diff --git a/tests/unit/test_conversion.py b/tests/unit/test_conversion.py
index f5abc129..b0cd2929 100644
--- a/tests/unit/test_conversion.py
+++ b/tests/unit/test_conversion.py
@@ -30,20 +30,164 @@ def test_conversion_from_FAO_to_IPCC2006_PRIMAP():
     # drop UNFCCC data
     ds = ds.drop_sel(source="UNFCCC")
 
-    # We need a comversion CSV file for each entity
-    # agg_info_fao = {
-    #     "category (FAOSTAT)" : {
-    #         "4" : {
-    #             "sources" : [
-    #                 "4.A",
-    #                 "4.B",
-    #             ],
-    #             "sel": {"variable": ["CO2"]},
-    #         },
-    #     }
-    # }
-    # ds_checked = ds.pr.add_aggregates_coordinates(agg_info=agg_info_fao)
+    # Checking consistency of category tree in FAO categorisation
+    # There are discrepancies of up to 100% due to rounding errors for small values
+    # theoretical example, 0.0001 (rounded from 0.00006) + 0.0004 (rounded from 0.00036)
+    # = 0.00042 which is then rounded to 0.0004, while the consistency check expects 0.0005
+    agg_info_fao = {
+        "category (FAOSTAT)": {
+            "1": {
+                "tolerance": 0.01,
+                "sources": [
+                    "1.A",
+                    "1.B",
+                ],
+                "sel": {"variable": ["N2O", "CH4"]},
+            },
+            # 1.A.1 wheat
+            # rounding errors
+            "1.A.1.a": {
+                "tolerance": 1,
+                "sources": [
+                    "1.A.1.a.i",
+                    "1.A.1.a.ii",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            "1.A.1": {
+                "tolerance": 0.01,
+                "sources": [
+                    "1.A.1.a",
+                    "1.A.1.b",
+                ],
+                "sel": {"variable": ["N2O", "CH4"]},
+            },
+            # 1.A.2 rice
+            # rounding errors
+            "1.A.2.a": {
+                "tolerance": 1,
+                "sources": [
+                    "1.A.2.a.i",
+                    "1.A.2.a.ii",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            "1.A.2": {
+                "tolerance": 0.01,
+                "sources": [
+                    "1.A.2.a",
+                    "1.A.2.b",
+                    "1.A.2.c",  # rice cultivation CH4
+                ],
+                "sel": {"variable": ["N2O", "CH4"]},
+            },
+            # "1.A" : {
+            #     "tolerance" : 0.01,
+            #     "sources" : [
+            #         "1.A.1",
+            #         "1.A.2",
+            #         "1.A.3",
+            #         "1.A.4",
+            #         "1.A.5",
+            #         "1.A.6",
+            #         "1.A.7",
+            #         "1.A.8",
+            #         "1.A.9",
+            #         "1.A.10",
+            #         "1.A.10",
+            #     ],
+            # },
+            # potatoes
+            "1.A.3.a": {
+                "tolerance": 1,
+                "sources": [
+                    "1.A.3.a.i",
+                    "1.A.3.a.ii",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            "1.A.3": {
+                "tolerance": 0.01,
+                "sources": [
+                    "1.A.3.a",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            # millet
+            "1.A.4.a": {
+                "tolerance": 1,
+                "sources": [
+                    "1.A.4.a.i",
+                    "1.A.4.a.ii",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            "1.A.4": {
+                "tolerance": 0.01,
+                "sources": [
+                    "1.A.4.a",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            # barley
+            "1.A.5.a": {
+                "tolerance": 1,
+                "sources": [
+                    "1.A.5.a.i",
+                    "1.A.5.a.ii",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            "1.A.5": {
+                "tolerance": 0.01,
+                "sources": [
+                    "1.A.5.a",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            # barley
+            "1.A.6.a": {
+                "tolerance": 1,
+                "sources": [
+                    "1.A.6.a.i",
+                    "1.A.6.a.ii",
+                ],
+                "sel": {"variable": ["N2O"]},
+            },
+            "1.A.6": {
+                "tolerance": 0.01,
+                "sources": [
+                    "1.A.6.a",
+                    "1.A.6.b",
+                ],
+                "sel": {"variable": ["N2O", "CH4"]},
+            },
+            "4": {
+                "tolerance": 0.01,
+                "sources": [
+                    "4.A",
+                    "4.B",
+                ],
+                "sel": {"variable": ["CO2"]},
+            },
+            "6.B": {
+                # rounding errors, NLD looks problematic but hard to tell which value is right
+                "tolerance": 1,
+                "sources": [
+                    "6.B.1",
+                    "6.B.2",
+                    "6.B.3",
+                    "6.B.4",
+                    "6.B.5",
+                ],
+                "sel": {"variable": ["CH4", "N2O", "CO2"]},
+            },
+        }
+    }
+    ds_checked = ds.pr.add_aggregates_coordinates(agg_info=agg_info_fao)  # noqa: F841
 
+    # ds_checked_if = ds_checked.pr.to_interchange_format()
+    # We need a comversion CSV file for each entity
     # That's a temporary workaround until convert function can filter for data variables (entities)
     conv = {}
     gases = ["CO2", "CH4", "N2O"]