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Rmd 3d4254c ajpelu 2021-09-10 include analysis of time with browsing; add to index

Introduction

  • Analysis of temporal evolution of soil parameters along time.

  • Only for Autumn treatment (i.e. zona == “P”; zona == “NP”)

  • Interpret zona as “grazing effect”:

    • zona == “P” corresponds to Browsing
    • zona == “NP” corresponds to No Browsing

Prepare data

raw_soil <- readxl::read_excel(here::here("data/Resultados_Suelos_2018_2021_v2.xlsx"), 
    sheet = "SEGUIMIENTO_SUELOS_sin_ouliers") %>% janitor::clean_names() %>% mutate(treatment_name = case_when(str_detect(geo_parcela_nombre, 
    "NP_") ~ "Autumn Burning / No Browsing", str_detect(geo_parcela_nombre, "PR_") ~ 
    "Spring Burning / Browsing", str_detect(geo_parcela_nombre, "P_") ~ "Autumn Burning / Browsing"), 
    zona = case_when(str_detect(geo_parcela_nombre, "NP_") ~ "QOt_NP", str_detect(geo_parcela_nombre, 
        "PR_") ~ "QPr_P", str_detect(geo_parcela_nombre, "P_") ~ "QOt_P"), fecha = lubridate::ymd(fecha), 
    pre_post_quema = case_when(pre_post_quema == "Prequema" ~ "0 preQuema", pre_post_quema == 
        "Postquema" ~ "1 postQuema"))
  • Compute date as months after fire
autumn_fire <- lubridate::ymd("2018-12-18")

soil <- raw_soil %>% filter(zona != "QPr_P") %>% mutate(zona = as.factor(zona)) %>% 
    mutate(meses = as.factor(case_when(fecha == "2018-12-11" ~ as.character("-1"), 
        fecha != "2018-12-11" ~ as.character(lubridate::interval(autumn_fire, lubridate::ymd(fecha))%/%months(1))))) %>% 
    mutate(pastoreo = as.factor(case_when(zona == "QOt_P" ~ "Browsing", zona == "QOt_NP" ~ 
        "No Browsing"))) %>% relocate(pastoreo, fecha, meses) %>% dplyr::select(-pre_post_quema, 
    -tratamiento)

xtabs(~meses + pastoreo, data = soil)
     pastoreo
meses Browsing No Browsing
   -1       24          24
   0        24          24
   22       25          25
   29       24          24

Modelize

  • For each response variable, the approach modelling is

\(Y \sim pastoreo (Browsing|NoBrowsing)+ Fecha(-1|0|22|29) + zona \times Fecha\)

  • using the “(1|pastoreo:geo_parcela_nombre)” as nested random effects

Humedad

humedad ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Type III Analysis of Variance Table with Satterthwaite's method
               Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
pastoreo         2.88   2.879     1   6.004  0.3883  0.556131    
meses          457.42 152.472     3 179.021 20.5621 1.705e-11 ***
pastoreo:meses 137.33  45.775     3 179.021  6.1732  0.000514 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean    SE   df lower.CL upper.CL
 Browsing      11.5 0.973 6.01     9.07     13.8
 No Browsing   10.6 0.973 5.99     8.22     13.0

Results are averaged over the levels of: meses 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate   SE df t.ratio p.value
 Browsing - No Browsing    0.857 1.38  6 0.623   0.5561 

Results are averaged over the levels of: meses 
Degrees-of-freedom method: kenward-roger 
$`emmeans of meses`
 meses emmean    SE   df lower.CL upper.CL
 -1     12.22 0.768 9.28    10.49     13.9
 0      12.13 0.768 9.28    10.40     13.9
 22      8.46 0.764 9.10     6.74     10.2
 29     11.29 0.770 9.39     9.56     13.0

Results are averaged over the levels of: pastoreo 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate    SE  df t.ratio p.value
 (-1) - 0    0.0907 0.556 179  0.163  0.9984 
 (-1) - 22   3.7540 0.550 179  6.820  <.0001 
 (-1) - 29   0.9245 0.559 179  1.654  0.3513 
 0 - 22      3.6633 0.550 179  6.655  <.0001 
 0 - 29      0.8338 0.559 179  1.492  0.4447 
 22 - 29    -2.8295 0.554 179 -5.110  <.0001 

Results are averaged over the levels of: pastoreo 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean   SE   df lower.CL upper.CL
 -1     12.45 1.09 9.28    10.00     14.9
 0      11.39 1.09 9.28     8.95     13.8
 22     10.07 1.08 9.10     7.63     12.5
 29     11.91 1.09 9.50     9.46     14.4

pastoreo = No Browsing:
 meses emmean   SE   df lower.CL upper.CL
 -1     11.99 1.09 9.28     9.54     14.4
 0      12.86 1.09 9.28    10.42     15.3
 22      6.86 1.08 9.10     4.42      9.3
 29     10.67 1.09 9.28     8.23     13.1

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0     1.054 0.786 179  1.341  0.5384 
 (-1) - 22    2.380 0.778 179  3.058  0.0136 
 (-1) - 29    0.535 0.795 179  0.673  0.9074 
 0 - 22       1.327 0.778 179  1.704  0.3245 
 0 - 29      -0.519 0.795 179 -0.653  0.9143 
 22 - 29     -1.846 0.788 179 -2.343  0.0922 

pastoreo = No Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0    -0.872 0.786 179 -1.110  0.6839 
 (-1) - 22    5.128 0.778 179  6.587  <.0001 
 (-1) - 29    1.314 0.786 179  1.672  0.3415 
 0 - 22       6.000 0.778 179  7.708  <.0001 
 0 - 29       2.187 0.786 179  2.782  0.0302 
 22 - 29     -3.813 0.778 179 -4.899  <.0001 

Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 4 estimates 

CIC

cic ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Type III Analysis of Variance Table with Satterthwaite's method
               Sum Sq Mean Sq NumDF   DenDF F value Pr(>F)    
pastoreo         9.00   9.001     1   5.985  2.5997 0.1581    
meses          438.74 146.248     3 179.999 42.2385 <2e-16 ***
pastoreo:meses  11.43   3.811     3 179.999  1.1007 0.3503    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean    SE df lower.CL upper.CL
 Browsing      17.0 0.504  6     15.8     18.2
 No Browsing   15.9 0.504  6     14.6     17.1

Results are averaged over the levels of: meses 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate    SE df t.ratio p.value
 Browsing - No Browsing     1.15 0.712  6 1.612   0.1580 

Results are averaged over the levels of: meses 
Degrees-of-freedom method: kenward-roger 
$`emmeans of meses`
 meses emmean    SE   df lower.CL upper.CL
 -1      16.6 0.426 12.2     15.7     17.5
 0       15.3 0.426 12.2     14.4     16.3
 22      15.0 0.422 11.8     14.1     15.9
 29      18.8 0.426 12.2     17.9     19.8

