diff --git a/R/xfaostat_L102_ProductionArea.R b/R/xfaostat_L102_ProductionArea.R
index 9de75453..3e78bcc0 100644
--- a/R/xfaostat_L102_ProductionArea.R
+++ b/R/xfaostat_L102_ProductionArea.R
@@ -23,11 +23,11 @@ module_xfaostat_L102_ProductionArea <- function(command, ...) {
       "FBSH_CBH_wide")
 
   MODULE_OUTPUTS <-
-    c("QCL_PROD",
-      "QCL_AN_LIVEANIMAL",
-      "QCL_AN_PRIMARY_MILK",
-      "QCL_AN_LIVEANIMAL_MEATEQ",
-      "QCL_CROP_PRIMARY")
+    c("L102.QCL_PROD",
+      "L102.QCL_AN_LIVEANIMAL",
+      "L102.QCL_AN_PRIMARY_MILK",
+      "L102.QCL_AN_LIVEANIMAL_MEATEQ",
+      "L102.QCL_CROP_PRIMARY")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -378,7 +378,7 @@ module_xfaostat_L102_ProductionArea <- function(command, ...) {
 
 
 
-    # Bind QCL_ALL and save RDS ----
+    # Bind L102.QCL_ALL and save RDS ----
     #*******************************************
 
     QCL_CROP_PRIMARY %>% mutate(item_set = "QCL_COMM_CROP_PRIMARY") %>%
@@ -392,10 +392,10 @@ module_xfaostat_L102_ProductionArea <- function(command, ...) {
       bind_rows(QCL_AN_LIVEANIMAL %>% mutate(item_set = "QCL_COMM_AN_LIVEANIMAL")) %>%
       bind_rows(QCL_AN_LIVEANIMAL_MEATEQ %>% mutate(item_set = "QCL_COMM_AN_LIVEANIMAL_MEATEQ")) %>%
       bind_rows(FBS_FISH %>% mutate(item_set = "FBS_COMM_FISH"))->
-      QCL_ALL
+      L102.QCL_ALL
 
 
-    QCL_ALL %>%
+    L102.QCL_ALL %>%
       filter(item_set %in% c("QCL_COMM_AN_PRIMARY_MEAT1",
                              "QCL_COMM_AN_PRIMARY_MEAT2",
                              "QCL_COMM_AN_PRIMARY_EGG",
@@ -408,60 +408,60 @@ module_xfaostat_L102_ProductionArea <- function(command, ...) {
       add_units("various") %>%
       add_comments("Detailed FAO QCL data processing for live animal and production") %>%
       add_precursors("QCL_wide", "FBS_wide", "FBSH_CBH_wide") ->
-      QCL_AN_LIVEANIMAL
+      L102.QCL_AN_LIVEANIMAL
 
     QCL_AN_PRIMARY_MILK %>%
       add_title("FAO milk animal stock and production") %>%
       add_units("various") %>%
       add_comments("Detailed FAO QCL data processing for dairy animal and production") %>%
-      same_precursors_as(QCL_AN_LIVEANIMAL) ->
-      QCL_AN_PRIMARY_MILK
+      same_precursors_as(L102.QCL_AN_LIVEANIMAL) ->
+      L102.QCL_AN_PRIMARY_MILK
 
     QCL_AN_LIVEANIMAL_MEATEQ %>%
       add_title("FAO live animal stock meat equivalent") %>%
       add_units("various") %>%
       add_comments("Detailed FAO QCL data processing for live animal stock meat equivalent") %>%
-      same_precursors_as(QCL_AN_LIVEANIMAL) ->
-      QCL_AN_LIVEANIMAL_MEATEQ
+      same_precursors_as(L102.QCL_AN_LIVEANIMAL) ->
+      L102.QCL_AN_LIVEANIMAL_MEATEQ
 
     QCL_CROP_PRIMARY %>%
       add_title("FAO primary crop area and production") %>%
       add_units("various") %>%
       add_comments("Detailed FAO QCL data processing for crop area and production") %>%
-      same_precursors_as(QCL_AN_LIVEANIMAL) ->
-      QCL_CROP_PRIMARY
+      same_precursors_as(L102.QCL_AN_LIVEANIMAL) ->
+      L102.QCL_CROP_PRIMARY
 
 
     # Production only
-    QCL_ALL %>% filter(element_code == 5510) ->
-      QCL_PROD
+    L102.QCL_ALL %>% filter(element_code == 5510) ->
+      L102.QCL_PROD
 
-    QCL_PROD %>%
+    L102.QCL_PROD %>%
       add_title("FAO primary production") %>%
       add_units("tonnes") %>%
       add_comments("FAO primary production") %>%
-      same_precursors_as(QCL_AN_LIVEANIMAL) ->
-      QCL_PROD
+      same_precursors_as(L102.QCL_AN_LIVEANIMAL) ->
+      L102.QCL_PROD
 
     # No NA
-    assertthat::assert_that(QCL_ALL %>% filter(is.na(value)) %>% nrow() == 0)
+    assertthat::assert_that(L102.QCL_ALL %>% filter(is.na(value)) %>% nrow() == 0)
 
-    #QCL_ALL %>% FF_check_count_plot -> p; p
+    #L102.QCL_ALL %>% FF_check_count_plot -> p; p
     # ggsave(file.path(DIR_DATAPROC_PLOT, "QCL_ALL.png"),
     #        plot = p + ggtitle("gcamdata-FAOSTAT (QCL & FBS) production data over time"),
     #        dpi = 200, width = 9, height = 5 )
     # rm(p)
 
-    QCL_ALL %>% distinct(year); # 53 years
-    QCL_ALL %>% distinct(element, element_code, unit) # QCL_COMM_AN_LIVEANIMAL_MEATEQ has no element_code
-    QCL_ALL %>% distinct(item)  # 158 primary crop + 45 primary an + 57 others + 17 +12
+    L102.QCL_ALL %>% distinct(year); # 53 years
+    L102.QCL_ALL %>% distinct(element, element_code, unit) # QCL_COMM_AN_LIVEANIMAL_MEATEQ has no element_code
+    L102.QCL_ALL %>% distinct(item)  # 158 primary crop + 45 primary an + 57 others + 17 +12
 
 
-    # QCL_ALL %>%
+    # L102.QCL_ALL %>%
     #   add_title("FAO crop and livestock production and crop area") %>%
     #   add_units("various") %>%
     #   add_comments("Detailed FAO QCL data processing. FBS fish data is used") ->
-    #   QCL_ALL
+    #   L102.QCL_ALL
 
 
     return_data(MODULE_OUTPUTS)
diff --git a/R/xfaostat_L103_ProducerPrices.R b/R/xfaostat_L103_ProducerPrices.R
index 3a6b4de5..8799d001 100644
--- a/R/xfaostat_L103_ProducerPrices.R
+++ b/R/xfaostat_L103_ProducerPrices.R
@@ -18,11 +18,11 @@
 module_xfaostat_L103_ProducerPrices <- function(command, ...) {
 
   MODULE_INPUTS <-
-    c("QCL_PROD",
+    c("L102.QCL_PROD",
       "PP_wide")
 
   MODULE_OUTPUTS <-
-    c("QCL_PRIMARY_PROD_PV")
+    c("L103.QCL_PRIMARY_PROD_PV")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -32,7 +32,7 @@ module_xfaostat_L103_ProducerPrices <- function(command, ...) {
 
     year <- value <- Year <- Value <- FAO_country <- iso <- NULL    # silence package check.
     PP_wide <- element_code <- element <- area_code <- item_code <- area <-
-      item <- unit <- QCL_PROD <- item_set <- GCAM_commodity <- QCL_PRIMARY_item <-
+      item <- unit <- L102.QCL_PROD <- item_set <- GCAM_commodity <- QCL_PRIMARY_item <-
       PP_item <- Production <- Prod_Value <- PP_index <- World_PP_index <-
       PP_Multiplier <- PP_item_world <- NULL
 
@@ -50,15 +50,15 @@ module_xfaostat_L103_ProducerPrices <- function(command, ...) {
 
 
     # Primary crops
-    QCL_PROD %>% filter(item_set == "QCL_COMM_CROP_PRIMARY") %>%
+    L102.QCL_PROD %>% filter(item_set == "QCL_COMM_CROP_PRIMARY") %>%
       distinct(item, item_code) -> QCL_COMM_CROP_PRIMARY
     # Primary animal products, including fat hides etc. 46
-    QCL_PROD %>% filter(grepl("AN_PRIMARY", item_set) ) %>%
+    L102.QCL_PROD %>% filter(grepl("AN_PRIMARY", item_set) ) %>%
       distinct(item, item_code) -> QCL_COMM_AN_PRIMARY
 
