Aerobic soil degradation data on dimethenamid and dimethenamid-P from the EU assessment in 2018

- - -

The datasets were extracted from the active substance evaluation dossier -published by EFSA. Kinetic evaluations shown for these datasets are intended -to illustrate and advance kinetic modelling. The fact that these data and -some results are shown here does not imply a license to use them in the -context of pesticide registrations, as the use of the data may be -constrained by data protection regulations.

- -

dimethenamid_2018

- -

Format

An mkindsg object grouping seven datasets with some meta information

Source

Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) -Renewal Assessment Report Dimethenamid-P Volume 3 - B.8 Environmental fate and behaviour -Rev. 2 - November 2017 -https://open.efsa.europa.eu/study-inventory/EFSA-Q-2014-00716

Details

The R code used to create this data object is installed with this package -in the 'dataset_generation' directory. In the code, page numbers are given for -specific pieces of information in the comments.

- -

Examples

print(dimethenamid_2018)
-#> <mkindsg> holding 7 mkinds objects
-#> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 
-#> Occurrence of observed compounds $observed_n:
-#> DMTAP   M23   M27   M31  DMTA 
-#>     3     7     7     7     4 
-#> Time normalisation factors $f_time_norm:
-#> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938
-#> Meta information $meta:
-#>                      study  usda_soil_type study_moisture_ref_type rel_moisture
-#> Calke        Unsworth 2014      Sandy loam                     pF2         1.00
-#> Borstel  Staudenmaier 2009            Sand                     pF1         0.50
-#> Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75
-#> Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75
-#> Flaach          König 1996 Sandy clay loam                     pF1         0.40
-#> BBA 2.2         König 1995      Loamy sand                     pF1         0.40
-#> BBA 2.3         König 1995      Sandy loam                     pF1         0.40
-#>          study_ref_moisture temperature
-#> Calke                    NA          20
-#> Borstel               23.00          20
-#> Elliot 1              33.37          23
-#> Elliot 2              33.37          23
-#> Flaach                   NA          20
-#> BBA 2.2                  NA          20
-#> BBA 2.3                  NA          20
-dmta_ds <- lapply(1:7, function(i) {
-  ds_i <- dimethenamid_2018$ds[[i]]$data
-  ds_i[ds_i$name == "DMTAP", "name"] <-  "DMTA"
-  ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i]
-  ds_i
-})
-names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title)
-dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]])
-dmta_ds[["Elliot 1"]] <- NULL
-dmta_ds[["Elliot 2"]] <- NULL
-# \dontrun{
-# We don't use DFOP for the parent compound, as this gives numerical
-# instabilities in the fits
-sfo_sfo3p <- mkinmod(
- DMTA = mkinsub("SFO", c("M23", "M27", "M31")),
- M23 = mkinsub("SFO"),
- M27 = mkinsub("SFO"),
- M31 = mkinsub("SFO", "M27", sink = FALSE),
- quiet = TRUE
-)
-dmta_sfo_sfo3p_tc <- mmkin(list("SFO-SFO3+" = sfo_sfo3p),
-  dmta_ds, error_model = "tc", quiet = TRUE)
-print(dmta_sfo_sfo3p_tc)
-#> <mmkin> object
-#> Status of individual fits:
-#> 
-#>            dataset
-#> model       Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot
