Farm characterization for Florenceās PhD project
Farm typology based on multivariate analysis
Deo
2024-10-08
A farm typology is proposed based on a Factor Analysis for Mixed Data (Data) run for the selected variables
Load the ācleanā dataset, rename and select variables
# set working directory ## Change accordingly
setwd('~/Harare 2023/diversification_rotation/Florence')
# load package
require(tidyverse)## Loading required package: tidyverse## āā Attaching core tidyverse packages āāāāāāāāāāāāāāāāāāāāāāāā tidyverse 2.0.0 āā
## ā dplyr 1.1.4 ā readr 2.1.5
## ā forcats 1.0.0 ā stringr 1.5.1
## ā ggplot2 3.5.1 ā tibble 3.2.1
## ā lubridate 1.9.3 ā tidyr 1.3.1
## ā purrr 1.0.2
## āā Conflicts āāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāāā tidyverse_conflicts() āā
## ā dplyr::filter() masks stats::filter()
## ā dplyr::lag() masks stats::lag()
## ā¹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsrm(list = ls())
clean_survey <- readxl::read_excel('2024-10-07.CLEANED DATA FARMER SEGMENTATION.xlsx',
sheet = 'Selected_data',
skip = 2,
trim_ws = T,
.name_repair = 'universal')## New names:
## ā¢ `ID NO` -> `ID.NO`
## ā¢ `AvgMZ_yield(kgs)` -> `AvgMZ_yield.kgs.`
## ā¢ `highestEdu_qualification(HHH)` -> `highestEdu_qualification.HHH.`
## ā¢ `Access_to_loan_during_2022/2023_season` ->
## `Access_to_loan_during_2022.2023_season`
## ā¢ `Do_you_practice__irrigation_farming?` ->
## `Do_you_practice__irrigation_farming.`
## ā¢ `Manure_application_in_Maize_field_during_2022/23_season` ->
## `Manure_application_in_Maize_field_during_2022.23_season`
## ā¢ `Do_you_pracatice_intercroping?` -> `Do_you_pracatice_intercroping.`
## ā¢ `Do_you_practice_cover_croping?` -> `Do_you_practice_cover_croping.`
## ā¢ `Do_you_practice_integrated_pest_management?` ->
## `Do_you_practice_integrated_pest_management.`
## ā¢ `Do_you_have_agroforestry_trees_in_your_farm?` ->
## `Do_you_have_agroforestry_trees_in_your_farm.`
## ā¢ `Do_you_have_fruit_trees?` -> `Do_you_have_fruit_trees.`
## ā¢ `Do_you_own_any_vegetable_garden?` -> `Do_you_own_any_vegetable_garden.`# List of variables
names(clean_survey)## [1] "ID.NO"
## [2] "Household_Head_Name"
## [3] "Age"
## [4] "HHSize"
## [5] "HH_income_"
## [6] "IncomeFarming"
## [7] "PercFarmIncome"
## [8] "Ncattle"
## [9] "Ngoats"
## [10] "Npigs"
## [11] "Nchickens"
## [12] "TLU"
## [13] "TLU_density"
## [14] "TotalLand"
## [15] "AvgMZ_yield.kgs."
## [16] "PercentageMaizeArea"
## [17] "PercentageLegumeArea"
## [18] "highestEdu_qualification.HHH."
## [19] "Access_to_loan_during_2022.2023_season"
## [20] "Do_you_practice__irrigation_farming."
## [21] "Manure_application_in_Maize_field_during_2022.23_season"
## [22] "Count_intens_options"
## [23] "Do_you_pracatice_intercroping."
## [24] "Do_you_practice_cover_croping."
## [25] "Do_you_practice_integrated_pest_management."
## [26] "Do_you_have_agroforestry_trees_in_your_farm."
## [27] "Do_you_have_fruit_trees."
## [28] "Do_you_own_any_vegetable_garden."
## [29] "Count_sust_practices"# Remove unnecessary columns and anonymize respondents
descrip_survey <- clean_survey |>
select(!c(Household_Head_Name ))
famd_df <- descrip_survey |>
select(!ID.NO) |>
na.omit()
pca_df <- famd_df |>
mutate(highestEdu_qualification.HHH. = case_when(
highestEdu_qualification.HHH. == 'never_attended' ~ 0,
highestEdu_qualification.HHH. == 'primary_school' ~ 1,
highestEdu_qualification.HHH. == 'secondary' ~ 2,
highestEdu_qualification.HHH. == 'tertiary' ~ 3,
.default = NA) ,
across(where(is.character),
function(x) ifelse(x == 'no', 0, ifelse(x == 'yes', 1, NA))))Matrix of scatterplots to see correlations for pairs of variables
Then, compare PCA for 3 subsets of the dataset, followed by hierarchical clustering
# Correlation matrix to visualize data
P00 <- GGally::ggpairs(famd_df)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2png('correlation_matrix.png', height = 25, width = 20, units = 'cm', res = 1000)
P00## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
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## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.dev.off()## png
## 2# Mock PCA on reduced data
res_pca <- FactoMineR::PCA(pca_df)## Warning: ggrepel: 6 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
# choose how many dimensions to incorporate in the typology
# the more dimensions, the more variability is captured, and the more noise is introduced
res_pca$eig## eigenvalue percentage of variance cumulative percentage of variance
## comp 1 5.307824e+00 1.965861e+01 19.65861
## comp 2 3.525534e+00 1.305753e+01 32.71614
## comp 3 3.310232e+00 1.226012e+01 44.97626
## comp 4 2.454007e+00 9.088916e+00 54.06518
## comp 5 1.910601e+00 7.076300e+00 61.14148
## comp 6 1.478511e+00 5.475966e+00 66.61744
## comp 7 1.416861e+00 5.247633e+00 71.86508
## comp 8 1.218511e+00 4.513003e+00 76.37808
## comp 9 1.069621e+00 3.961559e+00 80.33964
## comp 10 9.209886e-01 3.411069e+00 83.75071
## comp 11 8.211959e-01 3.041466e+00 86.79217
## comp 12 7.946434e-01 2.943124e+00 89.73530
## comp 13 5.232384e-01 1.937920e+00 91.67322
## comp 14 4.603624e-01 1.705046e+00 93.37826
## comp 15 4.066040e-01 1.505941e+00 94.88420
## comp 16 3.396770e-01 1.258063e+00 96.14227
## comp 17 3.226925e-01 1.195158e+00 97.33743
## comp 18 2.441237e-01 9.041619e-01 98.24159
## comp 19 1.791790e-01 6.636260e-01 98.90521
## comp 20 1.367356e-01 5.064283e-01 99.41164
## comp 21 1.008972e-01 3.736935e-01 99.78534
## comp 22 4.947718e-02 1.832488e-01 99.96858
## comp 23 6.353104e-03 2.353001e-02 99.99211
## comp 24 2.129264e-03 7.886165e-03 100.00000
## comp 25 5.661846e-31 2.096980e-30 100.00000
## comp 26 4.167562e-32 1.543541e-31 100.00000
## comp 27 4.050572e-32 1.500212e-31 100.00000# what variables are best represented by each dimension
factoextra::fviz_screeplot(res_pca, addlabels = T)
factoextra::fviz_pca_biplot(res_pca)
factoextra::fviz_pca_biplot(res_pca, axes = c(1, 3))
factoextra::fviz_pca_biplot(res_pca, axes = c(2, 3))
# choose appropriate palette for visualization
pal <- colorRampPalette(c('lightskyblue1', 'darkblue'))
# choose the most relevant variables to describe each dimension
res_pca$var$contrib[, 1:5]## Dim.1
## Age 0.636413327
## HHSize 3.005494980
## HH_income_ 9.127135054
## IncomeFarming 6.614174872
## PercFarmIncome 4.215889713
## Ncattle 4.340788365
## Ngoats 9.072873294
## Npigs 4.492103006
## Nchickens 3.475703708
## TLU 5.855721615
## TLU_density 4.773864188
## TotalLand 7.159026283
## AvgMZ_yield.kgs. 6.544345650
## PercentageMaizeArea 0.004803414
## PercentageLegumeArea 2.704109890
## highestEdu_qualification.HHH. 1.368361439
## Access_to_loan_during_2022.2023_season 0.937202535
## Do_you_practice__irrigation_farming. 2.109796369
## Manure_application_in_Maize_field_during_2022.23_season 2.220595889
## Count_intens_options 4.129428385
## Do_you_pracatice_intercroping. 2.582495671
## Do_you_practice_cover_croping. 2.727410880
## Do_you_practice_integrated_pest_management. 0.657415740
## Do_you_have_agroforestry_trees_in_your_farm. 0.337437216
## Do_you_have_fruit_trees. 0.060754505
## Do_you_own_any_vegetable_garden. 5.651780742
## Count_sust_practices 5.194873271
## Dim.2
## Age 1.101350347
## HHSize 2.224709379
## HH_income_ 8.380002845
## IncomeFarming 9.013985832
## PercFarmIncome 0.147844784
## Ncattle 11.457501746
## Ngoats 0.624240800
## Npigs 3.116445507
## Nchickens 2.287464593
## TLU 10.155375784
## TLU_density 12.406849598
## TotalLand 2.780094671
## AvgMZ_yield.kgs. 5.949048907
## PercentageMaizeArea 0.002028804
## PercentageLegumeArea 3.856739714
## highestEdu_qualification.HHH. 0.019618448
## Access_to_loan_during_2022.2023_season 8.276905145
## Do_you_practice__irrigation_farming. 0.136290182
## Manure_application_in_Maize_field_during_2022.23_season 0.464358337
## Count_intens_options 4.021048469
## Do_you_pracatice_intercroping. 0.253430397
## Do_you_practice_cover_croping. 1.646961282
## Do_you_practice_integrated_pest_management. 8.534389886
## Do_you_have_agroforestry_trees_in_your_farm. 1.088776681
## Do_you_have_fruit_trees. 1.450702089
## Do_you_own_any_vegetable_garden. 0.559343029
## Count_sust_practices 0.044492743
## Dim.3
## Age 0.34141706
## HHSize 1.00358649
## HH_income_ 1.48323460
## IncomeFarming 2.06738008
## PercFarmIncome 0.31890960
## Ncattle 8.14324882
## Ngoats 0.84125096
## Npigs 3.72297769
## Nchickens 0.42471859
## TLU 8.28333557
## TLU_density 7.82699693
## TotalLand 0.59430118
## AvgMZ_yield.kgs. 0.25528239
## PercentageMaizeArea 0.69905413
## PercentageLegumeArea 1.37291791
## highestEdu_qualification.HHH. 0.99453994
## Access_to_loan_during_2022.2023_season 2.25608242
## Do_you_practice__irrigation_farming. 13.09310635
## Manure_application_in_Maize_field_during_2022.23_season 0.45567080
## Count_intens_options 9.35929247
## Do_you_pracatice_intercroping. 0.74182293
## Do_you_practice_cover_croping. 0.46867385
## Do_you_practice_integrated_pest_management. 0.03629532
## Do_you_have_agroforestry_trees_in_your_farm. 8.05538977
## Do_you_have_fruit_trees. 12.89476253
## Do_you_own_any_vegetable_garden. 4.24556307
## Count_sust_practices 10.02018855
## Dim.4
## Age 21.218442760
## HHSize 5.632510850
## HH_income_ 0.226182275
## IncomeFarming 0.113540389
## PercFarmIncome 0.417078821
## Ncattle 0.015504875
## Ngoats 1.973253491
## Npigs 0.824745322
## Nchickens 1.379008781
## TLU 0.002922498
## TLU_density 0.001871272
## TotalLand 0.290945406
## AvgMZ_yield.kgs. 0.153457448
## PercentageMaizeArea 0.359423421
## PercentageLegumeArea 0.020366465
## highestEdu_qualification.HHH. 11.421245297
## Access_to_loan_during_2022.2023_season 1.376721657
## Do_you_practice__irrigation_farming. 1.588834664
## Manure_application_in_Maize_field_during_2022.23_season 14.962930693
## Count_intens_options 4.196151075
## Do_you_pracatice_intercroping. 0.410711661
## Do_you_practice_cover_croping. 11.336469697
## Do_you_practice_integrated_pest_management. 6.512336423
## Do_you_have_agroforestry_trees_in_your_farm. 5.035758987
## Do_you_have_fruit_trees. 0.002022253
## Do_you_own_any_vegetable_garden. 0.987627611
## Count_sust_practices 9.539935909
## Dim.5
## Age 0.002002792
## HHSize 0.013557173
## HH_income_ 0.153990927
## IncomeFarming 0.475631845
## PercFarmIncome 12.386628345
## Ncattle 0.448875215
## Ngoats 3.603933963
## Npigs 0.016460584
## Nchickens 0.178737714
## TLU 0.606343303
## TLU_density 0.476549762
## TotalLand 15.123732032
## AvgMZ_yield.kgs. 0.607718623
## PercentageMaizeArea 17.831746179
## PercentageLegumeArea 0.006862907
## highestEdu_qualification.HHH. 8.072203134
## Access_to_loan_during_2022.2023_season 11.730042022
## Do_you_practice__irrigation_farming. 1.142806731
## Manure_application_in_Maize_field_during_2022.23_season 1.266197893
## Count_intens_options 2.998927905
## Do_you_pracatice_intercroping. 2.119276149
## Do_you_practice_cover_croping. 2.956567168
## Do_you_practice_integrated_pest_management. 0.028614861
## Do_you_have_agroforestry_trees_in_your_farm. 0.937223950
## Do_you_have_fruit_trees. 8.761102756
## Do_you_own_any_vegetable_garden. 3.318581129
## Count_sust_practices 4.735684939for(i in list(c(1, 2), c(1, 3), c(1, 4), c(1, 5))){
for(j in names(pca_df)){
nb_col <- length(unique(pca_df[[j]]))
print(factoextra::fviz_ellipses(res_pca,
habillage = j,
axes = i,
geom = 'text',
addEllipses = T,
palette = pal(nb_col)
)
)
}
}## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
# Hierarchical clustering on the first 5 dimensions that capture 51% of total variability
eg_hc <- res_pca$ind$coord
dist_matrix <- dist(eg_hc)
hc <- hclust(dist_matrix, method = 'ward.D') # could be 'complete' or 'average'
plot(hc, main = 'Subset 1 - Dendogram on the first 5 components', xlab = '', sub = '', cex = 0.6)
# decide on the number of clusters to make: 2, 3, 4 or more
typo_1 <- pca_df
for(cl in 4:2){
clusters <- cutree(hc, k = cl)
typo_1$farm_type <- paste0('farm_', clusters)
print(paste0('================ Number of farm types = ', cl, ' ================'))
for(i in names(pca_df)){
res_anova <- lm(typo_1[[i]] ~ typo_1[['farm_type']])
print(paste0('--------------------', i, '----------------'))
print(anova(res_anova))
print('-----------')
print(emmeans::emmeans(res_anova,
pairwise ~ farm_type,
type = 'response',
adjust = 'bonferroni'))
}
}## [1] "================ Number of farm types = 4 ================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 75.13 25.043 0.4845 0.6949
## Residuals 40 2067.42 51.685
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 46.4 2.17 40 42.0 50.7
## farm_2 43.7 1.50 40 40.7 46.7
## farm_3 45.3 2.40 40 40.5 50.2
## farm_4 49.0 7.19 40 34.5 63.5
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.67 2.64 40 1.012 1.0000
## farm_1 - farm_3 1.03 3.23 40 0.319 1.0000
## farm_1 - farm_4 -2.64 7.51 40 -0.351 1.0000
## farm_2 - farm_3 -1.64 2.83 40 -0.579 1.0000
## farm_2 - farm_4 -5.30 7.34 40 -0.722 1.0000
## farm_3 - farm_4 -3.67 7.58 40 -0.484 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 42.823 14.2742 3.5088 0.02378 *
## Residuals 40 162.723 4.0681
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 7.73 0.608 40 6.50 8.96
## farm_2 5.43 0.421 40 4.58 6.28
## farm_3 6.89 0.672 40 5.53 8.25
## farm_4 6.00 2.017 40 1.92 10.08
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.292 0.739 40 3.101 0.0212
## farm_1 - farm_3 0.838 0.907 40 0.925 1.0000
## farm_1 - farm_4 1.727 2.107 40 0.820 1.0000
## farm_2 - farm_3 -1.454 0.793 40 -1.834 0.4449
## farm_2 - farm_4 -0.565 2.060 40 -0.274 1.0000
## farm_3 - farm_4 0.889 2.126 40 0.418 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 3.5571e+13 1.1857e+13 11.058 2.02e-05 ***
## Residuals 40 4.2891e+13 1.0723e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1515000 312218 40 883984 2146016
## farm_2 609696 215919 40 173308 1046083
## farm_3 2936667 345170 40 2239053 3634281
## farm_4 1500000 1035509 40 -592842 3592842
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 905304 379606 40 2.385 0.1315
## farm_1 - farm_3 -1421667 465427 40 -3.055 0.0240
## farm_1 - farm_4 15000 1081554 40 0.014 1.0000
## farm_2 - farm_3 -2326971 407140 40 -5.715 <.0001
## farm_2 - farm_4 -890304 1057781 40 -0.842 1.0000
## farm_3 - farm_4 1436667 1091523 40 1.316 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 2.0626e+13 6.8753e+12 10.554 3.031e-05 ***
## Residuals 40 2.6058e+13 6.5144e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1159091 243356 40 667250 1650932
## farm_2 551957 168296 40 211817 892096
## farm_3 2333333 269040 40 1789583 2877084
## farm_4 1000000 807120 40 -631251 2631251
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 607134 295881 40 2.052 0.2806
## farm_1 - farm_3 -1174242 362774 40 -3.237 0.0146
## farm_1 - farm_4 159091 843010 40 0.189 1.0000
## farm_2 - farm_3 -1781377 317342 40 -5.613 <.0001
## farm_2 - farm_4 -448044 824480 40 -0.543 1.0000
## farm_3 - farm_4 1333333 850780 40 1.567 0.7497
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 0.14711 0.049037 1.6659 0.1897
## Residuals 40 1.17745 0.029436
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.792 0.0517 40 0.688 0.897
## farm_2 0.902 0.0358 40 0.830 0.975
## farm_3 0.811 0.0572 40 0.695 0.927
## farm_4 0.667 0.1716 40 0.320 1.013
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.1102 0.0629 40 -1.752 0.5248
## farm_1 - farm_3 -0.0188 0.0771 40 -0.244 1.0000
## farm_1 - farm_4 0.1256 0.1792 40 0.701 1.0000
## farm_2 - farm_3 0.0913 0.0675 40 1.354 1.0000
## farm_2 - farm_4 0.2357 0.1753 40 1.345 1.0000
## farm_3 - farm_4 0.1444 0.1809 40 0.798 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 872.50 290.832 644.56 < 2.2e-16 ***
## Residuals 40 18.05 0.451
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.000 0.203 40 -0.40933 0.409
## farm_2 0.087 0.140 40 -0.19612 0.370
## farm_3 0.444 0.224 40 -0.00809 0.897
## farm_4 30.000 0.672 40 28.64240 31.358
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.087 0.246 40 -0.353 1.0000
## farm_1 - farm_3 -0.444 0.302 40 -1.472 0.8929
## farm_1 - farm_4 -30.000 0.702 40 -42.760 <.0001
## farm_2 - farm_3 -0.357 0.264 40 -1.354 1.0000
## farm_2 - farm_4 -29.913 0.686 40 -43.594 <.0001
## farm_3 - farm_4 -29.556 0.708 40 -41.742 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 126.16 42.054 6.5309 0.001063 **
## Residuals 40 257.57 6.439
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.091 0.765 40 1.54 4.64
## farm_2 0.739 0.529 40 -0.33 1.81
## farm_3 4.444 0.846 40 2.73 6.15
## farm_4 7.000 2.538 40 1.87 12.13
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.35 0.930 40 2.528 0.0931
## farm_1 - farm_3 -1.35 1.141 40 -1.187 1.0000
## farm_1 - farm_4 -3.91 2.650 40 -1.475 0.8884
## farm_2 - farm_3 -3.71 0.998 40 -3.714 0.0037
## farm_2 - farm_4 -6.26 2.592 40 -2.415 0.1223
## farm_3 - farm_4 -2.56 2.675 40 -0.955 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 69.806 23.269 7.0554 0.0006455 ***
## Residuals 40 131.921 3.298
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.273 0.548 40 0.166 2.38
## farm_2 0.478 0.379 40 -0.287 1.24
## farm_3 2.333 0.605 40 1.110 3.56
## farm_4 8.000 1.816 40 4.330 11.67
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.794 0.666 40 1.193 1.0000
## farm_1 - farm_3 -1.061 0.816 40 -1.299 1.0000
## farm_1 - farm_4 -6.727 1.897 40 -3.547 0.0061
## farm_2 - farm_3 -1.855 0.714 40 -2.598 0.0784
## farm_2 - farm_4 -7.522 1.855 40 -4.055 0.0014
## farm_3 - farm_4 -5.667 1.914 40 -2.960 0.0309
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 1083 361.00 12.175 8.44e-06 ***
## Residuals 40 1186 29.65
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 12.27 1.64 40 8.955 15.59
## farm_2 2.91 1.14 40 0.618 5.21
## farm_3 13.67 1.82 40 9.998 17.34
## farm_4 5.00 5.45 40 -6.005 16.01
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 9.36 2.00 40 4.689 0.0002
## farm_1 - farm_3 -1.39 2.45 40 -0.570 1.0000
## farm_1 - farm_4 7.27 5.69 40 1.279 1.0000
## farm_2 - farm_3 -10.75 2.14 40 -5.023 0.0001
## farm_2 - farm_4 -2.09 5.56 40 -0.375 1.0000
## farm_3 - farm_4 8.67 5.74 40 1.510 0.8335
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 497.20 165.734 411.15 < 2.2e-16 ***
## Residuals 40 16.12 0.403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.623 0.191 40 0.2358 1.010
## farm_2 0.236 0.132 40 -0.0319 0.503
## farm_3 1.242 0.212 40 0.8145 1.670
## farm_4 22.950 0.635 40 21.6668 24.233
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.387 0.233 40 1.663 0.6247
## farm_1 - farm_3 -0.619 0.285 40 -2.171 0.2156
## farm_1 - farm_4 -22.327 0.663 40 -33.669 <.0001
## farm_2 - farm_3 -1.007 0.250 40 -4.032 0.0014
## farm_2 - farm_4 -22.714 0.649 40 -35.023 <.0001
## farm_3 - farm_4 -21.708 0.669 40 -32.436 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 13.6639 4.5546 362.11 < 2.2e-16 ***
## Residuals 40 0.5031 0.0126
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0982 0.0338 40 0.0299 0.167
## farm_2 0.0650 0.0234 40 0.0177 0.112
## farm_3 0.1756 0.0374 40 0.1000 0.251
## farm_4 3.8250 0.1122 40 3.5983 4.052
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0333 0.0411 40 0.809 1.0000
## farm_1 - farm_3 -0.0774 0.0504 40 -1.535 0.7966
## farm_1 - farm_4 -3.7268 0.1171 40 -31.815 <.0001
## farm_2 - farm_3 -0.1106 0.0441 40 -2.508 0.0977
## farm_2 - farm_4 -3.7600 0.1146 40 -32.820 <.0001
## farm_3 - farm_4 -3.6494 0.1182 40 -30.870 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 164.33 54.775 4.7267 0.006472 **
## Residuals 40 463.54 11.589
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 8.41 1.03 40 6.33 10.48
## farm_2 3.95 0.71 40 2.51 5.38
## farm_3 6.89 1.13 40 4.60 9.18
## farm_4 6.00 3.40 40 -0.88 12.88
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 4.463 1.25 40 3.577 0.0056
## farm_1 - farm_3 1.520 1.53 40 0.994 1.0000
## farm_1 - farm_4 2.409 3.56 40 0.678 1.0000
## farm_2 - farm_3 -2.943 1.34 40 -2.199 0.2023
## farm_2 - farm_4 -2.054 3.48 40 -0.591 1.0000
## farm_3 - farm_4 0.889 3.59 40 0.248 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 51359965 17119988 4.7387 0.006392 **
## Residuals 40 144513349 3612834
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2993 573 40 1835 4151
## farm_2 1526 396 40 725 2327
## farm_3 4222 634 40 2942 5503
## farm_4 2000 1901 40 -1842 5842
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1467 697 40 2.106 0.2492
## farm_1 - farm_3 -1229 854 40 -1.439 0.9482
## farm_1 - farm_4 993 1985 40 0.500 1.0000
## farm_2 - farm_3 -2696 747 40 -3.608 0.0051
## farm_2 - farm_4 -474 1942 40 -0.244 1.0000
## farm_3 - farm_4 2222 2004 40 1.109 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 0.03002 0.010006 0.4775 0.6997
## Residuals 40 0.83817 0.020954
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.391 0.0436 40 0.303 0.479
## farm_2 0.443 0.0302 40 0.382 0.504
## farm_3 0.454 0.0483 40 0.357 0.552
## farm_4 0.500 0.1448 40 0.207 0.793
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0516 0.0531 40 -0.973 1.0000
## farm_1 - farm_3 -0.0631 0.0651 40 -0.970 1.0000
## farm_1 - farm_4 -0.1089 0.1512 40 -0.720 1.0000
## farm_2 - farm_3 -0.0115 0.0569 40 -0.202 1.0000
## farm_2 - farm_4 -0.0572 0.1479 40 -0.387 1.0000
## farm_3 - farm_4 -0.0458 0.1526 40 -0.300 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 0.17434 0.058113 2.3665 0.08523 .
