From 735c7aa37b8aae665f7f6d70921e7d76fe20b0ce Mon Sep 17 00:00:00 2001
From: "Simon, Wolfram" <wolfram.simon@wur.nl>
Date: Fri, 4 Feb 2022 18:10:46 +0000
Subject: [PATCH] Conflict with the local repo
---
.../Crop_cifos_DataPreparation_DB.R | 2276 -----------------
1 file changed, 2276 deletions(-)
delete mode 100644 R_to_SQL_database/Crop_cifos_DataPreparation_DB.R
diff --git a/R_to_SQL_database/Crop_cifos_DataPreparation_DB.R b/R_to_SQL_database/Crop_cifos_DataPreparation_DB.R
deleted file mode 100644
index 95ef88a..0000000
--- a/R_to_SQL_database/Crop_cifos_DataPreparation_DB.R
+++ /dev/null
@@ -1,2276 +0,0 @@
-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",
- "validate")
-
-# https://www.youtube.com/watch?v=CydajdNRJOU -----------------------------
-
-# con <- dbConnect(odbc::odbc(),
-# .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
-# Server = "localhost", Database = "data", UID = "root", PWD = "154236w.S",
-# Port = 3306)
-#
-#
-#
-# install.packages("quantmod"); require("quantmod")
-#
-# ticker = "AAPL"
-# stock = getSymbols(ticker, auto.assign = FALSE)
-#
-# # extracting index
-# idx = as.data.frame(index(stock))
-# stock = as.data.frame(cbind(idx,coredata(stock)))
-# colnames(stock) = c("Date", "Open", "High", "Low", "Close", "Volume" , "Adjusted")
-# head(stock)
-#
-# stock$Symbol = ticker
-#
-#
-# TYPES = list(Date="date",
-# Open="double(10,2)",
-# High="double(10,2)",
-# Low="double(10,2)",
-# Close="double(10,2)",
-# Volume="Int(25)",
-# Adjusted="double(10,2)",
-# Symbol="varchar(5)")
-#
-# # Writing data into the database -----------------------------------------
-# system.time(
-# dbWriteTable(con, name="temp", value=stock, field.types=TYPES, row.names=FALSE))
-#
-#
-# # adding another table for AMZN
-# ticker = "AMZN"
-# stock = getSymbols(ticker, auto.assign = FALSE)
-#
-# # extracting index
-# idx = as.data.frame(index(stock))
-# stock = as.data.frame(cbind(idx,coredata(stock)))
-# colnames(stock) = c("Date", "Open", "High", "Low", "Close", "Volume" , "Adjusted")
-# head(stock)
-#
-# stock$Symbol = ticker
-#
-# tail(stock)
-#
-# # Writing data into the database -----------------------------------------
-# system.time(
-# dbWriteTable(con, name="temp", value=stock, field.types=TYPES, row.names=FALSE))
-#
-# dbSendQuery(con, "insert into stk select * from temp")
-# dbSendQuery(con, "drop table temp")
-#
-# # End connection
-# dbDisconnect(con)
-#
-# getSymbols(data_map, auto.assign = FALSE)
-
-# # https://www.youtube.com/watch?v=F4HP3CXijC8 -----------------------------
-# install.packages(c("DBI", "RSQLite", "dbplyr"))
-# .libPaths(.libPaths()[2])
-# library("DBI")
-# library("RSQLite")
-# library("dplyr") #loads dbplyr automatically
-#
-# portaldb = dbConnect(SQLite(), "portal.sqlite")
-# portaldb = dbConnect(RSQLite::SQLite(), "portal.sqlite")
-#
-#
-# dbListTables((con)) #shows the tables in the connection
-# dbListFields(con, "stk") #gives the fields in the tables
-#
-# # Displaying the table in the database -- this table is not actually an R object yet.
-# stk = tbl(con, "stk")
-# stk
-#
-# stk_df = collect(stk) #this pulls the SQL table into R tbl
-#
-# odbc::odbc(),
-# .connection_string = "Driver={MySQL ODBC 8.0 Unicode Driver};",
-# Server = "localhost", Database = "data", UID = "root", PWD = "154236w.S",
-# Port = 3306)
-
-
-
-# Writing dfs into the cifos_crop schema (db: CIFOS_model_WJS) ------------
-
-# 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 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)
-
-# https://db.rstudio.com/getting-started/database-queries
-# Loading the data sets from Database -------------------------------------
-data_map = tbl(con_crop_map, "data_map")
-data_map = as_tibble(tbl(con_crop_map, "data_map"))
-
-# Crops -------------------------------------------------------------------
-# Issue: THis will not work anymore as the crop file on the database has been uploaded since.
-crop_disag_map = as_tibble(tbl(con_crop_map, "crop_disag_map"))
-# The crop dataset: Job is to add grass and get the original names back to column crop_cifos
-crop = as_tibble(tbl(con, "crop"))
-mapping_grasscrops = read_csv("Input_data/mapping_grasscrops_new.csv") %>%
- dplyr::rename(crop_cifos = crops_cifos) %>% dplyr::select(crop_cifos)
-
-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)) %>%
- 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"))
-# 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.
-#Be careful when changing again.
-dat_crop2=read_csv("Input_data/dat_crop_with_grass.csv") %>%
- dplyr::mutate(crop_cifos = dplyr::recode(crop_cifos,
- "dark_green_vegetables" = "green_vegetables",
- "red_and_orange_vegetables" = "red_vegetables",
- "other_vegetables" = "other_vegetables"))
-
-write_csv(dat_crop2, "Input_data/dat_crop_with_grass_final.csv")
-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)
-
-# class(data_map)
-#
-# # Loading data into R from database - 152 rows
-# # Comment: Complete! ProcOutH -> Dietary Products
-# #
-# procOutH_dietprod = data_map %>%
-# dplyr::select(`ProcOutH...1`:DietProd) %>%
-# dplyr::arrange() %>%
-# drop_na()
-#
-# # crops - 111 rows
-# procOut_all = data_map %>%
-# dplyr::select(`ProcOutH...5`) %>%
-# dplyr::arrange() %>%
-# drop_na()
-#
-# # crops_co product - rows 31
-# # comments: mapping is not correct!!
-# crops_coproduct = data_map %>%
-# dplyr::select(`CROPS FAO...7`:`Co-Product...8`) %>%
-# dplyr::arrange() %>%
-# drop_na()
-#
-# # Crops and coproducts - 110 rows
-# crops_coproduct_2 = data_map %>%
-# dplyr::select(c(22,23)) %>%
-# dplyr::arrange(c(1)) %>%
-# drop_na()
-#
-# # Crops and coproducts and dietary products - 110 rows
-# # # Not important! Ignore
-# # crops_coproduct_dietaryprod = data_map %>%
-# # dplyr::select(c(22,23,24)) %>%
-# # dplyr::arrange(c(1)) %>%
-# # drop_na()
-#
-# # Livestock - 11 rows
-# livestock_co_product = data_map %>%
-# dplyr::select(c(11,12)) %>%
-# dplyr::arrange(LIVESTOCK) %>%
-# drop_na()
-#
-# # Fish products - 18 rows
-# Fish_coproducts = data_map %>%
-# dplyr::select(c(15,16)) %>%
-# dplyr::arrange(FISH) %>%
-# drop_na()
-#
-# # #aquatic plants 14 rows
-# aquatic_plants = data_map %>% dplyr::select(c(25)) %>%
-# dplyr::arrange(c(1)) %>%
-# drop_na()
-#
-# # crops-coprod-dietaryProd - 18 rows
-# crops_coproduct_dietaryprod = data_map %>%
-# dplyr::select(c(29, 30, 31)) %>%
-# dplyr::arrange(c(1)) %>%
-# drop_na()
-#
-# # Processing - Fraction - CropsFAO
-# processingfrac_cropsfao = data_map %>%
-# dplyr::select(`Processing Fraction - Products`, c(41))
-# #
-# # CropsFAO - FoodProducts
-# Yield_food_prod = data_map %>% dplyr::select(`CROPS FAO - yield`, `Food Products`)
-#
-# # Checking the crops with the dietary sheet ----------------------------------------------------
-# crop = as_tibble(tbl(con, "crop"))
-dat_cifos <- read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Animal_Nutr")
-
-# Processing sheet --------------------------------------------------------
-Processing_sheet = read_csv("Input_data/Copy of European_CiFoS_model_data_ANITANov21.csv")
-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 %>%
- dplyr::select(c(1:5)) %>%
- stringdist_right_join(drop_na(dat_crop_final, old_cifos_crop),
- by = c("ProRaw" = "old_cifos_crop"),
- max_dist = 2) %>% #This results in the 59 crops (incl grass) - Good!
- dplyr::select(crop_cifos, ProRaw, ProcIn,ProcOut, Value, `Dietary Products`) %>%
- dplyr::rename(Proc_Raw=ProRaw) %>% clean_names() %>%
-
- # Adding the forage crops and grass to the processing sheet
- dplyr::mutate(across(c(proc_raw, proc_in, proc_out),
- ~ case_when(str_detect(crop_cifos, "for") ~ crop_cifos,
- str_detect(crop_cifos, "grass") ~ crop_cifos,
- crop_cifos == "tobacco" ~ crop_cifos,
- crop_cifos == "seed_cotton" ~ crop_cifos,
- 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 ~ .)))
-
-# removing disaggregated group elemetns that are not needed -
-# Aim: To have only one surrogate crop per crop_group (temperate_fruits = Apples)
-# First paragraph is about the aggregated crop groups
-# The second paragraph is about the crops that show weird relative crops
-# with no values. LIke this we can get rid of the unwanted crop rows in one go.
-# Assumptions:
-# - I deleted the potato starch as it had a value of 0 (Why?)
-
-dat_2nd_level = Processing_sheet %>%
- dplyr::select(c(1:5)) %>%
- stringdist_right_join(drop_na(dat_crop_final, old_cifos_crop),
- by = c("ProRaw" = "old_cifos_crop"),
- max_dist = 2)
-
-dat_2nd_level2 = anti_join(Processing_sheet,dat_2nd_level, by = "ProRaw") %>%
- dplyr::select(-c(6:7)) %>%
- dplyr::rename(proc_raw = ProRaw) %>%
- drop_na() %>%
- filter(`Dietary Products` %in% c("Sugar (Raw Equivalent)",
- "Rice and products",
- "Beer")) %>%
- mutate(crop_cifos = c("sugar_beet", "sugar_beet", "rice", "rice",
- "barley", "barley")) %>%
- relocate(crop_cifos, .before = 1) %>%
- clean_names()
-
-
-
-dat_group = sqldf('SELECT * FROM dat_proc WHERE
- crop_cifos == "temperate_fruit" AND proc_in == "Apple" OR
- crop_cifos == "other_vegetables" AND proc_in == "Cabbage_White" OR
- crop_cifos == "green_vegetables" AND proc_in == "Spinach" OR
- crop_cifos == "red_vegetables" AND proc_in == "Carrot_Winter" OR
- crop_cifos == "other_cereals" AND proc_in == "Oat_Food" OR
- crop_cifos == "other_cereals" AND proc_in == "Oat_Feed" OR
- crop_cifos == "other_pulses" AND proc_in == "Bean_french" OR
- crop_cifos == "other_oil_crops" AND proc_raw == "Olives" OR
- crop_cifos == "other_crops" OR
- crop_cifos == "other_fibres" OR
- crop_cifos == "tropical_fruits" AND proc_in == "oranges_for_juice" OR
- crop_cifos == "tropical_fruits" AND proc_in == "Orange" OR
- crop_cifos == "other_tubers" AND proc_raw == "Yautia" OR
- crop_cifos == "treenuts" AND proc_in == "Walnut" OR
-
- crop_cifos == "sunflower" AND proc_raw == "Sunflower seed" OR
- crop_cifos == "bananas" AND proc_in == "Banana" OR
- crop_cifos == "sugar_beet" AND proc_in == "Sugarbeet" OR
- crop_cifos == "soybeans" AND proc_in == "Soyabean_for_oil" OR
- crop_cifos == "soybeans" AND proc_in == "Soyabean_for_table" OR
- crop_cifos == "maize" AND proc_in == "Maize" OR
- crop_cifos == "pigeon_peas" AND proc_in == "Pea"
- ') %>% as_tibble()
-
-# Remove the groupped crops from the mother df and bind the final dataset togehter
-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
-dat_proc_2 %>% filter(grepl("Cocoa", dietary_products))
-# From FAO supply sheet for year 2014 - here i filter for strings in the FAO database
-dat_foodsupply = read_csv("Input_data/FAOSTAT_FoodSupply_data_11-23-2021.csv")
-dat_foodsupply %>% dplyr::filter(Element=="Processing") %>%
- filter(grepl("Cocoa", Item)) %>%
- distinct(Item)
-
-# df with missing dietary products
-dat_missin_dietprod = dat_proc_2 %>%
- dplyr::filter(is.na(dietary_products)) %>% pull(crop_cifos)
-
-# Adding the missing dietary prodcucts to the dataframe
-dat_dietprod_comp = dat_proc_2 %>%
- # Adding dietary products to the missing crops
- dplyr::mutate(dietary_products = case_when(
-
- # Forage crops --- Fodder crops and products
- crop_cifos == "beetfor" ~ "Sugar beet",
- crop_cifos == "cabbagefor" ~ "Vegetables, Other",
- crop_cifos == "carrotfor" ~ "Vegetables, Other",
- crop_cifos == "oilseedfor" ~ "Soyabean Oil",
- crop_cifos == "fornesfor" ~ "Vegetables, Other",
- crop_cifos == "legumenesfor" ~ "Pulses, Other and products",
- crop_cifos == "maizefor" ~ "Maize and products",
- crop_cifos == "ryefor" ~ "Rye and products", # newly added to cifos
- crop_cifos == "swedefor" ~ "Sugar beet",
- crop_cifos == "turnipfor" ~ "Sugar beet",
- crop_cifos == "vegfor" ~ "Vegetables, Other",
-
- # Grassy crops
- crop_cifos == "grass_pasture" ~ "Roughage", #no real FAO category
- crop_cifos == "cloverfor" ~ "Roughage",
- str_detect(crop_cifos, "grass") ~ "Roughage", #no real FAO category
-
- # Food crops
- crop_cifos == "cassava" ~ "Cassava and products", # newly added to cifos
- crop_cifos == "coconuts" ~ "Coconuts - Incl Copra",
- crop_cifos == "cow_peas_dry" ~ "Pulses, Other and products",
- crop_cifos == "pearl_millet" ~ "Millet and products", # newly added to cifos
- crop_cifos == "oil_palm_fruit" ~ "Palm kernels", # newly added to cifos; options: 1 Palm kernels, 2 Palmkernel Oil 3 Palm Oil
- crop_cifos == "plantains" ~ "Plantains", # newly added to cifos;
- crop_cifos == "seed_cotton" ~ "Fibres", #no real FAO category
- crop_cifos == "sorghum" ~ "Sorghum and products",# newly added to cifos;
- crop_cifos == "sugar_cane" ~ "Sugar cane", # newly added to cifos;
- crop_cifos == "sweet_potatoes" ~ "Sweet potatoes", # newly added to cifos;
- crop_cifos == "tea" ~ "Tea (including mate)", #newly added to cifos;
- crop_cifos == "tobacco" ~ "Tobacco",#no real FAO category; newly added to cifos;
- crop_cifos == "yams" ~ "Vegetables, Other",
- crop_cifos == "other_crops" ~ "Pulses, Other and products",
- crop_cifos == "other_fibres" ~ "Fibres",#no real FAO category;newly added to cifos;
- 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)
-
-dat_comp = dat_dietprod_comp %>% dplyr::filter(is.na(proc_in)) %>%
- tibble::add_column(na_replace = vec_proc_raw) %>%
- dplyr::mutate(proc_in = na_replace,
- proc_out = na_replace,
- value = 1) %>%
- dplyr::select(-na_replace)
-
-# Merging dfs - FInal processing sheet
-dat_complete = dat_dietprod_comp %>%
- dplyr::filter(!is.na(proc_in)) %>%
- 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") %>%
- dplyr::distinct(dietary_products, .keep_all = T)
-
-# This is the ones that are freely added and have a correspondant entry in the FAO supply data sheet
-new_dietprods_dist_FAO = new_dietprods_dist %>%
- dplyr::filter(!dietary_products %in% c("Roughage", "Fibres", "Tea", "Tobacco", "Fibres"))
-
-# This is the ones that are freely added and have NO correspondant entry in the FAO supply data sheet
-new_dietprods_dist_noFAO = new_dietprods_dist %>%
- dplyr::filter(dietary_products %in% c("Roughage", "Fibres", "Tea", "Tobacco", "Fibres"))
-
-# Adding the missing proc outs
-# 1. Identifying the missing proc-outs
-# new_procout = dat_complete %>%
-# dplyr::filter(!is.na(proc_out)) %>%
-# distinct_all() %>%
-# dplyr::anti_join(.,Processing_sheet, by=c("proc_out" = "ProcOut"))
-
-new_procin = dat_complete %>%
- dplyr::filter(!is.na(proc_in)) %>%
- distinct_all() %>%
- dplyr::anti_join(.,Processing_sheet, by=c("proc_in" = "ProcIn"))
-
-# Conclusion: There are 33 newly added processing products that need to be processed into different processing products
-# Rules:
-# - Forage gets value = 1.
