diff --git a/R/utils-scenario.R b/R/utils-scenario.R
index 1448aa8c..7cb489ad 100644
--- a/R/utils-scenario.R
+++ b/R/utils-scenario.R
@@ -334,7 +334,7 @@ raster_to_stars <- function(obj){
     dims$time <- stars:::create_dimension(values = times[i])
     o <- stars::st_redimension(o,new_dims = dims)
 
-    new_env[[names(obj)[i]]] <- o
+    new_env[[paste0(names(obj)[i],"__",i)]] <- o
   }
 
   new_env <- do.call(c, new_env)
diff --git a/vignettes/articles/02_train_simple_model.Rmd b/vignettes/articles/02_train_simple_model.Rmd
index 1fa702f9..068ff9b9 100644
--- a/vignettes/articles/02_train_simple_model.Rmd
+++ b/vignettes/articles/02_train_simple_model.Rmd
@@ -299,7 +299,7 @@ x <- distribution(background) |>
   add_predictors(predictors, transform = 'scale', derivates = "none") |>
   # Since we are adding the koeppen layer as zonal layer, we disgard it from the predictors
   rm_predictors("koeppen_50km") |> 
-  add_control_extrapolation(layer = predictors$koeppen_50km, method = "zones") |> 
+  add_limits_extrapolation(layer = predictors$koeppen_50km, method = "zones") |> 
   engine_xgboost(iter = 3000, learning_rate = 0.01)
 
 # Spatially limited prediction