more than intracluster distances. The highest intercluster distance was observed between cluster I and cluster IV followed by clusters I and V. The other intercluster distances were of low magnitude. Hence crossing between genotypes of cluster I with those from cluster IV and V will be rewarding. Based on intercluster distances and cluster mean for different characters, parents were identified which upon crossing may yield desirable recombinants.http://ejplantbreeding.org/index.php/EJPB/article/view/1163grapesfruit yield componentsd2 analysis |
| spellingShingle | Navjot Gupta MIS Gill and N.K. Arora Cluster analysis for fruit yield components in grapes Electronic Journal of Plant Breeding grapes fruit yield components d2 analysis |
| title | Cluster analysis for fruit yield components in grapes |
| title_full | Cluster analysis for fruit yield components in grapes |
| title_fullStr | Cluster analysis for fruit yield components in grapes |
| title_full_unstemmed | Cluster analysis for fruit yield components in grapes |
| title_short | Cluster analysis for fruit yield components in grapes |
| title_sort | cluster analysis for fruit yield components in grapes |
| topic | grapes fruit yield components d2 analysis |
| url | http://ejplantbreeding.org/index.php/EJPB/article/view/1163 |
| work_keys_str_mv | AT navjotgupta clusteranalysisforfruityieldcomponentsingrapes AT misgilland clusteranalysisforfruityieldcomponentsingrapes AT nkarora clusteranalysisforfruityieldcomponentsingrapes |