diff --git a/src/scripts/find_dadis_local_ids.py b/src/scripts/find_dadis_local_ids.py
index 8ee1168..832e3de 100644
--- a/src/scripts/find_dadis_local_ids.py
+++ b/src/scripts/find_dadis_local_ids.py
@@ -45,6 +45,7 @@ def full_local_match_workflow(
                 "dadis_update_date",
             ],
         )
+        matched_breeds = clean_output(matched_breeds)
         matched_breeds.to_csv(temp_out, sep="\t", index=False, header=False)
         temp_out.close()
         logger.info("Output written to temp file.")
@@ -61,8 +62,17 @@ def full_local_match_workflow(
 
 def read_vbo_data(filename: str) -> pd.DataFrame:
     vbo_breeds = pd.read_table(
-        filename, sep="\t", skiprows=[1], low_memory=False
-    ).convert_dtypes()
+        filename,
+        sep="\t",
+        skiprows=[1],
+        dtype={"obsolete": str, "description_of_origin": str},
+        na_values=[],
+        low_memory=False
+    ).convert_dtypes(
+        infer_objects=False,
+        convert_string=False,
+        convert_boolean=False
+    )
     return vbo_breeds
 
 
@@ -155,6 +165,18 @@ def write_tsv_header(
             csv_out.writerow(header)
 
 
+def clean_output(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Clean the dataframe before writing
+
+    * Convert any None values to empty strings
+    """
+    string_columns = df.select_dtypes(include="object").columns
+    for column in string_columns:
+        df[column] = df[column].fillna("")
+    return df
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Find DADIS entries matching VBO breeds"
diff --git a/src/scripts/find_dadis_transboundary_ids.py b/src/scripts/find_dadis_transboundary_ids.py
index be99db3..4af3293 100644
--- a/src/scripts/find_dadis_transboundary_ids.py
+++ b/src/scripts/find_dadis_transboundary_ids.py
@@ -38,6 +38,7 @@ def full_matching_workflow(
             output_file=temp_out,
             extra_cols=["dadis_transboundary_id"],
         )
+        matched_breeds = clean_output(matched_breeds)
         # Write the actual data below the headers
         matched_breeds.to_csv(temp_out, sep="\t", index=False, header=False)
         temp_out.close()
@@ -48,7 +49,16 @@ def full_matching_workflow(
 
 
 def read_vbo_data(filename: str) -> pd.DataFrame:
-    df = pd.read_table(filename, skiprows=[1]).convert_dtypes()
+    df = pd.read_table(
+        filename,
+        skiprows=[1],
+        na_values=[],
+        dtype={"obsolete": str},
+    ).convert_dtypes(
+        infer_objects=False,
+        convert_string=False,
+        convert_boolean=False
+    )
     return df
 
 
@@ -215,6 +225,18 @@ def write_tsv_header(
             csv_out.writerow(header)
 
 
+def clean_output(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Clean the dataframe before writing
+
+    * Convert any None values to empty strings
+    """
+    string_columns = df.select_dtypes(include="object").columns
+    for column in string_columns:
+        df[column] = df[column].fillna("")
+    return df
+
+
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description="Find DADIS entries matching VBO breeds"