alfam2: Predict Ammonia Emission from Field-Applied Manure

In sashahafner/ALFAM2: Model on Ammonia Emission from Field-Applied Manure

Predict Ammonia Emission from Field-Applied Manure

Description

An implementation of the ALFAM2 model for predicting ammonia emission from field-applied manure. The model is described in Hafner et al. (2019).

Usage

alfam2(
  dat, 
  pars = ALFAM2::alfam2pars02, 
  add.pars = NULL,
  app.name = 'TAN.app', 
  time.name = 'ct', 
  time.incorp = NULL, 
  group = NULL, 
  center = TRUE, 
  cmns = c(app.rate  = 40, 
           man.dm    =  6.0, 
           man.tan   =  1.2, 
           man.ph    =  7.5, 
           air.temp  = 13, 
           wind.2m   =  2.7, 
           wind.sqrt =  sqrt(2.7), 
           crop.z    = 10), 
  check.NA = TRUE, 
  pass.col = NULL, 
  incorp.names = c('incorp', 'deep', 'shallow'),
  add.incorp.rows = FALSE, 
  prep = FALSE,
  warn = TRUE,
  parallel = FALSE, 
  n.cpus = 1,
  ...)

Arguments

dat	data frame containing values for predictor variables
pars	A numeric vector (or a list of vectors) with model parameters (secondary parameters). Two parameter sets are provided with the package: pars01 and pars02, with pars02 recommended. The earlier set is described in Hafner et al. (2019). Note that the function could be called with pars = alfam2pars02 (omitting the ALFAM2:: bit) but for clarity and safety, and to avoid a package check problem, the package name is included by default. See details for more information.
add.pars	Additional parameter values that will extend or overwrite the pars.
app.name	name of column in dat that contains total ammonia nitrogen (TAN) application rate (usually kg/ha)
time.name	name of column in dat that contains cumulative time since manure was applied (h)
time.incorp	either name of column in dat that contains time at which incorporation occurred (h), or length-one numeric value giving time that incorporation occurred (h). Omit if there was no incorporation or if incorporation is not a predictor variable. Optional.
group	name of column in dat that contains a grouping variable for identifying individual plots or locations. Optional.
center	should numeric predictors be centered? Default parameters are based on centered values.
cmns	numeric vector with means for centering.
check.NA	should the function check for NA values in calculation of primary parameters? Usually makes sense to leave as TRUE.
pass.col	character vector with name(s) of column(s) in dat that should be passed through to the returned data frame.
incorp.names	character vector with name(s) of column(s) in dat that contain binary incorporation variables.
add.incorp.rows	function will add additional rows that exactly match the incorporation time(s) (no more than one per level of group) if they are not already present. Should these be returned or left out (default)? Length one logical vector.
prep	set to TRUE to automatically prepare dummy variables from input data. If FALSE (default), any necessary dummay variables must already be present in dat. See vignette.
warn	set to FALSE to suppress some warnings and messages.
parallel	should parallel processing be used? Helpful for very large dat.
n.cpus	numeric. Defines the number of cores used if parallel is TRUE.
...	additional optional arguments as length-one vectors that set values of fixed predictor variables. See examples.

Details

Parameter sets are provided for convenience. Both are numeric vectors. Set 1 (alfam2pars01) (Hafner et al., 2019) contains the following: c(int.f0 = -0.7364889, int.r1 = -1.1785848, int.r2 = -0.9543731, int.r3 = -2.9012937, app.mthd.os.f0 = -1.1717859, app.rate.f0 = -0.0134681, man.dm.f0 = 0.407466, incorp.deep.f4 = -3.6477259, incorp.shallow.f4 = -0.4121023, app.mthd.bc.r1 = 0.6283396, man.dm.r1 = -0.075822, air.temp.r1 = 0.0492777, wind.2m.r1 = 0.0486651, man.ph.r1 = 0.5327231, air.temp.r3 = 0.0152419, incorp.deep.r3 = -0.3838862, app.mthd.os.r3 = -0.122883, man.ph.r3 = 0.2663616, rain.rate.r2 = 0.4327281, rain.cum.r3 = -0.0300936)

For Set 2, some parameters were removed, and others added, and all values changed at least slightly. Calculation of this set is described in a report on Danish emission factors (Hafner et al., 2021). c(int.f0 = -0.605683, app.mthd.os.f0 = -1.74351, app.rate.ni.f0 = -0.0111490, man.dm.f0 = 0.399670, man.source.pig.f0 = -0.592028, app.mthd.cs.f0 = -7.63373, int.r1 = -0.939215, app.mthd.bc.r1 = 0.793524, man.dm.r1 = -0.139881, air.temp.r1 = 0.0735427, wind.2m.r1 = 0.150267, app.mthd.ts.r1 = -0.459071, ts.cereal.hght.r1 = -0.244712, man.ph.r1 = 0.665, int.r2 = -1.79919, rain.rate.r2 = 0.394022, int.r3 = -3.22841, app.mthd.bc.r3 = 0.561540, app.mthd.cs.r3 = -0.666474, man.ph.r3 = 0.238, incorp.shallow.f4 = -0.964967, incorp.shallow.r3 = -0.580527, incorp.deep.f4 = -3.69495, incorp.deep.r3 = -1.26570)