-# - 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
-# crop_cifos proc_raw proc_in proc_out value dietary_products
-# <chr> <chr> <chr> <chr> <dbl> <chr>
-# 1 grass_pasture grass_pasture grass_pasture grass_pasture 1 Roughage
-# 2 beetfor beetfor beetfor beetfor 1 Sugar beet
-# 3 cabbagefor cabbagefor cabbagefor cabbagefor 1 Vegetables, Other
-# 4 carrotfor carrotfor carrotfor carrotfor 1 Vegetables, Other
-# 5 cloverfor cloverfor cloverfor cloverfor 1 Roughage
-# 6 fornesfor fornesfor fornesfor fornesfor 1 Vegetables, Other
-# 7 legumenesfor legumenesfor legumenesfor legumenesfor 1 Pulses, Other and products
-# 8 maizefor maizefor maizefor maizefor 1 Maize and products
-# 9 oilseedfor oilseedfor oilseedfor oilseedfor 1 Soyabean Oil
-# 10 ryefor ryefor ryefor ryefor 1 Rye and products
-# 11 seed_cotton seed_cotton seed_cotton seed_cotton 1 Fibres
-# 12 swedefor swedefor swedefor swedefor 1 Sugar beet
-# 13 tobacco tobacco tobacco tobacco 1 Tobacco
-# 14 turnipfor turnipfor turnipfor turnipfor 1 Sugar beet
-# 15 vegfor vegfor vegfor vegfor 1 Vegetables, Other
-# 16 grass_pasture grass_pasture grass_pasture grass_pasture 1 Roughage
-# 17 grass_arable grass_arable grass_arable grass_arable 1 Roughage
-# 18 grass_rangeland grass_rangeland grass_rangeland grass_rangeland 1 Roughage
-# 19 cassava Cassava Cassava Cassava 1 Cassava and products
-# 20 cocoa_beans Cocoa beans Cocoa beans Cocoa beans 1 Cocoa Beans and products
-# 21 coconuts Coconuts Coconuts Coconuts 1 Coconuts - Incl Copra
-# 22 cow_peas_dry Cow peas, dry Cow peas, dry Cow peas, dry 1 Pulses, Other and products
-# 23 pearl_millet Millet Millet Millet 1 Millet and products
-# 24 oil_palm_fruit Oil palm fruit Oil palm fruit Oil palm fruit 1 Palm kernels
-# 25 plantains Plantains Plantains Plantains 1 Plantains
-# 26 sorghum Sorghum Sorghum Sorghum 1 Sorghum and products
-# 27 sugar_cane Sugar cane Sugar cane Sugar cane 1 Sugar cane
-# 28 sweet_potatoes Sweet potatoes Sweet potatoes Sweet potatoes 1 Sweet potatoes
-# 29 tea Tea Tea Tea 1 Tea
-# 30 yams Yams Yams Yams 1 Vegetables, Other
-# 31 other_crops Chilies and peppers, green Chilies and peppers, green Chilies and peppers, green 1 Pulses, Other and products
-# 32 other_fibres Flax fiber and tow Flax fiber and tow Flax fiber and tow 1 Fibres
-# 33 other_tubers Yautia Yautia Yautia 1 Vegetables, Other
-
-
-# Processing hardcode -----------------------------------------------------
-proc_new = tibble("crop_cifos" = NA, "proc_raw" = NA, "proc_in" = NA, "proc_out" = NA, "value" = NA, "dietary_products" = NA) %>%
-
- # Cocoa -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf)
- # Assumptions: we just look at chocolate production from cocoa comodity tree up to 2nd level
- # level: second level where available.
- add_row(crop_cifos = "cocoa_beans" , proc_raw = "Cocoa beans" , proc_in = "cocoa_for_chocolate", proc_out = "cocoa_butter", value = 0.47 *.8, dietary_products = "Cocoa Beans and products") %>%
- add_row(crop_cifos = "cocoa_beans" , proc_raw = "Cocoa beans" , proc_in = "cocoa_for_chocolate", proc_out = "cocoa_powder_cake", value = 0.53 *.8, dietary_products = "Cocoa Beans and products") %>%
- add_row(crop_cifos = "cocoa_beans" , proc_raw = "Cocoa beans" , proc_in = "cocoa_for_chocolate", proc_out = "cocoa_husks", value = 0.2, dietary_products = "Cocoa Beans and products") %>%
- drop_na() %>%
-
- # Coconut -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf)
- # Assumptions: It adds not up to 1 due to water loss
- # levels: second level where available, no data for copra only!
- add_row(crop_cifos = "coconuts" , proc_raw = "coconuts" , proc_in = "coconuts_dessicatedin", proc_out = "coconuts_dessicated", value = 0.20, dietary_products = "Coconuts - Incl Copra") %>%
- 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") %>%
- add_row(crop_cifos = "pearl_millet" , proc_raw = "pearl_millet" , proc_in = "pearl_millet_flourin", proc_out = "pearl_millet_bran", value = 0.10, dietary_products = "Millet and products") %>%
- add_row(crop_cifos = "pearl_millet" , proc_raw = "pearl_millet" , proc_in = "pearl_millet_beerin", proc_out = "pearl_millet_beer", value = 4.5, dietary_products = "Millet and products") %>%
-
- # Oil palm -- Source: from Technical Conversion Factors for Agricultural Commodities (http://countrystat.org/resources/documents/tcf.pdf); adjusted commodity tree; fractions from "OIL PALM FRUIT"
- # Assumption: oil_palm_kernels_fibre is just the difference of the sum of the listed categories to 1.
- # Both kernels and fruits are always both processed
- add_row(crop_cifos = "oil_palm_fruit" , proc_raw = "oil_palm_fruit" , proc_in = "oil_palm_fruitin", proc_out = "oil_palm_fruits_oil", value = 0.19, dietary_products = "Palm Oil") %>%
- add_row(crop_cifos = "oil_palm_fruit" , proc_raw = "oil_palm_fruit" , proc_in = "oil_palm_fruitin", proc_out = "oil_palm_kernels_cake", value = 0.46*0.06, dietary_products = "Palm kernels") %>%
- add_row(crop_cifos = "oil_palm_fruit" , proc_raw = "oil_palm_fruit" , proc_in = "oil_palm_fruitin", proc_out = "oil_palm_kernels_oil", value = 0.53*0.06, dietary_products = "Palmkernel Oil") %>%
-
- # plantains -- tables from countries. Assumptions: the whole plantain is to be kept whole! Question: Is that a fair assumption?
- add_row(crop_cifos = "plantains" , proc_raw = "plantains" , proc_in = "plantains", proc_out = "plantains", value = 1, dietary_products = "Plantains") %>%
-
- # sorghum -- adjusted commodity table "Sorghum"
- # levels, only level 1 used (level 2 does not make)
- add_row(crop_cifos = "sorghum" , proc_raw = "sorghum" , proc_in = "sorghum_flourin", proc_out = "sorghum_flour", value = 0.9, dietary_products = "Sorghum and products") %>%
- add_row(crop_cifos = "sorghum" , proc_raw = "sorghum" , proc_in = "sorghum_flourin", proc_out = "sorghum_bran", value = 0.08, dietary_products = "Sorghum and products") %>%
- add_row(crop_cifos = "sorghum" , proc_raw = "sorghum" , proc_in = "sorghum_beerin", proc_out = "sorghum_beer", value = 4.85, dietary_products = "Sorghum and products") %>% #on first level
-
- # sugar_cane
- # add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_cane", proc_out = "beverage_non_alc", value = 0.13*0.5, dietary_products = "Sugar cane") %>%
-
- # add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_cane", proc_out = "beverage_dist_alc", value = 0.13*0.5, dietary_products = "Sugar cane") %>%
- add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_cane" , proc_in = "sugar_canein", proc_out = "sugar_raw", value = 0.11, dietary_products = "Sugar (Raw Equivalent)") %>%
- add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_raw" , proc_in = "for_sugar_white", proc_out = "sugar_white", value = 0.935, dietary_products = "Sugar (Raw Equivalent)") %>% # 2nd level of sugar raw
- # add_row(crop_cifos = "sugar_cane" , proc_raw = "sugar_raw" , proc_in = "for_sugar_white", proc_out = "sugar_factorylime", value = 1-0.935, dietary_products = "Sugar (Raw Equivalent)") %>% # 2nd level of sugar raw
-
- 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 --
- add_row(crop_cifos = "tea" , proc_raw = "tea" , proc_in = "teain", proc_out = "tea_leave", value = 1, dietary_products = "Tea (including mate)") %>%
-
- # yams --
- # 1 . level only (other tubers)
- add_row(crop_cifos = "yams" , proc_raw = "yams" , proc_in = "yams_flourin", proc_out = "yams_flour", value = 0.25, dietary_products = "Roots, Other") %>%
- add_row(crop_cifos = "yams" , proc_raw = "yams" , proc_in = "yams_dryin", proc_out = "yams_dry", value = 0.3, dietary_products = "Roots, Other") %>%
-
- # other_crops --
- add_row(crop_cifos = "other_crops" , proc_raw = "chilies_and_peppers_green" , proc_in = "chilies_and_peppers_green", proc_out = "chilies_and_peppers_green", value = 0.9, dietary_products = "Pulses, Other and products") %>% # Commodity tree, from Egypt "Chillies, green"
-
- # other_fibres -- cotton fibre fractions
- add_row(crop_cifos = "other_fibres" , proc_raw = "flax_fiber_and_tow" , proc_in = "flax_fiber_and_tow", proc_out = "flax_fiber_and_tow", value = 0.33, dietary_products = "Fibres") %>%
-
- # other_tubers
- # 1 . level only (other tubers)
- add_row(crop_cifos = "other_tubers" , proc_raw = "other_tubers" , proc_in = "other_tubers_flourin", proc_out = "other_tubers_flour", value = 0.25, dietary_products = "Roots, Other") %>%
- add_row(crop_cifos = "other_tubers" , proc_raw = "other_tubers" , proc_in = "other_tubers_dryin", proc_out = "other_tubers_dry", value = 0.3, dietary_products = "Roots, Other") %>%
-
- # cotton - cotton fibre fractions
- add_row(crop_cifos = "seed_cotton" , proc_raw = "seed_cotton" , proc_in = "seed_cotton_fibresin", proc_out = "seed_cotton_fibres", value = 0.33, dietary_products = "Fibres") %>%
-
- # tobacco
- add_row(crop_cifos = "tobacco" , proc_raw = "tobacco" , proc_in = "tobacco_leavesin", proc_out = "tobacco_leaves", value = 1, dietary_products = "Tobacco") %>%
-
- # Chick peas
- # Levels: all pulses only one level
- add_row(crop_cifos = "chick_peas" , proc_raw = "chick_peas" , proc_in = "chickpea_vegetablein", proc_out = "chickpea_vegetable", value = 0.95, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "chick_peas" , proc_raw = "chick_peas" , proc_in = "chickpea_flourin", proc_out = "chickpea_flour", value = 0.72, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "chick_peas" , proc_raw = "chick_peas" , proc_in = "chickpea_flourin", proc_out = "chickpea_bran", value = 0.23, dietary_products = "Pulses, Other and products") %>%
-
- # Beans dry
- # Levels: all pulses only one level
- add_row(crop_cifos = "beans_dry" , proc_raw = "beans_dry" , proc_in = "beans_dry_vegetable", proc_out = "beans_dry_vegetable", value = 0.95, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "beans_dry" , proc_raw = "beans_dry" , proc_in = "beans_dry_flourin", proc_out = "beans_dry_flour", value = 0.72, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "beans_dry" , proc_raw = "beans_dry" , proc_in = "beans_dry_flourin", proc_out = "beans_dry_bran", value = 0.23, dietary_products = "Pulses, Other and products") %>%
-
- # Other pulses
- # Levels: all pulses only one level
- add_row(crop_cifos = "other_pulses" , proc_raw = "other_pulses" , proc_in = "other_pulses_vegetable", proc_out = "other_pulses_vegetable", value = 0.95, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "other_pulses" , proc_raw = "other_pulses" , proc_in = "other_pulses_flourin", proc_out = "other_pulses_flour", value = 0.72, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "other_pulses" , proc_raw = "other_pulses" , proc_in = "other_pulses_flourin", proc_out = "other_pulses_bran", value = 0.23, dietary_products = "Pulses, Other and products") %>%
-
- # lentils
- # Levels: all pulses only one level
- add_row(crop_cifos = "lentils" , proc_raw = "lentils" , proc_in = "lentils_vegetable", proc_out = "lentils_vegetable", value = 0.95, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "lentils" , proc_raw = "lentils" , proc_in = "lentils_flourin", proc_out = "lentils_flour", value = 0.72, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "lentils" , proc_raw = "lentils" , proc_in = "lentils_flourin", proc_out = "lentils_bran", value = 0.23, dietary_products = "Pulses, Other and products") %>%
-
- # pigeon_peas
- # Levels: all pulses only one level
- add_row(crop_cifos = "pigeon_peas" , proc_raw = "pigeon_peas" , proc_in = "pigeon_peas_vegetable", proc_out = "pigeon_peas_vegetable", value = 0.95, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "pigeon_peas" , proc_raw = "pigeon_peas" , proc_in = "pigeon_peas_flourin", proc_out = "pigeon_peas_flour", value = 0.72, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "pigeon_peas" , proc_raw = "pigeon_peas" , proc_in = "pigeon_peas_flourin", proc_out = "pigeon_peas_bran", value = 0.23, dietary_products = "Pulses, Other and products") %>%
-
- # cow_peas_dry
- # Levels: all pulses only one level
- add_row(crop_cifos = "cow_peas_dry" , proc_raw = "cow_peas_dry" , proc_in = "cow_peas_dry_vegetable", proc_out = "cow_peas_dry_vegetable", value = 0.95, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "cow_peas_dry" , proc_raw = "cow_peas_dry" , proc_in = "cow_peas_dry_flourin", proc_out = "cow_peas_dry_flour", value = 0.72, dietary_products = "Pulses, Other and products") %>%
- add_row(crop_cifos = "cow_peas_dry" , proc_raw = "cow_peas_dry" , proc_in = "cow_peas_dry_flourin", proc_out = "cow_peas_dry_bran", value = 0.23, dietary_products = "Pulses, Other and products") %>%
