Overview

Boxplots per treatment

## Warning: Removed 4 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).

## Warning: Removed 4 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 2 rows containing non-finite values (`stat_boxplot()`).
## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).

Overview plots split per treatment

## Warning: Removed 7 rows containing missing values (`geom_col()`).

## Warning: Removed 7 rows containing missing values (`geom_col()`).

Depth plots

## Warning: Using an external vector in selections was deprecated in tidyselect
## 1.1.0.

## Warning: Please use `all_of()` or `any_of()` instead.

## Warning: # Was:

## Warning: data %>% select(vector)

## Warning:

## Warning: # Now:

## Warning: data %>% select(all_of(vector))

## Warning:

## Warning: See <https://tidyselect.r-lib.org/reference/faq-external-vector.html>.

General results

For this document it is important to keep in mind that for the measurements in 2020 the cover crop treatment was still undergoing. It is however interesting to look at possible effects of the mechanical treatment in this year.

Significant constrats for the different depths:

00.05 cm

LB - SS 0.1926 0.0629 29 3.064 0.0229

06.10 cm

Ref - SS 0.2856 0.106 29 2.689 0.0541

LB - SS 0.3678 0.106 29 3.463 0.0086

SB - SS 0.3515 0.106 29 3.310 0.0126

11.15 cm

Ref - SS 0.66852 0.111 29 6.001 <.0001

LB - SS 0.61667 0.111 29 5.535 <.0001

SB - SS 0.62333 0.111 29 5.595 <.0001

16.20 cm

Model assumtpions violated.

Ref - SS 1.01667 0.117 29 8.666 <.0001

LB - SS 0.96889 0.117 29 8.259 <.0001

SB - SS 1.02407 0.117 29 8.729 <.0001

21.25 cm

Ref - LB 0.2830 0.104 29 2.731 0.0492

Ref - SS 1.3778 0.104 29 13.299 <.0001

LB - SS 1.0948 0.104 29 10.568 <.0001

SB - SS 1.2900 0.104 29 12.452 <.0001

26.30 cm

Ref - LB 0.422 0.102 29 4.128 0.0015

Ref - SS 1.667 0.102 29 16.296 <.0001

LB - SB -0.307 0.102 29 -3.002 0.0266

LB - SS 1.245 0.102 29 12.169 <.0001

SB - SS 1.552 0.102 29 15.170 <.0001

31.35 cm

Ref - LB 0.506 0.134 29 3.766 0.0040

Ref - SS 1.665 0.134 29 12.393 <.0001

LB - SS 1.159 0.134 29 8.627 <.0001

SB - SS 1.402 0.134 29 10.438 <.0001

36.40 cm

Ref - SS 1.4369 0.193 29 7.456 <.0001

LB - SS 1.0267 0.193 29 5.328 0.0001

SB - SS 1.3702 0.193 29 7.111 <.0001

41.40 cm

Ref - SS 1.1351 0.25 29 4.536 0.0005

LB - SS 0.6837 0.25 29 2.732 0.0492

SB - SS 1.2222 0.25 29 4.884 0.0002

Result

0 - 05cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           0.61505            0.08765            0.02006           -0.10494  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.01458           -0.08495           -0.03183  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq  Mean Sq F value  Pr(>F)  
## ondgr_bw   3 0.17186 0.057288  3.2214 0.03708 *
## gr_bem     2 0.04953 0.024764  1.3925 0.26457  
## blok       1 0.02431 0.024314  1.3672 0.25181  
## Residuals 29 0.51571 0.017783                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.97035, p-value = 0.4352
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.10432, p-value = 0.7902
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean     SE df lower.CL upper.CL
##  Ref       0.518 0.0445 29    0.427    0.609
##  LB        0.606 0.0445 29    0.515    0.697
##  SB        0.538 0.0445 29    0.447    0.629
##  SS        0.413 0.0445 29    0.322    0.504
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate     SE df t.ratio p.value
##  Ref - LB  -0.0877 0.0629 29  -1.394  0.5129
##  Ref - SB  -0.0201 0.0629 29  -0.319  0.9885
##  Ref - SS   0.1049 0.0629 29   1.669  0.3576
##  LB - SB    0.0676 0.0629 29   1.075  0.7071
##  LB - SS    0.1926 0.0629 29   3.064  0.0229
##  SB - SS    0.1250 0.0629 29   1.988  0.2156
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

