Package 'soilassessment'

Description

This function attaches soil textural classes according to different soil texture classification systems

Usage

appendTextureclass(df, method)

Arguments

Details

df is an output of createTexturedata with spatial reference or similar dataframe with normalized proportions summing to 100 method is the texture classification method for textural class calculation. Exanple methods are USDA, FAO, Australian, German, etc.

Value

Output is a soil texture dataframe with textural classes for every row (or pixel) in the dataframe. The output may sometimes return double class such as "SaLo, Lo" implying possibility of a tie for two classes. Such outputs should be edited outside the package for meaningful representation of soil textural classes when necessary

Note

This function can sometimes return double classes such as "SaLo, Lo" implying possibility of a tie for two classes.

Author(s)

Christian Thine Omuto

References

Moyes J. 2018. The soil texture wizard: R functions for plotting, classifying, transforming and exploring soil texture data. https://cran.r-project.org/web/packages/soiltexture/vignettes/soiltexture_vignette.pdf

See Also

textureSuit, createTexturedata

Examples

library(soiltexture)
newtxt=textureinput

texturedata=createTexturedata(newtxt$clay, newtxt$silt, newtxt$sand)
newtxt1=appendTextureclass(as.data.frame(texturedata), method = "USDA")
levels(as.factor(newtxt1$TEXCLASS))

Description

This function determines the suitability classes for soil calcium carbonate requirements of selected agricultural crops and forest trees

Usage

carbonateSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of calcium carbonate in percent

Value

The output is calcium carbonate suitability class for the crop. The output is an integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, ESPSuit, fertilitySuit

Examples

library(sp)
newmap=suitabinput
newmap$saffron=carbonateSuit(newmap$cac03,"saffron")
summary(newmap$saffron)
spplot(newmap,"saffron")

Description

This function provides alternatives for implementing RothC carbon turnover model

Usage

carbonTurnover(tt,clay,C0,In,Dr=1.44,effcts,solver)

Arguments

Details

vector t can be years or months sequentially arranged with the start-time as the minimum and end-time as the maximum time. Initial carbon pools are also provided as a vector of five items: C1,C2,C3,C4,C5 in that order where C1 is the pool in the decomposable plant material (DPM) compartment, C2 is pool in the resistant plant material (RPM) compartment, C3 is the pool in the microbial biomass (BIO) compartment, C4 is the pool in the humified organic matter (HUM) compartment, and C5 is the pool in the inert organic matter (IOM) compartment.

Value

nx6 matrix of carbon pools with time in the five compartments DPM, RPM, BIO, HUM, and IOM in that order (time, C1, C2, c3, C4, C5).

Author(s)

Christian Thine Omuto

References

Coleman, K. and Jenkinson, D. 2014. ROTHC-26.3 A model for the turnover of carbon in soils: Model description and users guide (Windows version). Rothamsted Research Harpenden Herts AL5 2JQ

Jenkinson, D. S., Andrew, S. P. S., Lynch, J. M., Goss, M. J., Tinker, P. B. 1990. The Turnover of Organic Carbon and Nitrogen in Soil. Philosophical Transactions: Biological Sciences, 329: 361–368.

See Also

RotCmoistcorrection, NPPmodel

Examples

library(deSolve)
Cin=c(0.6,0.1,0.3,0.1,2.7)
T=seq(1/12,200,by=1/12)
hw=carbonTurnover(tt=T,clay=23.4,C0=Cin,In=1.2,Dr=1.44,effcts=0.85,"euler")
matplot(T,hw[,2:6], type="l", lty=1, xlab="Time", ylab="C stocks (Mg/ha)")
legend("topright", c("DPM", "RPM", "BIO", "HUM", "IOM"),lty=1, col=1:5, bty="n")

Description

This function determines the suitability classes for Cation Exchange Capacity (CEC) requirements of selected agricultural crops and forest trees

Usage

CECSuit(value, crop)

Arguments

Details

input value can be a map or just a numerical entry of CEC (cmol(+)/kg)

Value

The output is CEC suitability class for the crop. The output is an integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

The output raster map of CEC suitability is given if the input value is raster map

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, tempSuit, rainSuit

Examples

CECSuit(22.4,"pineaple")

Description

This function displays names of integer classes (or levels) of derived codes of soil conditions produced in the package

Usage

classCode(value, indicator)

Arguments

Details

This is for interpretation of the integer codes of the soil conditions generated in the package

Value

Name of the level of soil condition

See Also

classLUT, erodFUN, classnames

Examples

classCode(2,"texture")
suitclas=classCode(4,"suitability")
levels(suitclas)

Description

This function develops a Look-Up Table (LUT) for the class type map of soil condition. LUT is important map legends or maps re-classification.

Usage

classLUT(fgrid,indicator)

Arguments

Details

The input raster map should contain only one band for the soil indicator for clear identification of the band.

Value

The output is a dataframe containing classes in the map and corresponding unique integers

Author(s)

Christian Thine Omuto

See Also

classCode, classnames

Examples

textrd=suitabinput["texture"]

LUT=classLUT(textrd,"texture")
LUT

Description

This is a database function for displaying the class names and codes used in the soil assessment package

Usage

classnames(indicator)

Arguments

Value

Table of soil condition code and name

See Also

classCode, classLUT

Examples

x="texture"
classnames(x)

Description

This function normalizes the decision ranking table and determines consistency of the decisions

Usage

comparisonTable(df)

Arguments

Details

The column names of the rank-decision table should correspond with the names of the criteria maps

Value

nmtx: a normalized pairwise comparison matrix crt: consistency index and message on whether the input decisions are consistent for analysis

Author(s)

Christian Thine Omuto

References

Barzilai J. and Golany B., 1990. Deriving Weights from Pairwise Comparison Matrices: the Additive Case. Operations Research Letters 9: 407–410.

See Also

suitability, fertilitySuit

Examples

data(nutrient)
library(FuzzyAHP)
comparisonTable(nutrient)

Description

This function enables development of own function for harmonizing soil indicators to standard values

Usage

conversion(x, A, B, method)

Arguments

Details

model for the relationship between target and predictor variables can be "linear", "power", "exponential", "log". Default is "linear"

Value

model object containing predictive parameters of the conversion model

Author(s)

Christian Thine Omuto

References

van Looy k, Bouma J, Herbst M, Koestel J, Minasny B, Mishra U, Montzka C, Nemes A, Pachepsky AY, Padarian J, Schaap MG, Tóth B, Verhoef A, Jan Vanderborght, van der Ploeg MJ, Weihermüller L, Zacharias S, Zhang Y, Vereecken H. 2017. Pedotransfer functions in Earth System Science: Challenges and Perspectives. Reviews of Geophysics 55(4): 1199-1256.

Sudduth KA, Kitchen RN, Wiebold WJ, Batchelor W. 2005. Relating apparent electrical conductivity to soil properties across the North-Central USA. Computers and Electronics in Agriculture, 46(1-3):263-283

See Also

ECconversion1, ECconversion2, ECconversion4

Examples

x=as.vector(c(0.800,2.580,0.980,0.532,1.870, 18.500,0.430,0.302,0.345,2.700))
y=as.vector(c(17.88,  6.43,  3.83,  7.18,  6.64, 14.83,  4.19,  7.31,  3.21, 18.41))
xy=as.data.frame(cbind(x,y))
names(xy)=c("ECa", "EC")
EC3.ml=nls(EC~conversion(ECa,A,B), start=c(A=0.1, B=0.8), data=xy)
cor.test(fitted(EC3.ml),xy$EC)
plot(fitted(EC3.ml)~xy$EC)
abline(0,1)

Description

The function creates spatial dataframe of normalized soil texture proportions. They are normalized to 100 percent

Usage

createTexturedata(clay,silt,sand)

Arguments

Details

the input data of soil texture proportions are imported into R as spatial raster or dataframe. They need to have uniform coordinate reference system (CRS) and same pixel size (resolution) if in raster map format. The sum of the proportions should be close to 100 per cent for each row

Value

The output is a spatial pixel dataframe of normalized soil texture proportions (for each pixel)

Note

It's important to ensure the input data does not have negative values nor add up to far below or above 100 per cent. It's also important to adhere to the order of the input data: clay, silt, sand

Author(s)

Christian Thine Omuto

See Also

createTexturedata, appendTextureclass

Examples

#data(textureinput)
newmap=textureinput

texturedata=createTexturedata(newmap$clay, newmap$silt, newmap$sand)
cor(texturedata$CLAY,texturedata$CLAY_n)^2

Description

An index map of density of sampling points in a geographic area

Usage

DataAvailabilityIndex(Boundary, Scale, CP,Data)

Arguments

Details

The input spreadsheet database should contain spatial coordinates of available samples. Example input spatial spreadsheet database is the global soil database.The Scale should be provided in area units e.g., 0.5, 1, 20, 30 (square km). Large areas cover more data than small areas. Hence, they take time to process. Coordinate projection (CP) for Boundary polygon should be of formal class CRS (coordinate reference system). It's preferrable to provide CP for Boundary area similar to CP for input data

Value

A spatial raster map of density of sample locations per unit (specified) area

Note

Scales less than 0.1 square km may be too small for search. Large scales (say 10000 square km) may be too large and take time to process

Author(s)

Christian Thine Omuto

Examples

library(sp)
library(raster)
library(terra)
x <- c(20.02,25.69,25.69,20.02)
y <- c(-28.40,-32.76,-32.76,-34.84)
yx=data.frame(cbind(x, y))
CRs="+proj=longlat +datum=WGS84 +no_defs"
Data=SASglobeData("ph","ZAF")
coordinates(Data)=~Longitude+Latitude
crs(Data)=CRs
Index=DataAvailabilityIndex(yx,60,CRs,Data)
plot(Index)

Description

A function to harmonize soil property between uniform depth intervals in a set of observation pits

Usage

depthharm(soildata, var.name, lam, d)

Arguments

Details

Input soil data must be a dataframe or class of ProfileCollection. The smoothing factor improves prediction of the target soil propert. Its default value is 0.1. Desired depth intervals are seperated by comma and should be choosen between minimum and maximum depths in the soil data.