     QCL_COMM_CROP_PRIMARY %>%
       bind_rows(QCL_COMM_AN_PRIMARY) %>%
-      left_join(QCL_PROD, by = c("item_code", "item")) ->
+      left_join(L102.QCL_PROD, by = c("item_code", "item")) ->
       QCL_PRIMARY
 
     QCL_PRIMARY %>% distinct(item, element, item_set, unit)
@@ -129,17 +129,17 @@ module_xfaostat_L103_ProducerPrices <- function(command, ...) {
       left_join(UnitMap %>%
                   bind_rows(UnitMap %>% mutate(element = "Prod_Value", unit = "USD")),
                 by = "element") ->
-      QCL_PRIMARY_PROD_PV
+      L103.QCL_PRIMARY_PROD_PV
 
     rm(QV1, QV)
 
-    QCL_PRIMARY_PROD_PV %>%
+    L103.QCL_PRIMARY_PROD_PV %>%
       add_title("FAO crop and livestock production and crop area") %>%
       add_units("USD and tonne") %>%
       add_comments("Detailed FAO QCL data processing. FBS fish data is used") %>%
-      add_precursors("QCL_PROD",
+      add_precursors("L102.QCL_PROD",
                      "PP_wide") ->
-      QCL_PRIMARY_PROD_PV
+      L103.QCL_PRIMARY_PROD_PV
 
     return_data(MODULE_OUTPUTS)
 
diff --git a/R/xfaostat_L104_ProductionAreaAddFodder.R b/R/xfaostat_L104_ProductionAreaAddFodder.R
index 3083b0e9..bda2429b 100644
--- a/R/xfaostat_L104_ProductionAreaAddFodder.R
+++ b/R/xfaostat_L104_ProductionAreaAddFodder.R
@@ -22,7 +22,7 @@ module_xfaostat_L104_ProductionAreaAddFodder <- function(command, ...) {
       "QCL_area_code_map")
 
   MODULE_OUTPUTS <-
-    c("QCL_FODDERCROP")
+    c("L104.QCL_FODDERCROP")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -95,17 +95,17 @@ module_xfaostat_L104_ProductionAreaAddFodder <- function(command, ...) {
       # Remove area x year that should no exist
       FAOSTAT_AREA_RM_NONEXIST %>%
       left_join(UnitMap, by = "element") ->
-      QCL_FODDERCROP
+      L104.QCL_FODDERCROP
 
     rm(FAO_fodder_Prod_t_HA_ha_PRODSTAT_2011, UnitMap)
 
-    QCL_FODDERCROP %>%
+    L104.QCL_FODDERCROP %>%
       add_title("Processed fodder crop production and area") %>%
       add_units("tonne and ha") %>%
       add_comments("Data is from old GCAM data v5.4") %>%
       add_precursors(file.path(DIR_RAW_DATA_FAOSTAT, "Other_supplementary/FAO_fodder_Prod_t_HA_ha_PRODSTAT_2011"),
                      "QCL_area_code_map")->
-      QCL_FODDERCROP
+      L104.QCL_FODDERCROP
 
 
     # FAO_ag_items_PRODSTAT is not read in anymore
diff --git a/R/xfaostat_L105_DataConnectionToSUA.R b/R/xfaostat_L105_DataConnectionToSUA.R
index 183c4c02..5d740ab9 100644
--- a/R/xfaostat_L105_DataConnectionToSUA.R
+++ b/R/xfaostat_L105_DataConnectionToSUA.R
@@ -19,8 +19,8 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 
   MODULE_INPUTS <-
     c(FILE = file.path(DIR_RAW_DATA_FAOSTAT, "FAO_items"),
-      "QCL_PROD",
-      "QCL_AN_LIVEANIMAL_MEATEQ",
+      "L102.QCL_PROD",
+      "L102.QCL_AN_LIVEANIMAL_MEATEQ",
       "TCL_wide",
       "TM_bilateral_wide",
       "FBSH_CBH_wide",
@@ -28,7 +28,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
       "SCL_wide")
 
   MODULE_OUTPUTS <-
-    c("Bal_new_all")
+    c("L105.Bal_new_all")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -39,10 +39,10 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
     year <- value <- Year <- Value <- FAO_country <- iso <- NULL    # silence package check.
     SCL_wide <- element_code <- element <- area_code <- item_code <- area <-
       item <- unit <- FBS_wide <- FBSH_CBH_wide <- TCL_wide <- TM_bilateral_wide <-
-      QCL_PROD <- FAO_items <- tier <- QCL <- oil <-
+      L102.QCL_PROD <- FAO_items <- tier <- QCL <- oil <-
       cake <- SCL_item_oil <- SCL_item_cake <- cake_rate <- cake_rate_world <-
       DS_key_coproduct_item <- Production <- Import <- Export <- DS_demand <-
-      DS_production <- CoproductRate <- QCL_AN_LIVEANIMAL_MEATEQ <- `Closing stocks` <-
+      DS_production <- CoproductRate <- L102.QCL_AN_LIVEANIMAL_MEATEQ <- `Closing stocks` <-
       `Opening stocks` <- `Stock Variation` <- NULL
 
     all_data <- list(...)[[1]]
@@ -78,7 +78,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 
 
     # Get area code in QCL that is consistent with FBS e.g., after 2010 only
-    QCL_PROD %>% filter(year >= min (FAOSTAT_Hist_Year_FBS)) %>%  distinct(area_code) %>% pull ->
+    L102.QCL_PROD %>% filter(year >= min (FAOSTAT_Hist_Year_FBS)) %>%  distinct(area_code) %>% pull ->
       QCL_area_code_FBS
 
     ## 1.2. Get FAO supply-utilization SCL ready ----
@@ -161,7 +161,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
     # Merge Sudan regions to be consistent with data
     # Mainly for storage data concerns
     # And only keep data > min(FAOSTAT_Hist_Year_FBS)
-    for (.DF in c("SCL", "TCL_TM", "TCL_gross", "FBSH_CBH", "FBS", "QCL_PROD")) {
+    for (.DF in c("SCL", "TCL_TM", "TCL_gross", "FBSH_CBH", "FBS", "L102.QCL_PROD")) {
       get(.DF) %>% filter(year >= min(FAOSTAT_Hist_Year_FBS)) %>%
         # merge Sudan and South Sudan
         FAO_AREA_DISAGGREGATE_HIST_DISSOLUTION_ALL(SUDAN2012_MERGE = T) %>%
@@ -169,7 +169,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 
 
     # Update area code in QCL
-    QCL_PROD %>% filter(year %in% FAOSTAT_Hist_Year_FBS) %>%  distinct(area_code) %>% pull ->
+    L102.QCL_PROD %>% filter(year %in% FAOSTAT_Hist_Year_FBS) %>%  distinct(area_code) %>% pull ->
       QCL_area_code_FBS
 
     # 2. Create helper functions to simplify join by data set ----
@@ -214,19 +214,19 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 
       ## Start SUA_TEMPLATE_LEFT_JOIN ----
 
-      ## a. Join QCL_PROD when .DS == "QCL" ----
+      ## a. Join L102.QCL_PROD when .DS == "QCL" ----
       if (.DS == "QCL") {
 
-        # assert QCL_PROD exist
-        assertthat::assert_that(is.data.frame(QCL_PROD))
+        # assert L102.QCL_PROD exist
+        assertthat::assert_that(is.data.frame(L102.QCL_PROD))
 
         # assert items exist in joined DF
         assertthat::assert_that(.DF %>% distinct(item_code) %>% pull %>%
-                                  setdiff(QCL_PROD %>% distinct(item_code) %>% pull) %>%
+                                  setdiff(L102.QCL_PROD %>% distinct(item_code) %>% pull) %>%
                                   length() == 0 )
         # Join
         .DF %>% left_join(
-          QCL_PROD %>%
+          L102.QCL_PROD %>%
             select(area_code, item_code, element, year, QCL = value),
           by = c("area_code", "item_code", "element", "year")
         ) -> .DF1
@@ -237,7 +237,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
       ## b. Join TCL_TM when .DS == "TM" ----
       if (.DS == "TM") {
 
-        # assert QCL_PROD exist
+        # assert L102.QCL_PROD exist
         assertthat::assert_that(is.data.frame(TCL_TM))
 
         if (.DS_TM_Assert_Item == T) {
@@ -259,7 +259,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
       ## c. Join TCL_gross when .DS == "TCL_gross" ----
       if (.DS == "TCL_gross") {
 
