R Markdown for Duke Farms - Agriculture Field (Grazed fields)

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library(gstat)




 

Part 1: When ‘n = 100’


 

1.1 Compute undirected (isotropic) empirical semivariogram
var1 <- variogram(Total_C ~ 1, ~x.axis + y.axis, cutoff = 500, width = 30, data = mc)

# create figure of empirical semivariogram
plot(var1, plot.numbers = TRUE, xlab = "Distance", ylab = "Semivariance", cex = 1, cex.axis = 2, xlim=c(0, 550), ylim=c(0.2, 0.7))

Figure 1: Undirected semivariogram of Total_C .


 

1.1.2 Semivariogram models

To fit theoretical semivariograms, the function ‘fit.variogram’ of the package ‘gstat’ is used. Sill, nugget and range are set to be calculated based on the empirical variogram data, which is also used to fit the model ‘(fit.method=1)’. This method fits the variogram model to the experimental variogram, using weighted least squares with weight = Nj, where Nj is the number of observations in the j -th distance class (bin) (from: http://www.gstat. org/gstat.pdf, Table 4.2). The exponential model is used here. Semivariogram models are only fitted to undirected empirical semivariograms as the number of point pairs per bin in the directed ones is very low and predictions therefore have less power (the number of points per bin (np) for the undirected semivariograms is as high as for all directed semivariograms together).
 

compute semivariogram model based on the empiral semivariogram ‘var1’**
mod1 <- fit.variogram(var1, vgm(psill = NA, "Exp", range = NA, 1), fit.sills = TRUE, fit.ranges = TRUE, fit.method = 1)
## Warning in fit.variogram(var1, vgm(psill = NA, "Exp", range = NA, 1), fit.sills
## = TRUE, : No convergence after 200 iterations: try different initial values?
## Warning in fit.variogram(object, model, fit.sills = fit.sills, fit.ranges =
## fit.ranges, : singular model in variogram fit

 






** SUMMARY STATISTICS GRAZED FIELD**


Table 1:

Descriptive statistics of soil organic C (Total_C) concentration, nitrogen concentration (Total_N), and organic matter (OM). Values are based on the 1st sample set (n = 150). Soil properties represent the top 15 cm of Duke Farm’s agriculture (grazed field) soil

N C OM
Soil series n Min Median Max Mean SD Min Median Max Mean SD Min Median Max Mean SD
KkoC 25 0.100 0.180 0.310 0.188 0.046 0.990 1.800 3.120 1.874 0.464 1.700 3.100 5.380 3.220 0.801
KkoD 13 0.130 0.220 0.390 0.231 0.076 1.240 2.180 4.050 2.317 0.841 2.140 3.760 6.970 3.992 1.449
PenB 36 0.060 0.205 0.310 0.192 0.060 0.250 2.150 3.520 1.952 0.696 0.420 3.705 6.070 3.366 1.202
PeoC 26 0.120 0.210 0.390 0.218 0.065 1.260 2.090 3.800 2.199 0.677 2.170 3.600 6.540 3.790 1.167

where N = Total Nitrogen, C = Total Carbon and OM = organic matter



Table 2:

Analysis of variance for soil organic C (Total_C) concentration, nitrogen concentration (Total_N), and organic matter (OM) for Duke Farm’s agriculture (grazed field) soil n = 150.

Parameters df Sum Sq Mean Sq F value Pr(>F)
Total_C S_Series 3 2.620 0.875 1.996 0.120
Residuals 96 42.070 0.438 - -
Total_N S_Series 3 0.026 0.009 2.373 0.075
Residuals 96 0.354 0.004 - -
OM S_Series 3 7.800 2.599 1.993 0.120
Residuals 96 125.200 1.305 - -






Spatial plot of samples:

You can use the plotting functions spplot or bubble as illustrated below (note: the x- and y-axis are the spatial coordinates)**

data = data.frame(x,y,Total_C)
library(sp)
coordinates(data) = ~x+y
class(data)
## [1] "SpatialPointsDataFrame"
## attr(,"package")
## [1] "sp"
spplot(data, "Total_C", colorkey = TRUE, main = " Grazed feilds 30m Triangular sampling C concentrations (n = 150 Random)")






# The End


 

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