-
- # groundnuts - commodity tree
- #1 level
- add_row(crop_cifos = "groundnuts" , proc_raw = "groundnuts_in_shell" , proc_in = "groundnuts_shellin", proc_out = "groundnut_shelled", value = 0.7, dietary_products = "Groundnuts (Shelled Eq)") %>%
- add_row(crop_cifos = "groundnuts" , proc_raw = "groundnuts_in_shell" , proc_in = "groundnuts_shellin", proc_out = "groundnut_shell", value = 0.3, dietary_products = "Groundnuts (Shelled Eq)") %>%
- # 2nd level
- add_row(crop_cifos = "groundnuts" , proc_raw = "groundnut_shelled" , proc_in = "groundnuts_oilin", proc_out = "groundnut_oil", value = 0.43, dietary_products = "Groundnut Oil") %>%
- 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)") %>%
- add_row(crop_cifos = "other_oil_crops" , proc_raw = "olives" , proc_in = "olive_oilin", proc_out = "olive_oil", value = 0.25, dietary_products = "Olive Oil") %>% #virgine and residue oil mixed! Therfore 5% more oil
- add_row(crop_cifos = "other_oil_crops" , proc_raw = "olives" , proc_in = "olive_oilin", proc_out = "olive_cake", value = 0.35, dietary_products = "Olives (including preserved)") %>%
-
- # temperate_fruit - assumption: Whole fruit is used and no losses occur
- add_row(crop_cifos = "temperate_fruit" , proc_raw = "temperate_fruit" , proc_in = "temperate_fruit_whole", proc_out = "temperate_fruit_whole", value =mean(c(0.5, 0.35,0.29, 0.25)) , dietary_products = "Apples and products") %>% #tcf fao, fractions from apples, plums, apricots, raisins
- add_row(crop_cifos = "temperate_fruit" , proc_raw = "temperate_fruit" , proc_in = "temperate_fruit_juice", proc_out = "temperate_fruit_juice", value = mean(c(0.22, 0.75)) , dietary_products = "Apples and products") %>% #tcf fao, taken from apple, raisins
- add_row(crop_cifos = "temperate_fruit" , proc_raw = "temperate_fruit" , proc_in = "temperate_fruit_juice", proc_out = "temperate_fruit_pulp", value = mean(c(0.4)) , dietary_products = "Apples and products") %>% #not in fao. taken from apple
-
- # tropical_fruits - assumption: Whole fruit is used and no losses occur
- add_row(crop_cifos = "tropical_fruits" , proc_raw = "tropical_fruits" , proc_in = "tropical_fruits_whole", proc_out = "tropical_fruits_whole", value = mean(c(0.65,0.52)), dietary_products = "Oranges, Mandarines") %>% #tcf fao(canned), orange canned, pineapples
- add_row(crop_cifos = "tropical_fruits" , proc_raw = "tropical_fruits" , proc_in = "tropical_fruits_juice", proc_out = "tropical_fruits_juice", value = mean(c(0.1,0.1,0.2)), dietary_products = "Oranges, Mandarines") %>%#tcf fao, mean of oranges, mango, pineapple
- add_row(crop_cifos = "tropical_fruits" , proc_raw = "tropical_fruits" , proc_in = "tropical_fruits_juice", proc_out = "tropical_fruits_pulp", value = mean(c(0.4,0.55)), dietary_products = "Oranges, Mandarines") %>%#tcf fao, mean of oranges, mango
-
- #Adding more details to existing crops:
- # other_vegetables
- add_row(crop_cifos = "other_vegetables" , proc_raw = "cabbages" , proc_in = "cabbage_whitein", proc_out = "cabbage_white", value = 0.65, dietary_products = "Vegetables, Other") %>%
-
- # red_vegetables
- add_row(crop_cifos = "red_vegetables" , proc_raw = "carrots" , proc_in = "carrot_winterin", proc_out = "carrot_winter", value = 0.65, dietary_products = "Vegetables, Other") %>%
-
- # green_vegetables
- add_row(crop_cifos = "green_vegetables" , proc_raw = "spinach" , proc_in = "spinachin", proc_out = "spinach", value = 0.65, dietary_products = "Vegetables, Other") %>%
-
- # Maize - commodity tree
- # 2 levels
- add_row(crop_cifos = "maize" , proc_raw = "maize_flour" , proc_in = "maize_flourin", proc_out = "maize_flour", value = 0.82, dietary_products = "Maize and products") %>%
- add_row(crop_cifos = "maize" , proc_raw = "maize_flour" , proc_in = "maize_flourin", proc_out = "maize_bran", value = 0.11, dietary_products = "Maize and products") %>% #for feed
- add_row(crop_cifos = "maize" , proc_raw = "maize_flour" , proc_in = "maize_flourin", proc_out = "maize_germ", value = 0.06, dietary_products = "Maize and products") %>%
- add_row(crop_cifos = "maize" , proc_raw = "maize_germ" , proc_in = "maize_oilin", proc_out = "maize_cake", value = 0.52, dietary_products = "Maize and products") %>%
- add_row(crop_cifos = "maize" , proc_raw = "maize_germ" , proc_in = "maize_oilin", proc_out = "maize_oil", value = 0.45, dietary_products = "Maize Germ Oil") %>%
-
- # Grass crops
- ## grass_arable - Prefereably this should represent a grass type like lollium ssp.
- add_row(crop_cifos = "grass_arable" , proc_raw = "grass_arable" , proc_in = "grass_arable_dry", proc_out = "grass_arable_hay", value = 0.88, dietary_products = "Roughage") %>% # Timothy (Phleum pratense), hay (feedepedia)
- add_row(crop_cifos = "grass_arable" , proc_raw = "grass_arable" , proc_in = "grass_arable_fresh", proc_out = "grass_arable_fresh", value = (1-0.86), dietary_products = "Forage and silage, grasses nes") %>% # Source: https://doi.org/10.1111/j.1469-8137.1994.tb04036.x - I take the upper bound
- add_row(crop_cifos = "grass_arable" , proc_raw = "grass_arable" , proc_in = "grass_arable_silage", proc_out = "grass_arable_silage", value = 0.308, dietary_products = "Roughage") %>% #, silage (feedepedia)
-
- ## grass_pasture - Source: https://www.feedipedia.org/node/16886; Surrogate specie: Timothy grass (Phleum pratense)
- add_row(crop_cifos = "grass_pasture" , proc_raw = "grass_pasture" , proc_in = "grass_pasture_dry", proc_out = "grass_pasture_hay", value = 0.88, dietary_products = "Roughage") %>% #Timothy (Phleum pratense), hay (feedepedia)
- add_row(crop_cifos = "grass_pasture" , proc_raw = "grass_pasture" , proc_in = "grass_pasture_fresh", proc_out = "grass_pasture_fresh", value = 0.27, dietary_products = "Forage and silage, grasses nes") %>% # Timothy (Phleum pratense), aerial part, fresh, (feedepedia)
- add_row(crop_cifos = "grass_pasture" , proc_raw = "grass_pasture" , proc_in = "grass_pasture_silage", proc_out = "grass_pasture_silage", value = 0.308, dietary_products = "Roughage") %>% #Timothy grass (Phleum pratense) silage, (feedepedia)
-
- ## 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)
- add_row(crop_cifos = "alfalfafor" , proc_raw = "alfalfafor" , proc_in = "alfalfafor_fresh", proc_out = "alfalfafor_fresh", value = 0.199, dietary_products = "Forage and silage, legumes") %>% #Alfalfa fresh (feedepedia)
- add_row(crop_cifos = "alfalfafor" , proc_raw = "alfalfafor" , proc_in = "alfalfafor_silage", proc_out = "alfalfafor_silage", value = 0.308, dietary_products = "Forage and silage, legumes") %>% #Alfalfa, silage (feedepedia)
-
- # cloverfor - Source: https://www.feedipedia.org/node/246
- add_row(crop_cifos = "cloverfor" , proc_raw = "cloverfor" , proc_in = "cloverfor_dry", proc_out = "cloverfor_hay", value = 0.894, dietary_products = "Roughage") %>% # Alfalfa, hay, (feedepedia)
- add_row(crop_cifos = "cloverfor" , proc_raw = "cloverfor" , proc_in = "cloverfor_fresh", proc_out = "cloverfor_fresh", value = 0.19, dietary_products = "Forage and silage, legumes") %>% #Red clover (Trifolium pratense), aerial part, fresh (feedepedia)
- add_row(crop_cifos = "cloverfor" , proc_raw = "cloverfor" , proc_in = "cloverfor_silage", proc_out = "cloverfor_silage", value = 0.277, dietary_products = "Roughage") %>% #Red clover (Trifolium pratense), silage (feedepedia)
-
- # ryefor - Source: https://www.feedipedia.org/node/385
- # According to FAO this might relate to fast growing grass which is covered by grass already -- https://www.fao.org/es/faodef/fdef11e.htm -- RYE GRASS FOR FORAGE Italian ryegrass (Lolium multiflorum); English, perennial ryegrass (L. perenne) (feedepedia)
- add_row(crop_cifos = "ryefor" , proc_raw = "ryefor" , proc_in = "ryefor_freshin", proc_out = "ryefor_fresh", value = 0.16, dietary_products = "Forage and silage, legumes") %>% # Reference: Feedipedia: Rye, aerial part, fresh (feedepedia)
- add_row(crop_cifos = "ryefor" , proc_raw = "ryefor" , proc_in = "ryefor_dryin", proc_out = "ryefor_hay", value = 0.92, dietary_products = "Forage and silage, legumes") %>% # Reference: Feedipedia: Rye straw (feedepedia)
- add_row(crop_cifos = "ryefor" , proc_raw = "ryefor" , proc_in = "ryefor_silage", proc_out = "ryefor_silage", value = 0.277, dietary_products = "Forage and silage, legumes") %>% # #Red clover (Trifolium pratense), silage (feedepedia)
-
- # fornesfor
- add_row(crop_cifos = "fornesfor" , proc_raw = "fornesfor_feed" , proc_in = "fornesfor_feedin", proc_out = "fornesfor_plant", value = 0.65, dietary_products = "Forage products") %>% #comes from cabbage
-
- # beetfor - Source: https://www.feedipedia.org/node/534
- add_row(crop_cifos = "beetfor" , proc_raw = "beetfor_feed" , proc_in = "beetfor_feedin", proc_out = "beetfor_leaves", value = 0.179, dietary_products = "Forage products") %>% # Beet leaves, fresh
- add_row(crop_cifos = "beetfor" , proc_raw = "beetfor_feed" , proc_in = "beetfor_feedin", proc_out = "beetfor_root", value = 0.161, dietary_products = "Forage products") %>% # Beet root, fodder type, fresh
-
- # cabbagefor
- add_row(crop_cifos = "cabbagefor" , proc_raw = "cabbagefor_feed" , proc_in = "cabbagefor_feedin", proc_out = "cabbagefor_plant", value = 0.65, dietary_products = "Forage products") %>%
-
- # carrotfor - Source: https://www.feedipedia.org/node/539
- add_row(crop_cifos = "carrotfor" , proc_raw = "carrotfor_feed" , proc_in = "carrotfor_feedin", proc_out = "carrots_fresh", value = 0.107 , dietary_products = "Forage products") %>% # Beet leaves, fresh
- add_row(crop_cifos = "carrotfor" , proc_raw = "carrotfor_feed" , proc_in = "carrotfor_feedin", proc_out = "carrots_tops_fresh", value = 0.164, dietary_products = "Forage products") %>% # Beet root, fodder type, fresh
-
- # legumenesfor - Source: https://www.feedipedia.org/node/4926 --> Faba Bean taken as a proxy
- # 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
- add_row(crop_cifos = "maizefor" , proc_raw = "maize_whole_feed" , proc_in = "maize_oil", proc_out = "maize_cake", value = 0.52*0.06, dietary_products = "Forage products") %>% # Reference: Commodidty tree fao same as with humans
-
- # oilseedfor - from Commodity tree (Soyabeans taken as a proxy)
- add_row(crop_cifos = "oilseedfor" , proc_raw = "oilseedfor" , proc_in = "oilseedfor_for_oil", proc_out = "oilseedfor_for_oil", value = 0.19, dietary_products = "Forage products") %>% # Reference: Soyabean tcf
- add_row(crop_cifos = "oilseedfor" , proc_raw = "oilseedfor" , proc_in = "oilseedfor_for_oil", proc_out = "oilseedfor_cake", value = 0.71, dietary_products = "Forage products") %>% # Reference:Soyabean tcf
- add_row(crop_cifos = "oilseedfor" , proc_raw = "oilseedfor" , proc_in = "oilseedfor_for_oil", proc_out = "oilseedfor_hulls", value = 0.07, dietary_products = "Forage products") %>% # Reference: Soyabean tcf
-
-
- # swedefor - https://www.feedipedia.org/node/534
- add_row(crop_cifos = "swedefor" , proc_raw = "swedefor_feed" , proc_in = "swedefor_feedin", proc_out = "swedefor_leave", value = 0.179, dietary_products = "Forage products") %>% # Beet leaves, fresh
- add_row(crop_cifos = "swedefor" , proc_raw = "swedefor_feed" , proc_in = "swedefor_feedin", proc_out = "swedefor_root", value = 0.161, dietary_products = "Forage products") %>% # Beet root, fodder type, fresh
-
- # turnipfor - https://www.feedipedia.org/node/534
- add_row(crop_cifos = "turnipfor" , proc_raw = "turnipfor_feed" , proc_in = "turnipfor_feedin", proc_out = "turnipfor_leave", value = 0.179, dietary_products = "Forage products") %>% # Beet leaves, fresh
- add_row(crop_cifos = "turnipfor" , proc_raw = "turnipfor_feed" , proc_in = "turnipfor_feedin", proc_out = "turnipfor_root", value = 0.161, dietary_products = "Forage products") %>% # Beet root, fodder type, fresh
-
- # vegfor
- add_row(crop_cifos = "vegfor" , proc_raw = "vegfor_feed" , proc_in = "vegfor_feedin", proc_out = "vegfor_whole", value = 0.65, dietary_products = "Forage products") %>%
-
- # Here I change the existing processing products that are not according to FAO tcf document
- # Barley from fao tcf document
- add_row(crop_cifos = "barley" , proc_raw = "barley" , proc_in = "barley_for_pearlin", proc_out = "barley_pearl", value = 0.72*0.55, dietary_products = "Barley and products") %>% #
- add_row(crop_cifos = "barley" , proc_raw = "barley" , proc_in = "barley_for_pearlin", proc_out = "barley_flour", value = 0.72*.45, dietary_products = "Barley and products") %>% #