Value

The output is a list of two dataframes: harmonized.d is a dataframe of harmonized soil property at target depth intervals. obs_n_pred is ugmented dataframe of observed and harmonized soil properties

References

Bishop, T.F.A., McBratney, A.B., Laslett, G.M., 1999. Modelling soil attribute depth functions with equal-area quadratic smoothing splines. Geoderma 91, 27–45. https://doi.org/10.1016/S0016-7061(99)00003-8

Malone, B.P., McBratney, A.B., Minasny, B., Laslett, G.M., 2009. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 154, 138–152. https://doi.org/10.1016/j.geoderma.2009.10.007

Ponce-Hernandez, R., Marriott, F.H.C., Beckett, P.H.T., 1986. An improved method for reconstructing a soil profile from analyses of a small number of samples. Journal of Soil Science 37, 455–467. https://doi.org/10.1111/j.1365-2389.1986.tb00377.x

Examples

library(aqp)
library(plyr)
library(sp)
x=c(rep(2.12,4),rep(2.05,4))
y=c(rep(9.34,4),rep(8.17,4))
pit=c(rep(1,4),rep(2,4))
depthcode=c(1,2,3,4,1,2,3,4)
upper=c(0,18,25,35,0,12,33,50)
lower=c(10,25,35,67,12,33,50,100)
pH=c(6.7,5.5,5.1,6.7,6.4,5.8,5.3,5.0)
df=data.frame(pit,x,y,upper,lower,depthcode,pH)
lat=df$x;lon=df$y;id=df$pit;top=df$upper;
bottom=df$lower;horizon=df$depthcode;Varn=df$pH

soildata <- join(data.frame(id, top, bottom, Varn, horizon),
              data.frame(id, lat, lon), type='inner')
depths(soildata) <- id ~ top + bottom
site(soildata) <- ~ lat + lon
coordinates(soildata) <- ~ lat + lon
proj4string(soildata) <- CRS("+proj=longlat +datum=WGS84")
depth.s = depthharm(soildata, var.name= "Varn",
lam=0.01,d = t(c(0,10,40,80,100,150)))
plot(soildata, color= "Varn", name="horizon")

Description

This function determines the suitability classes for soil depth requirements of selected agricultural crops and forest trees

Usage

depthSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of soil depth in cm

Value

The output is soil depth suitability class for the crop. The output is integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, tempSuit, fertilitySuit

Examples

#data(suitabinput)
library(sp)
library(raster)
LUT=data.frame(map=c(1,2,3,4,5,6),new=c(100,20,30,40,60,80))
newmap=(suitabinput["depthcodes"])
newmap$depth=reclassifyMap(newmap["depthcodes"],LUT)
newmap$melon=depthSuit(newmap$depth,"melon")
summary(newmap$depth)
spplot(newmap["depth"])

Description

This function determines the suitability classes for drainage requirements for selected agricultural crops and forest trees

Usage

drainageSuit(value, crop)

Arguments

Details

The input value can be a map or an integer of drainage class code. The textural class code is obtained using classCode("drainage")

Value

The output is drainage suitability class for the crop. The output is an integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

If the input value is raster map, then the output will also be a raster map of drainage suitability for the crop of interest

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, PHSuit, rainSuit

Examples

drainageSuit(6,"cassava")

Description

This function converts electrical conductivity measurements of a soil solution to that of soil paste extract. It considers the influence of texture, organic matter content, and clay content on electrical conductivity conversion. These factors and ratio of soil:water mix for the solution and conversion method must be indicated.

Usage

ECconversion1(ec,texture,method,extract,oc=NULL,clay=NULL)

Arguments

Details

This function considers the influence of texture and soil-water solution on conversion of electrical conductivities. The functions includes FAO, sonmez, and hogg conversion models. FAO model requires information on clay content and organic carbon content.

Value

equivalent electrical conductivity of saturated soil extract

Author(s)

Christian Thine Omuto

References

FAO. 2006. Soil description guidelines. FAO, Rome.

Sonmez S, Buyuktas D, Asri FO. 2008. Assessment of different soil to water ratios (1:1, 1:2.5, 1:5) in soil salinity studies. Geoderma, 144: 361-369

Hogg TTJ, Henry JL. 1984. Comparison of 1:1 and 1:2 suspensions and extracts with the saturation extracts in estimating salinity in Saskatchewan. Can. J. Soil Sci. 1984, 64, 699–704

See Also

ECconversion2, ECconversion3, ECconversion4

Examples

library(sp)
library(raster)
ECconversion1(7.31,"SiCl","FAO","1:2.5",0.91,22.5)
ec=suitabinput["ec"]
soc=nutrindicator["soc"]
clay=textureinput["clay"]
texture=suitabinput["texture"]
newmap=ec
newmap$ECe=ECconversion1(ec$ec,texture$texture,"FAO","1:2.5",soc$soc,clay$clay)
spplot(newmap["ECe"], main="Equivalent ECse")

Description

This function converts electrical conductivity measurements of soil solution to that of soil paste extract. The ratio of soil:water mix for the solution and conversion method must be indicated

Usage

ECconversion2(ec, method, extract)

Arguments

Details

This function assumes no influence of texture, clay content, etc on the conversion of electrical conductivities

Value

electrical conductivity equivalent for saturated soil extract in dS/m

Note

Models that work with soil solutions in 1:1 soil-water mix are: USDA, landon, kargas, ozkan,hogg, and zhang. Models for 1:2 solutions are: USDA and hogg. Models for 1:2.5 are: ozkan and shahid. landon model also works for 1:3 soil solution. Models for 1:5 are: USDA, landon, kargas, ozkan, chi, park, visconti, korsandi, klaustermeier, and he. The function only works for soil solution mix ratio handled by the respective model.

Author(s)

Christian Thine Omuto

References

Sonmez S, Buyuktas D, Asri FO. 2008. Assessment of different soil to water ratios (1:1, 1:2.5, 1:5) in soil salinity studies. Geoderma, 144: 361-369

Kargas G, Chatzigiakoumis I, Kollias A, Spiliotis D, Massas I, Kerkides P. 2018. Soil salinity assessment using saturated paste and mass soil:water 1:1 and 1:5 ratios extracts. Water, 10:1589, doi:10.3390/w10111589

See Also

ECconversion1, ECconversion3, ECconversion4

Examples

library(sp)
ECconversion2(0.75, "USDA","1:1")
newmap = suitabinput["ec"]
newmap$salinity=ECconversion2(newmap$ec,"hogg","1:1")
str(newmap$salinity)
spplot(newmap["salinity"])

Description

This function enables development of own function for harmonizing soil indicators to standard values

Usage

ECconversion3(x, A, B, method)

Arguments

Details

model for the relationship between target and predictor variables can be "linear", "power", "exponential", "log". Default is "linear"

Value

model object containing predictive parameters of the conversion model

Author(s)

Christian Thine Omuto

References

van Looy k, Bouma J, Herbst M, Koestel J, Minasny B, Mishra U, Montzka C, Nemes A, Pachepsky AY, Padarian J, Schaap MG, Tóth B, Verhoef A, Jan Vanderborght, van der Ploeg MJ, Weihermüller L, Zacharias S, Zhang Y, Vereecken H. 2017. Pedotransfer functions in Earth System Science: Challenges and Perspectives. Reviews of Geophysics 55(4): 1199-1256.

Sudduth KA, Kitchen RN, Wiebold WJ, Batchelor W. 2005. Relating apparent electrical conductivity to soil properties across the North-Central USA. Computers and Electronics in Agriculture, 46(1-3):263-283

See Also

ECconversion1, ECconversion2, ECconversion4

Examples

x=as.vector(c(0.800,2.580,0.980,0.532,1.870, 18.500,0.430,0.302,0.345,2.700))
y=as.vector(c(17.88,  6.43,  3.83,  7.18,  6.64, 14.83,  4.19,  7.31,  3.21, 18.41))
xy=as.data.frame(cbind(x,y))
names(xy)=c("ECa", "EC")
EC3.ml=nls(EC~ECconversion3(ECa,A,B), start=c(A=0.1, B=0.8), data=xy)
cor.test(fitted(EC3.ml),xy$EC)
plot(fitted(EC3.ml)~xy$EC)
abline(0,1)

Description

This function allows approximate conversion of other soil salt measurements into equivalent electrical conductivity (EC) in dS/m. These measurements include total soluble salts (TSS), total dissolved solids (TDS) and EC in mmho/cm

Usage

ECconversion4(x,target)

Arguments

Details

The target is specified as TDS or TSS or mmho.

