diff --git a/R_to_SQL_database/Crop_cifos_DataPreparation_DB.R b/R_to_SQL_database/Crop_cifos_DataPreparation_DB.R
index 1a74574a4f80a2db996ac85100b9a3baaecd1e09..95ef88ae5f12a6931fbeaded15bbf56388683c3b 100644
--- a/R_to_SQL_database/Crop_cifos_DataPreparation_DB.R
+++ b/R_to_SQL_database/Crop_cifos_DataPreparation_DB.R
@@ -1,12 +1,11 @@
-
-# Loading packages --------------------------------------------------------
+Loading packages --------------------------------------------------------
# Loading the packages
# .libPaths("C:/Wolfram_Admin/R-4.1.2/R-4.1.2/library")
# install.packages("easypackages", dependencies = T)
# library("easypackages")
# Loading packages
easypackages::packages("odbc","DBI", "RSQLite", "dbplyr", "readxl", "fuzzyjoin",
- "sqldf", "downloader", "tidyverse", "janitor", "FAOSTAT",
+ "sqldf", "downloader", "tidyverse", "janitor", "FAOSTAT",
"validate")
# https://www.youtube.com/watch?v=CydajdNRJOU -----------------------------
@@ -103,18 +102,18 @@ easypackages::packages("odbc","DBI", "RSQLite", "dbplyr", "readxl", "fuzzyjoin",
# Connection to a structured EER diagramm
con = dbConnect(odbc::odbc(),
- .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
- Server = "localhost", Database = "cifos_crop", UID = "root", PWD = "154236w.S",
- Port = 3306)
+ .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
+ Server = "localhost", Database = "cifos_crop", UID = "root", PWD = "154236w.S",
+ Port = 3306)
# Connection to a messy schema (no eer diagram)
con_crop_map = dbConnect(odbc::odbc(),
- .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
- Server = "localhost",
- Database = "crop_mapping",
- UID = "root",
- PWD = "154236w.S",
- Port = 3306)
+ .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
+ Server = "localhost",
+ Database = "crop_mapping",
+ UID = "root",
+ PWD = "154236w.S",
+ Port = 3306)
# https://db.rstudio.com/getting-started/database-queries
# Loading the data sets from Database -------------------------------------
@@ -132,23 +131,23 @@ mapping_grasscrops = read_csv("Input_data/mapping_grasscrops_new.csv") %>%
dat_crop = as_tibble(crop) %>%
dplyr::mutate(old_cifos_crop = crop_cifos) %>%
dplyr::mutate(crop_cifos = ifelse(!is.na(crop_name_monfreda),
- crop_name_monfreda,
- crop_long_spam)) %>%
+ crop_name_monfreda,
+ crop_long_spam)) %>%
dplyr::filter(!grepl("grass", crop_cifos)) %>%
bind_rows(.,mapping_grasscrops) %>%
mutate(crop_cifos = tolower(crop_cifos),
crop_cifos = gsub("\\,", "", crop_cifos),
crop_cifos = gsub(" ", "_", crop_cifos)) %>%
dplyr::mutate(crop_cifos = dplyr::recode(crop_cifos,
- "tobacco_unmanufactured" = "tobacco",
- "groundnuts_with_shell" = "groundnuts",
- # "dark_green_vegetables " = "green_vegetables",
- "other_roots_and_tubers" = "other_tubers",
- # grepl("red_and_orange",crop_cifos) = "red_vegetables",
- # "other_vegetables " = "other_vegetables",
- "sunflower_seed" = "sunflower",
- "other_cereal_crops" = "other_cereals",
- "sesame_seed" = "sesame"))
+ "tobacco_unmanufactured" = "tobacco",
+ "groundnuts_with_shell" = "groundnuts",
+ # "dark_green_vegetables " = "green_vegetables",
+ "other_roots_and_tubers" = "other_tubers",
+ # grepl("red_and_orange",crop_cifos) = "red_vegetables",
+ # "other_vegetables " = "other_vegetables",
+ "sunflower_seed" = "sunflower",
+ "other_cereal_crops" = "other_cereals",
+ "sesame_seed" = "sesame"))
# write_csv(dat_crop, "Input_data/dat_crop_with_grass.csv")
#ISSUE: The vegetable values are behaving weird. Had to remove two spaces at the end manually in CSV.