06 - 10cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           1.20657            0.08222            0.06593           -0.28556  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.07917           -0.10694           -0.06042  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq  Mean Sq F value   Pr(>F)   
## ondgr_bw   3 0.79136 0.263785  5.1975 0.005377 **
## gr_bem     2 0.07390 0.036952  0.7281 0.491449   
## blok       1 0.08760 0.087604  1.7261 0.199208   
## Residuals 29 1.47182 0.050752                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.97267, p-value = 0.5029
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.096055, p-value = 0.8626
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean     SE df lower.CL upper.CL
##  Ref       1.024 0.0751 29    0.870    1.177
##  LB        1.106 0.0751 29    0.952    1.260
##  SB        1.090 0.0751 29    0.936    1.243
##  SS        0.738 0.0751 29    0.585    0.892
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB  -0.0822 0.106 29  -0.774  0.8654
##  Ref - SB  -0.0659 0.106 29  -0.621  0.9246
##  Ref - SS   0.2856 0.106 29   2.689  0.0541
##  LB - SB    0.0163 0.106 29   0.153  0.9987
##  LB - SS    0.3678 0.106 29   3.463  0.0086
##  SB - SS    0.3515 0.106 29   3.310  0.0126
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

11 - 15cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           1.80389           -0.05185           -0.04519           -0.66852  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.10917           -0.03750           -0.10639  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value    Pr(>F)    
## ondgr_bw   3 2.74616 0.91539 16.3902 2.028e-06 ***
## gr_bem     2 0.07384 0.03692  0.6611   0.52391    
## blok       1 0.27165 0.27165  4.8639   0.03551 *  
## Residuals 29 1.61963 0.05585                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.94042, p-value = 0.05235
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.14011, p-value = 0.4398
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean     SE df lower.CL upper.CL
##  Ref       1.542 0.0788 29    1.381     1.70
##  LB        1.490 0.0788 29    1.329     1.65
##  SB        1.497 0.0788 29    1.336     1.66
##  SS        0.874 0.0788 29    0.713     1.03
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB  0.05185 0.111 29   0.465  0.9660
##  Ref - SB  0.04519 0.111 29   0.406  0.9770
##  Ref - SS  0.66852 0.111 29   6.001  <.0001
##  LB - SB  -0.00667 0.111 29  -0.060  0.9999
##  LB - SS   0.61667 0.111 29   5.535  <.0001
##  SB - SS   0.62333 0.111 29   5.595  <.0001
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

16 - 20cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##          2.105093          -0.047778           0.007407          -1.016667  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##         -0.058056           0.048056          -0.083472  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## ondgr_bw   3 6.8096 2.26985 36.6515 5.371e-10 ***
## gr_bem     2 0.0678 0.03388  0.5470    0.5845    
## blok       1 0.1672 0.16722  2.7002    0.1111    
## Residuals 29 1.7960 0.06193                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.90341, p-value = 0.004234
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.1613, p-value = 0.2753
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref       1.935 0.083 29    1.765     2.10
##  LB        1.887 0.083 29    1.717     2.06
##  SB        1.942 0.083 29    1.773     2.11
##  SS        0.918 0.083 29    0.748     1.09
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB  0.04778 0.117 29   0.407  0.9768
##  Ref - SB -0.00741 0.117 29  -0.063  0.9999
##  Ref - SS  1.01667 0.117 29   8.666  <.0001
##  LB - SB  -0.05519 0.117 29  -0.470  0.9650
##  LB - SS   0.96889 0.117 29   8.259  <.0001
##  SB - SS   1.02407 0.117 29   8.729  <.0001
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