## Residuals 40 0.98226 0.024556
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.393 0.0472 40 0.2979 0.489
## farm_2 0.465 0.0327 40 0.3987 0.531
## farm_3 0.306 0.0522 40 0.2008 0.412
## farm_4 0.333 0.1567 40 0.0166 0.650
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0713 0.0574 40 -1.241 1.0000
## farm_1 - farm_3 0.0871 0.0704 40 1.236 1.0000
## farm_1 - farm_4 0.0601 0.1637 40 0.367 1.0000
## farm_2 - farm_3 0.1584 0.0616 40 2.570 0.0840
## farm_2 - farm_4 0.1314 0.1601 40 0.821 1.0000
## farm_3 - farm_4 -0.0270 0.1652 40 -0.163 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 0.4705 0.15682 0.4324 0.7309
## Residuals 40 14.5068 0.36267
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.36 0.182 40 0.997 1.73
## farm_2 1.48 0.126 40 1.224 1.73
## farm_3 1.56 0.201 40 1.150 1.96
## farm_4 2.00 0.602 40 0.783 3.22
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.1146 0.221 40 -0.519 1.0000
## farm_1 - farm_3 -0.1919 0.271 40 -0.709 1.0000
## farm_1 - farm_4 -0.6364 0.629 40 -1.012 1.0000
## farm_2 - farm_3 -0.0773 0.237 40 -0.326 1.0000
## farm_2 - farm_4 -0.5217 0.615 40 -0.848 1.0000
## farm_3 - farm_4 -0.4444 0.635 40 -0.700 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 1.3123 0.43742 2.3596 0.0859 .
## Residuals 40 7.4150 0.18538
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.364 0.1298 40 0.101 0.626
## farm_2 0.304 0.0898 40 0.123 0.486
## farm_3 0.000 0.1435 40 -0.290 0.290
## farm_4 1.000 0.4306 40 0.130 1.870
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0593 0.158 40 0.376 1.0000
## farm_1 - farm_3 0.3636 0.194 40 1.879 0.4052
## farm_1 - farm_4 -0.6364 0.450 40 -1.415 0.9887
## farm_2 - farm_3 0.3043 0.169 40 1.798 0.4785
## farm_2 - farm_4 -0.6957 0.440 40 -1.582 0.7296
## farm_3 - farm_4 -1.0000 0.454 40 -2.203 0.2003
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 3.4481 1.14936 7.1407 0.0005958 ***
## Residuals 40 6.4383 0.16096
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.818 0.1210 40 0.5737 1.063
## farm_2 0.217 0.0837 40 0.0483 0.386
## farm_3 0.111 0.1337 40 -0.1592 0.381
## farm_4 0.000 0.4012 40 -0.8108 0.811
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.601 0.147 40 4.085 0.0012
## farm_1 - farm_3 0.707 0.180 40 3.921 0.0020
## farm_1 - farm_4 0.818 0.419 40 1.953 0.3474
## farm_2 - farm_3 0.106 0.158 40 0.674 1.0000
## farm_2 - farm_4 0.217 0.410 40 0.530 1.0000
## farm_3 - farm_4 0.111 0.423 40 0.263 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 1.4387 0.47958 2.632 0.0631 .
## Residuals 40 7.2885 0.18221
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.818 0.129 40 0.558 1.078
## farm_2 0.565 0.089 40 0.385 0.745
## farm_3 1.000 0.142 40 0.712 1.288
## farm_4 1.000 0.427 40 0.137 1.863
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.253 0.156 40 1.617 0.6830
## farm_1 - farm_3 -0.182 0.192 40 -0.948 1.0000
## farm_1 - farm_4 -0.182 0.446 40 -0.408 1.0000
## farm_2 - farm_3 -0.435 0.168 40 -2.591 0.0798
## farm_2 - farm_4 -0.435 0.436 40 -0.997 1.0000
## farm_3 - farm_4 0.000 0.450 40 0.000 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 7.1714 2.39046 3.5792 0.02202 *
## Residuals 40 26.7150 0.66787
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2.00 0.246 40 1.502 2.50
## farm_2 1.09 0.170 40 0.743 1.43
## farm_3 1.11 0.272 40 0.561 1.66
## farm_4 2.00 0.817 40 0.348 3.65
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.9130 0.300 40 3.048 0.0244
## farm_1 - farm_3 0.8889 0.367 40 2.420 0.1210
## farm_1 - farm_4 0.0000 0.854 40 0.000 1.0000
## farm_2 - farm_3 -0.0242 0.321 40 -0.075 1.0000
## farm_2 - farm_4 -0.9130 0.835 40 -1.094 1.0000
## farm_3 - farm_4 -0.8889 0.861 40 -1.032 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 0.9738 0.32459 1.7844 0.1656
## Residuals 40 7.2762 0.18191
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.909 0.1286 40 0.649 1.169
## farm_2 0.609 0.0889 40 0.429 0.788
## farm_3 0.889 0.1422 40 0.602 1.176
## farm_4 1.000 0.4265 40 0.138 1.862
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.3004 0.156 40 1.921 0.3711
## farm_1 - farm_3 0.0202 0.192 40 0.105 1.0000
## farm_1 - farm_4 -0.0909 0.445 40 -0.204 1.0000
## farm_2 - farm_3 -0.2802 0.168 40 -1.671 0.6153
## farm_2 - farm_4 -0.3913 0.436 40 -0.898 1.0000
## farm_3 - farm_4 -0.1111 0.450 40 -0.247 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 1.6957 0.56522 3.7484 0.01832 *
## Residuals 40 6.0316 0.15079
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.455 0.117 40 0.2179 0.691
## farm_2 0.174 0.081 40 0.0103 0.338
## farm_3 0.000 0.129 40 -0.2616 0.262
## farm_4 1.000 0.388 40 0.2152 1.785
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.281 0.142 40 1.971 0.3337
## farm_1 - farm_3 0.455 0.175 40 2.604 0.0772
## farm_1 - farm_4 -0.545 0.406 40 -1.345 1.0000
## farm_2 - farm_3 0.174 0.153 40 1.139 1.0000
## farm_2 - farm_4 -0.826 0.397 40 -2.083 0.2624
## farm_3 - farm_4 -1.000 0.409 40 -2.443 0.1144
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 1.0953 0.36510 1.6072 0.2028
## Residuals 40 9.0865 0.22716
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.636 0.1437 40 0.346 0.927
## farm_2 0.565 0.0994 40 0.364 0.766
## farm_3 0.889 0.1589 40 0.568 1.210
## farm_4 0.000 0.4766 40 -0.963 0.963
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0711 0.175 40 0.407 1.0000
## farm_1 - farm_3 -0.2525 0.214 40 -1.179 1.0000
## farm_1 - farm_4 0.6364 0.498 40 1.278 1.0000
## farm_2 - farm_3 -0.3237 0.187 40 -1.727 0.5511
## farm_2 - farm_4 0.5652 0.487 40 1.161 1.0000
## farm_3 - farm_4 0.8889 0.502 40 1.769 0.5068
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 1.5652 0.52174 2.9139 0.04596 *
## Residuals 40 7.1621 0.17905
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.545 0.1276 40 0.2876 0.803
## farm_2 0.261 0.0882 40 0.0825 0.439
## farm_3 0.000 0.1410 40 -0.2851 0.285
## farm_4 0.000 0.4231 40 -0.8552 0.855
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.285 0.155 40 1.835 0.4441
## farm_1 - farm_3 0.545 0.190 40 2.868 0.0394
## farm_1 - farm_4 0.545 0.442 40 1.234 1.0000
## farm_2 - farm_3 0.261 0.166 40 1.568 0.7486
## farm_2 - farm_4 0.261 0.432 40 0.604 1.0000
## farm_3 - farm_4 0.000 0.446 40 0.000 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 2.7826 0.92754 5.0142 0.004806 **
## Residuals 40 7.3992 0.18498
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.727 0.1297 40 0.465 0.989
## farm_2 0.348 0.0897 40 0.167 0.529
## farm_3 0.000 0.1434 40 -0.290 0.290
## farm_4 0.000 0.4301 40 -0.869 0.869
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.379 0.158 40 2.407 0.1249
## farm_1 - farm_3 0.727 0.193 40 3.762 0.0032
## farm_1 - farm_4 0.727 0.449 40 1.619 0.6799
## farm_2 - farm_3 0.348 0.169 40 2.057 0.2776
## farm_2 - farm_4 0.348 0.439 40 0.792 1.0000
## farm_3 - farm_4 0.000 0.453 40 0.000 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 3.4797 1.15990 8.1691 0.0002322 ***
## Residuals 40 5.6794 0.14199
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.727 0.1136 40 0.4977 0.957
## farm_2 0.130 0.0786 40 -0.0284 0.289
## farm_3 0.111 0.1256 40 -0.1427 0.365
## farm_4 1.000 0.3768 40 0.2384 1.762
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.5968 0.138 40 4.321 0.0006
## farm_1 - farm_3 0.6162 0.169 40 3.638 0.0047
## farm_1 - farm_4 -0.2727 0.394 40 -0.693 1.0000
## farm_2 - farm_3 0.0193 0.148 40 0.130 1.0000
## farm_2 - farm_4 -0.8696 0.385 40 -2.259 0.1764
## farm_3 - farm_4 -0.8889 0.397 40 -2.238 0.1852
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 3 32.194 10.7314 7.5686 0.0004005 ***
## Residuals 40 56.715 1.4179
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 4.00 0.359 40 3.274 4.73
## farm_2 2.09 0.248 40 1.585 2.59
## farm_3 1.89 0.397 40 1.087 2.69
## farm_4 3.00 1.191 40 0.593 5.41
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.913 0.437 40 4.383 0.0005
## farm_1 - farm_3 2.111 0.535 40 3.945 0.0019
## farm_1 - farm_4 1.000 1.244 40 0.804 1.0000
## farm_2 - farm_3 0.198 0.468 40 0.423 1.0000
## farm_2 - farm_4 -0.913 1.216 40 -0.751 1.0000
## farm_3 - farm_4 -1.111 1.255 40 -0.885 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "================ Number of farm types = 3 ================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 69.88 34.938 0.6911 0.5068
## Residuals 41 2072.67 50.553
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 45.9 1.59 41 42.7 49.1
## farm_2 43.7 1.48 41 40.7 46.7
## farm_3 49.0 7.11 41 34.6 63.4
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.2 2.17 41 1.014 0.9495
## farm_1 - farm_3 -3.1 7.29 41 -0.425 1.0000
## farm_2 - farm_3 -5.3 7.26 41 -0.730 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 39.343 19.6716 4.8527 0.01284 *
## Residuals 41 166.202 4.0537
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 7.35 0.45 41 6.44 8.26
## farm_2 5.43 0.42 41 4.59 6.28
## farm_3 6.00 2.01 41 1.93 10.07
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.915 0.616 41 3.111 0.0102
## farm_1 - farm_3 1.350 2.063 41 0.654 1.0000
## farm_2 - farm_3 -0.565 2.057 41 -0.275 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 2.5566e+13 1.2783e+13 9.9083 0.0003088 ***
## Residuals 41 5.2896e+13 1.2901e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2154750 253982 41 1641822 2667678
## farm_2 609696 236840 41 131388 1088003
## farm_3 1500000 1135844 41 -793883 3793883
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1545054 347275 41 4.449 0.0002
## farm_1 - farm_3 654750 1163894 41 0.563 1.0000
## farm_2 - farm_3 -890304 1160274 41 -0.767 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 1.3800e+13 6.9002e+12 8.6035 0.0007587 ***
## Residuals 41 3.2883e+13 8.0202e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1687500 200253 41 1283081 2091919
## farm_2 551957 186737 41 174834 929079
## farm_3 1000000 895558 41 -808617 2808617
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1135544 273810 41 4.147 0.0005
## farm_1 - farm_3 687500 917674 41 0.749 1.0000
## farm_2 - farm_3 -448044 914820 41 -0.490 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.14536 0.072678 2.5269 0.09228 .
## Residuals 41 1.17921 0.028761
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.801 0.0379 41 0.724 0.877
## farm_2 0.902 0.0354 41 0.831 0.974
## farm_3 0.667 0.1696 41 0.324 1.009
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.102 0.0519 41 -1.961 0.1699
## farm_1 - farm_3 0.134 0.1738 41 0.771 1.0000
## farm_2 - farm_3 0.236 0.1732 41 1.361 0.5431
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 871.52 435.76 939.03 < 2.2e-16 ***
## Residuals 41 19.03 0.46
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.200 0.152 41 -0.108 0.508
## farm_2 0.087 0.142 41 -0.200 0.374
## farm_3 30.000 0.681 41 28.624 31.376
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.113 0.208 41 0.543 1.0000
## farm_1 - farm_3 -29.800 0.698 41 -42.691 <.0001
## farm_2 - farm_3 -29.913 0.696 41 -42.987 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 117.09 58.546 9.0026 0.0005739 ***
## Residuals 41 266.63 6.503
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.700 0.570 41 2.548 4.85
## farm_2 0.739 0.532 41 -0.335 1.81
## farm_3 7.000 2.550 41 1.850 12.15
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.96 0.78 41 3.797 0.0014
## farm_1 - farm_3 -3.30 2.61 41 -1.263 0.6413
## farm_2 - farm_3 -6.26 2.61 41 -2.403 0.0626
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 64.238 32.119 9.5781 0.0003862 ***
## Residuals 41 137.489 3.353
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.750 0.409 41 0.923 2.58
## farm_2 0.478 0.382 41 -0.293 1.25
## farm_3 8.000 1.831 41 4.302 11.70
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.27 0.56 41 2.271 0.0853
## farm_1 - farm_3 -6.25 1.88 41 -3.331 0.0055
## farm_2 - farm_3 -7.52 1.87 41 -4.021 0.0007
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 1073.4 536.69 18.404 1.977e-06 ***
## Residuals 41 1195.6 29.16
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 12.90 1.21 41 10.461 15.34
## farm_2 2.91 1.13 41 0.639 5.19
## farm_3 5.00 5.40 41 -5.906 15.91
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 9.99 1.65 41 6.049 <.0001
## farm_1 - farm_3 7.90 5.53 41 1.428 0.4829
## farm_2 - farm_3 -2.09 5.52 41 -0.378 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 495.30 247.65 563.36 < 2.2e-16 ***
## Residuals 41 18.02 0.44
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.901 0.148 41 0.6021 1.201
## farm_2 0.236 0.138 41 -0.0435 0.515
## farm_3 22.950 0.663 41 21.6110 24.289
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.666 0.203 41 3.285 0.0063
## farm_1 - farm_3 -22.049 0.679 41 -32.453 <.0001
## farm_2 - farm_3 -22.714 0.677 41 -33.537 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 13.6343 6.8171 524.65 < 2.2e-16 ***
## Residuals 41 0.5327 0.0130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.133 0.0255 41 0.0816 0.185
## farm_2 0.065 0.0238 41 0.0170 0.113
## farm_3 3.825 0.1140 41 3.5948 4.055
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0681 0.0349 41 1.953 0.1730
## farm_1 - farm_3 -3.6919 0.1168 41 -31.608 <.0001
## farm_2 - farm_3 -3.7600 0.1164 41 -32.291 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 152.89 76.443 6.5985 0.003278 **
## Residuals 41 474.98 11.585
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 7.72 0.761 41 6.188 9.26
## farm_2 3.95 0.710 41 2.512 5.38
## farm_3 6.00 3.404 41 -0.874 12.87
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 3.78 1.04 41 3.632 0.0023
## farm_1 - farm_3 1.73 3.49 41 0.495 1.0000
## farm_2 - farm_3 -2.05 3.48 41 -0.591 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 43882790 21941395 5.9188 0.005518 **
## Residuals 41 151990524 3707086
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3546 431 41 2677 4416
## farm_2 1526 401 41 715 2337
## farm_3 2000 1925 41 -1888 5888
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2020 589 41 3.432 0.0041
## farm_1 - farm_3 1546 1973 41 0.784 1.0000
## farm_2 - farm_3 -474 1967 41 -0.241 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.01030 0.0051496 0.2461 0.783
## Residuals 41 0.85789 0.0209242
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.420 0.0323 41 0.354 0.485
## farm_2 0.443 0.0302 41 0.382 0.504
## farm_3 0.500 0.1447 41 0.208 0.792
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0232 0.0442 41 -0.525 1.0000
## farm_1 - farm_3 -0.0805 0.1482 41 -0.543 1.0000
## farm_2 - farm_3 -0.0572 0.1478 41 -0.387 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.13682 0.068412 2.7505 0.0757 .