@@ -164,9 +163,9 @@ dat_crop_final = read_csv("Input_data/dat_crop_with_grass_final.csv")
- # crop_cifos = case_when(crop_cifos != is.na(crop_cifos) ~ crop_long_spam)) %>%
- # dplyr::mutate(crop_cifos_2 = case_when(crop_cifos = is.na(crop_cifos) ~ crop_name_monfreda),
- # TRUE ~ crop_cifos)
+# crop_cifos = case_when(crop_cifos != is.na(crop_cifos) ~ crop_long_spam)) %>%
+# dplyr::mutate(crop_cifos_2 = case_when(crop_cifos = is.na(crop_cifos) ~ crop_name_monfreda),
+# TRUE ~ crop_cifos)
# class(data_map)
#
@@ -244,7 +243,7 @@ dat_crop_final = read_csv("Input_data/dat_crop_with_grass_final.csv") %>%
# Adding the grass to the old crop name columns so it will not result in NA later.
dplyr::mutate(old_cifos_crop = case_when(is.na(old_cifos_crop) ~ crop_cifos,
TRUE ~ old_cifos_crop))
-
+
# Fuzzy join of processing sheet and the crop map
# crop_map_cifos_procraw =
dat_proc = Processing_sheet %>%
@@ -261,12 +260,12 @@ dat_proc = Processing_sheet %>%
str_detect(crop_cifos, "grass") ~ crop_cifos,
crop_cifos == "tobacco" ~ crop_cifos,
crop_cifos == "seed_cotton" ~ crop_cifos,
- TRUE ~ .)),
+ TRUE ~ .)),
across(c(value), ~ case_when(str_detect(crop_cifos, "for") ~ 1,
str_detect(crop_cifos, "grass") ~ 1,
crop_cifos == "tobacco" ~ 1,
crop_cifos == "seed_cotton" ~ 1,
- TRUE ~ .)))
+ TRUE ~ .)))
# removing disaggregated group elemetns that are not needed -
# Aim: To have only one surrogate crop per crop_group (temperate_fruits = Apples)
@@ -293,7 +292,7 @@ dat_2nd_level2 = anti_join(Processing_sheet,dat_2nd_level, by = "ProRaw") %>%
"barley", "barley")) %>%
relocate(crop_cifos, .before = 1) %>%
clean_names()
-
+
dat_group = sqldf('SELECT * FROM dat_proc WHERE
@@ -325,7 +324,7 @@ dat_group = sqldf('SELECT * FROM dat_proc WHERE
dat_proc_2 = dat_proc %>%
anti_join(., dat_group, by = "crop_cifos") %>% #then I delete all the rows with unwanted duplicates
bind_rows(dat_group)#then I add the nicely filtered rows and get a clean df
-
+
# Adding missing dietary products
# Cifos dietary products - here i filter for strings in the FAO database
@@ -382,7 +381,7 @@ dat_dietprod_comp = dat_proc_2 %>%
crop_cifos == "cocoa_beans" ~ "Cocoa Beans and products", # newly added to cifos;
crop_cifos == "other_tubers" ~ "Vegetables, Other",
TRUE ~ dietary_products))
-
+
# Removing Nas in the columns procin and procout. Rough assumption: all prcessing fractions = 1. Names from proc_raw
vec_proc_raw = dat_dietprod_comp %>% dplyr::filter(is.na(proc_in)) %>% pull(proc_raw)
@@ -400,14 +399,14 @@ dat_complete = dat_dietprod_comp %>%
bind_rows(., dat_comp) %>%
distinct_all() %>% # tobacco was triplicated
bind_rows(dat_2nd_level2)
-
+
# write_csv(dat_complete, "Input_data/processing_sheet_2.csv")
# dat_complete = read_csv("Input_data/processing_sheet_2.csv")
# Isolate new dietary products
new_dietprods = dat_complete %>%
anti_join(., dat_proc_2, by="dietary_products")
-
+
# Distinct new dietary products
new_dietprods_dist = dat_complete %>%
anti_join(., dat_proc_2, by="dietary_products") %>%
@@ -439,7 +438,7 @@ new_procin = dat_complete %>%
# - If forage crops are represented as well as food crops they have to be merged to the food crop;
# That means they should get the same procin name but then get the different procout ("wheat_feed" or so)
print(new_procin, n=33)
-
+
print(new_procin, n=33)
# A tibble: 33 x 6
@@ -498,14 +497,14 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "coconuts" , proc_raw = "coconuts" , proc_in = "coconuts_copra", proc_out = "coconuts_oil_copra", value = 0.21*0.64, dietary_products = "Coconut Oil") %>%