Results are averaged over the levels of: pastoreo 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate    SE  df t.ratio p.value
 (-1) - 0     1.271 0.380 180   3.346 0.0055 
 (-1) - 22    1.605 0.376 180   4.268 0.0002 
 (-1) - 29   -2.229 0.380 180  -5.869 <.0001 
 0 - 22       0.335 0.376 180   0.889 0.8103 
 0 - 29      -3.500 0.380 180  -9.215 <.0001 
 22 - 29     -3.835 0.376 180 -10.195 <.0001 

Results are averaged over the levels of: pastoreo 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean    SE   df lower.CL upper.CL
 -1      17.5 0.602 12.2     16.2     18.9
 0       15.6 0.602 12.2     14.3     16.9
 22      15.6 0.597 11.8     14.3     16.9
 29      19.4 0.602 12.2     18.1     20.7

pastoreo = No Browsing:
 meses emmean    SE   df lower.CL upper.CL
 -1      15.7 0.602 12.2     14.4     17.0
 0       15.1 0.602 12.2     13.8     16.4
 22      14.4 0.597 11.8     13.1     15.7
 29      18.3 0.602 12.2     17.0     19.6

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0    1.9583 0.537 180  3.646  0.0020 
 (-1) - 22   1.9748 0.532 180  3.713  0.0015 
 (-1) - 29  -1.8333 0.537 180 -3.413  0.0044 
 0 - 22      0.0165 0.532 180  0.031  1.0000 
 0 - 29     -3.7917 0.537 180 -7.059  <.0001 
 22 - 29    -3.8081 0.532 180 -7.159  <.0001 

pastoreo = No Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0    0.5833 0.537 180  1.086  0.6986 
 (-1) - 22   1.2360 0.532 180  2.324  0.0965 
 (-1) - 29  -2.6250 0.537 180 -4.887  <.0001 
 0 - 22      0.6526 0.532 180  1.227  0.6106 
 0 - 29     -3.2083 0.537 180 -5.973  <.0001 
 22 - 29    -3.8610 0.532 180 -7.259  <.0001 

Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 4 estimates 

C

c_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Type III Analysis of Variance Table with Satterthwaite's method
                Sum Sq Mean Sq NumDF   DenDF F value   Pr(>F)   
pastoreo        2.9209  2.9209     1   5.997  1.5209 0.263626   
meses          31.1686 10.3895     3 180.001  5.4097 0.001382 **
pastoreo:meses  0.5844  0.1948     3 180.001  0.1014 0.959107   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean   SE df lower.CL upper.CL
 Browsing      8.03 0.78  6     6.12     9.94
 No Browsing   6.67 0.78  6     4.76     8.58

Results are averaged over the levels of: meses 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate  SE df t.ratio p.value
 Browsing - No Browsing     1.36 1.1  6 1.233   0.2636 

Results are averaged over the levels of: meses 
Degrees-of-freedom method: kenward-roger 
$`emmeans of meses`
 meses emmean    SE   df lower.CL upper.CL
 -1      7.22 0.578 7.25     5.86     8.58
 0       8.00 0.578 7.25     6.64     9.35
 22      6.90 0.577 7.18     5.55     8.26
 29      7.27 0.578 7.25     5.92     8.63

Results are averaged over the levels of: pastoreo 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate    SE  df t.ratio p.value
 (-1) - 0   -0.7779 0.283 180 -2.750  0.0330 
 (-1) - 22   0.3158 0.280 180  1.127  0.6732 
 (-1) - 29  -0.0523 0.283 180 -0.185  0.9977 
 0 - 22      1.0937 0.280 180  3.904  0.0008 
 0 - 29      0.7256 0.283 180  2.565  0.0537 
 22 - 29    -0.3681 0.280 180 -1.314  0.5552 

Results are averaged over the levels of: pastoreo 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean    SE   df lower.CL upper.CL
 -1      7.94 0.817 7.25     6.02     9.86
 0       8.73 0.817 7.25     6.81    10.65
 22      7.49 0.815 7.18     5.58     9.41
 29      7.96 0.817 7.25     6.04     9.87

pastoreo = No Browsing:
 meses emmean    SE   df lower.CL upper.CL
 -1      6.50 0.817 7.25     4.58     8.42
 0       7.27 0.817 7.25     5.35     9.19
 22      6.31 0.815 7.18     4.40     8.23
 29      6.59 0.817 7.25     4.67     8.51

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0   -0.7904 0.400 180 -1.976  0.2010 
 (-1) - 22   0.4423 0.396 180  1.117  0.6798 
 (-1) - 29  -0.0187 0.400 180 -0.047  1.0000 
 0 - 22      1.2328 0.396 180  3.112  0.0115 
 0 - 29      0.7717 0.400 180  1.929  0.2197 
 22 - 29    -0.4611 0.396 180 -1.164  0.6503 

pastoreo = No Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0   -0.7654 0.400 180 -1.913  0.2261 
 (-1) - 22   0.1892 0.396 180  0.478  0.9639 
 (-1) - 29  -0.0859 0.400 180 -0.215  0.9965 
 0 - 22      0.9546 0.396 180  2.410  0.0789 
 0 - 29      0.6795 0.400 180  1.699  0.3275 
 22 - 29    -0.2751 0.396 180 -0.694  0.8991 

Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 4 estimates 

Fe

fe_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: fe_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df den Df        F Pr(>F)    
pastoreo            1      6   0.4418 0.5310    
meses               3    180 101.4492 <2e-16 ***
pastoreo:meses      3    180   1.6605 0.1772    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean     SE  df asymp.LCL asymp.UCL
 Browsing     0.562 0.0564 Inf     0.452     0.673
 No Browsing  0.480 0.0602 Inf     0.362     0.598

Results are averaged over the levels of: meses 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate     SE  df z.ratio p.value
 Browsing - No Browsing   0.0827 0.0824 Inf 1.004   0.3152 

Results are averaged over the levels of: meses 
Note: contrasts are still on the inverse scale 
$`emmeans of meses`
 meses emmean     SE  df asymp.LCL asymp.UCL
 -1     0.566 0.0426 Inf     0.483     0.650
 0      0.583 0.0427 Inf     0.499     0.666
 22     0.538 0.0423 Inf     0.455     0.621
 29     0.397 0.0417 Inf     0.315     0.479

Results are averaged over the levels of: pastoreo 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate     SE  df z.ratio p.value
 (-1) - 0   -0.0164 0.0168 Inf -0.974  0.7645 
 (-1) - 22   0.0287 0.0159 Inf  1.801  0.2727 
 (-1) - 29   0.1694 0.0141 Inf 12.007  <.0001 
 0 - 22      0.0450 0.0162 Inf  2.783  0.0276 
 0 - 29      0.1858 0.0144 Inf 12.893  <.0001 
 22 - 29     0.1407 0.0133 Inf 10.551  <.0001 

