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Multi-environment Trial Analysis

Tiago Olivoto

2023-03-05

metan provides tools for computing several world-known stability statistics. The following stability methods are implemented.

The easiest way to compute the above-mentioned stability indexes is by using the function ge_stats(). This is a wrapper that basically returns a summary of each method. If you are looking for more details from each method like plot() and print(), I’d suggest computing the methods using their own function.

The complete functionality of the package is described at https://tiagoolivoto.github.io/metan/index.html. You’re welcome to check it out!

Brief examples

Brief examples will be shown using the dataset data_ge that contains data on two variables assessed in 10 genotypes growing in 14 environments.

Checking data

First of all, we will check the data for possible problems with the function inspect().

library(metan)
inspect(data_ge)
# # A tibble: 5 × 10
#   Variable Class   Missing Levels Valid_n   Min Median   Max Outlier Text 
#   <chr>    <chr>   <chr>   <chr>    <int> <dbl>  <dbl> <dbl>   <dbl> <lgl>
# 1 ENV      factor  No      14         420 NA     NA    NA         NA NA   
# 2 GEN      factor  No      10         420 NA     NA    NA         NA NA   
# 3 REP      factor  No      3          420 NA     NA    NA         NA NA   
# 4 GY       numeric No      -          420  0.67   2.61  5.09       0 NA   
# 5 HM       numeric No      -          420 38     48    58          0 NA

Then, the details of the multi-environment trial can be obtained with the function ge_details(). Note that to apply the function to all numeric variables quickly, we can use the select helper everything() in the argument resp.

ge_details(data_ge,
           env = ENV,
           gen = GEN,
           resp = everything())
# # A tibble: 10 × 3
#    Parameters GY                  HM              
#    <chr>      <chr>               <chr>           
#  1 Mean       "2.67"              "48.09"         
#  2 SE         "0.05"              "0.21"          
#  3 SD         "0.92"              "4.37"          
#  4 CV         "34.56"             "9.09"          
#  5 Min        "0.67 (G10 in E11)" "38 (G2 in E14)"
#  6 Max        "5.09 (G8 in E5)"   "58 (G8 in E11)"
#  7 MinENV     "E11 (1.37)"        "E14 (41.03)"   
#  8 MaxENV     "E3 (4.06)"         "E11 (54.2)"    
#  9 MinGEN     "G10 (2.47) "       "G2 (46.66) "   
# 10 MaxGEN     "G8 (3) "           "G5 (49.3) "

We can create a plot to show the performance of the genotypes across the environments with ge_plot().

ge_plot(data_ge, GEN, ENV, GY)

Or obtain the means for genotypes, environments or genotype-environment interaction with ge_means(). Note that the function round_cols() provided by metan round all numeric columns of a data frame to two (default) significant figures.

mge <- ge_means(data_ge,
                env = ENV,
                gen = GEN,
                resp = everything())
# Genotype-environment means
get_model_data(mge) %>% round_cols()
# Class of the model: ge_means
# Variable extracted: ge_means
# # A tibble: 140 × 4
#    ENV   GEN      GY    HM
#    <fct> <fct> <dbl> <dbl>
#  1 E1    G1     2.37  46.5
#  2 E1    G10    1.97  46.9
#  3 E1    G2     2.9   45.3
#  4 E1    G3     2.89  45.9
#  5 E1    G4     2.59  48.3
#  6 E1    G5     2.19  49.9
#  7 E1    G6     2.3   48.2
#  8 E1    G7     2.77  47.4
#  9 E1    G8     2.9   48.0
# 10 E1    G9     2.33  47.7
# # … with 130 more rows
# Environment means
get_model_data(mge, what = "env_means") %>% round_cols()
# Class of the model: ge_means
# Variable extracted: env_means
# # A tibble: 14 × 3
#    ENV      GY    HM
#    <fct> <dbl> <dbl>
#  1 E1     2.52  47.4
#  2 E10    2.18  44.3
#  3 E11    1.37  54.2
#  4 E12    1.61  49.6
#  5 E13    2.91  46.6
#  6 E14    1.78  41.0
#  7 E2     3.18  44.1
#  8 E3     4.06  52.9
#  9 E4     3.68  50  
# 10 E5     3.91  52.2
# 11 E6     2.66  45.9
# 12 E7     1.99  48.5
# 13 E8     2.54  45.2
# 14 E9     3.06  51.3
# Genotype means
get_model_data(mge, what = "gen_means") %>% round_cols()
# Class of the model: ge_means
# Variable extracted: gen_means
# # A tibble: 10 × 3
#    GEN      GY    HM
#    <fct> <dbl> <dbl>
#  1 G1     2.6   47.1
#  2 G10    2.47  48.5
#  3 G2     2.74  46.7
#  4 G3     2.96  47.6
#  5 G4     2.64  48.0
#  6 G5     2.54  49.3
#  7 G6     2.53  48.7
#  8 G7     2.74  48.0
#  9 G8     3     49.1
# 10 G9     2.51  47.9