add_row(crop_cifos = "coconuts" , proc_raw = "coconuts" , proc_in = "coconuts_copra", proc_out = "coconuts_cake_copra", value = 0.21*0.36, dietary_products = "Coconuts - Incl Copra") %>%
-
+
# Cassava -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf); adjusted commodity tree.
# Levels: only one level in tcf doc!
add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_flourin", proc_out = "cassava_flour", value = 0.3, dietary_products = "Cassava and products") %>%
add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_tapiocain", proc_out = "cassava_tapioca", value = 0.2, dietary_products = "Cassava and products") %>%
add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_dryin", proc_out = "cassava_dry", value = 0.35, dietary_products = "Cassava and products") %>%
add_row(crop_cifos = "cassava" , proc_raw = "cassava" , proc_in = "cassava_starchin", proc_out = "cassava_starch", value = 0.25, dietary_products = "Cassava and products") %>%
-
+
# Pearl millet -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf); adjusted commodity tree; fractions from "cereals/millet"
# levels: only one level
add_row(crop_cifos = "pearl_millet" , proc_raw = "pearl_millet" , proc_in = "pearl_millet_flourin", proc_out = "pearl_millet_flour", value = 0.86, dietary_products = "Millet and products") %>%
@@ -538,12 +537,12 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_canein", proc_out = "sugarcane_molasse", value = 0.05, dietary_products = "Sugar cane") %>%
add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_canein", proc_out = "sugarcane_bagasse", value = 0.25, dietary_products = "Sugar cane") %>%
-
+
#sweet_potatoes -- commodity tree
add_row(crop_cifos = "sweet_potatoes" , proc_raw = "sweet_potatoes" , proc_in = "sweet_potatoe_flourin", proc_out = "sweet_potatoe_flour", value = 0.25, dietary_products = "Sweet potatoes") %>%
add_row(crop_cifos = "sweet_potatoes" , proc_raw = "sweet_potatoes" , proc_in = "sweet_potatoe_starchin", proc_out = "sweet_potatoe_starch", value = 0.2, dietary_products = "Sweet potatoes") %>%
-
- # tea --
+
+ # tea --
add_row(crop_cifos = "tea" , proc_raw = "tea" , proc_in = "teain", proc_out = "tea_leave", value = 1, dietary_products = "Tea (including mate)") %>%
# yams --
@@ -613,12 +612,12 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "groundnuts" , proc_raw = "groundnut_shelled" , proc_in = "groundnuts_oilin", proc_out = "groundnut_cake", value = 0.54, dietary_products = "Groundnut Oil") %>%
add_row(crop_cifos = "groundnuts" , proc_raw = "groundnut_shelled" , proc_in = "groundnuts_butterin", proc_out = "groundnut_butter", value = 0.85, dietary_products = "Groundnuts (Shelled Eq)") %>%
-
+
# Other cereals - commodity tree
add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_flourin", proc_out = "other_cereals_flour", value = 0.8, dietary_products = "Cereals, Other") %>%
add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_flourin", proc_out = "other_cereals_bran", value = 0.2, dietary_products = "Cereals, Other") %>%
add_row(crop_cifos = "other_cereals" , proc_raw = "other_cereals" , proc_in = "other_cereals_wholein", proc_out = "other_cereals_whole", value = 1, dietary_products = "Cereals, Other") %>%
-
+
# other_oil_crops - commodity tree (tables)
# 1 level
add_row(crop_cifos = "other_oil_crops" , proc_raw = "olives" , proc_in = "olive_tablein", proc_out = "olive_table", value = 1, dietary_products = "Olives (including preserved)") %>%