Results are averaged over the levels of: pastoreo 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean     SE  df asymp.LCL asymp.UCL
 -1     0.618 0.0584 Inf     0.504     0.733
 0      0.629 0.0585 Inf     0.514     0.744
 22     0.562 0.0578 Inf     0.448     0.675
 29     0.440 0.0570 Inf     0.328     0.552

pastoreo = No Browsing:
 meses emmean     SE  df asymp.LCL asymp.UCL
 -1     0.514 0.0618 Inf     0.393     0.635
 0      0.536 0.0619 Inf     0.415     0.658
 22     0.514 0.0617 Inf     0.393     0.635
 29     0.354 0.0607 Inf     0.235     0.473

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2          estimate     SE  df z.ratio p.value
 (-1) - 0  -0.010670 0.0248 Inf -0.430  0.9733 
 (-1) - 22  0.056553 0.0230 Inf  2.459  0.0665 
 (-1) - 29  0.178094 0.0209 Inf  8.538  <.0001 
 0 - 22     0.067223 0.0233 Inf  2.890  0.0201 
 0 - 29     0.188764 0.0211 Inf  8.925  <.0001 
 22 - 29    0.121541 0.0190 Inf  6.405  <.0001 

pastoreo = No Browsing:
 2          estimate     SE  df z.ratio p.value
 (-1) - 0  -0.022056 0.0227 Inf -0.972  0.7656 
 (-1) - 22  0.000763 0.0220 Inf  0.035  1.0000 
 (-1) - 29  0.160696 0.0190 Inf  8.458  <.0001 
 0 - 22     0.022818 0.0225 Inf  1.015  0.7408 
 0 - 29     0.182751 0.0196 Inf  9.338  <.0001 
 22 - 29    0.159933 0.0188 Inf  8.529  <.0001 

Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 

K

k_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: k_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df den Df        F    Pr(>F)    
pastoreo            1   6.00   3.9693  0.093415 .  
meses               3 180.01 333.1065 < 2.2e-16 ***
pastoreo:meses      3 180.01   4.3516  0.005489 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean    SE  df asymp.LCL asymp.UCL
 Browsing      2.47 0.157 Inf      2.17      2.78
 No Browsing   1.68 0.159 Inf      1.37      1.99

Results are averaged over the levels of: meses 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate    SE  df z.ratio p.value
 Browsing - No Browsing    0.796 0.222 Inf 3.578   0.0003 

Results are averaged over the levels of: meses 
Note: contrasts are still on the inverse scale 
$`emmeans of meses`
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     2.393 0.138 Inf     2.122      2.66
 0      2.733 0.149 Inf     2.441      3.03
 22     2.195 0.131 Inf     1.937      2.45
 29     0.982 0.110 Inf     0.765      1.20

Results are averaged over the levels of: pastoreo 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate     SE  df z.ratio p.value
 (-1) - 0    -0.340 0.1407 Inf -2.420  0.0733 
 (-1) - 22    0.198 0.1214 Inf  1.632  0.3604 
 (-1) - 29    1.411 0.0976 Inf 14.464  <.0001 
 0 - 22       0.539 0.1337 Inf  4.028  0.0003 
 0 - 29       1.752 0.1125 Inf 15.569  <.0001 
 22 - 29      1.213 0.0872 Inf 13.919  <.0001 

Results are averaged over the levels of: pastoreo 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     2.908 0.208 Inf     2.500      3.32
 0      3.558 0.236 Inf     3.096      4.02
 22     2.378 0.186 Inf     2.013      2.74
 29     1.049 0.151 Inf     0.753      1.35

pastoreo = No Browsing:
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     1.878 0.181 Inf     1.522      2.23
 0      1.908 0.182 Inf     1.551      2.27
 22     2.011 0.184 Inf     1.650      2.37
 29     0.914 0.159 Inf     0.603      1.23

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate    SE  df z.ratio p.value
 (-1) - 0   -0.6499 0.243 Inf -2.669  0.0381 
 (-1) - 22   0.5300 0.196 Inf  2.706  0.0344 
 (-1) - 29   1.8593 0.162 Inf 11.483  <.0001 
 0 - 22      1.1799 0.225 Inf  5.248  <.0001 
 0 - 29      2.5092 0.196 Inf 12.800  <.0001 
 22 - 29     1.3293 0.132 Inf 10.078  <.0001 

pastoreo = No Browsing:
 2         estimate    SE  df z.ratio p.value
 (-1) - 0   -0.0309 0.141 Inf -0.219  0.9963 
 (-1) - 22  -0.1336 0.144 Inf -0.931  0.7884 
 (-1) - 29   0.9631 0.109 Inf  8.852  <.0001 
 0 - 22     -0.1027 0.145 Inf -0.709  0.8934 
 0 - 29      0.9941 0.110 Inf  9.007  <.0001 
 22 - 29     1.0968 0.114 Inf  9.633  <.0001 

Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 

Mg

mg_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mg_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df den Df      F  Pr(>F)  
pastoreo            1      6 0.8038 0.40448  
meses               3    180 3.1550 0.02614 *
pastoreo:meses      3    180 3.2034 0.02455 *
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean    SE  df asymp.LCL asymp.UCL
 Browsing     0.973 0.145 Inf     0.689      1.26
 No Browsing  0.766 0.155 Inf     0.463      1.07

Results are averaged over the levels of: meses 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate    SE  df z.ratio p.value
 Browsing - No Browsing    0.206 0.212 Inf 0.973   0.3307 

Results are averaged over the levels of: meses 
Note: contrasts are still on the inverse scale 
$`emmeans of meses`
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     0.913 0.110 Inf     0.698      1.13
 0      0.870 0.109 Inf     0.655      1.08
 22     0.900 0.109 Inf     0.685      1.11
 29     0.796 0.108 Inf     0.584      1.01

Results are averaged over the levels of: pastoreo 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate     SE  df z.ratio p.value
 (-1) - 0    0.0439 0.0442 Inf  0.993  0.7536 
 (-1) - 22   0.0139 0.0444 Inf  0.313  0.9894 
 (-1) - 29   0.1176 0.0415 Inf  2.831  0.0240 
 0 - 22     -0.0300 0.0432 Inf -0.696  0.8988 
 0 - 29      0.0737 0.0402 Inf  1.832  0.2582 
 22 - 29     0.1037 0.0404 Inf  2.569  0.0500 

Results are averaged over the levels of: pastoreo 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     1.029 0.152 Inf     0.732      1.33
 0      1.037 0.152 Inf     0.739      1.33
 22     0.986 0.151 Inf     0.691      1.28
 29     0.839 0.148 Inf     0.548      1.13

pastoreo = No Browsing:
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     0.797 0.158 Inf     0.487      1.11
 0      0.702 0.157 Inf     0.394      1.01
 22     0.813 0.159 Inf     0.502      1.12
 29     0.753 0.158 Inf     0.444      1.06

