Coffee-Soils Research
Andrew Gerlicz
2024-04-24
Q1: Do inherent and farmer-modified attributes of coffee agroecosystems appear to have an effect on properties associated with soil health?
Method of Analysis
To explore the covariance of within the groups of variables (independent and dependent variables), below we use: /n - Correlation matrices of continuous variables
Factors of Soil Health (Independent Variables)
Correlation matrices show relationships at p <0.01 Key: Green = positive relationship, Red = negative relationship
Factors of Soil Health (Independent Variables)
Probability of observing erosion based on the other factors
Here we use a multinomial logistic regression to understand the influence of the other factors on the probability of observing erosion within a study block.
Since not all dependent variables were collected at each site, below there are correlation matrices that relate to differently-sized datasets depending on the completeness of the data set. The n for these datasets is described in the table below.
Core Soil Dependent Variables
Correlation matrices show relationships at p <0.01
Core Soil Dependent Variables Plus Bulk Density and Aggregate Stability
Correlation matrices show relationships at p <0.01
Core Soil Dependent Variables Plus Bulk Density, Aggregate Stability and Macrofauna
Correlation matrices show relationships at p <0.05
Simple correlations between continuous IVs and DVs
Showing results significant at p-value <0.01
Q1: To what extent do attributes of soil health correspond to yields within coffee plots when controling for other factors?
Descriptives
Yield by Community
Understanding the Relationship between Agroecosystem Attributes on Soil Health Indicators
“Redundancy analysis is a type of asymmetric canonical analysis, that is, a method that analyses two or more data frames simultaneously by combining ordination and regression. Simple ordination via principal components analysis (PCA) is likely familiar to most readers as a method for characterizing the main axes of variance in a matrix (Y) into uncorrelated (orthogonal) synthetic composite variables (the PC axes). For example, PCA of a genetic data matrix (i.e. n individuals or populations genotyped at p loci) can be used to infer similarities and differences among sampled individuals/populations based on their multilocus genotypes. By comparison, multiple regression is used to model a single response variable (y) using a set of multiple predictor variables (X). If we extend multiple regression to the multivariate response matrix used in PCA (Y), we are conducting an RDA. RDA therefore identifies linear combinations of the explanatory variables (X) that maximize the variance explained in linear combinations of the response (Y).”
| n |
|---|
| 43 |
Above shows the n for the subset of data analyzed
| Proportion of Variation Explained by Factors | 0.61 |
| Proportion of Variation Unexplained by Factors | 0.39 |
The rows above indicate the proportion of variation explained by the included agroecosystem variables.
A forward selection can help us select variables that are statistically important. In the forward selection the following variables are retained:
## rda_1_DV ~ coop + slopePerc + altFinal + sandPerc_labThe following provide the R^2 and Adjusted R^2 for the selected variables (shown above) as well as the significance.
## $r.squared
## [1] 0.2802593
##
## $adj.r.squared
## [1] 0.2044971## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = rda_1_DV ~ coop + slopePerc + altFinal + sandPerc_lab, data = rda_1_IV)
## Df Variance F Pr(>F)
## Model 4 4.4841 3.6992 0.001 ***
## Residual 38 11.5159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for rda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = rda_1_DV ~ coop + slopePerc + altFinal + sandPerc_lab, data = rda_1_IV)
## Df Variance F Pr(>F)
## coop 1 1.8970 6.2597 0.001 ***
## slopePerc 1 1.2426 4.1004 0.001 ***
## altFinal 1 0.6479 2.1378 0.031 *
## sandPerc_lab 1 0.6967 2.2989 0.018 *
## Residual 38 11.5159
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The following shows an RDA plot for the selected variables:
Partial Redundancy Analysis
We can also run a partial RDA “in which the response variables (Y) are related to explanatory variables (X) in the presence of additional explanatory variables (W), called covariates (or covariables). In this case, we can treat our explanatory variables of interest (Y) as those that can be changed through management while unchangeable agroecosystem variables as the covariates (W).
