Type:	Package
Title:	Soil Physical Analysis
Version:	5.0
Date:	2022-06-06
Description:	Basic and model-based soil physical analyses.
Author:	Anderson Rodrigo da Silva [aut, cre], Renato Paiva de Lima [aut]
Maintainer:	Anderson Rodrigo da Silva <anderson.agro@hotmail.com>
URL:	https://arsilva87.github.io/soilphysics/
Imports:	utils, stats, graphics, boot, grDevices, datasets, MASS, shiny, rhandsontable, shinydashboard, fields
Suggests:	knitr, rmarkdown, rpanel, tcltk
VignetteBuilder:	knitr
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Packaged:	2022-06-07 15:25:34 UTC; User
Repository:	CRAN
Date/Publication:	2022-06-07 16:10:02 UTC

Soil Physical Analysis

Description

Basic and model-based soil physical analyses.

Details

Functions for modelling the load bearing capacity and the penetration resistance, and for predicting the stress applied by agricultural machines in the soil profile. The package allows one to model the soil water retention through six different models. There are some useful and easy-to-use functions to perform parameter estimation of these models. Methods to obtain the preconsolidation stress are available, such as the standard of Casagrande (1936) and so on. It is possible to quantify soil water availability for plants through the Least Limiting Water Range approach as well as the Integral Water Capacity. Moreover, it is possible to determine the water suction at the point of hydraulic cut-off. Also, users can deal with the high-energy-moisture-characteristics (HEMC) methodology proposed by Pierson and Mulla (1989), which is used to analyze the aggregate stability. There is a function to determine the soil critical moisture and the maximum bulk density for one or more samples, based on the Proctor (1933) compaction test. Other utilities like a function to calculate the soil liquid limit, the void ratio and to determine the maximum curvature point are available.

Note

soilphysics is an ongoing project. We welcome any and all criticism, comments and suggestions.

Author(s)

Anderson Rodrigo da Silva, Renato Paiva de Lima

Maintainer: Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

da Silva, A.R.; de Lima, R.P. (2015) soilphysics: an R package to determine soil preconsolidation pressure. Computers and Geosciences, 84: 54-60.

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.

da Silva, A.R.; 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. (2021) soilphysics: An R package for simulation of soil compaction induced by agricultural field traffic. SOIL and TILLAGE RESEARCH, 206: 104824.

Unsaturated Hydraulic Conductivity

Description

A closed-form analytical expressions for calculating the relative unsaturated hydraulic conductivity as a function of soil water tension (h) based on van Genuchten's water retention curve.

Usage

Kr_h(Ks, alpha, n, h, f=0.5)

Arguments

Value

numeric, the value of unsaturated hydraulic conductivity.

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

Guarracino, L. (2007). Estimation of saturated hydraulic conductivity Ks from the van Genuchten shape parameter alpha. Water Resources Research, 43(11).

Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. Soil Science Society of America Journal 44(5):892-898.

Examples

# EXAMPLE 1
Kr_h(Ks = 1.06*10^2, alpha = 0.048, n = 1.5,h=100, f=0.5)

# EXAMPLE 2
x <- seq(log10(1), log10(1000),len=100)
h <- 10^x
y <- Kr_h(Ks = 1.06*10^2, alpha = 0.048, n = 1.5,h=h, f=0.5)
plot(x=h,y=y, log="yx", xlab="h (hPa)", yaxt='n',
    ylab="", ylim=c(0.001,100), xlim=c(1,10000))
mtext(expression(K[r] ~ (cm~d^-1)), 2, line=2)
ax <- c(0.001, 0.01, 0.1, 1, 10, 100)
axis(2,at=ax, labels=ax)

# End (not run)

Unsaturated Hydraulic Conductivity as a function of water content

Description

A closed-form analytical expressions for calculating the relative unsaturated hydraulic conductivity as a function of soil water content based on van Genuchten's water retention curve.

Usage

Kr_theta(theta, thetaS, thetaR, n, Ks, f=0.5)

Arguments

Value

numeric, the value of unsaturated hydraulic conductivity.

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

Guarracino, L. (2007). Estimation of saturated hydraulic conductivity Ks from the van Genuchten shape parameter alpha. Water Resources Research, 43(11).

Van Genuchten, M. T. (1980). A closed-form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal 44(5):892-898.

Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resour. Res. 43(11): 513-522,

Examples

# EXAMPLE 1
Kr_theta(theta=0.45,thetaS=0.5,thetaR=0.15, 
         n = 2, Ks = 1.06*10^2, f=0.5)

# EXAMPLE 2
thetaS <- 0.50
thetaR <- 0.15
theta <- seq(thetaS, thetaR, len=50)
y <- Kr_theta(theta=theta,thetaS=thetaS,thetaR=thetaR, 
             n = 2, Ks = 1.06*10^2, f=0.5)
             
# Just for this example, we are removing the "0" value 
# for plotting the graph in log scale, sence log10(0) results in "-Inf"
Kr <- y[-50]
w <- theta[-50]

plot(x=w,y=Kr,xlab=expression(theta~(m^3~m^-3)),
     ylim=c(0.001,100), log="y",yaxt='n',
     ylab="", xlim=c(0.15,0.50))
mtext(expression(K[r] ~ (cm~d^-1)), 2, line=2)
ax <- c(0.001, 0.01, 0.1, 1, 10, 100)
axis(2,at=ax, labels=ax)

# End (not run)

A shiny for calculation of the usual least limiting water range

Description

A shiny for calculation of the usual least limiting water range

Usage

LLWR_App()

Value

A shiny app

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

See Also

stressTraffic

A shiny for calculation of least limiting water and matric potential ranges of agricultural soils with calculated physical restriction thresholds

Description

A shiny for calculation of least limiting water and matric potential ranges of agricultural soils with calculated physical restriction thresholds

Usage

LLWR_LLMPR_App()

Value

A shiny app

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

See Also

stressTraffic

A shiny for simulation of soil compaction

Description

A shiny for for simulation of soil compaction

Usage

PredComp()

Value

A shiny app

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

See Also

stressTraffic

Multiple R-squared

Description

Function to calculate the multiple R-squared and the adjusted R-squared from a fitted model via lm or aov, i.e., linear models. For a model fitted via nls, nonlinear models, the pseudo R-squared is returned.

Usage

 Rsq(model) 

Arguments

Value

A list of

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

See Also

lm, summary.lm, aov, nls

Examples

# example 1 [linear model]
y <- rnorm(10)
x <- 1:10
fit <- lm(y ~ x)
summary(fit)
Rsq(fit)

# example 2 [nonlinear model for Load Bearing Capacity]
data(compaction)
attach(compaction)
out <- fitlbc(theta = Mois, sigmaP = PS)
summary(out)
Rsq(out)

# End (not run)

Self-Starting Nls Load Bearing Capacity Model

Description

A selfStart model that evaluates the Load Bearing Capacity (Dias Jr., 1994) function and its gradient. It has an initial attribute that creates initial estimates of the parameters b0 and b1.

Usage

 SSlbc(theta, b0, b1) 

Arguments

Value

a numeric vector with the same length of theta. It is the value of the expression 10^{(b0 + b1 * \theta)}. Also, the gradient matrix with respect to the parameters is attached as an attribute named gradient.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Dias Junior, M. S. (1994). Compression of three soils under longterm tillage and wheel traffic. 1994. 114p. Ph.D. Thesis - Michigan State University, East Lansing.

See Also

getInitiallbc, fitlbc, selfStart, nls, sigmaP

Examples

data(compaction)
attach(compaction)
ss <- SSlbc(Mois, 2.79, -2.33)
ss[1:50] # prediction
PS # original data of preconsolidation stress
ss # prediction and gradient

# End (not run)

The S Index

Description

Function to calculate the S index (Dexter, 2004) for evaluating the soil physical quality based on the Water Retention Curve (van Genuchten, 1980).

S = - n * (\theta_S - \theta_R) * (1 + 1/m)^{-(1 + m)}

Usage

Sindex(theta_R, theta_S, alpha, n, m = 1 - 1/n, vcov = NULL,
	nsim = 999, conf.level = 0.95, graph = TRUE, ...)

Arguments

Value

A list of

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Dexter, A. R. (2004). Soil physical quality Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma, 120:201-214.

Genuchten, M. T. van. (1980). A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44:892-898.

Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resource Research, 12:513-522.

See Also

soilwater, fitsoilwater

Examples

# Dexter (2004, Table 1)
Sindex(0, 0.395, 0.0217, 1.103, xlim = c(0, 1000))
Sindex(0, 0.335, 0.0616, 1.139, xlim = c(0, 1000))
# ...
Sindex(0, 0.226, 0.0671, 1.581, xlim = c(0, 1000))

# End (not run)

Soil Aggregate Size Data Set

Description

Data set for determining soil aggregate size distribution.

Usage

data(SoilAggregate)

Format

A data frame with 12 observations on 7 variables.

ID

a factor with the names of the soil samples.

D3

D1.5

D0.75

D0.375

D0.178

D0.053

Examples

data(SoilAggregate)
summary(SoilAggregate)

A shiny to automatically fit the water retention curve

Description

Use the maximum likelihood method to find the water retention curve that best fits the data.

Usage

WRC_App()

Value

A shiny app

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

See Also

fitsoilwater_App

Soil Aggregate-Size Distribution

Description

It calculates the mean weight diameter (MWD), the geometric mean diameter (GMD) and the soil aggregates size distribution per class based on the mass of the aggregates retained in each sieve from a total soil mass used for the soil aggregate stability test.

Usage

aggreg.stability(sample.id = NA, dm.classes, aggre.mass)

Arguments

Details

The user must arrange a data.frame with lines representing the samples and the columns representing the mass of the aggregates retained in each one of the meshes (corresponding to each size class) in the aggregate stability test.

Value

A data.frame containing valor of MWD, GMD, total soil mass (total.mass) used in the aggregate stability test and the percentage of soil aggregate size distribution per class.

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

W. Kemper, W. Chepil. (1965). Size distribution of aggregates. C. Black (Ed.). Methods of Soil Analysis, American Society Agronomy, Madison. pp. 499-510.

Yoder, R. A. (1936). A direct method of aggregate analysis of soils and a study of the physical nature of erosion losses. Journal of the American Society of Agronomy, 28:337-351.