@@ -666,7 +665,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
## grass_rangeland -
add_row(crop_cifos = "grass_rangeland" , proc_raw = "grass_rangeland" , proc_in = "grass_rangeland_fresh", proc_out = "grass_rangeland_fresh", value = 0.30, dietary_products = "Forage and silage, grasses nes") %>% # Source: https://doi.org/10.1111/j.1469-8137.1994.tb04036.x - I take the lower bound
-
+
## alfalfafor
# grass_pasture - Source: Source: https://www.feedipedia.org/node/275
add_row(crop_cifos = "alfalfafor" , proc_raw = "alfalfafor" , proc_in = "alfalfafor_dry", proc_out = "alfalfafor_hay", value = 0.894, dietary_products = "Forage and silage, legumes") %>% #Alfalfa, hay, (feedepedia)
@@ -702,7 +701,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# Levels: all pulses only one level
add_row(crop_cifos = "legumenesfor" , proc_raw = "legumenesfor_feed" , proc_in = "legumenesfor_feedin", proc_out = "legumenesfor_bean", value = 0.866 , dietary_products = "Forage and silage, legumes") %>% # Faba bean (Vicia faba), all cultivars
add_row(crop_cifos = "legumenesfor" , proc_raw = "legumenesfor_feed" , proc_in = "legumenesfor_feedin", proc_out = "legumenesfor_straw", value = 0.194 , dietary_products = "Forage and silage, legumes") %>% # Faba bean (Vicia faba), aerial part, fresh
-
+
# maizefor - https://www.feedipedia.org/node/13883 AND commodity tree FAO
add_row(crop_cifos = "maizefor" , proc_raw = "maize_whole_feed" , proc_in = "maize_silagein", proc_out = "maize_silage", value = 0.325, dietary_products = "Forage products") %>% # Reference: Maize silage, dry matter 30-35% from https://www.feedipedia.org/node/12871
add_row(crop_cifos = "maizefor" , proc_raw = "maize_whole_feed" , proc_in = "maize_oilin", proc_out = "maize_oil", value = 0.45*0.06, dietary_products = "Forage products") %>% # Reference: Commodidty tree fao same as with humans
@@ -769,7 +768,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_starch", proc_out = "potatoes_starch", value = 0.19, dietary_products = "Potatoes and products") %>%
add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_starch", proc_out = "tuber_peel", value = 0.02, dietary_products = "Potatoes and products") %>%
-# Soybeans - tcf table if not otherwise stated
+ # Soybeans - tcf table if not otherwise stated
add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_oil", value = 0.19, dietary_products = "Soyabean Oil") %>%
add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_cake", value = 0.71, dietary_products = "Soyabeans") %>%
add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_oil", proc_out = "soyabean_hulls", value = 0.07, dietary_products = "Soyabeans") %>%
@@ -784,11 +783,11 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# sunflower - tcf fao
add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "sunflower_seed", proc_out = "sunflower_oil", value = 0.41, dietary_products = "Soyabean Oil") %>%
- add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "soyabean_for_oil", proc_out = "sunflower_cake", value = 0.47, dietary_products = "Soyabeans") %>%
- add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "soyabean_for_oil", proc_out = "sunflower_hulls", value = 0.07, dietary_products = "Soyabeans") %>% #ref from ollie (old proccessing sheet)
+ add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "sunflower_for_oil", proc_out = "sunflower_cake", value = 0.47, dietary_products = "Soyabeans") %>%