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate     SE  df z.ratio p.value
 (-1) - 0  -0.00738 0.0735 Inf -0.101  0.9996 
 (-1) - 22  0.04298 0.0702 Inf  0.612  0.9282 
 (-1) - 29  0.19094 0.0650 Inf  2.935  0.0175 
 0 - 22     0.05036 0.0705 Inf  0.714  0.8916 
 0 - 29     0.19833 0.0654 Inf  3.031  0.0130 
 22 - 29    0.14796 0.0616 Inf  2.401  0.0768 

pastoreo = No Browsing:
 2         estimate     SE  df z.ratio p.value
 (-1) - 0   0.09518 0.0492 Inf  1.934  0.2139 
 (-1) - 22 -0.01523 0.0543 Inf -0.280  0.9923 
 (-1) - 29  0.04420 0.0517 Inf  0.856  0.8276 
 0 - 22    -0.11041 0.0498 Inf -2.218  0.1183 
 0 - 29    -0.05098 0.0468 Inf -1.090  0.6959 
 22 - 29    0.05943 0.0522 Inf  1.139  0.6650 

Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 

C/N

c_n ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: c_n ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df      F Pr(>F)
pastoreo            1   6.0001 0.4794 0.5146
meses               3 179.0155 1.2883 0.2799
pastoreo:meses      3 179.0155 0.2166 0.8848

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean      SE  df asymp.LCL asymp.UCL
 Browsing    0.0280 0.00333 Inf    0.0214    0.0345
 No Browsing 0.0309 0.00380 Inf    0.0235    0.0384

Results are averaged over the levels of: meses 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate      SE  df z.ratio p.value
 Browsing - No Browsing -0.00297 0.00458 Inf -0.649  0.5167 

Results are averaged over the levels of: meses 
Note: contrasts are still on the inverse scale 
$`emmeans of meses`
 meses emmean      SE  df asymp.LCL asymp.UCL
 -1    0.0275 0.00292 Inf    0.0218    0.0333
 0     0.0299 0.00299 Inf    0.0240    0.0357
 22    0.0287 0.00299 Inf    0.0228    0.0345
 29    0.0316 0.00304 Inf    0.0257    0.0376

Results are averaged over the levels of: pastoreo 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate      SE  df z.ratio p.value
 (-1) - 0  -0.00234 0.00186 Inf -1.254  0.5921 
 (-1) - 22 -0.00111 0.00178 Inf -0.624  0.9245 
 (-1) - 29 -0.00410 0.00195 Inf -2.100  0.1530 
 0 - 22     0.00123 0.00189 Inf  0.647  0.9166 
 0 - 29    -0.00176 0.00204 Inf -0.863  0.8240 
 22 - 29   -0.00299 0.00198 Inf -1.509  0.4323 

Results are averaged over the levels of: pastoreo 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean      SE  df asymp.LCL asymp.UCL
 -1    0.0262 0.00331 Inf    0.0197    0.0327
 0     0.0287 0.00374 Inf    0.0214    0.0361
 22    0.0263 0.00376 Inf    0.0190    0.0337
 29    0.0305 0.00381 Inf    0.0230    0.0380

pastoreo = No Browsing:
 meses emmean      SE  df asymp.LCL asymp.UCL
 -1    0.0289 0.00416 Inf    0.0207    0.0370
 0     0.0310 0.00419 Inf    0.0228    0.0392
 22    0.0310 0.00417 Inf    0.0228    0.0392
 29    0.0328 0.00429 Inf    0.0244    0.0412

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2          estimate      SE  df z.ratio p.value
 (-1) - 0  -2.52e-03 0.00242 Inf -1.044  0.7237 
 (-1) - 22 -9.62e-05 0.00223 Inf -0.043  1.0000 
 (-1) - 29 -4.30e-03 0.00252 Inf -1.702  0.3225 
 0 - 22     2.43e-03 0.00247 Inf  0.984  0.7586 
 0 - 29    -1.77e-03 0.00271 Inf -0.655  0.9138 
 22 - 29   -4.20e-03 0.00257 Inf -1.634  0.3596 

pastoreo = No Browsing:
 2          estimate      SE  df z.ratio p.value
 (-1) - 0  -2.16e-03 0.00281 Inf -0.767  0.8695 
 (-1) - 22 -2.13e-03 0.00277 Inf -0.771  0.8678 
 (-1) - 29 -3.90e-03 0.00295 Inf -1.322  0.5485 
 0 - 22     2.54e-05 0.00288 Inf  0.009  1.0000 
 0 - 29    -1.75e-03 0.00305 Inf -0.572  0.9404 
 22 - 29   -1.77e-03 0.00301 Inf -0.589  0.9355 

Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 

MO

mo ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mo ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F   Pr(>F)    
pastoreo            1   5.9995  1.4822   0.2691    
meses               3 180.0402 15.1444 7.86e-09 ***
pastoreo:meses      3 180.0402  0.5464   0.6512    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean     SE  df asymp.LCL asymp.UCL
 Browsing     0.207 0.0179 Inf     0.172     0.242
 No Browsing  0.236 0.0184 Inf     0.200     0.272

Results are averaged over the levels of: meses 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate     SE  df z.ratio p.value
 Browsing - No Browsing   -0.029 0.0256 Inf -1.133  0.2573 

Results are averaged over the levels of: meses 
Note: contrasts are still on the inverse scale 
$`emmeans of meses`
 meses emmean     SE  df asymp.LCL asymp.UCL
 -1     0.192 0.0157 Inf     0.162     0.223
 0      0.171 0.0149 Inf     0.142     0.200
 22     0.227 0.0170 Inf     0.194     0.261
 29     0.296 0.0205 Inf     0.256     0.336

Results are averaged over the levels of: pastoreo 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate     SE  df z.ratio p.value
 (-1) - 0    0.0214 0.0147 Inf  1.459  0.4628 
 (-1) - 22  -0.0348 0.0170 Inf -2.052  0.1692 
 (-1) - 29  -0.1033 0.0204 Inf -5.061  <.0001 
 0 - 22     -0.0563 0.0161 Inf -3.489  0.0027 
 0 - 29     -0.1248 0.0197 Inf -6.327  <.0001 
 22 - 29    -0.0685 0.0215 Inf -3.194  0.0077 