21 - 25cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           2.48435           -0.28296           -0.08778           -1.37778  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.07861            0.12972           -0.04792  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value   Pr(>F)    
## ondgr_bw   3 10.9954  3.6651 75.8893 7.74e-14 ***
## gr_bem     2  0.2656  0.1328  2.7501   0.0806 .  
## blok       1  0.0551  0.0551  1.1410   0.2943    
## Residuals 29  1.4006  0.0483                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.97757, p-value = 0.663
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.10042, p-value = 0.8258
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean     SE df lower.CL upper.CL
##  Ref        2.41 0.0733 29    2.256     2.56
##  LB         2.12 0.0733 29    1.973     2.27
##  SB         2.32 0.0733 29    2.168     2.47
##  SS         1.03 0.0733 29    0.878     1.18
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB   0.2830 0.104 29   2.731  0.0492
##  Ref - SB   0.0878 0.104 29   0.847  0.8314
##  Ref - SS   1.3778 0.104 29  13.299  <.0001
##  LB - SB   -0.1952 0.104 29  -1.884  0.2568
##  LB - SS    1.0948 0.104 29  10.568  <.0001
##  SB - SS    1.2900 0.104 29  12.452  <.0001
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

26 - 30 cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##            3.0600            -0.4222            -0.1152            -1.6670  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##           -0.1942             0.1042            -0.1061  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq  F value    Pr(>F)    
## ondgr_bw   3 15.8009  5.2670 111.8527 4.858e-16 ***
## gr_bem     2  0.5502  0.2751   5.8424  0.007379 ** 
## blok       1  0.2702  0.2702   5.7388  0.023272 *  
## Residuals 29  1.3656  0.0471                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.97872, p-value = 0.7022
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.094194, p-value = 0.8772
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean     SE df lower.CL upper.CL
##  Ref        2.82 0.0723 29     2.67     2.97
##  LB         2.40 0.0723 29     2.25     2.54
##  SB         2.70 0.0723 29     2.55     2.85
##  SS         1.15 0.0723 29     1.00     1.30
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB    0.422 0.102 29   4.128  0.0015
##  Ref - SB    0.115 0.102 29   1.126  0.6768
##  Ref - SS    1.667 0.102 29  16.296  <.0001
##  LB - SB    -0.307 0.102 29  -3.002  0.0266
##  LB - SS     1.245 0.102 29  12.169  <.0001
##  SB - SS     1.552 0.102 29  15.170  <.0001
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

31 - 35 cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           3.70028           -0.50593           -0.26259           -1.66481  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.19083           -0.02667           -0.17944  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value    Pr(>F)    
## ondgr_bw   3 14.5462  4.8487 59.7050 1.596e-12 ***
## gr_bem     2  0.2563  0.1282  1.5781  0.223584    
## blok       1  0.7728  0.7728  9.5160  0.004446 ** 
## Residuals 29  2.3551  0.0812                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.9601, p-value = 0.2165
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.10978, p-value = 0.7375
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref        3.27 0.095 29     3.07     3.46
##  LB         2.76 0.095 29     2.57     2.96
##  SB         3.01 0.095 29     2.81     3.20
##  SS         1.60 0.095 29     1.41     1.80
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB    0.506 0.134 29   3.766  0.0040
##  Ref - SB    0.263 0.134 29   1.955  0.2283
##  Ref - SS    1.665 0.134 29  12.393  <.0001
##  LB - SB    -0.243 0.134 29  -1.811  0.2886
##  LB - SS     1.159 0.134 29   8.627  <.0001
##  SB - SS     1.402 0.134 29  10.438  <.0001
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