-#>   SFO-SFO3+ OK    OK      OK     OK      OK      OK    
-#> 
-#> OK: No warnings
-# The default (test_log_parms = FALSE) gives an undue
-# influence of ill-defined rate constants that have
-# extremely small values:
-plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = FALSE)
-# If we disregards ill-defined rate constants, the results
-# look more plausible, but the truth is likely to be in
-# between these variants
-plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE)
-
-# We can also specify a default value for the failing
-# log parameters, to mimic FOCUS guidance
-plot(mixed(dmta_sfo_sfo3p_tc), test_log_parms = TRUE,
-  default_log_parms = log(2)/1000)
-# As these attempts are not satisfying, we use nonlinear mixed-effects models
-# f_dmta_nlme_tc <- nlme(dmta_sfo_sfo3p_tc)
-# nlme reaches maxIter = 50 without convergence
-f_dmta_saem_tc <- saem(dmta_sfo_sfo3p_tc)
-# I am commenting out the convergence plot as rendering them
-# with pkgdown fails (at least without further tweaks to the
-# graphics device used)
-#saemix::plot(f_dmta_saem_tc$so, plot.type = "convergence")
-summary(f_dmta_saem_tc)
-#> saemix version used for fitting:      3.2 
-#> mkin version used for pre-fitting:  1.2.3 
-#> R version used for fitting:         4.2.3 
-#> Date of fit:     Sun Apr 16 08:30:03 2023 
-#> Date of summary: Sun Apr 16 08:30:03 2023 
-#> 
-#> Equations:
-#> d_DMTA/dt = - k_DMTA * DMTA
-#> d_M23/dt = + f_DMTA_to_M23 * k_DMTA * DMTA - k_M23 * M23
-#> d_M27/dt = + f_DMTA_to_M27 * k_DMTA * DMTA - k_M27 * M27 + k_M31 * M31
-#> d_M31/dt = + f_DMTA_to_M31 * k_DMTA * DMTA - k_M31 * M31
-#> 
-#> Data:
-#> 563 observations of 4 variable(s) grouped in 6 datasets
-#> 
-#> Model predictions using solution type deSolve 
-#> 
-#> Fitted in 304.528 s
-#> Using 300, 100 iterations and 9 chains
-#> 
-#> Variance model: Two-component variance function 
-#> 
-#> Starting values for degradation parameters:
-#>       DMTA_0   log_k_DMTA    log_k_M23    log_k_M27    log_k_M31 f_DMTA_ilr_1 
-#>      95.5662      -2.9048      -3.8130      -4.1600      -4.1486       0.1341 
-#> f_DMTA_ilr_2 f_DMTA_ilr_3 
-#>       0.1385      -1.6700 
-#> 
-#> Fixed degradation parameter values:
-#> None
-#> 
-#> Starting values for random effects (square root of initial entries in omega):
-#>              DMTA_0 log_k_DMTA log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1
-#> DMTA_0        4.802     0.0000    0.0000     0.000    0.0000       0.0000
-#> log_k_DMTA    0.000     0.9834    0.0000     0.000    0.0000       0.0000
-#> log_k_M23     0.000     0.0000    0.6983     0.000    0.0000       0.0000
-#> log_k_M27     0.000     0.0000    0.0000     1.028    0.0000       0.0000
-#> log_k_M31     0.000     0.0000    0.0000     0.000    0.9841       0.0000
-#> f_DMTA_ilr_1  0.000     0.0000    0.0000     0.000    0.0000       0.7185
-#> f_DMTA_ilr_2  0.000     0.0000    0.0000     0.000    0.0000       0.0000
-#> f_DMTA_ilr_3  0.000     0.0000    0.0000     0.000    0.0000       0.0000
-#>              f_DMTA_ilr_2 f_DMTA_ilr_3
-#> DMTA_0             0.0000       0.0000
-#> log_k_DMTA         0.0000       0.0000
-#> log_k_M23          0.0000       0.0000
-#> log_k_M27          0.0000       0.0000
-#> log_k_M31          0.0000       0.0000
-#> f_DMTA_ilr_1       0.0000       0.0000
-#> f_DMTA_ilr_2       0.7378       0.0000
-#> f_DMTA_ilr_3       0.0000       0.4451
