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diff --git a/vignettes/references.bib b/vignettes/references.bib
index f7eb4692..55bd483c 100644
--- a/vignettes/references.bib
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@@ -133,6 +133,19 @@
 }
 
 
+@Article{ranke2021,
+  AUTHOR = {Ranke, Johannes and Wöltjen, Janina and Schmidt, Jana and Comets, Emmanuelle},
+  TITLE = {Taking Kinetic Evaluations of Degradation Data to the Next Level with Nonlinear Mixed-Effects Models},
+  JOURNAL = {Environments},
+  VOLUME = {8},
+  YEAR = {2021},
+  NUMBER = {8},
+  ARTICLE-NUMBER = {71},
+  URL = {https://www.mdpi.com/2076-3298/8/8/71},
+  ISSN = {2076-3298},
+  ABSTRACT = {When data on the degradation of a chemical substance have been collected in a number of environmental media (e.g., in different soils), two strategies can be followed for data evaluation. Currently, each individual dataset is evaluated separately, and representative degradation parameters are obtained by calculating averages of the kinetic parameters. However, such averages often take on unrealistic values if certain degradation parameters are ill-defined in some of the datasets. Moreover, the most appropriate degradation model is selected for each individual dataset, which is time consuming and then requires workarounds for averaging parameters from different models. Therefore, a simultaneous evaluation of all available data is desirable. If the environmental media are viewed as random samples from a population, an advanced strategy based on assumptions about the statistical distribution of the kinetic parameters across the population can be used. Here, we show the advantages of such simultaneous evaluations based on nonlinear mixed-effects models that incorporate such assumptions in the evaluation process. The advantages of this approach are demonstrated using synthetically generated data with known statistical properties and using publicly available experimental degradation data on two pesticidal active substances.},
+  DOI = {10.3390/environments8080071}
+}
 
 @Article{gao11,
   Title                    = {Improving uncertainty analysis in kinetic evaluations using iteratively reweighted least squares},