In this package you will find a series of functions for soil physics data analysis. These functions includes five models of water retention curve, seven methods of soil precompression stress, least limiting water range (LLWR), Integral Water Capacity (IWC), soil penetration resistance curve by Busscher’s model, calculation of Soil Aggregate-Size Distribution, S Index, critical soil moisture and maximum bulk density using data from Proctor test, calculation of equivalent pore radius as a function of soil water tension, simulation of sedimentation time of soil particles through Stokes’ law, simulation of soil pore size distribution, calculation of the hydraulic cut-off introduced by Dexter et al. (2008) and simulation of soil compaction induced by agricultural field traffic. Other utilities like functions to calculate the void ratio and to determine the maximum curvature point are available.
From CRAN:
install.packages("soilphysics")
Or you can install the development version from GitHub:
install.packages("devtools")
devtools::install_github("arsilva87/soilphysics")
Then, load it
library(soilphysics)
#> ---
#> soilphysics version 5.0
Using the function stressTraffic, it it possible calculate the contact area, stress distribuition and stress propagation based on the SoilFlex model.
stress <- stressTraffic(inflation.pressure=200,
recommended.pressure=200,
tyre.diameter=1.8,
tyre.width=0.4,
wheel.load=4000,
conc.factor=c(4,5,5,5,5,5),
layers=c(0.05,0.1,0.3,0.5,0.7,1),
plot.contact.area = TRUE)
Unsing the funtion soilDeformation, it is possible calculates the bulk density variation as a function of the applied mean normal stress using critical state theory, by O’Sullivan and Robertson (1996).
soilDeformation(stress = 300,
p.density = 2.67,
iBD = 1.55,
N = 1.9392,
CI = 0.06037,
k = 0.00608,
k2 = 0.01916,
m = 1.3,graph=TRUE,ylim=c(1.4,2.0))
#> iBD fBD vi vf I%
#> 1 1.55 1.6385 1.7226 1.6295 5.71
mois <- c(0.083, 0.092, 0.108, 0.126, 0.135)
bulk <- c(1.86, 1.92, 1.95, 1.90, 1.87)
criticalmoisture(theta = mois, Bd = bulk)
#>
#> Critical Moisture and Maximum Bulk Density
#>
#> Sample 1
#> Intercept 0.4825950
#> mois 26.9265767
#> mois^2 -123.7120431
#> R.squared 0.9515476
#> n 5.0000000
#> critical.mois 0.1088276
#> max.bulk 1.9477727
Quantifying the soil water availability for plants through the IWC approach:
iwc(theta_R = 0.166, theta_S = 0.569, alpha = 0.029, n = 1.308,
a = 0.203, b = 0.256, hos = 200, graph = TRUE)
#> IWC EI h.Range
#> EKa(h, hos) 0.0144000 0.9600 66.43 - 139.49
#> EK(h, hos) 0.0405000 5.3700 139.49 - 330
#> C(h, hos) 0.0846000 49.4800 330 - 2471.44
#> ER(h, hos) 0.0288000 87.1200 2471.44 - 15000
#> ERKdry(h, hos) 0.0006000 4.9100 12000 - 15000
#> Sum 0.1689139 147.8336 0 - 15000
Quantifying the soil water availability for plants through the LLWR approach:
# Usage
data(skp1994)
with(skp1994,
llwr(theta = W, h = h, Bd = BD, Pr = PR,
particle.density = 2.65, air = 0.1,
critical.PR = 2, h.FC = 100, h.WP = 15000))
#>
#> Least Limiting Water Range
#>
#> ----------
#> Limiting theta (6 first rows):
#> thetaA thetaPR thetaFC thetaWP
#> [1,] 0.3905660 0.2461742 0.3900669 0.2568751
#> [2,] 0.3226415 0.3104104 0.3694538 0.2433005
#> [3,] 0.3867925 0.2495628 0.3888922 0.2561015
#> [4,] 0.3490566 0.2846168 0.3773373 0.2484921
#> [5,] 0.3226415 0.3104104 0.3694538 0.2433005
#> [6,] 0.3566038 0.2774366 0.3796205 0.2499957
#>
#> ----------
#> Estimates of the soil water content model:
#>
#> Formula: theta ~ exp(a + b * Bd) * h^c
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> a -0.150316 0.147075 -1.022 0.31080
#> b -0.301627 0.099303 -3.037 0.00351 **
#> c -0.083368 0.004142 -20.128 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.02239 on 61 degrees of freedom
#>
#> Number of iterations to convergence: 3
#> Achieved convergence tolerance: 2.525e-07
#>
#> pseudo R-squared: 0.8855893
#> adjusted R-squared: 0.8818382
#>
#> ----------
#> Estimates of the soil penetration resistance model:
#>
#> Formula: Pr ~ b0 * (theta^b1) * (Bd^b2)
#>
#> Parameters:
#> Estimate Std. Error t value Pr(>|t|)
#> b0 0.07684 0.02537 3.029 0.003598 **
#> b1 -1.66486 0.18007 -9.246 3.28e-13 ***
#> b2 3.08404 0.77814 3.963 0.000196 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.6293 on 61 degrees of freedom