+ add_row(crop_cifos = "sunflower" , proc_raw = "sunflower_seed" , proc_in = "sunflower_for_oil", proc_out = "sunflower_hulls", value = 0.07, dietary_products = "Soyabeans") %>% #ref from ollie (old proccessing sheet)
# Paddy Rice - tcf fao
- # husked rice
+ # husked rice
add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_husk", proc_out = "rice_husked", value = 0.77, dietary_products = "Rice (Milled Equivalent)") %>%
add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_husk", proc_out = "rice_hulls", value = 0.25, dietary_products = "Rice (Milled Equivalent)") %>%
# 2nd stage
@@ -803,15 +802,15 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# 2nd stage for bran oil/cake
add_row(crop_cifos = "rice" , proc_raw = "bran_paddy_milled" , proc_in = "rice_bran_oil", proc_out = "rice_bran_oil", value = 0.80, dietary_products = "Ricebran Oil") %>%
add_row(crop_cifos = "rice" , proc_raw = "bran_paddy_milled" , proc_in = "rice_bran_oil", proc_out = "rice_bran_cake", value = 0.14, dietary_products = "Ricebran Oil") %>%
-
-# coffee - feedepedia -> https://www.feedipedia.org/node/549
+
+ # coffee - feedepedia -> https://www.feedipedia.org/node/549
add_row(crop_cifos = "arabica_coffee" , proc_raw = "coffee_green" , proc_in = "coffee_green", proc_out = "coffee_roasted", value = 0.8, dietary_products = "Coffee and products") %>%
add_row(crop_cifos = "arabica_coffee" , proc_raw = "coffee_green" , proc_in = "coffee_green", proc_out = "coffee_hulls", value = 0.2, dietary_products = "Cocoa Beans and products") %>%#fraction comes from the cocoa husk. Could be improbved
-
+
# Sesame
add_row(crop_cifos = "sesame" , proc_raw = "sesame" , proc_in = "sesame_seed", proc_out = "sesame_oil", value = 0.42, dietary_products = "Sesameseed Oil") %>% #ref from old processing sheet
add_row(crop_cifos = "sesame" , proc_raw = "sesame" , proc_in = "sesame_seed", proc_out = "sesame_cake", value = 0.57, dietary_products = "Sesame seed") %>%
-
+
# Banana
add_row(crop_cifos = "bananas" , proc_raw = "bananas" , proc_in = "bananas", proc_out = "bananas", value = 1, dietary_products = "Bananas") %>% #ref from old processing sheet
@@ -824,7 +823,7 @@ proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out"
# Treenuts
add_row(crop_cifos = "treenuts" , proc_raw = "walnuts" , proc_in = "walnuts", proc_out = "walnuts", value = 0.04, dietary_products = "Nuts and products") %>%
add_row(crop_cifos = "treenuts" , proc_raw = "walnuts" , proc_in = "walnuts", proc_out = "nut_shell", value = 0.04, dietary_products = "Nuts and products")
-
+
# Pulling crops that are updated
vec_proc_new = proc_new %>% distinct_at(vars(crop_cifos)) %>% pull(crop_cifos)
@@ -860,22 +859,22 @@ dat_proc_new %>% write_csv("Input_data/dat_proc_new.csv")
dat_proc_asf =
Processing_sheet %>% dplyr::select(-c(6,7)) %>%
- clean_names() %>% rename(proc_raw = pro_raw) %>%
- slice(269:363) %>%
+ clean_names() %>% rename(proc_raw = pro_raw) %>%
+ slice(269:363) %>%
dplyr::mutate(dietary_products= case_when(proc_raw == "Milk" ~ "Milk - Excluding Butter",
- TRUE ~ dietary_products),
- dietary_products= case_when(proc_out == "Butter" ~ "Butter, Ghee",
- TRUE ~ dietary_products),
- dietary_products= case_when(dietary_products == "Fish (Calculated CiFoS)" ~ "Fish, Seafood",
- TRUE ~ dietary_products),
- proc_out = case_when(proc_out == "Butter_Milk" ~ "Butter_milk",
- TRUE ~ proc_out)) #this was written with a uppercase here and a lower case in the food losses sheet