## Residuals 41 1.01977 0.024872
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.354 0.0353 41 0.2830 0.425
## farm_2 0.465 0.0329 41 0.3983 0.531
## farm_3 0.333 0.1577 41 0.0148 0.652
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.1105 0.0482 41 -2.291 0.0815
## farm_1 - farm_3 0.0209 0.1616 41 0.129 1.0000
## farm_2 - farm_3 0.1314 0.1611 41 0.816 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.2881 0.14407 0.4021 0.6715
## Residuals 41 14.6891 0.35827
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.45 0.134 41 1.180 1.72
## farm_2 1.48 0.125 41 1.226 1.73
## farm_3 2.00 0.599 41 0.791 3.21
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0283 0.183 41 -0.154 1.0000
## farm_1 - farm_3 -0.5500 0.613 41 -0.897 1.0000
## farm_2 - farm_3 -0.5217 0.611 41 -0.853 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.6577 0.32885 1.6708 0.2006
## Residuals 41 8.0696 0.19682
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.200 0.0992 41 -0.000341 0.400
## farm_2 0.304 0.0925 41 0.117528 0.491
## farm_3 1.000 0.4436 41 0.104046 1.896
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.104 0.136 41 -0.769 1.0000
## farm_1 - farm_3 -0.800 0.455 41 -1.760 0.2577
## farm_2 - farm_3 -0.696 0.453 41 -1.535 0.3974
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.9733 0.48666 2.2386 0.1195
## Residuals 41 8.9130 0.21739
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.500 0.1043 41 0.2894 0.711
## farm_2 0.217 0.0972 41 0.0211 0.414
## farm_3 0.000 0.4663 41 -0.9416 0.942
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.283 0.143 41 1.982 0.1625
## farm_1 - farm_3 0.500 0.478 41 1.047 0.9043
## farm_2 - farm_3 0.217 0.476 41 0.456 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 1.2751 0.63755 3.5076 0.03925 *
## Residuals 41 7.4522 0.18176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.900 0.0953 41 0.707 1.093
## farm_2 0.565 0.0889 41 0.386 0.745
## farm_3 1.000 0.4263 41 0.139 1.861
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.335 0.130 41 2.568 0.0419
## farm_1 - farm_3 -0.100 0.437 41 -0.229 1.0000
## farm_2 - farm_3 -0.435 0.436 41 -0.998 0.9719
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 3.2603 1.63014 2.1823 0.1257
## Residuals 41 30.6261 0.74698
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.60 0.193 41 1.210 1.99
## farm_2 1.09 0.180 41 0.723 1.45
## farm_3 2.00 0.864 41 0.255 3.75
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.513 0.264 41 1.942 0.1773
## farm_1 - farm_3 -0.400 0.886 41 -0.452 1.0000
## farm_2 - farm_3 -0.913 0.883 41 -1.034 0.9213
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.9717 0.48587 2.737 0.07661 .
## Residuals 41 7.2783 0.17752
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.900 0.0942 41 0.710 1.090
## farm_2 0.609 0.0879 41 0.431 0.786
## farm_3 1.000 0.4213 41 0.149 1.851
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.291 0.129 41 2.261 0.0873
## farm_1 - farm_3 -0.100 0.432 41 -0.232 1.0000
## farm_2 - farm_3 -0.391 0.430 41 -0.909 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.6729 0.33646 1.9555 0.1545
## Residuals 41 7.0543 0.17206
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.250 0.0928 41 0.06268 0.437
## farm_2 0.174 0.0865 41 -0.00076 0.349
## farm_3 1.000 0.4148 41 0.16230 1.838
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0761 0.127 41 0.600 1.0000
## farm_1 - farm_3 -0.7500 0.425 41 -1.765 0.2553
## farm_2 - farm_3 -0.8261 0.424 41 -1.950 0.1743
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.7796 0.38982 1.6999 0.1953
## Residuals 41 9.4022 0.22932
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.750 0.1071 41 0.534 0.966
## farm_2 0.565 0.0999 41 0.364 0.767
## farm_3 0.000 0.4789 41 -0.967 0.967
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.185 0.146 41 1.262 0.6422
## farm_1 - farm_3 0.750 0.491 41 1.528 0.4023
## farm_2 - farm_3 0.565 0.489 41 1.155 0.7638
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.0925 0.046245 0.2196 0.8038
## Residuals 41 8.6348 0.210604
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.300 0.1026 41 0.0928 0.507
## farm_2 0.261 0.0957 41 0.0676 0.454
## farm_3 0.000 0.4589 41 -0.9268 0.927
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0391 0.140 41 0.279 1.0000
## farm_1 - farm_3 0.3000 0.470 41 0.638 1.0000
## farm_2 - farm_3 0.2609 0.469 41 0.556 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 0.1644 0.082213 0.3365 0.7162
## Residuals 41 10.0174 0.244327
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.400 0.111 41 0.177 0.623
## farm_2 0.348 0.103 41 0.140 0.556
## farm_3 0.000 0.494 41 -0.998 0.998
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0522 0.151 41 0.345 1.0000
## farm_1 - farm_3 0.4000 0.507 41 0.790 1.0000
## farm_2 - farm_3 0.3478 0.505 41 0.689 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 1.6004 0.80020 4.3404 0.01951 *
## Residuals 41 7.5587 0.18436
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.45 0.0960 41 0.2561 0.644
## farm_2 0.13 0.0895 41 -0.0504 0.311
## farm_3 1.00 0.4294 41 0.1329 1.867
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.32 0.131 41 2.434 0.0581
## farm_1 - farm_3 -0.55 0.440 41 -1.250 0.6551
## farm_2 - farm_3 -0.87 0.439 41 -1.983 0.1624
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 2 10.133 5.0665 2.6369 0.08369 .
## Residuals 41 78.776 1.9214
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.05 0.310 41 2.424 3.68
## farm_2 2.09 0.289 41 1.503 2.67
## farm_3 3.00 1.386 41 0.201 5.80
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.963 0.424 41 2.272 0.0851
## farm_1 - farm_3 0.050 1.420 41 0.035 1.0000
## farm_2 - farm_3 -0.913 1.416 41 -0.645 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "================ Number of farm types = 2 ================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 60.72 60.724 1.2251 0.2747
## Residuals 42 2081.82 49.567
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 46.0 1.54 42 42.9 49.1
## farm_2 43.7 1.47 42 40.7 46.7
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.35 2.12 42 1.107 0.2747
##
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 37.608 37.608 9.4054 0.003776 **
## Residuals 42 167.938 3.999
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 7.29 0.436 42 6.41 8.17
## farm_2 5.43 0.417 42 4.59 6.28
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.85 0.604 42 3.067 0.0038
##
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 2.5158e+13 2.5158e+13 19.823 6.165e-05 ***
## Residuals 42 5.3304e+13 1.2691e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2123571 245836 42 1627454 2619689
## farm_2 609696 234905 42 135639 1083753
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1513876 340023 42 4.452 0.0001
##
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 1.3350e+13 1.3350e+13 16.822 0.0001843 ***
## Residuals 42 3.3333e+13 7.9365e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1654762 194403 42 1262440 2047084
## farm_2 551957 185759 42 177080 926833
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1102805 268885 42 4.101 0.0002
##
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.12825 0.128247 4.5025 0.03979 *
## Residuals 42 1.19632 0.028484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.794 0.0368 42 0.720 0.869
## farm_2 0.902 0.0352 42 0.831 0.973
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.108 0.0509 42 -2.122 0.0398
##
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 25.77 25.767 1.2514 0.2696
## Residuals 42 864.78 20.590
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.619 0.990 42 -0.379 3.62
## farm_2 0.087 0.946 42 -1.822 2.00
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.53 1.37 42 1.119 0.2696
##
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 106.72 106.721 16.181 0.0002348 ***
## Residuals 42 277.01 6.595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.857 0.560 42 2.726 4.99
## farm_2 0.739 0.535 42 -0.342 1.82
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 3.12 0.775 42 4.023 0.0002
##
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 27.036 27.0358 6.5 0.01453 *
## Residuals 42 174.692 4.1593
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2.048 0.445 42 1.15 2.95
## farm_2 0.478 0.425 42 -0.38 1.34
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.57 0.616 42 2.550 0.0145
##
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 1013.9 1013.94 33.931 7.089e-07 ***
## Residuals 42 1255.1 29.88
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 12.52 1.19 42 10.116 14.93
## farm_2 2.91 1.14 42 0.613 5.21
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 9.61 1.65 42 5.825 <.0001
##
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 32.32 32.316 2.8217 0.1004
## Residuals 42 481.01 11.453
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.951 0.738 42 0.461 3.44
## farm_2 0.236 0.706 42 -1.188 1.66
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.72 1.02 42 1.680 0.1004
##
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.6529 0.65286 2.029 0.1617
## Residuals 42 13.5142 0.32177
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.309 0.124 42 0.0591 0.559
## farm_2 0.065 0.118 42 -0.1737 0.304
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.244 0.171 42 1.424 0.1617
##
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 150.05 150.052 13.19 0.0007595 ***
## Residuals 42 477.82 11.377
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 7.64 0.736 42 6.16 9.13
## farm_2 3.95 0.703 42 2.53 5.36
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 3.7 1.02 42 3.632 0.0008
##
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 41605753 41605753 11.327 0.001642 **
## Residuals 42 154267561 3673037
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3473 418 42 2629 4317
## farm_2 1526 400 42 719 2332
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1947 578 42 3.366 0.0016
##
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.00413 0.0041329 0.2009 0.6563
## Residuals 42 0.86406 0.0205728
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.423 0.0313 42 0.360 0.487
## farm_2 0.443 0.0299 42 0.382 0.503
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0194 0.0433 42 -0.448 0.6563
##
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.13641 0.13641 5.6157 0.02247 *
## Residuals 42 1.02019 0.02429
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.353 0.0340 42 0.285 0.422
## farm_2 0.465 0.0325 42 0.399 0.530
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.111 0.047 42 -2.370 0.0225
##
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.000 0.00005 1e-04 0.9909
## Residuals 42 14.977 0.35660
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.48 0.130 42 1.21 1.74
## farm_2 1.48 0.125 42 1.23 1.73
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.00207 0.18 42 -0.011 0.9909
##
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.0482 0.048184 0.2332 0.6317
## Residuals 42 8.6791 0.206645
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.238 0.0992 42 0.0379 0.438
## farm_2 0.304 0.0948 42 0.1131 0.496
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0663 0.137 42 -0.483 0.6317
##
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.7352 0.73522 3.3744 0.0733 .
## Residuals 42 9.1511 0.21788
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.476 0.1019 42 0.271 0.682
## farm_2 0.217 0.0973 42 0.021 0.414
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.259 0.141 42 1.837 0.0733
##
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 1.2656 1.26558 7.1236 0.01077 *
## Residuals 42 7.4617 0.17766
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.905 0.0920 42 0.719 1.090
## farm_2 0.565 0.0879 42 0.388 0.743
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.34 0.127 42 2.669 0.0108
##
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 3.1079 3.10790 4.241 0.04569 *
## Residuals 42 30.7785 0.73282
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.62 0.187 42 1.242 2.00
## farm_2 1.09 0.178 42 0.727 1.45
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.532 0.258 42 2.059 0.0457
##
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.9622 0.96222 5.5453 0.02328 *
## Residuals 42 7.2878 0.17352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.905 0.0909 42 0.721 1.088
## farm_2 0.609 0.0869 42 0.433 0.784
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.296 0.126 42 2.355 0.0233
##
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.1372 0.13721 0.7593 0.3885
## Residuals 42 7.5901 0.18072
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.286 0.0928 42 0.09851 0.473
## farm_2 0.174 0.0886 42 -0.00497 0.353
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.112 0.128 42 0.871 0.3885
##
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.2439 0.24393 1.0309 0.3158
## Residuals 42 9.9379 0.23662
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.714 0.106 42 0.500 0.929
## farm_2 0.565 0.101 42 0.361 0.770
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.149 0.147 42 1.015 0.3158
##
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.0068 0.006776 0.0326 0.8575
## Residuals 42 8.7205 0.207631
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.286 0.0994 42 0.0850 0.486
## farm_2 0.261 0.0950 42 0.0691 0.453
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0248 0.138 42 0.181 0.8575
##
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 0.012 0.012046 0.0497 0.8246
## Residuals 42 10.170 0.242137
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.381 0.107 42 0.164 0.598
## farm_2 0.348 0.103 42 0.141 0.555
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0331 0.149 42 0.223 0.8246
##
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 1.3123 1.31230 7.0241 0.01129 *
## Residuals 42 7.8468 0.18683
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.476 0.0943 42 0.2858 0.667
## farm_2 0.130 0.0901 42 -0.0514 0.312
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.346 0.13 42 2.650 0.0113
##
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_1[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_1[["farm_type"]] 1 10.131 10.1306 5.401 0.02504 *
## Residuals 42 78.778 1.8757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.05 0.299 42 2.44 3.65
## farm_2 2.09 0.286 42 1.51 2.66
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.961 0.413 42 2.324 0.0250# Choose a few box-plots to display, or any other visual. Here is just an e.g. for per capita income
boxplot_variable <- function(var_name, var_title){
P00 <- ggplot(typo_1, aes_string('farm_type', var_name)) +
geom_boxplot() +
labs(x = 'Farm type', title = var_title) +
theme_bw()
png(paste0('plot_', var_name, '.png'), height = 7.5, width = 10, units = 'cm', res = 1000)
P00
ggsave(paste0('plot_', var_name, '.png'))
dev.off()
}
boxplot_variable('HHSize', 'Size of household')## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ā¹ Please use tidy evaluation idioms with `aes()`.
## ā¹ See also `vignette("ggplot2-in-packages")` for more information.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('HH_income_', 'Household income, USD')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('PercFarmIncome', 'Proportion of income derived from farming, %')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('Ngoats', 'Number of goats')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('Nchickens', 'Number of chickens')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('TotalLand', 'Farm size, ha ???')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('AvgMZ_yield.kgs.', 'Maize yield, kg/ha ???')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('PercentageLegumeArea', 'Share of farm area occupied by legume crops, %')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('Manure_application_in_Maize_field_during_2022.23_season', 'Manure application')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('Count_intens_options', 'Count_intens_options')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('Do_you_pracatice_intercroping.', 'Number of intensification options practised')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('Do_you_own_any_vegetable_garden.', 'Presence of vegetable garden')## Saving 3.94 x 2.95 in image## png
## 2boxplot_variable('Count_sust_practices', 'Count_sust_practices')## Saving 3.94 x 2.95 in image## png
## 2Compare multivariate techniques on the same subset of variables
##############################################################################
# Mock FADM
res_famd <- FactoMineR::FAMD(famd_df)## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps## Warning: ggrepel: 15 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 10 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
factoextra::fviz_screeplot(res_famd, addlabels = T)
# factoextra::fviz_famd(res_famd)
for(i in list(c(1, 2), c(1, 3), c(1, 4) )){
for(j in names(famd_df)){
nb_col <- length(unique(famd_df[[j]]))
print(factoextra::fviz_ellipses(res_famd,
habillage = j,
axes = i,
geom = 'text',
addEllipses = T,
palette = pal(nb_col)
)
)