Value

The output is a numeric value of equivalent electrical conductivity (EC) in dS/m

Note

TDS should be given in mg/l or ppm. TSS should be given in mmol/l. The function does not convert salt values between different measurement methods

Author(s)

Christian Thine Omuto

See Also

ECconversion1, ECconversion2, pedoTransfer

Examples

ECconversion4(200,"TSS")
ECconversion4(20,"TDS")
ECconversion4(2,"mmho")

Description

Information index for relative predictive performance of soil EC harmonization models

Usage

ECharm_Info(solution)

Arguments

Details

Ratio in text format for the soil-water solution for the extract used in measuring EC. It’s given in quotation marks. Current models consider “1:2”, “1:2.5”, and “1:5” ratios. Default ratio is “1:2”

Value

Graphical display of the predictive performance index for the harmonization models in different regions of the world: Africa, Asia, Near East and North Africa (NENA), Latin America and Caribbean (LAC), north America, and Europe. The performance index ranges between 0 (poor) to 1 (best).

Note

The function currently works for 1:2, 1:2.5, and 1:5. These ratios must be entered in quotation marks.Due to periodic update,internet connectivity is needed for the function to work.

Author(s)

Christian Thine Omuto

See Also

PHharm_Info, SASdata_densityInfo

Examples

ECharm_Info("1:2")

Description

This function determines the suitability classes for Electrical Conductivity requirements for selected agricultural crops and forest trees

Usage

ECSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of electrical conductivity (ECe) of saturated paste extract or its equivalent in dS/m

Value

The output is EC suitability class for the crop. The output is integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

Should the input value be raster map, then the output will also be a raster map of Electrical Conductivity suitability for the crop of interest

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, PHSuit, fertilitySuit

Examples

library(sp)
ECSuit(0.78,"yam")
ec=(suitabinput["ec"])
soc=(nutrindicator["soc"])
clay=(textureinput["clay"])
texture=(suitabinput["texture"])
newmap=ec
newmap$ECe=ECconversion1(ec$ec,texture$texture,"FAO","1:2.5",soc$soc,clay$clay)
newmap$wheat=ECSuit(newmap$ECe,"wheat")
spplot(newmap["wheat"], main="EC suitability for wheat")
summary(newmap$wheat)

Description

A function to determine soil erodibility factor from a choice of different erodibility models

Usage

erodFUN(sand,silt,clay,OC,texture,Struct,method)

Arguments

Value

soil erodibility factor ranging between 0 and 1

Author(s)

Christian Thine Omuto

References

Benavidez R, Bethana J, Maxwell D, Norton K. 2018. A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates. Hydrol. Earth Syst. Sci., 22, 6059–6086

Omuto CT and Vargas R. 2009. Combining pedometrics, remote sensing and field observations for assessing soil loss in challenging drylands: a case study of northwestern Somalia. Land Degrad. Develop. 20: 101–115

See Also

erosivFUN, erodibilityRisk, sloplenFUN

Examples

library(sp)
bx=suitabinput
sand=textureinput["sand"]
silt=textureinput["silt"]
clay=textureinput["clay"]
soc=nutrindicator["soc"]
bx$permeability=permeabilityClass(bx$texture)
bx$wsmith=erodFUN(sand$sand,silt$silt,clay$clay,soc$soc,bx$texture, bx$structure,"WSmith")
bx$renard=erodFUN(sand$sand,silt$silt,clay$clay,soc$soc,bx$texture, bx$structure,"Renard")
summary(bx$renard)
spplot(bx["wsmith"])

Description

This function classifies soil erodibility factor into classes of risk to erosion

Usage

erodibilityRisk(x)

Arguments

Details

Erodibility factor ranges between 0 (lowest risk) to 1 (highest risk)

Value

erodibility risk classes

Author(s)

Christian Thine Omuto

References

Wischmeier WH, Mannering JV. 1969. Relation of Soil Properties to its Erodibility, Soil and Water Management and Conservation, 15, 131–137 Benavidez R, Bethana J, Maxwell D, Norton K. 2018. A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates. Hydrol. Earth Syst. Sci., 22, 6059–6086

See Also

erosivFUN, erodFUN, sloplenFUN

Examples

library(sp)
erodibilityRisk(0.8)
x=suitabinput
sand=textureinput["sand"]
silt=textureinput["silt"]
clay=textureinput["clay"]
soc=nutrindicator["soc"]
x$permeability=permeabilityClass(x$texture)
x$renard=erodFUN(sand$sand,silt$silt,clay$clay,soc$soc,x$texture, x$structure,"Renard")
x$erodibilityrisk=erodibilityRisk(x$renard)
x$erodib=classCode(x$renard,"erodibility")
summary(x$erodib)
spplot(x["erodib"])

Description

This function assumes an algebraic relationship between annual rainfall amounts and rainfall erosivity. The relationship has constants that may depend of certain regions.

Usage

erosivFUN(rain,A,B, model)

Arguments

Value

rainfall erosivity in MJ mm/ha/hr/yr

Author(s)

Christian Thine

References

Morgan RPC. 2005. Soil erosion and conservation. Blackwell. UK Benavidez R, Bethana J, Maxwell D, Norton K. 2018. A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates. Hydrol. Earth Syst. Sci., 22, 6059–6086

See Also

erodFUN, sloplenFUN

Examples

erosivFUN(587,151, 0.63, "linear")

Description

This function determines the suitability classes for ESP requirements of selected agricultural crops and forest trees

Usage

ESPSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical value of Exchangeable Sodium Percent (ESP)

Value

The output is ESP suitability class for the crop. The output is integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

If the input value is raster map, then the output will also be a raster map of ESP suitability for the crop of interest

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, rainSuit, fertilitySuit

Examples

ESPSuit(8.6,"broccoli")

Description

This function establishes graphical representation of the landscape feature in the sample points. An approximation of Kolmogorov-Smirnov similarity test (D-statistic) between the sampled feature distribution and the population feature distribution is also given.

Usage

featureRep(fgrid,df )

Arguments

Details

The sampled points should have the same coordinate system as the landscape feature (raster map). The function extracts the raster map values, attaches them to the sample points, and creates histogram distributions: one for the feature map as contained in the sample points and another as contained in the raster map.

Value

Histograms on back-to-back showing distribution of the landscape feature in the sampled points and on the map for similarity comparison

Note

The input points dataframe and raster map must have similar coordinate reference system.

Author(s)

Christian Thine Omuto

References

Kolmogorov, A. N. 1933. Sulla determinazione empirica di una legge di distribuzione. Giornale dell’ Istituto Italiano degli Attuari 4: 83–91

Simard R, L'Ecuyer P. 2011. Computing the Two-Sided Kolmogorov–Smirnov Distribution. Journal of Statistical Software. 39 (11): 1–18. doi:10.18637/jss.v039.i11

See Also

imageIndices

Examples

library(Hmisc)
data(soil)
dem=suitabinput["dem"]
featureRep(dem,soil)

Description

This function determines the fertility levels given values of a soil property

Usage

fertilityRating(value, indicator = "nitrogen")

Arguments

Details

The units for input values are: nitrogen (percent), phosphorus (mg/kg); potassium (cmol(+)/kg);carbon(percent);iron(mg/kg);zinc(mg/kg);manganese(mg/kg);boron(mg/kg);copper(mg/kg);sulfur(mg/kg); CEC(cmol(+)/kg)

Value

soil fertility class code for the given soil property (fertility indicator)

Author(s)

Christian Thine Omuto

References

FAO, 1976. A framework for land evaluation. FAO Soils Bulletin 32 Sanchez PA, Couto W, Buol SW. 1982. The fertility capability soil classification system: Interpretation, applicability, and modification

Sanchez PA, Palm CA, Buol SW. 2003. Fertility capability soil classification: a tool to help assess soil quality in the tropics. Geoderma 114, 157 –185.

See Also

suitability, saltRating, fertilitySuit

Examples

library(sp)
newmap=nutrindicator["iron"]
newmap$ironclass=fertilityRating(newmap$iron,"iron")
summary(newmap$iron)
spplot(newmap["ironclass"])

Description

This function determines the suitability classes for soil fertility requirements of selected agricultural crops

Usage

fertilitySuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of soil fertility index

Value

The output is fertility suitability class for the crop. The output is integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

If the input value is raster map, then the output will also be a raster map of fertility suitability for the crop of interest

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, ESPSuit, fertilityRating

Examples

library(sp)
library(FuzzyAHP)
fertilitySuit(1.56, "melon")
newmap=(nutrindicator)
newmap$carbon=fertilityRating((nutrindicator$soc),"carbon")
newmap$nitrogen=fertilityRating((nutrindicator$nitrogen),"nitrogen")
newmap$potassium=fertilityRating((nutrindicator$potassium),"potassium")
newmap$phosphorus=fertilityRating((nutrindicator$phosphorus),"phosphorus")
newmap$iron=fertilityRating((nutrindicator$iron),"iron")
newmap$zinc=fertilityRating((nutrindicator$zinc),"zinc")
newmap$manganese=fertilityRating((nutrindicator$manganese),"manganese")
newmap$copper=fertilityRating((nutrindicator$copper),"copper")
newmap$cec=fertilityRating((nutrindicator$cec),"cec")
newmap$boron=fertilityRating((nutrindicator$boron),"boron")
newmap$sulfur=fertilityRating((nutrindicator$sulfur),"sulfur")
newmap$soc=NULL
newmapT1=newmap@data
valuT=as.matrix(newmapT1)
data("nutrient")
nutriens=comparisonTable(nutrient)

newmapT1$fertility=suitability(nutrient, valuT)
newmap@data$fertility=newmapT1$fertility
newmap$fertilityokra=fertilitySuit(newmap$fertility,"okra")
str(newmap$fertilityokra)
spplot(newmap["fertilityokra"], main="Fertility suitability map for Okra")

Description

A generic model for harmonizing soil data for salt-affected soils.