- add_row(crop_cifos = "barley" , proc_raw = "barley" , proc_in = "barley_for_pearlin", proc_out = "barley_bran", value = 0.19, dietary_products = "Barley and products") %>% #
- add_row(crop_cifos = "barley" , proc_raw = "barley" , proc_in = "barley_for_pearlin", proc_out = "barley_hulls", value = 0.07, dietary_products = "Barley and products") %>% #
-
- # add_row(crop_cifos = "barley" , proc_raw = "barley" , proc_in = "barley_for_malt", proc_out = "barley_maltculms", value = 0.24, dietary_products = "Barley and products") %>% #
- add_row(crop_cifos = "barley" , proc_raw = "barley" , proc_in = "barley_for_malt", proc_out = "barley_malt", value = 0.73, dietary_products = "Barley and products") %>% #
- # add_row(crop_cifos = "barley" , proc_raw = "barley_malt" , proc_in = "for_beer", proc_out = "brewers_grain", value = 8, dietary_products = "Barley and products") %>% #
- add_row(crop_cifos = "barley" , proc_raw = "barley_malt" , proc_in = "for_beer", proc_out = "barley_beer", value = 8*0.8, dietary_products = "Barley and products") %>% #
-
- # Barley for feed
- add_row(crop_cifos = "barley" , proc_raw = "barley_malt" , proc_in = "barley_feed", proc_out = "grain_barley", value = 0.98, dietary_products = "Barley and products") %>% #
-
- #Wheat- tcf table
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_for_wholemeal", proc_out = "wheat_wholemeal", value = 0.85, dietary_products = "Wheat and products") %>%
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_for_wholemeal", proc_out = "wheat_wholemeal_germ", value = 0.01, dietary_products = "Wheat and products") %>%
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_for_wholemeal", proc_out = "wheat_feedmeal", value = 0.02, dietary_products = "Wheat and products") %>% #
-
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_flour", proc_out = "wheat_germ", value = 0.02, dietary_products = "Wheat and products") %>% #ref: slightly adjusted from tcf FAO so that it adds up to 88% DM
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_flour", proc_out = "wheat_flour", value = 0.75, dietary_products = "Wheat and products") %>%
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_flour", proc_out = "wheat_branh", value = 0.11, dietary_products = "Wheat and products") %>%
-
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_feed", proc_out = "grain_wheat", value = 0.88, dietary_products = "Wheat and products") %>% #wheat harvested ca 12 %DM
-
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_flourba", proc_out = "wheat_flour", value = 0.73, dietary_products = "Wheat and products") %>%
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_flourba", proc_out = "wheat_feedmeal", value = 0.02, dietary_products = "Wheat and products") %>%
- add_row(crop_cifos = "wheat" , proc_raw = "wheat" , proc_in = "wheat_flourba", proc_out = "wheat_brana", value = 0.12, dietary_products = "Wheat and products") %>% #
-
- # Rapeseed- tcf table
- add_row(crop_cifos = "rapeseed" , proc_raw = "rapeseed" , proc_in = "rapeseed_oil", proc_out = "rapeseed_oil", value = 0.38, dietary_products = "Rape and Mustard Oil") %>%
- add_row(crop_cifos = "rapeseed" , proc_raw = "rapeseed" , proc_in = "rapeseed_oil", proc_out = "rapeseed_cake", value = 0.6, dietary_products = "Rape and Mustardseed") %>%
-
- # potatoes - tcf table
- add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_whole", proc_out = "potatoes_dry", value = 0.17, dietary_products = "Potatoes and products") %>%
- add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_whole", proc_out = "tuber_peel", value = 0.02, dietary_products = "Potatoes and products") %>%
-
- add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_flour", proc_out = "potatoes_flour", value = 0.2, dietary_products = "Potatoes and products") %>%
- add_row(crop_cifos = "potatoes" , proc_raw = "potatoes" , proc_in = "potatoes_flour", proc_out = "tuber_peel", value = 0.02, dietary_products = "Potatoes and products") %>%
-
- 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
- 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") %>%
-
- add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_table", proc_out = "soyabean_table", value = 0.71, dietary_products = "Soyabeans") %>% #ref: Ollie
- add_row(crop_cifos = "soybeans" , proc_raw = "soybeans" , proc_in = "soyabean_for_table", proc_out = "soyabean_hulls", value = 0.07, dietary_products = "Soyabeans") %>% #ref: Ollie
-
- # feed
- add_row(crop_cifos = "soybeans" , proc_raw = "soybeans_feed" , proc_in = "soyabean_for_feed", proc_out = "soyabean_feed_oil", value = 0.19, dietary_products = "Soyabean Oil") %>%
- add_row(crop_cifos = "soybeans" , proc_raw = "soybeans_feed" , proc_in = "soyabean_for_feed", proc_out = "soyabean_feed_cake", value = 0.71, dietary_products = "Soyabeans") %>%
- add_row(crop_cifos = "soybeans" , proc_raw = "soybeans_feed" , proc_in = "soyabean_for_feed", proc_out = "soyabean_feed_hulls", value = 0.07, dietary_products = "Soyabeans") %>%
-
- # 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 = "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
- 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
- add_row(crop_cifos = "rice" , proc_raw = "rice_husked" , proc_in = "rice_husked_milled", proc_out = "rice_husked_milled", value = 0.9, dietary_products = "Rice (Milled Equivalent)") %>%
- add_row(crop_cifos = "rice" , proc_raw = "rice_husked" , proc_in = "rice_husked_milled", proc_out = "rice_bran", value = 0.15, dietary_products = "Rice (Milled Equivalent)") %>%
-
- # Milled paddy rice
- add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_paddy_milled", proc_out = "rice_paddy_milled", value = 0.67, dietary_products = "Rice (Milled Equivalent)") %>%
- add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_paddy_milled", proc_out = "hulls_paddy_milled", value = 0.25, dietary_products = "Rice (Milled Equivalent)") %>%
- add_row(crop_cifos = "rice" , proc_raw = "rice" , proc_in = "rice_paddy_milled", proc_out = "bran_paddy_milled", value = 0.8, dietary_products = "Rice (Milled Equivalent)") %>%
-
- # 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
- 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
-
- # Sugar beets - tcf fao
- add_row(crop_cifos = "sugar_beet" , proc_raw = "sugar_beet" , proc_in = "for_sugar_refined", proc_out = "sugarbeet_toptails", value = 0.1, dietary_products = "Sugar beet") %>% #no reference
- add_row(crop_cifos = "sugar_beet" , proc_raw = "sugar_beet" , proc_in = "for_sugar_refined", proc_out = "sugarbeet_pulp", value = 0.07, dietary_products = "Sugar beet") %>%
- add_row(crop_cifos = "sugar_beet" , proc_raw = "sugar_beet" , proc_in = "for_sugar_refined", proc_out = "sugarbeet_molasses", value = 0.04, dietary_products = "Sugar beet") %>%
- add_row(crop_cifos = "sugar_beet" , proc_raw = "sugar_beet" , proc_in = "for_sugar_refined", proc_out = "sugar_refined", value = 0.935*0.14, dietary_products = "Sugar (Raw Equivalent)") %>%
-
- # 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)
-
-# # Anti joim
-# dat_proc_old = dat_complete %>% anti_join(., vec_proc_new)
-#
-# Creating the final processing sheet
-dat_proc_new = dat_complete %>%
- dplyr::filter(!crop_cifos %in% vec_proc_new,
- !proc_out == "sugarbeet_toptails") %>%
- bind_rows(.,proc_new) %>%
- mutate(dietary_products = ifelse(proc_out == "Sugar_raw" & proc_raw == "Sugar beets" |
- proc_out == "sugar_raw" & proc_raw == "sugar_cane",
- "Sugar (Raw Equivalent)", dietary_products)) %>%
- dplyr::mutate(across(c("crop_cifos":"proc_out"), tolower)) %>%
- dplyr::mutate(proc_raw = gsub("\\,", "", proc_raw),
- proc_raw = gsub(" ", "_", proc_raw),
- proc_raw = case_when(
- proc_raw %in% (dat_proc_new %>% pull(proc_out)) &
- proc_raw != proc_out~ proc_raw,
- TRUE ~ crop_cifos))
-
-dat_proc_new %>% write_csv("Input_data/dat_proc_new.csv")
-# dat = read_csv("Input_data/dat_proc_new.csv")
-
-# Adding the animal part to the processing
-
-# Downloading the FAO stat Food balance sheet
-# FoodBalanceSheetFAO = get_faostat_bulk("FBS", "Input_data") #downloads the data bulk from faostat
-
-# FoodBalanceSheetFAO
-
-dat_proc_asf =
- Processing_sheet %>% dplyr::select(-c(6,7)) %>%
- 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
-
-
-
-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
-dat_humnutr = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Human_Nutr")
-
-# Checking what is missing in the humann nutrition shheet so that we can add what is missing from the RIVM sheet
-dat_humnutr_cifos = dat_humnutr %>% janitor::row_to_names(2) %>% janitor::clean_names() %>%
- dplyr::filter(!family %in% c("Dairy", "Fish", "Meat", "Egg")) %>% mutate(product_lc = tolower(product))
-# write_csv(dat_humnutr_cifos, "Input_data/dat_humnutr_cifos.csv")
-
-dat_humnutr_cifos %>%
- dplyr::select(c(4:5), product_lc) %>%
- right_join(., (dat_proc_newdat_humnutr_cifos),
- by = c("product_lc"="proc_out")) %>%
- dplyr::filter(is.na(product)) %>%
- dplyr::anti_join(., dat_procout_demi, by = c("product_lc"="product")) %>%
- dplyr::anti_join(.,dat_map_old_new_procout,by = c("product"="new_procout"))
-
-# write_csv(proc_out_new, "Input_data/proc_out_new.csv")
-
-# Checking the overlap of updated processing sheet with demis nutritional sheet
-dat_procout_demi = read_csv("Input_data/demi_human_food.csv") %>%
- filter(!product=="g" &
- !product =="barley_maltculms") %>%
- dplyr::select(c(4:5), product)
-
-dat_procout_to_find_demi = dat_procout_all %>% dplyr::filter(is.na(original_id)) %>%# this needs to be checked for in the old human nutrition sheet
- write_csv("Input_data/humNutr_demitofind.csv")
-
-
-# Manual Mapping Human Nutrition ------------------------------------------
-# Careful -- Manual mapping -- Which procout is in the old human nutrition sheet that was not detected by the join above. Also mapping for non-human food procouts
-dat_map_old_new_procout =
- read_csv("Input_data/map_old_new_procout_2.csv") %>%
- dplyr::filter(food_cat == "food" & is.na(old_procout))
-
-
-# These prcouts are neither in the old processing sheet nor in demis sheet
-proc_out_missing =
- dat_proc_new %>% dplyr::select(proc_out, dietary_products) %>%
- dplyr::left_join(., dat_humnutr_cifos %>% select(product_lc, product) , by = c("proc_out" ="product_lc")) %>%
- dplyr::left_join(.,dat_map_old_new_procout,by = c("proc_out"="new_procout")) %>%
- dplyr::left_join(., dat_procout_demi, by = c("proc_out"="product")) %>%
- dplyr::filter(is.na(original_id)) %>%
- # dplyr::filter(!is.na(dietary_products)) %>%
- dplyr::filter(food_cat == "food") %>%
- distinct_at(vars(proc_out))
-
-
-
-# new procout added from demi's sheet
-# Food waste crops
-food_psf_new = read_csv("Input_data/food_sheet_demi_distinct.csv") %>%
- slice(-1) %>%
- dplyr::select(-c(2,3,4,41)) %>%
- rename(na_2 = `na...19`,
- na = `na...5`) %>%
- type.convert() %>%
- mutate(epa = as.numeric(epa),
- dha = as.numeric(dha ),
- cholestrol = as.numeric(cholestrol ),
- i =as.numeric(i),
- a = as.numeric(a) ,
- b12 = as.numeric(b12),
- d = as.numeric(d),
- e = as.numeric(e),
- vit_k = as.numeric(vit_k)) %>%
- drop_na(product) %>%
- filter(!product == "groundnut_shelled") %>%
- select(-raw_cooked , -na)
-
-vec_food_new = food_psf_new %>% pull(product)
-
-
-# These are the procouts from the old sheet that we matched the new procouts
-food_old_match =
- read_csv("Input_data/map_old_new_procout_2.csv") %>%
- dplyr::filter(food_cat == "food" & !is.na(old_procout)) %>%
- left_join(dat_humnutr_cifos, by = c("old_procout"="product"))%>%
- type.convert() %>%
- group_by(new_procout) %>%
- summarise_if(is.numeric, mean, na.rm=T ) %>%
- mutate(raw_nevo = "agg",
- cooked_nevo = "agg",
- na = "Food",
- family = "NA",
- product = new_procout) %>%
- relocate(product, .before = "weight") %>%
- relocate(family, .before = "product") %>%
- relocate( na, .before = "family") %>%
- relocate(cooked_nevo, .before = "na") %>%
- relocate(raw_nevo, .before = "cooked_nevo") %>%
- select(-new_procout,-family , -na,-raw_nevo ,-cooked_nevo) %>%
- filter(!product %in% vec_food_new)
-
-
-# procout asf
-food_asf = dat_humnutr %>% janitor::row_to_names(2) %>% janitor::clean_names() %>%
- dplyr::filter(family %in% c("Dairy", "Fish", "Meat", "Egg")) %>%
- mutate(product_lc = tolower(product)) %>%
- type.convert() %>%
- select(-product_lc,-family , -na, -cooked_nevo, -raw_nevo)
-
-# Combining all dfs
-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
-dat_animnutr = readxl::read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Animal_Nutr")
-# %>% janitor::row_to_names(1)
-
-# Which procouts are needed?
