--- title: "Organic farms statistics in FADN" author: Sebastian Neuenfeldt date: "`r format(Sys.time(), '%d %B %Y')`" output: word_document: reference_docx: "git/MIND STEP deliverable template - empty.docx" --- # Organic farms statistics in FADN This analysis crawls some descriptive statistics about organic farming in the FADN 2004-2018. # Prelimenaries ## Clean environment ```{r echo=TRUE,message=FALSE, results = 'hide'} rm(list=ls()) ``` ## Load libraries ```{r echo=TRUE,message=FALSE, results = 'hide'} requiredPackages = c('tidyverse','readxl','skimr','data.table','fadnUtils') for(p in requiredPackages){ if(!require(p,character.only = TRUE)) install.packages(p) library(p,character.only = TRUE) } ``` # Declare folder structure and settings ```{r echo=T, message=FALSE,results = 'hide'} # set project directory CurrentProjectDirectory <- "D:/data/fadn/lieferung_20210414/neuenfeldt/test_fadnutils" # the path of the fadn files for loading fadn.data.dir <- "D:/data/fadn/lieferung_20210414/csv/" # ceate a data.dir create.data.dir(folder.path = CurrentProjectDirectory) # Once the data.dir is created, we must declare that we are working with it set.data.dir(CurrentProjectDirectory) get.data.dir() ``` # Load raw Rdata Load raw FADN data. ```{r,echo=T,message=FALSE,results='hide'} ## LOAD RAW R-DATA countries_all <- get.available.fadn.raw.rds() %>% filter(str_detect(pattern = "compressed",string = COUNTRY,negate = TRUE)) %>% pull(COUNTRY) %>% unique() system.time({ my.data <- load.fadn.raw.rds(countries = countries_all,years = 2018) }) # ~ 10 sec ``` # Select relevant variables Je nach MS und Hauptbetriebstyp (TF8, wenn möglich) für 2018: • Zahl der Ökobetriebe und gesamtzahl der Betriebe in der Gruppe ORGANIC A_CL_140_C Organic farming Code • • Durchschnittliche Fläche (UAA) pro Betrieb für Öko und insgesamt, je Gruppe • SE025 Total Utilised Agricultural Area Area in ha • • Durchscnittlice Betriebseinkommen pro Betrieb, Öko und insgesamt, je Gruppe • SE420 SE420 Farm Net Income • • Durchnittliche Subventionen pro Betrieb für Öko und insgesamt, je Gruppe SBPS_V M_S_1150_V BPS (Basic payment scheme) subsidy. Value in EUR SE621 SE621 Environmental subsidies Die Frage ist ob a) AUKM damit gemeint sind und b) ob Ökoprämien auch dabei sind. Eigentlich möchte ich nur AUKM haben. in EUR • SE624 SE624 Total support for rural development in EUR • • Durchnittliche Ökoprämien pro ha UAA und insgesamt, je Gruppe (bin nicht sicher, was fi2 und fi3 bedeutet - kann sein das SORGSUB_V reicht SORGSUB_2_V M_S_3350_2_V Organic farming subsidy. Value with fi2 in EUR SORGSUB_3_V M_S_3350_3_V Organic farming subsidy. Value with fi3 in EUR SORGSUB_V M_S_3350_V Organic farming subsidy. Value in EUR ORGANIC: 1. the holding does not apply organic production methods 2. the holding applies only organic production methods for all its products 3. the holding applies both organic and other production methods 4. the holding is converting to organic production methods If the holding is converting only a part of its production to organic production methods, it should be reported under code 4 * 5 M_S_3300_2_V Agri-environment and animal welfare payments Value with fi 2 SAEAWSUB_2_V 1 * 6 M_S_3300_3_V Agri-environment and animal welfare payments Value with fi 3 SAEAWSUB_3_V 1 * 7 M_S_3300_V Agri-environment and animal welfare payments Value SAEAWSUB_V 1 *19 M_S_3350_2_V Organic farming subsidy. Value with fi2 SORGSUB_2_V 1 *20 M_S_3350_3_V Organic farming subsidy. Value with fi3 SORGSUB_3_V 1 *21 M_S_3350_V Organic farming subsidy. Value SORGSUB_V 1 ```{r echo=T, message=FALSE} # necessary variables and years selected_vars <- c("YEAR","COUNTRY","TF8","ORGANIC","SYS02","SE025","SE420", "SBPS_V","SE621","SE624", # "SORGSUB_2_V","SORGSUB_3_V", "SORGSUB_V", # "SAEAWSUB_2_V","SAEAWSUB_3_V", "SAEAWSUB_V") selected_years <- c(2018) # filter files <- my.data %>% select(all_of(selected_vars)) %>% filter(YEAR %in% selected_years) # tidy data files <- files %>% pivot_longer(-c(YEAR,COUNTRY,TF8,ORGANIC,SYS02)) # summarise for fully organic vs conventional files_summary <- files %>% filter(ORGANIC %in% c("1","2")) %>% mutate(ORGANIC=if_else(condition = ORGANIC==2, true = "Fully organic", false = "Conventional")) %>% group_by(YEAR,COUNTRY,TF8,ORGANIC,name) %>% summarise(Mean_sample=mean(value,na.rm=TRUE), Sum_sample=sum(value,na.rm=TRUE), Nobs_sample=n(), Mean_weighted=weighted.mean(x=value,w=SYS02,na.rm=TRUE), Sum_weighted=sum(value*SYS02,na.rm=TRUE), Nobs_weighted=sum(SYS02,na.rm=TRUE)) files_summary # export writexl::write_xlsx(files_summary, path = paste0(CurrentProjectDirectory,"/../test_organic/Organic_descriptives_1-2.xlsx")) # summarise for fully organic vs not fully organic files_summary <- files %>% mutate(ORGANIC=if_else(condition = ORGANIC==1, true = "Conventional", false = "Fully and partially organic")) %>% group_by(YEAR,COUNTRY,TF8,ORGANIC,name) %>% summarise(Mean_sample=mean(value,na.rm=TRUE), Sum_sample=sum(value,na.rm=TRUE), Nobs_sample=n(), Mean_weighted=weighted.mean(x=value,w=SYS02,na.rm=TRUE), Sum_weighted=sum(value*SYS02,na.rm=TRUE), Nobs_weighted=sum(SYS02,na.rm=TRUE)) files_summary writexl::write_xlsx(files_summary, path = paste0(CurrentProjectDirectory,"/../test_organic/Organic_descriptives_conv_fully-partly-organic.xlsx")) ```