}
}## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
## Warning: Computation failed in `stat_conf_ellipse()`.
## Caused by error in `if (scale[1] > 0) ...`:
## ! missing value where TRUE/FALSE needed
# Hierarchical clustering on the first 5 dimensions that capture 64% of total variability
eg_hc <- res_famd$ind$coord[, 1:5]
dist_matrix <- dist(eg_hc)
hc <- hclust(dist_matrix, method = 'ward.D') # could be 'complete' or 'average'
plot(hc, main = 'Subset 3 - Dendogram on the first 5 components', xlab = '', sub = '', cex = 0.6)
# decide on the number of clusters to make: 2, 3, 4 or more
typo_3 <- famd_df
for(cl in 4:2){
clusters <- cutree(hc, k = cl)
typo_3$farm_type <- paste0('farm_', clusters)
print(paste0('================ Number of farm types = ', cl, '================'))
for(i in names(famd_df)[c(1, 3:10, 12:15)]){
res_anova <- lm(typo_3[[i]] ~ typo_3[['farm_type']])
print(paste0('--------------------', i, '----------------'))
print(anova(res_anova))
print('-----------')
print(emmeans::emmeans(res_anova,
pairwise ~ farm_type,
type = 'response',
adjust = 'bonferroni'))
}
}## [1] "================ Number of farm types = 4================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 837.05 279.017 8.549 0.0001656 ***
## Residuals 40 1305.50 32.637
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 41.2 1.17 40 38.9 43.6
## farm_2 45.7 2.16 40 41.4 50.1
## farm_3 51.9 1.81 40 48.2 55.6
## farm_4 47.7 3.30 40 41.0 54.3
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -4.46 2.45 40 -1.819 0.4583
## farm_1 - farm_3 -10.65 2.15 40 -4.953 0.0001
## farm_1 - farm_4 -6.42 3.50 40 -1.834 0.4445
## farm_2 - farm_3 -6.19 2.82 40 -2.197 0.2032
## farm_2 - farm_4 -1.95 3.94 40 -0.495 1.0000
## farm_3 - farm_4 4.23 3.76 40 1.126 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 4.2492e+13 1.4164e+13 15.751 6.484e-07 ***
## Residuals 40 3.5970e+13 8.9924e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1141792 193567 40 750578 1533006
## farm_2 1466429 358417 40 742041 2190816
## farm_3 645000 299873 40 38934 1251066
## farm_4 4833333 547491 40 3726813 5939853
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -324637 407346 40 -0.797 1.0000
## farm_1 - farm_3 496792 356920 40 1.392 1.0000
## farm_1 - farm_4 -3691542 580702 40 -6.357 <.0001
## farm_2 - farm_3 821429 467318 40 1.758 0.5186
## farm_2 - farm_4 -3366905 654377 40 -5.145 <.0001
## farm_3 - farm_4 -4188333 624235 40 -6.710 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 2.0474e+13 6.8248e+12 10.416 3.392e-05 ***
## Residuals 40 2.6209e+13 6.5522e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 999375 165230 40 665432 1333318
## farm_2 1057143 305947 40 438801 1675485
## farm_3 556000 255974 40 38658 1073342
## farm_4 3500000 467342 40 2555467 4444533
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -57768 347713 40 -0.166 1.0000
## farm_1 - farm_3 443375 304670 40 1.455 0.9204
## farm_1 - farm_4 -2500625 495691 40 -5.045 0.0001
## farm_2 - farm_3 501143 398906 40 1.256 1.0000
## farm_2 - farm_4 -2442857 558580 40 -4.373 0.0005
## farm_3 - farm_4 -2944000 532852 40 -5.525 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 0.16525 0.055082 1.9005 0.145
## Residuals 40 1.15932 0.028983
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.888 0.0348 40 0.817 0.958
## farm_2 0.752 0.0643 40 0.622 0.882
## farm_3 0.874 0.0538 40 0.765 0.983
## farm_4 0.710 0.0983 40 0.512 0.909
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.1355 0.0731 40 1.853 0.4273
## farm_1 - farm_3 0.0138 0.0641 40 0.215 1.0000
## farm_1 - farm_4 0.1773 0.1043 40 1.701 0.5808
## farm_2 - farm_3 -0.1217 0.0839 40 -1.451 0.9273
## farm_2 - farm_4 0.0417 0.1175 40 0.355 1.0000
## farm_3 - farm_4 0.1635 0.1121 40 1.459 0.9145
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 356.28 118.760 8.8914 0.0001227 ***
## Residuals 40 534.27 13.357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0 0.746 40 -1.51 1.51
## farm_2 0.0 1.381 40 -2.79 2.79
## farm_3 0.2 1.156 40 -2.14 2.54
## farm_4 11.3 2.110 40 7.07 15.60
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0 1.57 40 0.000 1.0000
## farm_1 - farm_3 -0.2 1.38 40 -0.145 1.0000
## farm_1 - farm_4 -11.3 2.24 40 -5.064 0.0001
## farm_2 - farm_3 -0.2 1.80 40 -0.111 1.0000
## farm_2 - farm_4 -11.3 2.52 40 -4.494 0.0004
## farm_3 - farm_4 -11.1 2.41 40 -4.628 0.0002
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 191.17 63.723 13.237 3.807e-06 ***
## Residuals 40 192.56 4.814
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.29 0.448 40 0.387 2.20
## farm_2 4.00 0.829 40 2.324 5.68
## farm_3 1.20 0.694 40 -0.202 2.60
## farm_4 9.00 1.267 40 6.440 11.56
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -2.7083 0.942 40 -2.874 0.0388
## farm_1 - farm_3 0.0917 0.826 40 0.111 1.0000
## farm_1 - farm_4 -7.7083 1.344 40 -5.737 <.0001
## farm_2 - farm_3 2.8000 1.081 40 2.590 0.0800
## farm_2 - farm_4 -5.0000 1.514 40 -3.302 0.0122
## farm_3 - farm_4 -7.8000 1.444 40 -5.400 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 36.669 12.2230 2.9621 0.04355 *
## Residuals 40 165.058 4.1265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.04 0.415 40 0.204 1.88
## farm_2 2.00 0.768 40 0.448 3.55
## farm_3 0.30 0.642 40 -0.998 1.60
## farm_4 4.00 1.173 40 1.630 6.37
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.958 0.873 40 -1.098 1.0000
## farm_1 - farm_3 0.742 0.765 40 0.970 1.0000
## farm_1 - farm_4 -2.958 1.244 40 -2.378 0.1336
## farm_2 - farm_3 1.700 1.001 40 1.698 0.5834
## farm_2 - farm_4 -2.000 1.402 40 -1.427 0.9685
## farm_3 - farm_4 -3.700 1.337 40 -2.767 0.0512
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 398.45 132.815 2.8401 0.04992 *
## Residuals 40 1870.55 46.764
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 6.54 1.40 40 3.720 9.36
## farm_2 13.29 2.58 40 8.062 18.51
## farm_3 4.50 2.16 40 0.129 8.87
## farm_4 11.67 3.95 40 3.687 19.65
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -6.74 2.94 40 -2.296 0.1620
## farm_1 - farm_3 2.04 2.57 40 0.793 1.0000
## farm_1 - farm_4 -5.12 4.19 40 -1.224 1.0000
## farm_2 - farm_3 8.79 3.37 40 2.607 0.0766
## farm_2 - farm_4 1.62 4.72 40 0.343 1.0000
## farm_3 - farm_4 -7.17 4.50 40 -1.592 0.7155
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 233.71 77.904 11.144 1.885e-05 ***
## Residuals 40 279.62 6.990
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.351 0.540 40 -0.74 1.44
## farm_2 0.833 0.999 40 -1.19 2.85
## farm_3 0.350 0.836 40 -1.34 2.04
## farm_4 9.550 1.526 40 6.46 12.64
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.482024 1.136 40 -0.424 1.0000
## farm_1 - farm_3 0.000833 0.995 40 0.001 1.0000
## farm_1 - farm_4 -9.199167 1.619 40 -5.682 <.0001
## farm_2 - farm_3 0.482857 1.303 40 0.371 1.0000
## farm_2 - farm_4 -8.717143 1.824 40 -4.778 0.0001
## farm_3 - farm_4 -9.200000 1.740 40 -5.286 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 194.00 64.668 5.9621 0.00185 **
## Residuals 40 433.86 10.847
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 4.75 0.672 40 3.39 6.11
## farm_2 9.36 1.245 40 6.84 11.87
## farm_3 4.17 1.041 40 2.07 6.28
## farm_4 10.00 1.901 40 6.16 13.84
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -4.607 1.41 40 -3.257 0.0138
## farm_1 - farm_3 0.575 1.24 40 0.464 1.0000
## farm_1 - farm_4 -5.250 2.02 40 -2.603 0.0774
## farm_2 - farm_3 5.182 1.62 40 3.193 0.0165
## farm_2 - farm_4 -0.643 2.27 40 -0.283 1.0000
## farm_3 - farm_4 -5.825 2.17 40 -2.687 0.0627
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 52171707 17390569 4.8407 0.005749 **
## Residuals 40 143701606 3592540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2263 387 40 1481.1 3045
## farm_2 3418 716 40 1970.0 4866
## farm_3 1278 599 40 66.6 2489
## farm_4 5667 1094 40 3455.0 7878
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1155 814 40 -1.418 0.9830
## farm_1 - farm_3 985 713 40 1.381 1.0000
## farm_1 - farm_4 -3404 1161 40 -2.932 0.0332
## farm_2 - farm_3 2140 934 40 2.291 0.1639
## farm_2 - farm_4 -2249 1308 40 -1.719 0.5597
## farm_3 - farm_4 -4389 1248 40 -3.517 0.0066
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 0.03813 0.012710 0.6125 0.6109
## Residuals 40 0.83006 0.020752
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.423 0.0294 40 0.364 0.482
## farm_2 0.393 0.0544 40 0.283 0.503
## farm_3 0.468 0.0456 40 0.376 0.560
## farm_4 0.498 0.0832 40 0.330 0.666
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0298 0.0619 40 0.481 1.0000
## farm_1 - farm_3 -0.0448 0.0542 40 -0.826 1.0000
## farm_1 - farm_4 -0.0747 0.0882 40 -0.847 1.0000
## farm_2 - farm_3 -0.0746 0.0710 40 -1.050 1.0000
## farm_2 - farm_4 -0.1045 0.0994 40 -1.051 1.0000
## farm_3 - farm_4 -0.0299 0.0948 40 -0.315 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 3 0.1534 0.051132 2.0388 0.1238
## Residuals 40 1.0032 0.025080
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.420 0.0323 40 0.3546 0.485
## farm_2 0.383 0.0599 40 0.2616 0.504
## farm_3 0.470 0.0501 40 0.3685 0.571
## farm_4 0.218 0.0914 40 0.0335 0.403
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0374 0.0680 40 0.549 1.0000
## farm_1 - farm_3 -0.0498 0.0596 40 -0.836 1.0000
## farm_1 - farm_4 0.2016 0.0970 40 2.078 0.2648
## farm_2 - farm_3 -0.0872 0.0780 40 -1.117 1.0000
## farm_2 - farm_4 0.1642 0.1093 40 1.503 0.8449
## farm_3 - farm_4 0.2514 0.1042 40 2.411 0.1235
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "================ Number of farm types = 3================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 36.42 18.210 0.3545 0.7037
## Residuals 41 2106.12 51.369
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 44.4 1.23 41 41.9 46.9
## farm_2 45.7 2.71 41 40.2 51.2
## farm_3 47.7 4.14 41 39.3 56.0
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.33 2.97 41 -0.448 1.0000
## farm_1 - farm_3 -3.28 4.32 41 -0.761 1.0000
## farm_2 - farm_3 -1.95 4.95 41 -0.395 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 4.0750e+13 2.0375e+13 22.152 3.001e-07 ***
## Residuals 41 3.7712e+13 9.1980e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 995676 164478 41 663507 1327845
## farm_2 1466429 362491 41 734364 2198493
## farm_3 4833333 553714 41 3715086 5951581
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -470752 398061 41 -1.183 0.7313
## farm_1 - farm_3 -3837657 577626 41 -6.644 <.0001
## farm_2 - farm_3 -3366905 661814 41 -5.087 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 1.9087e+13 9.5434e+12 14.179 2.088e-05 ***
## Residuals 41 2.7597e+13 6.7309e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 868971 140701 41 584820 1153122
## farm_2 1057143 310090 41 430904 1683381
## farm_3 3500000 473670 41 2543405 4456595
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -188172 340518 41 -0.553 1.0000
## farm_1 - farm_3 -2631029 494125 41 -5.325 <.0001
## farm_2 - farm_3 -2442857 566143 41 -4.315 0.0003
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 0.1639 0.081952 2.8949 0.06667 .
## Residuals 41 1.1607 0.028309
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.884 0.0289 41 0.825 0.942
## farm_2 0.752 0.0636 41 0.624 0.880
## farm_3 0.710 0.0971 41 0.514 0.906
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.1315 0.0698 41 1.883 0.2005
## farm_1 - farm_3 0.1732 0.1013 41 1.709 0.2848
## farm_2 - farm_3 0.0417 0.1161 41 0.360 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 356.00 177.998 13.652 2.856e-05 ***
## Residuals 41 534.55 13.038
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0588 0.619 41 -1.19 1.31
## farm_2 0.0000 1.365 41 -2.76 2.76
## farm_3 11.3333 2.085 41 7.12 15.54
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0588 1.50 41 0.039 1.0000
## farm_1 - farm_3 -11.2745 2.17 41 -5.184 <.0001
## farm_2 - farm_3 -11.3333 2.49 41 -4.548 0.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 191.11 95.555 20.34 7.308e-07 ***
## Residuals 41 192.62 4.698
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.26 0.372 41 0.514 2.02
## farm_2 4.00 0.819 41 2.346 5.65
## farm_3 9.00 1.251 41 6.473 11.53
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -2.74 0.90 41 -3.040 0.0123
## farm_1 - farm_3 -7.74 1.31 41 -5.925 <.0001
## farm_2 - farm_3 -5.00 1.50 41 -3.343 0.0053
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 32.786 16.3930 3.9784 0.02636 *
## Residuals 41 168.941 4.1205
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.824 0.348 41 0.120 1.53
## farm_2 2.000 0.767 41 0.451 3.55
## farm_3 4.000 1.172 41 1.633 6.37
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.18 0.843 41 -1.396 0.5103
## farm_1 - farm_3 -3.18 1.223 41 -2.598 0.0389
## farm_2 - farm_3 -2.00 1.401 41 -1.428 0.4828
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 369.02 184.511 3.9816 0.02629 *
## Residuals 41 1899.98 46.341
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 5.94 1.17 41 3.58 8.3
## farm_2 13.29 2.57 41 8.09 18.5
## farm_3 11.67 3.93 41 3.73 19.6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -7.34 2.83 41 -2.599 0.0388
## farm_1 - farm_3 -5.73 4.10 41 -1.396 0.5103
## farm_2 - farm_3 1.62 4.70 41 0.345 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 233.71 116.86 17.134 3.903e-06 ***
## Residuals 41 279.62 6.82
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.351 0.448 41 -0.554 1.26
## farm_2 0.833 0.987 41 -1.161 2.83
## farm_3 9.550 1.508 41 6.505 12.59
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.482 1.08 41 -0.445 1.0000
## farm_1 - farm_3 -9.199 1.57 41 -5.849 <.0001
## farm_2 - farm_3 -8.717 1.80 41 -4.837 0.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 191.67 95.835 9.008 0.0005717 ***
## Residuals 41 436.20 10.639
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 4.58 0.559 41 3.45 5.71
## farm_2 9.36 1.233 41 6.87 11.85
## farm_3 10.00 1.883 41 6.20 13.80
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -4.776 1.35 41 -3.528 0.0031
## farm_1 - farm_3 -5.419 1.96 41 -2.759 0.0259
## farm_2 - farm_3 -0.643 2.25 41 -0.286 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 45322481 22661240 6.1714 0.00454 **
## Residuals 41 150550833 3671972
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1973 329 41 1310 2637
## farm_2 3418 724 41 1955 4881
## farm_3 5667 1106 41 3432 7901
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1445 795 41 -1.816 0.2299
## farm_1 - farm_3 -3693 1154 41 -3.200 0.0080
## farm_2 - farm_3 -2249 1322 41 -1.701 0.2898
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 0.02395 0.011977 0.5817 0.5635
## Residuals 41 0.84424 0.020591
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.436 0.0246 41 0.386 0.486
## farm_2 0.393 0.0542 41 0.284 0.503
## farm_3 0.498 0.0828 41 0.330 0.665
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0429 0.0596 41 0.721 1.0000
## farm_1 - farm_3 -0.0615 0.0864 41 -0.712 1.0000
## farm_2 - farm_3 -0.1045 0.0990 41 -1.055 0.8929
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 2 0.13587 0.067934 2.7287 0.07717 .
## Residuals 41 1.02073 0.024896
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.435 0.0271 41 0.3799 0.489
## farm_2 0.383 0.0596 41 0.2621 0.503
## farm_3 0.218 0.0911 41 0.0344 0.402
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.052 0.0655 41 0.794 1.0000
## farm_1 - farm_3 0.216 0.0950 41 2.275 0.0846
## farm_2 - farm_3 0.164 0.1089 41 1.508 0.4176
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "================ Number of farm types = 2================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 28.42 28.416 0.5645 0.4566
## Residuals 42 2114.13 50.336
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 44.4 1.22 42 41.9 46.8
## farm_2 46.3 2.24 42 41.8 50.8
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.92 2.55 42 -0.751 0.4566
##
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 1.6945e+13 1.6945e+13 11.569 0.001483 **
## Residuals 42 6.1517e+13 1.4647e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 995677 207556 42 576812 1414541
## farm_2 2476500 382714 42 1704152 3248848
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1480824 435373 42 -3.401 0.0015
##
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 6.5550e+12 6.5550e+12 6.8607 0.01221 *
## Residuals 42 4.0128e+13 9.5544e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 868971 167634 42 530671 1207270
## farm_2 1790000 309102 42 1166207 2413793
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -921029 351632 42 -2.619 0.0122
##
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 0.16024 0.160244 5.7804 0.02069 *
## Residuals 42 1.16432 0.027722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.884 0.0286 42 0.826 0.941
## farm_2 0.740 0.0527 42 0.633 0.846
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.144 0.0599 42 2.404 0.0207
##
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 86.26 86.263 4.5047 0.03974 *
## Residuals 42 804.28 19.150
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0588 0.75 42 -1.456 1.57
## farm_2 3.4000 1.38 42 0.607 6.19
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -3.34 1.57 42 -2.122 0.0397
##
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 138.61 138.610 23.75 1.604e-05 ***
## Residuals 42 245.12 5.836
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.26 0.414 42 0.429 2.10
## farm_2 5.50 0.764 42 3.958 7.04
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -4.24 0.869 42 -4.873 <.0001
##
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 24.386 24.3861 5.7754 0.02075 *
## Residuals 42 177.341 4.2224
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.824 0.352 42 0.112 1.53
## farm_2 2.600 0.650 42 1.289 3.91
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.78 0.739 42 -2.403 0.0207
##
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 363.52 363.52 8.0125 0.007099 **
## Residuals 42 1905.48 45.37
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 5.94 1.16 42 3.61 8.27
## farm_2 12.80 2.13 42 8.50 17.10
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -6.86 2.42 42 -2.831 0.0071
##
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 74.14 74.135 7.0896 0.01094 *
## Residuals 42 439.19 10.457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.351 0.555 42 -0.769 1.47
## farm_2 3.448 1.023 42 1.384 5.51
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -3.1 1.16 42 -2.663 0.0109
##
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 190.80 190.803 18.335 0.0001053 ***
## Residuals 42 437.07 10.406
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 4.58 0.553 42 3.46 5.7
## farm_2 9.55 1.020 42 7.49 11.6
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -4.97 1.16 42 -4.282 0.0001
##
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 34702478 34702478 9.0432 0.004439 **
## Residuals 42 161170836 3837401
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1973 336 42 1295 2651
## farm_2 4092 619 42 2842 5343
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -2119 705 42 -3.007 0.0044
##
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 0.00104 0.0010395 0.0503 0.8235
## Residuals 42 0.86715 0.0206465
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.436 0.0246 42 0.386 0.486
## farm_2 0.425 0.0454 42 0.333 0.516
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0116 0.0517 42 0.224 0.8235
##
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_3[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_3[["farm_type"]] 1 0.07925 0.079250 3.0895 0.08608 .
