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