Design of Agricultural Field Experiments Accounting for both Complex Blocking Structures and Network Effects

Design of Agricultural Field Experiments Accounting for both Complex Blocking Structures and Network Effects
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考虑复杂阻塞结构和网络效应的农业田间实验设计

DOI:
10.1007/s13253-023-00544-3
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发表时间:
2023
期刊:
Journal of Agricultural, Biological and Environmental Statistics
影响因子:
--
通讯作者:
Koutra V
Koutra V
中科院分区:
--
文献类型:
--
作者:
Koutra V

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我们提出了一种新的基于模型的方法来构造最优设计与复杂的块结构和网络效应的应用在农业田间试验。通过邻接矩阵定义的网络结构描述了应用于不同地块的治疗之间的潜在干扰。我们认为,在Rothamsted研究的现场试运行,并提供了各种不同的模型下,特别是新的网络设计和常用的设计在这种情况下的最佳设计的比较。结果表明,当相邻地块上的治疗之间存在干扰时,将网络效应模型化这种干扰的设计至少与随机行列设计一样有效,而且往往比随机行列设计更有效。一般来说,网络设计的优点是,我们可以通过图形来构建邻居结构,即使是不规则的布局,以解决实验的特定特征。正如我们通过激励的例子所证明的那样,在设计实验时未能考虑网络结构可能会导致治疗参数的不精确估计和无效结论。
We propose a novel model-based approach for constructing optimal designs with complex blocking structures and network effects for application in agricultural field experiments. The potential interference among treatments applied to different plots is described via a network structure, defined via the adjacency matrix. We consider a field trial run at Rothamsted Research and provide a comparison of optimal designs under various different models, specifically new network designs and the commonly used designs in such situations. It is shown that when there is interference between treatments on neighboring plots, designs incorporating network effects to model this interference are at least as efficient as, and often more efficient than, randomized row–column designs. In general, the advantage of network designs is that we can construct the neighbor structure even for an irregular layout by means of a graph to address the particular characteristics of the experiment. As we demonstrate through the motivating example, failing to account for the network structure when designing the experiment can lead to imprecise estimates of the treatment parameters and invalid conclusions.Supplementary materials accompanying this paper appear online.
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