Value

A data frame with the same number of rows as dat (unless add.incorp.rows = TRUE). Deafult columns are:

ct

cumulative time since manure was applied

dt

interval duration (time step)

f0

f0 parameter

r1

r1 parameter

r2

r2 parameter

r3

r3 parameter

f4

r4 parameter

f

fast pool size at ct (kg/ha)

s

slow pool size at ct (kg/ha)

j

average NH3 flux in interval (kg/ha-h)

ei

interval emission (kg/ha)

e

cumulative emission (from time = 0 to ct) (kg/ha)

er

relative cumulative emission (fraction of applied TAN)

Note

ALFAM2mod() is an alias for alfam2. The parameter sets also have aliases with capital ALFAM2.

Author(s)

Sasha D. Hafner

References

Hafner, S.D., Pacholski, A., Bittman, S., Carozzi, M., Chantigny, M., Genermont, S., Haeni, C., Hansen, M., Huijsmans, J., Kupper, T., Misselbrook, T., Neftel, A., Nyord, T., Sommer, S. 2019. A flexible semi-empirical model for estimating ammonia volatilization from field-applied slurry. Atmospheric Environment 199 474-484.

Hafner, S.D., Nyord, T., Sommer, S.G., Adamsen, A.P.S. 2021. Estimation of Danish emission factors for ammonia from field-applied liquid manure for 1980 to 2019. Danish Centre for Food and Agriculture, Aarhus University, Aarhus, Denmark. Report no. 2021-0251862. https://pure.au.dk/portal/files/223538048/EFreport23092021.pdf

The AlFAM2 project website. https://projects.au.dk/alfam/

Examples

# Example 1
# Create predictor variable data
dat1 <- data.frame(ctime = 0:12*4, TAN.app = 100, man.dm = 8, air.temp = 15, app.mthd.bc = TRUE)

# Run model, using default parameter values
pred1 <- alfam2(dat1, app.name = 'TAN.app', time.name = 'ctime')
pred1
plot(e ~ ctime, data = pred1, type = 'o')

# For fixed variables (constant over time), they can be given as optional argument.
dat1b <- data.frame(ctime = 0:12*4)

# Run model, using default parameter values
pred1b <- alfam2(dat1b, app.name = 'TAN.app', time.name = 'ctime', TAN.app = 100, 
                    man.dm = 8, air.temp = 15, app.mthd.bc = TRUE)
all.equal(pred1, pred1b)


# Example 2
# Add incorporation (can occur at any time)
dat2 <- dat1
dat2$incorp.deep <- TRUE
dat2$t.incorp <- 4

pred2 <- alfam2(dat2, app.name = 'TAN.app', time.name = 'ctime', time.incorp = 't.incorp')
# See change in r3
pred2
lines(e ~ ctime, data = pred2, type = 'o', col = 'red')

# Example 3
# Time step doesn't matter
dat3 <- data.frame(ctime = c(0, 48), TAN.app = 100, man.dm = 8, air.temp = 15, app.mthd.bc = TRUE)
pred3 <- alfam2(dat3, app.name = 'TAN.app', time.name = 'ctime')
lines(e ~ ctime, data = pred3, type = 'o', col = 'blue')

# Example 4
# Incorporation does not need to occur at end of interval
dat4 <- dat3
dat4$incorp.deep <- TRUE
dat4$t.incorp <- 4
pred4 <- alfam2(dat4, app.name = 'TAN.app', time.name = 'ctime', time.incorp = 't.incorp')
lines(e ~ ctime, data = pred4, type = 'o', col = 'orange')

# Incorporation time can be numeric also (not very practical for groups)
alfam2(dat4, app.name = 'TAN.app', time.name = 'ctime', time.incorp = 4)

# To see incorporation time in output, use add.incorp.rows
alfam2(dat4, app.name = 'TAN.app', time.name = 'ctime', time.incorp = 4, add.incorp.rows = TRUE)

# Example 5
# Function accepts multiple groups
dat5 <- data.frame(field.plot = 1:5, ctime = 48, TAN.app = 100, man.dm = 5, air.temp = 15, 
                   app.mthd.bc = TRUE, t.incorp = 4)
pred5 <- alfam2(dat5, app.name = 'TAN.app', time.name = 'ctime', group = 'field.plot', 
                   time.incorp = 't.incorp')
pred5

# See vignette for more examples. Run:
# vignette("ALFAM2-start")

sashahafner/ALFAM2 documentation built on Jan. 31, 2024, 11:16 a.m.