| Proportion of Variation Explained by Inherent Factors | 0.41 |
| Proportion of Variation Explained by Management Factors | 0.19 |
| Proportion of Variation Unexplained by Any Included Factors | 0.39 |
## $r.squared
## [1] 0.1948699
##
## $adj.r.squared
## [1] 0.05408979## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(X = rda_1_DV, Y = rda_IV2, Z = rda_IV1)
## Df Variance F Pr(>F)
## Model 9 3.1179 1.2682 0.103
## Residual 23 6.2829
Variation Partitioning
## No. of explanatory tables: 2
## Total variation (SS): 672
## Variance: 16
## No. of observations: 43
##
## Partition table:
## Df R.squared Adj.R.squared Testable
## [a+c] = X1 11 0.37933 0.15909 TRUE
## [b+c] = X2 10 0.41245 0.22884 TRUE
## [a+b+c] = X1+X2 19 0.60732 0.28293 TRUE
## Individual fractions
## [a] = X1|X2 9 0.05409 TRUE
## [b] = X2|X1 8 0.12383 TRUE
## [c] 0 0.10500 FALSE
## [d] = Residuals 0.71707 FALSE
## ---
## Use function 'rda' to test significance of fractions of interest
…Removing bulk density and aggregates to increase n
| n |
|---|
| 68 |
Above shows the n for the subset of data analyzed
| Proportion of Variation Explained by Factors | 0.44 |
| Proportion of Variation Unexplained by Factors | 0.56 |
## rda_1_DV ~ coop + aspectQuad## $r.squared
## [1] 0.1671153
##
## $adj.r.squared
## [1] 0.1142337## Permutation test for rda under reduced model
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = rda_1_DV ~ coop + aspectQuad, data = rda_1_IV)
## Df Variance F Pr(>F)
## Model 4 2.1725 3.1602 0.001 ***
## Residual 63 10.8275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## Permutation test for rda under reduced model
## Terms added sequentially (first to last)
## Permutation: free
## Number of permutations: 999
##
## Model: rda(formula = rda_1_DV ~ coop + aspectQuad, data = rda_1_IV)
## Df Variance F Pr(>F)
## coop 1 1.1610 6.7555 0.001 ***
## aspectQuad 3 1.0115 1.9617 0.004 **
## Residual 63 10.8275
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
PCA exploring trends among variables of both factors and the soil health indicators.
CESMACH
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = pca_ces_nm)
