Algorithmic models of seed yield and its components in smooth bromegrass (<Emphasis Type="Italic">Bromus inermis</Emphasis> L.) via large sample size under field conditions |
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| Authors: | Quanzhen Wang Jian Cui Xianguo Wang Tiejun Zhang He Zhou Tianming Hu Jianguo Han |
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| Institution: | (1) Department of Grassland Science, College of Animal Science and Technology, Northwest A & F University, Yangling, 712100, Shaanxi, People’s Republic of China;(2) Department of Plant Science, College of Life Science, Northwest A & F University, Yangling, 712100, Shaanxi, People’s Republic of China;(3) Institute of Grassland Science, College of Animal Science and Technology, China Agricultural University, Beijing, 100094, People’s Republic of China;(4) Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People’s Republic of China |
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| Abstract: | Based on multi-factor orthogonally designed field experimental plots, the correlations among yield components as well as their
direct and indirect effects on the seed yield of Bromus inermis L. cv. ‘Carlton’ were investigated. The seed yield parameters fertile tillers/m2 (Y1), spikelets/fertile tiller (Y2), florets/spikelet (Y3), seed number/spikelet (Y4), seed weight (Y5), and seed yield (Z) were determined by hand in the field for the years 2003–2005. Via ridge regression analysis, a steady
algorithmic model of seed yield with its five components was found that could closely estimate the seed yield. The component
Y1 had the largest correlation coefficient with Z, followed by Y2. The contributions of the five components to the seed yield in decreasing order are Y1 > Y4 > Y2 > Y5 > Y3. The inter-correlation among the components Y1 to Y5 and Z exhibited significance but Y1 was not correlated with Y3. Therefore, direct selection for large Y1, Y2, and Y4 would be an effective means of selection for high seed yield in the grass. |
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