#>
#> Number of iterations to convergence: 8
#> Achieved convergence tolerance: 4.833e-06
#>
#> pseudo R-squared: 0.6593648
#> adjusted R-squared: 0.6481964
#>
#> ----------
#> Shaded area: 0.02494455
Quantifying the LLWR using van Genuchten’s parameters:
par(mfrow=c(1,2))
llwr_llmpr(thetaR=0.1180, thetaS=0.36, alpha=0.133, n=1.30,
d=0.005, e=-2.93, f=3.54, PD=2.65,
critical.PR=4, h.FC=100, h.PWP=15000, air.porosity=0.1,
labels=c("AFP", "FC","PWP", "PR"),
graph1=TRUE,graph2=FALSE, ylab=expression(LLMPR~(hPa)), ylim=c(15000,1))
#> $CRITICAL_LIMITS
#> theta potential
#> AIR 0.2600 41.02
#> FC 0.2285 100.00
#> PWP 0.1428 15000.00
#> PR 0.1939 356.65
#>
#> $LLRW_LLMPR
#> Upper Lower Range
#> LLWR 0.2285 0.1939 0.0346
#> LLMPR 100.0000 356.6500 256.6500
mtext(expression("Bulk density"~(Mg~m^-3)),1,line=2.2, cex=0.8)
llwr_llmpr(thetaR=0.1180, thetaS=0.36, alpha=0.133, n=1.30,
d=0.005, e=-2.93, f=3.54, PD=2.65,
critical.PR=4, h.FC=100, h.PWP=15000, air.porosity=0.1,
labels=c("AFP", "FC","PWP", "PR"),
graph1=FALSE,graph2=TRUE, ylab=expression(LLMPR~(hPa)), ylim=c(0.1,0.5))
#> $CRITICAL_LIMITS
#> theta potential
#> AIR 0.2600 41.02
#> FC 0.2285 100.00
#> PWP 0.1428 15000.00
#> PR 0.1939 356.65
#>
#> $LLRW_LLMPR
#> Upper Lower Range
#> LLWR 0.2285 0.1939 0.0346
#> LLMPR 100.0000 356.6500 256.6500
mtext(expression("Bulk density"~(Mg~m^-3)),1,line=2.2, cex=0.8)
Estimating the precompression stress by several methods:
pres <- c(1, 12.5, 25, 50, 100, 200, 400, 800, 1600)
VR <- c(0.846, 0.829, 0.820, 0.802, 0.767, 0.717, 0.660, 0.595, 0.532)
sigmaP(VR, pres, method = "casagrande", n4VCL = 2)
#> Preconsolidation stress: 104.2536
#> Method: casagrande, with mcp equal to 1.7885
#> Compression index: 0.2093
#> Swelling index: 0.0179
Fitting (interactive!) water retention curve using van Genuchten’s model
Sindex(theta_R=0, theta_S=0.395, alpha=0.0217, n=1.103, xlim = c(0, 1000))
#>
#> The S Index
#>
#> h_i : 395.4757
#> theta_i : 0.3139
#> |S| : 0.0296
#> Soil physical quality : Poor
data(SoilAggregate)
head(SoilAggregate)
#> ID D3 D1.5 D0.75 D0.375 D0.178 D0.053
#> 1 A1 25.80 7.55 5.50 5.10 3.00 3.05
#> 2 A2 19.85 5.30 7.45 7.30 4.40 5.70
#> 3 A3 7.10 9.80 11.60 8.10 2.35 11.05
#> 4 B1 6.10 4.85 11.20 13.10 7.15 7.60
#> 5 B2 12.00 6.30 16.10 7.35 3.70 4.55
#> 6 B3 14.10 6.15 8.80 11.05 4.60 5.30
classes <- c(3, 1.5, 0.75, 0.375, 0.178, 0.053)
out <- aggreg.stability(sample.id = SoilAggregate[ ,1],
dm.classes = classes,
aggre.mass = SoilAggregate[ ,-1])
head(out)
#> [sample.id] [MWD (mm)] [GMD (mm)] [Total mass (g)] 3 1.5 0.75 0.375 0.178
#> 1 A1 1.909163 1.2382214 50 52 15 11 10 6
#> 2 A2 1.538206 0.8239103 50 40 11 15 15 9
#> 3 A3 0.974829 0.4865272 50 14 20 23 16 5
#> 4 B1 0.811260 0.4311214 50 12 10 22 26 14
#> 5 B2 1.223620 0.7282644 50 24 13 32 15 7
#> 6 B3 1.267369 0.6853162 50 28 12 18 22 9
#> 0.053
#> 1 6
#> 2 11
#> 3 22
#> 4 15
#> 5 9
#> 6 11
De Lima, R.P.; Da Silva, A.R.; Da Silva, A.P. (2021) soilphysics: An R package for simulation of soil compaction induced by agricultural field traffic. SOIL and TILLAGE RESEARCH, 206: 104824. DOI: https://doi.org/10.1016/j.still.2020.104824
De Lima, R.P.; Tormena, C.A.; Figueiredo, G.C; Da Silva, A.R.; Rolim, M.M. (2020) Least limiting water and matric potential ranges of agricultural soils with calculated physical restriction thresholds. Agricultural Water Management, 240: 106299. DOI: https://doi.org/10.1016/j.agwat.2020.106299
Da Silva, A.R.; De Lima, R.P. (2017) Determination of maximum curvature point with the R package soilphysics. International Journal of Current Research, 9: 45241-45245.
De Lima, R.P.; Da Silva, A.R.; Da Silva, A.P.; Leao, T.P.; Mosaddeghi, M.R. (2016) soilphysics: an R package for calculating soil water availability to plants by different soil physical indices. Computers and Eletronics in Agriculture, 120: 63-71. DOI: https://doi.org/10.1016/j.compag.2015.11.003
Da Silva, A.R.; De Lima, R.P. (2015) soilphysics: an R package to determine soil preconsolidation pressure. Computers and Geosciences, 84: 54-60. DOI: https://doi.org/10.1016/j.cageo.2015.08.008
soilphysics is an ongoing project. Then, contributions are very welcome. If you have a question or have found a bug, please open an