## Overall MSA = 0.46
## MSA for each item =
## Slope.... Altitude..masl.
## 0.28 0.64
## Sand.... Shade.Cover....
## 0.79 0.27
## Groundcover.... Plant.Density
## 0.49 0.24
## Bulk.Density Agg..Stablity..Lg.Aggs.
## 0.73 0.71
## Agg..Stablity..All.Aggs. Active.Carbon..POxC.
## 0.77 0.45
## Organic.Matter Available.Phosphorus..On.Site.
## 0.80 0.56
## Phosphorous..Bray. Calcium
## 0.59 0.42
## Magnesium Potassium
## 0.14 0.28
## Boron Sulfur
## 0.57 0.35
## Zinc pH
## 0.31 0.32
## CEC
## 0.41##
## Call:
## factanal(x = ~., factors = 6, data = pca_ces_nm, na.action = na.exclude, rotation = "none")
##
## Uniquenesses:
## Slope.... Altitude..masl.
## 0.118 0.331
## Sand.... Shade.Cover....
## 0.352 0.522
## Groundcover.... Plant.Density
## 0.413 0.960
## Bulk.Density Agg..Stablity..Lg.Aggs.
## 0.261 0.256
## Agg..Stablity..All.Aggs. Active.Carbon..POxC.
## 0.285 0.609
## Organic.Matter Available.Phosphorus..On.Site.
## 0.094 0.226
## Phosphorous..Bray. Calcium
## 0.005 0.005
## Magnesium Potassium
## 0.005 0.568
## Boron Sulfur
## 0.632 0.626
## Zinc pH
## 0.005 0.439
## CEC
## 0.005
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## Slope.... -0.331 0.307 -0.392 0.210 -0.275 0.637
## Altitude..masl. -0.360 0.632 0.255 0.199 -0.184
## Sand.... 0.423 0.630 -0.210 -0.154
## Shade.Cover.... 0.232 0.235 -0.100 0.590
## Groundcover.... 0.300 0.446 -0.467 -0.269
## Plant.Density 0.116 -0.132
## Bulk.Density -0.801 0.156 -0.205 -0.167
## Agg..Stablity..Lg.Aggs. 0.774 -0.178 0.295 -0.139
## Agg..Stablity..All.Aggs. 0.163 0.628 0.115 -0.283 0.316 -0.317
## Active.Carbon..POxC. 0.386 0.331 0.351
## Organic.Matter 0.329 0.830 -0.242 0.218
## Available.Phosphorus..On.Site. 0.660 0.409 -0.389
## Phosphorous..Bray. 0.600 0.642 0.178 -0.438
## Calcium 0.921 0.101 -0.234 0.287
## Magnesium 0.405 -0.397 0.819
## Potassium 0.123 0.546 0.111 0.140 0.275 0.108
## Boron 0.270 -0.410 0.113 0.257 0.220
## Sulfur -0.107 0.200 0.365 0.424
## Zinc 0.565 -0.557 -0.435 -0.419
## pH 0.484 -0.337 0.207 0.407
## CEC 0.963 0.261
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 4.183 3.947 1.624 1.561 1.529 1.444
## Proportion Var 0.199 0.188 0.077 0.074 0.073 0.069
## Cumulative Var 0.199 0.387 0.465 0.539 0.612 0.680
##
## Test of the hypothesis that 6 factors are sufficient.
## The chi square statistic is 192.49 on 99 degrees of freedom.
## The p-value is 5.67e-08## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 0.9657388 0.8132009 0.46549179 0.38912954 0.3163160
## Proportion of Variance 0.4082482 0.2894680 0.09484818 0.06628166 0.0437973
## Cumulative Proportion 0.4082482 0.6977163 0.79256444 0.85884609 0.9026434
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 0.27148557 0.23410872 0.21418243 0.128477417 0.103276269
## Proportion of Variance 0.03226253 0.02399055 0.02008041 0.007225344 0.004668808
## Cumulative Proportion 0.93490592 0.95889647 0.97897688 0.986202222 0.990871030
## Comp.11 Comp.12 Comp.13 Comp.14
## Standard deviation 0.092194727 0.085469564 0.0460755477 0.034379920
## Proportion of Variance 0.003720635 0.003197628 0.0009292786 0.000517386
## Cumulative Proportion 0.994591666 0.997789294 0.9987185723 0.999235958
## Comp.15 Comp.16 Comp.17 Comp.18
## Standard deviation 0.0262954226 0.0215674478 0.0185139927 1.310229e-02
## Proportion of Variance 0.0003026671 0.0002036116 0.0001500393 7.514485e-05
## Cumulative Proportion 0.9995386255 0.9997422370 0.9998922764 9.999674e-01
## Comp.19 Comp.20 Comp.21
## Standard deviation 8.526631e-03 1.312815e-03 6.469884e-09
## Proportion of Variance 3.182437e-05 7.544175e-07 1.832306e-17
## Cumulative Proportion 9.999992e-01 1.000000e+00 1.000000e+00## Comp.1 Comp.2
## Slope.... 0.01549651 0.322362317
## Altitude..masl. 0.30962025 0.193274392
## Sand.... 0.25128398 -0.184307754
## Shade.Cover.... -0.14974174 0.147559863
## Groundcover.... 0.06011613 -0.276903820
## Plant.Density 0.07949282 0.057856903
## Bulk.Density -0.37125050 0.001039050
## Agg..Stablity..Lg.Aggs. 0.39701880 0.003331749
## Agg..Stablity..All.Aggs. 0.37058852 -0.119307259
## Active.Carbon..POxC. 0.14927167 -0.204705187
## Organic.Matter 0.37800928 -0.149116670
## Available.Phosphorus..On.Site. -0.09684428 -0.353350414
## Phosphorous..Bray. -0.06939126 -0.272441176
## Calcium -0.01806432 -0.432755963
## Magnesium -0.12193272 -0.007128951
## Potassium 0.24921900 0.020827743
## Boron -0.24026658 -0.072841062
## Sulfur -0.07929518 0.060621678
## Zinc -0.04287664 -0.245391342
## pH -0.22758327 -0.182114205
## CEC -0.03591411 -0.394167970
ASOBAGRI
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = pca_aso_nm)