-# Checking what is missing in the humann nutrition shheet so that we can add what is missing from the RIVM sheet
-dat_animal_nutr_cifos = dat_animnutr %>%
- janitor::row_to_names(1) %>%
- janitor::clean_names() %>%
- mutate(product_lc = tolower(product)) %>%
- relocate(product_lc, .before = "product")
-
-dat_animal_nutr_cifos %>% colnames(.) %>% as_tibble() %>%
- write_csv("Input_data/dat_animal_cifos.csv")
-
-dat_miss_animal = dat_animal_nutr_cifos %>%
- # dplyr::select(c(4:5), product_lc) %>%
- right_join(., (dat_proc_new %>% dplyr::select(proc_out, dietary_products)),
- by = c("product_lc"="proc_out"))%>%
- dplyr::filter(is.na(product))
-
-# Substract the procouts that are already in the old animalnutrient file
-# Manual mapping: Which crops that are missing in the join above are already in the old animal nutrition sheet?
-# File used as input:
-# write_csv(dat_miss_animal %>% dplyr::select(product_lc, product), "Input_data/dat_miss_animal.csv")
-mapd_animnutr_missing = read_csv("Input_data/dat_miss_animal_out_mapd.csv")
-
-# Missing animal procouts that need to be added from various sources
-proc_out_anim_miss_tbl = mapd_animnutr_missing %>%
- dplyr::filter(feed %in% c("feed", "grass") &
- is.na(proc_out_old_animsheet)) %>%
- dplyr::select(-proc_out_old_animsheet)
-
-proc_out_anim_miss_vec = proc_out_anim_miss_tbl %>% pull(proc_out_new_missing)
-
-
-# Function that does all the foratting, cacluation and extraction of the cvb data from downloaded excel files
-FunAnimDatExtract = function(dat, cvb_code, cvb_name, product){
-
- # function that subsets the cvb data sheets based on the row headers (caution: indexed subsetting did not work due to differences in rownumbers between sheets)
- FunSubsetCVB = function(dat, char_upper, char_lower, suffix_char=NULL){
- table_subset = cvb_table[which.max(cvb_table$nutrient == char_upper) : which.max(cvb_table$nutrient == char_lower), ,drop = FALSE]
- table_subset = table_subset %>% dplyr::filter(nutrient != char_lower & nutrient != char_upper)
- table_subset = table_subset %>% mutate(nutrient= paste(nutrient, suffix_char, sep = "")) %>% dplyr::filter(!unit == "kcal/kg - in DM")
- return(table_subset)
- }
-
- # cvb_table = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 23 57 PM.xlsx", col_names = F) #%>%
-
- cvb_table = dat %>% rename(nutrient = ...1,
- fresh = ...2,
- dm = ...3,
- unit = ...4) %>%
- filter(if_any(everything(), ~ !is.na(.)))
-
-
- # Creating the subsets
- weende = FunSubsetCVB(dat = cvb_table, char_upper = "Weende analysis", char_lower = "Minerals and trace elements", suffix_char = NULL)
- mineral = FunSubsetCVB(dat = cvb_table, char_upper = "Minerals and trace elements", char_lower = "Fermentation products", suffix_char = NULL)
- ferment = FunSubsetCVB(dat = cvb_table, char_upper = "Fermentation products", char_lower = "Digestibility coefficients ruminants", suffix_char = NULL)
-
- digest_rum = FunSubsetCVB(dat = cvb_table, char_upper = "Digestibility coefficients ruminants", char_lower = "Digestibility coefficients pigs", suffix_char = "r")
- digest_rum2 = FunSubsetCVB(dat = cvb_table, char_upper = "DVE/OEB 2007", char_lower = "Feeding values ruminants", suffix_char = "r")
- digest_pig = FunSubsetCVB(dat = cvb_table, char_upper = "Digestibility coefficients pigs", char_lower = "Digestibility coefficients roosters and laying hens",suffix_char = "p")
- digest_lay = FunSubsetCVB(dat = cvb_table, char_upper = "Digestibility coefficients roosters and laying hens", char_lower = "Digestibility coefficients broilers",suffix_char = "l")
- digest_bro = FunSubsetCVB(dat = cvb_table, char_upper = "Digestibility coefficients broilers", char_lower = "Digestibility coefficients rabbits", suffix_char = "b")
- feeding_rum = FunSubsetCVB(dat = cvb_table, char_upper = "Feeding values ruminants", char_lower = "Feeding values rabbits", suffix_char = "r")
- feeding_lay = FunSubsetCVB(dat = cvb_table, char_upper = "Feeding values roosters/laying hens", char_lower = "Feeding values broilers", suffix_char = "l")
- feeding_bro = FunSubsetCVB(dat = cvb_table, char_upper = "Feeding values roosters/laying hens", char_lower = "Feeding values broilers", suffix_char = "b")
- feeding_bro = FunSubsetCVB(dat = cvb_table, char_upper = "Feeding values broilers", char_lower = "Feeding values pigs", suffix_char = "b")
- feeding_pig = FunSubsetCVB(dat = cvb_table, char_upper = "Feeding values pigs", char_lower = "Standardized digestibility coefficients amino acids pigs", suffix_char = "p")
-
- amino_acids_pigs = FunSubsetCVB(dat = cvb_table, char_upper = "Standardized digestible amino acid contents pigs", char_lower = "Apparent digestible amino acid contents pigs", suffix_char = "p")
- amino_acids_bro = FunSubsetCVB(dat = cvb_table, char_upper = "Standardized digestible amino acid contents poultry", char_lower = "Fatty acids", suffix_char = "b")
- amino_acids_lay = FunSubsetCVB(dat = cvb_table, char_upper = "Standardized digestible amino acid contents poultry", char_lower = "Fatty acids", suffix_char = "l")
-
- # Combining the data
- dat_comb_cvb = bind_rows(weende,mineral,ferment,digest_rum,digest_rum2, digest_pig,digest_lay,digest_bro,
- feeding_rum, feeding_lay,feeding_bro,feeding_pig,amino_acids_pigs,amino_acids_bro,amino_acids_lay)
-
- # matching the data
- dat_cvb_final =
- read_csv("Input_data/dat_animal_cifos_cvb_map.csv") %>%
- dplyr::select(c(1:4)) %>%
- dplyr::left_join(dat_comb_cvb, by = c("adj_col_cvb"="nutrient"))%>%
- dplyr::rename(
- content = dm,
- unit = unit,
- nutrient = cifos_cols) %>%
- dplyr::select(nutrient,content) %>%
- tidyr::pivot_wider(names_from = nutrient,
- values_from = content) %>%
- dplyr::mutate(cvb_code = !!cvb_code,
- cvb_name = !!cvb_name,
- product = !!product) %>%
- relocate(cvb_name, .after = cvb_code) %>%
- relocate(cvb_code, .before = DM) %>%
- relocate(cvb_name, .after = cvb_code) %>%
- relocate(product, .after = cvb_name) %>%
- clean_names() %>%
- dplyr::mutate(origin = "co-product",
- family = NA) %>%
- relocate(origin, .before = product) %>%
- relocate(family, .before = product)%>%
- # Calculations
- #[F.H11] GE (kJ/kg) = 24.14 x CP + 36.57 x CFAT + 20.92 x CF + 16.99 x NFE - 0.63 x SUG*
- dplyr::mutate(
-
- ge = case_when(
- sug > 80 ~ round((cp*24.14+cfat*36.57+cf*13.81+nfe*16.99-0.63*sug)/1000,1),
- sug <= 80 ~ round((cp*24.14+cfat*36.57+cf*13.81+nfe*16.99-0.63)/1000,1)),
-
- # #[F.H12] ME (kJ/kg) from cvb feed table 2018
- # me = case_when(
- # sug > 80 ~ round((15.90 * dcp + 37.66 * dcfat + 13.81 * dcf + 14.64 *dnfe - 0.63 * sug)/1000,1),
- # sug <= 80 ~ round((15.90 * dcp + 37.66 * dcfat + 13.81 * dcf + 14.64 *dnfe - 0.63)/1000,1)),
- #
-
- # Calculating nitrogen from protein
- n = cp/6.25)
-}
-
-# Extract and format from cvb feed calculator: https://vvdb.cvbdiervoeding.nl/Manage/Tools/VwCalc.aspx
-{
- soyabean_hulls = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 23 57 PM.xlsx", col_names = F) %>% #CF 320-360 g/kg
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "soyabean_hulls")
-
- soyabean_feed_hulls = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 23 57 PM.xlsx", col_names = F) %>% #CF 320-360 g/kg
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "soyabean_feed_hulls")
-
-
- coconuts_cake_copra = read_xlsx("Input_data/CVB_procout/Copra cake (3015.401)_12_15_2021 6 35 06 PM.xlsx", col_names = F) %>% #CFAT > 100g/kg
- FunAnimDatExtract(cvb_code = "3015.401" ,
- cvb_name = "Copra cake",
- product = "coconuts_cake_copra")
-
- cow_peas_bran = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 44 56 PM.xlsx", col_names = F) %>% #CF 320-360 g/kg
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "cow_peas_dry_bran")
-
- pearl_millet_bran = read_excel("Input_data/CVB_procout/Rice bran meal, solvent extracted (1003.416).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1003.416" ,
- cvb_name = "Rice bran meal",
- product = "pearl_millet_bran")
-
- oil_palm_kernels_cake = read_xlsx("Input_data/CVB_procout/Palm kernel expeller (3001.401)_12_15_2021 6 54 41 PM.xlsx", col_names = F) %>% #CF >180 g/kg
- FunAnimDatExtract(cvb_code = "3001.401" ,
- cvb_name = "Palm kernel expeller",
- product = "oil_palm_kernels_cake")
-
-
- sorghum_bran = read_xlsx("Input_data/CVB_procout/Rice bran meal, solvent extracted (1003.416).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1003.416" ,
- cvb_name = "Rice bran meal",
- product = "sorghum_bran")
-
-
- bagasse = read_xlsx("Input_data/CVB_procout/Sugarbeet pulp, pressed, ensiled (4004.244).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4004.244" ,
- cvb_name = "Sugarbeet pulp",
- product = "sugarcane_bagasse")
-
-
- chickpea_bran = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 44 56 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "chickpea_bran")
-
- beans_dry_bran =read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 44 56 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "beans_dry_bran")
-
-
- other_pulses_bran = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 44 56 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "other_pulses_bran")
-
- lentils_bran = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 44 56 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "lentils_bran")
-
-
- pigeon_peas_bran = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 44 56 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "pigeon_peas_bran")
-
-
- cow_peas_dry_bran = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 44 56 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "cow_peas_dry_bran")
-
-
- groundnut_cake = read_xlsx("Input_data/CVB_procout/Groundnut expeller (2013.401).xlsx", col_names = F) %>% #partly dehulled CF75 - 145 g/kg
- FunAnimDatExtract(cvb_code = "2013.401" ,
- cvb_name = "Groundnut expeller",
- product = "groundnut_cake")
-
-
- # other_cereals_bran = read_xlsx("Input_data/CVB_procout/Rice bran meal, solvent extracted (1003.416).xlsx", col_names = F) %>%
- # FunAnimDatExtract(cvb_code = "1003.416" ,
- # cvb_name = "Rice bran meal",
- # product = "other_cereals_bran")
-
- olive_cake = read_xlsx("Input_data/CVB_procout/Sunflower seed meal, solvent extracted (3003.407).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3003.407" ,
- cvb_name = "Sunflower seed meal, solvent extracted",
- product = "olive_cake")
-
- maize_bran = read_xlsx("Input_data/CVB_procout/Maize bran (1002.108).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1002.108" ,
- cvb_name = "Maize bran",
- product = "maize_bran")
-
- maize_cake = read_xlsx("Input_data/CVB_procout/Maize germs expeller (1002.417).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1002.417" ,
- cvb_name = "Maize germs expeller",
- product = "maize_cake")
-
- maize_silage = read_xlsx("Input_data/CVB_procout/Maize silage (5008.602).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5008.602" ,
- cvb_name = "Maize silage",
- product = "maize_silage")
-
- grass_rangeland_fresh = read_xlsx("Input_data/CVB_procout/Grass hay, a) poor quality (5010.701).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.701" ,
- cvb_name = "Grass hay, a) poor quality",
- product = "grass_rangeland_fresh")
-
- grass_pasture_fresh = read_xlsx("Input_data/CVB_procout/Grass hay, b) average quality (5010.702).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.702" ,
- cvb_name = "Grass hay, b) average quality",
- product = "grass_pasture_fresh")
-
- grass_arable_fresh = read_xlsx("Input_data/CVB_procout/Grass hay, c) good quality (5010.703).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.703" ,
- cvb_name = "Grass hay, c) good quality",
- product = "grass_arable_fresh")
-
-
-
- alfalfafor_fresh = read_xlsx("Input_data/CVB_procout/Lucerne (alfalfa), hay (5004.606).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5004.606" ,
- cvb_name = "Lucerne (alfalfa), hay",
- product = "alfalfafor_fresh")
-
-
- cloverfor_fresh = read_xlsx("Input_data/CVB_procout/Clover red, hay (5003.606).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5003.606" ,
- cvb_name = "Clover red, hay",
- product = "cloverfor_fresh")
-
- beetfor_leaves = read_xlsx("Input_data/CVB_procout/Sugarbeet leaves, fresh (4004.642).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4004.642" ,
- cvb_name = "Sugarbeet leaves, fresh",
- product = "beetfor_leaves")
-
- beetfor_root = read_xlsx("Input_data/CVB_procout/Fodderbeets, fresh (4005.000).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4005.000" ,
- cvb_name = "Fodderbeets, fresh",
- product = "beetfor_root")
-
- cabbagefor_plant = read_xlsx("Input_data/CVB_procout/Cabbage (red_white_sav.), fresh (6023.000).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "6023.000" ,
- cvb_name = "Cabbage (red_white_sav.), fresh",
- product = "cabbagefor_plant")
-
- # carrots_fresh = read_xlsx("Input_data/CVB_procout/Carrots (4006.000).xlsx", col_names = F) %>%
- # FunAnimDatExtract(cvb_code = "4006.000" ,
- # cvb_name = "Carrots",
- # product = "carrots_fresh")
-
-
- carrots_tops_fresh = read_xlsx("Input_data/CVB_procout/Sugarbeet leaves with tops, fresh (4004.647).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4004.647" ,
- cvb_name = "Sugarbeet leaves with tops, fresh",
- product = "carrots_tops_fresh")
-
- turnipfor_leave = read_xlsx("Input_data/CVB_procout/Sugarbeet leaves, fresh (4004.642).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4004.642" ,