-#> 
-#> Starting values for error model parameters:
-#> a.1 b.1 
-#>   1   1 
-#> 
-#> Results:
-#> 
-#> Likelihood computed by importance sampling
-#>    AIC  BIC logLik
-#>   2276 2273  -1120
-#> 
-#> Optimised parameters:
-#>                    est.   lower   upper
-#> DMTA_0          88.3192 83.8656 92.7729
-#> log_k_DMTA      -3.0530 -3.5686 -2.5373
-#> log_k_M23       -4.0620 -4.9202 -3.2038
-#> log_k_M27       -3.8633 -4.2668 -3.4598
-#> log_k_M31       -3.9731 -4.4763 -3.4699
-#> f_DMTA_ilr_1     0.1346 -0.2150  0.4841
-#> f_DMTA_ilr_2     0.1449 -0.2593  0.5491
-#> f_DMTA_ilr_3    -1.3882 -1.7011 -1.0753
-#> a.1              0.9156  0.8217  1.0095
-#> b.1              0.1383  0.1216  0.1550
-#> SD.DMTA_0        3.7280 -0.6949  8.1508
-#> SD.log_k_DMTA    0.6431  0.2781  1.0080
-#> SD.log_k_M23     1.0096  0.3782  1.6409
-#> SD.log_k_M27     0.4583  0.1541  0.7625
-#> SD.log_k_M31     0.5738  0.1942  0.9533
-#> SD.f_DMTA_ilr_1  0.4119  0.1528  0.6709
-#> SD.f_DMTA_ilr_2  0.4780  0.1806  0.7754
-#> SD.f_DMTA_ilr_3  0.3657  0.1383  0.5931
-#> 
-#> Correlation: 
-#>              DMTA_0  l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2
-#> log_k_DMTA    0.0303                                                    
-#> log_k_M23    -0.0229 -0.0032                                            
-#> log_k_M27    -0.0372 -0.0049  0.0041                                    
-#> log_k_M31    -0.0245 -0.0032  0.0022  0.0815                            
-#> f_DMTA_ilr_1 -0.0046 -0.0006  0.0415 -0.0433  0.0324                    
-#> f_DMTA_ilr_2 -0.0008 -0.0002  0.0214 -0.0267 -0.0893 -0.0361            
-#> f_DMTA_ilr_3 -0.1755 -0.0135  0.0423  0.0775  0.0377 -0.0066    0.0060  
-#> 
-#> Random effects:
-#>                   est.   lower  upper
-#> SD.DMTA_0       3.7280 -0.6949 8.1508
-#> SD.log_k_DMTA   0.6431  0.2781 1.0080
-#> SD.log_k_M23    1.0096  0.3782 1.6409
-#> SD.log_k_M27    0.4583  0.1541 0.7625
-#> SD.log_k_M31    0.5738  0.1942 0.9533
-#> SD.f_DMTA_ilr_1 0.4119  0.1528 0.6709
-#> SD.f_DMTA_ilr_2 0.4780  0.1806 0.7754
-#> SD.f_DMTA_ilr_3 0.3657  0.1383 0.5931
-#> 
-#> Variance model:
-#>       est.  lower upper
-#> a.1 0.9156 0.8217 1.009
-#> b.1 0.1383 0.1216 0.155
-#> 
-#> Backtransformed parameters:
-#>                   est.     lower    upper
-#> DMTA_0        88.31924 83.865625 92.77286
-#> k_DMTA         0.04722  0.028196  0.07908
-#> k_M23          0.01721  0.007298  0.04061
-#> k_M27          0.02100  0.014027  0.03144
-#> k_M31          0.01882  0.011375  0.03112
-#> f_DMTA_to_M23  0.14608        NA       NA
-#> f_DMTA_to_M27  0.12077        NA       NA
-#> f_DMTA_to_M31  0.11123        NA       NA
-#> 
-#> Resulting formation fractions:
-#>               ff
-#> DMTA_M23  0.1461
-#> DMTA_M27  0.1208
-#> DMTA_M31  0.1112
-#> DMTA_sink 0.6219
-#> 
-#> Estimated disappearance times:
-#>       DT50   DT90
-#> DMTA 14.68  48.76
-#> M23  40.27 133.76
-#> M27  33.01 109.65
-#> M31  36.84 122.38
-# As the confidence interval for the random effects of DMTA_0
-# includes zero, we could try an alternative model without
-# such random effects
-# f_dmta_saem_tc_2 <- saem(dmta_sfo_sfo3p_tc,
-#   covariance.model = diag(c(0, rep(1, 7))))
-# saemix::plot(f_dmta_saem_tc_2$so, plot.type = "convergence")
-# This does not perform better judged by AIC and BIC
-# saemix::compare.saemix(f_dmta_saem_tc$so, f_dmta_saem_tc_2$so)
-# }
-

- -