AMMI model

Fitting the model

The AMMI model may be fitted with with both functions performs_ammi() and waas(), which is the acronym for the weighted average of absolute scores (Olivoto, Lúcio, Da silva, Marchioro, et al. 2019).

ammi_model <- performs_ammi(data_ge, ENV, GEN, REP, resp = c(GY, HM))
# variable GY 
# ---------------------------------------------------------------------------
# AMMI analysis table
# ---------------------------------------------------------------------------
#     Source  Df  Sum Sq Mean Sq F value   Pr(>F) Proportion Accumulated
#        ENV  13 279.574 21.5057   62.33 0.00e+00         NA          NA
#   REP(ENV)  28   9.662  0.3451    3.57 3.59e-08         NA          NA
#        GEN   9  12.995  1.4439   14.93 2.19e-19         NA          NA
#    GEN:ENV 117  31.220  0.2668    2.76 1.01e-11         NA          NA
#        PC1  21  10.749  0.5119    5.29 0.00e+00       34.4        34.4
#        PC2  19   9.924  0.5223    5.40 0.00e+00       31.8        66.2
#        PC3  17   4.039  0.2376    2.46 1.40e-03       12.9        79.2
#        PC4  15   3.074  0.2049    2.12 9.60e-03        9.8        89.0
#        PC5  13   1.446  0.1113    1.15 3.18e-01        4.6        93.6
#        PC6  11   0.932  0.0848    0.88 5.61e-01        3.0        96.6
#        PC7   9   0.567  0.0630    0.65 7.53e-01        1.8        98.4
#        PC8   7   0.362  0.0518    0.54 8.04e-01        1.2        99.6
#        PC9   5   0.126  0.0252    0.26 9.34e-01        0.4       100.0
#  Residuals 252  24.367  0.0967      NA       NA         NA          NA
#      Total 536 389.036  0.7258      NA       NA         NA          NA
# ---------------------------------------------------------------------------
# 
# variable HM 
# ---------------------------------------------------------------------------
# AMMI analysis table
# ---------------------------------------------------------------------------
#     Source  Df  Sum Sq Mean Sq F value   Pr(>F) Proportion Accumulated
#        ENV  13 5710.32 439.255   57.22 1.11e-16         NA          NA
#   REP(ENV)  28  214.93   7.676    2.70 2.20e-05         NA          NA
#        GEN   9  269.81  29.979   10.56 7.41e-14         NA          NA
#    GEN:ENV 117 1100.73   9.408    3.31 1.06e-15         NA          NA
#        PC1  21  381.13  18.149    6.39 0.00e+00       34.6        34.6
#        PC2  19  319.43  16.812    5.92 0.00e+00       29.0        63.6
#        PC3  17  114.26   6.721    2.37 2.10e-03       10.4        74.0
#        PC4  15   81.96   5.464    1.92 2.18e-02        7.4        81.5
#        PC5  13   68.11   5.240    1.84 3.77e-02        6.2        87.7
#        PC6  11   59.07   5.370    1.89 4.10e-02        5.4        93.0
#        PC7   9   46.69   5.188    1.83 6.33e-02        4.2        97.3
#        PC8   7   26.65   3.808    1.34 2.32e-01        2.4        99.7
#        PC9   5    3.41   0.682    0.24 9.45e-01        0.3       100.0
#  Residuals 252  715.69   2.840      NA       NA         NA          NA
#      Total 536 9112.21  17.000      NA       NA         NA          NA
# ---------------------------------------------------------------------------
# 
# All variables with significant (p < 0.05) genotype-vs-environment interaction
# Done!
waas_index <- waas(data_ge, ENV, GEN, REP, GY, verbose = FALSE)

Cross-validation procedures

The cross-validation procedures implemented in the metan are based on the splitting of the original data into a training set and a validation set. The model is fitted using the training set and the predicted value is compared with the validation set. This process is iterated many times, say, 1000 times. The lesser the difference between predicted and validation data, the higher the predictive accuracy of the model. More information may be found here.