Examples

data(SoilAggregate)
classes <- c(3, 1.5, 0.75, 0.375, 0.178, 0.053)
aggreg.stability(sample.id = SoilAggregate[ ,1],
   dm.classes = classes, aggre.mass = SoilAggregate[ ,-1])

# End (not run)

A shiny for Soil Aggregate-Size Distribution

Description

A shiny for Soil Aggregate-Size Distribution

Usage

aggreg.stability_App()

Value

A shiny app

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

See Also

stressTraffic

Soil Bulk Density Data Set

Description

This data set refers to five observations of soil bulk density and soil moisture per sample. There are four soil samples.

Usage

 data(bulkDensity) 

Format

A data frame with 20 observations on the following 3 variables.

Id

a factor with levels s1 s2 s3 s4, the 'ID' of each soil sample.

MOIS

a numeric vector containing soil moisture values (cm^3 / cm^3).

BULK

a numeric vector containing soil bulk density values (g / cm^3).

Source

Simulated data.

Examples

data(bulkDensity)
summary(bulkDensity)

Soil Compaction Data Set

Description

This data set refers to physical soil variables related to soil compaction.

Usage

 data(compaction) 

Format

A data frame with 50 observations on the following 4 variables.

PR

a numeric vector containing soil penetration resistance values (MPa).

BD

a numeric vector containing soil bulk density values (g / cm^3).

Mois

a numeric vector containing soil moisture values (cm^3 / cm^3).

PS

a numeric vector containing soil preconsolidation stress values (kPa).

Source

Simulated data.

Examples

data(compaction)
summary(compaction)

Estimation of compressive properties by Defossez et al. (2003)

Description

It calculates the compressive parameters N and lambda using the pedo-transfer function from Defossez et al. (2003)

Usage

 
compressive_properties(water.content, soil=c("Loess", "Calcareous"))

Arguments

Details

In Defossez et al. (2003), the recompression index, kappa, was assumed as 0.0058 for both soil group.

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Defossez, P., Richard, G., Boizard, H., & O'Sullivan, M. F., 2003. Modeling change in soil compaction due to agricultural traffic as function of soil water content. Geoderma, 116: 89–105.

See Also

stressTraffic

Examples

# EXAMPLE 1 - For Loess and Calcareous soil

water.content <- 25
compressive_properties(water.content=water.content, soil="Loess") 
compressive_properties(water.content=water.content, soil="Calcareous") 

# EXAMPLE 2 - For Loess soil

water.content <- seq(from=5,to=30,len=20)
out <- compressive_properties(water.content=water.content, soil="Loess") 
plot(x=water.content ,y=out$N, ylab="N", xlab="Bulk density") # plot for N
plot(x=water.content ,y=out$CI, ylab="Lambda", xlab="Bulk density") # plot for compression index


# EXAMPLE 3 - For Calcareous soil

water.content <- seq(from=5,to=30,len=20)
out <- compressive_properties(water.content=water.content, soil="Calcareous") 
plot(x=water.content ,y=out$N, ylab="N", xlab="Bulk density") # plot for N
plot(x=water.content ,y=out$CI, ylab="Lambda", xlab="Bulk density") # plot for compression index

# End (not run)

Estimation of compressive properties by Keller and Arvidsson (2007)

Description

It calculates the compressive parameters N and lambda using the pedo-transfer function from Keller and Arvidsson (2007)

Usage

 
compressive_properties2(particle.density, bulk.density)

Arguments

Details

In Keller and Arvidsson (2007), the recompression index, kappa, was found as 0.042 for all soil.

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Keller, T., Arvidsson, J., 2007. Compressive properties of some Swedish and Danish structured agricultural soils measured in uniaxial compression tests. European Journal of Soil Science , 58: 1373-1381.

See Also

stressTraffic

Examples

# EXAMPLE 1

 compressive_properties2(particle.density=2.65, bulk.density=1.5)

# EXAMPLE 2

BD <- seq(from=1.2,to=1.8, by=0.01) # range of bulk density from 1.2 to 1.8
out <- compressive_properties2(particle.density=2.65, bulk.density=BD)

plot(x=BD,y=out$N, ylab="N", xlab="Bulk density") # for N
plot(x=BD,y=out$CI, ylab="Compression index (CI)", xlab="Bulk density") # for compression index

# End (not run)

Estimation of compressive properties by de Lima et al. (2018)

Description

It calculates the compressive parameters N, lambda and kappa using the pedo-transfer function from de Lima et al. (2018)

Usage

 
compressive_properties3(bulk.density, matric.suction, soil=c("SandyLoam","SandyClayLoam"))

Arguments

Details

Pedo-transfer function deveploed under no-till condition. See de Lima et al. (2018)

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

de Lima, R. P., da Silva, A. P., Giarola, N. F., da Silva, A. R., Rolim, M. M., Keller, T., 2018. Impact of initial bulk density and matric suction on compressive properties of two Oxisols under no-till. Soil and Tillage Research, 175: 168-177.

See Also

stressTraffic

Examples

# EXAMPLE 1

compressive_properties3(bulk.density=1.5, matric.suction=100, soil="SandyLoam")
compressive_properties3(bulk.density=1.5, matric.suction=100, soil="SandyClayLoam")

# EXAMPLE 2 for SandyLoam soil

matric.suction <- seq(from=30,to=1000,len=100)
out <- compressive_properties3(bulk.density=1.5, 
	matric.suction=matric.suction, soil="SandyLoam")
plot(x=matric.suction,y=out$N, ylab="N", 
	xlab="Matric suction (hPa)", log="x") # plot for N
# plot for lambda
plot(x=matric.suction,y=out$lambda, ylab="lambda", 
	xlab="Matric suction (hPa)", log="x") 
# plot for kappa
plot(x=matric.suction,y=out$k, ylab="kappa", 
	xlab="Matric suction (hPa)", log="x") 


# EXAMPLE 3 for SandyClayLoam soil

matric.suction <- seq(from=30,to=1000,len=100)
out <- compressive_properties3(bulk.density=1.5, 
				matric.suction=matric.suction, 
				soil="SandyClayLoam")
plot(x=matric.suction,y=out$N, ylab="N", 
     xlab="Matric suction (hPa)", log="x") # plot for N
# plot for lambda
plot(x=matric.suction,y=out$lambda, 
     ylab="lambda", xlab="Matric suction (hPa)", log="x")
# plot for kappa
plot(x=matric.suction,y=out$k, 
     ylab="kappa", xlab="Matric suction (hPa)", log="x")

# End (not run)

Estimation of compressive properties by de Lima et al. (2020)

Description

It calculates the compressive parameters N, lambda and kappa using the pedo-transfer function from de Lima et al. (2020)

Usage

 
compressive_properties4(matric.suction, soil=c("PloughLayer","PloughPan"))

Arguments

Details

Pedo-transfer function developed for a sandy loam soil texture. See de Lima et al. (2018)

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

de Lima, R. P., Rolim, M. M., da C. Dantas, D., da Silva, A. R., Mendonca, E. A., 2020. Compressive properties and least limiting water range of plough layer and plough pan in sugarcane fields. Soil Use and Management, 00: 1-12.

See Also

stressTraffic

Examples

# EXAMPLE 1

compressive_properties4(matric.suction=100, soil="PloughLayer")
compressive_properties4(matric.suction=100, soil="PloughPan")



# EXAMPLE 2 for "PloughLayer"

matric.suction <- seq(from=10,to=10000,len=100)
out <- compressive_properties4(matric.suction=matric.suction, soil="PloughLayer")
plot(x=matric.suction,y=out$N, ylab="N", 
	xlab="Matric suction (hPa)", log="x") # plot for N
# plot for lambda
plot(x=matric.suction,y=out$lambda, ylab="lambda", 
	xlab="Matric suction (hPa)", log="x") 
# plot for kappa
plot(x=matric.suction,y=out$k, ylab="kappa", 
	xlab="Matric suction (hPa)", log="x")


# EXAMPLE 3 for "PloughPan"

matric.suction <- seq(from=10,to=10000,len=100)
out <- compressive_properties4(matric.suction=matric.suction, 
                               soil="PloughPan")
# plot for N
plot(x=matric.suction,y=out$N, 
     ylab="N", xlab="Matric suction (hPa)", log="x") 
# plot for lambda
plot(x=matric.suction,y=out$lambda, 
     ylab="lambda", xlab="Matric suction (hPa)", log="x") 
# plot for kappa
plot(x=matric.suction,y=out$k, ylab="kappa", 
     xlab="Matric suction (hPa)", log="x") 

# End (not run)

Estimation of compressive properties by O'Sullivan et al. (1999)

Description

It calculates the compressive parameteres N, lambda and kappa using the pedo-transfer function from O'Sullivan et al. (1999)

Usage

 
compressive_properties5(water.content, soil=c("SandyLoam","ClayLoam"))

Arguments

Details

See O'Sullivan et al. (1999).

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

O'sullivan, M. F., Henshall, J. K., Dickson, J. W., 1999. A simplified method for estimating soil compaction. Soil and Tillage Research, 49: 325-335.

See Also

stressTraffic

Examples

# EXAMPLE 1
water.content <- 15
compressive_properties5(water.content=water.content, soil="SandyLoam") 
compressive_properties5(water.content=water.content, soil="ClayLoam") 


# EXAMPLE 2 - SandyLoam

water.content <- seq(from=5,to=20,len=20)
out <- compressive_properties5(water.content=water.content, soil="SandyLoam") 
plot(x=water.content ,y=out$N, 
     ylab="N", xlab="Bulk density") # plot for N
plot(x=water.content ,y=out$lambda, 
    ylab="lambda", xlab="Bulk density") # plot for lambda
plot(x=water.content ,y=out$kappa, 
    ylab="kappa", xlab="Bulk density") # plot for kappa


# EXAMPLE 3 - ClayLoam 

water.content <- seq(from=10,to=25,len=20)
out <- compressive_properties5(water.content=water.content, soil="ClayLoam") 
plot(x=water.content ,y=out$N, 
     ylab="N", xlab="Bulk density") # plot for N
plot(x=water.content ,y=out$lambda, 
     ylab="lambda", xlab="Bulk density") # plot for lambda
plot(x=water.content ,y=out$kappa, 
     ylab="kappa", xlab="Bulk density") # plot for rkappa

# End (not run)

Critical Moisture and Maximum Bulk Density

Description

Function to determine the soil Critical Moisture and the Maximum Bulk Density based on the Proctor (1933) compaction test. It estimates compaction curve by fitting a quadradtic regression model.

Usage

criticalmoisture(theta, Bd, samples = NULL, graph = TRUE, ...)

maxbulkdensity(theta, Bd, samples = NULL, graph = TRUE, ...)

Arguments

Details

If samples is ispecified, then it must has the same length of theta and Bd.