-        # assert QCL_PROD exist
+        # assert TCL exist
         assertthat::assert_that(is.data.frame(TCL_gross))
 
         if (.DS_TM_Assert_Item == T) {
@@ -280,7 +280,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
       ## d. Join SCL when .DS == "SCL" ----
       if (.DS == "SCL") {
 
-        # assert QCL_PROD exist
+        # assert SCL exist
         assertthat::assert_that(is.data.frame(SCL))
 
         # assert items exist in joined DF
@@ -580,7 +580,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 
     ### 3.8.1 Process the live animal meat equivalent data APE_live_an_MeatEQ ----
 
-    # read in QCL_AN_LIVEANIMAL_MEATEQ live animal meat equivalent
+    # read in L102.QCL_AN_LIVEANIMAL_MEATEQ live animal meat equivalent
     # Treat live animal as stock and adjust using production or other demand
     # Milk cattle is not included
     # Note that only stock variation is used
@@ -588,7 +588,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
     # But accounting delta allows more accurate estimate of feed uses
     # E.g., additional feed demand due to animal expansion
 
-    QCL_AN_LIVEANIMAL_MEATEQ %>%
+    L102.QCL_AN_LIVEANIMAL_MEATEQ %>%
       select(area_code, item_code, year, value) %>%
       #mutate(item = gsub("Meat", "AnMeatEq", item)) %>%
       # convert units back to tonne!!! And adjust item_code
@@ -622,14 +622,14 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
       Bal_new_tier8
 
     assert_FBS_balance(.DF = Bal_new_tier8)
-    rm(QCL_AN_LIVEANIMAL_MEATEQ, APE_live_an_MeatEQ)
+    rm(L102.QCL_AN_LIVEANIMAL_MEATEQ, APE_live_an_MeatEQ)
 
 
     # 4. Bind all to get Bal_new_all ----
     #[ToDo loop not working in package]
     # lapply(paste0("Bal_new_tier", 1:9), get) %>% bind_rows() %>%
     #   # Add area_code
-    #   left_join(QCL_PROD %>% distinct(area, area_code), by = "area_code") ->
+    #   left_join(L102.QCL_PROD %>% distinct(area, area_code), by = "area_code") ->
     #   Bal_new_all
 
       Bal_new_tier1 %>%
@@ -641,28 +641,28 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
         bind_rows(Bal_new_tier7) %>%
         bind_rows(Bal_new_tier8) %>%
       # Add area_code
-      left_join(QCL_PROD %>% distinct(area, area_code), by = "area_code")->
-      Bal_new_all
+      left_join(L102.QCL_PROD %>% distinct(area, area_code), by = "area_code")->
+      L105.Bal_new_all
 
-    assert_FBS_balance(.DF = Bal_new_all)
+    assert_FBS_balance(.DF = L105.Bal_new_all)
 
     rm(TCL_gross, TCL_TM, SCL, FBS, FBSH_CBH, FAO_items)
     rm(list = ls(pattern = "Bal_new_tier*"))
 
 
-    Bal_new_all %>%
-      add_title("Bal_new_all") %>%
+    L105.Bal_new_all %>%
+      add_title("L105.Bal_new_all") %>%
       add_units("Ktonne") %>%
       add_comments("Preprocessed FAO SUA 2010 - 2021") %>%
       add_precursors(file.path(DIR_RAW_DATA_FAOSTAT, "FAO_items"),
-                     "QCL_PROD",
-                     "QCL_AN_LIVEANIMAL_MEATEQ",
+                     "L102.QCL_PROD",
+                     "L102.QCL_AN_LIVEANIMAL_MEATEQ",
                      "TCL_wide",
                      "TM_bilateral_wide",
                      "FBSH_CBH_wide",
                      "FBS_wide",
                      "SCL_wide")->
-      Bal_new_all
+      L105.Bal_new_all
 
 
     return_data(MODULE_OUTPUTS)
@@ -690,7 +690,7 @@ module_xfaostat_L105_DataConnectionToSUA <- function(command, ...) {
 #
 #  TCL_TM %>% mutate(item = 1) -> TCL_TM1
 #
-#  FF_join_checkmap(c("SCL", "QCL_PROD", "TCL_TM1", "FBSH_CB"), COL_by = c("item_code"), COL_rename = "item" ) ->
+#  FF_join_checkmap(c("SCL", "L102.QCL_PROD", "TCL_TM1", "FBSH_CB"), COL_by = c("item_code"), COL_rename = "item" ) ->
 #    JoinItemMap
 #
 #
diff --git a/R/xfaostat_L106_FoodMacroNutrient.R b/R/xfaostat_L106_FoodMacroNutrient.R
index aca1a8f5..53017d7e 100644
--- a/R/xfaostat_L106_FoodMacroNutrient.R
+++ b/R/xfaostat_L106_FoodMacroNutrient.R
@@ -25,7 +25,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
       FILE = file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_FAO_FBS_SUA"))
 
   MODULE_OUTPUTS <-
-    c("SUA_food_macronutrient_rate")
+    c("L106.SUA_food_macronutrient_rate")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -235,11 +235,11 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
           left_join(FAO_an_items_cal_SUA %>%
                       select(item_code, calperg = Mcal_t,fatperc = fat_Perc,
                              proteinperc = protein_Perc), by = "item_code" )
-      ) -> SUA_food_macronutrient_rate
+      ) -> L106.SUA_food_macronutrient_rate
 
-    unique(SUA_food_macronutrient_rate$area_code) %>% length()
-    unique(SUA_food_macronutrient_rate$item_code) %>% length()
-    SUA_food_macronutrient_rate %>% distinct(item, item_code) -> SUA_COMM_FOOD_NUTRIENT
+    unique(L106.SUA_food_macronutrient_rate$area_code) %>% length()
+    unique(L106.SUA_food_macronutrient_rate$item_code) %>% length()
+    L106.SUA_food_macronutrient_rate %>% distinct(item, item_code) -> SUA_COMM_FOOD_NUTRIENT
     # 76244 =179 area * (414 items + 12 fish items)
     # remove processing data
 
@@ -252,7 +252,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
 
 
 
-    SUA_food_macronutrient_rate %>%
+    L106.SUA_food_macronutrient_rate %>%
       add_title("FAO food calories and macronutrient rate") %>%
       add_units("rates") %>%
       add_comments("Detailed FAO food calories and macrotunitent info for 414 SUA items + 12 fish items") %>%
@@ -261,7 +261,7 @@ module_xfaostat_L106_FoodMacroNutrient <- function(command, ...) {
                      "OA",
                      file.path(DIR_RAW_DATA_FAOSTAT, "FAO_an_items_cal_SUA"),
                      file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_FAO_FBS_SUA")) ->
-      SUA_food_macronutrient_rate
+      L106.SUA_food_macronutrient_rate
 
     # P.S. ----
     # China Wheat, bran for food? Need to fix in SUA later; no changes needed here for this
diff --git a/R/yextension_L100_FoodBalanceSheet.R b/R/xfaostat_L107_FoodBalanceSheet.R
similarity index 86%
rename from R/yextension_L100_FoodBalanceSheet.R
rename to R/xfaostat_L107_FoodBalanceSheet.R
index 2d75cc2c..9f99c2c7 100644
--- a/R/yextension_L100_FoodBalanceSheet.R
+++ b/R/xfaostat_L107_FoodBalanceSheet.R
@@ -1,13 +1,13 @@
 # Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
 
-#' module_yextension_L100_FoodBalanceSheet
+#' module_xfaostat_L107_FoodBalanceSheet
 #'
-#' Generate supply utilization balance in primary equivalent
+#' Generate FBS supply utilization balance in primary equivalent
 #'
 #' @param command API command to execute
 #' @param ... other optional parameters, depending on command
 #' @return Depends on \code{command}: either a vector of required inputs, a vector of output names, or (if
-#'   \code{command} is "MAKE") all the generated outputs: \code{FAO_Food_Macronutrient_All},
+#'   \code{command} is "MAKE") all the generated outputs: \code{L107.FAO_Food_Macronutrient_All},
 #'   \code{FAO_Food_MacronutrientRate_2010_2019_MaxValue}
 #' @details This chunk aggregates supply utilization accounts into food balance sheet items
 #' @importFrom assertthat assert_that
@@ -16,22 +16,19 @@
 #' @importFrom tibble tibble is_tibble
 #' @importFrom tidyr complete drop_na gather nesting spread replace_na
 #' @author XZ 2024
-module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
+module_xfaostat_L107_FoodBalanceSheet <- function(command, ...) {
 
