diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 69c8d00b6cafb1d8471f1e2a45c946145dfeff59..ac6ef0b8155271286a47f49f5adb5bc5bbaac610 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -676,8 +676,11 @@ class PlotSectorialSkillScore(AbstractPlotClass):  # pragma: no cover
         :return:
         """
         limit = 5
-        lower = np.max([-limit, np.min([0, helpers.float_round(data["data"].min(), 2) - 0.1])])
-        upper = np.min([limit, helpers.float_round(data["data"].max(), 2) + 0.1])
+        try:
+            lower = np.max([-limit, np.min([0, helpers.float_round(data["data"].min(), 2) - 0.1])])
+            upper = np.min([limit, helpers.float_round(data["data"].max(), 2) + 0.1])
+        except ValueError:
+            lower, upper = (-limit, limit)
         return lower, upper
 
 
@@ -765,7 +768,7 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass):  # pragma: no cover
 
     def _set_title(self, model_name, branch=None, n_branches=None):
         title_d = {"single input": "Single Inputs", "branch": "Input Branches", "variable": "Variables",
-                   "group_of_variables_sector": "grouped variables by sector",
+                       "group_of_variables_sector": "grouped variables by sector",
                    "group_of_variables_var_in_sectors": "grouped variables across sectors"}
         base_title = f"{model_name}\nImportance of {title_d[self._boot_type]}"
 
@@ -1083,15 +1086,17 @@ class PlotFeatureImportanceSkillScore(AbstractPlotClass):  # pragma: no cover
 
         #>>>>>>> c1f8954cc667698400527de794aaf928bcfbdefb
         plot_data = self._data if branch is None else self._data[self._data["branch"] == str(branch)]
+        alphabetical_order = sorted(plot_data[self._x_name].unique().tolist(), key=str.casefold)
         if self._boot_type == "branch":
             fig, ax = plt.subplots(figsize=(0.5 + 2 / len(plot_data[self._x_name].unique()) + len(plot_data[self._x_name].unique()),4))
             sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1.,
                         palette="Blues_r", showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
-                        showfliers=False, width=0.8)
+                        showfliers=False, width=0.8, order=alphabetical_order)
         else:
             fig, ax = plt.subplots()
             sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1.5, palette="Blues_r",
-                        showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, showfliers=False)
+                        showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, showfliers=False,
+                        order=alphabetical_order)
         ax.axhline(y=0, color="grey", linewidth=.5)
         #<<<<<<< HEAD
         #=======