ArticlePloS one2011
Path and ridge regression analysis of seed yield and seed yield components of Russian wildrye (Psathyrostachys juncea Nevski) under field conditions.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
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- Nitrogen-phosphorus stoichiometry shapes seedling growth and yield performance of perennial cool-season forage grasses in saline-alkali soil.BMC plant biology · 2025Article
- Article
- Article
- Using Genomic Selection to Develop Performance-Based Restoration Plant Materials.International journal of molecular sciences · 2022Review
- Incredible Role of Osmotic Adjustment in Grain Yield Sustainability under Water Scarcity Conditions in Wheat (Plants (Basel, Switzerland) · 2020Article
- Article
- Effects of ultrasonication on increased germination and improved seedling growth of aged grass seeds of tall fescue and Russian wildrye.Scientific reports · 2016Article
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Authors and funding
7 authors.
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Abstract
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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