Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.

The correlations among seed yield components, and their direct and indirect effects on the seed yield (Z) of Russina wildrye (Psathyrostachys juncea Nevski) were investigated. The seed yield components: fertile tillers m(-2) (Y(1)), spikelets per fertile tillers (Y(2)), florets per spikelet(-) (Y(3)...

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Autores principales:	Quanzhen Wang, Tiejun Zhang, Jian Cui, Xianguo Wang, He Zhou, Jianguo Han, René Gislum
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7884fecb49a84b58bef3a3e50c9a85f4
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spelling	oai:doaj.org-article:7884fecb49a84b58bef3a3e50c9a85f42021-11-18T06:55:37ZPath and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.1932-620310.1371/journal.pone.0018245https://doaj.org/article/7884fecb49a84b58bef3a3e50c9a85f42011-04-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21533153/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203The correlations among seed yield components, and their direct and indirect effects on the seed yield (Z) of Russina wildrye (Psathyrostachys juncea Nevski) were investigated. The seed yield components: fertile tillers m(-2) (Y(1)), spikelets per fertile tillers (Y(2)), florets per spikelet(-) (Y(3)), seed numbers per spikelet (Y(4)) and seed weight (Y(5)) were counted and the Z were determined in field experiments from 2003 to 2006 via big sample size. Y(1) was the most important seed yield component describing the Z and Y(2) was the least. The total direct effects of the Y(1), Y(3) and Y(5) to the Z were positive while Y(4) and Y(2) were weakly negative. The total effects (directs plus indirects) of the components were positively contributed to the Z by path analyses. The seed yield components Y(1), Y(2), Y(4) and Y(5) were significantly (P<0.001) correlated with the Z for 4 years totally, while in the individual years, Y(2) were not significant correlated with Y(3), Y(4) and Y(5) by Peason correlation analyses in the five components in the plant seed production. Therefore, selection for high seed yield through direct selection for large Y(1), Y(2) and Y(3) would be effective for breeding programs in grasses. Furthermore, it is the most important that, via ridge regression, a steady algorithm model between Z and the five yield components was founded, which can be closely estimated the seed yield via the components.Quanzhen WangTiejun ZhangJian CuiXianguo WangHe ZhouJianguo HanRené GislumPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 4, p e18245 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Quanzhen Wang Tiejun Zhang Jian Cui Xianguo Wang He Zhou Jianguo Han René Gislum Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.
description	The correlations among seed yield components, and their direct and indirect effects on the seed yield (Z) of Russina wildrye (Psathyrostachys juncea Nevski) were investigated. The seed yield components: fertile tillers m(-2) (Y(1)), spikelets per fertile tillers (Y(2)), florets per spikelet(-) (Y(3)), seed numbers per spikelet (Y(4)) and seed weight (Y(5)) were counted and the Z were determined in field experiments from 2003 to 2006 via big sample size. Y(1) was the most important seed yield component describing the Z and Y(2) was the least. The total direct effects of the Y(1), Y(3) and Y(5) to the Z were positive while Y(4) and Y(2) were weakly negative. The total effects (directs plus indirects) of the components were positively contributed to the Z by path analyses. The seed yield components Y(1), Y(2), Y(4) and Y(5) were significantly (P<0.001) correlated with the Z for 4 years totally, while in the individual years, Y(2) were not significant correlated with Y(3), Y(4) and Y(5) by Peason correlation analyses in the five components in the plant seed production. Therefore, selection for high seed yield through direct selection for large Y(1), Y(2) and Y(3) would be effective for breeding programs in grasses. Furthermore, it is the most important that, via ridge regression, a steady algorithm model between Z and the five yield components was founded, which can be closely estimated the seed yield via the components.
format	article
author	Quanzhen Wang Tiejun Zhang Jian Cui Xianguo Wang He Zhou Jianguo Han René Gislum
author_facet	Quanzhen Wang Tiejun Zhang Jian Cui Xianguo Wang He Zhou Jianguo Han René Gislum
author_sort	Quanzhen Wang
title	Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.
title_short	Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.
title_full	Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.
title_fullStr	Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.
title_full_unstemmed	Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.
title_sort	path and ridge regression analysis of seed yield and seed yield components of russian wildrye (psathyrostachys juncea nevski) under field conditions.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/7884fecb49a84b58bef3a3e50c9a85f4
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Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.

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