Value

An object of class 'criticalmoisture', i.e., a matrix containing the quadratic model coefficients (rows 1 to 3), the R-squared (row 4), the sample size (row 5), the critical soil moisture (row 6) and the maximum bulk density (row 7), per sample.

Note

maxbulkdensity is just an alias of criticalmoisture.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Proctor, E. R. (1933). Design and construction of rolled earth dams. Eng. News Record, 3: 245-284, 286-289, 348-351, 372-376.

Silva, A. P. et al. (2010). Indicadores da qualidade fisica do solo. In: Jong Van Lier, Q. (Ed). Fisica do solo. Vicosa (MG): Sociedade Brasileira de Ciencia do Solo. p.541-281.

See Also

maxcurv

Examples

# example 1 (1 sample)
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)

# example 2 (4 samples)
data(bulkDensity)
attach(bulkDensity)
criticalmoisture(theta = MOIS, Bd = BULK, samples = Id)

# End (not run)

Self-starting Nls Busscher's (1990) Model for Soil Penetration Resistance

Description

Function to self start the nonlinear Busscher's (1990) model for penetration resistance, i.e., Pr = b0 * (\theta ^ {b1}) * (Bd ^ {b2}). It creates initial estimates (by log-linearization) of the parameters b0, b1 and b2 and uses them to provide its least-squares estimates through nls.

Usage

 fitbusscher(Pr, theta, Bd, ...) 

Arguments

Value

A nls output (see help(nls)).

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Busscher, W. J. (1990). Adjustment of flat-tipped penetrometer resistance data to common water content. Transactions of the ASAE, 3:519-524.

See Also

fitlbc, nls, summary.nls, predict.nls, Rsq

Examples

data(compaction)
attach(compaction)
out <- fitbusscher(Pr = PR, theta = Mois, Bd = BD)
summary(out)
Rsq(out)

# 3D plot
X <- seq(min(Mois), max(Mois), len = 30) # theta
Y <- seq(min(BD), max(BD), len = 30) # Bd
f <- function(x, y) coef(out)[1] * (x^coef(out)[2]) * (y^coef(out)[3])
Z <- outer(X, Y, f)
persp(X, Y, Z,
	xlab = "Soil moisture",
	ylab = "Soil bulk density",
	zlab = "Penetration resistance",
	ticktype = "detailed",
	phi = 20, theta = 30)

# End (not run)

Parameter Estimation of the Load Bearing Capacity Model

Description

This function creates initial parameter estimates of the nonlinear Load Bearing Capacity (Dias Jr., 1994) model, i.e., \sigma_P = 10 ^ {(b0 + b1 * \theta)}, by using two methods: a getInitial method or a log-linearization. Then, it uses them to provide its least-squares estimates via nls.

Usage

 fitlbc(theta, sigmaP, ...) 

Arguments

Value

A nls object.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Dias Junior, M. S. (1994). Compression of three soils under longterm tillage and wheel traffic. 1994. 114p. Ph.D. Thesis - Michigan State University, East Lansing.

See Also

sigmaP, fitbusscher, maxcurv, Rsq

Examples

data(compaction)
attach(compaction)
out <- fitlbc(theta = Mois, sigmaP = PS)
summary(out)
Rsq(out)
curve(10^(coef(out)[1] + coef(out)[2]*x))

# End (not run)

Interactive Estimation of van Genuchten's (1980) Model Parameters

Description

An interactive graphical adjustment of the soil water retention curve via van Genuchten's (1980) formula. The nonlinear least-squares estimates can be achieved taking the graphical initial values.

Usage

fitsoilwater(theta, x, xlab = NULL, ylab = NULL, ...)

Arguments

Value

A plot of theta versus x and the curve of the current fitted model according to the adjusted parameters in an external interactive panel. Pressing the button "NLS estimates" a nls summary of the fitted model is printed on console whether convergence is achieved, otherwise a warning box of "No convergence" is shown.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Genuchten, M. T. van. (1980). A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44:892-898.

See Also

nls, soilwater

Examples

# Liu et al. (2011)
h <- c(0.001, 50.65, 293.77, 790.14, 992.74, 5065, 10130, 15195)
w <- c(0.5650, 0.4013, 0.2502, 0.2324, 0.2307, 0.1926, 0.1812, 0.1730)
fitsoilwater(w, h)

# End (not run)

Interactive Estimation of the Groenevelt and Grant (2004) Model Parameters

Description

An interactive graphical adjustment of the soil water retention curve via Groenevelt and Grant (2004) formula. The nonlinear least-squares estimates can be achieved taking the graphical initial values.

Usage

fitsoilwater2(theta, x, x0 = 6.653, xlab = NULL, ylab = NULL, ...)

Arguments

Value

A plot of theta versus x and the curve of the current fitted model according to the adjusted parameters in an external interactive panel. Pressing the button "NLS estimates" a nls summary of the fitted model is printed on console whether convergence is achieved, otherwise a warning box of "No convergence" is shown.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Groenevelt & Grant (2004). A newmodel for the soil-water retention curve that solves the problem of residualwater contents. European Journal of Soil Science, 55:479-485.

See Also

nls, soilwater2, soilwater

Examples

w <- c(0.417, 0.354, 0.117, 0.048, 0.029, 0.017, 0.007, 0)
pF <- 0:7
fitsoilwater2(w, pF)

# End (not run)

Interactive Estimation of the Dexter's (2008) Model Parameters

Description

An interactive graphical adjustment of the soil water retention curve through the Dexter's (2008) formula. The nonlinear least-squares estimates can be achieved taking the graphical initial values.

Usage

fitsoilwater3(theta, x, xlab = NULL, ylab = NULL, ...)

Arguments

Value

A plot of theta versus x and the curve of the current fitted model according to the adjusted parameters in an external interactive panel. Pressing the button "NLS estimates" a nls summary of the fitted model is printed on console whether convergence is achieved, otherwise a warning box of "No convergence" is shown.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Dexter et al. (2008). A user-friendly water retention function that takes account of the textural and structural pore spaces in soil. Geoderma, 143:243–253.

See Also

soilwater3, nls, fitsoilwater2

Examples

# data extracted from Liu et al. (2011)
h <- c(0.001, 50.65, 293.77, 790.14, 992.74, 5065, 10130, 15195)
w <- c(0.5650, 0.4013, 0.2502, 0.2324, 0.2307, 0.1926, 0.1812, 0.1730)
fitsoilwater3(w, h)

# End (not run)

Self-starting Nls Power Models for Soil Water Retention

Description

Function to self start the following nonlinear power models for soil water retention:

\theta = \exp(a + b*Bd) \psi^c

(Silva et al., 1994)

\theta = a \psi^c

(Ross et al., 1991)

where \theta is the soil water content.

fitsoilwater() creates initial estimates (by log-linearization) of the parameters a, b and c and uses them to provide its least-squares estimates through nls.

Usage

fitsoilwater4(theta, psi, Bd, model = c("Silva", "Ross"))

Arguments

Value

A "nls" object containing the fitted model.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Ross et al. (1991). Equation for extending water-retention curves to dryness. Soil Science Society of America Journal, 55:923-927.

Silva et al. (1994). Characterization of the least limiting water range of soils. Soil Science Society of America Journal, 58:1775-1781.

See Also

fitsoilwater4, soilwater, soilwater2, soilwater3

Examples

data(skp1994)
# Example 1
ex1 <- with(skp1994,
    fitsoilwater4(theta = W, psi = h, model = "Ross"))
ex1
summary(ex1)

# Example 2
ex2 <- with(skp1994,
    fitsoilwater4(theta = W, psi = h, Bd = BD, model = "Silva"))
ex2
summary(ex2)

# Not run

Interactive Estimation of the Modified van Genuchten's Model Parameters

Description

An interactive graphical adjustment of the soil water retention curve via the van Genuchten's formula, modified by Pierson and Mulla (1989). The nonlinear least-squares estimates can be achieved taking the graphical initial values. It may be useful to estimate the parameters needed in the high-energy-moisture-characteristics (HEMC) method, which is used to analyze the aggregate stability.

Usage

fitsoilwater5(theta, x, theta_S, xlab = NULL, ylab = NULL, ...)

Arguments

Details

The parameter theta_S must be passed as an argument. It is recommended to consider it as the highest water content value in the data set or the water content at saturation.

Value

A plot of theta versus x and the curve of the current fitted model according to the adjusted parameters in an external interactive panel. Pressing the button "NLS estimates" a nls summary of the fitted model is printed on console whether convergence is achieved, otherwise a warning box of "No convergence" is shown.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Pierson, F.B.; Mulla, D.J. (1989) An Improved Method for Measuring Aggregate Stability of a Weakly Aggregated Loessial Soil. Soil Sci. Soc. Am. J., 53:1825–1831.

See Also

nls, soilwater5

Examples

h <- seq(0.1, 40, by = 2)
w <- c(0.735, 0.668, 0.635, 0.612, 0.559, 0.462, 0.369, 0.319, 0.296, 0.282, 
	0.269, 0.256, 0.249, 0.246, 0.239, 0.236, 0.229, 0.229, 0.226, 0.222)
plot(w ~ h)

# suggestions of starting values: thetaR = 0.35, alpha = 0.1, n = 10, 
# b0 = 0.02, b1 = -0.0057, b2 = 0.00004 (Not run)

fitsoilwater5(theta = w, x = h, theta_S = 0.70)

# End (Not run)

A shiny for fitting soil water retention curves

Description

A shiny for fitting soil water retention curves

Usage

fitsoilwater_App()

Value

A shiny app

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

See Also

stressTraffic

Converting Function to Formula

Description

An accessorial function to convert an object of class 'function' to an object of class 'formula'.

Usage

 fun2form(fun, y = NULL) 

Arguments

Value

An object of class formula.

Warning

Numerical values into fun with three or more digits may cause miscalculation.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

See Also

function, formula

Examples

g <- function(x) Asym * exp(-b2 * b3 ^ x) # Gompertz Growth Model
fun2form(g, "y")

# f1 <- function(w) {exp(w)} # error
# fun2form(f1, "x") 
f2 <- function(w) exp(w) # ok
fun2form(f2, "x")

# End (not run)

Get Initial Parameter Estimates for the Load Bearing Capacity Model

Description

This is a getInitial function that evaluates initial parameter estimates for the Load Bearing Capacity model via SSlbc.

Usage

 getInitiallbc(theta, sigmaP) 

Arguments

Value

A numeric vector containing the estimates of the parameters b0 and b1.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Dias Junior, M. S. (1994). Compression of three soils under longterm tillage and wheel traffic. 1994. 114p. Ph.D. Thesis - Michigan State University, East Lansing.