- cvb_name = "Sugarbeet leaves, fresh",
- product = "turnipfor_leave")
-
-
- turnipfor_root = read_xlsx("Input_data/CVB_procout/Cabbage (turnip cabbage), fresh (4012.000).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4012.000" ,
- cvb_name = "Cabbage (turnip cabbage), fresh",
- product = "turnipfor_root")
-
-
- swedefor_leave = read_xlsx("Input_data/CVB_procout/Sugarbeet leaves, fresh (4004.642).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4004.642" ,
- cvb_name = "Sugarbeet leaves, fresh",
- product = "swedefor_leave")
-
-
- swedefor_root = read_xlsx("Input_data/CVB_procout/Fodderbeets, fresh (4005.000).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4005.000" ,
- cvb_name = "Fodderbeets, fresh",
- product = "swedefor_root")
-
- legumenesfor_straw = read_xlsx("Input_data/CVB_procout/Bean straw (Phaseolus) (2001.508).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "2001.508" ,
- cvb_name = "Bean straw (Phaseolus)",
- product = "legumenesfor_straw")
-
- # legumenesfor_bean = read_xlsx("Input_data/CVB_procout/Field beans (Vicia faba) ensiled (5001.602).xlsx", col_names = F) %>%
- # FunAnimDatExtract(cvb_code = "5001.602" ,
- # cvb_name = "Bean straw (Phaseolus)",
- # product = "legumenesfor_bean")
-
- # vegfor_whole = read_xlsx("Input_data/CVB_procout/Cabbage (red_white_sav.), fresh (6023.000).xlsx", col_names = F) %>%
- # FunAnimDatExtract(cvb_code = "6023.000" ,
- # cvb_name = "Cabbage (red_white_sav.), fresh",
- # product = "vegfor_whole")
-
-
- # ryefor_fresh = read_xlsx("Input_data/CVB_procout/Rye (1007.000).xlsx", col_names = F) %>%
- # FunAnimDatExtract(cvb_code = "1007.000" ,
- # cvb_name = "Rye",
- # product = "ryefor_fresh")
-
-
- fornesfor_plant =read_xlsx("Input_data/CVB_procout/Fodderbeets, fresh (4005.000).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4005.000" ,
- cvb_name = "Fodderbeets, fresh",
- product = "fornesfor_plant")
-
- # Added after revision
- coconuts_oil_copra = read_xlsx("Input_data/CVB_procout/Fat_oil, Coconut oil (3015.421)_1_18_2022 1 41 16 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3015.421" ,
- cvb_name = "Fat_oil, Coconut oil",
- product = "coconuts_oil_copra")
-
- cassava_starch = read_xlsx("Input_data/CVB_procout/Tapioca starch (4008.201)_1_18_2022 2 41 14 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4008.201" ,
- cvb_name = "Tapioca starch",
- product = "cassava_starch")
-
-
- cassava_tapioca = read_xlsx("Input_data/CVB_procout/Tapioca, dried (4008.611)_1_18_2022 1 55 24 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4008.611" ,
- cvb_name = "Tapioca, dried",
- product = "cassava_tapioca")
-
- cassava_dry = read_xlsx("Input_data/CVB_procout/Tapioca, dried (4008.611)_1_18_2022 1 55 24 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4008.611" ,
- cvb_name = "Tapioca, dried",
- product = "cassava_dry")
-
- cassava_flour = read_xlsx("Input_data/CVB_procout/Tapioca, dried (4008.611)_1_18_2022 1 55 24 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4008.611" ,
- cvb_name = "Tapioca, dried",
- product = "cassava_flour")
-
- pearl_millet_flour = read_xlsx("Input_data/CVB_procout/Millet, (pearl millet) (1013.000)_1_18_2022 2 01 23 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1013.000" ,
- cvb_name = "Millet, (pearl millet)",
- product = "pearl_millet_flour")
-
- oil_palm_kernels_oil = read_xlsx("Input_data/CVB_procout/Fat_oil, Palm oil, chemically refined (3001.437).xlsx", col_names = F)%>%
- FunAnimDatExtract(cvb_code = "3001.421" ,
- cvb_name = "Palmkernel oil, chemically refined",
- product = "oil_palm_kernels_oil")
-
- oil_palm_fruits_oil = read_xlsx("Input_data/CVB_procout/Fat_oil, Palm oil, chemically refined (3001.437).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "3001.437" ,
- cvb_name = "Palm oil, chemically refined",
- product = "oil_palm_fruits_oil")
-
- sorghum_flour = read_xlsx("Input_data/CVB_procout/Sorghum (1008.000)_1_18_2022 2 24 03 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1008.000" ,
- cvb_name = "Sorghum",
- product = "sorghum_flour")
-
- sugarcane_molasse = read_xlsx("Input_data/CVB_procout/Molasses, sugarcane (7002.210).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "7002.210" ,
- cvb_name = "Molasses, sugarcane",
- product = "sugarcane_molasse")
-
- sweet_potatoe_flour = read_xlsx("Input_data/CVB_procout/Sweet potatoes, dried (4007.611)_1_18_2022 2 30 17 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4007.611" ,
- cvb_name = "Sweet potatoes, dried",
- product = "sweet_potatoe_flour")
-
- sweet_potatoe_starch = read_xlsx("Input_data/CVB_procout/Potato starch, dried (4001.201)_1_18_2022 2 32 03 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4001.201" ,
- cvb_name = "Potato starch, dried",
- product = "sweet_potatoe_starch")
-
- beans_dry_vegetable = read_xlsx("Input_data/CVB_procout/Feed beans, heat treated (2001.616)_1_18_2022 2 56 41 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "2001.616" ,
- cvb_name = "Feed beans, heat treated",
- product = "beans_dry_vegetable")
-
- other_tubers_flour = read_xlsx("Input_data/CVB_procout/Sweet potatoes, dried (4007.611)_1_18_2022 2 30 17 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4007.611" ,
- cvb_name = "Sweet potatoes, dried",
- product = "other_tubers_flour")
-
- other_tubers_dry = read_xlsx("Input_data/CVB_procout/Sweet potatoes, dried (4007.611)_1_18_2022 2 30 17 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "4007.611" ,
- cvb_name = "Sweet potatoes, dried",
- product = "other_tubers_dry")
-
- groundnut_oil = read_xlsx("Input_data/CVB_procout/Fat_oil, Groundnut oil (2013.421)_1_18_2022 3 08 32 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "2013.421" ,
- cvb_name = "Fat_oil, Groundnut oil",
- product = "groundnut_oil")
-
- groundnut_butter = read_xlsx("Input_data/CVB_procout/Fat_oil, Groundnut oil (2013.421)_1_18_2022 3 08 32 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "2013.421" ,
- cvb_name = "Fat_oil, Groundnut oil",
- product = "groundnut_butter")
-
- temperate_fruit_pulp = read_xlsx("Input_data/CVB_procout/Citrus pulp (6022.305)_1_18_2022 3 10 48 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "6022.305" ,
- cvb_name = "Citrus pulp",
- product = "temperate_fruit_pulp")
-
- tropical_fruits_pulp = read_xlsx("Input_data/CVB_procout/Citrus pulp (6022.305)_1_18_2022 3 10 48 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "6022.305" ,
- cvb_name = "Citrus pulp",
- product = "tropical_fruits_pulp")
-
- maize_germ = read_xlsx("Input_data/CVB_procout/Maize germs (1002.102)_1_18_2022 3 14 58 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1002.102" ,
- cvb_name = "Maize germs",
- product = "maize_germ")
-
- maize_germ = read_xlsx("Input_data/CVB_procout/Maize germs (1002.102)_1_18_2022 3 14 58 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1002.102" ,
- cvb_name = "Maize germs",
- product = "maize_germ")
-
- maize_oil = read_xlsx("Input_data/CVB_procout/Fat_oil, Maize oil (1002.421)_1_18_2022 3 16 25 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1002.421" ,
- cvb_name = "Fat_oil, Maize oil",
- product = "maize_oil")
-
- grass_arable_hay = read_xlsx("Input_data/CVB_procout/Grass hay, c) good quality (5010.703)_1_18_2022 3 20 24 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.703" ,
- cvb_name = "Grass hay, c) good quality",
- product = "grass_arable_hay")
-
- grass_arable_silage = read_xlsx("Input_data/CVB_procout/Grass silage, a) clay soil, before 21 June (5010.140).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.140" ,
- cvb_name = "Grass silage, a) clay soil, before 21 June",
- product = "grass_arable_silage")
-
-
- grass_pasture_hay = read_xlsx("Input_data/CVB_procout/Grass hay, b) average quality (5010.702)_1_18_2022 3 24 59 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.702" ,
- cvb_name = "Grass hay, b) average quality",
- product = "grass_pasture_hay")
-
-
- grass_pasture_silage = read_xlsx("Input_data/CVB_procout/Grass silage, j) average (5010.170)_1_18_2022 3 34 20 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.170" ,
- cvb_name = "Grass silage, j) average",
- product = "grass_pasture_silage")
-
- alfalfafor_silage = read_xlsx("Input_data/CVB_procout/Lucerne (alfalfa), ensiled (5004.602)_1_18_2022 3 51 44 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5004.602" ,
- cvb_name = "Lucerne (alfalfa), ensiled",
- product = "alfalfafor_silage")
-
- alfalfafor_hay = read_xlsx("Input_data/CVB_procout/Lucerne (alfalfa), hay (5004.606)_1_18_2022 3 51 06 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5004.606" ,
- cvb_name = "Lucerne (alfalfa), hay",
- product = "alfalfafor_hay")
-
- cloverfor_hay = read_xlsx("Input_data/CVB_procout/Clover red, hay (5003.606)_1_18_2022 3 52 43 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5003.606" ,
- cvb_name = "Clover red, hay",
- product = "cloverfor_hay")
-
- cloverfor_silage= read_xlsx("Input_data/CVB_procout/Clover red, ensiled (5003.602)_1_18_2022 3 53 06 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5003.602" ,
- cvb_name = "Clover red, ensiled",
- product = "cloverfor_silage")
-
-
- cloverfor_silage= read_xlsx("Input_data/CVB_procout/Clover red, ensiled (5003.602)_1_18_2022 3 53 06 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5003.602" ,
- cvb_name = "Clover red, ensiled",
- product = "cloverfor_silage")
-
-
- wheat_wholemeal_germ= read_xlsx("Input_data/CVB_procout/Wheat germs (1010.102)_1_18_2022 4 10 25 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "1010.102" ,
- cvb_name = "Wheat germs",
- product = "wheat_wholemeal_germ")
-
- oilseedfor_hulls = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 23 57 PM.xlsx", col_names = F) %>% #CF 320-360 g/kg
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "oilseedfor_hulls")
-
- barley_hulls = read_xlsx("Input_data/CVB_procout/Oats husk meal (1004.111)_1_18_2022 4 13 11 PM.xlsx", col_names = F) %>% #CF 320-360 g/kg
- FunAnimDatExtract(cvb_code = "1004.111" ,
- cvb_name = "Oats husk meal",
- product = "barley_hulls")
-
- other_pulses_bran = read_xlsx("Input_data/CVB_procout/Soya bean hulls (3012.505)_12_15_2021 6 23 57 PM.xlsx", col_names = F) %>% #CF 320-360 g/kg
- FunAnimDatExtract(cvb_code = "3012.505" ,
- cvb_name = "Soya bean hulls",
- product = "other_pulses_bran")
-
- # Grass from monfreda
- ryefor_fresh = read_xlsx("Input_data/CVB_procout/Grass hay, c) good quality (5010.703).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.703" ,
- cvb_name = "Grass hay, c) good quality",
- product = "ryefor_fresh")
-
- ryefor_silage = read_xlsx("Input_data/CVB_procout/Grass silage, a) clay soil, before 21 June (5010.140).xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.140" ,
- cvb_name = "Grass silage, a) clay soil, before 21 June",
- product = "ryefor_silage")
-
-
- ryefor_dry = read_xlsx("Input_data/CVB_procout/Grass hay, c) good quality (5010.703)_1_18_2022 3 20 24 PM.xlsx", col_names = F) %>%
- FunAnimDatExtract(cvb_code = "5010.703" ,
- cvb_name = "Grass hay, c) good quality",
- product = "ryefor_hay")
-
-
-}
-# Combining all feeds
-dat_anim = bind_rows(soyabean_hulls, coconuts_cake_copra, cow_peas_bran, pearl_millet_bran,
- oil_palm_kernels_cake,sorghum_bran, bagasse,chickpea_bran,
- beans_dry_bran,lentils_bran, pigeon_peas_bran, cow_peas_dry_bran, groundnut_cake,
- # other_cereals_bran,
- olive_cake, maize_bran, maize_cake,maize_silage, grass_rangeland_fresh,
- grass_pasture_fresh,grass_arable_fresh, alfalfafor_fresh,
- cloverfor_fresh, beetfor_leaves, beetfor_root, cabbagefor_plant,
- # carrots_fresh,
- carrots_tops_fresh,turnipfor_root,turnipfor_leave,swedefor_leave,swedefor_root, legumenesfor_straw,
- #legumenesfor_bean, vegfor_whole, ryefor_fresh,
- fornesfor_plant, coconuts_oil_copra, cassava_starch, cassava_tapioca,
- cassava_dry, cassava_flour, pearl_millet_flour, oil_palm_kernels_oil,
- oil_palm_fruits_oil,sorghum_flour, sugarcane_molasse, sweet_potatoe_flour,
- sweet_potatoe_starch,beans_dry_vegetable, other_tubers_dry,
- other_tubers_flour, groundnut_oil,groundnut_butter,
- temperate_fruit_pulp,tropical_fruits_pulp,maize_germ,maize_oil,
- grass_arable_hay, grass_arable_silage, grass_pasture_hay,grass_pasture_silage,
- alfalfafor_silage, alfalfafor_hay, cloverfor_hay,cloverfor_silage,
- soyabean_feed_hulls,wheat_wholemeal_germ,oilseedfor_hulls,barley_hulls,other_pulses_bran,
- ryefor_fresh, ryefor_silage, ryefor_dry)
-
-
-write_csv(dat_anim, "Input_Data/dat_anim.csv")
-
-# Combining the feed crops that are still missing and the one downloaded from cvb
-dat_miss_downl =
- proc_out_anim_miss_tbl %>%
- left_join(dat_anim, by = c("proc_out_new_missing" = "product")) %>%
- filter(is.na(cvb_name)) %>%
- dplyr::select(-feed) %>%
- rename(product = proc_out_new_missing) %>%
- relocate(product, .before = dm ) %>%
- bind_rows(dat_anim,.) %>%
- mutate(family = "notyet",
- origin = "co-product")# %>%
-# type.convert()
-
-
-
-# Now we combine that df with the one that are in the old animal nutrition sheet
-prod_vec = dat_miss_downl %>% pull(product)
-
-dat_com_all = dat_animal_nutr_cifos %>% select(-product) %>%
- rename(product = product_lc,
- cvb_name = raw_nevo,
- origin = na) %>%
- relocate(cvb_code, .before = cvb_name) %>%
- # type.convert(.)
