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Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions

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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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Acknowledgments

The National Science & Technology Pillar Program of China (2011BAD17B05), The National Basic Research and Development Programme (973 project, 2007CB106805) and 948 Research Project (No. 202099) funded this study. We are grateful for the skilful technical assistance of Mr. Zhang Bing, Miss Yan Xuehua, Miss Han Juhoung, Mr. Zhang Xijun, Mr. Wang Shouguo, and Mr. Zhang Guoqi, who are animal husbandry engineers at the Daye Institute of Forage & Grass Products in Jiuquan, at the Gansu Branch of Chengdu Daye International Interest Co. Ltd.

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  1. Department of Grassland Science, College of Animal Science and Technology, Northwest A & F University, Yangling, 712100, Shaanxi, People’s Republic of China

    Quanzhen Wang & Tianming Hu

  2. Department of Plant Science, College of Life Science, Northwest A & F University, Yangling, 712100, Shaanxi, People’s Republic of China

    Jian Cui

  3. Institute of Grassland Science, College of Animal Science and Technology, China Agricultural University, Beijing, 100094, People’s Republic of China

    Xianguo Wang, He Zhou & Jianguo Han

  4. Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, 100193, People’s Republic of China

    Tiejun Zhang

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  1. Quanzhen Wang
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Correspondence to Quanzhen Wang.

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Wang, Q., Cui, J., Wang, X. et al. Algorithmic models of seed yield and its components in smooth bromegrass (Bromus inermis L.) via large sample size under field conditions. Euphytica 185, 363–375 (2012). https://doi.org/10.1007/s10681-011-0541-8

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