   MODULE_INPUTS <-
-    c("FBS_wide",
-      "TM_bilateral_wide",
-      "Bal_new_all",
-      "QCL_CROP_PRIMARY",
-      "QCL_PROD",
-      "SUA_food_macronutrient_rate",
+    c("TM_bilateral_wide",
+      "L105.Bal_new_all",
+      "L106.SUA_food_macronutrient_rate",
       FILE = file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_SUA_PrimaryEquivalent"),
       FILE = file.path(DIR_RAW_DATA_FAOSTAT, "SUA_item_code_map"),
       FILE = file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_FAO_iso_reg") )
 
   MODULE_OUTPUTS <-
-    c("GCAM_APE_after2010",
-      "FAO_Food_Macronutrient_All")
+    c("L107.APE_after2010",
+      "L107.FAO_Food_Macronutrient_All")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -47,26 +44,6 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
 
     get_data_list(all_data, MODULE_INPUTS, strip_attributes = TRUE)
 
-    for (.DF in c("QCL_CROP_PRIMARY", "QCL_PROD")) {
-      get(.DF) %>%
-        # merge Sudan and South Sudan
-        FAO_AREA_DISAGGREGATE_HIST_DISSOLUTION_ALL(SUDAN2012_MERGE = T) %>%
-        assign(x = .DF, envir = Curr_Envir)
-    }
-
-    ## *NonFodderProdArea ----
-    QCL_CROP_PRIMARY %>%
-      filter(element == "Area harvested") %>%
-      mutate(item_set = "QCL_COMM_CROP_PRIMARY") %>%
-      bind_rows(QCL_PROD %>%
-                  filter(!is.na(year))) %>%
-      select(-element_code) %>%
-      mutate(value = value / 1000,
-             unit = if_else(unit == "tonnes", "1000 tonnes", "1000 ha")) %>%
-      spread(year, value) ->
-      GCAMFAOSTAT_NonFodderProdArea
-
-
 
     TM_bilateral_wide %>%
       gather_years() %>%
@@ -91,36 +68,37 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
       ) %>%
       filter(value != 0.0) %>%
       transmute(area_code, item_code, source_code, year, value) ->
-      GCAMFAOSTAT_BiTrade
+      FAO_BiTrade_Kt
 
-    Bal_new_all %>% filter(value != 0.0) %>%
+    # GCAMFAOSTAT_SUA
+    L105.Bal_new_all %>%
+      filter(value != 0.0) %>%
       transmute(area_code, item_code, element, year, value) ->
-      GCAMFAOSTAT_SUA
+      FAO_SUA_Kt
 
-    SUA_food_macronutrient_rate -> GCAMFAOSTAT_MacroNutrientRate
 
     # Fix Sudan with code 206 and 276 after 2012
-    if (206 %in% GCAMFAOSTAT_MacroNutrientRate$area_code == F) {
-      GCAMFAOSTAT_MacroNutrientRate %>%
+    if (206 %in% L106.SUA_food_macronutrient_rate$area_code == F) {
+      L106.SUA_food_macronutrient_rate %>%
         filter(area_code  == 276) %>% mutate(area_code  = 206) %>%
-        bind_rows(GCAMFAOSTAT_MacroNutrientRate) ->
-        GCAMFAOSTAT_MacroNutrientRate
+        bind_rows(L106.SUA_food_macronutrient_rate) ->
+        L106.SUA_food_macronutrient_rate
     }
 
 
 
 
     # Get Supply-utilization account (SUA) elements and use as factor
-    All_Bal_element <- levels(GCAMFAOSTAT_SUA$element)
+    All_Bal_element <- levels(FAO_SUA_Kt$element)
     All_Bal_element <- factor(All_Bal_element, levels = All_Bal_element)
 
     # Bilateral trade item indicator is added to SUA_item_code_map
-    # filter GCAMFAOSTAT_BiTrade to only include bilateral trade item to be
-    # consistent with GCAMFAOSTAT_SUA
+    # filter FAO_BiTrade_Kt to only include bilateral trade item to be
+    # consistent with FAO_SUA_Kt
     BilaterialTrade_ItemCode <- SUA_item_code_map %>% filter(TM == TRUE) %>% distinct(item_code) %>% pull
-    GCAMFAOSTAT_BiTrade %>%
+    FAO_BiTrade_Kt %>%
       filter(item_code %in% BilaterialTrade_ItemCode) ->
-      GCAMFAOSTAT_BiTrade
+      FAO_BiTrade_Kt
     SUA_item_code_map %>% select(item, item_code) -> SUA_item_code_map
 
     # Section1: [2010-2019] Region aggregation of supply-utilization-accounting data ----
@@ -133,26 +111,28 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
     # trap doors where we need to be extra careful to complete / refill zeros or risk loosing
     # rows of legitimate data.
 
-    # create a complete area / iso / GCAM region mapping
-    GCAMFAOSTAT_NonFodderProdArea %>%
-      distinct(area_code, area) %>%
+    # create a complete area / iso  region mapping
+    FAO_SUA_Kt %>%
+      distinct(area_code) %>%
       left_join_error_no_match(Mapping_FAO_iso_reg %>% distinct(area_code, iso, region_ID), by = c("area_code")) ->
       Area_Region_Map
 
     # 1.1 Regional aggregation for SUA ----
+    # [this section is not doing anything if no regional aggregation is needed]
 
-    # Aggregate SUA to GCAM regions
+    # Aggregate SUA regions if needed
     # Intra regional trade is removed when bilateral trade data is available
 
-    GCAMFAOSTAT_SUA %>%
-      left_join_error_no_match(Area_Region_Map %>% select(area_code, region_ID), by="area_code") %>%
+    FAO_SUA_Kt %>%
+      left_join_error_no_match(
+        Area_Region_Map %>% select(area_code, region_ID), by="area_code") %>%
       group_by(region_ID, item_code, element, year) %>%
       summarize(value = sum(value), .groups = "drop") %>%
       ungroup() ->
       DF_SUA_Agg
 
     # Calculate intra regional trade
-    GCAMFAOSTAT_BiTrade %>%
+    FAO_BiTrade_Kt %>%
       left_join_error_no_match(Area_Region_Map %>% select(area_code, region_ID), by="area_code") %>%
       left_join_error_no_match(Area_Region_Map %>% select(source_code = area_code, source_region_ID = region_ID), by="source_code") %>%
       filter(region_ID == source_region_ID) %>%
@@ -193,18 +173,20 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
       group_by(region_ID, item_code, element, year) %>%
       summarize(value = sum(value), .groups = "drop") %>%
       ungroup() ->
-      FAO_SUA_Kt_2010to2019_R
+      FAO_SUA_Kt_R
 
+    # complete years and fill in zeros
+    FAO_SUA_Kt_R %>%
+      complete(nesting(region_ID, item_code, element), year) %>%
+      replace_na(list(value = 0)) ->
+      FAO_SUA_Kt_R
 
-    Min_SUA_Year <- min(FAO_SUA_Kt_2010to2019_R$year)
-    FAO_SUA_Kt_2010to2019 <- GCAMFAOSTAT_SUA
     ## Clean up
-    rm(GCAMFAOSTAT_SUA)
-    rm(GCAMFAOSTAT_BiTrade)
+    rm(FAO_BiTrade_Kt)
     ## Done Section1 ----
     #****************************----
 
-    # Section2: [2010-2019] Primary equivalent aggregation to GCAM commodities ----
+    # Section2: [2010-2019] Primary equivalent aggregation to PCe commodities ----
 
     Mapping_SUA_PrimaryEquivalent %>%
       left_join_error_no_match(SUA_item_code_map %>% rename(sink_item_code = item_code), by=c("sink_item" = "item")) %>%
@@ -231,6 +213,7 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
     # @return A data frame including regional, traded, and world extraction rates of a processing
 
