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.

Background: Commonly, several traits are assessed in agronomic experiments to better understand the factors under study. However, it is also common to see that even when several traits are available, researchers opt to follow the easiest way by applying univariate analyses and post-hoc tests for mean comparison for each trait, which arouses the hypothesis that the benefits of a multi-trait framework analysis may have not been fully exploited in this area. Results: In this paper, we extended the theoretical foundations of the multi-trait genotype-ideotype distance index (MGIDI) to analyze multivariate data either in simple experiments (e.g., one-way layout with few treatments and traits) or complex experiments (e.g., with a factorial treatment structure). We proposed an optional weighting process that makes the ranking of treatments that stands out in traits with higher weights more likely. Its application is illustrated using (1) simulated data and (2) real data from a strawberry experiment that aims to select better factor combinations (namely, cultivar, transplant origin, and substrate mixture) based on the desired performance of 22 phenological, productive, physiological, and qualitative traits. Our results show that most of the strawberry traits are influenced by the cultivar, transplant origin, cultivation substrates, as well as by the interaction between cultivar and transplant origin. The MGIDI ranked the Albion cultivar originated from Imported transplants and the Camarosa cultivar originated from National transplants as the better factor combinations. The substrates with burned rice husk as the main component (70%) showed satisfactory physical proprieties, providing higher water use efficiency. The strengths and weakness view provided by the MGIDI revealed that looking for an ideal treatment should direct the efforts on increasing fruit production of Albion transplants from Imported origin. On the other hand, this treatment has strengths related to productive precocity, total soluble solids, and flesh firmness. Conclusions: Overall, this study opens the door to the use of MGIDI beyond the plant breeding context, providing a unique, practical, robust, and easy-to-handle multi-trait-based framework to analyze multivariate data. There is an exciting possibility for this to open up new avenues of research, mainly because using the MGIDI in future studies will dramatically reduce the number of tables/figures needed, serving as a powerful tool to guide researchers toward better treatment recommendations.

A variância associada à produção em plantas com colheitas múltiplas nem sempre é homogênea, o que compromete a precisão experimental. Com o objetivo de identificar o comportamento da média e da variância da massa dos frutos de abobrinha-italiana (Curcubita pepo) com o passar das colheitas, entre as linhas de cultivo dentro de cada colheita e entre colheitas, e verificar a interferência de diferentes manejos nesse comportamento, conduziu-se um trabalho nas estações sazonais inverno-primavera (I/P) e verão-outono (V/O) 2004/2005. O experimento foi conduzido em dois túneis (T1 e T2), com três linhas de 25 plantas por túnel, com a cultivar Caserta. Em T1 utilizou-se irrigação por gotejamento, com mulching sobre os camalhões e menor aplicação de biocidas que em T2. Em T2 utilizou-se irrigação por aspersores, colocados a 1,8 m de altura, sem mulching sobre os camalhões. Aplicou-se o teste de Bartlett entre as seis variâncias das linhas de cultivo dentro de cada colheita e entre as variâncias médias das seis linhas para verificar a homogeneidade entre colheitas em cada estação de cultivo. Para comparação das médias de produção de cada linha de cultivo, em cada colheita, dentro de cada túnel, e entre linhas de cultivo, com a mesma posição nos túneis, foi aplicado o teste t. Na estação V/O, as variâncias foram homogêneas em duas das onze colheitas e, na estação I/P, em dez das treze colheitas, sendo que em ambas as estações houve maior homogeneidade das variâncias na primeira metade do ciclo. Na estação V/O, as variâncias em T1 foram, de modo geral, menores em todo o ciclo. Nas duas estações houve heterogeneidade na variância entre colheitas. Na estação V/O, ocorreram diferenças significativas em seis e três colheitas, respectivamente em T1 e T2, com as médias de produção atingindo 481,0 em T1 e 454,0 g planta-1 em T2. Na estação I/P ocorreram diferenças de produção na primeira (T1) e quarta e quinta colheitas (T2), com as médias atingindo 598,2 (T1) e 597,4 g planta-1 (T2).

The aim of this study was to select growth models to describe strawberry (Fragaria x ananassa)fruit production. To do this, data on 16 treatments (combination of 2 cultivars [Albion and Camarosa], 2 origins [National and Imported], and 4 organic substrates mixed [70% crushed sugar cane residue + 30% organic compost, 70% crushed sugar cane residue + 30% commercial substrate, 70% burnt rice husk + 30% organic compost, and 70% burnt rice husk + 30% commercial substrate]) conducted in a Randomized Complete Block Design (RCBD) with 4 replicates were used. Different parameterizations of the Logistic, Gompertz, and von Bertalanffy models were adjusted for number and weight of fruits per plant (g) accumulated in multiple harvests. The model adjustment and parameter estimation were obtained by ordinary least squares, using a Gauss Newton algorithm. The selection of the best model was chosen by intrinsic and parametric nonlinearity. Among the adjusted nonlinear models, the best adjustment for both variables was achieved by the parameterization 2 of the Logistic model and parameterization 1 of the Gompertz model, because they had lower results with less parametric and intrinsic nonlinearity. However, care should be taken when using the Gompertz model because it tends to overestimate the production estimate and may cause misunderstandings in interpretation.