Biplots

The well-known AMMI2 biplot may be obtained using the function plot_scores(). ggplot2-based graphics are obtained. Please, note that since performs_ammi() and , waas() functions allow analyzing multiple variables at the same time, e.g., resp = c(v1, v2, ...), the output ammi_model is a list that in this case has two elements, (GY and HM). To produce an AMMI2 biplot with IPCA1 and IPCA3, for example, we use the argument second to change the default value of the y axis.

a <- plot_scores(ammi_model)
b <- plot_scores(ammi_model,
                 type = 2,
                 second = "PC3")
c <- plot_scores(ammi_model,
                 type = 2,
                 polygon = TRUE,
                 col.gen = "black",
                 col.env = "gray70",
                 col.segm.env = "gray70",
                 axis.expand = 1.5)
arrange_ggplot(a, b, c, tag_levels = "a", ncol = 1)

Predict the response variable

The S3 method predict() is implemented for objects of class performs_ammi and may be used to estimate the response of each genotype in each environment considering different number of Interaction Principal Component Axis (IPCA). As a example, to predict the variables GY and HM we will use four and six IPCA (number of significant IPCAs, respectively). In addition, we will create a two way table with make_mat() to show the predicted values for the variable GY.

predicted <- predict(ammi_model, naxis = c(4, 6))
predicted %>% 
   subset(TRAIT == "GY") %>% 
   make_mat(GEN, ENV, YpredAMMI) %>% 
  round_cols()
#       E1  E10  E11  E12  E13  E14   E2   E3   E4   E5   E6   E7   E8   E9
# G1  2.52 2.15 1.30 1.60 3.05 1.62 3.01 4.06 3.53 4.02 2.63 1.87 2.39 2.71
# G10 1.96 1.52 0.89 1.04 1.83 1.90 3.13 4.16 4.21 3.34 2.54 2.18 2.75 3.15
# G2  2.88 2.28 1.48 1.93 3.02 1.48 3.23 4.62 3.62 3.84 2.69 1.90 2.05 3.39
# G3  2.76 2.46 1.71 1.84 3.28 2.07 3.62 4.22 4.05 4.21 2.94 2.09 2.88 3.24
# G4  2.54 2.24 1.41 1.66 2.70 1.78 3.16 3.88 3.38 3.61 2.51 2.06 2.40 3.65
# G5  2.31 2.09 1.32 1.44 2.64 1.77 3.21 3.62 3.47 3.55 2.44 1.83 2.49 3.34
# G6  2.30 2.17 1.40 1.43 2.88 1.77 3.26 3.40 3.41 3.68 2.43 1.67 2.56 3.12
# G7  2.75 2.48 1.36 1.88 3.12 1.88 2.63 4.05 3.05 4.20 2.70 2.57 2.50 3.19
# G8  2.84 2.57 1.72 1.92 3.62 2.10 3.43 4.26 3.97 4.59 3.06 2.19 2.97 2.79
# G9  2.34 1.79 1.10 1.34 2.96 1.43 3.11 4.38 4.06 4.07 2.69 1.53 2.37 1.99

BLUP model

The implementation of linear-mixed effect models to predict the response variable in MET is made with the function gamem_met(). By default, genotype and genotype-vs-environment interaction are assumed to have random effects. Use the argument random to change this default. In the following example the model is fitted to all numeric variables in data_ge.