- dplyr::filter(!product %in% prod_vec) %>%
- dplyr::mutate(origin = recode(origin, "Co-Product" = "co-product"))
-
-
-# Copy paste the col format so the rbind works. Useful template: dfWrong[] <- mapply(FUN = as,dfWrong,sapply(dfCorrect,class),SIMPLIFY = FALSE)
-dat_com_all[] <- mapply(FUN = as,dat_com_all,sapply(dat_miss_downl,class),SIMPLIFY = FALSE)
-
-dat_comb_all = bind_rows(dat_com_all, dat_miss_downl )
-
-# This contains all the NEW proc_outs
-procnew_vec = proc_new %>% pull(proc_out)
-
-# Co-products -------------------------------------------------------------
-# Df for co-products - allprocouts for which we have data
-dat_old_proc_crop_grass =
- dat_animal_nutr_cifos %>%
- right_join(., mapd_animnutr_missing %>%
- filter(!proc_out_new_missing %in% "sugar_factorylime"),
- by = c("product" = "proc_out_old_animsheet")) %>%
- drop_na(product_lc) %>%
- rename(origin = na) %>%
- dplyr::select(-c("product_lc", "product","feed")) %>%
- relocate(proc_out_new_missing, .before = "dm") %>%
- rename(product = proc_out_new_missing)
-
-# Pasting the formating
-dat_old_proc_crop_grass[] <- mapply(FUN = as,dat_old_proc_crop_grass,sapply(dat_miss_downl,class),SIMPLIFY = FALSE)
-
-dat_old_proc_crop_grass_sum =
- dat_old_proc_crop_grass %>%
- dplyr::group_by(raw_nevo, cvb_code , origin, family, product) %>%
- dplyr::summarise_if(is.numeric, mean, na.rm = T) %>%
- dplyr::mutate(origin = recode(origin, "Co-Product" = "co-product"),
- origin = recode(origin, "Foodwaste" = "co-product"))
-
-
-
-
-# All procouts that directly matched the new lower case procouts
-dat_match_dir = dat_animal_nutr_cifos %>%
- # dplyr::select(c(4:5), product_lc) %>%
- right_join(., (dat_proc_new %>% dplyr::select(proc_out, dietary_products)),
- by = c("product_lc"="proc_out"))%>%
- dplyr::filter(!is.na(product)) %>%
- dplyr::select(-c("product", "dietary_products")) %>%
- dplyr::rename("product" = "product_lc") %>%
- rename(origin = na) %>%
- dplyr::mutate(origin = recode(origin, "Co-Product" = "co-product"),
- origin = recode(origin, "Foodwaste" = "co-product"),
- origin = recode(origin, "HarvestWaste" = "co-product"))
-
-
-dat_match_dir[] <- mapply(FUN = as,dat_match_dir,sapply(dat_miss_downl,class),SIMPLIFY = FALSE)
-
-
-# These are all the newly added procouts
-dat_new_proc_crop_grass =
- dat_miss_downl
-
-
-# all co_products that are ASF (animal source food)
-dat_fish_meat_milk = dat_animal_nutr_cifos %>%
- dplyr::filter(family %in% c("Fish", "Milk", "Meat", "Egg", "Dairy")) %>%
- dplyr::filter(na == "Co-Product") %>%
- dplyr::select(-c("product_lc")) %>%
- rename(origin = na) %>%
- dplyr::mutate(origin = recode(origin, "Co-Product" = "co-product"))
-
-dat_fish_meat_milk[] <- mapply(FUN = as,dat_fish_meat_milk,sapply(dat_miss_downl,class),SIMPLIFY = FALSE)
-
-# Combining all the co-products
-animal_coproducts = bind_rows(dat_old_proc_crop_grass_sum, dat_new_proc_crop_grass, dat_match_dir, dat_fish_meat_milk) %>%
- dplyr::group_by(product) %>%
- dplyr::summarise_if(is.numeric, mean, na.rm = T) %>%
- dplyr::mutate(origin = "co-product",
- raw_nevo = "crop_agg",
- cvb_code = "crop_agg",
- family = "none") %>%
- relocate(product, .before = "dm") %>%
- relocate(family, .before = "product") %>%
- relocate(origin, .before = "family") %>%
- relocate(cvb_code, .before = "origin") %>%
- relocate(raw_nevo, .before = "cvb_code")
-
-
-# Checking the consistency with all possible procouts
-
-# Check consistency needded -> https://stackoverflow.com/questions/13774773/check-whether-values-in-one-data-frame-column-exist-in-a-second-data-frame
-# A$C[!A$C %in% B$C] # returns all values of A$C that are NOT in B$C
-animal_coproducts$product[!animal_coproducts$product %in% proc_new$proc_out]
-# All new procouts are in the df
-
-
-
-# HarvestWaste ------------------------------------------------------------
-# Df for harvest waste - cvb data from every crop we have
-crop_harvestwaste =
- proc_new %>%
- # dat_comb_all %>%
- # left_join(., proc_new, by = c("product"="proc_out")) %>%
- dplyr::select(-c(proc_raw, proc_in , value , dietary_products)) %>%
- # distinct_at(vars(crop_cifos), .keep_all = T) %>%
- left_join(., dat_comb_all , by=c("proc_out" = "product")) %>%
- dplyr::group_by(crop_cifos) %>%
- dplyr::summarise_if(is.numeric, mean, na.rm = T) %>%
- dplyr::mutate(raw_nevo = "crop_agg",
- cvb_code = "crop_agg",
- origin = "harvest_waste",
- family = "none",
- product = crop_cifos) %>%
- relocate(product, .before = "dm") %>%
- relocate(family, .before = "product") %>%
- relocate(origin, .before = "family") %>%
- relocate(cvb_code, .before = "origin") %>%
- relocate(raw_nevo, .before = "cvb_code") %>%
- dplyr::select(-crop_cifos) %>%
- dplyr::filter(!product %in% c("other_fibres", "seed_cotton", "tobacco",
- "tea"))
-# Validation: should be 59 crops minus the unedible crops: 55 crops included
-
-
-# proc_new$crop_cifos[!proc_new$crop_cifos %in% crop$crop_cifos]
-# proc_new$crop_cifos[!proc_new$crop_cifos %in% crop$crop_cifos]
-# [1] "tea" "other_fibres" "seed_cotton" "tobacco"
-
-# Harveste waste from ASF
-asf_harvestwaste = dat_animal_nutr_cifos %>%
- dplyr::filter(family %in% c("Fish", "Milk", "Meat", "Egg", "Dairy")) %>%
- dplyr::filter(na == "HarvestWaste") %>%
- dplyr::select(-c("product_lc")) %>%
- rename(origin = na) %>%
- dplyr::mutate(origin = recode(origin, "HarvestWaste" = "harvest_waste"))
-
-# align formatting
-asf_harvestwaste[] <- mapply(FUN = as,asf_harvestwaste,sapply(dat_miss_downl,class), SIMPLIFY = FALSE)
-
-# Combining all the harvest waste
-harvestwaste = bind_rows(crop_harvestwaste,asf_harvestwaste)
-
-# FoodWaste ---------------------------------------------------------------
-
-# Food waste crops - Food waste that matches directly with the co products from animal
-foodwaste = hum_nutr_all %>%
- distinct_at(vars(product), .keep_all = T)
-
-foodwaste_vec = foodwaste %>% pull(product)
-
-# foodwaste products that match coproducts animal
-match_coprods_foodwaste = animal_coproducts %>%
- dplyr::filter(product %in% foodwaste_vec) %>%
- dplyr::mutate(origin = "foodwaste")
-
-match_coprods_foodwaste[] <- mapply(FUN = as,match_coprods_foodwaste,sapply(dat_miss_downl,class), SIMPLIFY = FALSE)
-
-
-# Tea
-tea_food_waste =
- dat_animal_nutr_cifos %>%
- dplyr::filter(product %in% "Tea")%>%
- dplyr::mutate(na = "foodwaste") %>%
- select(-product_lc) %>%
- rename(origin = na) %>%
- mutate(product = recode(product, "Tea" = "tea_leave"))
-
-tea_food_waste[] <- mapply(FUN = as,tea_food_waste,sapply(dat_miss_downl,class), SIMPLIFY = FALSE)
-
-match_animal = dat_animal_nutr_cifos %>%
- dplyr::filter(product %in% foodwaste_vec) %>%
- dplyr::mutate(na = "foodwaste") %>%
- select(-product_lc) %>%
- rename(origin = na)
-
-match_animal[] <- mapply(FUN = as,match_animal,sapply(dat_miss_downl,class), SIMPLIFY = FALSE)
-
-
-match_rest = dat_animal_nutr_cifos %>%
- dplyr::filter(product_lc %in% foodwaste_vec) %>%
- dplyr::mutate(na = "foodwaste") %>%
- select(-product) %>%
- rename(product = product_lc)
-
-match_rest[] <- mapply(FUN = as,match_rest,sapply(dat_miss_downl,class), SIMPLIFY = FALSE)
-
-
-# Bind food waste
-foodwaste_all = bind_rows(match_coprods_foodwaste,match_animal, match_rest,tea_food_waste) %>%
- distinct_at(vars(product), .keep_all = T)
-
-
-# Checking what is not in the coproducts
-miss_foodwaste = foodwaste_all %>% full_join(.,foodwaste, by = "product")
-
-
-# Full animal nutrition sheet ---------------------------------------------
-animal_nutrition_all = bind_rows(harvestwaste, animal_coproducts, foodwaste_all) %>%
- dplyr::select(-raw_nevo, -cvb_code, -family)
-
-write_csv(animal_nutrition_all, "Input_data/animal_nutrition_all.csv")
-read_csv("Input_data/animal_nutrition_all.csv")
-
-
-# Loss fraction sheet ----------------------------------------------------
-dat_lossfrac = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Loss_Fraction_New")
-dat_lossfrac_join= dat_lossfrac %>% dplyr::select(c(1:10)) %>% janitor::row_to_names(1)%>% select(-ProdCat ,-Fam_SPAM) %>%
- rename(product = Prod)
-
-loss_values = read_csv("Input_data/gusatvsson_loss_table.csv") %>%
- rename(Fam_Gustavson = `...1`)
-
-# Crops - from animal nutrition sheet (postharvest)
-crop_loss = crop_harvestwaste %>% distinct_at(vars(product)) %>%
- mutate(ProdCat = "crop",
- product = product) %>%
- relocate(product, .after = "ProdCat")%>%
- distinct_at(vars(product), .keep_all = T)
-
-# Co-product - from animal nutrition sheet
-coproduct_loss = animal_coproducts %>% distinct_at(vars(product)) %>%
- mutate(ProdCat = "co-product",
- product = product) %>%
- relocate(product, .after = "ProdCat")%>%
- distinct_at(vars(product), .keep_all = T)
-
-
-# Foodwaste - from Animal nutrition sheet
-foodwaste_loss = foodwaste_all %>% distinct_at(vars(product)) %>%
- mutate(ProdCat = "foodwaste",
- product = product) %>%
- relocate(product, .after = "ProdCat")%>%
- distinct_at(vars(product), .keep_all = T)
-
-# HarvestWaste
-harvest_loss = crop_harvestwaste %>% distinct_at(vars(product)) %>%
- mutate(ProdCat = "harvest_waste",
- product = product) %>%
- relocate(product, .after = "ProdCat")%>%
- distinct_at(vars(product), .keep_all = T)
-
-# Food - From human nutrtion sheet
-food_loss = hum_nutr_all %>% distinct_at(vars(product)) %>%
- mutate(ProdCat = "food",
- product = product) %>%
- relocate(product, .after = "ProdCat")%>%
- distinct_at(vars(product), .keep_all = T)
-
-
-other_losses = tibble(
- ProdCat = "other",
- product = c('flax_fiber_and_tow',
- 'seed_cotton_fibres',
- 'tobacco_leaves')
-)
-
-# Fish - from the old loss fraction sheet
-fish_vec = c('Atlantic_herring',
- 'Atlantic_cod',
- 'Blue_whiting',
- 'Atlantic_mackerel',
- 'European_sprat',
- 'Haddock',
- 'Pollock',
- 'European_plaice',
- 'European_hake',
- 'Atlantic_horse_mackerel',
- 'Sandeels',
- 'Norway_pout',
- 'Norway_lobster',
- 'Northern_prawn',
- 'Ling',
- 'European_pilchard')
-
-fish_loss = food_loss %>% filter(product == "alien") %>%
- rbind(data.frame(product = fish_vec)) %>%
- mutate(ProdCat = "fish",
- product = product) %>%
- relocate(product, .after = "ProdCat")%>%
- distinct_at(vars(product), .keep_all = T)
-
-
-# MeatMilkEggs -
-milkmeategg_vec = c('Milk',
- 'Egg',
- 'Pig_LW',
- 'Dairy_LW',
- 'Beef_LW',
- 'Broiler_LW',
- 'Layer_LW',
- 'Salmon_LW',
- 'Tilapia_LW',
- 'Sheep_LW',
- 'Goat_LW')
-
-mmegg_loss = food_loss %>% filter(product == "alien") %>%
- rbind(data.frame(product = milkmeategg_vec)) %>%
- mutate(ProdCat = "meatmilkeggs",
- product = product) %>%
- relocate(product, .after = "ProdCat")%>%
- distinct_at(vars(product), .keep_all = T)
-
-# Grass
-grass_loss = proc_new %>% filter(grepl('grass', proc_out)) %>%
- distinct(proc_out) %>%
- mutate(ProdCat = "grass",
- product = proc_out) %>%
- relocate(product, .after = "ProdCat") %>%
- select(-proc_out) %>%
- distinct_at(vars(product), .keep_all = T)
-
-
-loss_fraction_1 = bind_rows(crop_loss, coproduct_loss,foodwaste_loss,harvest_loss,
- food_loss,fish_loss,mmegg_loss, grass_loss,other_losses)
-
-# Fuzzy join
-loss_frac_join = loss_fraction_1 %>% drop_na(product) %>% group_by(ProdCat) %>%
- stringdist_left_join(dat_lossfrac_join %>% drop_na(product),
- by = c("product"),
- max_dist = 4) %>%
- distinct_at(vars(ProdCat, product.x), .keep_all = T)
-# write_csv(loss_frac_join, "Input_data/lossfraction_mapping_in.csv")
-
-# MANUAL MAPPING required - I mapped the ProdFam and Fam_Gustavson from the old loss fraction sheet.