Usage

harmonization(x,A,B)

Arguments

Details

This is a generic linear model for harmonizing input soil data for assessing salt-affected soils.

Value

a numeric output of harmonized ec or ph

Author(s)

Christian Thine Omto

See Also

ME_ECharmserve, ME_PHharmserve

Examples

A = 1.08
B = 0.303
ec=2.45
harmonization(2.45,1.08,0.303)

Description

The function determines commonly used remote sensing indices with relationship with soil surface or vegetation cover characteristics.

Usage

imageIndices(blue, green,red,nir,swir1,swir2,index)

Arguments

Details

The indices are based on multispectral bands: blue, green, red, NIR(near infrared), SWIR1 (short-wave infrared1) and SWIR2(short-wave infrared2)

Value

dimensionless remote sensing index

Author(s)

Christian Thine Omuto

References

Gorji T, Yildirim A, Sertel E, Tanik A. 2019. Remote sensing approaches and mapping methods for monitoring soil salinity under different climate regimes. International Journal of Environment and Geoinformatics 6(1): 33-49 (2019)

See Also

featureRep

Examples

imageIndices(0.15,0.05,0.18,0.25,0.36,0.45,"SAVI")

Description

This function determines the suitability classes for length of growing period (LGP) requirements for selected agricultural crops and forest trees

Usage

LGPSuit(value, crop)

Arguments

Details

The input value can be map or an integer value of LGP in days

Value

The output is LGP suitability class for the crop. The output is an integer for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

If the input value is raster map, then the output will also be a raster map of LGP suitability for the crop of interest

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, PHSuit, fertilitySuit

Examples

library(sp)
LGPSuit(138,"cotton")
newmap = data.frame(LGP = c(1:6,158,160,211),
                    lon = c(1,1,1,2,2,2,3,3,3),
                    lat = c(rep(c(0, 1.5, 3),3)))
coordinates(newmap) = ~lon+lat
gridded(newmap) = TRUE
newmap = as(newmap, "SpatialGridDataFrame")
newmap$LGPmillet=LGPSuit(newmap$LGP,"millet")
spplot(newmap["LGPmillet"], main="LGP suitability map for finger millet")

Description

A function for harmonizing soil electrical conductivity to the equivalent conductivity of saturated paste extract using mixed effects approach

Usage

ME_ECharm(EC, TEXCLASS, model, soilsolution)

Arguments

Details

EC harmonization models, which were developed using global datasets, are designed to standardize soil electrical conductivity for applications in soil salt classification

Value

numeric value of equivalent EC of saturated soil paste extract

Note

The models are currently developed for soil solutions from 1:2, 1:2.5 and 1:5 soil:water mix ratios. The function only works with USDA soil textural classes. Convert other soil textural classes to USDA classes for all applications with this function.

Author(s)

Christian Thine Omuto

References

Omuto, C. T., Vargas, R.-R., EL Mobarak, A., Nuha, M., Viatkin, K., & Yigini, Y. (2020). Mapping of salt-affected soils – Technical manual. FAO. https://doi.org/10.4060/ca9215en

Omuto, C. T., Minasny, B., McBratney, A. B., & Biamah, E. K. (2006). Nonlinear mixed effect modelling for improved estimation of water retention and infiltration parameters. Journal of Hydrology, 330(3–4), 748–758. https://doi.org/10.1016/j.jhydrol.2006.05.006

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-Effects Models in Sand S-PLUS. Springer New York. https://doi.org/10.1007/978-1-4419-0318-1

See Also

ME_PHharm, ECconversion1, ECconversion2

Examples

ndata=data.frame(EC=c(1,0.34,5.07,12.17, 2.219),TEX=c("Cl","SaCl","LoSa", "SiCl","SaClLo"))
ndata$ESa1=ME_ECharm(ndata$EC,ndata$TEX,"power","1:5")

Description

Mixed effects models for harmonizing electrical conductivity (EC)

Details

Suit of mixed-effects models

Note

Internet connectivity is needed for the function to work.

Description

A function for harmonizing soil pH (KCl or CaCl2) to the equivalent pH (water) using mixed effects approach

Usage

ME_PHharm(ph, TEXCLASS, model, phtype)

Arguments

Details

ph harmonization models, which were developed using global datasets, are designed to standardize soil ph for applications in soil salt classification

Value

numeric value of equivalent ph (water)

Note

The function only works with USDA soil textural classes. Convert other soil textural classes to USDA classes for all applications with this function.

Author(s)

Christian Thine Omuto

See Also

ME_ECharm, ECconversion1, ECconversion2

Examples

newdata=data.frame(ph=c(1.6,8.3,5.7,12.1,2.2),tex=c("Cl","SaCl","LoSa", "Si","SaClLo"))
newdata$pH2=ME_PHharm(newdata$ph,newdata$tex,"exponential","kcl")

Description

Mixed effects models for harmonizing soil pH

Details

Suit of mixed-effects models

Note

Internet connectivity is needed for the function to work.

Description

Function to handle negative entries when assessing salt-affected soils

Usage

negData(vg,x)

Arguments

Details

Three tags for soil properties are allowed: "ec", "ph", "esp"

Value

numeric value of soil property to correct. It return NA where negative "ec" or "esp" is involved or where ph<1 or ph>14

Author(s)

Christian Thine Omuto

Examples

negData("ph",14)

Description

This is an empirical function for deriving net primary production using climatic variables (mean temperature and rainfall amounts)

Usage

NPPmodel(rain,temperature,model)

Arguments

Details

This function is based on empirical models for calculating annual net primary production (NPP) of dry matter

Value

Net primary production (NPP) of dry matter in grams per square meter per year

Note

This empirical function estimates annual NPP in g/m2/year. It is a general model for all land cover types. It may be necessary to adjust it for certain cover types or geolocations

Author(s)

Christian Thine Omuto

References

Schuur, E. A. G. 2003. Productivity and global climate revisited: the sensitivity of tropical forest growth to precipitation. Ecology 84:1165–1170

Lieth, H. 1975. Modeling the primary productivity of the world. Pages 237–264 in H. Lieth and R. H. Whittaker, editors. Primary productivity of the biosphere. Springer-Verlag, New York, New York, USA

Del Grosso, S., Parton, W., Stohlgren, T., Zheng, D., Bachelef, D., Prince, S., Hibbard, K., Olson, R. 2008. Global potential net primary production predicted from vegetation class, precipitation, and temperature. Ecology, 89(8): 2117-2126

See Also

carbonTurnover, RotCmoistcorrection

Examples

NPPmodel(800,23,"miami")
NPPmodel(800,23,"schuur")
NPPmodel(800,23,"NCEAS")

Description

This is an 11-factor table of decision ranking of soil nutrient indicators

Usage

data("nutrient")

Format

A dataframe with 11 factors for pairwise decision ranking of soil nutrient indicators.

Details

The ranks are the recipricals of Saaty's scale of relative importance which are between 1 and 9

Source

Hypothetical data of rank between soil nutrient indicators

Examples

data(nutrient)
str(nutrient)
plot(nutrient)

Description

A grid stack map of eleven variables for assessing soil fertility

Usage

data("nutrindicator")

Format

Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots ..@ data :'data.frame': 16900 obs. of 11 variables: .. ..$ soc : num [1:16900] 0.163 0.242 0.233 0.218 0.179 ... .. ..$ nitrogen : num [1:16900] 0.0272 0.0242 0.0266 0.0275 0.0256 ... .. ..$ phosphorus: num [1:16900] 9.4 8.22 8.92 7.45 8.3 ... .. ..$ manganese : num [1:16900] 2.84 2.7 2.95 2.88 3.19 ... .. ..$ potassium : num [1:16900] 93.2 102.3 93.5 96.5 87.8 ... .. ..$ cec : num [1:16900] 10.9 10.7 10 10.1 10.2 ... .. ..$ boron : num [1:16900] 0.172 0.16 0.171 0.172 0.174 ... .. ..$ copper : num [1:16900] 0.368 0.421 0.37 0.369 0.412 ... .. ..$ iron : num [1:16900] 0.238 0.231 0.241 0.239 0.242 ... .. ..$ zinc : num [1:16900] 0.816 0.652 0.816 0.818 0.814 ... .. ..$ sulfur : num [1:16900] 153 131 119 135 163 ... ..@ grid :Formal class 'GridTopology' [package "sp"] with 3 slots .. .. ..@ cellcentre.offset: Named num [1:2] 383216 3341506 .. .. .. ..- attr(*, "names")= chr [1:2] "x" "y" .. .. ..@ cellsize : num [1:2] 357 357 .. .. ..@ cells.dim : int [1:2] 130 130 ..@ bbox : num [1:2, 1:2] 383038 3341327 429478 3387767 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "x" "y" .. .. ..$ : chr [1:2] "min" "max" ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot .. .. ..@ projargs: chr "+proj=utm +zone=41 +datum=WGS84 +units=m +no_defs"

Examples

data(nutrindicator)
str(nutrindicator)
#spplot(nutrindicator["nitrogen"])

Description

This generic pedo-transfer function is used to approximate EC values from other existing and easy-to-measure soil data

Usage

pedoTransfer(method="linear", df, ...)

