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Meta-analysis is a powerful tool to summarize variety mixture effects - exemplified by grain yield and weed suppression of spring barley

机译:荟萃分析是总结各种混合效应的有力工具-以谷物产量和春大麦的杂草抑制为例

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摘要

In a new project, we aim to increase the general understanding of the power of meta-analysis to combine existing experimental results on variety mixtures. In this way, explanatory power can be increased compared to separate analyses and overall measures and relationships may be revealed. We will thus pursue to uncover a number of critical issues, including the relative importance of various traits and trait combinations on mixing success. To demonstrate the methods of meta-analysis applicable for variety mixture data, we considered a data set consisting of grain yield and weed ground cover assessments in 16 field trials of six 3-component variety mixtures and their components (part of the Danish BAR-OF field trials). The effects of mixing were analysed separately for each field trial and the results used in a meta-analysis in combination with their standard errors. We also analysed the mixing effects of each mixture by fitting a linear model to the entire data set. Both methods showed an overall positive mixing effect on grain yield and a trend for less weed to be found in variety mixtures. Finally, strengths and shortcomings of the methods are highlighted.
机译:在一个新项目中,我们旨在加深对荟萃分析功能的一般理解,以结合各种混合物的现有实验结果。这样,与单独的分析相比,可以提高解释力,并且可以揭示总体度量和关系。因此,我们将寻求发现许多关键问题,包括各种特质和特质组合对成功混合的相对重要性。为了证明适用于各种混合物数据的荟萃分析方法,我们在16种六种3组分混合物及其组分的田间试验(丹麦BAR-OF的一部分)中考虑了由谷物产量和杂草地被量评估组成的数据集。现场试验)。每种田间试验均单独分析混合效果,并将结果与​​标准误差结合用于荟萃分析。我们还通过将线性模型拟合到整个数据集来分析每种混合物的混合效果。两种方法均显示出对谷物产量的整体正混合效应,并且在各种混合物中发现杂草较少的趋势。最后,重点介绍了该方法的优缺点。

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