     Get_GROSS_EXTRACTION_RATE <- function(DF_CURR_NEST, DF_ALL) {
+
       curr_sink_items = unique(DF_CURR_NEST$item_code)
       Mapping_SUA_PrimaryEquivalent_ID %>%
         filter(sink_item_code %in% curr_sink_items) ->
@@ -267,12 +250,13 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
                # Regional extraction rate = prod of an aggregated processed item  / Processed use of an aggregated primary item
                # Use world average to fill in NA or zero
                extraction_rate = if_else(is.na(extraction_rate) | extraction_rate == 0, extraction_rate_world, extraction_rate),
-               # Using minimum extraction rate here
+               # Using minimum extraction rate here as an upper threshold to avoid extremely large scaling later
                extraction_rate = pmax(extraction_rate, minimium_extraction_rate),
+               # Set rate to Inf when Processed == 0
+               PositiveProd_ZeroProc = Production > 0 & Processed == 0,
+               extraction_rate = if_else(PositiveProd_ZeroProc == TRUE, Inf, extraction_rate),
                extraction_rate_trade = sum(Export) / sum(Export / extraction_rate),
-               extraction_rate_trade = if_else(is.na(extraction_rate_trade), extraction_rate, extraction_rate_trade),
-               # both processed and production > 0
-               positive_prod = Production > 0 & Processed > 0) %>%
+               extraction_rate_trade = if_else(is.na(extraction_rate_trade), extraction_rate, extraction_rate_trade)) %>%
         ungroup() %>%
         group_by(APE_comm, region_ID) %>%
         # Calculate lagged extraction_rate but replace NA with current rate (first period)
@@ -426,11 +410,13 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
 
     # Primary equivalent aggregation
     # @param DF_ALL Input supply-utilization accounting data frame with all levels of data nested which need to be primarized
-    # @return A supply-utilization accounting data frame with all levels processed and aggregated to GCAM_commodity
+    # @return A supply-utilization accounting data frame with all levels processed and aggregated to APE_comm_Agg
 
     Proc_primarize <- function(DF_ALL){
+
       MaxNest = max(DF_ALL$nest_level)
       MinNest = 1
+
       for(curr_nest in MaxNest:MinNest) {
         # get the current tier to process
         DF_ALL %>%
@@ -495,9 +481,9 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
           complete(region_ID = unique(Area_Region_Map$region_ID), nesting(APE_comm, item_code, nest_level, year), fill=list(value=0)) %>%
           complete(.df2 %>% distinct(APE_comm, item_code, nest_level), nesting(region_ID, year), fill=list(value=0)) %>%
           # Use global value (year is specified now!)
-          group_by(APE_comm, item_code, year) %>%
-          mutate(value = sum(value)) %>%
-          ungroup() %>%
+          # group_by(APE_comm, item_code, year) %>%
+          # mutate(value = sum(value)) %>%
+          # ungroup() %>%
           group_by(APE_comm, region_ID, year) %>%
           mutate(share = value/ sum(value),
                  share = if_else(is.finite(share), share, dplyr::n()/sum(dplyr::n()))) %>%
@@ -572,28 +558,28 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
         gather(element, value, -region_ID, -APE_comm, -year) ->
         APE_AGG
 
-      # Aggregate by GCAM_commodity
+      # Aggregate by APE_comm_Agg
       # At this point we ditch the ID codes and factors as we return the data and
       # make it available for the rest of the original processing
       APE_AGG %>%
-        left_join_error_no_match(Mapping_SUA_PrimaryEquivalent %>% select(GCAM_commodity, APE_comm) %>% distinct(),
+        left_join_error_no_match(Mapping_SUA_PrimaryEquivalent %>% select(APE_comm_Agg, APE_comm) %>% distinct(),
                                  by = c("APE_comm")) %>%
-        group_by(region_ID, GCAM_commodity, element, year) %>%
+        group_by(region_ID, APE_comm_Agg, element, year) %>%
         summarize(value = sum(value), .groups = "drop") %>%
         ungroup() %>%
         mutate(element = as.character(element)) %>%
-        select(region_ID, year, GCAM_commodity, element, value) ->
-        GCAM_APE_after2010
+        select(region_ID, year, APE_comm_Agg, element, value) ->
+        .df_APE
 
-      return(GCAM_APE_after2010)
+      return(.df_APE)
     }
 
     # 2.2. Execution: process data into APE ----
 
 
-    ## Loop through all GCAM_commodity with available data ----
+    ## Loop through all APE_comm_Agg with available data ----
 
-    FAO_SUA_Kt_2010to2019_R %>%
+    FAO_SUA_Kt_R %>%
       left_join(Mapping_SUA_PrimaryEquivalent_ID %>%
                   select(APE_comm, item_code = sink_item_code, nest_level) %>% distinct(), by = c("item_code")) %>%
       left_join(Mapping_SUA_PrimaryEquivalent_ID %>%
@@ -610,12 +596,11 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
       # although we end up needed to unnest at times as well so ultimately,
       # it likely makes little difference in performance
       tidyr::nest(data = -nest_level) %>%
-      # we are now ready to recursively primarize APE commodities then aggregate
-      # to GCAM commodities
+      # we are now ready to recursively primarize APE commodities then aggregate to APE
       Proc_primarize() ->
-      GCAM_APE_after2010
+      L107.APE_after2010
 
-    rm(FAO_SUA_Kt_2010to2019_R)
+    rm(FAO_SUA_Kt_R)
 
     ## Done Section2 ----
     #****************************----
@@ -632,15 +617,15 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
     # about 486 items (out of 530) used in GCAM
 
     Mapping_SUA_PrimaryEquivalent %>%
-      select(GCAM_commodity, item = source_item) %>%
+      select(APE_comm_Agg, item = source_item) %>%
       bind_rows(Mapping_SUA_PrimaryEquivalent %>%
-                  select(GCAM_commodity, item = sink_item)) %>%
+                  select(APE_comm_Agg, item = sink_item)) %>%
       distinct() %>%
       left_join_error_no_match(SUA_item_code_map, by = "item") ->
-      SUA_Items_GCAM
+      SUA_Items_APE
 
     assertthat::assert_that(
-      SUA_Items_GCAM %>% distinct(item_code) %>% nrow() == SUA_Items_GCAM %>% nrow(),
+      SUA_Items_APE %>% distinct(item_code) %>% nrow() == SUA_Items_APE %>% nrow(),
       msg = "Check duplicates in Mapping_SUA_PrimaryEquivalent SUA items"
     )
 
@@ -648,16 +633,16 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
     # (e.g., wine, infant food, or other nonfood items etc.)
 
     SUA_item_code_map %>%
-      filter(!item_code %in% unique(SUA_Items_GCAM$item_code)) -> SUA_Items_NonGCAM
+      filter(!item_code %in% unique(SUA_Items_APE$item_code)) -> SUA_Items_NEC
 
-    # b. There are 426 FAO food items, all included in FAO_SUA_Kt_2010to2019 (530 items)
+    # b. There are 426 FAO food items, all included in FAO_SUA_Kt_2010to2022 (530 items)
     # SUA_Items_Food includes both GCAM and NonGCAM(NEC)
     SUA_item_code_map %>%
-      filter(item_code %in% unique(GCAMFAOSTAT_MacroNutrientRate$item_code)) %>%
-      left_join(SUA_Items_GCAM %>% select(-item), by = "item_code") %>%
-      # For NA GCAM_commodity: not elsewhere classified (NEC)
+      filter(item_code %in% unique(L106.SUA_food_macronutrient_rate$item_code)) %>%
+      left_join(SUA_Items_APE %>% select(-item), by = "item_code") %>%
+      # For NA APE_comm_Agg: not elsewhere classified (NEC)
       # So we would know % of food calories not included in GCAM commodities
-      mutate(GCAM_commodity = if_else(is.na(GCAM_commodity), "NEC", GCAM_commodity)) ->
+      mutate(APE_comm_Agg = if_else(is.na(APE_comm_Agg), "NEC", APE_comm_Agg)) ->
       SUA_Items_Food
 
 
@@ -666,7 +651,7 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
     ### a. Get world average macronutrient ----
     # For filling in missing values
 
-    GCAMFAOSTAT_MacroNutrientRate %>%
+    L106.SUA_food_macronutrient_rate %>%
       tidyr::gather(macronutrient, macronutrient_value, calperg:proteinperc) %>%
       group_by(item, item_code, macronutrient) %>%
       summarise(macronutrient_value_World = mean(macronutrient_value), .groups = "drop") %>%
@@ -676,7 +661,7 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
 