Arguments

Details

This generic model can be used even with other soil properties. For example, it can be used to predict porosity from bulk density, carbon, and texture components as long as they are in the database and have known/suspected relationship

Value

model for predicting EC given similar input data

Note

This function can also be used to predict EC from apparent electrical conductivity of bulk soil, texture, and other important soil properties

Author(s)

Christian Thine Omuto

References

van Looy k, Bouma J, Herbst M, Koestel J, Minasny B, Mishra U, Montzka C, Nemes A, Pachepsky AY, Padarian J, Schaap MG, Tóth B, Verhoef A, Jan Vanderborght, van der Ploeg MJ, Weihermüller L, Zacharias S, Zhang Y, Vereecken H. 2017. Pedotransfer functions in Earth System Science: Challenges and Perspectives. Reviews of Geophysics 55(4): 1199-1256.

Sudduth KA, Kitchen RN, Wiebold WJ, Batchelor W. 2005. Relating apparent electrical conductivity to soil properties across the North-Central USA. Computers and Electronics in Agriculture, 46(1-3):263-283

See Also

ECconversion4, conversion

Examples

library(caret)
clay=as.data.frame(runif(120, 1,100))
silt=as.data.frame (runif(120,20,70))
sand=as.data.frame(runif(120,10.1,50.5))
pH=as.data.frame(runif(120,1,14))
EC=as.data.frame(runif(120,0.5,20.5))
OC=as.data.frame(runif(120,0.1,1.25))
soil4=cbind(EC,clay,silt,sand,OC,pH)
names(soil4)=c("EC","clay","silt","sand","OC","pH")
bound <- floor((nrow(soil4)/4)*3)
df.train <- soil4[sample(nrow(soil4)), ][1:bound, ]
df.test <- soil4[sample(nrow(soil4)), ][(bound+1):nrow(soil4[sample(nrow(soil4)), ]), ]
EC1.lm=pedoTransfer("randomforest",df.train,EC, clay,sand,silt,OC,pH)
df.test$EC1=predict(EC1.lm,newdata = df.test)
cor(df.test$EC,df.test$EC1)^2
plot(df.test$EC~df.test$EC1)
abline(1,1)

Description

This function determines the soil permeability classes according to the USDA soil textural classes

Usage

permeabilityClass(texture)

Arguments

Details

Soil textural class is according to USDA textural triangle such as SiLo, Si, SaLo. The code is represented by first two letters of the class with the first letter in upper case, e.g., Si, Lo, Cl. Class codes with combination of textures have the first two letters included in the combination, e.g., SiLo, SaClLo, etc.

Value

permeability class code. 1-very slow, 2-slow, 3-moderately slow, 4-moderate, 5-moderately rapid, 6-rapid, and 7-very rapid

Author(s)

Christian Thine Omuto

References

O'Geen, A. T. (2013) Soil Water Dynamics. Nature Education Knowledge 4(5):9

Soil Survey Staff. Soil Taxonomy A Basic System of Soil Classification for Making and Interpreting Soil Surveys. Agricultural Handbook No. 436. U.S. Government Printing Office Washington, DC, 1999.

See Also

drainageSuit, erodFUN

Examples

library(sp)
permeabilityClass("SaLo")

texture=suitabinput["texture1"]
texture$permeability=ifelse(texture$texture1=="Lo",
permeabilityClass("Lo"),ifelse(texture$texture1=="SaLo",
permeabilityClass("SaLo"),permeabilityClass("SiLo")))
str(texture$permeability)
texture$Perm=classCode(texture$permeability,"permeability")
spplot(texture["Perm"])

Description

A suit of functions for converting soil pH (KCl or CaCl2) to the equivalent pH (water)

Usage

PHConversion(ph, model,phtype)

Arguments

Details

ph conversion models are those in the literature

Value

numeric value of equivalent ph (water)

Note

ph ranges between 1 and 14

Author(s)

Christian Thine Omuto

References

Davies, B.E. (1971). A Statistical Comparison of pH Values of some English Soils after Measurement in both Water and 0.01M Calcium Chloride. Soil Science Society of America Journal 35, 551–552. https://doi.org/10.2136/sssaj1971.03615995003500040022x

Kabała, C., Musztyfaga, E., Gałka, B., Łabuńska, D., Mańczyńska, P. (2016). Conversion of Soil pH 1:2.5 KCl and 1:2.5 H2O to 1:5 H2O: Conclusions for Soil Management, Environmental Monitoring, and International Soil Databases. Pol. J. Environ. Stud. 25, 647–653. https://doi.org/10.15244/pjoes/61549

Miller, R.O., Kissel, D.E. (2010). Comparison of Soil pH Methods on Soils of North America. Soil Sci. Soc. Am. J. 74, 310–316. https://doi.org/10.2136/sssaj2008.0047

Sadovski, A.N. (2019). Study on pH in water and potassium chloride for Bulgarian soils. EURASIAN JOURNAL OF SOIL SCIENCE (EJSS) 8, 11–16. https://doi.org/10.18393/ejss.477560

See Also

ME_PHharm, ME_ECharm, ECconversion1

Examples

testdata=data.frame(PHKC=c(6.45,8.34,5.07,12.17, 4.219),TEX=c("Cl","SaCl","LoSa", "Si","SaClLo"))
testdata$PHs1=PHConversion(testdata$PHKC,"kabala","kcl")

Description

Information index for relative predictive performance of soil pH harmonization models

Usage

PHharm_Info(solution)

Arguments

Details

Solution for measuring soil pH. It’s given in quotation marks. Current models consider “cacl2” and “kcl” ratios. Default solution is “cacl2”

Value

Graphical display of the predictive performance index for the harmonization models in different regions of the world: Africa, Asia, Near East and North Africa (NENA), Latin America and Caribbean (LAC), north America, and Europe. The performance index ranges between 0 (poor) to 1 (best).

Note

The function currently works for cacl2 and kcl. These solutions must be entered in quotation marks.Due to periodic update,internet connectivity is needed for the function to work.

Author(s)

Christian Thine Omuto

See Also

ECharm_Info, SASdata_densityInfo

Examples

PHharm_Info("kcl")

Description

This function determines the suitability classes for soil pH requirements for selected agricultural crops and forest trees

Usage

PHSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of soil pH of a saturated paste extract

Value

The output is pH suitability class for the crop. The output is integer value of suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

If the input value is raster map, then the output will also be a raster map of pH suitability for the crop of interest

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, ECSuit, fertilitySuit

Examples

PHSuit(8.4,"cauliflower")

Description

This function calculates statistical indices for accuracy between an array of two variables such as calibration and validation vectors. The indices are Bias, RMSE, R-squared, and NSE

Usage

predAccuracy(x,y)

Arguments

Details

The function calculates four indices for accuracy: bias, root mean square error (RMSE), r-squared, Nash-sutcliffe efficiency (NSE)

Value

A table of four variables: bias, RMSE, Rsquared, and NSE

Author(s)

Christian Thine Omuto

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. 1988. The New S Language. Wadsworth & Brooks/Cole.

Nash, J. E.; Sutcliffe, J. V. (1970). "River flow forecasting through conceptual models part I — A discussion of principles". Journal of Hydrology. 10 (3): 282–290

See Also

predUncertain, featureRep

Examples

xy=data.frame(a=c(2,3,4,5,6,7,8,9),b=c(1,1.5,8,10,12,3.5,NA,18))
predAccuracy(xy$a,xy$b)$Rsquared

Description

This functions uses bootstrap approach to estimate spatial maps of modelling prediction interval width and standard deviation

Usage

predUncertain(indata,fgrid, k, z, model="rf")

Arguments

Details

One-variable input dataframe is prefered or at least the first column should have the target soil variable to predict. It should not contain NAs. The number of realizations k need not be too high because the software multiplies it exponentially and may slow down the computing process if set to a high value. For example k=5 will results into more than 40 realizations created

Value

a two-layer raster stack map of prediction width and standard deviation

Note

The input dataframe and predictors need to have similar coordinate reference system (CRS). In addition, the input dataframe should not have missing entrie (NAs)

Author(s)

Christian Thine Omuto

References

Efron B. 1992. Jackknife-after-bootstrap standard errors and influence functions. Journal of the Royal Statistical Society. Series B (Methodological), 83–127.