36 - 40 cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           4.27398           -0.41011           -0.06663           -1.43685  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.20419            0.01122           -0.29085  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value   Pr(>F)    
## ondgr_bw   3 11.8954  3.9651 23.7285 5.87e-08 ***
## gr_bem     2  0.3529  0.1765  1.0559 0.360864    
## blok       1  2.0302  2.0302 12.1493 0.001583 ** 
## Residuals 29  4.8460  0.1671                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.96648, p-value = 0.3373
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.085935, p-value = 0.9324
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref        3.63 0.136 29     3.35     3.91
##  LB         3.22 0.136 29     2.94     3.50
##  SB         3.56 0.136 29     3.28     3.84
##  SS         2.19 0.136 29     1.91     2.47
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB   0.4101 0.193 29   2.128  0.1681
##  Ref - SB   0.0666 0.193 29   0.346  0.9855
##  Ref - SS   1.4369 0.193 29   7.456  <.0001
##  LB - SB   -0.3435 0.193 29  -1.782  0.3019
##  LB - SS    1.0267 0.193 29   5.328  0.0001
##  SB - SS    1.3702 0.193 29   7.111  <.0001
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

41 - 45 cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           4.83744           -0.45144            0.08711           -1.13511  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.25142            0.06847           -0.28197  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df Sum Sq Mean Sq F value    Pr(>F)    
## ondgr_bw   3 8.4401 2.81335  9.9847 0.0001103 ***
## gr_bem     2 0.6809 0.34046  1.2083 0.3133061    
## blok       1 1.9082 1.90820  6.7723 0.0144343 *  
## Residuals 29 8.1713 0.28177                      
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.9538, p-value = 0.1377
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.14909, p-value = 0.364
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref        4.21 0.177 29     3.85     4.57
##  LB         3.76 0.177 29     3.40     4.12
##  SB         4.30 0.177 29     3.94     4.66
##  SS         3.08 0.177 29     2.72     3.44
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate   SE df t.ratio p.value
##  Ref - LB   0.4514 0.25 29   1.804  0.2919
##  Ref - SB  -0.0871 0.25 29  -0.348  0.9852
##  Ref - SS   1.1351 0.25 29   4.536  0.0005
##  LB - SB   -0.5386 0.25 29  -2.152  0.1609
##  LB - SS    0.6837 0.25 29   2.732  0.0492
##  SB - SS    1.2222 0.25 29   4.884  0.0002
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

46 - 50 cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##          4.982602          -0.535407          -0.055370          -0.519519  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##         -0.443806          -0.004778           0.009833  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value Pr(>F)
## ondgr_bw   3  2.2629 0.75431  2.0739 0.1255
## gr_bem     2  1.5589 0.77946  2.1430 0.1355
## blok       1  0.0023 0.00232  0.0064 0.9369
## Residuals 29 10.5478 0.36372               
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.9639, p-value = 0.2828
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.071499, p-value = 0.9865
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref        4.85 0.201 29     4.44     5.26
##  LB         4.32 0.201 29     3.91     4.73
##  SB         4.80 0.201 29     4.39     5.21
##  SS         4.33 0.201 29     3.92     4.74
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB   0.5354 0.284 29   1.883  0.2572
##  Ref - SB   0.0554 0.284 29   0.195  0.9973
##  Ref - SS   0.5195 0.284 29   1.827  0.2814
##  LB - SB   -0.4800 0.284 29  -1.688  0.3478
##  LB - SS   -0.0159 0.284 29  -0.056  0.9999
##  SB - SS    0.4641 0.284 29   1.633  0.3769
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

51 - 55 cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           5.66244           -0.58256            0.06348           -0.06737  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.66192           -0.31669           -0.06442  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value  Pr(>F)  
## ondgr_bw   3  2.3576 0.78588  1.8526 0.15976  
## gr_bem     2  2.6304 1.31521  3.1005 0.06022 .
## blok       1  0.0996 0.09959  0.2348 0.63165  
## Residuals 29 12.3017 0.42420                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.97514, p-value = 0.5813
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.067602, p-value = 0.9928
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref        5.21 0.217 29     4.76     5.65
##  LB         4.62 0.217 29     4.18     5.07
##  SB         5.27 0.217 29     4.83     5.71
##  SS         5.14 0.217 29     4.70     5.58
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB   0.5826 0.307 29   1.897  0.2513
##  Ref - SB  -0.0635 0.307 29  -0.207  0.9968
##  Ref - SS   0.0674 0.307 29   0.219  0.9962
##  LB - SB   -0.6460 0.307 29  -2.104  0.1757
##  LB - SS   -0.5152 0.307 29  -1.678  0.3532
##  SB - SS    0.1309 0.307 29   0.426  0.9735
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