See Also

getInitial, SSlbc, nls, sigmaP

Examples

data(compaction)
attach(compaction)
getInitiallbc(theta = Mois, sigmaP = PS)

# End (not run)

High-Energy-Moisture-Characteristics Aggregate Stability

Description

A function to determine the modal suction, volume of drainable pores, structural index and stability ratio using the high-energy-moisture-characteristics (HEMC) method by Pierson & Mulla (1989), which is used to analyze the aggregate stability. Before using hemc(), the user may estimate the parameters of the Modified van Genuchten's Model through the function fitsoilwater5().

Usage

hemc(x, theta_R, theta_S, alpha, n, b1, b2, 
	graph = TRUE, from = 1, to = 30, 
	xlab = expression(Psi ~ (J~kg^{-1})), 
	ylab = expression(d ~ theta/d ~ Psi), ...)

Arguments

Value

A list of a two objects: 1) a matrix containing the Modal Suction, the Volume od Drainable Pores (VDP) and the Structural Index for both, fast and slow wetting; and 2) the value of Stability Ratio.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Pierson, F.B.; Mulla, D.J. (1989). An Improved Method for Measuring Aggregate Stability of a Weakly Aggregated Loessial Soil. Soil Sci. Soc. Am. J., 53:1825–1831.

See Also

fitsoilwater5

Examples

hemc(x = seq(1, 30), theta_R = c(0.27, 0.4), theta_S = c(0.65, 0.47), 
	alpha = c(0.1393, 0.0954), n = c(6.37, 7.47), 
	b1 = c(-0.008421, -0.011970), b2 = c(0.0001322, 0.0001552))

# End (Not run)

The matric potential at the point of hydraulic cut-off obtained from DE (Dexter et al., 2008) and GG (Groenevelt & Grant, 2004) water retention curves.

Description

The pore water suction at the point of hydraulic cut-off occurs at the point where the residual water content, obtained from Dexter et al. (2008), intercepts with the Groenevelt & Grant (2004) retention curve.

Usage

hydraulicCutOff(theta_R, k0, k1, n, x0 = 6.653)

Arguments

Value

The water suction at the point of hydraulic cut-off.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Dexter, A.R.; Czyz, E.A.; Richard, G.; Reszkowska, A. (2008). A user-friendly water retention function that takes account of the textural and structural pore spaces in soil. Geoderma, 143:243–253.

Groenevelt, P.H.; Grnat, C.D. (2004). A new model for the soil-water retention curve that solves the problem of residual water contents. European Journal of Soil Science, 55:479–485.

See Also

fitsoilwater2, fitsoilwater3

Examples

# Dexter et al. (2012), Table 4A
hydraulicCutOff(0.1130, 6.877, 0.6508, 1.0453)
hydraulicCutOff(0.1122, 12.048, 0.4338, 2.0658)

# End (not run)

The matric potential at the point of hydraulic cut-off using the point of maximum curvature of DE (Dexter et al. 2008) water retention curve.

Description

The pore water suction at the point of hydraulic cut-off occurs at the point where the residual water content, obtained from Dexter et al. (2008), intercepts with the Groenevelt & Grant (2004) retention curve. This function calculates the Hydraulic Cut-Off using the point of maximum curvature of the DE (Dexter et al. 2008) curve.

Usage

hydraulicCutOff2(theta_R, a1, a2, p1, p2, graph = FALSE, ...)

Arguments

Details

The arguments are the fitting parameters from Dexter's (2008) water retention curve, which can be fitted using fitsoilwater3. Further examples of how to use these parameters are given in Dexter et al. (2012).

Value

A data.frame containing the values of matric potential (hPa), pF and water content (w) at the hydraulic cut-off (hco) point.

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

Dexter, A.R.; Czyz, E.A.; Richard, G.; Reszkowska, A. (2008). A user-friendly water retention function that takes account of the textural and structural pore spaces in soil. Geoderma, 143:243–253.

Dexter, A.R., Czyz, E.A., Richard, G. (2012). Equilibrium, non-equilibrium and residual water: consequences for soil water retention. Geoderma, 177:63–71.

See Also

hydraulicCutOff, fitsoilwater3

Examples

# Example 1:  soils from Dexter et al. (2012), Table 4

hydraulicCutOff2(theta_R=0.1130,a1=0.0808,a2=0.0576,p1=4043.2,p2=269.1,
                  graph = TRUE, ylim=c(-0.05,0.15)) # Soil 1

hydraulicCutOff2(theta_R=0.0998,a1=0.1456,a2=0.0162,p1=3156.0,p2=71.51,
                  graph = TRUE, ylim=c(-0.20,0.30)) # Soil 4

hydraulicCutOff2(theta_R=0.0709,a1=0.0195,a2=0.1794,p1=4467.5,p2=1395.5,
                  graph = TRUE, ylim=c(-0.20,0.30)) # Soil 7

hydraulicCutOff2(theta_R=0.0359,a1=0.1014,a2=0.0459,p1=1282.4,p2=56.93,
                  graph = TRUE, ylim=c(-0.10,0.20)) # Soil 10

hydraulicCutOff2(theta_R=0.0736,a1=0.0522,a2=0.0321,p1=3516.2,p2=90.54,
                  graph = TRUE, ylim=c(-0.05,0.15)) # Soil 14

# Example 2:

# Fitting the water retention curve through the Dexter's (2008) curve
h <- c(0.001, 50.65, 293.77, 790.14, 992.74, 5065, 10130, 15195)
w <- c(0.5650, 0.4013, 0.2502, 0.2324, 0.2307, 0.1926, 0.1812, 0.1730)
if (interactive()) {
   fitsoilwater3(theta=w, x=h)
}

# Using the fitted parameter
hydraulicCutOff2(theta_R=0.1738,a1=0.07505,a2=0.316,p1=3673,p2=70.38,
                  graph = TRUE, ylim=c(-0.40,0.60))


# End (not run)

Integral Water Capacity (IWC)

Description

Quantifying the soil water availability for plants through the IWC approach. The theory was based on the work of Groenevelt et al. (2001), Groenevelt et al. (2004) and Asgarzadeh et al. (2014), using the van Genuchten-Mualem Model for estimation of the water retention curve and a simple power model for penetration resistance. The salinity effect on soil available water is also implemented here, according to Groenevelt et al. (2004).

Usage

iwc(theta_R, theta_S, alpha, n, a, b, hos = 0, 
    graph = TRUE, 
    xlab = "Matric head (cm)", 
    ylab = expression(cm^-1), 
    xlim1 = NULL,
    xlim2 = NULL, 
    xlim3 = NULL,
    ylim1 = NULL, 
    ylim2 = NULL, 
    ylim3 = NULL,
    col12 = c("black", "blue", "red"),
    col3 = c("orange", "black"),
    lty12 = c(1, 3, 2), 
    lty3 = c(2, 1), ...)

Arguments

Details

The parameters of the van Genuchten-Mualem Model can be estimated through the function fitsoilwater(). The soil penetration resistance model is given by: PR = a*h^b, where h is the soil water content and a and b are the fitting parameters.

Value

A table containing each integration of IWC (integral water capacity, in m/m) and EI (integral energy calculation, in J/kg).

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Asgarzadeh, H.; Mosaddeghi, M.R.; Nikbakht, A.M. (2014) SAWCal: A user-friendly program for calculating soil available water quantities and physical quality indices. Computers and Electronics in Agriculture, 109:86–93.

Groenevelt, P.H.; Grant, C.D.; Semetsa, S. (2001) A new procedure to determine soil water availability. Australian Journal Soil Research, 39:577–598.

Groenevelt, P.H., Grant, C.D., Murray, R.S. (2004) On water availability in saline soils. Australian Journal Soil Research, 42:833–840.

See Also

soilwater, fitsoilwater, llwr

Examples

# example 1 (Fig 1b, Asgarzadeh et al., 2014)
iwc(theta_R = 0.0160, theta_S = 0.4828, alpha = 0.0471, n = 1.2982, 
	a = 0.2038, b = 0.2558, graph = TRUE) 

# example 2 (Table 1, Asgarzadeh et al., 2014)
iwc(theta_R = 0.166, theta_S = 0.569, alpha = 0.029, n = 1.308, 
	a = 0.203, b = 0.256, graph = TRUE) 

# example 3: evaluating the salinity effect
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)

# End (Not run)

Soil Liquid Limit

Description

Function to determine the soil Liquid Limit by using the Sowers (1965) method.

LL = \theta * (n / 25) ^ {0.12}

.

Usage

 liquidlimit(theta, n) 

Arguments

Value

The soil moisture value corresponding to the Liquid Limit.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Sowers, G. F. (1965). Consistency. In: BLACK, C.A. (Ed.). Methods of soil analysis. Madison: American Society of Agronomy. Part 1, p.391-399. (Agronomy, 9).

Sowers, G. F. (1965). Consistency. In: KLUTE, A. (Ed.). 2 ed. Methods of soil analysis. Madison: American Society of Agronomy. Part 1, p.545-566.

See Also

criticalmoisture

Examples

liquidlimit(theta = 0.34, n = 22)

M <- c(0.34, 0.29, 0.27, 0.25, 0.20)
N <- c(22, 24, 25, 26, 28)
liquidlimit(theta = M, n = N)

# End (not run)

Least Limiting Water Range (LLWR)

Description

Graphical solution for the Least Limiting Water Range and parameter estimation of the related water retention and penetration resistance curves. A summary containing standard errors and statistical significance of the parameters is also given.

Usage

llwr(theta, h, Bd, Pr, 
    particle.density, air, 
    critical.PR, h.FC, h.WP, 
    water.model = c("Silva", "Ross"), 
    Pr.model = c("Busscher", "noBd"),
    pars.water = NULL, pars.Pr = NULL, 
    graph = TRUE, graph2 = TRUE, 
    xlab = expression(Bulk~Density~(Mg~m^{-3})), 
    ylab = expression(theta~(m^{3}~m^{-3})), 
    main = "Least Limiting Water Range", ...) 

Arguments

Details

The numeric vectors theta, h, Bd and Pr are supposed to have the same length, and their values should have appropriate unit of measurement. For fitting purposes, it is not advisable to use vectors with less than five values. It is possible to calculate the LLWR for a especific (unique) value of bulk density. In This case, Bd should be a vector of length 1 and, therfore, it is not possible to fit the models "Silva" and "Busscher", for water content and penetration resistance, respectively.

