metan: An R package for multi‐environment trial analysis

• Published in: Methods in ecology and evolution Vol. 11; no. 6; pp. 783 - 789
• Main Authors: Olivoto, Tiago, Lúcio, Alessandro Dal'Col, Jarman, Simon
• Format: Journal Article
• Language: English
• Published: London John Wiley & Sons, Inc 01-06-2020
• Subjects: additive main effect and multiplicative interaction; biometry; Crop production; Data analysis; Food security; genotype–environment interaction; GGE biplot; Learning curves; multi‐environment trials; Plant breeding; R software; stability; Stability analysis; Statistical analysis; statistics; Workflow; Working conditions
• ISSN: 2041-210X; 2041-210X
• DOI: 10.1111/2041-210X.13384

Multi‐environment trials (MET) are crucial steps in plant breeding programs that aim at increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization and modelling. As new methods are proposed, analysing MET data correctly and completely remains a challenge, often intractable with existing tools.
Here we describe the metan R package, a collection of functions that implement a workflow‐based approach to (a) check, manipulate and summarize typical MET data; (b) analyse individual environments using both fixed and mixed‐effect models; (c) compute parametric and nonparametric stability statistics; (d) implement biometrical models widely used in MET analysis and (e) plot typical MET data quickly.
In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyse MET data. We guide the user along a gentle learning curve and show how adding only a few commands or options at a time, powerful analyses can be implemented.
metan offers a flexible, intuitive and richly documented working environment with tools that will facilitate the implementation of a complete analysis of MET datasets.