TY  - CONF
AU  - Peric, Vesna
AU  - Srebric, Mirjana
AU  - Ristic, Danijela
AU  - Tabaković, Marijenka
AU  - Nikolić, Valentina
AU  - Mladenović Drinić, Snežana
AU  - 
PY  - 2022
UR  - http://rik.mrizp.rs/handle/123456789/1159
AB  - Principal Component Analysis (PCA) is a useful tool for processing multiple data, which are
often encountered in breeding practice. This method is suitable for the evaluation of genotypes
on the basis of multiple traits and graphical presentation of relationships between traits. This
study included 16 soybean mid-season genotypes (maturity group I), originated from different
regions of the world, maintained in soybean collection of Maize Research Institute Zemun Polje.
Field trials were carried out at two locations, during two years, according to a RCB design with
three replications. The genotypes were evaluated in respect to eight major agronomic traits: PH –
plant height, NN – node number, PN – pod number, SN – seed number, TSW – 1000 seed weight,
SYP – seed yield per plant, PROT – protein content, OIL – oil content. First two PCA axes
encompassed a large portion of the variance of standardized data (75,9%). Biplot distinguished
genotypes of potential importance for various breeding targets. Two genotypes stood out with the
largest 1000 seed weight. One variety formed a larger number of pods and the seed number per
plant as compared to the group average, achieving the highest grain yield per plant. Two
genotypes were among the most productive ones, with a larger number of pods as well as a
higher 1000 seed weight, compared to the average. The most promising variety was Laura,
which had a high yield and higher protein content than the average, and could be used as a
potential germplasm source for the simultaneous improvement of both traits. Correlations among
traits determined by PC biplot were in accordance with Pearson’s correlation coefficients.
PB  - Banja Luka : Narodna i univerzitetska biblioteka Republike Srpske
C3  - 13. International scientific agriculture symposium “AGROSYM 2022 - Book of proceedings
T1  - Multivariate analysis of agronomic traits in mid-season soybean varieties
UR  - https://hdl.handle.net/21.15107/rcub_rik_1159
ER  - 

@conference{
author = "Peric, Vesna and Srebric, Mirjana and Ristic, Danijela and Tabaković, Marijenka and Nikolić, Valentina and Mladenović Drinić, Snežana and ",
year = "2022",
abstract = "Principal Component Analysis (PCA) is a useful tool for processing multiple data, which are
often encountered in breeding practice. This method is suitable for the evaluation of genotypes
on the basis of multiple traits and graphical presentation of relationships between traits. This
study included 16 soybean mid-season genotypes (maturity group I), originated from different
regions of the world, maintained in soybean collection of Maize Research Institute Zemun Polje.
Field trials were carried out at two locations, during two years, according to a RCB design with
three replications. The genotypes were evaluated in respect to eight major agronomic traits: PH –
plant height, NN – node number, PN – pod number, SN – seed number, TSW – 1000 seed weight,
SYP – seed yield per plant, PROT – protein content, OIL – oil content. First two PCA axes
encompassed a large portion of the variance of standardized data (75,9%). Biplot distinguished
genotypes of potential importance for various breeding targets. Two genotypes stood out with the
largest 1000 seed weight. One variety formed a larger number of pods and the seed number per
plant as compared to the group average, achieving the highest grain yield per plant. Two
genotypes were among the most productive ones, with a larger number of pods as well as a
higher 1000 seed weight, compared to the average. The most promising variety was Laura,
which had a high yield and higher protein content than the average, and could be used as a
potential germplasm source for the simultaneous improvement of both traits. Correlations among
traits determined by PC biplot were in accordance with Pearson’s correlation coefficients.",
publisher = "Banja Luka : Narodna i univerzitetska biblioteka Republike Srpske",
journal = "13. International scientific agriculture symposium “AGROSYM 2022 - Book of proceedings",
title = "Multivariate analysis of agronomic traits in mid-season soybean varieties",
url = "https://hdl.handle.net/21.15107/rcub_rik_1159"
}

Peric, V., Srebric, M., Ristic, D., Tabaković, M., Nikolić, V., Mladenović Drinić, S.,& . (2022). Multivariate analysis of agronomic traits in mid-season soybean varieties. in 13. International scientific agriculture symposium “AGROSYM 2022 - Book of proceedings
Banja Luka : Narodna i univerzitetska biblioteka Republike Srpske..
https://hdl.handle.net/21.15107/rcub_rik_1159

Peric V, Srebric M, Ristic D, Tabaković M, Nikolić V, Mladenović Drinić S, . Multivariate analysis of agronomic traits in mid-season soybean varieties. in 13. International scientific agriculture symposium “AGROSYM 2022 - Book of proceedings. 2022;.
https://hdl.handle.net/21.15107/rcub_rik_1159 .

Peric, Vesna, Srebric, Mirjana, Ristic, Danijela, Tabaković, Marijenka, Nikolić, Valentina, Mladenović Drinić, Snežana, , "Multivariate analysis of agronomic traits in mid-season soybean varieties" in 13. International scientific agriculture symposium “AGROSYM 2022 - Book of proceedings (2022),
https://hdl.handle.net/21.15107/rcub_rik_1159 .