The model employed by Silva et al. (1994) for the soil water content (\theta) as a function of the soil bulk density (\rho) and the matric head (h) is:

\theta = exp(a + b \rho)h^c

The model proposed by Ross et al. (1991) for the soil water content (\theta) as a function of the matric head (h) is:

\theta = a h^c

The penetration resistance model, as presented by Busscher (1990), is given by

Pr = b0 * (\theta^{b1}) * (\rho^{b2})

If the agrument Bd receives a single value of bulk density, then llwr() fits the following simplified model (option noBd):

Pr = b0 * \theta^{b1}

Value

A list of

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Busscher, W. J. (1990). Adjustment of flat-tipped penetrometer resistance data to common water content. Transactions of the ASAE, 3:519-524.

Leao et al. (2005). An Algorithm for Calculating the Least Limiting Water Range of Soils. Agronomy Journal, 97:1210-1215.

Leao et al. (2006). Least limiting water range: A potential indicator of changes in near-surface soil physical quality after the conversion of Brazilian Savanna into pasture. Soil & Tillage Research, 88:279-285.

Ross et al. (1991). Equation for extending water-retention curves to dryness. Soil Science Society of America Journal, 55:923-927.

Silva et al. (1994). Characterization of the least limiting water range of soils. Soil Science Society of America Journal, 58:1775-1781.

See Also

fitbusscher

Examples

# Example 1 - part of the data set used by Leao et al. (2005)
data(skp1994)
ex1 <- 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))
ex1

# Example 2 - specifying the parameters (Leao et al., 2005)
a <- c(-0.9175, -0.3027, -0.0835) # Silva et al. model of water content
b <- c(0.0827, -1.6087, 3.0570)   # Busscher's model
ex2 <- with(skp1994,
	llwr(theta = W, h = h, Bd = BD, Pr = PR,
		particle.density = 2.65, air = 0.1,
		critical.PR = 2, h.FC = 0.1, h.WP = 1.5,
		pars.water = a, pars.Pr = b))
ex2

# Example 3 - specifying a single value for Bd
ex3 <- with(skp1994,
	llwr(theta = W, h = h, Bd = 1.45, Pr = PR,
		particle.density = 2.65, air = 0.1,
		critical.PR = 2, h.FC = 100, h.WP = 15000))
ex3

# End (not run)

Least Limiting Water Range (LLWR) Using Pedo-Transfer Functions

Description

It calculates Least Limiting Water Range (LLWR) using pedo-transfer functions in according to Silva \& Kay (1997) and Silva et al. (2008), for Canadian and Brazilian soils, respectively.

Usage

 
llwrPTF(air, critical.PR, h.FC, h.WP, p.density, Bd, clay.content, org.carbon = NULL)

Arguments

Details

Note that org.carbon is only required for Canadian soil. If it is not passed, LLWR for Canadian soil is calculated with 2\% of organic carbon.

Value

A list of

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

Alvaro Pires da Silva <apisilva@usp.br>

References

Keller, T; Silva, A.P.; Tormena, C.A.; Giarola, N.B.F., Cavalieri, K.M.V., Stettler, M.; Arvidsson, J. 2015. SoilFlex-LLWR: linking a soil compaction model with the least limiting water range concept. Soil Use and Management, 31:321-329.

Silva, A.P.; Kay, B.D. 1997. Estimating the least limiting water range of soil from properties and management. Soil Science Society of America Journal, 61:877-883.

Silva, A.P., Kay, B.D.; Perfect, E. 1994. Characterization of the least limiting water range. Soil Science Society of America Journal, 61:877-883.

Silva, A.P., Tormena, C.A., Jonez, F.; Imhoff, S. 2008. Pedotransfer functions for the soil water retention and soil resistance to penetration curves. Revista Brasileira de Ciencia do Solo, 32:1-10.

Examples

# EXEMPLE 1 (for Brazilian Soils) 
llwrPTF(air=0.1,critical.PR=2, h.FC=100, h.WP=15000,p.density=2.65,
        Bd=c(1.2,1.3,1.4,1.5,1.35),clay.content=c(30,30,35,38,40))

# EXEMPLE 2 (for Canadian Soils)
llwrPTF(air=0.1,critical.PR=2, h.FC=100, h.WP=15000,p.density=2.65,
        Bd=c(1.2,1.3,1.4),clay.content=c(30,30,30), org.carbon=c(1.3,1.5,2))

# EXEMPLE 3 (combining it with soil stress)
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 = FALSE)

stress.p <- stress$Stress$sigma_mean
layers <- stress$Stress$Layers
n <- length(layers)
def <- soilDeformation(stress = stress.p,
                p.density = rep(2.67,n),
	          iBD = rep(1.55,n), 
                N = rep(1.9392,n), 
                CI = rep(0.06037,n), 
                k = rep(0.00608,n), 
                k2 = rep(0.01916,n),
                m = rep(1.3,n),graph=TRUE,ylim=c(1.4,1.8))

# Grapth LLWR, considering Brazilian soils
plot(x = 1, y = 1, 
     xlim=c(0,0.2),ylim=c(1,0),xaxt = "n",
     ylab = "Soil Depth",xlab ="", type="l", main="")
axis(3)
mtext("LLWR",side=3,line=2.5)

i.LLWR <- llwrPTF(air=0.1,critical.PR=2, h.FC=100, 
                        h.WP=15000,p.density=2.65,
                        Bd=def$iBD,clay.content=rep(20,n))
f.LLWR <- llwrPTF(air=0.1,critical.PR=2, h.FC=100, 
                        h.WP=15000,p.density=2.65,
                        Bd=def$fBD,clay.content=rep(20,n))

points(x=i.LLWR$LLWR.B, y=layers, type="l"); points(x=i.LLWR$LLWR.B, y=layers,pch=15)
points(x=f.LLWR$LLWR.B, y=layers, type="l", col=2); points(x=f.LLWR$LLWR.B, y=layers,pch=15, col=2)

# End (not run)

Least Limiting Water (LLWR) and Matric Potential Ranges (LLMPR)

Description

A graphical solution and calculation of the least limiting water range and least limiting water matric potential range, including the corresponding the water content and water tensions limits.

Usage

 
llwr_llmpr(thetaR, thetaS, alpha, n, d, e, f = NULL, critical.PR, PD, Bd = NULL, 
         h.FC, h.PWP, air.porosity,
         labels = c("AIR", "FC", "PWP", "PR"), ylab = "",
         graph1 = TRUE, graph2 = FALSE, ...)

Arguments

Details

The penetration resistance model, as presented by Busscher (1990), is given by PR = d * \theta^{e} * BD^{f}. In this model, BD (bulk density) is calculated from thetaS (soil total porosity) and PD (particles density), i.e., BD = PD * thetaS^{-1}. If the argument f is not passed, the model becomes PR = d * \theta^{e} .

Value

A list of the LLWR and LLMPR, including the corresponding the water content and water tensions limits.

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

Leon, H. N., Almeida, B. G., Almeida, C. D. G. C., Freire, F. J., Souza, E. R., Oliveira, E. C. A., Silva, E. P. 2019. Medium-term influence of conventional tillage on the physical quality of a Typic Fragiudult with hardsetting behavior cultivated with sugarcane under rainfed conditions. Catena, 175: 37-46.

Busscher, W. J. 1990. Adjustment of flat-tipped penetrometer resistance data to common water content. Transactions of the ASAE, 3: 519-524.

van Genuchten, M. T. 1980. A closed-form equation for predicting the hydraulic conductivity of unsaturated soils 1. Soil Science Society of America journal, 44: 892-898.

Silva et al. 1994. Characterization of the least limiting water range of soils. Soil Science Society of America Journal, 58: 1775-1781.

Assouline, S., Or, D. 2014. The concept of field capacity revisited: Defining intrinsic static and dynamic criteria for soil internal drainage dynamics. Water Resources Research, 50: 4787-4802.

Millington, R. J., Quirk, J. P. 1961. Permeability of porous solids. Transactions of the Faraday Society, 57: 1200-1207.

Dexter, A. R., Czyz, E. A., Richard, G. 2012. Equilibrium, non-equilibrium and residual water: consequences for soil water retention. Geoderma, 177: 63-71.

Moraes, M. T., Bengough, A. G., Debiasi, H., Franchini, J. C., Levien, R., Schnepf, A., Leitner, D., 2018. Mechanistic framework to link root growth models with weather and soil physical properties, including example applications to soybean growth in Brazil. Plant and Soil, 428: 67-92.

Examples

# Parameters from Leon et al. (2018), for usual physical restrictions threshold
  
  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(psi~(hPa)), ylim=c(15000,1))
  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,
        graph1=FALSE,graph2=TRUE,
        labels=c("Air-filled porosity", "Field capacity", 
                "Permanent wilting point", "Penetration resistance"),
        ylim=c(0.1,0.30), ylab=expression(theta~(m^3~m^-3)))
  mtext(expression("Bulk density"~(Mg~m^-3)),1,line=2.2, cex=0.8)


# Without bulk density effects in Busscher's model (i.e. f=NULL)
 
  llwr_llmpr(thetaR=0.1180, thetaS=0.36, alpha=0.133, n=1.30, 
        d=0.0165, e=-2.93, PD=2.65,
        critical.PR=3, h.FC=100, h.PWP=15000, air.porosity=0.1,
        graph1=TRUE,graph2=FALSE,ylim=c(15000,1),
        ylab=expression(psi~(hPa)))
  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=0.0165, e=-2.93, PD=2.65,
        critical.PR=3, h.FC=100, h.PWP=15000,air.porosity=0.1,
        graph1=FALSE,graph2=TRUE,
        ylim=c(0.1,0.30), ylab=expression(theta~(m^3~m^-3)))
  mtext(expression("Bulk density"~(Mg~m^-3)),1,line=2.2, cex=0.8)


# Parameters from Leon et al. (2018), calculated physical restrictions threshold

  thetaR <- 0.1180
  thetaS <- 0.36
  alpha <- 0.133
  n <- 1.30 
  clay.content <- 15 # clay content 15 %
  mim.gas.difusion <- 0.005
  root.elongation.rate <- 0.3 # root elogation rate 30%

  FC <- (1/alpha)*((n-1)/n)^((1-2*n)/n) # Assouline and Or (2014)
  PWP <- 10^(3.514 + 0.0250*clay.content) # Dexter et al. (2012)
  AIR.critical <-  (mim.gas.difusion*(thetaS)^2)^(1/(10/3)) # Millington and Quirk (1961)
  PR.critical <- log(root.elongation.rate)/-0.4325 # Moraes et al. (2018)

  llwr_llmpr(thetaR=thetaR, thetaS=thetaS, alpha=alpha, n=n, 
        d=0.005, e=-2.93, f=3.54, PD=2.65,ylim=c(15000,1),
        critical.PR=PR.critical, h.FC=FC, h.PWP=PWP, air.porosity=AIR.critical,
        graph1=TRUE,graph2=FALSE, ylab=expression(psi~(hPa)))
  mtext(expression("Bulk density"~(Mg~m^-3)),1,line=2.2, cex=0.8)

  llwr_llmpr(thetaR=thetaR, thetaS=thetaS, alpha=alpha, n=n, 
        d=0.005, e=-2.93, f=3.54, PD=2.65,
        critical.PR=PR.critical, h.FC=FC, h.PWP=PWP, air.porosity=AIR.critical,
        graph1=FALSE,graph2=TRUE,
        ylim=c(0.1,0.30), ylab=expression(theta~(m^3~m^-3)))
  mtext(expression("Bulk density"~(Mg~m^-3)),1,line=2.2, cex=0.8)

# End (not run)

Maximum Curvature Point

Description

Function to determine the maximum curvature point of an univariate nonlinear function of x.