     ### b. Calculate SUA food Calories consumption by joining macronutrient rates and SUA food ----
 
-    FAO_SUA_Kt_2010to2019 %>%
+    FAO_SUA_Kt %>%
       filter(element == "Food", item_code %in% SUA_Items_Food$item_code) %>%
       # ensure we at least have a complete series across time otherwise it may
       # throw off moving avg calculations
@@ -687,7 +672,7 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
       repeat_add_columns(
         tibble(macronutrient = c("calperg", "fatperc", "proteinperc"))) %>%
       left_join(
-        GCAMFAOSTAT_MacroNutrientRate %>%
+        L106.SUA_food_macronutrient_rate %>%
           select(-item) %>%
           tidyr::gather(macronutrient, macronutrient_value, calperg:proteinperc),
         by = c("area_code", "item_code", "macronutrient")
@@ -709,20 +694,8 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
         macronutrient == "fatperc" ~ "MtFat",
         macronutrient == "proteinperc" ~ "MtProtein" )) %>%
       left_join_error_no_match(Area_Region_Map, by = "area_code") ->
-      FAO_Food_Macronutrient_All
-
-
-    # Compare recompiled FBS with the original FBS ----
-
-    FBS_wide %>% gather_years() %>%
-      filter(year >= min(FAOSTAT_Hist_Year_FBS)) %>%
-      FAOSTAT_AREA_RM_NONEXIST() -> FBS
+      L107.FAO_Food_Macronutrient_All
 
-    for (.DF in c("FBS")) {
-      get(.DF) %>% filter(year >= min(FAOSTAT_Hist_Year_FBS)) %>%
-        # merge Sudan and South Sudan
-        FAO_AREA_DISAGGREGATE_HIST_DISSOLUTION_ALL(SUDAN2012_MERGE = T) %>%
-        assign(x = .DF, envir = parent.env(environment()))  }
 
     #****************************----
     # Produce outputs ----
@@ -732,28 +705,18 @@ module_yextension_L100_FoodBalanceSheet <- function(command, ...) {
     #   add_title("GCAM_AgLU_SUA_APE_1973_2019") %>%
     #   add_units("kton") %>%
     #   add_comments("Supply utilization balance for GCAM commodities and regions in primary equivalent") %>%
-    #   add_precursors("aglu/AGLU_ctry",
-    #                  "common/iso_GCAM_regID",
-    #                  "aglu/FAO/GCAMFAOSTAT_SUA",
-    #                  "aglu/FAO/GCAMFAOSTAT_BiTrade",
-    #                  "aglu/FAO/Mapping_SUA_PrimaryEquivalent",
-    #                  "aglu/FAO/SUA_item_code_map",
-    #                  "aglu/FAO/GCAMFAOSTAT_NonFodderProdArea") ->
+    #   add_precursors(MODULE_INPUTS) ->
     #   GCAM_AgLU_SUA_APE_1973_2019
 
 
 
     #
-    #     FAO_Food_Macronutrient_All %>%
+    #     L107.FAO_Food_Macronutrient_All %>%
     #       add_title("GCAM_AgLU_SUA_APE_1973_2019") %>%
     #       add_units("MKcal, MtFat, MtProtein") %>%
     #       add_comments("Macronutrient consumption values connected to food consumption in GCAM_AgLU_SUA_APE_1973_2019") %>%
-    #       add_precursors("aglu/AGLU_ctry",
-    #                      "common/iso_GCAM_regID",
-    #                      "aglu/FAO/GCAMFAOSTAT_SUA",
-    #                      "aglu/FAO/GCAMFAOSTAT_MacroNutrientRate",
-    #                      "aglu/FAO/Mapping_SUA_PrimaryEquivalent") ->
-    #       FAO_Food_Macronutrient_All
+    #       add_precursors(MODULE_INPUTS) ->
+    #       L107.FAO_Food_Macronutrient_All
     #
 
     return_data(MODULE_OUTPUTS)
diff --git a/R/xfaostat_L201_Forestry.R b/R/xfaostat_L201_Forestry.R
index 9815d4ba..a3d0a07c 100644
--- a/R/xfaostat_L201_Forestry.R
+++ b/R/xfaostat_L201_Forestry.R
@@ -21,8 +21,8 @@ module_xfaostat_L201_Forestry <- function(command, ...) {
     c("FO_RoundwoodProducts")
 
   MODULE_OUTPUTS <-
-    c("For_Balance",
-      "FO_RoundwoodProducts_Export_Q_V")
+    c("L201.For_Balance",
+      "L201.FO_RoundwoodProducts_Export_Q_V")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -186,21 +186,19 @@ module_xfaostat_L201_Forestry <- function(command, ...) {
     # clean up
     rm(FO_area_item, UnitMap,  L201.FO_RoundwoodProducts,  L201.FO_RoundwoodProducts_Prod,  L201.FO_RoundwoodProducts_Import_Q_V)
 
-
     L201.For_Balance %>%
       add_title("FAO forestry consumption, production, export, and import (roundwood total) by country_year") %>%
       add_units("m3 ") %>%
       add_comments("Data is preprocessed and generated by gcamdata-FAOSTAT. Gross trade is balanced") %>%
       add_precursors("FO_RoundwoodProducts") ->
-      For_Balance
+      L201.For_Balance
 
     L201.FO_RoundwoodProducts_Export_Q_V %>%
       add_title("FAO forests export qunatity and export value by country_year") %>%
       add_units("m3 and 1000 USD") %>%
       add_comments("Data is generated by gcamdata-FAOSTAT for deriving export prices") %>%
       add_precursors("FO_RoundwoodProducts") ->
-      FO_RoundwoodProducts_Export_Q_V
-
+      L201.FO_RoundwoodProducts_Export_Q_V
 
     return_data(MODULE_OUTPUTS)
 
diff --git a/R/xfaostat_L301_LandCover.R b/R/xfaostat_L301_LandCover.R
index af8a6d10..6038f21e 100644
--- a/R/xfaostat_L301_LandCover.R
+++ b/R/xfaostat_L301_LandCover.R
@@ -21,7 +21,7 @@ module_xfaostat_L301_LandCover <- function(command, ...) {
     c("RL")
 
   MODULE_OUTPUTS <-
-    c("RL_LandCover")
+    c("L301.RL_LandCover")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -125,14 +125,14 @@ module_xfaostat_L301_LandCover <- function(command, ...) {
     RL1_ArableLand %>%
       bind_rows(RL2_TemporaryCrop) %>%
       bind_rows(RL3_FallowLand) ->
-      RL_LandCover
+      L301.RL_LandCover
 
-    RL_LandCover %>%
+    L301.RL_LandCover %>%
       add_title("FAO cropland, Temporary Crop (harvested cropland), and fallow area by country_year") %>%
       add_units("1000 Ha") %>%
       add_comments("Data is cleaned and generated by gcamdata-FAOSTAT") %>%
       add_precursors("RL") ->
-      RL_LandCover
+      L301.RL_LandCover
 
 
     return_data(MODULE_OUTPUTS)
diff --git a/R/xfaostat_L401_Fertilizer.R b/R/xfaostat_L401_Fertilizer.R
index 43b8517f..7fdc735b 100644
--- a/R/xfaostat_L401_Fertilizer.R
+++ b/R/xfaostat_L401_Fertilizer.R
@@ -21,7 +21,7 @@ module_xfaostat_L401_Fertilizer <- function(command, ...) {
     c("RFN")
 
   MODULE_OUTPUTS <-
-    c("RFN_ProdDemand")
+    c("L401.RFN_ProdDemand")
 
   if(command == driver.DECLARE_INPUTS) {
     return(MODULE_INPUTS)
@@ -110,14 +110,14 @@ module_xfaostat_L401_Fertilizer <- function(command, ...) {
 
     RFN1_Production %>%
       bind_rows(RFN1_Demand) ->
-      RFN_ProdDemand
+      L401.RFN_ProdDemand
 
-    RFN_ProdDemand %>%
+    L401.RFN_ProdDemand %>%
       add_title("FAO fertilizer production by country_year") %>%
       add_units("tonnes N") %>%
       add_comments("Data is preprocessed and generated by gamdata-FAOSTAT") %>%
       add_precursors("RFN") ->
-      RFN_ProdDemand
+      L401.RFN_ProdDemand
 
 
     return_data(MODULE_OUTPUTS)
diff --git a/R/yfaostat_SUA_FBS_CSVExport.R b/R/xfaostat_L999_DataExport.R
similarity index 89%
rename from R/yfaostat_SUA_FBS_CSVExport.R
rename to R/xfaostat_L999_DataExport.R
index ac706488..ba280c68 100644
--- a/R/yfaostat_SUA_FBS_CSVExport.R
+++ b/R/xfaostat_L999_DataExport.R
@@ -1,6 +1,6 @@
 # Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
 