## Overall MSA = 0.49
## MSA for each item =
## Slope.... Altitude..masl.
## 0.60 0.53
## Sand.... Shade.Cover....
## 0.35 0.51
## Groundcover.... Plant.Density
## 0.22 0.16
## Bulk.Density Agg..Stablity..Lg.Aggs.
## 0.78 0.65
## Agg..Stablity..All.Aggs. Active.Carbon..POxC.
## 0.58 0.41
## Organic.Matter Available.Phosphorus..On.Site.
## 0.67 0.66
## Phosphorous..Mehlich.III. Calcium
## 0.25 0.53
## Magnesium Potassium
## 0.55 0.40
## Boron Sulfur
## 0.56 0.36
## Zinc pH
## 0.39 0.65
## CEC
## 0.53##
## Call:
## factanal(x = ~., factors = 6, data = pca_aso_nm, na.action = na.exclude, rotation = "none", lower = 0.01)
##
## Uniquenesses:
## Slope.... Altitude..masl.
## 0.258 0.205
## Sand.... Shade.Cover....
## 0.469 0.518
## Groundcover.... Plant.Density
## 0.615 0.276
## Bulk.Density Agg..Stablity..Lg.Aggs.
## 0.346 0.010
## Agg..Stablity..All.Aggs. Active.Carbon..POxC.
## 0.090 0.606
## Organic.Matter Available.Phosphorus..On.Site.
## 0.201 0.364
## Phosphorous..Mehlich.III. Calcium
## 0.603 0.010
## Magnesium Potassium
## 0.010 0.271
## Boron Sulfur
## 0.369 0.384
## Zinc pH
## 0.010 0.219
## CEC
## 0.010
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## Slope.... -0.122 0.533 0.189 0.301 0.562
## Altitude..masl. 0.386 0.320 0.493 0.435 0.177 0.284
## Sand.... -0.563 0.130 0.119 -0.409
## Shade.Cover.... 0.396 -0.216 0.448 0.274
## Groundcover.... -0.379 0.403 0.268
## Plant.Density -0.107 -0.331 0.109 0.507 -0.577
## Bulk.Density 0.202 0.720 0.240 0.173
## Agg..Stablity..Lg.Aggs. 0.683 -0.454 -0.561
## Agg..Stablity..All.Aggs. 0.716 -0.361 -0.373 0.230 0.227 -0.153
## Active.Carbon..POxC. 0.224 0.122 -0.174 -0.138 -0.526
## Organic.Matter -0.168 -0.195 -0.528 -0.565 -0.365
## Available.Phosphorus..On.Site. 0.102 0.474 0.362 0.477 -0.205
## Phosphorous..Mehlich.III. 0.500 -0.133 0.351
## Calcium 0.955 0.166 0.213
## Magnesium 0.931 0.136 0.201 -0.249
## Potassium -0.184 0.495 0.209 0.259 0.363 0.455
## Boron 0.346 0.525 0.264 -0.135 -0.358 0.135
## Sulfur -0.371 0.205 0.250 0.518 0.324
## Zinc -0.188 0.892 -0.397
## pH 0.672 0.401 0.306 0.208 -0.146 0.101
## CEC 0.964 0.212 0.112
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 5.251 3.180 2.551 1.503 1.350 1.322
