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FieldSimR: an R package for simulating plot data in multi-environment field trials.

Christian R Werner^1,2, Dorcus C Gemenet^1,2, Daniel J Tolhurst³

¹Accelerated Breeding Initiative (ABI), Consultative Group of International Agricultural Research (CGIAR), Texcoco, Mexico.

Frontiers in Plant Science

|April 19, 2024

Summary

This summary is machine-generated.

A new R package, FieldSimR, simulates realistic plot data for plant breeding trials. This tool optimizes experimental designs and statistical analyses in multi-environment field trials.

Keywords:

linear mixed models multi-environment field trials plot error simulation spatial variation

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Area of Science:

Agricultural Science
Plant Breeding
Statistical Modeling

Background:

Accurate simulation of plot data is crucial for optimizing plant breeding programs.
Existing software lacks comprehensive tools for simulating multi-environment field trial data.
Realistic simulation aids in the design and analysis of complex agricultural experiments.

Purpose of the Study:

To present a general framework for simulating plot data in multi-environment field trials.
To introduce the R package FieldSimR for generating realistic plot errors.
To demonstrate FieldSimR's utility in optimizing experimental designs and statistical analyses.

Main Methods:

Developed a framework embedded in the R package FieldSimR.
Core function generates plot errors capturing global trend, local variation, and extraneous variation.
Utilized simulated data to compare spatial models for prediction and variance estimation.

Main Results:

FieldSimR simulates realistic plot data, incorporating user-defined ratios of variation.
The package offers functionality missing in other plant breeding simulation software.
Demonstrated application in optimizing experimental design for maize hybrid trials.

Conclusions:

FieldSimR is a flexible and powerful tool for simulating plant breeding data.
It enhances the optimization of experimental designs and statistical analyses in field trials.
The framework has broader applications in agricultural trial simulations, including glasshouse experiments.