-#' module_yfaostat_SUA_FBS_CSVExport
+#' module_xfaostat_L999_DataExport
 #'
 #' Generate supply utilization balance in primary equivalent
 #'
@@ -15,16 +15,16 @@
 #' @importFrom tibble tibble
 #' @importFrom tidyr complete drop_na gather nesting spread replace_na
 #' @author XZ Sep2024
-module_yfaostat_SUA_FBS_CSVExport <- function(command, ...) {
+module_xfaostat_L999_DataExport <- function(command, ...) {
 
   MODULE_INPUTS <-
     c(FILE = file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_SUA_PrimaryEquivalent"),
       FILE = file.path(DIR_RAW_DATA_FAOSTAT, "SUA_item_code_map"),
       FILE = file.path(DIR_RAW_DATA_FAOSTAT, "Mapping_FAO_iso_reg"),
-      "GCAM_APE_after2010",
-      "Bal_new_all",
+      "L107.APE_after2010",
+      "L105.Bal_new_all",
       "FBS_wide",
-      "FAO_Food_Macronutrient_All")
+      "L107.FAO_Food_Macronutrient_All")
 
     MODULE_OUTPUTS <-
       c(CSV = "GCAMFAOSTAT_DataArchive_SUA")
@@ -53,12 +53,12 @@ module_yfaostat_SUA_FBS_CSVExport <- function(command, ...) {
       # SUA and FBS ----
       ## *SUA ----
 
-      Bal_new_all %>% filter(value != 0.0) %>%
+      L105.Bal_new_all %>% filter(value != 0.0) %>%
         transmute(area_code, item_code, element, year, value) %>%
         add_title("GCAMFAOSTAT_DataArchive_SUA") %>%
         add_units("1000 tonnes") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("Bal_new_all") ->
+        add_precursors("L105.Bal_new_all") ->
         GCAMFAOSTAT_DataArchive_SUA
 
 
@@ -73,7 +73,7 @@ module_yfaostat_SUA_FBS_CSVExport <- function(command, ...) {
         out_dir = DIR_OUTPUT_CSV,
         GZIP = F)
 
-      rm(Bal_new_all)
+      rm(L105.Bal_new_all)
 
 
       }
diff --git a/R/yfaostat_GCAM_CSVExport.R b/R/yfaostat_GCAM_CSVExport.R
index a4198ef7..14d888c3 100644
--- a/R/yfaostat_GCAM_CSVExport.R
+++ b/R/yfaostat_GCAM_CSVExport.R
@@ -21,20 +21,20 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
     c("CS",
       "PD",
       "MK",
-      "Bal_new_all",
       "FBSH_CBH_wide",
-      "QCL_PROD",
-      "QCL_AN_LIVEANIMAL",
-      "QCL_AN_PRIMARY_MILK",
-      "QCL_CROP_PRIMARY",
-      "QCL_FODDERCROP",
-      "QCL_PRIMARY_PROD_PV",
+      "L102.QCL_PROD",
+      "L102.QCL_AN_LIVEANIMAL",
+      "L102.QCL_AN_PRIMARY_MILK",
+      "L102.QCL_CROP_PRIMARY",
+      "L104.QCL_FODDERCROP",
+      "L103.QCL_PRIMARY_PROD_PV",
+      "L105.Bal_new_all",
       "TM_bilateral_wide",
-      "SUA_food_macronutrient_rate",
-      "For_Balance",
-      "FO_RoundwoodProducts_Export_Q_V",
-      "RL_LandCover",
-      "RFN_ProdDemand")
+      "L106.SUA_food_macronutrient_rate",
+      "L201.For_Balance",
+      "L201.FO_RoundwoodProducts_Export_Q_V",
+      "L301.RL_LandCover",
+      "L401.RFN_ProdDemand")
 
     MODULE_OUTPUTS <-
       c("yfaostat_GCAM_GCAMFAOSTAT_CSV",
@@ -61,9 +61,9 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
   } else if(command == driver.MAKE) {
 
     year <- value <- Year <- Value <- FAO_country <- iso <- NULL    # silence package check.
-    area_code <- source_code <- area_code1 <- item_code <- Bal_new_all <- element <- FBSH_CBH_wide <-
-      element_code <- QCL_CROP_PRIMARY <- QCL_PROD <- unit <- QCL_FODDERCROP <- QCL_AN_LIVEANIMAL <-
-      QCL_AN_PRIMARY_MILK <- QCL_PRIMARY_PROD_PV <- area <- PD <- SUA_food_macronutrient_rate  <-
+    area_code <- source_code <- area_code1 <- item_code <- L105.Bal_new_all <- element <- FBSH_CBH_wide <-
+      element_code <- L102.QCL_CROP_PRIMARY <- L102.QCL_PROD <- unit <- L104.QCL_FODDERCROP <- L102.QCL_AN_LIVEANIMAL <-
+      L102.QCL_AN_PRIMARY_MILK <- L103.QCL_PRIMARY_PROD_PV <- area <- PD <- L106.SUA_food_macronutrient_rate  <-
       TM_bilateral_wide <- NULL
 
     all_data <- list(...)[[1]]
@@ -160,12 +160,12 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       # SUA and FBS ----
       ## *SUA ----
 
-      Bal_new_all %>% filter(value != 0.0) %>%
+      L105.Bal_new_all %>% filter(value != 0.0) %>%
         transmute(area_code, item_code, element, year, value) %>%
         add_title("GCAMFAOSTAT_SUA") %>%
         add_units("1000 tonnes") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("Bal_new_all",
+        add_precursors("L105.Bal_new_all",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_SUA
 
@@ -182,7 +182,7 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
         out_dir = DIR_OUTPUT_CSV,
         GZIP = T)
 
-      rm(Bal_new_all)
+      rm(L105.Bal_new_all)
 
       ## *FBSH and CB ----
       FBSH_CBH_wide %>%
@@ -224,7 +224,7 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       # Production and Area harvested ----
 
 
-      for (.DF in c("QCL_CROP_PRIMARY", "QCL_FODDERCROP", "QCL_PROD")) {
+      for (.DF in c("L102.QCL_CROP_PRIMARY", "L104.QCL_FODDERCROP", "L102.QCL_PROD")) {
         get(.DF) %>%
           # merge Sudan and South Sudan
           FAO_AREA_DISAGGREGATE_HIST_DISSOLUTION_ALL(SUDAN2012_MERGE = T) %>%
@@ -232,10 +232,10 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       }
 
       ## *NonFodderProdArea ----
-      QCL_CROP_PRIMARY %>%
+      L102.QCL_CROP_PRIMARY %>%
         filter(element == "Area harvested") %>%
         mutate(item_set = "QCL_COMM_CROP_PRIMARY") %>%
-        bind_rows(QCL_PROD %>%
+        bind_rows(L102.QCL_PROD %>%
                     filter(!is.na(year))) %>%
         select(-element_code) %>%
         mutate(value = value / 1000,
@@ -244,8 +244,8 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
         add_title("GCAMFAOSTAT_NonFodderProdArea") %>%
         add_units("1000 tonnes or 1000 ha") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("QCL_CROP_PRIMARY",
-                       "QCL_PROD",
+        add_precursors("L102.QCL_CROP_PRIMARY",
+                       "L102.QCL_PROD",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_NonFodderProdArea
 
@@ -263,7 +263,7 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       )
 
       ## *FodderProdArea ----
-      QCL_FODDERCROP %>%
+      L104.QCL_FODDERCROP %>%
         filter(!is.na(year)) %>%
         select(-element_code) %>%
         mutate(value = value / 1000,
@@ -272,7 +272,7 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
         add_title("GCAMFAOSTAT_FodderProdArea") %>%
         add_units("1000 tonnes or 1000 ha") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("QCL_FODDERCROP",
+        add_precursors("L104.QCL_FODDERCROP",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_FodderProdArea
 
@@ -289,17 +289,17 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
         GZIP = T
       )
 
-      rm(QCL_CROP_PRIMARY, QCL_FODDERCROP, QCL_PROD)
+      rm(L102.QCL_CROP_PRIMARY, L104.QCL_FODDERCROP, L102.QCL_PROD)
 
       ##*AnimalStocks ----
       # Laying is not needed for now; live animal stocks and milk animals are used for water demand calculation in GCAM
       # 1171 "Animals live nes" and 1083 "Pigeons, other birds" are not available
 
 
-      QCL_AN_LIVEANIMAL %>% filter(element == "Stocks") %>%
+      L102.QCL_AN_LIVEANIMAL %>% filter(element == "Stocks") %>%
         spread(year, value) -> FAO_an_Stocks
 