-loss_frac_all = read_csv("Input_data/lossfraction_mapping_done.csv") %>%
- dplyr::select(ProdCat, product.x,ProdFam,Fam_Gustavson) %>%
- left_join(loss_values, by = "Fam_Gustavson") %>%
- rename(Product = product.x,
- Postharvest = `Postharvest handling & storage`,
- ProcessingPackaging = `Processing and packaging`,
- Distribution = Distribution ,
- Consumption = Consumption ,
- Feeding = Feeding) %>%
- relocate(Product, .after = "Fam_Gustavson")
-
-write_csv(loss_frac_all, "Input_data/loss_frac_all.csv")
-
-
-
-
-# Consistency checks -----------------------------------------------------
-# RULE: What is in the procout ani nutrition should be in the processing and the loss fraction sheet product
-# A$C[!A$C %in% B$C] # returns all values of A$C that are NOT in B$C
-
-# All values of animal nutrition that are not in loss fraction
-animal_nutrition_all$product[!animal_nutrition_all$product %in% loss_frac_all$Product]
-# NONE -->
-
-# All values of human nutrition that are not in loss fraction
-hum_nutr_all$product[!hum_nutr_all$product %in% loss_frac_all$Product]
-# [1] "NONE" --> GOOD!
-
-# All values of animal nutrition that are not in processing fraction
-animal_coproducts$product[!animal_coproducts$product %in% Processing_sheet_final$proc_out]
-# [1] None! Good
-
-# All values of humanutrition that are not in processing sheet
-hum_nutr_all$product[!hum_nutr_all$product %in% Processing_sheet_final$proc_out]
-# None -- Good!
-
-# All values that of processing that are not in loss fraction
-Processing_sheet_final$proc_out[!Processing_sheet_final$proc_out %in% loss_frac_all$Product]
-# None!
-
-# All the values that are in the loss fraction but not in the processing
-loss_frac_all$Product[!loss_frac_all$Product %in% rbind(Processing_sheet_final$proc_out, Processing_sheet_final$proc_raw)]
-# 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)
-
-table_fert_suit = dat_fertsuitability %>% distinct_at(vars(scenario), .keep_all = T) %>%
- dplyr::select(-crop_cifos)
-
-# List of all crops possible
-crop_list = proc_new %>% distinct_at(vars(crop_cifos)) %>% pull()
-
-scen_baseline = tibble(scenario = "Baseline",
- crop_cifos = crop_list)
-
-scen_circular = tibble(scenario = "Circular",
- 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))
-
-# Country map for subsetting EU
-country_map_FAO_code = read.csv(here::here("Input_data", "Mappings", "dat_map_SPAM_GAUL_CIFOS_FAO.csv")) %>%
- as_tibble() %>% dplyr::select(country_code_FAO, iso3_SPAM )
-
-# EU28 - Subsetting -------------------------------------------------------
-EU_iso3 = c("AUT","BEL", "BGR", "HRV", "CZE", "DNK", "EST","FIN", "FRA", "DEU", "GRC", "HUN", "IRL", "ITA","LVA",
- "LTU", "LUX", "MLT", "NLD", "POL", "PRT","ROU", "SVK","SVN", "ESP", "SWE", "GBR", "CYP")
-
-country_eu = country_map_FAO_code %>% filter(iso3_SPAM %in% EU_iso3) %>%
- pull(country_code_FAO)
-
-# country map
-country_map_FAO_code_namecifos = read.csv(here::here("Input_data", "Mappings", "dat_map_SPAM_GAUL_CIFOS_FAO.csv")) %>%
- as_tibble() %>% dplyr::select(country_code_FAO,name_cntr_CIFOS2021)
-
-# Dietary products from the processing sheet
-diet_prod_dist = Processing_sheet_final %>% distinct_at(vars(dietary_products))
-
-# Downloading the FAO stat Food balance sheet
-FoodBalanceSheetFAO = get_faostat_bulk("FBS", "Input_data") #downloads the data bulk from faostat
-
-Baseline_import_export = FoodBalanceSheetFAO %>% as_tibble() %>%
- dplyr::filter(year %in% c("2010":"2018"), #for a 3 year average
- area_code %in% country_eu, #filtering for EU+UKD countries
- element == "import_quantity" | element == "export_quantity") %>% # choosing import and export quantity in 1000 tonnes
- dplyr::group_by(item, element) %>%
- dplyr::summarise(value = sum(value)) %>%
- dplyr::mutate(value = value *1000) %>% #converting to tonnes
- pivot_wider(values_from = value,
- names_from = element) %>%
- rename(export_ton = export_quantity,
- import_ton = import_quantity,
- 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)
-
-write_csv(Baseline_import_export, "Input_data/Baseline_import_export.csv")
-
-# Baseline production -------------------------------------------------------------
-Baseline_production = FoodBalanceSheetFAO %>% as_tibble() %>%
- dplyr::filter(year %in% c("2010":"2018"),
- area_code %in% country_eu, #filtering for EU+UKD countries
- element == "production" ) %>% # choosing import and export quantity in 1000 tonnes
- dplyr::group_by(area_code, item, element) %>%
- dplyr::summarise(value = sum(value)) %>%
- dplyr::mutate(value = value *1000) %>% #converting to tonnes from 1000 tonnes
- pivot_wider(values_from = value,
- names_from = element) %>%
- rename(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
- left_join(country_map_FAO_code_namecifos, by= c("area_code"="country_code_FAO")) %>%
- ungroup() %>%
- dplyr::select(name_cntr_CIFOS2021, dietary_products, production) %>%
- dplyr::rename(production_t=production,
- country_cifos = name_cntr_CIFOS2021)
-
-write_csv(Baseline_production, "Input_data/Baseline_production.csv")
-
-# validation %>%distinct_at(vars(country_cifos)) # all good - 28 countries
-
-# Baseline_food_supply ----------------------------------------------------
-Baseline_food_supply =
- FoodBalanceSheetFAO %>% as_tibble() %>%
- dplyr::filter(year %in% c("2010":"2018"), #for a year average
- area_code %in% country_eu, #filtering for EU+UKD countries
- element=='protein_supply_quantity__g_capita_day_')%>% # unit is in g/capita/day
- dplyr::group_by(area_code, item, element) %>%
- dplyr::summarise(value = sum(value)) %>%
- pivot_wider(values_from = value,
- names_from = element) %>%
- rename(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
- left_join(country_map_FAO_code_namecifos, by= c("area_code"="country_code_FAO")) %>%
- ungroup() %>%
- dplyr::select(name_cntr_CIFOS2021, dietary_products,protein_supply_quantity__g_capita_day_) %>%
- dplyr::rename(food_supply_gppp=protein_supply_quantity__g_capita_day_ ,
- country_cifos = name_cntr_CIFOS2021)
-
-write_csv(Baseline_food_supply, "Input_data/Baseline_food_supply.csv")
-
-
-# Baseline animal number --------------------------------------------------
-# Loading the animal production data sheet from FAO:
-# Livestock_Crop = get_faostat_bulk("QCL", "Input_data") #downloads the data bulk from faostat
-# Livestock_Crop %>% write_csv("Input_data/Livestock_Crop_QCL.csv")
-Livestock_Crop = read_csv("Input_data/Livestock_Crop_QCL.csv")
-
-Baseline_animal_number =
- Livestock_Crop %>% as_tibble() %>%
- dplyr::filter(year %in% c("2010":"2018"), #for a year average
- 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::summarise(value = mean(value, na.rm=T)) #%>%
-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
- left_join(country_map_FAO_code_namecifos, by= c("area_code"="country_code_FAO")) %>%
- ungroup() %>%
- dplyr::select(name_cntr_CIFOS2021, crops,area_harvested) %>%
- dplyr::rename(area_harvested_ha=area_harvested,
- country_cifos = name_cntr_CIFOS2021)
-
-write_csv(Baseline_food_supply, "Input_data/Baseline_food_supply.csv")
-
-# Baseline crop area ------------------------------------------------------
-
-# Crop map
-dat_crop_fao_cifos = dat_crop_final %>% dplyr::select(crop_cifos, crop_name_fao)
-dat_crop_fao_cifos_vec = dat_crop_fao_cifos %>% drop_na() %>% pull(crop_name_fao)
-
-
-
-
-Baseline_area_crop_ha = Livestock_Crop %>% as_tibble() %>%
- dplyr::filter(year %in% c("2010":"2018"), #for a year average
- area_code %in% country_eu, #filtering for EU+UKD countries
- element=='area_harvested') %>% # unit is in ha
- dplyr::group_by(area_code, item, element) %>%
- dplyr::summarise(value = sum(value, na.rm=T)) %>%
- 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
- left_join(country_map_FAO_code_namecifos, by= c("area_code"="country_code_FAO")) %>%
- ungroup() %>%
- dplyr::select(name_cntr_CIFOS2021, crops,area_harvested) %>%
- dplyr::rename(area_harvested_ha=area_harvested,
- country_cifos = name_cntr_CIFOS2021)
-
-write_csv(Baseline_area_crop_ha, "Input_data/Baseline_area_crop_ha.csv")
-
-
-# Crop_others sheet -------------------------------------------------------
-# crop_other_old = read_excel("C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx", sheet = "Crop_Other")
-# crop_other_old %>% write_csv("Input_data/crop_other.csv")
-crop_other_old = read_csv("Input_data/crop_other.csv")
-
-# Crop map
-crop_map = read_csv("Input_data/dat_crop_with_grass.csv")
-
-crop_other_new =
- crop_other_old %>% dplyr::select(-c(27:52, DM:HI)) %>%
- dplyr::rename(crop_cifos_old = `...1`,
- Rotation=`Rotation...26`) %>%
- 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) %>%
- relocate(arable, .after = Rotation) %>%
- # dplyr::filter(!grepl("Grass", crop_cifos))
- 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)) %>%
- 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")
-
-# match the new crops
-cropnutr_new =
- cropnutr %>% dplyr::rename(crop_cifos_old = `...1`) %>%
- 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")
-
-# 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`) %>%
- full_join(crop_map %>% select(old_cifos_crop, crop_cifos), by = c("crop_cifos_old"="old_cifos_crop")) %>%
- relocate(crop_cifos , .before = crop_cifos_old) %>%
- pivot_wider(names_from = CropNutr,
- values_from = value)
-
-write_csv(CropFert, "Input_data/cropfert_inter.csv")
-
-
-
-# Data maps ---------------------------------------------------------------
-# Anitas excel -- differs a bit to mine
-data_map = read_excel("Input_data/Copy of European_CiFoS_model_data_ANITANov21.xlsx", sheet = "Data_Map")
-proc_sheet_crops = bind_rows(dat_proc_new ,dat_proc_asf) #%>% write_csv("Input_data/proc_sheet_crops.csv")
-
-# 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) %>%
- dplyr::select(proc_out, dietary_products) %>%
- dplyr::filter(proc_out %in%(hum_nutr_all %>% distinct_at(vars(product)) %>% pull())) %>%
- rename(ProcOutH = proc_out)
-
-write_csv(ProcOutH_dietProduct, "Input_data/ProcOutH_dietProduct.csv")
-
-# ProcOutH only
-ProcOutH =Processing_sheet_final %>% distinct_at(vars(proc_out), .keep_all = F) %>%
- dplyr::select(proc_out) %>%
- dplyr::filter(proc_out %in%(hum_nutr_all %>% distinct_at(vars(product)) %>% pull())) %>%
- rename(ProcOutH = proc_out)
-
-write_csv(ProcOutH_dietProduct, "Input_data/ProcOutH.csv")
-
-# Crops and co-products (proc_outs)
-crop_procouts = dat_proc_new %>%
- dplyr::select(crop_cifos, proc_out) %>%
- distinct_at(vars(crop_cifos,proc_out))
-
-write_csv(crop_procouts, "Input_data/crop_procouts.csv")
-
-# Livestock-Co_product
-# livestock_procouts = dat_proc_asf %>%
-# dplyr::select(crop_cifos, proc_out) %>%
-# distinct_at(vars(crop_cifos,proc_out))
-#
-# write_csv(crop_procouts, "Input_data/crop_procouts.csv")
-
-# Crops and co-products (proc_outs)
-crop_procouts_diet_products = dat_proc_new %>%
- dplyr::select(crop_cifos, proc_out,dietary_products) %>%
- distinct_at(vars(crop_cifos,proc_out,dietary_products))
-
-write_csv(crop_procouts_diet_products, "Input_data/crop_procouts_diet_products.csv")
-
-# My excel
-Crop_Map2 = read_excel("C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx", sheet = "Data_Map")
-
-# Crop_Map2 = read_excel("C:/Wolfram_Admin/GAMS/EU_model_frmSep21/cifos-model_eu/European_CiFoS_model_data.xlsx", sheet = "Data_Map")
-# Crop_Map2 %>% write_csv("Input_data/crop_map_mine.csv")
-Crop_Map2 =read_csv("Input_data/crop_map_mine.csv")
-
-# This is the only map that is actually used in the model 30.01.22
-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`) %>%
- 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
-
-
-# Logic - Whole data in the database
-# In yields, area, crop_other
-# 1. Crops > FAO_crops
-# # in processing sheet > remove everything that is not used in the model. All products can have a max of three functions(animal feed, human food, fertilizer)
-# 2. FAO_crops > Proc_Raw (processing_sheet(ProcRaw matched with crops --> Only put in crops that get a value >> delete them))
-# 3. Proc_raw > ProcIn (supply_sheets FAO)
-# 4. ProcIn > ProcOut
-# 5. ProcOutc > Dietary_product (group of the FAO >> that is matched to Food groups (Fruits, vegetables, fibres,...))
-# # each Proc_out have to have a or many functions (feed, fertilizer, etc.)
-# 6. Proc_out > Product (Human_Nutr sheet) # function: human food
-# 7. Proc_out > Product (Animal_Nutr sheet) # function:animal feed
-# 8. Proc_out > Prod (Loss_fraction_new sheet) # function: Proc_raw for fertilizer OR animal feed
-# 9. Proc_out > column B (fert_suitability sheet)
-
-# 10 . Dietary Products ("processing sheet") > Item("import sheet")
-# 11. Item(import_sheet) > Dietary_product (processing fraction sheet) >> Everything that is imported in that country, everything that is exported
-
-# Workflow
-# Processing sheet: Make sure that we only have plant based products that are represented in the crops_coproduct
-### If you do not find products in the sheet that are in the crop sheet (disaggregated) we need to find the "Value" and the nutritional content.
-### Or report the assumptions >> Renee and Wendy and Demy could also help. Demy is wendy's friend and is the project assistant!
-### Contact Hannah when your done with PF sheet.
-
-# IMPORTANT: Naming: Crops > Just name it >> Use original name from SPAM and Monfreda
-# Forage crops: Just assume processing fraction of 1
-
-# Single mappings
-# Check following.
-## ProcOutH should be element of ProcOut
-
-## Note: Animals ok. Crops not --> Update!
-
-# IMPORT sheet: Already mapped!
-# When you import your crop data in the processing.
-
-# ProcOutH (cifos procout) > DietProd (FAO supply sheet) ## we can have four products to supply the
-# DietProd (DataMap) > DietaryProducts (processing fration sheet)
-
-# # Animals
-# #Animal_yield sheet
-# 1. Yields (column D row 2)
--
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