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

Vasiliki Koutra (), Steven G. Gilmour, Ben M. Parker and Andrew Mead
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Vasiliki Koutra: King’s College London
Steven G. Gilmour: King’s College London
Ben M. Parker: Brunel University London
Andrew Mead: Rothamsted Research

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 3, No 8, 526-548

Abstract: Abstract 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.

Keywords: Design of experiments; Connected experimental units; Neighbor effects; Nested row–column designs; Treatment interference (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13253-023-00544-3

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