model2 <- gamem_met(data_ge, ENV, GEN, REP, everything())
# Evaluating trait GY |======================                      | 50% 00:00:00 
Evaluating trait HM |============================================| 100% 00:00:01 
# Method: REML/BLUP
# Random effects: GEN, GEN:ENV
# Fixed effects: ENV, REP(ENV)
# Denominador DF: Satterthwaite's method
# ---------------------------------------------------------------------------
# P-values for Likelihood Ratio Test of the analyzed traits
# ---------------------------------------------------------------------------
#     model       GY       HM
#  COMPLETE       NA       NA
#       GEN 1.11e-05 5.07e-03
#   GEN:ENV 2.15e-11 2.27e-15
# ---------------------------------------------------------------------------
# All variables with significant (p < 0.05) genotype-vs-environment interaction

Residual plots

Several residual plots may be obtained using the S3 generic function plot()..

plot(model2, which = c(1, 2, 7), ncol = 1)

Distribution of random effects

The distribution of the random effects may be obtained using the argument type = "re".

plot(model2, type = "re", nrow = 3)

Genetic parameters and variance components

We can get easily the model results such as the Likelihood Ration Test for random effects, the variance components, and the BLUPs for genotypes with get_model_data(). By default, the function returns the genetic parameters.

get_model_data(model2) %>% round_cols(digits = 3)
# Class of the model: waasb
# Variable extracted: genpar
# # A tibble: 9 × 3
#   Parameters              GY    HM
#   <chr>                <dbl> <dbl>
# 1 Phenotypic variance  0.181 5.52 
# 2 Heritability         0.154 0.089
# 3 GEIr2                0.313 0.397
# 4 h2mg                 0.815 0.686
# 5 Accuracy             0.903 0.828
# 6 rge                  0.37  0.435
# 7 CVg                  6.26  1.46 
# 8 CVr                 11.6   3.50 
# 9 CV ratio             0.538 0.415

Plotting the BLUPs for genotypes

library(ggplot2)
d <- plot_blup(model2)
e <- plot_blup(model2,
               prob = 0.1,
               col.shape  =  c("gray20", "gray80")) +
      coord_flip()
arrange_ggplot(d, e, tag_levels = list(c("d", "e")), ncol = 1)

BLUPS for genotype-vs-environment interaction

get_model_data(model2, what = "blupge") %>% 
  round_cols()
# Class of the model: waasb
# Variable extracted: blupge
# # A tibble: 140 × 4
#    ENV   GEN      GY    HM
#    <fct> <fct> <dbl> <dbl>
#  1 E1    G1     2.4   46.6
#  2 E1    G10    2.11  47.2
#  3 E1    G2     2.78  45.7
#  4 E1    G3     2.84  46.2
#  5 E1    G4     2.55  48.0
#  6 E1    G5     2.27  49.4
#  7 E1    G6     2.34  48.1
#  8 E1    G7     2.7   47.4
#  9 E1    G8     2.86  48.0
# 10 E1    G9     2.35  47.6
# # … with 130 more rows

BLUP-based stability index

The WAASB index (Olivoto, Lúcio, Da silva, Marchioro, et al. 2019) is a quantitative stability measure based on the weighted average of the absolute scores from the singular value decomposition of the BLUPs for genotype-vs-interaction effects. We can obtain this statistic with the function waasb() combined with get_model_data() using what = "WAASB".

model3 <- waasb(data_ge, ENV, GEN, REP, everything(), verbose = FALSE)
get_model_data(model3, what = "WAASB") %>% 
  round_cols()
# Class of the model: waasb
# Variable extracted: WAASB
# # A tibble: 10 × 3
#    GEN      GY    HM
#    <chr> <dbl> <dbl>
#  1 G1     0.13  0.38
#  2 G10    0.46  1.03
#  3 G2     0.21  0.79
#  4 G3     0.1   0.36
#  5 G4     0.25  0.6 
#  6 G5     0.22  0.88
#  7 G6     0.17  0.41
#  8 G7     0.32  0.68
#  9 G8     0.26  0.44
# 10 G9     0.37  0.56

The function blup_indexes() can be used to compute the harmonic mean of genotypic values (HMGV), the relative performance of the genotypic values (RPGV) and the harmonic mean of the relative performance of genotypic values (HMRPGV). See Alves et al. (2018) for more details. We use the function get_model_data() to get the HMRPGV (default) for all analyzed variables.

