Browsings effects on Soil propierties after fire
ajpelu
2021-09-08
Last updated: 2022-02-17
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Knit directory: soil_alcontar/
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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 24Modelize
- 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 ' ' 1Post-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 ' ' 1Post-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 ' ' 1Post-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 ' ' 1Post-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 ' ' 1Post-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 ' ' 1Post-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 4.2407 0.08514 .
meses 3 179.0338 45.2654 < 2e-16 ***
pastoreo:meses 3 179.0338 0.8210 0.48386
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Post-hoc
$`emmeans of pastoreo`
pastoreo emmean SE df asymp.LCL asymp.UCL
Browsing 0.0800 0.00450 Inf 0.0712 0.0888
No Browsing 0.0916 0.00441 Inf 0.0830 0.1003
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 -0.0117 0.00629 Inf -1.856 0.0635
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 0.0666 0.00353 Inf 0.0597 0.0735
0 0.0797 0.00379 Inf 0.0723 0.0871
22 0.0769 0.00370 Inf 0.0696 0.0841
29 0.1201 0.00478 Inf 0.1107 0.1295
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 -0.01313 0.00327 Inf -4.013 0.0003
(-1) - 22 -0.01032 0.00317 Inf -3.254 0.0063
(-1) - 29 -0.05351 0.00439 Inf -12.194 <.0001
0 - 22 0.00281 0.00346 Inf 0.812 0.8486
0 - 29 -0.04038 0.00460 Inf -8.772 <.0001
22 - 29 -0.04319 0.00453 Inf -9.532 <.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 0.0609 0.00491 Inf 0.0513 0.0705
0 0.0747 0.00528 Inf 0.0643 0.0850
22 0.0708 0.00514 Inf 0.0607 0.0809
29 0.1136 0.00653 Inf 0.1008 0.1264
pastoreo = No Browsing:
meses emmean SE df asymp.LCL asymp.UCL
-1 0.0723 0.00504 Inf 0.0624 0.0821
0 0.0847 0.00542 Inf 0.0741 0.0954
22 0.0830 0.00531 Inf 0.0726 0.0934
29 0.1266 0.00698 Inf 0.1129 0.1403
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 -0.01379 0.00425 Inf -3.244 0.0065
(-1) - 22 -0.00991 0.00407 Inf -2.436 0.0705
(-1) - 29 -0.05269 0.00574 Inf -9.181 <.0001
0 - 22 0.00387 0.00451 Inf 0.859 0.8260
0 - 29 -0.03890 0.00606 Inf -6.422 <.0001
22 - 29 -0.04278 0.00593 Inf -7.210 <.0001
pastoreo = No Browsing:
2 estimate SE df z.ratio p.value
(-1) - 0 -0.01247 0.00498 Inf -2.507 0.0589
(-1) - 22 -0.01072 0.00486 Inf -2.204 0.1221
(-1) - 29 -0.05432 0.00664 Inf -8.183 <.0001
0 - 22 0.00175 0.00526 Inf 0.334 0.9872
0 - 29 -0.04185 0.00693 Inf -6.038 <.0001
22 - 29 -0.04360 0.00685 Inf -6.365 <.0001
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 ' ' 1Post-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 ' ' 1Post-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 ' ' 1Post-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 44NO3
- 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 47P
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 ' ' 1Post-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 ' ' 1Post-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 ' ' 1Post-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 | 16.77 (0.87) | 13.59 (0.74) | 14.34 (0.58) | 8.87 (0.35) | 13.96 (0.89) | 11.88 (0.72) | 12.12 (0.38) | 7.94 (0.29) |
| 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)
|
||||||||
Anovas table
| Variables | F | p | F | p | F | p |
|---|---|---|---|---|---|---|
| c_n | 4.241 | 0.085 | 45.265 | 0.000 | 0.821 | 0.484 |
| 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_0.8.0 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_1.7.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 plyr_1.8.6
[4] TMB_1.7.19 splines_4.0.2 digest_0.6.27
[7] foreach_1.5.1 htmltools_0.5.2 fansi_0.4.2
[10] magrittr_2.0.1 checkmate_2.0.0 openxlsx_4.2.3
[13] modelr_0.1.8 sandwich_3.0-0 colorspace_2.0-2
[16] rvest_1.0.0 haven_2.3.1 xfun_0.23
[19] callr_3.7.0 crayon_1.4.1 jsonlite_1.7.2
[22] zoo_1.8-8 iterators_1.0.13 glue_1.4.2
[25] gtable_0.3.0 R2admb_0.7.16.2 webshot_0.5.2
[28] abind_1.4-5 scales_1.1.1.9000 DBI_1.1.1
[31] rstatix_0.6.0 Rcpp_1.0.7 viridisLite_0.4.0
[34] xtable_1.8-4 foreign_0.8-81 httr_1.4.2
[37] ellipsis_0.3.2 pkgconfig_2.0.3 farver_2.1.0
[40] sass_0.3.1 dbplyr_2.1.1 utf8_1.1.4
[43] tidyselect_1.1.1 labeling_0.4.2 rlang_0.4.12
[46] reshape2_1.4.4 later_1.1.0.1 munsell_0.5.0
[49] cellranger_1.1.0 tools_4.0.2 cli_2.5.0
[52] generics_0.1.0 broom_0.7.9 evaluate_0.14
[55] fastmap_1.1.0 yaml_2.2.1 processx_3.5.1
[58] fs_1.5.0 zip_2.1.1 nlme_3.1-152
[61] whisker_0.4 formatR_1.8 xml2_1.3.2
[64] compiler_4.0.2 pbkrtest_0.5-0.1 rstudioapi_0.13
[67] curl_4.3 ggsignif_0.6.0 gt_0.3.0
[70] reprex_2.0.0 broom.helpers_1.4.0 bslib_0.2.4
[73] stringi_1.7.4 highr_0.8 ps_1.5.0
[76] lattice_0.20-41 commonmark_1.7 nloptr_1.2.2.2
[79] vctrs_0.3.8 pillar_1.6.1 lifecycle_1.0.1
[82] jquerylib_0.1.3 estimability_1.3 data.table_1.14.0
[85] insight_0.14.4 httpuv_1.5.5 R6_2.5.1
[88] bookdown_0.21.6 promises_1.2.0.1 rio_0.5.16
[91] codetools_0.2-18 boot_1.3-26 assertthat_0.2.1
[94] rprojroot_2.0.2 withr_2.4.1 parallel_4.0.2
[97] hms_1.0.0 grid_4.0.2 coda_0.19-4
[100] minqa_1.2.4
[ reached getOption("max.print") -- omitted 6 entries ]