-      QCL_AN_PRIMARY_MILK %>% filter(element == "Milk Animals") %>%
+      L102.QCL_AN_PRIMARY_MILK %>% filter(element == "Milk Animals") %>%
         spread(year, value) -> FAO_an_Dairy_Stocks
 
       FAO_an_Stocks %>%
@@ -329,13 +329,13 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       # Producer prices in quantity and value ----
       ##*Producer Price ----
 
-      QCL_PRIMARY_PROD_PV %>%
+      L103.QCL_PRIMARY_PROD_PV %>%
         filter(year >= MIN_HIST_PP_YEAR) %>%
         spread(year, value) %>%
         add_title("GCAMFAOSTAT_ProdPrice") %>%
         add_units("Quantity in tonne; Value in US$") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("QCL_PRIMARY_PROD_PV",
+        add_precursors("L103.QCL_PRIMARY_PROD_PV",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_ProdPrice
 
@@ -352,7 +352,7 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
         out_dir = DIR_OUTPUT_CSV,
         GZIP = T
       )
-      rm(QCL_PRIMARY_PROD_PV)
+      rm(L103.QCL_PRIMARY_PROD_PV)
 
 
       ##*GDP deflators ----
@@ -384,7 +384,7 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       ## *MacroNutrientRate----
 
 
-      SUA_food_macronutrient_rate ->
+      L106.SUA_food_macronutrient_rate ->
         GCAMFAOSTAT_MacroNutrientRate
 
 
@@ -400,7 +400,7 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
         add_title("GCAMFAOSTAT_MacroNutrientRate") %>%
         add_units("calories per g or (fat or protein) percentage") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("SUA_food_macronutrient_rate",
+        add_precursors("L106.SUA_food_macronutrient_rate",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_MacroNutrientRate
 
@@ -421,13 +421,13 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       # Forestry data output ----
 
       ##*ForProdTrade ----
-      For_Balance %>%
+      L201.For_Balance %>%
         filter(element != "Demand") %>%
         spread(year, value) %>%
         add_title("GCAMFAOSTAT_ForProdTrade for forest products") %>%
         add_units("m3") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("For_Balance",
+        add_precursors("L201.For_Balance",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_ForProdTrade
 
@@ -445,12 +445,12 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       )
 
       ##*ForExpPrice ----
-      FO_RoundwoodProducts_Export_Q_V %>%
+      L201.FO_RoundwoodProducts_Export_Q_V %>%
         spread(year, value) %>%
         add_title("GCAMFAOSTAT_ForExpPrice") %>%
         add_units("Export Quantity in m3; Export Value in 1000 US$") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("FO_RoundwoodProducts_Export_Q_V",
+        add_precursors("L201.FO_RoundwoodProducts_Export_Q_V",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_ForExpPrice
 
@@ -471,12 +471,12 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       # Land Cover data output ----
 
       ##*LandCover ----
-      RL_LandCover %>%
+      L301.RL_LandCover %>%
         spread(year, value) %>%
         add_title("GCAMFAOSTAT_LandCover") %>%
         add_units("1000 Ha") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("RL_LandCover",
+        add_precursors("L301.RL_LandCover",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_LandCover
 
@@ -497,12 +497,12 @@ module_yfaostat_GCAM_CSVExport <- function(command, ...) {
       # Fertilizer data output----
 
       ##* N Fertilizer ----
-      RFN_ProdDemand %>%
+      L401.RFN_ProdDemand %>%
         spread(year, value) %>%
         add_title("GCAMFAOSTAT_NFertilizer") %>%
         add_units("tonnes N") %>%
         add_comments("gcamfaostat Export CSV") %>%
-        add_precursors("RFN_ProdDemand",
+        add_precursors("L401.RFN_ProdDemand",
                        "yfaostat_GCAM_GCAMFAOSTAT_CSV") ->
         GCAMFAOSTAT_NFertilizer
 
diff --git a/inst/extdata/FAOSTAT/Mapping_SUA_PrimaryEquivalent.csv b/inst/extdata/FAOSTAT/Mapping_SUA_PrimaryEquivalent.csv
index 828f5948..106d464a 100644
--- a/inst/extdata/FAOSTAT/Mapping_SUA_PrimaryEquivalent.csv
+++ b/inst/extdata/FAOSTAT/Mapping_SUA_PrimaryEquivalent.csv
@@ -20,7 +20,7 @@
 # Last update: 06-09-2024 (XZ)
 # Column types: ccilccncnnl
 # ----------
-GCAM_commodity,APE_comm,nest_level,source_primary,source_item,sink_item,output_specific_extraction_rate,sink_FBS_item,extraction_rate_Q25,extraction_rate_world2019,Q25asMin
+APE_comm_Agg,APE_comm,nest_level,source_primary,source_item,sink_item,output_specific_extraction_rate,sink_FBS_item,extraction_rate_Q25,extraction_rate_world2019,Q25asMin
 Wheat,Wheat,1,TRUE,Wheat,"Food preparations, flour, malt extract",,Wheat and products,0.942971612,0.985277283,FALSE
 Wheat,Wheat,1,TRUE,Wheat,Bulgur,,Wheat and products,0.942971612,0.985277283,FALSE
 Wheat,Wheat,1,TRUE,Wheat,"Cereals, breakfast",,Wheat and products,0.942971612,0.985277283,FALSE
diff --git a/man/module_yextension_L100_FoodBalanceSheet.Rd b/man/module_xfaostat_L107_FoodBalanceSheet.Rd
similarity index 59%
rename from man/module_yextension_L100_FoodBalanceSheet.Rd
rename to man/module_xfaostat_L107_FoodBalanceSheet.Rd
index 349a41bf..bfbf903e 100644
--- a/man/module_yextension_L100_FoodBalanceSheet.Rd
+++ b/man/module_xfaostat_L107_FoodBalanceSheet.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/yextension_L100_FoodBalanceSheet.R
-\name{module_yextension_L100_FoodBalanceSheet}
-\alias{module_yextension_L100_FoodBalanceSheet}
-\title{module_yextension_L100_FoodBalanceSheet}
+% Please edit documentation in R/xfaostat_L107_FoodBalanceSheet.R
+\name{module_xfaostat_L107_FoodBalanceSheet}
+\alias{module_xfaostat_L107_FoodBalanceSheet}
+\title{module_xfaostat_L107_FoodBalanceSheet}
 \usage{
-module_yextension_L100_FoodBalanceSheet(command, ...)
+module_xfaostat_L107_FoodBalanceSheet(command, ...)
 }
 \arguments{
 \item{command}{API command to execute}
@@ -13,11 +13,11 @@ module_yextension_L100_FoodBalanceSheet(command, ...)
 }
 \value{
 Depends on \code{command}: either a vector of required inputs, a vector of output names, or (if
-  \code{command} is "MAKE") all the generated outputs: \code{FAO_Food_Macronutrient_All},
+  \code{command} is "MAKE") all the generated outputs: \code{L107.FAO_Food_Macronutrient_All},
   \code{FAO_Food_MacronutrientRate_2010_2019_MaxValue}
 }
 \description{
-Generate supply utilization balance in primary equivalent
+Generate FBS supply utilization balance in primary equivalent
 }
 \details{
 This chunk aggregates supply utilization accounts into food balance sheet items
diff --git a/man/module_yfaostat_SUA_FBS_CSVExport.Rd b/man/module_xfaostat_L999_DataExport.Rd
similarity index 69%
rename from man/module_yfaostat_SUA_FBS_CSVExport.Rd
rename to man/module_xfaostat_L999_DataExport.Rd
index 45d39597..4085da7a 100644
--- a/man/module_yfaostat_SUA_FBS_CSVExport.Rd
+++ b/man/module_xfaostat_L999_DataExport.Rd
@@ -1,10 +1,10 @@
 % Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/yfaostat_SUA_FBS_CSVExport.R
-\name{module_yfaostat_SUA_FBS_CSVExport}
-\alias{module_yfaostat_SUA_FBS_CSVExport}
-\title{module_yfaostat_SUA_FBS_CSVExport}
+% Please edit documentation in R/xfaostat_L999_DataExport.R
+\name{module_xfaostat_L999_DataExport}
+\alias{module_xfaostat_L999_DataExport}
+\title{module_xfaostat_L999_DataExport}
 \usage{
-module_yfaostat_SUA_FBS_CSVExport(command, ...)
+module_xfaostat_L999_DataExport(command, ...)
 }
 \arguments{
 \item{command}{API command to execute}