Designs with complex blocking structures and network effects for agricultural field experiments

TL;DR

This paper introduces a model-based method for designing agricultural experiments that accounts for complex blocking and network effects, improving efficiency and accuracy over traditional designs especially in irregular layouts.

Contribution

It presents a novel approach to constructing optimal experimental designs incorporating network effects and complex blocking structures, tailored for irregular agricultural field layouts.

Findings

Network-based designs outperform traditional row-column designs when interference exists.

Incorporating network effects improves the precision of treatment effect estimates.

Designs can be adapted to irregular layouts using graph-based neighbor structures.

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, including the commonly used designs in such situations. It is shown that when there is interference between treatments on neighbouring plots, due to the spatial arrangement of the plots, designs incorporating network effects are at least as, and often more efficient than, randomised row-column designs. The advantage of network designs is that we can construct the neighbour structure even for an irregular layout by means of a graph to address the particular characteristics of the experiment. The need for such designs arises when it is required to account for treatment-induced patterns of heterogeneity. Ignoring the network structure can lead to imprecise estimates of the treatment parameters and invalid conclusions.