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.

+ +

Usage

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:      3.3 
+#> mkin version used for pre-fitting:  1.2.10 
+#> R version used for fitting:         4.4.2 
+#> Date of fit:     Fri Feb 14 07:29:22 2025 
+#> Date of summary: Fri Feb 14 07:29:22 2025 
+#> 
+#> 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 295.57 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          88.4862 84.1127 92.8598
+#> log_k_DMTA      -3.0512 -3.5674 -2.5351
+#> log_k_M23       -4.0576 -4.9013 -3.2139
+#> log_k_M27       -3.8584 -4.2572 -3.4595
+#> log_k_M31       -3.9779 -4.4844 -3.4714
+#> f_DMTA_ilr_1     0.1264 -0.2186  0.4714
+#> f_DMTA_ilr_2     0.1509 -0.2547  0.5565
+#> f_DMTA_ilr_3    -1.3891 -1.6962 -1.0819
+#> a.1              0.9196  0.8307  1.0085
+#> b.1              0.1377  0.1205  0.1549
+#> SD.DMTA_0        3.5956 -0.8167  8.0078
+#> SD.log_k_DMTA    0.6437  0.2784  1.0091
+#> SD.log_k_M23     0.9929  0.3719  1.6139
+#> SD.log_k_M27     0.4530  0.1522  0.7537
+#> SD.log_k_M31     0.5773  0.1952  0.9595
+#> SD.f_DMTA_ilr_1  0.4063  0.1505  0.6621
+#> SD.f_DMTA_ilr_2  0.4800  0.1817  0.7783
+#> SD.f_DMTA_ilr_3  0.3582  0.1350  0.5814
+#> 
+#> Correlation: 
+#>              DMTA_0  l__DMTA lg__M23 lg__M27 lg__M31 f_DMTA__1 f_DMTA__2
+#> log_k_DMTA    0.0306                                                    
+#> log_k_M23    -0.0234 -0.0032                                            
+#> log_k_M27    -0.0380 -0.0049  0.0041                                    
+#> log_k_M31    -0.0247 -0.0031  0.0022  0.0817                            
+#> f_DMTA_ilr_1 -0.0046 -0.0006  0.0425 -0.0438  0.0319                    
+#> f_DMTA_ilr_2 -0.0008 -0.0002  0.0216 -0.0267 -0.0890 -0.0349            
+#> f_DMTA_ilr_3 -0.1805 -0.0136  0.0434  0.0791  0.0390 -0.0061    0.0053  
+#> 
+#> Random effects:
+#>                   est.   lower  upper
+#> SD.DMTA_0       3.5956 -0.8167 8.0078
+#> SD.log_k_DMTA   0.6437  0.2784 1.0091
+#> SD.log_k_M23    0.9929  0.3719 1.6139
+#> SD.log_k_M27    0.4530  0.1522 0.7537
+#> SD.log_k_M31    0.5773  0.1952 0.9595
+#> SD.f_DMTA_ilr_1 0.4063  0.1505 0.6621
+#> SD.f_DMTA_ilr_2 0.4800  0.1817 0.7783
+#> SD.f_DMTA_ilr_3 0.3582  0.1350 0.5814
+#> 
+#> Variance model:
+#>       est.  lower  upper
+#> a.1 0.9196 0.8307 1.0085
+#> b.1 0.1377 0.1205 0.1549
+#> 
+#> Backtransformed parameters:
+#>                   est.     lower    upper
+#> DMTA_0        88.48621 84.112654 92.85977
+#> k_DMTA         0.04730  0.028230  0.07926
+#> k_M23          0.01729  0.007437  0.04020
+#> k_M27          0.02110  0.014162  0.03144
+#> k_M31          0.01872  0.011283  0.03107
+#> f_DMTA_to_M23  0.14551        NA       NA
+#> f_DMTA_to_M27  0.12169        NA       NA
+#> f_DMTA_to_M31  0.11062        NA       NA
+#> 
+#> Resulting formation fractions:
+#>               ff
+#> DMTA_M23  0.1455
+#> DMTA_M27  0.1217
+#> DMTA_M31  0.1106
+#> DMTA_sink 0.6222
+#> 
+#> Estimated disappearance times:
+#>       DT50   DT90
+#> DMTA 14.65  48.68
+#> M23  40.09 133.17
+#> M27  32.85 109.11
+#> M31  37.02 122.97
+# 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)
+# }
+