See Also

regmodelSuit, imageIndices,predAccuracy

Examples

library(raster)
library(caret)
soil1=soil[,c("OC")]
predictere=suitabinput[c("depthcodes","rain","texture","dem")]

pred_uncert=predUncertain(soil1,predictere,3,90,"rf")
plot(pred_uncert)

Description

This function determines the suitability classes for rainfall requirements of selected agricultural crops and forest trees

Usage

rainSuit(value, crop)

Arguments

Details

The input value can be map or just numerical entry of annual rainfall amount in mm

Value

The output is rainfall suitability class for the crop. The output is an integer for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

This function assumes rainfall as the source of water for crop development

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability, ESPSuit, fertilitySuit

Examples

library(sp)
rain=(suitabinput["rain"])
rain$rainmiaz=rainSuit(rain$rain,"wheat")
summary(rain$rainmiaz)
spplot(rain["rainmiaz"])

Description

This function re-classifies an input raster maps based on input look-up table that specifies transition from map classes (or range of classes) to a new class (or range of classes)

Usage

reclassifyMap(fgrid,df)

Arguments

Details

The look-up table should have at least two columns in which the first column contains the classes in the input map and the second column contains the new classes to be assigned

Value

The output is a reclassified raster map

Author(s)

Christian Thine Omuto

References

Robert Hijman. Raster Package in R. https://www.rdocumentation.org/packages/raster

See Also

classCode, classLUT, classnames

Examples

library(sp)
LUT=data.frame(map=c(1,2,3,4,5,6),new=c(100,20,30,40,60,80))
newmap=(suitabinput["depthcodes"])
newmap$depth=reclassifyMap(newmap["depthcodes"],LUT)
newmap$melon=depthSuit(newmap$depth,"melon")
summary(newmap$depth)
spplot(newmap["depth"])

Description

This function evaluates suitability of most predition models in mapping soil properties using a set of predictors

Usage

regmodelSuit(df, ...)

Arguments

Details

The name of the target soil variable to predict and names of its predictors are seperated by commas and are similar to column names of the corresponding variables in the supplied dataframe. The name of the target soil variable starts the list and followed by the names of its predictors. For example, if the dataframe has EC, landcover,DEM, Slope, NDVI, etc., then the input could be (soil,EC,landcover,Slope,DEM).

Value

A table of model statistics such as root mean square error (RMSE), mean absolute error (MAE), r-squared (R2) and Nash-Sutcliffe coefficient of efficiency (NSE) for the popular models in digital soil mapping

Note

The function carries 5-fold cross-validation. Sometimes it may give a warning of missing resample performance with some models. It's important to ensure no NA in the data used for modelling

Author(s)

Christian Thine Omuto

References

Nash, J. E.; Sutcliffe, J. V. 1970. River flow forecasting through conceptual models part I — A discussion of principles. Journal of Hydrology. 10 (3): 282–290

See Also

pedoTransfer, predUncertain, ECconversion3

Examples

library(caret)
library(sp)
data(soil)
soil1=soil[,c("EC")]
soil1=subset(soil1,!is.na(soil1$EC))
overlay.ov=over(soil1, suitabinput)
soil1$dem=overlay.ov$dem
soil1$rain=overlay.ov$rain
soil1$ph=overlay.ov$ph
soil2=soil1@data[,c("EC","dem","rain","ph")]

regmodelSuit(soil2,EC,dem,rain,ph)

Description

This function estimates the scalar constant representing the moisture effects in RothC carbon turnover modelling in the soil

Usage

RotCmoistcorrection(P, E, S.Thick, clay, pE, fk)

Arguments

Details

E can be given as pan evapotranspiration or potential evapotranspiration. If potential evapotranspiration is used for E, then pE = 1 and if pan evapotranspiration is used for E then pE=0.75.

Value

A scalar constant for moisture effects on carbon decomposition rates

Note

This function can be used with monthly or annual input data to produce time-dependent scalars

Author(s)

Christian Thine Omuto

References

Burke, I., Kaye, J., Bird, S., Hall, S., McCulley, R., Sommerville, G. 2003. Evaluating and testing models of terrestrial biogeochemistry: the role of temperature in controlling decomposition, Models in ecosystem science, Princeton University Press, Princeton, New Jersey, USA, 225–253, 2003

Adair, E., Parton, W., Del Grosso, S., Silver, W., Harmon, M.,Hall, S., Burke, I., and Hart, S. 2008. Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates, Global Change Biology, 14: 2636–2660

Coleman, K. and Jenkinson, D. 2014. ROTHC-26.3 A model for the turnover of carbon in soils: Model description and users guide (Windows version). Rothamsted Research Harpenden Herts AL5 2JQ

See Also

carbonTurnover, RotCtempcorrection, NPPmodel

Examples

clay=34.5
depth=30
precip=c(73,59,63,51,52,57,34,55,58,56,76,71)
evapo=c(8,10,27,49,83,99,103,91,69,34,16,8)
inCl=data.frame(seq(1,12,1),precip,evapo)
colnames(inCl)=c("month","rain","ET")
inCl$mcor=RotCmoistcorrection(inCl$rain,inCl$ET,depth,clay,0.75,1)
inCl$mcor

Description

This function estimates the scalar constant for temperature effects in RothC carbon turnover modelling in the soil

Usage

RotCtempcorrection(temperature)

Arguments

Details

mean air temperature canbe monthly or annual mean temperature

Value

A scalar constant for temperature effects on carbon decomposition rates

Note

This function can be used with monthly or annual input data to produce time-dependent scalars The function works with temperatures greater than -18.2 degrees Celsius

Author(s)

Christian Thine Omuto

References

Burke, I., Kaye, J., Bird, S., Hall, S., McCulley, R., Sommerville, G. 2003. Evaluating and testing models of terrestrial biogeochemistry: the role of temperature in controlling decomposition, Models in ecosystem science, Princeton University Press, Princeton, New Jersey, USA, 225–253, 2003

Adair, E., Parton, W., Del Grosso, S., Silver, W., Harmon, M.,Hall, S., Burke, I., and Hart, S. 2008. Simple three-pool model accurately describes patterns of long-term litter decomposition in diverse climates, Global Change Biology, 14: 2636–2660

Coleman, K. and Jenkinson, D. 2014. ROTHC-26.3 A model for the turnover of carbon in soils: Model description and users guide (Windows version). Rothamsted Research Harpenden Herts AL5 2JQ

See Also

carbonTurnover, RotCmoistcorrection, NPPmodel

Examples

airTemp=22.1
RotCtempcorrection(airTemp)

Description

This function determines the major classes of salt-affected soils using Electrical Conductivity (EC), soil reaction (pH), and Exchangeable Sodium Percent (ESP) according to FAO or USDA classification schemes

Usage

saltClass(ec,ph,esp)

Arguments

Value

saltClass returns integer classes of salt problems in the soil. The classes are 1, 2, 3, 4, 5 corresponding to None, Saline, Saline-sodic, Sodic, and Alkaline categories.

Note

ESP is mandatory when using this function. The "error: 1 * ESP : non-numeric argument to binary operator" is flagged when ESP entry is missing. In case ESP is missing, saltRating is suggested.

Author(s)

Christian Thine Omuto

References

FAO.2006. Guidelines for soil description. FAO. Rome

Richards, L. A. (ed.) 1954. Diagnosis and Improvement of Saline and Alkali Soils. U.S. Department Agriculture Handbook 60. U.S. Gov. Printing Office, Washington, DC.

See Also

saltRating, saltSeverity, classCode

Examples

saltClass(6.12,7.84,1)

Description

This function determines classes of salt-affected soils using Electrical Conductivity and pH according to FAO or USDA salt classification schemes

Usage

saltRating(ec,ph,criterion="FAO")

Arguments

Value

The output is an integer value for soil salt class. The class name for any integer code is obtained from classCode function

Note

This function gives approximate classification. A better classification is achieved when indicator of sodium ions is included (e.g. ESP)

Author(s)

Christian Thine Omuto

References

FAO.2006. Guidelines for soil description. FAO. Rome

Richards, L. A. (ed.) 1954. Diagnosis and Improvement of Saline and Alkali Soils. U.S. Department Agriculture Handbook 60. U.S. Gov. Printing Office, Washington, DC.

See Also

saltClass, saltSeverity, classCode

Examples

library(sp)
saltRating(11.2,8.14, "USDA")

ec=suitabinput["ec"]
ph=suitabinput["ph"]
soc=nutrindicator["soc"]
clay=textureinput["clay"]
texture=suitabinput["texture"]
newmap=ec
newmap$ph=ph$ph
newmap$ECe=ECconversion1(ec$ec,texture$texture,"FAO","1:1", soc$soc,clay$clay)
newmap$salinity=saltRating(newmap$ECe,newmap$ph,"FAO")
newmap$salineclass=classCode(newmap$salinity,"saltclass")
newmap$salineclass1=as.factor(newmap$salineclass)
spplot(newmap["salineclass"], main="Soil Salinity Class")
summary(newmap$salinity)

Description

This function classifies salt intensity in soil based on EC, pH and ESP levels

Usage

saltSeverity(ec,ph,esp,criterion="FAO")

Arguments

Details

This function requires input EC, pH and ESP values to process the classification. They can be maps or numerical entries. PSALT criterion uses percent salt content instead of EC.

Value

Integer classes of ranging between 1-17. The names of integer codes are obtained using classCode function

Note

The function strictly requires input EC, pH, and ESP. Percent salt content can be used in place of EC if the criterion is PSALT

Author(s)

Christian Thine Omuto

References

Abrol, IP, Yadav JSP, Massoud FI. 1988. Salt-affected soils and their management. FAO Soils Bulletin 39. FAO, Rome

Amrhein C. 1996. Australian sodic soils: Distribution, properties, and management. Soil Science 161. pp412.