56 - 60 cm

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##           5.91562           -0.40807            0.11066            0.11259  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##          -0.79558           -0.48018           -0.07881  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value  Pr(>F)  
## ondgr_bw   3  1.8882 0.62941  1.3524 0.27920  
## gr_bem     2  3.2453 1.62264  3.4865 0.04556 *
## blok       1  0.1244 0.12445  0.2674 0.60946  
## Residuals 26 12.1007 0.46541                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.97375, p-value = 0.5903
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.087335, p-value = 0.9439
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref        5.33 0.261 26     4.79     5.86
##  LB         4.92 0.242 26     4.42     5.42
##  SB         5.44 0.228 26     4.97     5.90
##  SS         5.44 0.228 26     4.97     5.91
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB  0.40807 0.355 26   1.151  0.6623
##  Ref - SB -0.11066 0.348 26  -0.318  0.9886
##  Ref - SS -0.11259 0.348 26  -0.324  0.9880
##  LB - SB  -0.51873 0.333 26  -1.558  0.4189
##  LB - SS  -0.52066 0.333 26  -1.564  0.4157
##  SB - SS  -0.00193 0.322 26  -0.006  1.0000
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates

61 - 65cm

Only the blocking factor show significant effects. Applies to all layers until 66 cm.

##                 
##                  Fodder radish Black oats Tall fescue
##   Ref                        3          3           3
##   Compost                    0          0           0
##   LB                         3          3           3
##   LB extensive               0          0           0
##   LB Sand                    0          0           0
##   SB                         3          3           3
##   SS                         3          3           3
##   SS Caterpillar             0          0           0

## $model
## 
## Call:
## lm(formula = var1[[1]] ~ ondgr_bw + gr_bem + blok, data = dataset)
## 
## Coefficients:
##       (Intercept)         ondgr_bwLB         ondgr_bwSB         ondgr_bwSS  
##            6.3205            -0.3315            -0.1699            -0.1372  
##  gr_bemBlack oats  gr_bemTall fescue               blok  
##           -0.7865            -0.2892            -0.2038  
## 
## 
## $anova
## Analysis of Variance Table
## 
## Response: var1[[1]]
##           Df  Sum Sq Mean Sq F value Pr(>F)
## ondgr_bw   3  0.5352 0.17839  0.2536 0.8580
## gr_bem     2  3.2651 1.63257  2.3212 0.1189
## blok       1  0.7707 0.77066  1.0957 0.3052
## Residuals 25 17.5833 0.70333               
## 
## $model.shapiro
## 
##  Shapiro-Wilk normality test
## 
## data:  model$residuals
## W = 0.95517, p-value = 0.2017
## 
##  Exact one-sample Kolmogorov-Smirnov test
## 
## data:  resid(functie.model(var1))
## D = 0.10652, p-value = 0.8236
## alternative hypothesis: two-sided
## $emmeans
##  ondgr_bw emmean    SE df lower.CL upper.CL
##  Ref        5.54 0.322 25     4.88     6.20
##  LB         5.21 0.298 25     4.60     5.82
##  SB         5.37 0.300 25     4.75     5.99
##  SS         5.40 0.280 25     4.83     5.98
## 
## Results are averaged over the levels of: gr_bem 
## Confidence level used: 0.95 
## 
## $contrasts
##  contrast estimate    SE df t.ratio p.value
##  Ref - LB   0.3315 0.436 25   0.760  0.8715
##  Ref - SB   0.1699 0.447 25   0.380  0.9808
##  Ref - SS   0.1372 0.428 25   0.320  0.9883
##  LB - SB   -0.1616 0.426 25  -0.380  0.9810
##  LB - SS   -0.1943 0.409 25  -0.475  0.9640
##  SB - SS   -0.0327 0.409 25  -0.080  0.9998
## 
## Results are averaged over the levels of: gr_bem 
## P value adjustment: tukey method for comparing a family of 4 estimates