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首页> 外文期刊>African Journal of Biotechnology >Modelling of seed yield and its components in tall fescue (Festuca arundinacea) based on a large sample
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Modelling of seed yield and its components in tall fescue (Festuca arundinacea) based on a large sample

机译:基于大量样本的高羊茅(Festuca arundinacea)种子产量及其组成模型

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Tall fescue (Festuca arundinacea?Schreb.) is a primary?cool-season?grass species that is widely used as a cold-season forage and turfgrass?throughout the temperate regions of the world.?The key seed yield components, namely fertile tillers m-2?(Y1),spikelets fertile tiller?-1?(Y2), florets spikelet?-1?(Y3), seed number spikelet?-1?(Y4), seed weight (Y5), and the seed yield (Z) of tall fescue were determined in field experiments from 2003 to 2005. The experiments produced a large sample for analysis. The correlations among Y1?to Y5?and their direct and indirect effects on Z were investigated. All of the direct effects of the Y1, Y3, Y4?and Y5?components on the seed yield were significantly positive. However, the effect of Y2?was not significant. In decreasing order, the contributions of the five components to seed yield are Y1?>Y4?>Y3?>Y5?>Y2. Y4?and Y5?were not significantly correlated with Z. However, the components Y1, Y2?and Y3?were positively correlated with Z in all the three experimental years and the intercorrelations among the components Y1, Y2and Y3?were significant. Ridge regression analysis was used to derive a steady algorithmic model that related Z to the five components; Y1?to Y5. This model can estimate Z precisely from the values of these components. Furthermore, an approach based on the exponents of the algorithmic model could be applied to the selection for high seed yield via direct selection for large Y2, Y3?and Y5?values in a breeding program for tall fescue.
机译:高羊茅(Festuca arundinacea?Schreb。)是主要的“冷季”草种,在世界温带地区广泛用作冷季牧草和草皮草。关键的种子产量成分,即肥沃的分till m-2?(Y1),小穗可分till?-1?(Y2),小穗小穗?-1?(Y3),种子数小穗?-1?(Y4),种子重量(Y5)和种子产量在2003年至2005年的野外实验中确定了高羊茅的(Z)。这些实验产生了一个大样本用于分析。研究了Y1到Y5之间的相关性及其对Z的直接和间接影响。 Y1,Y3,Y4′和Y5′组分对种子产量的所有直接影响均显着为正。但是,Y 2的作用并不显着。这五种成分对种子产量的递减顺序为Y1→Y4→Y3→Y5→Y2。 Y4和Y5与Z没有显着相关。但是,在所有的三个实验年中,Y1,Y2和Y3与Z都呈正相关,并且Y1,Y2和Y3之间的相互关系也很显着。使用Ridge回归分析得出一个稳定的算法模型,该模型将Z与这五个组件相关联。从Y1到Y5。该模型可以根据这些分量的值精确估算Z。此外,基于算法模型指数的方法可通过在高羊茅育种程序中直接选择大的Y2,Y3和Y5α值而应用于高种子产量的选择。

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