FAO. 2006. Guidelines for soil description. FAO, Rome

Richards LA. 1954. Diagnosis and improvements of saline and alkali soils. Agriculture Handbook No. 60. USDA, Washington

See Also

saltClass, saltRating, classCode

Examples

library(sp)
saltSeverity(4.5,7.8,11.6,"USDA")
ec=suitabinput["ec"]
ph=suitabinput["ph"]
soc=nutrindicator["soc"]
clay=textureinput["clay"]
texture=suitabinput["texture"]
newmap=ec
newmap$ph=ph$ph-1
newmap$ECe=ECconversion1(ec$ec*0.25,texture$texture,"FAO","1:5",soc$soc,clay$clay)
newmap$salt=saltSeverity(newmap$ECe,newmap$ph,6.84,"FAO")
newmap$salineclass=classCode(newmap$salt,"saltseverity")
spplot(newmap["salineclass"], main="Salinity Code")

Description

Global distribution of sampling points with measured soil property data

Usage

SASdata_densityInfo(data)

Arguments

Details

The function accepts three input alternatives for querrying available SAs information: "ec", "ph", and "texture". The default is "ec"

Value

Spatial maps of sampling locations with measured soil data for SAS information. They include maps of electrical conductivity (ec), pH, texture (sand, silt, clay percentages), and cation exchange capacity (CEC). Locations for CEC are similar to those for texture.

Note

The function currently works for ec, ph, and texture. Distribution of locations for texture is similar to those for CEC. The input for this function must be entered in quotation marks. Internet connectivity is needed for the function to work.

Author(s)

Christian Thine Omuto

References

Batjes, N.H., Ribeiro, E., van Oostrum, A., 2020. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth Syst. Sci. Data 12, 299–320. https://doi.org/10.5194/essd-12-299-2020

FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO and IIASA, Rome.

See Also

DataAvailabilityIndex, PHharm_Info

Examples

SASdata_densityInfo("ec")

Description

A function to query soil data availability in the global SAS database

Usage

SASglobeData(dframe,ISO,Region)

Arguments

Details

Options for type of soil data for querrying the database are "ecse","ec2","ec2.5","ec5","ph","phkcl","phcacl2","sand","silt","clay".Options for regions of the world in the SAS database are "Africa", "Asia", "Europe", "Eurasia", "NENA", "LAC", "N.America", and "Pacific". NENA is Near East and North Africa. LAC is Latin America and Caribbean. N. America is North America. Any of these Regions may be specified if desired. The default Region is NULL

Value

The query returns a dataframe with the soil attribute querried, coordinates of sampling locations, and name of country where the samples are located

Note

Internet connectivity is needed for the function to work.

Author(s)

Christian Thine Omuto

References

Batjes, N. H., Ribeiro, E. & van Oostrum, A. Standardised soil profile data to support global mapping and modelling (WoSIS snapshot 2019). Earth Syst. Sci. Data 12, 299–320 (2020).

FAO/IIASA/ISRIC/ISS-CAS/JRC. Harmonized World Soil Database (version 1.2). (FAO and IIASA, 2012).

Omuto, C. T., Vargas. R., Abdelmagin, E.A., Mohamed, N., Viatkin, K., Yusuf, Y. Mapping of salt-affected soils – Technical manual. (FAO, 2020). doi:10.4060/ca9215en

Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A. & Fernández‐Ugalde, O. LUCAS Soil, the largest expandable soil dataset for Europe: a review. Eur J Soil Sci 69, 140–153 (2018).

Examples

wrter=SASglobeData("sand","ZAF")# For South Africa profiles
plot(Longitude~Latitude, wrter)

Description

This information function shows the list of EC and pH harmonization models contained in the SAS harmonization service

Usage

SASmodels(data="ec", extract="1:1")

Arguments

Details

This information function shows available models in the SAS harmonization service. The models are divided into two major categories: ec and ph models. The function returns a list of available SAS harmonization models under each data category. ec is the default data category.

Value

A list of SAS harmonization models

Author(s)

Christian Thine Omuto

See Also

SASglobeData, DataAvailabilityIndex

Examples

SASmodels("ec", "1:1")
SASmodels("ph","kcl")

Description

This function determines the suitability classes for slope requirements of selected agricultural crops and forest trees

Usage

slopeSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of slope in degrees

Value

The output is slope suitability class for the crop. The output is an integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

The input slope value must be in degrees

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

LGPSuit, tempSuit, suitability

Examples

slopeSuit(23.4,"carrot")
library(sp)
slopeSuit(23.4,"carrot")
slope=suitabinput["slope"]
slope$tea=slopeSuit(slope$slope,"tea")
slope$carrot=slopeSuit(slope$slope,"carrot")
summary(slope$carrot)
spplot(slope["carrot"])

Description

Th function estimates slope length factor for erosion risk assessment. It has options for choosing different algorithms

Usage

sloplenFUN(ls,slope,method)

Arguments

Details

Slope (degrees) and length of slope (metres) are relief parameters in erosion risk assessment.

Value

a dimensionless quantity of slope-length factor of erosion risk

Note

The slope must be in degrees. The warning given is a reminder to that the slope is given in degrees

Author(s)

Christian Thine Omuto

References

Benavidez R, Bethana J, Maxwell D, Norton K. 2018. A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates. Hydrol. Earth Syst. Sci., 22, 6059–6086

Omuto CT and Vargas R. 2009. Combining pedometrics, remote sensing and field observations for assessing soil loss in challenging drylands: a case study of northwestern Somalia. Land Degrad. Develop. 20: 101–115

See Also

erosivFUN, erodFUN, slopeSuit

Examples

library(sp)
sloplenFUN(60,14.88,"Renard")
newmap=suitabinput["slope"]
newmap$LSrenard=sloplenFUN(60,(newmap$slope),"Renard")
newmap$LSwsmith=sloplenFUN(60,(newmap$slope),"WSmith")
spplot(newmap["LSrenard"])
spplot(newmap["LSwsmith"])

Description

This function determines the suitability classes for soil organic carbon requirements of selected agricultural crops and forest trees

Usage

SOCSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of soil organic carbon in percent

Value

The output is SOC suitability class for the crop. The output is an integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

depthSuit, carbonateSuit, suitability

Examples

library(sp)
soc1=nutrindicator["soc"]
soc1$pyrethrum=SOCSuit(soc1$soc,"pyrethrum")
summary(soc1$pyrethrum)
spplot(soc1["pyrethrum"])

Description

Horizon sample dataset for mapping soil salinity

Usage

data("soil")

Format

The format is: Formal class 'SpatialPointsDataFrame' [package "sp"] with 5 slots ..@ data :'data.frame': 152 obs. of 17 variables: .. ..$ Sample : Factor w/ 152 levels "1","10","100",..: 1 65 76 87 98 109 120 131 142 2 ... .. ..$ ProfileID: Factor w/ 87 levels "1","2","3","4",..: 5 53 53 55 55 56 6 7 57 8 ... .. ..$ Latitude : num [1:152] -30.2 -30.3 -30.3 -30.3 -30.3 ... .. ..$ Longitude: num [1:152] 62.2 62.1 62.1 62.1 62.1 ... .. ..$ Horizon : Factor w/ 2 levels "A","B": 1 1 2 1 2 2 1 1 2 1 ... .. ..$ Depth : Factor w/ 43 levels "0 - 100","0 - 17",..: 8 14 37 8 29 42 8 8 38 8 ... .. ..$ Sand : num [1:152] 43.2 61.2 57.2 55.2 65.2 83.2 63.2 63.2 45.2 59.2 ... .. ..$ Silt : num [1:152] 44 24 29 32 22 9 24 24 40 24 ... .. ..$ Clay : num [1:152] 12.8 14.8 13.8 12.8 12.8 7.8 12.8 12.8 14.8 16.8 ... .. ..$ OC : num [1:152] 0.36 0.465 0.39 0.36 0.42 0.87 0.075 0.375 0.84 0.33 ... .. ..$ PH : num [1:152] 8.6 8.37 8.31 8.76 7.81 ... .. ..$ EC : num [1:152] 0.8 2.58 0.98 0.532 1.87 18.5 0.43 0.302 0.345 2.7 ... .. ..$ CaCo3 : num [1:152] 15.2 18.5 20.5 15.8 20 ... .. ..$ K : num [1:152] 67 162 120 124 177 91 127 72 123 158 ... .. ..$ Na : num [1:152] 1073 707 689 646 691 ... .. ..$ CEC : num [1:152] 6 11 18 9 10.4 6 6.4 16 10 4.9 ... .. ..$ ESP : Factor w/ 22 levels "0","1","10","11",..: 11 19 17 20 20 8 17 20 15 11 ... ..@ coords.nrs : num(0) ..@ coords : num [1:152, 1:2] 420924 418226 418226 415334 415334 ... .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : NULL .. .. ..$ : chr [1:2] "coords.x1" "coords.x2" ..@ bbox : num [1:2, 1:2] 386582 3343117 427796 3386711 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "coords.x1" "coords.x2" .. .. ..$ : chr [1:2] "min" "max" ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot .. .. ..@ projargs: chr "+proj=utm +zone=41 +datum=WGS84 +units=m +no_defs"

Details

A dataset with 87 points of soil horizons for mapping salinity

Source

Hypothetical dataset for salinity mapping

References

Hypothetical dataset for salinity mapping

Examples

data(soil)
str(soil)

Description

This function determines the suitability classes for stoniness requirements of selected agricultural crops and forest trees

Usage

stoneSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of stoniness in percent

Value

The output is stoniness suitability class for the crop. The output is an integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

Output raster map of stoniness for the crop of interest is given if the input value is raster map

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

tempSuit, PHSuit, rainSuit

Examples

stoneSuit(15,"grape")

Description

This function determines soil condition classes (such as suitability, fertility, etc.) given a set of indicators.