## Residuals 42 1.07735 0.025651
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.435 0.0275 42 0.379 0.490
## farm_2 0.333 0.0506 42 0.231 0.435
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.101 0.0576 42 1.758 0.0861################################################################################
# Mock hierarchical clustering on original selected variables
eg_hc <- pca_df
dist_matrix <- dist(eg_hc)
hc <- hclust(dist_matrix, method = 'ward.D') # could be 'complete' or 'avrage'
plot(hc, main = "Dendrogram for original variables", xlab = "", sub = "", cex = 0.6)
# decide on the number of clusters to make: 2, 3, 4 or more
typo_4 <- pca_df
for(cl in 4:2){
print(paste0('================ Number of farm types = ', cl, '================'))
clusters <- cutree(hc, k = cl)
typo_4$farm_type <- paste0('farm_', clusters)
for(i in names(eg_hc)){
res_anova <- lm(typo_4[[i]] ~ typo_4[['farm_type']])
print(paste0('--------------------', i, '----------------'))
print(anova(res_anova))
print('-----------')
print(emmeans::emmeans(res_anova,
pairwise ~ farm_type,
type = 'response',
adjust = 'bonferroni'))
}
ggplot(typo_4, aes(hh_size, cultivated_area_ha, colour = farm_type)) +
geom_point() +
theme_bw() +
labs(title = "Hierarchical Clustering")
ggplot(typo_4, aes(cluster, cultivated_area_ha, colour = farm_type)) +
geom_boxplot() +
theme_bw() +
labs(title = "Hierarchical Clustering")
}## [1] "================ Number of farm types = 4================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 34.17 11.388 0.2161 0.8847
## Residuals 40 2108.38 52.710
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 43.6 1.94 40 39.7 47.6
## farm_2 45.3 1.76 40 41.7 48.9
## farm_3 45.2 2.19 40 40.8 49.6
## farm_4 47.0 5.13 40 36.6 57.4
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.651 2.62 40 -0.630 1.0000
## farm_1 - farm_3 -1.539 2.93 40 -0.526 1.0000
## farm_1 - farm_4 -3.357 5.49 40 -0.612 1.0000
## farm_2 - farm_3 0.112 2.81 40 0.040 1.0000
## farm_2 - farm_4 -1.706 5.43 40 -0.314 1.0000
## farm_3 - farm_4 -1.818 5.58 40 -0.326 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 38.519 12.8396 3.0749 0.0384 *
## Residuals 40 167.027 4.1757
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 6.50 0.546 40 5.40 7.60
## farm_2 5.41 0.496 40 4.41 6.41
## farm_3 6.91 0.616 40 5.66 8.15
## farm_4 9.50 1.445 40 6.58 12.42
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.088 0.737 40 1.476 0.8873
## farm_1 - farm_3 -0.409 0.823 40 -0.497 1.0000
## farm_1 - farm_4 -3.000 1.545 40 -1.942 0.3551
## farm_2 - farm_3 -1.497 0.791 40 -1.894 0.3931
## farm_2 - farm_4 -4.088 1.528 40 -2.676 0.0644
## farm_3 - farm_4 -2.591 1.571 40 -1.649 0.6414
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 7.4319e+13 2.4773e+13 239.19 < 2.2e-16 ***
## Residuals 40 4.1428e+12 1.0357e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1886429 86011 40 1712594 2060263
## farm_2 349000 78054 40 191248 506752
## farm_3 1206818 97033 40 1010706 1402930
## farm_4 6500000 227563 40 6040077 6959923
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1537429 116147 40 13.237 <.0001
## farm_1 - farm_3 679610 129666 40 5.241 <.0001
## farm_1 - farm_4 -4613571 243275 40 -18.964 <.0001
## farm_2 - farm_3 -857818 124530 40 -6.888 <.0001
## farm_2 - farm_4 -6151000 240577 40 -25.568 <.0001
## farm_3 - farm_4 -5293182 247387 40 -21.396 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 4.2558e+13 1.4186e+13 137.53 < 2.2e-16 ***
## Residuals 40 4.1258e+12 1.0315e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1671429 85834 40 1497951 1844907
## farm_2 308529 77894 40 151101 465958
## farm_3 845455 96834 40 649745 1041164
## farm_4 4750000 227097 40 4291020 5208980
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1362899 115909 40 11.758 <.0001
## farm_1 - farm_3 825974 129400 40 6.383 <.0001
## farm_1 - farm_4 -3078571 242777 40 -12.681 <.0001
## farm_2 - farm_3 -536925 124275 40 -4.320 0.0006
## farm_2 - farm_4 -4441471 240084 40 -18.500 <.0001
## farm_3 - farm_4 -3904545 246880 40 -15.816 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.1810 0.060335 2.1104 0.1141
## Residuals 40 1.1436 0.028589
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.886 0.0452 40 0.795 0.978
## farm_2 0.897 0.0410 40 0.814 0.980
## farm_3 0.756 0.0510 40 0.653 0.859
## farm_4 0.732 0.1196 40 0.491 0.974
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0106 0.0610 40 -0.174 1.0000
## farm_1 - farm_3 0.1304 0.0681 40 1.914 0.3768
## farm_1 - farm_4 0.1542 0.1278 40 1.206 1.0000
## farm_2 - farm_3 0.1410 0.0654 40 2.155 0.2234
## farm_2 - farm_4 0.1648 0.1264 40 1.304 1.0000
## farm_3 - farm_4 0.0238 0.1300 40 0.183 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 60.60 20.200 0.9735 0.4148
## Residuals 40 829.95 20.749
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.000 1.22 40 -2.4604 2.46
## farm_2 0.118 1.10 40 -2.1152 2.35
## farm_3 2.727 1.37 40 -0.0485 5.50
## farm_4 2.000 3.22 40 -4.5097 8.51
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.118 1.64 40 -0.072 1.0000
## farm_1 - farm_3 -2.727 1.84 40 -1.486 0.8707
## farm_1 - farm_4 -2.000 3.44 40 -0.581 1.0000
## farm_2 - farm_3 -2.610 1.76 40 -1.481 0.8794
## farm_2 - farm_4 -1.882 3.41 40 -0.553 1.0000
## farm_3 - farm_4 0.727 3.50 40 0.208 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 167.32 55.773 10.309 3.704e-05 ***
## Residuals 40 216.41 5.410
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2.143 0.622 40 0.886 3.4
## farm_2 0.765 0.564 40 -0.375 1.9
## farm_3 3.182 0.701 40 1.764 4.6
## farm_4 10.000 1.645 40 6.676 13.3
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.38 0.839 40 1.642 0.6509
## farm_1 - farm_3 -1.04 0.937 40 -1.109 1.0000
## farm_1 - farm_4 -7.86 1.758 40 -4.469 0.0004
## farm_2 - farm_3 -2.42 0.900 40 -2.686 0.0629
## farm_2 - farm_4 -9.24 1.739 40 -5.311 <.0001
## farm_3 - farm_4 -6.82 1.788 40 -3.813 0.0028
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 21.585 7.1951 1.5976 0.2051
## Residuals 40 180.142 4.5036
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.857 0.567 40 0.711 3.00
## farm_2 0.353 0.515 40 -0.687 1.39
## farm_3 1.636 0.640 40 0.343 2.93
## farm_4 2.000 1.501 40 -1.033 5.03
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.504 0.766 40 1.964 0.3391
## farm_1 - farm_3 0.221 0.855 40 0.258 1.0000
## farm_1 - farm_4 -0.143 1.604 40 -0.089 1.0000
## farm_2 - farm_3 -1.283 0.821 40 -1.563 0.7557
## farm_2 - farm_4 -1.647 1.586 40 -1.038 1.0000
## farm_3 - farm_4 -0.364 1.631 40 -0.223 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 544.05 181.351 4.2054 0.01121 *
## Residuals 40 1724.95 43.124
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 10.43 1.76 40 6.881 13.98
## farm_2 3.35 1.59 40 0.134 6.57
## farm_3 8.82 1.98 40 4.816 12.82
## farm_4 15.00 4.64 40 5.615 24.38
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 7.08 2.37 40 2.985 0.0289
## farm_1 - farm_3 1.61 2.65 40 0.609 1.0000
## farm_1 - farm_4 -4.57 4.96 40 -0.921 1.0000
## farm_2 - farm_3 -5.47 2.54 40 -2.151 0.2255
## farm_2 - farm_4 -11.65 4.91 40 -2.373 0.1354
## farm_3 - farm_4 -6.18 5.05 40 -1.225 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 45.47 15.157 1.2958 0.2891
## Residuals 40 467.86 11.696
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.597 0.914 40 -1.250 2.44
## farm_2 0.245 0.829 40 -1.431 1.92
## farm_3 2.561 1.031 40 0.477 4.64
## farm_4 2.850 2.418 40 -2.038 7.74
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.352 1.23 40 0.285 1.0000
## farm_1 - farm_3 -1.964 1.38 40 -1.425 0.9713
## farm_1 - farm_4 -2.253 2.59 40 -0.871 1.0000
## farm_2 - farm_3 -2.316 1.32 40 -1.750 0.5270
## farm_2 - farm_4 -2.605 2.56 40 -1.019 1.0000
## farm_3 - farm_4 -0.289 2.63 40 -0.110 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.9885 0.32949 1.0001 0.4028
## Residuals 40 13.1786 0.32946
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.1099 0.153 40 -0.2001 0.420
## farm_2 0.0697 0.139 40 -0.2117 0.351
## farm_3 0.4330 0.173 40 0.0832 0.783
## farm_4 0.2472 0.406 40 -0.5731 1.068
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0402 0.207 40 0.194 1.0000
## farm_1 - farm_3 -0.3230 0.231 40 -1.397 1.0000
## farm_1 - farm_4 -0.1373 0.434 40 -0.316 1.0000
## farm_2 - farm_3 -0.3633 0.222 40 -1.636 0.6586
## farm_2 - farm_4 -0.1775 0.429 40 -0.414 1.0000
## farm_3 - farm_4 0.1858 0.441 40 0.421 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 190.31 63.437 5.7991 0.002174 **
## Residuals 40 437.56 10.939
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 6.07 0.884 40 4.28 7.86
## farm_2 3.57 0.802 40 1.95 5.19
## farm_3 7.41 0.997 40 5.39 9.42
## farm_4 12.00 2.339 40 7.27 16.73
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.50 1.19 40 2.093 0.2566
## farm_1 - farm_3 -1.34 1.33 40 -1.004 1.0000
## farm_1 - farm_4 -5.93 2.50 40 -2.371 0.1358
## farm_2 - farm_3 -3.84 1.28 40 -2.997 0.0280
## farm_2 - farm_4 -8.43 2.47 40 -3.408 0.0090
## farm_3 - farm_4 -4.59 2.54 40 -1.806 0.4710
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 76644399 25548133 8.5711 0.0001624 ***
## Residuals 40 119228915 2980723
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2812 461 40 1880 3745
## farm_2 1311 419 40 465 2158
## farm_3 2850 521 40 1798 3902
## farm_4 7500 1221 40 5033 9967
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1501.1 623 40 2.409 0.1241
## farm_1 - farm_3 -37.5 696 40 -0.054 1.0000
## farm_1 - farm_4 -4687.5 1305 40 -3.592 0.0053
## farm_2 - farm_3 -1538.6 668 40 -2.303 0.1593
## farm_2 - farm_4 -6188.6 1291 40 -4.795 0.0001
## farm_3 - farm_4 -4650.0 1327 40 -3.504 0.0069
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.05348 0.017826 0.8752 0.462
## Residuals 40 0.81471 0.020368
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.445 0.0381 40 0.368 0.522
## farm_2 0.454 0.0346 40 0.384 0.524
## farm_3 0.376 0.0430 40 0.289 0.463
## farm_4 0.497 0.1009 40 0.293 0.700
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0096 0.0515 40 -0.186 1.0000
## farm_1 - farm_3 0.0687 0.0575 40 1.195 1.0000
## farm_1 - farm_4 -0.0519 0.1079 40 -0.481 1.0000
## farm_2 - farm_3 0.0783 0.0552 40 1.418 0.9829
## farm_2 - farm_4 -0.0423 0.1067 40 -0.396 1.0000
## farm_3 - farm_4 -0.1206 0.1097 40 -1.099 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.27025 0.090083 4.0653 0.01301 *
## Residuals 40 0.88635 0.022159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.367 0.0398 40 0.2864 0.447
## farm_2 0.492 0.0361 40 0.4194 0.565
## farm_3 0.389 0.0449 40 0.2985 0.480
## farm_4 0.161 0.1053 40 -0.0519 0.374
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.1256 0.0537 40 -2.337 0.1471
## farm_1 - farm_3 -0.0224 0.0600 40 -0.373 1.0000
## farm_1 - farm_4 0.2059 0.1125 40 1.830 0.4481
## farm_2 - farm_3 0.1032 0.0576 40 1.791 0.4850
## farm_2 - farm_4 0.3315 0.1113 40 2.979 0.0294
## farm_3 - farm_4 0.2283 0.1144 40 1.995 0.3170
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.8676 0.28922 0.8199 0.4906
## Residuals 40 14.1096 0.35274
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.50 0.159 40 1.18 1.82
## farm_2 1.35 0.144 40 1.06 1.64
## farm_3 1.55 0.179 40 1.18 1.91
## farm_4 2.00 0.420 40 1.15 2.85
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.1471 0.214 40 0.686 1.0000
## farm_1 - farm_3 -0.0455 0.239 40 -0.190 1.0000
## farm_1 - farm_4 -0.5000 0.449 40 -1.114 1.0000
## farm_2 - farm_3 -0.1925 0.230 40 -0.838 1.0000
## farm_2 - farm_4 -0.6471 0.444 40 -1.457 0.9169
## farm_3 - farm_4 -0.4545 0.457 40 -0.996 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 1.2269 0.40896 2.181 0.1053
## Residuals 40 7.5004 0.18751
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.143 0.116 40 -0.091 0.377
## farm_2 0.235 0.105 40 0.023 0.448
## farm_3 0.545 0.131 40 0.282 0.809
## farm_4 0.000 0.306 40 -0.619 0.619
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0924 0.156 40 -0.591 1.0000
## farm_1 - farm_3 -0.4026 0.174 40 -2.308 0.1577
## farm_1 - farm_4 0.1429 0.327 40 0.436 1.0000
## farm_2 - farm_3 -0.3102 0.168 40 -1.851 0.4293
## farm_2 - farm_4 0.2353 0.324 40 0.727 1.0000
## farm_3 - farm_4 0.5455 0.333 40 1.639 0.6548
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.7431 0.24771 1.0837 0.367
## Residuals 40 9.1432 0.22858
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.286 0.128 40 0.027465 0.544
## farm_2 0.235 0.116 40 0.000937 0.470
## farm_3 0.545 0.144 40 0.254110 0.837
## farm_4 0.500 0.338 40 -0.183262 1.183
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0504 0.173 40 0.292 1.0000
## farm_1 - farm_3 -0.2597 0.193 40 -1.348 1.0000
## farm_1 - farm_4 -0.2143 0.361 40 -0.593 1.0000
## farm_2 - farm_3 -0.3102 0.185 40 -1.677 0.6086
## farm_2 - farm_4 -0.2647 0.357 40 -0.741 1.0000
## farm_3 - farm_4 0.0455 0.368 40 0.124 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.3514 0.11714 0.5594 0.6449
## Residuals 40 8.3759 0.20940
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.714 0.122 40 0.467 0.961
## farm_2 0.647 0.111 40 0.423 0.871
## farm_3 0.818 0.138 40 0.539 1.097
## farm_4 1.000 0.324 40 0.346 1.654
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0672 0.165 40 0.407 1.0000
## farm_1 - farm_3 -0.1039 0.184 40 -0.564 1.0000
## farm_1 - farm_4 -0.2857 0.346 40 -0.826 1.0000
## farm_2 - farm_3 -0.1711 0.177 40 -0.966 1.0000
## farm_2 - farm_4 -0.3529 0.342 40 -1.032 1.0000
## farm_3 - farm_4 -0.1818 0.352 40 -0.517 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 4.9983 1.6661 2.307 0.0912 .
## Residuals 40 28.8881 0.7222
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.14 0.227 40 0.684 1.60
## farm_2 1.12 0.206 40 0.701 1.53
## farm_3 1.91 0.256 40 1.391 2.43
## farm_4 1.50 0.601 40 0.286 2.71
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0252 0.307 40 0.082 1.0000
## farm_1 - farm_3 -0.7662 0.342 40 -2.238 0.1852
## farm_1 - farm_4 -0.3571 0.642 40 -0.556 1.0000
## farm_2 - farm_3 -0.7914 0.329 40 -2.407 0.1248
## farm_2 - farm_4 -0.3824 0.635 40 -0.602 1.0000
## farm_3 - farm_4 0.4091 0.653 40 0.626 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.8661 0.28871 1.564 0.2131
## Residuals 40 7.3839 0.18460
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.786 0.115 40 0.554 1.018
## farm_2 0.588 0.104 40 0.378 0.799
## farm_3 0.909 0.130 40 0.647 1.171
## farm_4 1.000 0.304 40 0.386 1.614
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.1975 0.155 40 1.274 1.0000
## farm_1 - farm_3 -0.1234 0.173 40 -0.713 1.0000
## farm_1 - farm_4 -0.2143 0.325 40 -0.660 1.0000
## farm_2 - farm_3 -0.3209 0.166 40 -1.930 0.3644
## farm_2 - farm_4 -0.4118 0.321 40 -1.282 1.0000
## farm_3 - farm_4 -0.0909 0.330 40 -0.275 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 1.1757 0.39190 2.3927 0.08273 .