index <- blup_indexes(model3)
get_model_data(index) %>% round_cols()
# Class of the model: blup_ind
# Variable extracted: HMRPGV
# # A tibble: 10 × 3
#    GEN      GY    HM
#    <chr> <dbl> <dbl>
#  1 G1     0.97  0.98
#  2 G10    0.9   1.01
#  3 G2     1.02  0.97
#  4 G3     1.1   0.99
#  5 G4     0.99  1   
#  6 G5     0.95  1.02
#  7 G6     0.95  1.01
#  8 G7     1.03  1   
#  9 G8     1.12  1.02
# 10 G9     0.92  1

GGE model

Fitting the model

The GGE model is fitted with the function gge(). This function produces a GGE model based on both a two-way table (in our case the object table) with genotypes in the rows and environments in columns, or a data.frame containing at least the columns for genotypes, environments and the response variable(s).

gge_model <- gge(data_ge, ENV, GEN, GY)

Visualizing the Biplot

The generic function plot() is used to generate a biplot using as input a fitted model of class gge. The type of biplot is chosen by the argument type in the function. Ten biplots type are available according to {Yan and Kang (2003)}.

f <- plot(gge_model)
g <- plot(gge_model, type = 2)
arrange_ggplot(e, f, tag_levels = list(c("e", "f")), ncol = 1)

Wrapper function ge_stats()

To compute all the stability statistics at once, we can use the function ge_stats(). Again we get the results with get_model_data().

stat_ge <- ge_stats(data_ge, ENV, GEN, REP, GY)
# Evaluating trait GY |============================================| 100% 00:00:08 
get_model_data(stat_ge) %>% 
  round_cols()
# Class of the model: ge_stats
# Variable extracted: stats
# # A tibble: 10 × 44
#    var   GEN       Y    CV   ACV POLAR   Var Shukla  Wi_g  Wi_f  Wi_u Ecoval
#    <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>  <dbl>
#  1 GY    G1     2.6   35.2  34.1  0.03 10.9    0.03  84.4  89.2  81.1   1.22
#  2 GY    G10    2.47  42.4  38.6  0.14 14.2    0.24  59.2  64.6  54.4   7.96
#  3 GY    G2     2.74  34.0  35.2  0.06 11.3    0.09  82.8  95.3  75.6   3.03
#  4 GY    G3     2.96  29.9  33.8  0.02 10.1    0.01 104.   99.7 107.    0.72
#  5 GY    G4     2.64  31.4  31   -0.05  8.93   0.06  85.9  79.6  91.9   2.34
#  6 GY    G5     2.54  30.6  28.8 -0.12  7.82   0.05  82.7  82.2  82.4   1.84
#  7 GY    G6     2.53  29.6  27.8 -0.15  7.34   0.05  83.0  83.7  81.8   1.81
#  8 GY    G7     2.74  27.4  28.3 -0.13  7.33   0.12  83.9  77.6  93.4   4.16
#  9 GY    G8     3     30.4  35.0  0.05 10.8    0.07  98.8  90.5 107.    2.57
# 10 GY    G9     2.51  42.4  39.4  0.15 14.8    0.17  68.8  68.9  70.3   5.56
# # … with 32 more variables: bij <dbl>, Sij <dbl>, R2 <dbl>, ASTAB <dbl>,
# #   ASI <dbl>, ASV <dbl>, AVAMGE <dbl>, DA <dbl>, DZ <dbl>, EV <dbl>, FA <dbl>,
# #   MASI <dbl>, MASV <dbl>, SIPC <dbl>, ZA <dbl>, WAAS <dbl>, WAASB <dbl>,
# #   HMGV <dbl>, RPGV <dbl>, HMRPGV <dbl>, Pi_a <dbl>, Pi_f <dbl>, Pi_u <dbl>,
# #   Gai <dbl>, S1 <dbl>, S2 <dbl>, S3 <dbl>, S6 <dbl>, N1 <dbl>, N2 <dbl>,
# #   N3 <dbl>, N4 <dbl>

Selection based on multiple traits

The multi-trait stability index (MTSI) was proposed by Olivoto, Lúcio, Da silva, Sari, et al. (2019) and is used for simultaneous selection considering mean performance and stability (of several traits) in the analysis of METs using both fixed and mixed-effect models. For more details see the complete vignette.

