metan: An R package for multi‐environment trial analysis

TLDR

Multi‑environment trials are essential for plant breeding to boost crop productivity, yet their analysis demands data manipulation, visualization, and modelling, and new methods make correct analysis challenging with existing tools. The metan R package provides a workflow‑based toolkit to check, manipulate, summarize, analyze, and visualize multi‑environment trial data using fixed, mixed, and biometrical models. Users learn the package gradually, adding commands incrementally to perform powerful analyses with minimal effort. metan delivers a flexible, intuitive, and well‑documented environment that enables comprehensive analysis of MET datasets.

Abstract

Abstract 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.