Results are averaged over the levels of: pastoreo 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean     SE  df asymp.LCL asymp.UCL
 -1     0.170 0.0209 Inf     0.129     0.211
 0      0.165 0.0207 Inf     0.124     0.205
 22     0.215 0.0234 Inf     0.169     0.261
 29     0.279 0.0279 Inf     0.225     0.334

pastoreo = No Browsing:
 meses emmean     SE  df asymp.LCL asymp.UCL
 -1     0.215 0.0233 Inf     0.170     0.261
 0      0.177 0.0211 Inf     0.136     0.219
 22     0.240 0.0246 Inf     0.191     0.288
 29     0.312 0.0299 Inf     0.254     0.371

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate     SE  df z.ratio p.value
 (-1) - 0   0.00482 0.0193 Inf  0.250  0.9945 
 (-1) - 22 -0.04537 0.0222 Inf -2.046  0.1711 
 (-1) - 29 -0.10954 0.0268 Inf -4.081  0.0003 
 0 - 22    -0.05018 0.0219 Inf -2.289  0.1005 
 0 - 29    -0.11436 0.0266 Inf -4.293  0.0001 
 22 - 29   -0.06418 0.0288 Inf -2.229  0.1155 

pastoreo = No Browsing:
 2         estimate     SE  df z.ratio p.value
 (-1) - 0   0.03807 0.0222 Inf  1.714  0.3162 
 (-1) - 22 -0.02429 0.0257 Inf -0.945  0.7806 
 (-1) - 29 -0.09712 0.0308 Inf -3.156  0.0087 
 0 - 22    -0.06236 0.0237 Inf -2.635  0.0418 
 0 - 29    -0.13519 0.0291 Inf -4.648  <.0001 
 22 - 29   -0.07283 0.0318 Inf -2.290  0.1003 

Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 

pH Agua

p_h_agua_eez ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_agua_eez ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F    Pr(>F)    
pastoreo            1   5.9998  0.7555   0.41814    
meses               3 180.0226 18.9834 9.647e-11 ***
pastoreo:meses      3 180.0226  3.3264   0.02092 *  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean       SE  df asymp.LCL asymp.UCL
 Browsing     0.127 0.000532 Inf     0.126     0.128
 No Browsing  0.126 0.000532 Inf     0.125     0.127

Results are averaged over the levels of: meses 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate       SE  df z.ratio p.value
 Browsing - No Browsing  0.00055 0.000752 Inf 0.731   0.4646 

Results are averaged over the levels of: meses 
Note: contrasts are still on the inverse scale 
$`emmeans of meses`
 meses emmean       SE  df asymp.LCL asymp.UCL
 -1     0.125 0.000444 Inf     0.125     0.126
 0      0.126 0.000445 Inf     0.126     0.127
 22     0.126 0.000442 Inf     0.125     0.127
 29     0.128 0.000448 Inf     0.127     0.129

Results are averaged over the levels of: pastoreo 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1          estimate       SE  df z.ratio p.value
 (-1) - 0  -0.000918 0.000387 Inf -2.368  0.0833 
 (-1) - 22 -0.000682 0.000383 Inf -1.780  0.2829 
 (-1) - 29 -0.002900 0.000391 Inf -7.426  <.0001 
 0 - 22     0.000235 0.000385 Inf  0.611  0.9287 
 0 - 29    -0.001982 0.000392 Inf -5.055  <.0001 
 22 - 29   -0.002217 0.000388 Inf -5.714  <.0001 

Results are averaged over the levels of: pastoreo 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean       SE  df asymp.LCL asymp.UCL
 -1     0.125 0.000627 Inf     0.124     0.127
 0      0.126 0.000629 Inf     0.125     0.128
 22     0.126 0.000625 Inf     0.125     0.128
 29     0.129 0.000635 Inf     0.128     0.131

pastoreo = No Browsing:
 meses emmean       SE  df asymp.LCL asymp.UCL
 -1     0.126 0.000629 Inf     0.124     0.127
 0      0.126 0.000630 Inf     0.125     0.128
 22     0.126 0.000625 Inf     0.125     0.127
 29     0.127 0.000632 Inf     0.126     0.129

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2          estimate       SE  df z.ratio p.value
 (-1) - 0  -1.00e-03 0.000547 Inf -1.833  0.2576 
 (-1) - 22 -1.03e-03 0.000541 Inf -1.900  0.2279 
 (-1) - 29 -4.06e-03 0.000553 Inf -7.337  <.0001 
 0 - 22    -2.66e-05 0.000545 Inf -0.049  1.0000 
 0 - 29    -3.06e-03 0.000557 Inf -5.495  <.0001 
 22 - 29   -3.03e-03 0.000551 Inf -5.498  <.0001 

pastoreo = No Browsing:
 2          estimate       SE  df z.ratio p.value
 (-1) - 0  -8.33e-04 0.000548 Inf -1.519  0.4262 
 (-1) - 22 -3.36e-04 0.000542 Inf -0.620  0.9258 
 (-1) - 29 -1.74e-03 0.000550 Inf -3.160  0.0086 
 0 - 22     4.97e-04 0.000544 Inf  0.913  0.7977 
 0 - 29    -9.06e-04 0.000552 Inf -1.641  0.3556 
 22 - 29   -1.40e-03 0.000546 Inf -2.570  0.0499 

Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 

pH KCl

p_h_k_cl ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_k_cl ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F    Pr(>F)    
pastoreo            1   5.9999  0.0763   0.79162    
meses               3 180.0141 12.4914 1.855e-07 ***
pastoreo:meses      3 180.0141  2.3135   0.07756 .  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean       SE  df asymp.LCL asymp.UCL
 Browsing     0.134 0.000717 Inf     0.132     0.135
 No Browsing  0.134 0.000718 Inf     0.132     0.135

Results are averaged over the levels of: meses 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate      SE  df z.ratio p.value
 Browsing - No Browsing  0.00025 0.00101 Inf 0.246   0.8054 

Results are averaged over the levels of: meses 
Note: contrasts are still on the inverse scale 
$`emmeans of meses`
 meses emmean       SE  df asymp.LCL asymp.UCL
 -1     0.134 0.000569 Inf     0.133     0.135
 0      0.133 0.000567 Inf     0.131     0.134
 22     0.133 0.000565 Inf     0.132     0.134
 29     0.135 0.000570 Inf     0.134     0.136

Results are averaged over the levels of: pastoreo 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1          estimate       SE  df z.ratio p.value
 (-1) - 0   0.001468 0.000416 Inf  3.528  0.0024 
 (-1) - 22  0.000959 0.000413 Inf  2.322  0.0930 
 (-1) - 29 -0.000963 0.000420 Inf -2.293  0.0996 
 0 - 22    -0.000509 0.000411 Inf -1.239  0.6018 
 0 - 29    -0.002431 0.000418 Inf -5.818  <.0001 
 22 - 29   -0.001922 0.000415 Inf -4.634  <.0001 