Usage

 maxcurv(x.range, fun, 
	method = c("general", "pd", "LRP", "spline"), 
	x0ini = NULL, 
	graph = TRUE, ...) 

Arguments

Details

The method "LRP" can be understood as an especial case of "spline". And both models are fitted via nls. The method "pd" is an adaptation of the method proposed by Lorentz et al. (2012). The "general" method should be preferred for finding global points. On the other hand, "pd", "LRP" and "spline" are suitable for finding local points of maximum curvature.

Value

A list of

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Lorentz, L.H.; Erichsen, R.; Lucio, A.D. (2012). Proposal method for plot size estimation in crops. Revista Ceres, 59:772–780.

See Also

function, curve

Examples

# Example 1: an exponential model
f <- function(x) exp(-x)
maxcurv(x.range = c(-2, 5), fun = f)

# Example 2: Gompertz Growth Model
Asym <- 8.5
b2 <- 2.3
b3 <- 0.6
g <- function(x) Asym * exp(-b2 * b3 ^ x)
maxcurv(x.range = c(-5, 20), fun = g)

# using "pd" method
maxcurv(x.range = c(-5, 20), fun = g, method = "pd")

# using "LRP" method
maxcurv(x.range = c(-5, 20), fun = g, method = "LRP", x0ini = 6.5)

# Example 3: Lessman & Atkins (1963) model for optimum plot size
a = 40.1
b = 0.72
cv <- function(x) a * x^-b
maxcurv(x.range = c(1, 50), fun = cv)

# using "spline" method
maxcurv(x.range = c(1, 50), fun = cv, method = "spline", x0ini = 6)

# End (not run)

Sedimentation time of soil particles in aqueous media

Description

It calculates the sedimentation time of soil particle in aqueous media using Stokes equation, i.e., the time needed for the particles of soil larger than the size attributed as input to sediment in aqueous media, usually water.

Usage

particle.sedimentation(d, h=0.2, g=9.81, v=0.001, Pd=2650, Wd=1000) 

Arguments

Value

A data.frame containing the estimated time for the sedimentation of particles.

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

Hillel, D. (2003). Introduction to environmental soil physics. Elsevier. p.39-51. Doi:10.1016/B978-012348655-4/50004-6

Examples

# Example 1
particle.sedimentation(d=2, h=0.2, g=9.81, v=1.002*10^-3, Pd=2650, Wd=1000) 

# Example 2
d <- c(2000, 200, 50, 10, 2, 1)
time <- particle.sedimentation(d=d, h=0.2, g=9.81, v=1.002*10^-3, Pd=2650, Wd=1000) 

plot(x=d, y=time$hours, log = "x", xaxt ="n", 
     ylab = "time of sedimentation (hours)", xlab = "particle diameter (micrometer)")
axis(1,at=d, labels=d)

# End (not run)

A shiny for time of particle sedimentation

Description

A shiny for time of particle sedimentation

Usage

particle.sedimentation_App()

Value

A shiny app

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

See Also

stressTraffic

Percentile Confidence Intervals for Simulated Preconsolidation Stress

Description

Build and plot percentile confidence intervals for preconsolidation stress simulated from simSigmaP.

Usage

plotCIsigmaP(msim, conf.level = 0.95, shade.col = "orange", 
	ordered = TRUE, xlim = NULL, xlab = expression(sigma[P]), 
	las = 1, mar = c(4.5, 6.5, 2, 1), ...)

Arguments

Value

A numeric matrix containing the simulated mean, coefficient of variation, lower and upper CI limits and the name of the method used to calculate the preconsolidation stress.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

See Also

simSigmaP, sigmaP

Examples

# input data: stress and void ratio
pres <- c(1, 12.5, 25, 50, 100, 200, 400, 800, 1600)
VR <- c(1.43, 1.41, 1.40, 1.39, 1.35, 1.31, 1.25, 1.18, 1.12)

# simulation (may take a few seconds)
simres <- simSigmaP(VR, pres, nsim = 30)
head(simres)

# percentile confidence intervals
ci <- plotCIsigmaP(simres, conf.level = 0.95, 
	shade.col = "blue", ordered = TRUE)
print(ci)

# End (Not run)

Soil Pore Size Distribution

Description

The unimodal soil pore size distribution based on van Genuchten's model.

Usage

psd(thetaS, thetaR, alpha, n, h)

Arguments

Value

A numeric vector containing the soil pore size distribution as a function of soil water tension.

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

Ghiberto, P. J., Imhoff, S., Libardi, P. L., Silva, A. P. D., Tormena, C. A., Pilatti, M. A. (2015). Soil physical quality of Mollisols quantified by a global index. Scientia Agricola, 72(2):167-174.

Asgarzadeh, H., Mosaddeghi, M. R., Nikbakht, A. M. (2014). SAWCal: A user-friendly program for calculating soil available water quantities and physical quality indices. Computers and Electronics in Agriculture, 109:86-93.

Examples

# EXAMPLE 1
x <- seq(log10(1),log10(15000),len=100)
h <- 10^x
y <- psd(thetaR = 0.15,thetaS = 0.55, alpha = 0.048, n = 1.5, h=h)
plot(x=h,y=y, log="x", xlab="h (hPa)", ylab=expression(delta*theta/delta*h), ylim=c(0,0.005))

# EXAMPLE 2
x <- seq(log10(1),log10(15000),len=100)
h <- 10^x
y <- psd(thetaR = 0.20,thetaS = 0.61, alpha = 0.1232, n = 1.3380,h=h)
plot(x=h,y=y, log="x", xlab="h (hPa)", ylab=expression(delta*theta/delta*h), ylim=c(0,0.01))

# EXAMPLE 3
x <- seq(log10(1),log10(15000),len=100)
h <- 10^x
y <- psd(thetaR = 0.154,thetaS = 0.600, alpha = 0.103, n = 2.365,h=h)
plot(x=h,y=y, log="x", xlab="h (hPa)", ylab=expression(delta*theta/delta*h), ylim=c(0,0.03))
ax <- c(1,10,100,1000,10000)
radius <- r(h=ax)
axis(3,at=ax, labels=round(radius,2))
mtext("Equivalent pore radius"~(mu*m),3,line=2.5, cex=0.9)

# End (not run)

Equation of capillary

Description

The equivalent pore radius as a function of soil water tension.

Usage

r(h, surface.tension.water=0.072, water.density=1000, water.pore.contact.angle=0)

Arguments

Value

The equivalent pore radius, in micrometer..

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

References

Ghiberto, P. J., Imhoff, S., Libardi, P. L., Silva, A. P. D., Tormena, C. A., Pilatti, M. A. (2015). Soil physical quality of Mollisols quantified by a global index. Scientia Agricola, 72(2):167-174.

Examples

x <- seq(log10(1), log10(15000), len=50)
h <- 10^x
y <- r(h=h)
plot(x=h, y=y, log="yx", xlab="h (hPa)", yaxt='n', ylab="", ylim=c(0.1, 1500))
ax <- c(0.1, 1, 10, 100, 1000, 1500)
axis(2,at=ax, labels=ax)
mtext("Pore radius"~ (mu*m), 2, line=2.5)

# End (not run)

A shiny for equation of capillarity

Description

A shiny for equation of capillarity

Usage

r_App()

Value

A shiny app

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

See Also

stressTraffic

Preconsolidation Stress

Description

A function to determine the preconsolidation stress (\sigma_P). It is a parameter obtained from the soil compression curve and has been used as an indicator of soil load-bearing capacity as well as to characterize the impacts suffered by the use of machines. The function sigmaP() contains implementations of the main methods for determining the pre-consolidation stress, such as the Casagrande method, the method of Pacheco Silva, the regression methods and the method of the virgin compression line intercept.

Usage

sigmaP(voidratio, stress, n4VCL = 3,
    method = c("casagrande", "VCLzero", "reg1", "reg2", "reg3", "reg4", "pacheco"),
    mcp = NULL, graph = TRUE, ...)

Arguments

Details

casagrande is the method proposed by Casagrande (1936). The preconsolidation stress obtained via VCLzero corresponds to the intersection of the soil Virgin Compression Line (VCL) with the x-axis at zero applied stress, as described by Arvidsson & Keller (2004). reg1, reg2, reg3 and reg4 are regression methods that obtain the preconsolidation stress value as the intercept of the VCL and a regression line fitted with the first two, three, four and five points of the curve, respectively, as described by Dias Junior & Pierce (1995). pacheco is the method of Pacheco Silva (ABNT, 1990).

You may follow the flowchart below to understand the determination of the preconsolidation stress through sigmaP().

Value

A list of

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

ABNT - Associacao Brasileira de Normas Tecnicas. (1990). Ensaio de adensamento unidimensional: NBR 12007. Rio de Janeiro. 13p.

Arvidsson, J.; Keller, T. (2004). Soil precompression stress I. A survey of Swedish arable soils. Soil & Tillage Research, 77:85-95.

Bowles, J. A. (1986). Engineering Properties of Soils and their Measurements, 3rd edition. McGraw-Hill Book Company, Inc. NY, 218pp.

Casagrande, A. (1936). The determination of the pre-consolidation load and its practical significance. In: Proceedings of the International Conference on Soil Mech. and Found. Eng. (ICSMFE), Cambridge, MA, 22-26 June 1936, vol. 3. Harvard University, Cambridge, MA, USA, pp. 60-64.