## Residuals 40 6.5516 0.16379
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.4286 0.1082 40 0.2100 0.647
## farm_2 0.0588 0.0982 40 -0.1396 0.257
## farm_3 0.2727 0.1220 40 0.0261 0.519
## farm_4 0.0000 0.2862 40 -0.5784 0.578
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.3697 0.146 40 2.531 0.0924
## farm_1 - farm_3 0.1558 0.163 40 0.956 1.0000
## farm_1 - farm_4 0.4286 0.306 40 1.401 1.0000
## farm_2 - farm_3 -0.2139 0.157 40 -1.366 1.0000
## farm_2 - farm_4 0.0588 0.303 40 0.194 1.0000
## farm_3 - farm_4 0.2727 0.311 40 0.877 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.8621 0.28737 1.2334 0.3102
## Residuals 40 9.3197 0.23299
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.786 0.129 40 0.525 1.046
## farm_2 0.529 0.117 40 0.293 0.766
## farm_3 0.545 0.146 40 0.251 0.840
## farm_4 1.000 0.341 40 0.310 1.690
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.256 0.174 40 1.471 0.8943
## farm_1 - farm_3 0.240 0.194 40 1.235 1.0000
## farm_1 - farm_4 -0.214 0.365 40 -0.587 1.0000
## farm_2 - farm_3 -0.016 0.187 40 -0.086 1.0000
## farm_2 - farm_4 -0.471 0.361 40 -1.304 1.0000
## farm_3 - farm_4 -0.455 0.371 40 -1.225 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.2659 0.088617 0.4189 0.7404
## Residuals 40 8.4614 0.211536
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.286 0.123 40 0.03728 0.534
## farm_2 0.235 0.112 40 0.00984 0.461
## farm_3 0.364 0.139 40 0.08337 0.644
## farm_4 0.000 0.325 40 -0.65729 0.657
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0504 0.166 40 0.304 1.0000
## farm_1 - farm_3 -0.0779 0.185 40 -0.420 1.0000
## farm_1 - farm_4 0.2857 0.348 40 0.822 1.0000
## farm_2 - farm_3 -0.1283 0.178 40 -0.721 1.0000
## farm_2 - farm_4 0.2353 0.344 40 0.684 1.0000
## farm_3 - farm_4 0.3636 0.354 40 1.029 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.3579 0.1193 0.4858 0.6941
## Residuals 40 9.8239 0.2456
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.357 0.132 40 0.0895 0.625
## farm_2 0.353 0.120 40 0.1100 0.596
## farm_3 0.455 0.149 40 0.1526 0.757
## farm_4 0.000 0.350 40 -0.7082 0.708
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0042 0.179 40 0.023 1.0000
## farm_1 - farm_3 -0.0974 0.200 40 -0.488 1.0000
## farm_1 - farm_4 0.3571 0.375 40 0.953 1.0000
## farm_2 - farm_3 -0.1016 0.192 40 -0.530 1.0000
## farm_2 - farm_4 0.3529 0.370 40 0.953 1.0000
## farm_3 - farm_4 0.4545 0.381 40 1.193 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 0.9528 0.31761 1.5481 0.217
## Residuals 40 8.2063 0.20516
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.357 0.121 40 0.112 0.602
## farm_2 0.118 0.110 40 -0.104 0.340
## farm_3 0.455 0.137 40 0.179 0.731
## farm_4 0.500 0.320 40 -0.147 1.147
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.2395 0.163 40 1.465 0.9043
## farm_1 - farm_3 -0.0974 0.182 40 -0.534 1.0000
## farm_1 - farm_4 -0.1429 0.342 40 -0.417 1.0000
## farm_2 - farm_3 -0.3369 0.175 40 -1.922 0.3703
## farm_2 - farm_4 -0.3824 0.339 40 -1.129 1.0000
## farm_3 - farm_4 -0.0455 0.348 40 -0.131 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 3 12.644 4.2148 2.2106 0.1018
## Residuals 40 76.265 1.9066
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.00 0.369 40 2.254 3.75
## farm_2 1.88 0.335 40 1.206 2.56
## farm_3 3.00 0.416 40 2.159 3.84
## farm_4 2.50 0.976 40 0.527 4.47
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.118 0.498 40 2.243 0.1831
## farm_1 - farm_3 0.000 0.556 40 0.000 1.0000
## farm_1 - farm_4 0.500 1.044 40 0.479 1.0000
## farm_2 - farm_3 -1.118 0.534 40 -2.092 0.2571
## farm_2 - farm_4 -0.618 1.032 40 -0.598 1.0000
## farm_3 - farm_4 0.500 1.061 40 0.471 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "================ Number of farm types = 3================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 19.58 9.788 0.189 0.8285
## Residuals 41 2122.97 51.780
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 44.3 1.44 41 41.4 47.2
## farm_2 45.3 1.75 41 41.8 48.8
## farm_3 47.0 5.09 41 36.7 57.3
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.974 2.26 41 -0.431 1.0000
## farm_1 - farm_3 -2.680 5.29 41 -0.507 1.0000
## farm_2 - farm_3 -1.706 5.38 41 -0.317 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 37.488 18.744 4.5728 0.01612 *
## Residuals 41 168.058 4.099
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 6.68 0.405 41 5.86 7.5
## farm_2 5.41 0.491 41 4.42 6.4
## farm_3 9.50 1.432 41 6.61 12.4
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.27 0.636 41 1.993 0.1590
## farm_1 - farm_3 -2.82 1.488 41 -1.895 0.1953
## farm_2 - farm_3 -4.09 1.513 41 -2.701 0.0300
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 7.1474e+13 3.5737e+13 209.68 < 2.2e-16 ***
## Residuals 41 6.9879e+12 1.7044e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1587400 82568 41 1420650 1754150
## farm_2 349000 100129 41 146786 551214
## farm_3 6500000 291922 41 5910451 7089549
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1238400 129781 41 9.542 <.0001
## farm_1 - farm_3 -4912600 303375 41 -16.193 <.0001
## farm_2 - farm_3 -6151000 308617 41 -19.931 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 3.8355e+13 1.9178e+13 94.41 4.505e-16 ***
## Residuals 41 8.3284e+12 2.0313e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1308000 90140 41 1125958 1490042
## farm_2 308529 109311 41 87771 529288
## farm_3 4750000 318694 41 4106385 5393615
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 999471 141683 41 7.054 <.0001
## farm_1 - farm_3 -3442000 331196 41 -10.393 <.0001
## farm_2 - farm_3 -4441471 336919 41 -13.183 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.07627 0.038137 1.2526 0.2965
## Residuals 41 1.24829 0.030446
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.829 0.0349 41 0.758 0.899
## farm_2 0.897 0.0423 41 0.811 0.982
## farm_3 0.732 0.1234 41 0.483 0.981
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0680 0.0549 41 -1.239 0.6668
## farm_1 - farm_3 0.0968 0.1282 41 0.755 1.0000
## farm_2 - farm_3 0.1648 0.1304 41 1.263 0.6408
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 14.78 7.3904 0.346 0.7096
## Residuals 41 875.76 21.3601
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.200 0.924 41 -0.667 3.07
## farm_2 0.118 1.121 41 -2.146 2.38
## farm_3 2.000 3.268 41 -4.600 8.60
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.08 1.45 41 0.745 1.0000
## farm_1 - farm_3 -0.80 3.40 41 -0.236 1.0000
## farm_2 - farm_3 -1.88 3.45 41 -0.545 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 160.67 80.334 14.766 1.48e-05 ***
## Residuals 41 223.06 5.440
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2.600 0.466 41 1.658 3.54
## farm_2 0.765 0.566 41 -0.378 1.91
## farm_3 10.000 1.649 41 6.669 13.33
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.84 0.733 41 2.503 0.0492
## farm_1 - farm_3 -7.40 1.714 41 -4.317 0.0003
## farm_2 - farm_3 -9.24 1.744 41 -5.297 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 21.285 10.643 2.4182 0.1017
## Residuals 41 180.442 4.401
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.760 0.420 41 0.913 2.61
## farm_2 0.353 0.509 41 -0.675 1.38
## farm_3 2.000 1.483 41 -0.996 5.00
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.41 0.659 41 2.134 0.1167
## farm_1 - farm_3 -0.24 1.542 41 -0.156 1.0000
## farm_2 - farm_3 -1.65 1.568 41 -1.050 0.8993
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 528.08 264.039 6.2183 0.004379 **
## Residuals 41 1740.92 42.462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 9.72 1.30 41 7.088 12.35
## farm_2 3.35 1.58 41 0.161 6.54
## farm_3 15.00 4.61 41 5.695 24.31
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 6.37 2.05 41 3.108 0.0102
## farm_1 - farm_3 -5.28 4.79 41 -1.103 0.8298
## farm_2 - farm_3 -11.65 4.87 41 -2.391 0.0644
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 21.71 10.857 0.9055 0.4123
## Residuals 41 491.61 11.991
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.461 0.693 41 0.0626 2.86
## farm_2 0.245 0.840 41 -1.4508 1.94
## farm_3 2.850 2.449 41 -2.0949 7.79
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.22 1.09 41 1.117 0.8115
## farm_1 - farm_3 -1.39 2.54 41 -0.546 1.0000
## farm_2 - farm_3 -2.60 2.59 41 -1.006 0.9606
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.3457 0.17283 0.5127 0.6027
## Residuals 41 13.8214 0.33711
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.2521 0.116 41 0.0176 0.487
## farm_2 0.0697 0.141 41 -0.2147 0.354
## farm_3 0.2472 0.411 41 -0.5819 1.076
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.18238 0.183 41 0.999 0.9706
## farm_1 - farm_3 0.00486 0.427 41 0.011 1.0000
## farm_2 - farm_3 -0.17752 0.434 41 -0.409 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 179.29 89.644 8.1934 0.001015 **
## Residuals 41 448.58 10.941
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 6.66 0.662 41 5.32 8.00
## farm_2 3.57 0.802 41 1.95 5.19
## farm_3 12.00 2.339 41 7.28 16.72
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 3.09 1.04 41 2.968 0.0149
## farm_1 - farm_3 -5.34 2.43 41 -2.197 0.1012
## farm_2 - farm_3 -8.43 2.47 41 -3.408 0.0044
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 76635736 38317868 13.176 3.81e-05 ***
## Residuals 41 119237578 2908234
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2829 341 41 2140 3518
## farm_2 1311 414 41 476 2147
## farm_3 7500 1206 41 5065 9935
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1518 536 41 2.831 0.0215
## farm_1 - farm_3 -4671 1253 41 -3.727 0.0018
## farm_2 - farm_3 -6189 1275 41 -4.854 0.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.02438 0.012188 0.5922 0.5578
## Residuals 41 0.84382 0.020581
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.414 0.0287 41 0.356 0.472
## farm_2 0.454 0.0348 41 0.384 0.524
## farm_3 0.497 0.1014 41 0.292 0.701
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0398 0.0451 41 -0.883 1.0000
## farm_1 - farm_3 -0.0821 0.1054 41 -0.779 1.0000
## farm_2 - farm_3 -0.0423 0.1072 41 -0.394 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.26716 0.133580 6.1576 0.004588 **
## Residuals 41 0.88944 0.021694
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.377 0.0295 41 0.3171 0.436
## farm_2 0.492 0.0357 41 0.4202 0.564
## farm_3 0.161 0.1041 41 -0.0495 0.371
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.116 0.0463 41 -2.499 0.0497
## farm_1 - farm_3 0.216 0.1082 41 1.994 0.1586
## farm_2 - farm_3 0.332 0.1101 41 3.011 0.0133
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.8549 0.42746 1.241 0.2997
## Residuals 41 14.1224 0.34445
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.52 0.117 41 1.28 1.76
## farm_2 1.35 0.142 41 1.07 1.64
## farm_3 2.00 0.415 41 1.16 2.84
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.167 0.184 41 0.905 1.0000
## farm_1 - farm_3 -0.480 0.431 41 -1.113 0.8166
## farm_2 - farm_3 -0.647 0.439 41 -1.475 0.4437
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.2284 0.11422 0.551 0.5806
## Residuals 41 8.4988 0.20729
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.320 0.0911 41 0.1361 0.504
## farm_2 0.235 0.1104 41 0.0123 0.458
## farm_3 0.000 0.3219 41 -0.6502 0.650
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0847 0.143 41 0.592 1.0000
## farm_1 - farm_3 0.3200 0.335 41 0.956 1.0000
## farm_2 - farm_3 0.2353 0.340 41 0.691 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.3275 0.16377 0.7024 0.5012
## Residuals 41 9.5588 0.23314
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.400 0.0966 41 0.20497 0.595
## farm_2 0.235 0.1171 41 -0.00121 0.472
## farm_3 0.500 0.3414 41 -0.18952 1.190
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.165 0.152 41 1.085 0.8527
## farm_1 - farm_3 -0.100 0.355 41 -0.282 1.0000
## farm_2 - farm_3 -0.265 0.361 41 -0.733 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.2849 0.14246 0.6919 0.5064
## Residuals 41 8.4424 0.20591
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.760 0.0908 41 0.577 0.943
## farm_2 0.647 0.1101 41 0.425 0.869
## farm_3 1.000 0.3209 41 0.352 1.648
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.113 0.143 41 0.792 1.0000
## farm_1 - farm_3 -0.240 0.333 41 -0.720 1.0000
## farm_2 - farm_3 -0.353 0.339 41 -1.040 0.9127
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 1.382 0.69083 0.8714 0.426
## Residuals 41 32.505 0.79280
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.48 0.178 41 1.120 1.84
## farm_2 1.12 0.216 41 0.682 1.55
## farm_3 1.50 0.630 41 0.228 2.77
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.362 0.280 41 1.295 0.6082
## farm_1 - farm_3 -0.020 0.654 41 -0.031 1.0000
## farm_2 - farm_3 -0.382 0.666 41 -0.574 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.7724 0.38618 2.1174 0.1333
## Residuals 41 7.4776 0.18238
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.840 0.0854 41 0.668 1.012
## farm_2 0.588 0.1036 41 0.379 0.797
## farm_3 1.000 0.3020 41 0.390 1.610
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.252 0.134 41 1.875 0.2037
## farm_1 - farm_3 -0.160 0.314 41 -0.510 1.0000
## farm_2 - farm_3 -0.412 0.319 41 -1.290 0.6130
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 1.0261 0.51305 3.139 0.0539 .
## Residuals 41 6.7012 0.16344
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.3600 0.0809 41 0.197 0.523
## farm_2 0.0588 0.0981 41 -0.139 0.257
## farm_3 0.0000 0.2859 41 -0.577 0.577
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.3012 0.127 41 2.370 0.0678
## farm_1 - farm_3 0.3600 0.297 41 1.212 0.6976
## farm_2 - farm_3 0.0588 0.302 41 0.195 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.5065 0.25326 1.0732 0.3513
## Residuals 41 9.6753 0.23598
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.680 0.0972 41 0.484 0.876
## farm_2 0.529 0.1178 41 0.291 0.767
## farm_3 1.000 0.3435 41 0.306 1.694
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.151 0.153 41 0.986 0.9896
## farm_1 - farm_3 -0.320 0.357 41 -0.896 1.0000
## farm_2 - farm_3 -0.471 0.363 41 -1.296 0.6068
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.2284 0.11422 0.551 0.5806
## Residuals 41 8.4988 0.20729
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.320 0.0911 41 0.1361 0.504
## farm_2 0.235 0.1104 41 0.0123 0.458
## farm_3 0.000 0.3219 41 -0.6502 0.650
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0847 0.143 41 0.592 1.0000
## farm_1 - farm_3 0.3200 0.335 41 0.956 1.0000
## farm_2 - farm_3 0.2353 0.340 41 0.691 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.2995 0.14973 0.6212 0.5423
## Residuals 41 9.8824 0.24103
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.400 0.0982 41 0.202 0.598
## farm_2 0.353 0.1191 41 0.112 0.593
## farm_3 0.000 0.3472 41 -0.701 0.701
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0471 0.154 41 0.305 1.0000
## farm_1 - farm_3 0.4000 0.361 41 1.109 0.8220
## farm_2 - farm_3 0.3529 0.367 41 0.962 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 0.8944 0.44719 2.2185 0.1217
## Residuals 41 8.2647 0.20158
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.400 0.0898 41 0.219 0.581
## farm_2 0.118 0.1089 41 -0.102 0.338
## farm_3 0.500 0.3175 41 -0.141 1.141
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.282 0.141 41 2.001 0.1563
## farm_1 - farm_3 -0.100 0.330 41 -0.303 1.0000
## farm_2 - farm_3 -0.382 0.336 41 -1.139 0.7837
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 2 12.644 6.3222 3.3988 0.04308 *
## Residuals 41 76.265 1.8601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.00 0.273 41 2.449 3.55
## farm_2 1.88 0.331 41 1.214 2.55
## farm_3 2.50 0.964 41 0.552 4.45
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.118 0.429 41 2.607 0.0381
## farm_1 - farm_3 0.500 1.002 41 0.499 1.0000
## farm_2 - farm_3 -0.618 1.020 41 -0.606 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "================ Number of farm types = 2================"
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 6.28 6.275 0.1234 0.7272
## Residuals 42 2136.27 50.864
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 44.5 1.37 42 41.7 47.3
## farm_2 45.3 1.73 42 41.8 48.8
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.776 2.21 42 -0.351 0.7272
##
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 22.761 22.761 5.23 0.02731 *
## Residuals 42 182.784 4.352
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 6.89 0.401 42 6.08 7.70
## farm_2 5.41 0.506 42 4.39 6.43
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.48 0.646 42 2.287 0.0273
##
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 2.6782e+13 2.6782e+13 21.766 3.131e-05 ***
## Residuals 42 5.1680e+13 1.2305e+12
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1951296 213478 42 1520479 2382113
## farm_2 349000 269037 42 -193938 891938
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1602296 343444 42 4.665 <.0001
##
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 1.6416e+13 1.6416e+13 22.778 2.219e-05 ***
## Residuals 42 3.0268e+13 7.2067e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1562963 163375 42 1233259 1892667
## farm_2 308529 205893 42 -106980 724039
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1254434 262837 42 4.773 <.0001
##
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.05892 0.058919 1.9552 0.1694
## Residuals 42 1.26564 0.030134
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.822 0.0334 42 0.754 0.889
## farm_2 0.897 0.0421 42 0.812 0.982
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0752 0.0537 42 -1.398 0.1694
##
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 13.60 13.596 0.6511 0.4243
## Residuals 42 876.95 20.880
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.259 0.879 42 -0.515 3.03
## farm_2 0.118 1.108 42 -2.119 2.35
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.14 1.41 42 0.807 0.4243
##
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 59.26 59.261 7.6709 0.00832 **
## Residuals 42 324.47 7.725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.148 0.535 42 2.069 4.23
## farm_2 0.765 0.674 42 -0.596 2.13
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 2.38 0.861 42 2.770 0.0083
##
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 21.178 21.1783 4.9266 0.0319 *
## Residuals 42 180.549 4.2988
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.778 0.399 42 0.973 2.58
## farm_2 0.353 0.503 42 -0.662 1.37
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.42 0.642 42 2.220 0.0319
##
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 476.45 476.45 11.163 0.00176 **
## Residuals 42 1792.55 42.68
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 10.11 1.26 42 7.574 12.65
## farm_2 3.35 1.58 42 0.155 6.55
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 6.76 2.02 42 3.341 0.0018
##
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 18.14 18.143 1.5388 0.2217
## Residuals 42 495.18 11.790
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.564 0.661 42 0.231 2.90
## farm_2 0.245 0.833 42 -1.435 1.93
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.32 1.06 42 1.240 0.2217
##
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.3456 0.34562 1.0502 0.3113
## Residuals 42 13.8214 0.32908
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.2517 0.110 42 0.0289 0.474
## farm_2 0.0697 0.139 42 -0.2111 0.350
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.182 0.178 42 1.025 0.3113
##
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 126.48 126.481 10.595 0.002244 **
## Residuals 42 501.39 11.938
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 7.06 0.665 42 5.71 8.40
## farm_2 3.57 0.838 42 1.88 5.26
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 3.48 1.07 42 3.255 0.0022
##
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 36231586 36231586 9.5321 0.00357 **
## Residuals 42 159641728 3800994
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3175 375 42 2418 3932
## farm_2 1311 473 42 357 2266
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1864 604 42 3.087 0.0036
##
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.01189 0.011888 0.5831 0.4494
## Residuals 42 0.85630 0.020388
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.420 0.0275 42 0.365 0.476
## farm_2 0.454 0.0346 42 0.384 0.524
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0338 0.0442 42 -0.764 0.4494
##
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.18092 0.18092 7.7883 0.007877 **
## Residuals 42 0.97567 0.02323
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.361 0.0293 42 0.301 0.420
## farm_2 0.492 0.0370 42 0.418 0.567
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.132 0.0472 42 -2.791 0.0079
##
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.4283 0.42825 1.2363 0.2725
## Residuals 42 14.5490 0.34641
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.56 0.113 42 1.33 1.78
## farm_2 1.35 0.143 42 1.06 1.64
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.203 0.182 42 1.112 0.2725
##
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.0388 0.03882 0.1877 0.6671
## Residuals 42 8.6885 0.20687
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.296 0.0875 42 0.1197 0.473
## farm_2 0.235 0.1103 42 0.0127 0.458
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.061 0.141 42 0.433 0.6671
##
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.3090 0.30902 1.3552 0.2509
## Residuals 42 9.5773 0.22803
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.407 0.0919 42 0.22195 0.593
## farm_2 0.235 0.1158 42 0.00157 0.469
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.172 0.148 42 1.164 0.2509
##
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.1783 0.17825 0.8757 0.3547
## Residuals 42 8.5490 0.20355
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.778 0.0868 42 0.603 0.953
## farm_2 0.647 0.1094 42 0.426 0.868
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.131 0.14 42 0.936 0.3547
##
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 1.381 1.38092 1.7843 0.1888
## Residuals 42 32.505 0.77394
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.48 0.169 42 1.140 1.82
## farm_2 1.12 0.213 42 0.687 1.55
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.364 0.272 42 1.336 0.1888
##
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.7249 0.72495 4.0462 0.05072 .