Results are averaged over the levels of: pastoreo 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean       SE  df asymp.LCL asymp.UCL
 -1     0.134 0.000804 Inf     0.133     0.136
 0      0.132 0.000801 Inf     0.131     0.134
 22     0.133 0.000798 Inf     0.131     0.135
 29     0.136 0.000806 Inf     0.134     0.137

pastoreo = No Browsing:
 meses emmean       SE  df asymp.LCL asymp.UCL
 -1     0.134 0.000804 Inf     0.132     0.135
 0      0.133 0.000803 Inf     0.131     0.135
 22     0.133 0.000799 Inf     0.132     0.135
 29     0.134 0.000805 Inf     0.133     0.136

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2          estimate       SE  df z.ratio p.value
 (-1) - 0   0.002240 0.000587 Inf  3.813  0.0008 
 (-1) - 22  0.001336 0.000584 Inf  2.289  0.1007 
 (-1) - 29 -0.001260 0.000595 Inf -2.117  0.1477 
 0 - 22    -0.000904 0.000580 Inf -1.559  0.4022 
 0 - 29    -0.003500 0.000591 Inf -5.921  <.0001 
 22 - 29   -0.002596 0.000588 Inf -4.419  0.0001 

pastoreo = No Browsing:
 2          estimate       SE  df z.ratio p.value
 (-1) - 0   0.000696 0.000589 Inf  1.182  0.6384 
 (-1) - 22  0.000581 0.000583 Inf  0.997  0.7513 
 (-1) - 29 -0.000666 0.000592 Inf -1.125  0.6740 
 0 - 22    -0.000114 0.000582 Inf -0.196  0.9973 
 0 - 29    -0.001362 0.000590 Inf -2.306  0.0966 
 22 - 29   -0.001247 0.000585 Inf -2.132  0.1429 

Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 4 estimates 

NH4

  • No data
# A tibble: 2 x 3
# Groups:   meses, fecha [2]
  meses fecha          n
  <fct> <date>     <int>
1 -1    2018-12-11    48
2 0     2018-12-20    48

NO3

  • No data
# A tibble: 2 x 3
# Groups:   meses, fecha [2]
  meses fecha          n
  <fct> <date>     <int>
1 -1    2018-12-11    48
2 0     2018-12-20    47

P

p ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Data: df_model
               num Df   den Df       F    Pr(>F)    
pastoreo            1   5.9997  0.0217    0.8876    
meses               3 180.0317 10.0437 3.746e-06 ***
pastoreo:meses      3 180.0317  1.2069    0.3087    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean     SE  df asymp.LCL asymp.UCL
 Browsing      1.40 0.0771 Inf      1.25      1.56
 No Browsing   1.39 0.0781 Inf      1.24      1.54

Results are averaged over the levels of: meses 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate   SE  df z.ratio p.value
 Browsing - No Browsing   0.0136 0.11 Inf 0.124   0.9012 

Results are averaged over the levels of: meses 
Results are given on the log (not the response) scale. 
$`emmeans of meses`
 meses emmean     SE  df asymp.LCL asymp.UCL
 -1      1.34 0.0843 Inf     1.172      1.50
 0       1.64 0.0756 Inf     1.493      1.79
 22      1.51 0.0782 Inf     1.355      1.66
 29      1.10 0.0926 Inf     0.923      1.29

Results are averaged over the levels of: pastoreo 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate     SE  df z.ratio p.value
 (-1) - 0    -0.304 0.0967 Inf -3.145  0.0090 
 (-1) - 22   -0.171 0.0990 Inf -1.728  0.3093 
 (-1) - 29    0.233 0.1102 Inf  2.114  0.1486 
 0 - 22       0.133 0.0917 Inf  1.452  0.4671 
 0 - 29       0.537 0.1041 Inf  5.159  <.0001 
 22 - 29      0.404 0.1061 Inf  3.807  0.0008 

Results are averaged over the levels of: pastoreo 
Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     1.341 0.118 Inf     1.110      1.57
 0      1.588 0.109 Inf     1.375      1.80
 22     1.452 0.113 Inf     1.231      1.67
 29     1.237 0.123 Inf     0.996      1.48

pastoreo = No Browsing:
 meses emmean    SE  df asymp.LCL asymp.UCL
 -1     1.333 0.119 Inf     1.099      1.57
 0      1.694 0.105 Inf     1.489      1.90
 22     1.564 0.108 Inf     1.351      1.78
 29     0.971 0.137 Inf     0.702      1.24

Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate    SE  df z.ratio p.value
 (-1) - 0    -0.247 0.136 Inf -1.811  0.2680 
 (-1) - 22   -0.111 0.140 Inf -0.795  0.8569 
 (-1) - 29    0.104 0.146 Inf  0.711  0.8927 
 0 - 22       0.135 0.133 Inf  1.019  0.7384 
 0 - 29       0.351 0.142 Inf  2.480  0.0631 
 22 - 29      0.216 0.145 Inf  1.489  0.4442 

pastoreo = No Browsing:
 2         estimate    SE  df z.ratio p.value
 (-1) - 0    -0.361 0.135 Inf -2.671  0.0379 
 (-1) - 22   -0.231 0.138 Inf -1.668  0.3405 
 (-1) - 29    0.362 0.161 Inf  2.252  0.1096 
 0 - 22       0.131 0.126 Inf  1.038  0.7272 
 0 - 29       0.723 0.151 Inf  4.782  <.0001 
 22 - 29      0.592 0.154 Inf  3.851  0.0007 

Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 4 estimates 

N

n_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: n_percent
                 Chisq Df Pr(>Chisq)   
pastoreo        0.0288  1   0.865347   
meses          14.7673  3   0.002027 **
pastoreo:meses  0.4107  3   0.938028   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean     SE  df lower.CL upper.CL
 Browsing     -1.19 0.0710 183    -1.33    -1.05
 No Browsing  -1.20 0.0714 183    -1.35    -1.06

Results are averaged over the levels of: meses 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate  SE  df t.ratio p.value
 Browsing - No Browsing    0.018 0.1 183 0.180   0.8572 

Results are averaged over the levels of: meses 
Results are given on the log odds ratio (not the response) scale. 
$`emmeans of meses`
 meses emmean     SE  df lower.CL upper.CL
 -1    -1.288 0.0869 183    -1.46    -1.12
 0     -0.961 0.0816 183    -1.12    -0.80
 22    -1.352 0.0866 183    -1.52    -1.18
 29    -1.182 0.0859 183    -1.35    -1.01

Results are averaged over the levels of: pastoreo 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate    SE  df t.ratio p.value
 (-1) - 0   -0.3271 0.111 183 -2.960  0.0181 
 (-1) - 22   0.0643 0.114 183  0.564  0.9425 
 (-1) - 29  -0.1061 0.113 183 -0.935  0.7862 
 0 - 22      0.3914 0.110 183  3.549  0.0027 
 0 - 29      0.2210 0.110 183  2.012  0.1872 
 22 - 29    -0.1704 0.113 183 -1.504  0.4369 