Getting help

References

Alves, Rodrigo Silva, Leonardo de Azevedo Peixoto, Paulo Eduardo Teodoro, Lidiane Aparecida Silva, Erina Vitório Rodrigues, Marcos Deon Vilela de Resende, Bruno Galveas Laviola, and Leonardo Lopes Bhering. 2018. “Selection of Jatropha curcas families based on temporal stability and adaptability of genetic values.” Industrial Crops and Products 119 (September): 290–93. https://doi.org/10.1016/J.INDCROP.2018.04.029.
Annicchiarico, P. 1992. “Cultivar adaptation and recommendation from alfalfa trials in Northern Italy.” Journal of Genetics and Breeding 46: 269–78.
Döring, Thomas F., Samuel Knapp, and Joel E. Cohen. 2015. “Taylor’s power law and the stability of crop yields.” Field Crops Research 183 (November): 294–302. https://doi.org/10.1016/j.fcr.2015.08.005.
Döring, Thomas F., and Moritz Reckling. 2018. “Detecting global trends of cereal yield stability by adjusting the coefficient of variation.” European Journal of Agronomy 99 (September): 30–36. https://doi.org/10.1016/j.eja.2018.06.007.
Eberhart, S. A., and W. A. Russell. 1966. “Stability parameters for comparing Varieties.” Crop Science 6 (1): 36–40. https://doi.org/10.2135/cropsci1966.0011183X000600010011x.
Fox, P. N., B. Skovmand, B. K. Thompson, H. J. Braun, and R. Cormier. 1990. “Yield and adaptation of hexaploid spring triticale.” Euphytica 47 (1): 57–64. https://doi.org/10.1007/BF00040364.
Huehn, M. 1979. “Beitrage zur erfassung der phanotypischen stabilitat.” EDV in Medizin Und Biologie 10: 112–17.
Lin, C. S., and M. R. Binns. 1988. “A superiority measure of cultivar performance for cultivar x location data.” Canadian Journal of Plant Science 68 (1): 193–98. https://doi.org/10.4141/cjps88-018.
Mohammadi, Reza, and Ahmed Amri. 2008. “Comparison of parametric and non-parametric methods for selecting stable and adapted durum wheat genotypes in variable environments.” Euphytica 159 (3): 419–32. https://doi.org/10.1007/s10681-007-9600-6.
Murakami, D. M., and C. D. Cruz. 2004. “Proposal of methodologies for environment stratification and analysis of genotype adaptability.” Crop Breeding and Applied Biotechnology 4 (1): 7–11.
Olivoto, T., A. D. C Lúcio, J. A. G. Da silva, V. S. Marchioro, V. Q. de Souza, and E. Jost. 2019. “Mean performance and stability in multi-environment trials I: Combining features of AMMI and BLUP techniques.” Agronomy Journal 111 (6): 2949–60. https://doi.org/10.2134/agronj2019.03.0220.
Olivoto, T., A. D. C Lúcio, J. A. G. Da silva, B. G. Sari, and M. I. Diel. 2019. “Mean performance and stability in multi-environment trials II: Selection based on multiple traits.” Agronomy Journal 111 (6): 2961–69. https://doi.org/10.2134/agronj2019.03.0221.
Schmildt, Edilson Romais, Adriel Lima Nascimento, Cosme Damião Cruz, and Jaqueline Aparecida Raminelli Oliveira. 2011. “Avaliação de metodologias de adaptabilidade e estabilidade de cultivares milho.” Acta Scientiarum - Agronomy 33 (1): 51–58. https://doi.org/10.4025/actasciagron.v33i1.5817.
Shukla, G. K. 1972. “Some statistical aspects of partitioning genotype-environmental components of variability.” Heredity 29 (2): 238–45. https://doi.org/10.1038/hdy.1972.87.
Thennarasu, K. 1995. “On certain non-parametric procedures for studying genotype-environment interactions.” Thesis, IARI.
Wricke, G. 1965. “Zur berechnung der okovalenz bei sommerweizen und hafer.” Z. Pflanzenzuchtg 52: 127–38.
Yan, Weikai., and Manjit S. Kang. 2003. GGE biplot analysis : a graphical tool for breeders, geneticists, and agronomists. CRC Press.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.