## Residuals 42 7.5251 0.17917
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.852 0.0815 42 0.687 1.016
## farm_2 0.588 0.1027 42 0.381 0.795
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.264 0.131 42 2.012 0.0507
##
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.7861 0.78610 4.7565 0.03484 *
## Residuals 42 6.9412 0.16527
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.3333 0.0782 42 0.175 0.491
## farm_2 0.0588 0.0986 42 -0.140 0.258
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.275 0.126 42 2.181 0.0348
##
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.3169 0.31689 1.3492 0.252
## Residuals 42 9.8649 0.23488
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.704 0.0933 42 0.515 0.892
## farm_2 0.529 0.1175 42 0.292 0.767
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.174 0.15 42 1.162 0.2520
##
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.0388 0.03882 0.1877 0.6671
## Residuals 42 8.6885 0.20687
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.296 0.0875 42 0.1197 0.473
## farm_2 0.235 0.1103 42 0.0127 0.458
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.061 0.141 42 0.433 0.6671
##
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.0032 0.003169 0.0131 0.9095
## Residuals 42 10.1786 0.242349
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.370 0.0947 42 0.179 0.562
## farm_2 0.353 0.1194 42 0.112 0.594
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0174 0.152 42 0.114 0.9095
##
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 0.8759 0.87587 4.4411 0.0411 *
## Residuals 42 8.2832 0.19722
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.407 0.0855 42 0.2349 0.580
## farm_2 0.118 0.1077 42 -0.0997 0.335
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.29 0.137 42 2.107 0.0411
##
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_4[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_4[["farm_type"]] 1 12.181 12.1814 6.668 0.01339 *
## Residuals 42 76.728 1.8268
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2.96 0.260 42 2.44 3.49
## farm_2 1.88 0.328 42 1.22 2.54
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 1.08 0.418 42 2.582 0.0134################################################################################
# Mock k-means clustering
eg_kmeans <- pca_df
# decide on the number of clusters to make: 2, 3, 4 or more
typo_5 <- eg_kmeans
for(cl in 2:4){
kmeans_result <- kmeans(eg_kmeans, centers = cl)
clusters <- as.factor(kmeans_result$cluster)
typo_5$farm_type <- paste0('farm_', clusters)
for(i in names(eg_kmeans)){
res_anova <- lm(typo_5[[i]] ~ typo_5[['farm_type']])
print(paste0('--------------------', i, '----------------'))
print(anova(res_anova))
print('-----------')
print(emmeans::emmeans(res_anova,
pairwise ~ farm_type,
type = 'response',
adjust = 'bonferroni'))
}
ggplot(typo_5, aes(hh_size, cultivated_area_ha, colour = farm_type)) +
geom_point() +
theme_bw() +
labs(title = paste0("kmeans clustering with ", cl, ' clusters') )
ggplot(typo_5, aes(kmeans_cluster, cultivated_area_ha, colour = farm_type)) +
geom_boxplot() +
theme_bw() +
labs(title = paste0("kmeans clustering with ", cl, ' clusters') )
}## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 9.97 9.974 0.1964 0.6599
## Residuals 42 2132.57 50.776
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 44.7 1.10 42 42.5 46.9
## farm_2 47.0 5.04 42 36.8 57.2
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -2.29 5.16 42 -0.443 0.6599
##
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 21.212 21.2121 4.8331 0.03348 *
## Residuals 42 184.333 4.3889
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 6.17 0.323 42 5.51 6.82
## farm_2 9.50 1.481 42 6.51 12.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -3.33 1.52 42 -2.198 0.0335
##
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 5.5955e+13 5.5955e+13 104.42 5.862e-13 ***
## Residuals 42 2.2507e+13 5.3588e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1086143 112956 42 858189 1314097
## farm_2 6500000 517628 42 5455385 7544615
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -5413857 529809 42 -10.219 <.0001
##
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 2.8247e+13 2.8247e+13 64.348 5.218e-10 ***
## Residuals 42 1.8437e+13 4.3897e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 903452 102233 42 697137 1109768
## farm_2 4750000 468492 42 3804545 5695455
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -3846548 479517 42 -8.022 <.0001
##
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.02951 0.029507 0.957 0.3336
## Residuals 42 1.29506 0.030835
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.856 0.0271 42 0.802 0.911
## farm_2 0.732 0.1242 42 0.482 0.983
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.124 0.127 42 0.978 0.3336
##
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 2.93 2.9264 0.1385 0.7117
## Residuals 42 887.62 21.1338
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.762 0.709 42 -0.67 2.19
## farm_2 2.000 3.251 42 -4.56 8.56
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.24 3.33 42 -0.372 0.7117
##
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 126.58 126.584 20.675 4.566e-05 ***
## Residuals 42 257.14 6.122
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.86 0.382 42 1.09 2.63
## farm_2 10.00 1.750 42 6.47 13.53
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -8.14 1.79 42 -4.547 <.0001
##
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 1.251 1.2511 0.2621 0.6114
## Residuals 42 200.476 4.7732
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.19 0.337 42 0.51 1.87
## farm_2 2.00 1.545 42 -1.12 5.12
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.81 1.58 42 -0.512 0.6114
##
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 117.86 117.857 2.3011 0.1368
## Residuals 42 2151.14 51.218
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 7.14 1.10 42 4.91 9.37
## farm_2 15.00 5.06 42 4.79 25.21
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -7.86 5.18 42 -1.517 0.1368
##
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 6.75 6.7543 0.56 0.4584
## Residuals 42 506.57 12.0612
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.969 0.536 42 -0.112 2.05
## farm_2 2.850 2.456 42 -2.106 7.81
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.88 2.51 42 -0.748 0.4584
##
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.0091 0.00908 0.0269 0.8704
## Residuals 42 14.1580 0.33709
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.178 0.0896 42 -0.00255 0.359
## farm_2 0.247 0.4105 42 -0.58131 1.076
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.069 0.42 42 -0.164 0.8704
##
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 82.89 82.890 6.3881 0.01534 *
## Residuals 42 544.98 12.976
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 5.41 0.556 42 4.29 6.53
## farm_2 12.00 2.547 42 6.86 17.14
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -6.59 2.61 42 -2.527 0.0153
##
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 53329013 53329013 15.713 0.0002808 ***
## Residuals 42 142544301 3393912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2215 284 42 1641 2788
## farm_2 7500 1303 42 4871 10129
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -5285 1333 42 -3.964 0.0003
##
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.00831 0.0083143 0.4061 0.5274
## Residuals 42 0.85988 0.0204733
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.431 0.0221 42 0.386 0.475
## farm_2 0.497 0.1012 42 0.292 0.701
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.066 0.104 42 -0.637 0.5274
##
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.13168 0.131678 5.396 0.0251 *
## Residuals 42 1.02492 0.024403
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.423 0.0241 42 0.3748 0.472
## farm_2 0.161 0.1105 42 -0.0621 0.384
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.263 0.113 42 2.323 0.0251
##
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.5725 0.57251 1.6693 0.2034
## Residuals 42 14.4048 0.34297
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.45 0.0904 42 1.27 1.63
## farm_2 2.00 0.4141 42 1.16 2.84
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.548 0.424 42 -1.292 0.2034
##
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.1558 0.15584 0.7636 0.3872
## Residuals 42 8.5714 0.20408
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.286 0.0697 42 0.145 0.426
## farm_2 0.000 0.3194 42 -0.645 0.645
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.286 0.327 42 0.874 0.3872
##
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.0530 0.05303 0.2265 0.6366
## Residuals 42 9.8333 0.23413
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.333 0.0747 42 0.183 0.484
## farm_2 0.500 0.3421 42 -0.190 1.190
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.167 0.35 42 -0.476 0.6366
##
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.1558 0.15584 0.7636 0.3872
## Residuals 42 8.5714 0.20408
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.714 0.0697 42 0.574 0.855
## farm_2 1.000 0.3194 42 0.355 1.645
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.286 0.327 42 -0.874 0.3872
##
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.053 0.05303 0.0658 0.7988
## Residuals 42 33.833 0.80556
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.33 0.138 42 1.054 1.61
## farm_2 1.50 0.635 42 0.219 2.78
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.167 0.65 42 -0.257 0.7988
##
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.131 0.13095 0.6774 0.4151
## Residuals 42 8.119 0.19331
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.738 0.0678 42 0.601 0.875
## farm_2 1.000 0.3109 42 0.373 1.627
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.262 0.318 42 -0.823 0.4151
##
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.1082 0.10823 0.5966 0.4442
## Residuals 42 7.6190 0.18141
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.238 0.0657 42 0.105 0.371
## farm_2 0.000 0.3012 42 -0.608 0.608
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.238 0.308 42 0.772 0.4442
##
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.2771 0.27706 1.1748 0.2846
## Residuals 42 9.9048 0.23583
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.619 0.0749 42 0.468 0.77
## farm_2 1.000 0.3434 42 0.307 1.69
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.381 0.351 42 -1.084 0.2846
##
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.1558 0.15584 0.7636 0.3872
## Residuals 42 8.5714 0.20408
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.286 0.0697 42 0.145 0.426
## farm_2 0.000 0.3194 42 -0.645 0.645
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.286 0.327 42 0.874 0.3872
##
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.2771 0.27706 1.1748 0.2846
## Residuals 42 9.9048 0.23583
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.381 0.0749 42 0.230 0.532
## farm_2 0.000 0.3434 42 -0.693 0.693
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.381 0.351 42 1.084 0.2846
##
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.0877 0.087662 0.4059 0.5275
## Residuals 42 9.0714 0.215986
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.286 0.0717 42 0.141 0.43
## farm_2 0.500 0.3286 42 -0.163 1.16
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.214 0.336 42 -0.637 0.5275
##
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 1 0.004 0.00433 0.002 0.9641
## Residuals 42 88.905 2.11678
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 2.55 0.224 42 2.095 3.00
## farm_2 2.50 1.029 42 0.424 4.58
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0476 1.05 42 0.045 0.9641
##
##
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 13.95 6.973 0.1343 0.8747
## Residuals 41 2128.60 51.917
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 45.1 1.65 41 41.7 48.4
## farm_2 47.0 5.09 41 36.7 57.3
## farm_3 44.4 1.50 41 41.4 47.5
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.947 5.36 41 -0.364 1.0000
## farm_1 - farm_3 0.618 2.23 41 0.277 1.0000
## farm_2 - farm_3 2.565 5.31 41 0.483 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 35.439 17.7195 4.2709 0.02066 *
## Residuals 41 170.106 4.1489
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 5.53 0.467 41 4.58 6.47
## farm_2 9.50 1.440 41 6.59 12.41
## farm_3 6.70 0.425 41 5.84 7.55
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -3.97 1.514 41 -2.624 0.0364
## farm_1 - farm_3 -1.17 0.631 41 -1.852 0.2138
## farm_2 - farm_3 2.80 1.502 41 1.868 0.2070
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 7.1958e+13 3.5979e+13 226.81 < 2.2e-16 ***
## Residuals 41 6.5040e+12 1.5863e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 407000 91374 41 222467 591533
## farm_2 6500000 281633 41 5931231 7068769
## farm_3 1647174 83049 41 1479453 1814895
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -6093000 296085 41 -20.579 <.0001
## farm_1 - farm_3 -1240174 123476 41 -10.044 <.0001
## farm_2 - farm_3 4852826 293623 41 16.527 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 3.8484e+13 1.9242e+13 96.214 3.273e-16 ***
## Residuals 41 8.1996e+12 1.9999e+11
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 360263 102595 41 153067 567459
## farm_2 4750000 316220 41 4111380 5388620
## farm_3 1352174 93248 41 1163855 1540493
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -4389737 332447 41 -13.204 <.0001
## farm_1 - farm_3 -991911 138640 41 -7.155 <.0001
## farm_2 - farm_3 3397826 329683 41 10.306 <.0001
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.08723 0.043616 1.4452 0.2474
## Residuals 41 1.23733 0.030179
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.897 0.0399 41 0.817 0.978
## farm_2 0.732 0.1228 41 0.484 0.980
## farm_3 0.823 0.0362 41 0.750 0.896
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.1651 0.1291 41 1.279 0.6248
## farm_1 - farm_3 0.0745 0.0539 41 1.383 0.5224
## farm_2 - farm_3 -0.0906 0.1281 41 -0.708 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 17.89 8.9432 0.4202 0.6597
## Residuals 41 872.66 21.2844
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.105 1.058 41 -2.032 2.24
## farm_2 2.000 3.262 41 -4.588 8.59
## farm_3 1.304 0.962 41 -0.638 3.25
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.895 3.43 41 -0.552 1.0000
## farm_1 - farm_3 -1.199 1.43 41 -0.838 1.0000
## farm_2 - farm_3 0.696 3.40 41 0.205 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 146.20 73.099 12.618 5.367e-05 ***
## Residuals 41 237.53 5.793
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.11 0.552 41 -0.00991 2.22
## farm_2 10.00 1.702 41 6.56281 13.44
## farm_3 2.48 0.502 41 1.46469 3.49
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -8.89 1.789 41 -4.971 <.0001
## farm_1 - farm_3 -1.37 0.746 41 -1.840 0.2190
## farm_2 - farm_3 7.52 1.774 41 4.239 0.0004
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 24.698 12.3488 2.86 0.06875 .
## Residuals 41 177.030 4.3178
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.368 0.477 41 -0.594 1.33
## farm_2 2.000 1.469 41 -0.967 4.97
## farm_3 1.870 0.433 41 0.995 2.74
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.63 1.545 41 -1.056 0.8911
## farm_1 - farm_3 -1.50 0.644 41 -2.330 0.0744
## farm_2 - farm_3 0.13 1.532 41 0.085 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 449.18 224.592 5.06 0.01086 *
## Residuals 41 1819.82 44.386
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 4.05 1.53 41 0.966 7.14
## farm_2 15.00 4.71 41 5.486 24.51
## farm_3 9.70 1.39 41 6.890 12.50
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -10.95 4.95 41 -2.210 0.0981
## farm_1 - farm_3 -5.64 2.07 41 -2.732 0.0277
## farm_2 - farm_3 5.30 4.91 41 1.080 0.8594
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 23.23 11.614 0.9716 0.387
## Residuals 41 490.10 11.954
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.28 0.793 41 -1.3219 1.88
## farm_2 2.85 2.445 41 -2.0873 7.79
## farm_3 1.54 0.721 41 0.0823 2.99
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -2.57 2.57 41 -1.000 0.9696
## farm_1 - farm_3 -1.26 1.07 41 -1.174 0.7416
## farm_2 - farm_3 1.31 2.55 41 0.515 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.3894 0.19470 0.5794 0.5648
## Residuals 41 13.7776 0.33604
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0735 0.133 41 -0.1950 0.342
## farm_2 0.2472 0.410 41 -0.5806 1.075
## farm_3 0.2647 0.121 41 0.0206 0.509
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.1737 0.431 41 -0.403 1.0000
## farm_1 - farm_3 -0.1912 0.180 41 -1.064 0.8809
## farm_2 - farm_3 -0.0175 0.427 41 -0.041 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 178.58 89.290 8.1482 0.001048 **
## Residuals 41 449.29 10.958
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.75 0.759 41 2.22 5.28
## farm_2 12.00 2.341 41 7.27 16.73
## farm_3 6.78 0.690 41 5.39 8.18
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -8.25 2.46 41 -3.352 0.0052
## farm_1 - farm_3 -3.03 1.03 41 -2.955 0.0155
## farm_2 - farm_3 5.22 2.44 41 2.138 0.1156
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 78822380 39411190 13.805 2.607e-05 ***
## Residuals 41 117050934 2854901
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1358 388 41 575 2140
## farm_2 7500 1195 41 5087 9913
## farm_3 2923 352 41 2211 3634
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -6142 1256 41 -4.890 <.0001
## farm_1 - farm_3 -1565 524 41 -2.988 0.0142
## farm_2 - farm_3 4577 1246 41 3.675 0.0021
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.01730 0.0086499 0.4168 0.6619
## Residuals 41 0.85089 0.0207535
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.447 0.033 41 0.380 0.513
## farm_2 0.497 0.102 41 0.291 0.702
## farm_3 0.417 0.030 41 0.357 0.478
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.0499 0.1071 41 -0.466 1.0000
## farm_1 - farm_3 0.0294 0.0447 41 0.658 1.0000
## farm_2 - farm_3 0.0793 0.1062 41 0.747 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.30952 0.15476 7.4907 0.001687 **
## Residuals 41 0.84708 0.02066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.495 0.033 41 0.4285 0.562
## farm_2 0.161 0.102 41 -0.0444 0.366
## farm_3 0.364 0.030 41 0.3038 0.425
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.334 0.1069 41 3.128 0.0097
## farm_1 - farm_3 0.131 0.0446 41 2.934 0.0164
## farm_2 - farm_3 -0.203 0.1060 41 -1.920 0.1854
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.6066 0.30328 0.8653 0.4285
## Residuals 41 14.3707 0.35051
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.42 0.136 41 1.15 1.70
## farm_2 2.00 0.419 41 1.15 2.85
## farm_3 1.48 0.123 41 1.23 1.73
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.5789 0.440 41 -1.315 0.5870
## farm_1 - farm_3 -0.0572 0.184 41 -0.312 1.0000
## farm_2 - farm_3 0.5217 0.436 41 1.195 0.7164
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.1735 0.086748 0.4158 0.6626
## Residuals 41 8.5538 0.208629
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.263 0.1048 41 0.0515 0.475
## farm_2 0.000 0.3230 41 -0.6523 0.652
## farm_3 0.304 0.0952 41 0.1120 0.497
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.2632 0.340 41 0.775 1.0000
## farm_1 - farm_3 -0.0412 0.142 41 -0.291 1.0000
## farm_2 - farm_3 -0.3043 0.337 41 -0.904 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.2239 0.11195 0.475 0.6253
## Residuals 41 9.6625 0.23567
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.263 0.111 41 0.0382 0.488
## farm_2 0.500 0.343 41 -0.1932 1.193
## farm_3 0.391 0.101 41 0.1869 0.596
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.237 0.361 41 -0.656 1.0000
## farm_1 - farm_3 -0.128 0.150 41 -0.851 1.0000
## farm_2 - farm_3 0.109 0.358 41 0.304 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.3932 0.19659 0.9671 0.3887
## Residuals 41 8.3341 0.20327
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.632 0.103 41 0.423 0.840
## farm_2 1.000 0.319 41 0.356 1.644
## farm_3 0.783 0.094 41 0.593 0.972
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.368 0.335 41 -1.099 0.8342
## farm_1 - farm_3 -0.151 0.140 41 -1.081 0.8586
## farm_2 - farm_3 0.217 0.332 41 0.654 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 1.121 0.56046 0.7013 0.5018
## Residuals 41 32.765 0.79916
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.16 0.205 41 0.744 1.57
## farm_2 1.50 0.632 41 0.223 2.78
## farm_3 1.48 0.186 41 1.102 1.85
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.3421 0.665 41 -0.515 1.0000
## farm_1 - farm_3 -0.3204 0.277 41 -1.156 0.7632
## farm_2 - farm_3 0.0217 0.659 41 0.033 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 1.0097 0.50486 2.8589 0.06881 .