+ TRUE ~ dietary_products),
+ dietary_products= case_when(proc_out == "Butter" ~ "Butter, Ghee",
+ TRUE ~ dietary_products),
+ dietary_products= case_when(dietary_products == "Fish (Calculated CiFoS)" ~ "Fish, Seafood",
+ TRUE ~ dietary_products),
+ proc_out = case_when(proc_out == "Butter_Milk" ~ "Butter_milk",
+ TRUE ~ proc_out)) #this was written with a uppercase here and a lower case in the food losses sheet
+
-
Processing_sheet_final = bind_rows(dat_proc_new %>% dplyr::select(-crop_cifos),dat_proc_asf)
write_csv(Processing_sheet_final, "Input_data/processing_sheet.csv")
-
+
# Human nutrition sheet ---------------------------------------------------
# Human nutrtion sheet
@@ -984,7 +983,7 @@ hum_nutr_all = bind_rows(food_old_match, food_psf_new, food_asf)
# check = full_join(hum_nutr_all, proc_new, by = c("product"="proc_out"))
write_csv(hum_nutr_all, "Input_data/hum_nutr_all.csv")
-
+
# Animal nutrition new ----------------------------------------------------
# Cifos sheet
@@ -1939,9 +1938,6 @@ loss_frac_all$Product[!loss_frac_all$Product %in% rbind(Processing_sheet_final$p
# NONE --> GOOOD!
-
-
-
# Fert_suitability sheet --------------------------------------------------
dat_fertsuitability = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Fert_Suitability") %>%
dplyr::rename(scenario=1,crop_cifos = 2)
@@ -1951,18 +1947,18 @@ table_fert_suit = dat_fertsuitability %>% distinct_at(vars(scenario), .keep_all
# List of all crops possible
crop_list = proc_new %>% distinct_at(vars(crop_cifos)) %>% pull()
-
+
scen_baseline = tibble(scenario = "Baseline",
- crop_cifos = crop_list)
+ crop_cifos = crop_list)
scen_circular = tibble(scenario = "Circular",
- crop_cifos = crop_list)
+ crop_cifos = crop_list)
fert_suit = bind_rows(scen_baseline, scen_circular) %>%
left_join(table_fert_suit, by= "scenario")
write_csv(fert_suit, "Input_data/fert_suitability.csv")
-
+
# Import sheet -----------------------------------------------------------
dat_ImportExport = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Import_Export") %>%
dplyr::select(c(1:3))
@@ -2002,7 +1998,7 @@ Baseline_import_export = FoodBalanceSheetFAO %>% as_tibble() %>%
dietary_products=item) %>%
replace(is.na(.), 0) %>% #all the NA values (crops that are not grown or imported to the EU) are 0.
dplyr::filter(dietary_products %in% (diet_prod_dist %>% pull(dietary_products))) %>%
- replace(is.na(.), 0) # The forage, fibre and tabacco crops that have no match in column item are set to 0 (no import export assumed)
+ replace(is.na(.), 0) # The forage, fibre and tabacco crops that have no match in column item are set to 0 (no import export assumed)
write_csv(Baseline_import_export, "Input_data/Baseline_import_export.csv")
@@ -2065,11 +2061,11 @@ Baseline_animal_number =
area_code %in% country_eu, #filtering for EU+UKD countries
item %in% c('Beef and Buffalo Meat','Chickens', 'Meat, Poultry',"Pigs", 'Milk, whole fresh cow'),
element %in% c("producing_animals_slaughtered", "stocks","milk_animals")) #%>%
- dplyr::group_by(area_code, item, element) %>%
+dplyr::group_by(area_code, item, element) %>%
dplyr::summarise(value = mean(value, na.rm=T)) #%>%
- pivot_wider(values_from = value,
- names_from = element) #%>%
- rename(crops=item) %>%
+pivot_wider(values_from = value,
+ names_from = element) #%>%
+rename(crops=item) %>%
replace(is.na(.), 0) %>% #all the NA values (crops that are not grown or imported to the EU) are 0.