Results are averaged over the levels of: pastoreo 
Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean    SE  df lower.CL upper.CL
 -1    -1.282 0.122 183    -1.52   -1.041
 0     -0.972 0.115 183    -1.20   -0.745
 22    -1.361 0.122 183    -1.60   -1.120
 29    -1.130 0.119 183    -1.36   -0.896

pastoreo = No Browsing:
 meses emmean    SE  df lower.CL upper.CL
 -1    -1.293 0.123 183    -1.54   -1.051
 0     -0.949 0.115 183    -1.18   -0.722
 22    -1.343 0.122 183    -1.58   -1.102
 29    -1.233 0.123 183    -1.48   -0.990

Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0   -0.3099 0.156 183 -1.983  0.1980 
 (-1) - 22   0.0789 0.161 183  0.490  0.9613 
 (-1) - 29  -0.1520 0.159 183 -0.958  0.7735 
 0 - 22      0.3888 0.156 183  2.490  0.0648 
 0 - 29      0.1579 0.154 183  1.028  0.7334 
 22 - 29    -0.2309 0.159 183 -1.456  0.4661 

pastoreo = No Browsing:
 2         estimate    SE  df t.ratio p.value
 (-1) - 0   -0.3443 0.156 183 -2.204  0.1261 
 (-1) - 22   0.0497 0.161 183  0.308  0.9898 
 (-1) - 29  -0.0601 0.162 183 -0.371  0.9826 
 0 - 22      0.3939 0.156 183  2.529  0.0588 
 0 - 29      0.2841 0.157 183  1.810  0.2721 
 22 - 29    -0.1098 0.162 183 -0.679  0.9049 

Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 4 estimates 

Na

na_percent ~ pastoreo * meses + (1 | pastoreo:geo_parcela_nombre)
Analysis of Deviance Table (Type II Wald chisquare tests)

Response: na_percent
                  Chisq Df Pr(>Chisq)    
pastoreo         5.6988  1  0.0169770 *  
meses          182.7172  3  < 2.2e-16 ***
pastoreo:meses  18.9523  3  0.0002797 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pastoreo`
 pastoreo    emmean     SE  df lower.CL upper.CL
 Browsing     -3.13 0.0901 184    -3.31    -2.95
 No Browsing  -2.77 0.0867 184    -2.94    -2.60

Results are averaged over the levels of: meses 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pastoreo`
 1                      estimate    SE  df t.ratio p.value
 Browsing - No Browsing   -0.361 0.125 184 -2.896  0.0042 

Results are averaged over the levels of: meses 
Results are given on the log odds ratio (not the response) scale. 
$`emmeans of meses`
 meses emmean     SE  df lower.CL upper.CL
 -1     -3.19 0.0831 184    -3.35    -3.02
 0      -3.29 0.0852 184    -3.45    -3.12
 22     -2.95 0.0774 184    -3.10    -2.80
 29     -2.38 0.0707 184    -2.52    -2.24

Results are averaged over the levels of: pastoreo 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses`
 1         estimate     SE  df t.ratio p.value
 (-1) - 0    0.0983 0.0882 184   1.114 0.6814 
 (-1) - 22  -0.2360 0.0810 184  -2.913 0.0208 
 (-1) - 29  -0.8061 0.0752 184 -10.724 <.0001 
 0 - 22     -0.3343 0.0832 184  -4.017 0.0005 
 0 - 29     -0.9044 0.0773 184 -11.698 <.0001 
 22 - 29    -0.5700 0.0689 184  -8.278 <.0001 

Results are averaged over the levels of: pastoreo 
Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 4 estimates 
$`emmeans of meses | pastoreo`
pastoreo = Browsing:
 meses emmean     SE  df lower.CL upper.CL
 -1     -3.46 0.1243 184    -3.70    -3.21
 0      -3.61 0.1289 184    -3.87    -3.36
 22     -3.02 0.1106 184    -3.23    -2.80
 29     -2.44 0.1006 184    -2.64    -2.24

pastoreo = No Browsing:
 meses emmean     SE  df lower.CL upper.CL
 -1     -2.92 0.1098 184    -3.13    -2.70
 0      -2.96 0.1106 184    -3.17    -2.74
 22     -2.89 0.1079 184    -3.10    -2.67
 29     -2.32 0.0991 184    -2.52    -2.13

Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of meses | pastoreo`
pastoreo = Browsing:
 2         estimate     SE  df t.ratio p.value
 (-1) - 0    0.1567 0.1390 184  1.127  0.6731 
 (-1) - 22  -0.4412 0.1225 184 -3.602  0.0023 
 (-1) - 29  -1.0210 0.1142 184 -8.938  <.0001 
 0 - 22     -0.5979 0.1275 184 -4.688  <.0001 
 0 - 29     -1.1776 0.1193 184 -9.875  <.0001 
 22 - 29    -0.5798 0.0996 184 -5.824  <.0001 

pastoreo = No Browsing:
 2         estimate     SE  df t.ratio p.value
 (-1) - 0    0.0399 0.1088 184  0.367  0.9831 
 (-1) - 22  -0.0309 0.1062 184 -0.291  0.9914 
 (-1) - 29  -0.5912 0.0976 184 -6.058  <.0001 
 0 - 22     -0.0708 0.1070 184 -0.662  0.9112 
 0 - 29     -0.6311 0.0981 184 -6.431  <.0001 
 22 - 29    -0.5603 0.0951 184 -5.893  <.0001 

Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 4 estimates 

General Overview

Mean + SE table

Characteristic Browsing No Browsing
-1, N = 241 0, N = 241 22, N = 251 29, N = 241 -1, N = 241 0, N = 241 22, N = 251 29, N = 241
humedad 12.45 (0.66) 11.39 (0.79) 10.07 (0.54) 11.91 (0.60) 11.99 (0.76) 12.86 (0.86) 6.94 (0.47) 10.67 (0.39)
fe_percent 1.76 (0.12) 1.72 (0.07) 1.98 (0.09) 2.61 (0.15) 1.97 (0.08) 1.89 (0.04) 1.97 (0.04) 2.89 (0.08)
k_percent 0.35 (0.03) 0.29 (0.02) 0.43 (0.02) 1.06 (0.05) 0.55 (0.04) 0.54 (0.03) 0.51 (0.03) 1.18 (0.04)
mg_percent 1.11 (0.09) 1.10 (0.07) 1.19 (0.08) 1.44 (0.10) 1.46 (0.15) 1.74 (0.21) 1.42 (0.12) 1.58 (0.16)
na_percent 0.03 (0.00) 0.03 (0.00) 0.05 (0.00) 0.08 (0.01) 0.05 (0.01) 0.05 (0.00) 0.05 (0.01) 0.09 (0.00)
n_percent 0.21 (0.02) 0.28 (0.03) 0.19 (0.01) 0.24 (0.02) 0.22 (0.02) 0.30 (0.04) 0.20 (0.02) 0.22 (0.02)
c_percent 7.94 (0.46) 8.73 (0.35) 7.46 (0.38) 7.96 (0.45) 6.50 (0.33) 7.27 (0.42) 6.31 (0.36) 6.59 (0.38)
c_n 42.29 (3.66) 37.98 (4.00) 41.90 (3.24) 35.46 (2.37) 37.15 (4.17) 34.30 (5.37) 34.69 (2.23) 32.21 (2.46)
cic 17.54 (0.49) 15.58 (0.47) 15.56 (0.35) 19.38 (0.30) 15.67 (0.38) 15.08 (0.36) 14.44 (0.42) 18.29 (0.50)
p 3.84 (0.24) 4.91 (0.35) 4.28 (0.47) 3.46 (0.38) 3.83 (0.23) 5.50 (0.33) 4.80 (0.80) 2.67 (0.25)
mo 5.97 (0.44) 6.15 (0.46) 4.68 (0.28) 3.60 (0.24) 4.77 (0.39) 5.86 (0.65) 4.24 (0.42) 3.25 (0.32)
p_h_k_cl 7.44 (0.03) 7.57 (0.03) 7.51 (0.02) 7.37 (0.03) 7.48 (0.02) 7.52 (0.03) 7.51 (0.03) 7.44 (0.03)
p_h_agua_eez 7.98 (0.03) 7.92 (0.03) 7.91 (0.03) 7.73 (0.03) 7.96 (0.03) 7.91 (0.02) 7.94 (0.02) 7.85 (0.03)

1 Mean (std.error)

Figures

Anovas table

zona
fecha
zona X fecha
Variables F p F p F p
c_n 0.479 0.515 1.288 0.280 0.217 0.885
cic 2.600 0.158 42.238 0.000 1.101 0.350
c_percent 1.521 0.264 5.410 0.001 0.101 0.959
k_percent 3.969 0.093 333.106 0.000 4.352 0.005
humedad 0.388 0.556 20.562 0.000 6.173 0.001
fe_percent 0.442 0.531 101.449 0.000 1.661 0.177
mg_percent 0.804 0.404 3.155 0.026 3.203 0.025
mo 1.482 0.269 15.144 0.000 0.546 0.651
p 0.022 0.888 10.044 0.000 1.207 0.309
p_h_agua_eez 0.756 0.418 18.983 0.000 3.326 0.021
p_h_k_cl 0.076 0.792 12.491 0.000 2.313 0.078
n_percent 0.029 0.865 14.767 0.002 0.411 0.938
na_percent 5.699 0.017 182.717 0.000 18.952 0.000

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] kableExtra_1.3.1   gtsummary_1.4.2    plotrix_3.8-1      car_3.0-10        
 [5] carData_3.0-4      glmmADMB_0.8.3.3   glmmTMB_1.0.2.1    DHARMa_0.3.3.0    
 [9] afex_0.28-1        performance_0.7.2  multcomp_1.4-16    TH.data_1.0-10    
[13] mvtnorm_1.1-1      emmeans_1.5.4      lmerTest_3.1-3     lme4_1.1-27.1     
[17] Matrix_1.3-2       fitdistrplus_1.1-3 survival_3.2-7     MASS_7.3-53       
[21] ggpubr_0.4.0       janitor_2.1.0      here_1.0.1         forcats_0.5.1     
[25] stringr_1.4.0      dplyr_1.0.6        purrr_0.3.4        readr_1.4.0       
[29] tidyr_1.1.3        tibble_3.1.2       ggplot2_3.3.5      tidyverse_1.3.1   
[33] rmdformats_1.0.1   knitr_1.31         workflowr_1.6.2   

loaded via a namespace (and not attached):
  [1] minqa_1.2.4         colorspace_2.0-0    ggsignif_0.6.0     
  [4] ellipsis_0.3.2      rio_0.5.16          rprojroot_2.0.2    
  [7] estimability_1.3    snakecase_0.11.0    fs_1.5.0           
 [10] rstudioapi_0.13     farver_2.0.3        fansi_0.4.2        
 [13] lubridate_1.7.10    xml2_1.3.2          codetools_0.2-18   
 [16] splines_4.0.2       jsonlite_1.7.2      nloptr_1.2.2.2     
 [19] gt_0.3.0            pbkrtest_0.5-0.1    broom_0.7.9        
 [22] dbplyr_2.1.1        compiler_4.0.2      httr_1.4.2         
 [25] backports_1.2.1     assertthat_0.2.1    fastmap_1.1.0      
 [28] cli_2.5.0           formatR_1.8         later_1.1.0.1      
 [31] htmltools_0.5.2     tools_4.0.2         coda_0.19-4        
 [34] gtable_0.3.0        glue_1.4.2          reshape2_1.4.4     
 [37] Rcpp_1.0.7          cellranger_1.1.0    jquerylib_0.1.3    
 [40] vctrs_0.3.8         nlme_3.1-152        broom.helpers_1.3.0
 [43] iterators_1.0.13    insight_0.14.4      xfun_0.23          
 [46] openxlsx_4.2.3      rvest_1.0.0         lifecycle_1.0.0    
 [49] rstatix_0.6.0       zoo_1.8-8           scales_1.1.1       
 [52] hms_1.0.0           promises_1.2.0.1    parallel_4.0.2     
 [55] sandwich_3.0-0      TMB_1.7.19          yaml_2.2.1         
 [58] curl_4.3            sass_0.3.1          stringi_1.7.4      
 [61] highr_0.8           foreach_1.5.1       checkmate_2.0.0    
 [64] boot_1.3-26         zip_2.1.1           R2admb_0.7.16.2    
 [67] commonmark_1.7      rlang_0.4.10        pkgconfig_2.0.3    
 [70] evaluate_0.14       lattice_0.20-41     labeling_0.4.2     
 [73] tidyselect_1.1.1    plyr_1.8.6          magrittr_2.0.1     
 [76] bookdown_0.21.6     R6_2.5.0            generics_0.1.0     
 [79] DBI_1.1.1           pillar_1.6.1        haven_2.3.1        
 [82] whisker_0.4         foreign_0.8-81      withr_2.4.1        
 [85] abind_1.4-5         modelr_0.1.8        crayon_1.4.1       
 [88] utf8_1.1.4          rmarkdown_2.8       grid_4.0.2         
 [91] readxl_1.3.1        data.table_1.14.0   git2r_0.28.0       
 [94] webshot_0.5.2       reprex_2.0.0        digest_0.6.27      
 [97] xtable_1.8-4        httpuv_1.5.5        numDeriv_2016.8-1.1
[100] munsell_0.5.0      
 [ reached getOption("max.print") -- omitted 2 entries ]