<abstract xmlns="http://www.w3.org/1999/xhtml"><p> This study examined 36 accessions of melon grown originally in dry and warm zones managed in the gene bank in the Institute of Seed and Plant Improvement, Karaj. To characterize and compare results of the field experiment statistical procedures, hierarchical cluster analysis, principal component analysis (PCA), and multiple linear regressions were applied. The results indicated a high degree of variability for single plant yield, fruit weight, fruit length, and cavity diameter. In addition, the variables of fruit size displayed a high differentiating power. Positive and negative correlation coefficients were identified among the studied traits as well. The highest significant positive correlation was found between the plant yield and fruit weight (0.88). Also, the highest significant negative correlation was observed between relative water content and canopy temperature (−0.58). Melon accessions were classified into three main clusters, indicating that the characterized melon collection has a high potential for particular breeding goals. The usefulness of the identified correlations among traits is valuable for the potential use in breeding projects directed for fruit size and yielding in dry condition. Multiple linear regressions were done and R<sup>2</sup> (Coefficient of determination) and RMSE (Root Mean Squared Error) were 0.97 and 229, respectively.</p></abstract>

This study examined 36 accessions of melon grown originally in dry and warm zones managed in the gene bank in the Institute of Seed and Plant Improvement, Karaj. To characterize and compare results of the field experiment statistical procedures, hierarchical cluster analysis, principal component analysis (PCA), and multiple linear regressions were applied. The results indicated a high degree of variability for single plant yield, fruit weight, fruit length, and cavity diameter. In addition, the variables of fruit size displayed a high differentiating power. Positive and negative correlation coefficients were identified among the studied traits as well. The highest significant positive correlation was found between the plant yield and fruit weight (0.88). Also, the highest significant negative correlation was observed between relative water content and canopy temperature (−0.58). Melon accessions were classified into three main clusters, indicating that the characterized melon collection has a high potential for particular breeding goals. The usefulness of the identified correlations among traits is valuable for the potential use in breeding projects directed for fruit size and yielding in dry condition. Multiple linear regressions were done and R2 (Coefficient of determination) and RMSE (Root Mean Squared Error) were 0.97 and 229, respectively.

Evaluation of Melon (Cucumis Melo. L) Genotypes Aiming Effective Selection of Parents for Breeding Directed at High Yield under Drought Stress Condition

Journal of Horticultural Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

This study examined 36 accessions of melon grown originally in dry and warm zones managed in the gene bank in the Institute of Seed and Plant Improvement,...

Evaluation of Melon (Cucumis Melo. L) Genotypes Aiming Effective Selection of Parents for Breeding Directed at High Yield under Drought Stress Condition

Online veröffentlicht: 30. Juni 2017

Seitenbereich: 125 - 134

Eingereicht: 01. Feb. 2017

Akzeptiert: 01. Mai 2017

DOI: https://doi.org/10.1515/johr-2017-0013

Schlüsselwörter
Melon, principal component, multiple regression

© by Mohammad Reza Naroui Rad

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Evaluation of Melon (Cucumis Melo. L) Genotypes Aiming Effective Selection of Parents for Breeding Directed at High Yield under Drought Stress Condition

Online veröffentlicht: 30. Juni 2017

Seitenbereich: 125 - 134

Eingereicht: 01. Feb. 2017

Akzeptiert: 01. Mai 2017

DOI: https://doi.org/10.1515/johr-2017-0013

SchlüsselwörterMelon, principal component, multiple regression

© by Mohammad Reza Naroui Rad

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
Melon, principal component, multiple regression