## Proportion Var 0.250 0.151 0.121 0.072 0.064 0.063
## Cumulative Var 0.250 0.401 0.523 0.594 0.659 0.722
##
## Test of the hypothesis that 6 factors are sufficient.
## The chi square statistic is 126.38 on 99 degrees of freedom.
## The p-value is 0.0331## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 1.0853881 0.9081977 0.5351515 0.38833252 0.2920401
## Proportion of Variance 0.4419481 0.3094297 0.1074372 0.05657293 0.0319953
## Cumulative Proportion 0.4419481 0.7513777 0.8588149 0.91538787 0.9473832
## Comp.6 Comp.7 Comp.8 Comp.9
## Standard deviation 0.23766163 0.161417897 0.146462682 0.12423017
## Proportion of Variance 0.02118943 0.009774724 0.008047391 0.00578969
## Cumulative Proportion 0.96857259 0.978347316 0.986394707 0.99218440
## Comp.10 Comp.11 Comp.12 Comp.13
## Standard deviation 0.108485765 0.060759793 0.0489865643 0.0386858082
## Proportion of Variance 0.004415162 0.001384949 0.0009002334 0.0005614415
## Cumulative Proportion 0.996599559 0.997984508 0.9988847415 0.9994461830
## Comp.14 Comp.15 Comp.16 Comp.17
## Standard deviation 0.0299952624 1.385135e-02 1.378500e-02 1.273918e-02
## Proportion of Variance 0.0003375254 7.197566e-05 7.128773e-05 6.088137e-05
## Cumulative Proportion 0.9997837084 9.998557e-01 9.999270e-01 9.999879e-01
## Comp.18 Comp.19 Comp.20 Comp.21
## Standard deviation 4.609918e-03 3.107742e-03 1.212223e-03 0
## Proportion of Variance 7.972371e-06 3.623189e-06 5.512719e-07 0
## Cumulative Proportion 9.999958e-01 9.999994e-01 1.000000e+00 1## Comp.1 Comp.2
## Slope.... 0.207541485 0.301913752
## Altitude..masl. -0.024499164 0.381901620
## Sand.... 0.303711873 -0.024622101
## Shade.Cover.... -0.182297960 0.145825769
## Groundcover.... -0.040776723 0.008838771
## Plant.Density 0.050660982 -0.008829326
## Bulk.Density 0.002898679 0.308385138
## Agg..Stablity..Lg.Aggs. -0.358607056 -0.135585763
## Agg..Stablity..All.Aggs. -0.360354062 -0.058578071
## Active.Carbon..POxC. -0.150594813 -0.059200983
## Organic.Matter -0.061700163 -0.381330550
## Available.Phosphorus..On.Site. 0.105563585 0.284619207
## Phosphorous..Mehlich.III. 0.101431130 0.127075453
## Calcium -0.324998863 0.209874684
## Magnesium -0.349617227 0.190848930
## Potassium 0.234708197 0.256542682
## Boron -0.018335818 0.241010096
## Sulfur 0.254654679 0.116290640
## Zinc 0.181480746 0.067455298
## pH -0.169351272 0.331516337
## CEC -0.332413518 0.213621023
ASOBAGRI
## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = pca_all_nm)