Dias Junior, M. S.; Pierce, F. J. (1995). A simple procedure for estimating preconsolidation pressure from soil compression curves. Soil Technology, 8:139-151.

See Also

voidratio, maxcurv, fitlbc

Examples

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)

plot(VR ~ log10(pres), type = "b") # find the 'mcp'
sigmaP(VR, pres, method = "casagrande", mcp = 1.6, n4VCL = 2)

# fitting the VCL
sigmaP(VR, pres, method = "casagrande", mcp = 1.6, n4VCL = 3)

# self-calculation of "mcp" argument for Casagrande method
sigmaP(VR, pres, method = "casagrande", n4VCL = 3)

# Pacheco method
sigmaP(VR, pres, method = "pacheco")

# Regression method
sigmaP(VR, pres, method = "reg3")

# End (not run)

Simulating Preconsolidation Stress

Description

Simulating preconsolidation pressure, compression and swelling indices, based on a multivariate Gaussian distribution for the parameters of the compression curve.

Usage

simSigmaP(voidratio, stress,
	what.out = c("sigmaP", "CI", "SI"),
	method = c("casagrande", "VCLzero", "reg1", "reg2", "reg3", "reg4", "pacheco"),
	n4VCL = 3, nsim = 100)

Arguments

Value

A numeric matrix containing the simulated values for each method selected as input.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

See Also

sigmaP, plotCIsigmaP

Examples

# input data: stress and void ratio
pres <- c(1, 12.5, 25, 50, 100, 200, 400, 800, 1600)
VR <- c(1.43, 1.41, 1.40, 1.39, 1.35, 1.31, 1.25, 1.18, 1.12)

# simulation (may take a few seconds)
simres <- simSigmaP(VR, pres, nsim = 30)
head(simres)

# plot percentile confidence intervals
ci <- plotCIsigmaP(simres, conf.level = 0.95,
	shade.col = "blue", ordered = TRUE)
print(ci)

# End (Not run)

LLWR Data Set

Description

Data set presented by Leao et al. (2005), for determining the Least Limiting Water Range.

Usage

data(skp1994)

Format

A data frame with 64 observations on the following 4 variables:

BD

a numeric vector containing soil bulk density values, in Mg/m3.

W

a numeric vector containing volumetric water content values, in m3/m3.

PR

a numeric vector containing penetration resistance values, in MPa.

h

a numeric vector containing matric head values, in cm.

Source

Leao et al. (2005). An Algorithm for Calculating the Least Limiting Water Range of Soils. Agronomy Journal, 97:1210-1215.

Examples

data(skp1994)
summary(skp1994)

Soil deformation by O'Sullivan and Robertson (1996)

Description

It calculates bulk density variation as a function of the applied mean normal stress using critical state theory, by O'Sullivan and Robertson (1996).

Usage

soilDeformation(stress, p.density, iBD, N, CI, k, k2, m, graph = FALSE, ...)

Arguments

Details

The specific volume (v) is given as v=PD/BD, where PD is particle density and BD is the bulk density. Please, check each parameter from O'Sullivan and Robertson (1996) model in the figure below.

Value

A list of

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

Alvaro Pires da Silva <apisilva@usp.br>

References

O'Sullivan, M.F.; Robertson, E.A.G. 1996. Critical state parameters from intact samples of two agricultural soils. Soil and Tillage Research, 39:161-173.

Keller, T.; Defossez, P.; Weisskopf, P.; Arvidsson, J.; Richard, G. 2007. SoilFlex: a model for prediction of soil stresses and soil compaction due to agricultural field traffic including a synthesis of analytical approaches. Soil and Tillage Research, 93:391-411.

Examples

# EXAMPLE 1
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(1.4,1.8))

# EXEMPLE 2 (combining it with soil stress)
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 = FALSE)

stress.mean <- stress$Stress$sigma_mean
layers <- stress$Stress$Layers
n <- length(layers)

def <- soilDeformation(stress = stress.mean,
                p.density = rep(2.67, n),
	        iBD = rep(1.55,n),
                N = rep(1.9392,n),
                CI = rep(0.06037,n),
                k = rep(0.00608,n),
                k2 = rep(0.01916,n),
                m = rep(1.3,n),graph=TRUE,ylim=c(1.4,1.8))

# Graph
plot(x = 1, y = 1,
     xlim=c(1.4,1.7),ylim=c(1,0),xaxt = "n",
     ylab = "Soil Depth",xlab ="", type="l", main="")
axis(3)
mtext("Bulk Density",side=3,line=2.5)

initial.BD <- def$iBD
final.BD <- def$fBD
points(x=initial.BD, y=layers, type="l")
points(x=initial.BD, y=layers,pch=15)
points(x=final.BD, y=layers, type="l", col=2)
points(x=final.BD, y=layers,pch=15, col=2)

# End (not run)

Estimation of precompression stress by Severiano et al. (2013)

Description

It calculates the precompression stress using the pedo-transfer function from Severiano et al. (2013)

Usage

soilStrength(clay.content, matric.suction = NULL, water.content = NULL)

Arguments

Details

Intervals of soil water content/matric suction to be used as input for estimating soil strength according to Severiano et al. (2013).

Value

A two-columns data frame:

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

Alvaro Pires da Silva <apisilva@usp.br>

References

Severiano, E.C; Oliveira, G.C.; Dias Junior, M.S.; Curi, N.C.; Costa, K. A.P.; Carducci, C.E. 2013. Preconsolidation pressure, soil water retention characteristics, and texture of Latosols in the Brazilian Cerrado. Soil Research, 51:193-202.

Stettler, M., Keller, T., Weisskopf, P., Lamande, M., Lassen, P., Schjonning, P., 2014. Terranimo - a web-based tool for evaluating soil compaction. Landtechnik, 69:132-137.

See Also

stressTraffic

Examples

# EXEMPLE 1 (using water content)
soilStrength(clay.content=c(25,28,30,30,30),
             water.content = c(0.26,0.27,0.29,0.32,0.32))


# EXEMPLE 2 (using matric suction)
soilStrength(clay.content=c(25,28,30,30,30),
             matric.suction = c(100,330,1000,3000,5000))


# EXAMPLE 3 (combining it with soil stress)
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 = FALSE)

strength <- soilStrength(clay.content=c(25,28,30,30,30,30),
              matric.suction = c(30,100,100,100,200,200))


# Graph
plot(x = 1, y = 1,
     xlim=c(0,300),ylim=c(1,0),xaxt = "n",
     ylab = "Soil Depth",xlab ="", type="l", main="")
axis(3)
mtext("Vertical Stress",side=3,line=2.5)

stressz <- stress$Stress$sigma_vertical
layers <- stress$Stress$Layers
points(x=stressz, y=layers, type="l")

# Green zone
x0 <- strength$LL.Pc
x1 <- rep(0,length(layers))
y0 <- layers
y1 <- rev(layers)
polygon(x=c(x0,x1), y = c(y0,y1),density = NA,
       col=rgb(red=0, green=1, blue=0, alpha=0.3))

# Yellow zone
x0 <- strength$UL.Pc
x1 <- rev(strength$LL.Pc)
y0 <- layers
y1 <- rev(layers)
polygon(x=c(x0,x1), y = c(y0,y1),density = NA,
       col=rgb(red=1, green=1, blue=0, alpha=0.3))

# Red zone
x0 <- rep(300,length(layers))
x1 <- rev(strength$UL.Pc)
y0 <- layers
y1 <- rev(layers)
polygon(x=c(x0,x1), y = c(y0,y1),density = NA,
       col=rgb(red=1, green=0, blue=0, alpha=0.3))

# End (not run)

Estimation of precompression stress by Schjonning and Lamande (2018)

Description

It calculates the precompression stress using the pedo-transfer function from Schjonning and Lamande (2018)

Usage

 
soilStrength2(bulk.density,  matric.suction, clay.content)

Arguments

Details

The function returns 0 for soil properties values beyond the range for which the function was built.

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Schjonning, P.; Lamande, M., 2018. Models for prediction of soil precompression stress from readily available soil properties. Geoderma, 320:115-125.

See Also

stressTraffic

Examples

# EXAMPLE 1

soilStrength2(bulk.density=1.46, matric.suction=100, clay.content=0.3)


# EXAMPLE 2

matric.suction <- seq(from=60,to=1000,len=50) # range of matric suction from 60 to 1200 hPa
out <- soilStrength2(bulk.density=1.46, matric.suction=matric.suction, clay.content=0.3)
plot(x=matric.suction,y=out, 
     ylab="Precompression stress (kPa)", xlab="Matric suction (hPa)")

# End (not run)

Estimation of precompression stress by Saffih-Hdadi et al. (2009)

Description

It calculates the precompression stress using the pedo-transfer function from Saffih-Hdadi et al. (2009)

Usage

 
soilStrength3(bulk.density, water.content, 
              texture=c("VeryFine","Fine","MediumFine","Medium","Coarse"))

Arguments

Details

The function returns 0 for soil properties values beyond the range for which the function was built.

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Saffih-Hdadi, K.; Defossez, P.; Richard, G.; Cui, Y. J.; Tang, A. M.; Chaplain, V, 2009. A method for predicting soil susceptibility to the compaction of surface layers as a function of water content and bulk density Geoderma, 115: 96-103.

See Also

stressTraffic

Examples

  
# EXAMPLE 1

soilStrength3(bulk.density=1.1, water.content=40, texture="VeryFine")
soilStrength3(bulk.density=1.2, water.content=20, texture="Fine")
soilStrength3(bulk.density=1.3, water.content=15, texture="MediumFine")
soilStrength3(bulk.density=1.4, water.content=15, texture="Medium")
soilStrength3(bulk.density=1.5, water.content=10, texture="Coarse")

# End (not run)

Estimation of precompression stress by Lebert and Horn (1991)

Description

It calculates the soil strength through precompression stress using the pedo-transfer function from Lebert and Horn (1991)

Usage

 
soilStrength4(BD=1.55,AC=10,AWC=15,PWP=26,Ks=0.29,
              OM=1.5,C=30,phi=36,texture="Clay>35", pF=1.8)

Arguments

Details

The function returns '0' for soil properties values beyond the range for which the function was built. The default for this function is the values given in the application example by Horn and Fleige (2003). In the 'texture' argument, the user must pass the textural classification 'Sand','SandLoam', 'Silt', 'Clay<35' or 'Clay>35'. See examples.

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Lebert, M., Horn, R. (1991) A method to predict the mechanical strength of agricultural soils. Soil and Tillage Research, 19: 275-256.