## Residuals 41 7.2403 0.17659
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.579 0.0964 41 0.384 0.774
## farm_2 1.000 0.2971 41 0.400 1.600
## farm_3 0.870 0.0876 41 0.693 1.047
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.421 0.312 41 -1.348 0.5553
## farm_1 - farm_3 -0.291 0.130 41 -2.231 0.0937
## farm_2 - farm_3 0.130 0.310 41 0.421 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 1.3016 0.65082 4.1527 0.02279 *
## Residuals 41 6.4256 0.15672
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0526 0.0908 41 -0.131 0.236
## farm_2 0.0000 0.2799 41 -0.565 0.565
## farm_3 0.3913 0.0825 41 0.225 0.558
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0526 0.294 41 0.179 1.0000
## farm_1 - farm_3 -0.3387 0.123 41 -2.760 0.0258
## farm_2 - farm_3 -0.3913 0.292 41 -1.341 0.5621
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 1.0102 0.5051 2.2579 0.1174
## Residuals 41 9.1716 0.2237
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.474 0.1085 41 0.255 0.693
## farm_2 1.000 0.3344 41 0.325 1.675
## farm_3 0.739 0.0986 41 0.540 0.938
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.526 0.352 41 -1.497 0.4262
## farm_1 - farm_3 -0.265 0.147 41 -1.810 0.2327
## farm_2 - farm_3 0.261 0.349 41 0.748 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.1735 0.086748 0.4158 0.6626
## Residuals 41 8.5538 0.208629
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.263 0.1048 41 0.0515 0.475
## farm_2 0.000 0.3230 41 -0.6523 0.652
## farm_3 0.304 0.0952 41 0.1120 0.497
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.2632 0.340 41 0.775 1.0000
## farm_1 - farm_3 -0.0412 0.142 41 -0.291 1.0000
## farm_2 - farm_3 -0.3043 0.337 41 -0.904 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.2825 0.14125 0.585 0.5617
## Residuals 41 9.8993 0.24145
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.368 0.113 41 0.141 0.596
## farm_2 0.000 0.347 41 -0.702 0.702
## farm_3 0.391 0.102 41 0.184 0.598
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.3684 0.365 41 1.009 0.9573
## farm_1 - farm_3 -0.0229 0.152 41 -0.150 1.0000
## farm_2 - farm_3 -0.3913 0.362 41 -1.080 0.8591
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 0.6545 0.32726 1.5777 0.2187
## Residuals 41 8.5046 0.20743
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.158 0.104 41 -0.0531 0.369
## farm_2 0.500 0.322 41 -0.1504 1.150
## farm_3 0.391 0.095 41 0.1995 0.583
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.342 0.339 41 -1.010 0.9546
## farm_1 - farm_3 -0.233 0.141 41 -1.653 0.3178
## farm_2 - farm_3 0.109 0.336 41 0.324 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 2 14.794 7.3968 4.0918 0.02397 *
## Residuals 41 74.116 1.8077
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.89 0.308 41 1.27 2.52
## farm_2 2.50 0.951 41 0.58 4.42
## farm_3 3.09 0.280 41 2.52 3.65
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.605 0.999 41 -0.606 1.0000
## farm_1 - farm_3 -1.192 0.417 41 -2.860 0.0199
## farm_2 - farm_3 -0.587 0.991 41 -0.592 1.0000
##
## P value adjustment: bonferroni method for 3 tests
##
## [1] "--------------------Age----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 44.14 14.714 0.2805 0.8392
## Residuals 40 2098.40 52.460
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 45.3 1.76 40 41.7 48.8
## farm_2 45.0 1.76 40 41.4 48.6
## farm_3 47.0 5.12 40 36.6 57.4
## farm_4 42.9 2.56 40 37.7 48.1
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.294 2.48 40 0.118 1.0000
## farm_1 - farm_3 -1.706 5.41 40 -0.315 1.0000
## farm_1 - farm_4 2.419 3.11 40 0.779 1.0000
## farm_2 - farm_3 -2.000 5.41 40 -0.369 1.0000
## farm_2 - farm_4 2.125 3.11 40 0.684 1.0000
## farm_3 - farm_4 4.125 5.73 40 0.720 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------HHSize----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 41.112 13.7039 3.3336 0.02883 *
## Residuals 40 164.434 4.1108
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 5.41 0.492 40 4.42 6.41
## farm_2 6.94 0.492 40 5.95 7.94
## farm_3 9.50 1.434 40 6.60 12.40
## farm_4 6.12 0.717 40 4.68 7.57
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.529 0.695 40 -2.199 0.2022
## farm_1 - farm_3 -4.088 1.516 40 -2.697 0.0611
## farm_1 - farm_4 -0.713 0.869 40 -0.820 1.0000
## farm_2 - farm_3 -2.559 1.516 40 -1.688 0.5949
## farm_2 - farm_4 0.816 0.869 40 0.939 1.0000
## farm_3 - farm_4 3.375 1.603 40 2.106 0.2494
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------HH_income_----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 7.4922e+13 2.4974e+13 282.18 < 2.2e-16 ***
## Residuals 40 3.5402e+12 8.8504e+10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 349000 72153 40 203172 494828
## farm_2 1332647 72153 40 1186819 1478475
## farm_3 6500000 210362 40 6074843 6925157
## farm_4 2128750 105181 40 1916172 2341328
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -983647 102040 40 -9.640 <.0001
## farm_1 - farm_3 -6151000 222392 40 -27.658 <.0001
## farm_1 - farm_4 -1779750 127551 40 -13.953 <.0001
## farm_2 - farm_3 -5167353 222392 40 -23.235 <.0001
## farm_2 - farm_4 -796103 127551 40 -6.241 <.0001
## farm_3 - farm_4 4371250 235192 40 18.586 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------IncomeFarming----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 4.3017e+13 1.4339e+13 156.44 < 2.2e-16 ***
## Residuals 40 3.6664e+12 9.1660e+10
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 308529 73428 40 160125 456934
## farm_2 1011765 73428 40 863360 1160169
## farm_3 4750000 214079 40 4317330 5182670
## farm_4 1937500 107040 40 1721165 2153835
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -703235 103844 40 -6.772 <.0001
## farm_1 - farm_3 -4441471 226322 40 -19.625 <.0001
## farm_1 - farm_4 -1628971 129804 40 -12.549 <.0001
## farm_2 - farm_3 -3738235 226322 40 -16.517 <.0001
## farm_2 - farm_4 -925735 129804 40 -7.132 <.0001
## farm_3 - farm_4 2812500 239348 40 11.751 <.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercFarmIncome----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.14325 0.047751 1.6169 0.2006
## Residuals 40 1.18131 0.029533
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.897 0.0417 40 0.813 0.981
## farm_2 0.793 0.0417 40 0.709 0.878
## farm_3 0.732 0.1215 40 0.487 0.978
## farm_4 0.904 0.0608 40 0.782 1.027
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.10349 0.0589 40 1.756 0.5208
## farm_1 - farm_3 0.16479 0.1285 40 1.283 1.0000
## farm_1 - farm_4 -0.00747 0.0737 40 -0.101 1.0000
## farm_2 - farm_3 0.06130 0.1285 40 0.477 1.0000
## farm_2 - farm_4 -0.11096 0.0737 40 -1.506 0.8396
## farm_3 - farm_4 -0.17226 0.1359 40 -1.268 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ncattle----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 31.72 10.574 0.4925 0.6895
## Residuals 40 858.82 21.471
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.118 1.12 40 -2.154 2.39
## farm_2 1.765 1.12 40 -0.507 4.04
## farm_3 2.000 3.28 40 -4.622 8.62
## farm_4 0.000 1.64 40 -3.311 3.31
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.647 1.59 40 -1.036 1.0000
## farm_1 - farm_3 -1.882 3.46 40 -0.543 1.0000
## farm_1 - farm_4 0.118 1.99 40 0.059 1.0000
## farm_2 - farm_3 -0.235 3.46 40 -0.068 1.0000
## farm_2 - farm_4 1.765 1.99 40 0.888 1.0000
## farm_3 - farm_4 2.000 3.66 40 0.546 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Ngoats----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 178.32 59.441 11.575 1.342e-05 ***
## Residuals 40 205.40 5.135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.765 0.550 40 -0.346 1.88
## farm_2 3.176 0.550 40 2.066 4.29
## farm_3 10.000 1.602 40 6.762 13.24
## farm_4 1.375 0.801 40 -0.244 2.99
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -2.41 0.777 40 -3.103 0.0211
## farm_1 - farm_3 -9.24 1.694 40 -5.452 <.0001
## farm_1 - farm_4 -0.61 0.972 40 -0.628 1.0000
## farm_2 - farm_3 -6.82 1.694 40 -4.028 0.0015
## farm_2 - farm_4 1.80 0.972 40 1.854 0.4266
## farm_3 - farm_4 8.62 1.791 40 4.814 0.0001
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Npigs----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 22.08 7.3601 1.6388 0.1956
## Residuals 40 179.65 4.4912
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.353 0.514 40 -0.6859 1.39
## farm_2 1.882 0.514 40 0.8435 2.92
## farm_3 2.000 1.499 40 -1.0286 5.03
## farm_4 1.500 0.749 40 -0.0143 3.01
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.529 0.727 40 -2.104 0.2502
## farm_1 - farm_3 -1.647 1.584 40 -1.040 1.0000
## farm_1 - farm_4 -1.147 0.909 40 -1.262 1.0000
## farm_2 - farm_3 -0.118 1.584 40 -0.074 1.0000
## farm_2 - farm_4 0.382 0.909 40 0.421 1.0000
## farm_3 - farm_4 0.500 1.675 40 0.298 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Nchickens----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 636.09 212.029 5.1939 0.003998 **
## Residuals 40 1632.91 40.823
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.35 1.55 40 0.221 6.48
## farm_2 8.29 1.55 40 5.162 11.43
## farm_3 15.00 4.52 40 5.869 24.13
## farm_4 12.75 2.26 40 8.184 17.32
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -4.94 2.19 40 -2.255 0.1782
## farm_1 - farm_3 -11.65 4.78 40 -2.439 0.1157
## farm_1 - farm_4 -9.40 2.74 40 -3.430 0.0085
## farm_2 - farm_3 -6.71 4.78 40 -1.404 1.0000
## farm_2 - farm_4 -4.46 2.74 40 -1.627 0.6700
## farm_3 - farm_4 2.25 5.05 40 0.445 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TLU----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 32.81 10.937 0.9105 0.4446
## Residuals 40 480.52 12.013
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.245 0.841 40 -1.454 1.94
## farm_2 1.918 0.841 40 0.219 3.62
## farm_3 2.850 2.451 40 -2.103 7.80
## farm_4 0.490 1.225 40 -1.987 2.97
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.673 1.19 40 -1.407 1.0000
## farm_1 - farm_3 -2.605 2.59 40 -1.005 1.0000
## farm_1 - farm_4 -0.245 1.49 40 -0.165 1.0000
## farm_2 - farm_3 -0.932 2.59 40 -0.360 1.0000
## farm_2 - farm_4 1.428 1.49 40 0.961 1.0000
## farm_3 - farm_4 2.360 2.74 40 0.861 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TLU_density----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.6647 0.22158 0.6564 0.5837
## Residuals 40 13.5023 0.33756
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0697 0.141 40 -0.2151 0.354
## farm_2 0.3296 0.141 40 0.0448 0.614
## farm_3 0.2472 0.411 40 -0.5831 1.078
## farm_4 0.0874 0.205 40 -0.3278 0.503
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.2599 0.199 40 -1.304 1.0000
## farm_1 - farm_3 -0.1775 0.434 40 -0.409 1.0000
## farm_1 - farm_4 -0.0177 0.249 40 -0.071 1.0000
## farm_2 - farm_3 0.0824 0.434 40 0.190 1.0000
## farm_2 - farm_4 0.2422 0.249 40 0.972 1.0000
## farm_3 - farm_4 0.1598 0.459 40 0.348 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------TotalLand----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 182.65 60.885 5.4702 0.003021 **
## Residuals 40 445.21 11.130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 3.57 0.809 40 1.94 5.21
## farm_2 6.91 0.809 40 5.28 8.55
## farm_3 12.00 2.359 40 7.23 16.77
## farm_4 6.12 1.180 40 3.74 8.51
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -3.338 1.14 40 -2.917 0.0346
## farm_1 - farm_3 -8.426 2.49 40 -3.379 0.0098
## farm_1 - farm_4 -2.551 1.43 40 -1.784 0.4923
## farm_2 - farm_3 -5.088 2.49 40 -2.040 0.2878
## farm_2 - farm_4 0.787 1.43 40 0.550 1.0000
## farm_3 - farm_4 5.875 2.64 40 2.227 0.1896
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------AvgMZ_yield.kgs.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 85725630 28575210 10.377 3.502e-05 ***
## Residuals 40 110147684 2753692
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1311 402 40 498 2125
## farm_2 3243 402 40 2429 4056
## farm_3 7500 1173 40 5128 9872
## farm_4 1950 587 40 764 3136
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1931 569 40 -3.393 0.0094
## farm_1 - farm_3 -6189 1240 40 -4.989 0.0001
## farm_1 - farm_4 -639 711 40 -0.898 1.0000
## farm_2 - farm_3 -4257 1240 40 -3.432 0.0084
## farm_2 - farm_4 1293 711 40 1.817 0.4604
## farm_3 - farm_4 5550 1312 40 4.231 0.0008
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageMaizeArea----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.02911 0.0097044 0.4626 0.71
## Residuals 40 0.83908 0.0209770
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.454 0.0351 40 0.383 0.525
## farm_2 0.424 0.0351 40 0.353 0.495
## farm_3 0.497 0.1024 40 0.290 0.703
## farm_4 0.394 0.0512 40 0.291 0.498
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0304 0.0497 40 0.612 1.0000
## farm_1 - farm_3 -0.0423 0.1083 40 -0.390 1.0000
## farm_1 - farm_4 0.0599 0.0621 40 0.965 1.0000
## farm_2 - farm_3 -0.0727 0.1083 40 -0.671 1.0000
## farm_2 - farm_4 0.0295 0.0621 40 0.475 1.0000
## farm_3 - farm_4 0.1022 0.1145 40 0.892 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------PercentageLegumeArea----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.30256 0.100853 4.7236 0.006493 **
## Residuals 40 0.85404 0.021351
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.492 0.0354 40 0.421 0.564
## farm_2 0.402 0.0354 40 0.331 0.474
## farm_3 0.161 0.1033 40 -0.048 0.370
## farm_4 0.322 0.0517 40 0.217 0.426
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 0.0899 0.0501 40 1.794 0.4826
## farm_1 - farm_3 0.3315 0.1092 40 3.035 0.0253
## farm_1 - farm_4 0.1706 0.0626 40 2.723 0.0573
## farm_2 - farm_3 0.2416 0.1092 40 2.212 0.1965
## farm_2 - farm_4 0.0807 0.0626 40 1.288 1.0000
## farm_3 - farm_4 -0.1609 0.1155 40 -1.393 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------highestEdu_qualification.HHH.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 1.1023 0.36742 1.0592 0.3771
## Residuals 40 13.8750 0.34687
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.35 0.143 40 1.064 1.64
## farm_2 1.59 0.143 40 1.300 1.88
## farm_3 2.00 0.416 40 1.158 2.84
## farm_4 1.38 0.208 40 0.954 1.80
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.2353 0.202 40 -1.165 1.0000
## farm_1 - farm_3 -0.6471 0.440 40 -1.470 0.8968
## farm_1 - farm_4 -0.0221 0.253 40 -0.087 1.0000
## farm_2 - farm_3 -0.4118 0.440 40 -0.935 1.0000
## farm_2 - farm_4 0.2132 0.253 40 0.844 1.0000
## farm_3 - farm_4 0.6250 0.466 40 1.342 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Access_to_loan_during_2022.2023_season----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 1.4332 0.47772 2.6197 0.06398 .
## Residuals 40 7.2941 0.18235
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.235 0.104 40 0.026 0.445
## farm_2 0.471 0.104 40 0.261 0.680
## farm_3 0.000 0.302 40 -0.610 0.610
## farm_4 0.000 0.151 40 -0.305 0.305
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.235 0.146 40 -1.606 0.6963
## farm_1 - farm_3 0.235 0.319 40 0.737 1.0000
## farm_1 - farm_4 0.235 0.183 40 1.285 1.0000
## farm_2 - farm_3 0.471 0.319 40 1.474 0.8896
## farm_2 - farm_4 0.471 0.183 40 2.570 0.0839
## farm_3 - farm_4 0.000 0.338 40 0.000 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice__irrigation_farming.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.5922 0.19742 0.8496 0.475
## Residuals 40 9.2941 0.23235
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.235 0.117 40 -0.000989 0.472
## farm_2 0.471 0.117 40 0.234305 0.707
## farm_3 0.500 0.341 40 -0.188877 1.189
## farm_4 0.250 0.170 40 -0.094438 0.594
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.2353 0.165 40 -1.423 0.9747
## farm_1 - farm_3 -0.2647 0.360 40 -0.735 1.0000
## farm_1 - farm_4 -0.0147 0.207 40 -0.071 1.0000
## farm_2 - farm_3 -0.0294 0.360 40 -0.082 1.0000
## farm_2 - farm_4 0.2206 0.207 40 1.067 1.0000
## farm_3 - farm_4 0.2500 0.381 40 0.656 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Manure_application_in_Maize_field_during_2022.23_season----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.4993 0.16644 0.8092 0.4963
## Residuals 40 8.2279 0.20570
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.647 0.110 40 0.425 0.869
## farm_2 0.824 0.110 40 0.601 1.046
## farm_3 1.000 0.321 40 0.352 1.648
## farm_4 0.625 0.160 40 0.301 0.949
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.1765 0.156 40 -1.134 1.0000
## farm_1 - farm_3 -0.3529 0.339 40 -1.041 1.0000
## farm_1 - farm_4 0.0221 0.194 40 0.113 1.0000
## farm_2 - farm_3 -0.1765 0.339 40 -0.520 1.0000
## farm_2 - farm_4 0.1985 0.194 40 1.021 1.0000
## farm_3 - farm_4 0.3750 0.359 40 1.046 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Count_intens_options----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 5.6878 1.89594 2.6894 0.05914 .
## Residuals 40 28.1985 0.70496
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.118 0.204 40 0.706 1.53
## farm_2 1.765 0.204 40 1.353 2.18
## farm_3 1.500 0.594 40 0.300 2.70
## farm_4 0.875 0.297 40 0.275 1.47
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.647 0.288 40 -2.247 0.1814
## farm_1 - farm_3 -0.382 0.628 40 -0.609 1.0000
## farm_1 - farm_4 0.243 0.360 40 0.674 1.0000
## farm_2 - farm_3 0.265 0.628 40 0.422 1.0000
## farm_2 - farm_4 0.890 0.360 40 2.472 0.1069
## farm_3 - farm_4 0.625 0.664 40 0.942 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_pracatice_intercroping.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.7868 0.26226 1.4056 0.2553
## Residuals 40 7.4632 0.18658
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.588 0.105 40 0.377 0.80
## farm_2 0.824 0.105 40 0.612 1.04
## farm_3 1.000 0.305 40 0.383 1.62
## farm_4 0.875 0.153 40 0.566 1.18
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.2353 0.148 40 -1.588 0.7208
## farm_1 - farm_3 -0.4118 0.323 40 -1.275 1.0000
## farm_1 - farm_4 -0.2868 0.185 40 -1.548 0.7764
## farm_2 - farm_3 -0.1765 0.323 40 -0.547 1.0000
## farm_2 - farm_4 -0.0515 0.185 40 -0.278 1.0000
## farm_3 - farm_4 0.1250 0.341 40 0.366 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice_cover_croping.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 1.6758 0.55860 3.6923 0.01947 *
## Residuals 40 6.0515 0.15129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.0588 0.0943 40 -0.132 0.249
## farm_2 0.4706 0.0943 40 0.280 0.661
## farm_3 0.0000 0.2750 40 -0.556 0.556
## farm_4 0.1250 0.1375 40 -0.153 0.403
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.4118 0.133 40 -3.086 0.0220
## farm_1 - farm_3 0.0588 0.291 40 0.202 1.0000
## farm_1 - farm_4 -0.0662 0.167 40 -0.397 1.0000
## farm_2 - farm_3 0.4706 0.291 40 1.618 0.6805
## farm_2 - farm_4 0.3456 0.167 40 2.072 0.2683
## farm_3 - farm_4 -0.1250 0.307 40 -0.407 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_practice_integrated_pest_management.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.5642 0.18806 0.7821 0.5109
## Residuals 40 9.6176 0.24044
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.529 0.119 40 0.289 0.770
## farm_2 0.647 0.119 40 0.407 0.887
## farm_3 1.000 0.347 40 0.299 1.701
## farm_4 0.750 0.173 40 0.400 1.100
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.118 0.168 40 -0.699 1.0000
## farm_1 - farm_3 -0.471 0.367 40 -1.284 1.0000
## farm_1 - farm_4 -0.221 0.210 40 -1.049 1.0000
## farm_2 - farm_3 -0.353 0.367 40 -0.963 1.0000
## farm_2 - farm_4 -0.103 0.210 40 -0.490 1.0000
## farm_3 - farm_4 0.250 0.388 40 0.645 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_have_agroforestry_trees_in_your_farm.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.6758 0.22527 1.1191 0.3527
## Residuals 40 8.0515 0.20129
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.235 0.109 40 0.0154 0.455
## farm_2 0.412 0.109 40 0.1918 0.632
## farm_3 0.000 0.317 40 -0.6412 0.641
## farm_4 0.125 0.159 40 -0.1956 0.446
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.176 0.154 40 -1.147 1.0000
## farm_1 - farm_3 0.235 0.335 40 0.702 1.0000
## farm_1 - farm_4 0.110 0.192 40 0.573 1.0000
## farm_2 - farm_3 0.412 0.335 40 1.228 1.0000
## farm_2 - farm_4 0.287 0.192 40 1.491 0.8632
## farm_3 - farm_4 -0.125 0.355 40 -0.352 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_have_fruit_trees.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.5642 0.18806 0.7821 0.5109
## Residuals 40 9.6176 0.24044
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.353 0.119 40 0.113 0.593
## farm_2 0.471 0.119 40 0.230 0.711
## farm_3 0.000 0.347 40 -0.701 0.701
## farm_4 0.250 0.173 40 -0.100 0.600
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.118 0.168 40 -0.699 1.0000
## farm_1 - farm_3 0.353 0.367 40 0.963 1.0000
## farm_1 - farm_4 0.103 0.210 40 0.490 1.0000
## farm_2 - farm_3 0.471 0.367 40 1.284 1.0000
## farm_2 - farm_4 0.221 0.210 40 1.049 1.0000
## farm_3 - farm_4 -0.250 0.388 40 -0.645 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Do_you_own_any_vegetable_garden.----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 0.9017 0.30058 1.4561 0.241
## Residuals 40 8.2574 0.20643
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 0.118 0.110 40 -0.1051 0.340
## farm_2 0.412 0.110 40 0.1891 0.634
## farm_3 0.500 0.321 40 -0.1493 1.149
## farm_4 0.375 0.161 40 0.0503 0.700
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -0.2941 0.156 40 -1.887 0.3984
## farm_1 - farm_3 -0.3824 0.340 40 -1.126 1.0000
## farm_1 - farm_4 -0.2574 0.195 40 -1.321 1.0000
## farm_2 - farm_3 -0.0882 0.340 40 -0.260 1.0000
## farm_2 - farm_4 0.0368 0.195 40 0.189 1.0000
## farm_3 - farm_4 0.1250 0.359 40 0.348 1.0000
##
## P value adjustment: bonferroni method for 6 tests
##
## [1] "--------------------Count_sust_practices----------------"
## Analysis of Variance Table
##
## Response: typo_5[[i]]
## Df Sum Sq Mean Sq F value Pr(>F)
## typo_5[["farm_type"]] 3 15.586 5.1952 2.8341 0.05026 .
## Residuals 40 73.324 1.8331
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## [1] "-----------"
## $emmeans
## farm_type emmean SE df lower.CL upper.CL
## farm_1 1.88 0.328 40 1.219 2.55
## farm_2 3.24 0.328 40 2.572 3.90
## farm_3 2.50 0.957 40 0.565 4.43
## farm_4 2.50 0.479 40 1.533 3.47
##
## Confidence level used: 0.95
##
## $contrasts
## contrast estimate SE df t.ratio p.value
## farm_1 - farm_2 -1.353 0.464 40 -2.913 0.0350
## farm_1 - farm_3 -0.618 1.012 40 -0.610 1.0000
## farm_1 - farm_4 -0.618 0.580 40 -1.064 1.0000
## farm_2 - farm_3 0.735 1.012 40 0.726 1.0000
## farm_2 - farm_4 0.735 0.580 40 1.267 1.0000
## farm_3 - farm_4 0.000 1.070 40 0.000 1.0000
##
## P value adjustment: bonferroni method for 6 testsI hope the cryptic codes were readable; otherwise, feel free to reach out.
I think there were too many variables selected. For 44 respondents, I would not have more than 8 or 10 variables. Consider selecting the most relevant.
I proposed several methods of clustering: but my subjective preference goes to the FAMD or the PCA, followed by a clustering in 2 or 3 groups. Anyway, whatever method, you would select the contrasting characteristics must be summarized; and I do prefer a series of boxplot for variables showing significant differences. It may be more challenging to give it a coherent explanation.
ALL THE BEST AND SEE YOU SOON!!!