dplyr::filter(crops %in% dat_crop_fao_cifos_vec) %>%
replace(is.na(.), 0) %>% #The forage, fibre and
@@ -2130,12 +2126,12 @@ crop_other_new =
dplyr::mutate(crop_cifos = case_when(crop_cifos_old == "Grass_Managed_HQ" ~ "grass_arable",
crop_cifos_old == "Grass_Managed_MQ" ~ "grass_pasture",
crop_cifos_old == "Grass_Natural_MQ" ~ "grass_rangeland",
- TRUE ~ crop_cifos)) %>%
+ TRUE ~ crop_cifos)) %>%
dplyr::select(-crop_cifos_old, -code_spam2010) %>%
distinct_at(vars(crop_cifos),.keep_all = T) %>% drop_na(crop_cifos)
write_csv(crop_other_new, "Input_Data/crop_other_new.csv")
-
+
# cropnutr = read_excel('C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx',sheet="CropNutr")
# write_csv(cropnutr,"Input_data/cropnutr_old")
cropnutr = read_csv("Input_data/cropnutr_old")
@@ -2146,14 +2142,14 @@ cropnutr_new =
full_join(crop_map %>% dplyr::select(old_cifos_crop, crop_cifos), by = c("crop_cifos_old"="old_cifos_crop")) %>%
relocate(crop_cifos, .before = crop_cifos_old) %>%
dplyr::select(-crop_cifos_old)
-
- write_csv(cropnutr_new, "Input_data/cropnutr_new.csv")
+
+write_csv(cropnutr_new, "Input_data/cropnutr_new.csv")
# CropFert_New NOT DONE! ------------------------------------------------------------
# CropFert_New = read_excel("C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx", sheet = "CropFert_New")
# CropFert_New %>% write_csv("Input_data/CropFert_New.csv")
CropFert_New = read_csv("Input_data/CropFert_New.csv")
-
+
CropFert =
CropFert_New %>%
dplyr::rename(crop_cifos_old = `...1`) %>%
@@ -2174,7 +2170,7 @@ proc_sheet_crops = bind_rows(dat_proc_new ,dat_proc_asf) #%>% write_csv("Input_
# Crops
crop_datmap = dat_proc_new %>% select(crop_cifos) %>% distinct_all()
write_csv(crop_datmap, "Input_data/crop_datmap.csv")
-
+
# ProcoutH and dietary products map
ProcOutH_dietProduct = Processing_sheet_final %>% distinct_at(vars(proc_out), .keep_all = T) %>%
@@ -2224,15 +2220,15 @@ Crop_Map2 =read_csv("Input_data/crop_map_mine.csv")
diets_animal_crop_datamap =
Crop_Map2 %>% slice(121:210) %>%
dplyr::select(c(1:2)) %>% dplyr::rename(crop_cifos = `CROPS FAO...1`,
- proc_out = `Co-Product...2`) %>%
+ proc_out = `Co-Product...2`) %>%
filter(proc_out != 'Grass_Natural_LQ') %>%
full_join(dat_proc_asf %>% dplyr::select(proc_out, dietary_products)) %>%
distinct_all() %>%
bind_rows(.,dat_proc_new %>% select(c(crop_cifos,4,6)))
-
+
write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv")
-
-
+
+
# crop_animal_co_prod_dietary
@@ -2278,14 +2274,3 @@ write_csv(animal_map, "Input_Data/diets_animal_crop_datamap.csv")
# # Animals
# #Animal_yield sheet
# 1. Yields (column D row 2)
-
-
-
-
-
-
-
-
-
-
-