Horn, R., Fleige, H. (2003) A method for assessing the impact of load on mechanical stability and on physical properties of soils. Soil and Tillage Research, 73: 89-99.

See Also

stressTraffic

Examples

soilStrength4(BD=1.55,AC=10,AWC=15,PWP=26,Ks=0.29,OM=1.5,
              C=30,phi=36,texture="Clay>35", pF=1.8) # Exemple from Horn and Fleige (2003), Table 7

# End (not run)

Estimation of precompression stress by Imhoff et al. (2004)

Description

It calculates the soil strength using precompression stress using the pedo-transfer function from Imhoff et al. (2004)

Usage

 
soilStrength5(bulk.density,  water.content, clay.content)

Arguments

Details

The function returns 0 for soil properties values beyond the range for which the function was built.

Value

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com> Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Imhoff, S., Da Silva, A. P., Fallow, D. (2004) Susceptibility to Compaction, Load Support Capacity, and Soil Compressibility of Hapludox. Soil Science Society of America Journal, 68: 17-24.

See Also

stressTraffic

Examples

# EXAMPLE 1

soilStrength5(clay.content=60, water.content=0.30, bulk.density=1.25)
soilStrength5(clay.content=35, water.content=0.23, bulk.density=1.40)
soilStrength5(clay.content=20, water.content=0.10, bulk.density=1.60)


# EXAMPLE 2
water.content <- seq(0.1,0.30,len=20) # range of water content from 0.1 to 0.30 (g g^-1)
out <- soilStrength5(clay.content=20, water.content=water.content , bulk.density=1.60)
plot(x=water.content,y=out, 
     ylab="Precompression stress (kPa)", xlab="Water content")


# End (not run)

Soil Water Retention, based on the van Genuchten's (1980) formula

Description

Function to calculate the soil water content based on the van Genuchten's (1980) formula:

\theta = \theta_R + (\theta_S - \theta_R)(1 + (\alpha x)^n)^{-m}

Usage

soilwater(x, theta_R, theta_S, alpha, n, m = 1 - 1/n,
    saturation.index = FALSE)

Arguments

Value

The the soil water content or the saturation index (a value between 0 and 1).

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com> (code adapted from the function swc(), package soilwater (Cordano et al., 2012).)

References

Genuchten, M. T. van. (1980). A closed form equation for predicting the hydraulic conductivity of unsaturated soils. Soil Science Society of America Journal, 44:892-898.

Mualem, Y. (1976). A new model for predicting the hydraulic conductivity of unsaturated porous media. Water Resources Research, 12:513-522.

See Also

fitsoilwater

Examples

# example 1
soilwater(x = 0.1, theta_R = 0.06, theta_S = 0.25, alpha = 21, n = 2.08)
curve(soilwater(x, theta_R = 0.06, theta_S = 0.25, alpha = 21, n = 2.08))

# example 2 (punctual predictions)
p <- seq(0, 1, length.out = 10)
m <- soilwater(x = p, theta_R = 0.06, theta_S = 0.25,
	alpha = 21, n = 2.08)
points(m ~ p, type = "b", col = "red")

# End (not run)

Soil Water Retention, based on the Groenevelt & Grant (2004) formula

Description

Function to calculate the soil water content based on the Groenevelt & Grant (2004) model. It is based on thermodynamic principles. Therefore, it is appropriate for the case in which thermodynamic equilibrium has been attained by diffusion of water. In this case, the water retention curve is given by:

\theta = k_1 \exp(-k_0 / x_0^n) - k_1 \exp(-k_0 / x^n)

where x = log h (pore water suction), and h is in units of hPa

Usage

soilwater2(x, x0 = 6.653, k0, k1, n)

Arguments

Value

The the soil water content.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Groenevelt & Grant (2004). A newmodel for the soil-water retention curve that solves the problem of residualwater contents. European Journal of Soil Science, 55:479-485.

See Also

fitsoilwater2, soilwater

Examples

pF <- 0:7
soilwater2(pF, k0 = 1.867, k1 = 0.426, n = 2.358)

# End (not run)

Soil Water Retention, based on the Dexter's (2008) formula

Description

Function to calculate the soil water content based on the Dexter's (2008) formula. It is based on the segregation of pore space in two categories what are called bi-modal pore size distributions. In this model, the pore space is divided into two parts: the textural porosity which occurs between the primary mineral particles, and the structural porosity which occurs between micro aggregates and/or any other compound particles. This is called the double-exponential (DE) water retention equation, given by:

\theta = \theta_R + a_1 \exp(-x/p_1) + a_2 \exp(-x/p_2)

where \theta is the gravimetric water content.

Usage

soilwater3(x, theta_R, a1, p1, a2, p2)

Arguments

Value

The the soil water content.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Dexter et al. (2008). A user-friendly water retention function that takes account of the textural and structural pore spaces in soil. Geoderma, 143:243-253.

See Also

fitsoilwater3, soilwater, soilwater2

Examples

soilwater3(x = 0, theta_R = 0.058, a1 = 0.233, p1 = 3.274, a2 = 0.070, p2 = 1.78)
soilwater3(x = 3, theta_R = 0.058, a1 = 0.233, p1 = 3.274, a2 = 0.070, p2 = 1.78)
soilwater3(x = 4, theta_R = 0.058, a1 = 0.233, p1 = 3.274, a2 = 0.070, p2 = 1.78)

# End (not run)

Soil Water Retention, based on Power Models

Description

Function to calculate the soil water content based on the following formulas:

\theta = \exp(a + b*Bd) \psi^c

(Silva et al., 1994)

\theta = a \psi^c

(Ross et al., 1991)

where \theta is the soil water content.

Usage

soilwater4(psi, Bd, a, b, c, model = c("Silva", "Ross"))

Arguments

Details

The parameters "a" and "c" have the same meaning in both models, but be aware that the parameter "a" of the model employed by Silva et al. (1994) is parameter "a" of the Ross et al. (1991) in a log10 scale.

Value

The the soil water content.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Ross et al. (1991). Equation for extending water-retention curves to dryness. Soil Science Society of America Journal, 55:923-927.

Silva et al. (1994). Characterization of the least limiting water range of soils. Soil Science Society of America Journal, 58:1775-1781.

See Also

fitsoilwater4, soilwater, soilwater2, soilwater3

Examples

# End (not run)

Soil Water Retention, based on the modified van Genuchten's formula

Description

Function to calculate the soil water content based on the modified van Genuchten's formula, as suggested by Pierson and Mulla (1989):

\theta = \theta_R + (\theta_S - \theta_R)(1 + (\alpha x)^n)^{-m} + b_0 + b_1 x + b_2 * x^2

Usage

soilwater5(x, theta_R, theta_S, alpha, n, m = 1 - 1/n, b0, b1, b2)

Arguments

Value

The the soil water content or the saturation index (a value between 0 and 1).

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

References

Pierson, F.B.; Mulla, D.J. (1989) An Improved Method for Measuring Aggregate Stability of a Weakly Aggregated Loessial Soil. Soil Sci. Soc. Am. J., 53:1825–1831.

See Also

fitsoilwater5

Examples

soilwater5(x = 20, theta_R = 0.2735, theta_S = 0.478, alpha = 0.1029, 
	n = 9.45, b0 = 0.2278, b1 = -0.0165, b2 = 0.000248)
curve(soilwater5(x, theta_R = 0.2735, theta_S = 0.478, alpha = 0.1029, 
	n = 9.45, b0 = 0.2278, b1 = -0.0165, b2 = 0.000248), 
	from = 0, to = 40,
	ylab = "Water content",
	xlab = "Matric potential")

# End (Not run)

Predicting Soil Stress Due to Agricultural Trafficability

Description

Contact area, stress distribuition and stress propagation based on the SoilFlex model (Keller 2005; Keller et al. 2007) are calculated.

Usage

stressTraffic(inflation.pressure, recommended.pressure, tyre.diameter, 
    tyre.width, wheel.load, conc.factor, layers, plot.contact.area = FALSE, ...) 

Arguments

Value

A list of

Author(s)

Renato Paiva de Lima <renato_agro_@hotmail.com>

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

Alvaro Pires da Silva <apisilva@usp.br>

References

Keller, T. 2005. A model to predict the contact area and the distribution of vertical stress below agricultural tyres from readily-available tyre parameters. Biosyst. Eng. 92, 85-96.

Keller, T.; Defossez, P.; Weisskopf, P.; Arvidsson, J.; Richard, G. 2007. SoilFlex: a model for prediction of soil stresses and soil compaction due to agricultural field traffic including a synthesis of analytical approaches. Soil and Tillage Research 93, 391-411.

Examples

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)

stress

# Building a fancier plot for the contact area
# library(fields)
# image.plot(x = as.numeric(rownames(stress$stress.matrix)), 
#	       y = as.numeric(colnames(stress$stress.matrix)), 
#	       z = stress$stress.matrix,  
#	       xlab="Tyre footprint length (m)", ylab="Tyre width (m)") 
# End (not run)

# Stress Propagation 
# Vertical Stress
stress.v <- stress$Stress$sigma_vertical
layers <- stress$Stress$Layers
plot(x = 1, y = 1, xlim=c(0,300),ylim=c(1,0),xaxt = "n",
     ylab = "Soil Depth",xlab ="", type="l", main="")
axis(3)
mtext("Stress (kPa)",side=3,line=2.5)
lines(x=stress.v, y=layers)

# Mean normal stress
stress.p <- stress$Stress$sigma_mean
lines(x=stress.p, y=layers, lty=2)
legend("bottomright", c("Vertical stress", "Normal mean stress"), lty = 1:2)

# End (not run)

Void Ratio

Description

A function to calculate the soil void ratio.

Usage

voidratio(wetsoil, drysoil, diam.cylinder, height.cylinder,
    dens.particle, deformation)

Arguments

Value

A numeric vector with same length of deformation containig void ratio values.

Author(s)

Anderson Rodrigo da Silva <anderson.agro@hotmail.com>

See Also

sigmaP

Examples

def <- c(0, 0.0230, 0.0352, 0.0605, 0.1070, 0.1750, 0.2525, 0.3395, 0.4250)
pres <- c(1, 12.5, 25, 50, 100, 200, 400, 800, 1600)
VR <- voidratio(wetsoil = 170.62, drysoil = 134.08, diam.cylinder = 6.95, 
               height.cylinder = 2.5, dens.particle = 2.61, def)
VR

plot(VR ~ pres, type = "b",
	ylab = "Void ratio",
	xlab = "Applied stress (kPa)",
	main = "Compression curve", 
	log = "x")

# End (not run)