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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

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: Wang, Quanzhen, Zhang, Tiejun, Cui, Jian, Wang, Xianguo, Zhou, He, Han, Jianguo, Gislum, René
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078908/
https://www.ncbi.nlm.nih.gov/pubmed/21533153
http://dx.doi.org/10.1371/journal.pone.0018245
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author Wang, Quanzhen
Zhang, Tiejun
Cui, Jian
Wang, Xianguo
Zhou, He
Han, Jianguo
Gislum, René
author_facet Wang, Quanzhen
Zhang, Tiejun
Cui, Jian
Wang, Xianguo
Zhou, He
Han, Jianguo
Gislum, René
author_sort Wang, Quanzhen
collection PubMed
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.
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spelling pubmed-30789082011-04-29 Path and Ridge Regression Analysis of Seed Yield and Seed Yield Components of Russian Wildrye (Psathyrostachys juncea Nevski) under Field Conditions Wang, Quanzhen Zhang, Tiejun Cui, Jian Wang, Xianguo Zhou, He Han, Jianguo Gislum, René PLoS One Research Article 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. Public Library of Science 2011-04-18 /pmc/articles/PMC3078908/ /pubmed/21533153 http://dx.doi.org/10.1371/journal.pone.0018245 Text en Wang et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Wang, Quanzhen
Zhang, Tiejun
Cui, Jian
Wang, Xianguo
Zhou, He
Han, Jianguo
Gislum, René
Path and Ridge Regression Analysis of Seed Yield and Seed Yield Components of Russian Wildrye (Psathyrostachys juncea Nevski) under Field Conditions
title 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_short 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
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078908/
https://www.ncbi.nlm.nih.gov/pubmed/21533153
http://dx.doi.org/10.1371/journal.pone.0018245
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