## Overall MSA = 0.53
## MSA for each item =
## Slope.... Altitude..masl.
## 0.43 0.55
## Sand.... Shade.Cover....
## 0.56 0.33
## Groundcover.... Plant.Density
## 0.51 0.23
## Bulk.Density Agg..Stablity..Lg.Aggs.
## 0.65 0.79
## Agg..Stablity..All.Aggs. Active.Carbon..POxC.
## 0.58 0.43
## Organic.Matter Available.Phosphorus..On.Site.
## 0.55 0.60
## Phosphorous..Comb.. Calcium
## 0.46 0.54
## Magnesium Potassium
## 0.56 0.25
## Boron Sulfur
## 0.66 0.38
## Zinc pH
## 0.43 0.76
## CEC
## 0.54##
## Call:
## factanal(x = ~., factors = 6, data = pca_all_nm, na.action = na.exclude, rotation = "none", lower = 0.01)
##
## Uniquenesses:
## Slope.... Altitude..masl.
## 0.020 0.685
## Sand.... Shade.Cover....
## 0.469 0.851
## Groundcover.... Plant.Density
## 0.643 0.887
## Bulk.Density Agg..Stablity..Lg.Aggs.
## 0.393 0.335
## Agg..Stablity..All.Aggs. Active.Carbon..POxC.
## 0.569 0.607
## Organic.Matter Available.Phosphorus..On.Site.
## 0.010 0.035
## Phosphorous..Comb.. Calcium
## 0.290 0.010
## Magnesium Potassium
## 0.010 0.245
## Boron Sulfur
## 0.550 0.386
## Zinc pH
## 0.790 0.289
## CEC
## 0.010
##
## Loadings:
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## Slope.... -0.236 -0.459 0.798 -0.272
## Altitude..masl. 0.172 -0.276 0.385 -0.137 0.176 0.102
## Sand.... -0.458 0.450 0.315 0.130
## Shade.Cover.... 0.256 -0.151 0.122 0.178
## Groundcover.... 0.179 0.229 -0.333 0.266 0.280 -0.111
## Plant.Density -0.307 -0.111
## Bulk.Density 0.259 -0.618 -0.334 0.194
## Agg..Stablity..Lg.Aggs. 0.478 0.535 -0.250 -0.237 -0.176
## Agg..Stablity..All.Aggs. 0.421 0.493
## Active.Carbon..POxC. 0.269 0.511 0.224
## Organic.Matter 0.970 0.192
## Available.Phosphorus..On.Site. 0.326 0.898 0.205
## Phosphorous..Comb.. 0.265 0.765 0.124 0.196
## Calcium 0.955 0.131 -0.220
## Magnesium 0.948 -0.129 0.265
## Potassium 0.137 -0.200 0.775 -0.302
## Boron 0.526 -0.142 -0.119 0.345 -0.144
## Sulfur -0.101 -0.211 -0.126 0.682 -0.279
## Zinc 0.395 0.192 0.109
## pH 0.749 -0.278 0.200 0.169
## CEC 0.992
##
## Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
## SS loadings 4.593 2.571 1.894 1.879 1.429 0.552
## Proportion Var 0.219 0.122 0.090 0.089 0.068 0.026
## Cumulative Var 0.219 0.341 0.431 0.521 0.589 0.615
##
## Test of the hypothesis that 6 factors are sufficient.
## The chi square statistic is 206.26 on 99 degrees of freedom.
## The p-value is 1.56e-09## Importance of components:
## Comp.1 Comp.2 Comp.3 Comp.4 Comp.5
## Standard deviation 0.9788067 0.6371410 0.5181965 0.3801299 0.33026774
## Proportion of Variance 0.4530417 0.1919620 0.1269794 0.0683295 0.05157944
## Cumulative Proportion 0.4530417 0.6450037 0.7719831 0.8403126 0.89189206
## Comp.6 Comp.7 Comp.8 Comp.9 Comp.10
## Standard deviation 0.2708738 0.25639508 0.17140767 0.16010531 0.115842589
## Proportion of Variance 0.0346959 0.03108592 0.01389328 0.01212148 0.006345718
## Cumulative Proportion 0.9265880 0.95767387 0.97156715 0.98368863 0.990034352
## Comp.11 Comp.12 Comp.13 Comp.14
## Standard deviation 0.096800196 0.05712464 0.052948463 0.0437061463
## Proportion of Variance 0.004430949 0.00154309 0.001325718 0.0009032944
## Cumulative Proportion 0.994465301 0.99600839 0.997334109 0.9982374033
## Comp.15 Comp.16 Comp.17 Comp.18
## Standard deviation 0.0405350365 0.0297676353 0.022618076 0.0201220949
## Proportion of Variance 0.0007769721 0.0004190183 0.000241911 0.0001914656
## Cumulative Proportion 0.9990143754 0.9994333937 0.999675305 0.9998667702
## Comp.19 Comp.20 Comp.21
## Standard deviation 0.0167520168 1.056163e-03 0
## Proportion of Variance 0.0001327023 5.274798e-07 0
## Cumulative Proportion 0.9999994725 1.000000e+00 1## Comp.1 Comp.2
## Slope.... 0.229501813 0.28311692
## Altitude..masl. 0.030592959 0.21515572
## Sand.... 0.354140288 -0.01973949
## Shade.Cover.... -0.124463860 0.18218019
## Groundcover.... -0.120353291 -0.13751832
## Plant.Density -0.076766656 -0.07732474
## Bulk.Density -0.096339918 0.40192064
## Agg..Stablity..Lg.Aggs. -0.334580800 -0.30934262
## Agg..Stablity..All.Aggs. -0.213789959 -0.30176434
## Active.Carbon..POxC. -0.141676270 -0.20339347
## Organic.Matter -0.005583571 -0.49733016
## Available.Phosphorus..On.Site. 0.205530549 0.07751402
## Phosphorous..Comb.. 0.173105809 0.04436856
## Calcium -0.333797775 0.11532452
## Magnesium -0.369504066 0.14244706
## Potassium -0.022473307 0.08619279
## Boron -0.260029456 0.13647229
## Sulfur -0.014877366 0.04532763
## Zinc 0.147013265 0.01149270
## pH -0.247491461 0.30184183
## CEC -0.355954933 0.13101617