Usage

suitability(df,data)

Arguments

Value

A vector of soil suitability (condition) class between 0 and 5.

Note

It's important to normalize and assess the adequacy of the decision matrix before using this function

Author(s)

Christian Thine Omuto

References

FAO, 1976. A framework for land evaluation. FAO Soils Bulletin 32

Saaty TL. 1980. The Analytic Hierarchy Process. McGraw-Hill, New York

See Also

fertilityRating, suitabilityClass

Examples

library(sp)
newmap=(nutrindicator)
newmap$carbon=fertilityRating((nutrindicator$soc),"carbon")
newmap$nitrogen=fertilityRating((nutrindicator$nitrogen),"nitrogen")
newmap$potassium=fertilityRating((nutrindicator$potassium),"potassium")
newmap$phosphorus=fertilityRating((nutrindicator$phosphorus),"phosphorus")
newmap$iron=fertilityRating((nutrindicator$iron),"iron")
newmap$zinc=fertilityRating((nutrindicator$zinc),"zinc")
newmap$manganese=fertilityRating((nutrindicator$manganese),"manganese")
newmap$copper=fertilityRating((nutrindicator$copper),"copper")
newmap$cec=fertilityRating((nutrindicator$cec),"cec")
newmap$boron=fertilityRating((nutrindicator$boron),"boron")
newmap$sulfur=fertilityRating((nutrindicator$sulfur),"sulfur")
newmap$soc=NULL
newmapT1=newmap@data
valuT=as.matrix(newmapT1)
data("nutrient")
nutriens=comparisonTable(nutrient)

newmapT1$fertility=suitability(nutrient, valuT)
newmap@data$fertility=newmapT1$fertility
newmap$fertilityclass2=classCode(newmap$fertility,"fertility")
spplot(newmap["fertility"])
summary(newmap$fertilityclass2)

Description

This function determines the suitability class to which a given indicator value falls based on the crop requirement

Usage

suitabilityClass(value,crop,factor)

Arguments

Value

The output is rainfall suitability class for the crop. The output is integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

This function assumes rainfall as the source of water for crop development. The input slope value must be in degrees

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

suitability,slopeSuit, tempSuit

Examples

library(sp)
library(raster)
suitabilityClass(20.14,"saffron","slope")
slope=suitabinput["slope"]
slope$tea=slopeSuit(slope$slope,"tea")
slope$saffron=suitabilityClass(slope$slope,"saffron","slope")
summary(slope$saffron)
spplot(slope["tea"], main="Slope suitability for tea")
spplot(slope["saffron"], main="Slope suitability for saffron")

Description

A grid stack map of nine variables for assessing crop suitabilities

Usage

data("suitabinput")

Format

The format is: Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots ..@ data :'data.frame': 16900 obs. of 12 variables: .. ..$ cac03 : num [1:16900] 21.8 20.6 21.2 22 22.3 ... .. ..$ ec : num [1:16900] 2.5 2.38 2.15 2.36 2.24 ... .. ..$ depthcodes: num [1:16900] 3 1 3 3 3 3 3 3 1 1 ... .. ..$ rain : num [1:16900] 282 279 260 279 276 ... .. ..$ texture : int [1:16900] 5 5 5 5 5 5 5 5 11 11 ... .. ..$ dem : num [1:16900] 489 489 489 485 487 ... .. ..$ drainage : int [1:16900] 2 5 2 2 2 5 7 5 5 5 ... .. ..$ stones : num [1:16900] 6 9 6 6 6 6 6 9 9 9 ... .. ..$ structure : int [1:16900] 3 8 7 5 5 5 7 5 9 9 ... .. ..$ ph : num [1:16900] 8.76 8.83 8.73 8.71 8.69 ... .. ..$ slope : num [1:16900] 0.969 0.969 0.969 0.969 0.969 ... .. ..$ texture1 : Factor w/ 3 levels "Lo","SaLo","SiLo": 2 2 ... ..@ grid :Formal class 'GridTopology' [package "sp"] with 3 slots .. .. ..@ cellcentre.offset: Named num [1:2] 383216 3341506 .. .. .. ..- attr(*, "names")= chr [1:2] "x" "y" .. .. ..@ cellsize : num [1:2] 357 357 .. .. ..@ cells.dim : int [1:2] 130 130 ..@ bbox : num [1:2, 1:2] 383038 3341327 429478 3387767 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "x" "y" .. .. ..$ : chr [1:2] "min" "max" ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot .. .. ..@ projargs: chr "+proj=utm +zone=41 +datum=WGS84 +units=m +no_defs"

Examples

data(suitabinput)
summary(suitabinput$depthcodes)
hist(suitabinput$dem)

Description

This function uses stratified random sampling to generate georeferenced locations for monitoring soil conditions

Usage

surveyPoints(soilmap,scorpan,conditionclass,mapproportion)

Arguments

Details

The number of scorpan factors can be assumed but need to be with respect to the soil forming factors.The maximum possible number of factors is 5 irrespective of number of layers in each factor while the minimum number is 1.The soil condition class is the class code in the map which is to be targeted

Value

A spatial points dataframe with projection similar to the soil condition map projection

Author(s)

Christian Thine Omuto

See Also

featureRep, imageIndices, pedoTransfer, classCode

Examples

library(sp)
library(raster)
ec=suitabinput["ec"]
ph=suitabinput["ph"]
soc=nutrindicator["soc"]
clay=textureinput["clay"]
texture=suitabinput["texture"]
newmap=ec
newmap$ph=ph$ph
newmap$ECe=ECconversion1(ec$ec*0.1,texture$texture,"FAO","1:5", soc$soc,clay$clay)
newmap$salt=saltSeverity(newmap$ECe,newmap$ph,0.84,"FAO")
newmap$salineclass=classCode(newmap$salt,"saltseverity")
newmap$salineclass1=as.factor(newmap$salineclass)
spplot(newmap["salineclass"], main="Salinity Code")
summary(newmap$salt)
summary(newmap$salineclass)
salt=raster(newmap["salt"])
salt1=newmap["salt"]
n_points=surveyPoints(salt1,4,11,80)
length(n_points$new)
spplot(salt1, scales=list(draw=TRUE),sp.layout=list("sp.points",n_points,pch=8,col="cyan"))
spplot(salt, scales=list(draw=TRUE),sp.layout=list("sp.points",n_points,pch=8,col="cyan"))

Description

This function determines the suitability classes for temperature requirements of selected agricultural crops and forest trees

Usage

tempSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of temperature in degrees Celsius

Value

The output is temperature suitability class for the crop. The output is integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

carbonSuit, depthSuite, fertilitySuit

Examples

tempgrape=tempSuit(23.5,"grape")
summary(tempgrape)

Description

Sample dataset for assessing soil texture

Usage

data("textureinput")

Format

The format is: Formal class 'SpatialGridDataFrame' [package "sp"] with 4 slots ..@ data :'data.frame': 16900 obs. of 3 variables: .. ..$ sand: num [1:16900] 61.5 59.8 60.6 58.2 59.1 ... .. ..$ clay: num [1:16900] 12.6 13.9 14.1 13.8 13.8 ... .. ..$ silt: num [1:16900] 25 26.9 25.3 28 26.9 ... ..@ grid :Formal class 'GridTopology' [package "sp"] with 3 slots .. .. ..@ cellcentre.offset: Named num [1:2] 383216 3341506 .. .. .. ..- attr(*, "names")= chr [1:2] "x" "y" .. .. ..@ cellsize : num [1:2] 357 357 .. .. ..@ cells.dim : int [1:2] 130 130 ..@ bbox : num [1:2, 1:2] 383038 3341327 429478 3387767 .. ..- attr(*, "dimnames")=List of 2 .. .. ..$ : chr [1:2] "x" "y" .. .. ..$ : chr [1:2] "min" "max" ..@ proj4string:Formal class 'CRS' [package "sp"] with 1 slot .. .. ..@ projargs: chr "+proj=utm +zone=41 +datum=WGS84 +units=m +no_defs"

Examples

data(textureinput)
summary(textureinput)

Description

This function determines the suitability classes for texture requirements of selected agricultural crops and forest trees

Usage

textureSuit(value, crop)

Arguments

Details

The input value can be map or just a numerical entry of textural class code. The textural class code is obtained using classCode("texture")

Value

The output is texture suitability class for the crop. The output is integer value for suitability class: 1- highly suitable; 2 - moderately suitable; 3 - marginally suitable; 4 - currently not suitable; 5 - not suitable

Note

If the input value is raster map, then the output will also be a raster map of texture suitability for the crop of interest

Author(s)

Christian Thine Omuto

References

Sys, C., Van Ranst, E., Debaveye, J. and Beerneaert, F.1993. Land evaluation: Part III: Crop requirements. Development Cooperation, Belgium.

Naidu, L.G.K., Ramamurthy, V., Challa O., Hegde, R. and Krishnan, P. 2006. Manual, Soil-site Suitability Criteria for Major Crops, National Bureau of Soil Survey and Land Use Planning, ICAR, Nagpur, India

FAO Crop Suitability Requirements: http://ecocrop.fao.org/ecocrop/srv/en/home

See Also

tempSuit, PHSuit, rainSuit

Examples

library(sp)
textureSuit(4,"mango")
texture=suitabinput["texture"]
texture$mango=textureSuit(texture$texture,"mango")
summary(texture$mango)
spplot(texture["mango"])