is.nan.data.frame <- function(x){
do.call(cbind, lapply(x, is.nan))}
data.site[is.nan(data.site)] <- NA
data.site$Publication <- NA
data.site$Credit  <- "Maj Rundlof, Henrik Smith and Riccardo Bommarco"
data.site$Email_contact <- "Maj.Rundlof@biol.lu.se"
data_raw_obs <- data_raw %>%
select(site,round,row,observation_location, names(data_raw[30:ncol(data_raw)]))
# Remove NAs
data_raw_obs <-
data_raw_obs[,colSums(is.na(data_raw_obs))<nrow(data_raw_obs)]
data_raw_gather <- data_raw_obs %>% gather(-site,key = "Organism_ID", value = 'Abundance', !contains("site"))
data_raw_gather$Family <- as.character(NA)
gild_list <- read_csv("C:/Users/USUARIO/Desktop/OBservData/Thesaurus_Pollinators/Table_organism_guild_META.csv")
data_raw_gather <- data_raw_gather %>% left_join(gild_list,by=c("Organism_ID","Family"))
#Check NA's in guild
data_raw_gather %>% filter(is.na(Guild))
# Remove entries with zero abundance
data_raw_gather <- data_raw_gather %>% filter(Abundance>0)
insect_sampling <- tibble(
study_id = "Maj_Rundlof_Brassica_napus_Sweden_2011",
site_id = data_raw_gather$site,
pollinator = data_raw_gather$Organism_ID,
guild = data_raw_gather$Guild,
sampling_method = "transects",
abundance = data_raw_gather$Abundance,
total_sampled_area = 2*150*1,
total_sampled_time = 2*15,
total_sampled_flowers = NA,
Description = "Two surveys per field, and surveys were conducted in one 150 x 1 m transect per field, 15 min each"
)
View(insect_sampling)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(insect_sampling, "insect_sampling_Maj_Rundlof_Brassica_napus_Sweden_2011.csv")
setwd(dir_ini)
data_raw_gather <-  data_raw_gather %>%
group_by(site,Organism_ID,Family,Guild) %>% summarise_all(sum,na.rm=TRUE)
abundance_aux <- data_raw_gather %>% rename(site_id=site) %>%
group_by(site_id,Guild) %>% count(wt=Abundance) %>%
spread(key=Guild, value=n)
names(abundance_aux)
abundance_aux <- abundance_aux %>% mutate(other_flies=0,lepidoptera=0,beetles=0,
non_bee_hymenoptera=0,other=0,humbleflies=0,total=0)
abundance_aux[is.na(abundance_aux)] <- 0
abundance_aux$total <- rowSums(abundance_aux[,c(2:ncol(abundance_aux))])
data.site <- data.site %>% left_join(abundance_aux, by = "site_id")
abundace_field <- data_raw_gather %>% rename(site_id=site) %>%
select(site_id,Organism_ID,Abundance)%>%
group_by(site_id,Organism_ID) %>% count(wt=Abundance)
abundace_field <- abundace_field %>% spread(key=Organism_ID,value=n)
abundace_field[is.na(abundace_field)] <- 0
abundace_field$r_obser <-  0
abundace_field$r_chao <-  0
for (i in 1:nrow(abundace_field)) {
x <- as.numeric(abundace_field[i,2:(ncol(abundace_field)-2)])
chao  <-  ChaoRichness(x, datatype = "abundance", conf = 0.95)
abundace_field$r_obser[i] <-  chao$Observed
abundace_field$r_chao[i] <-  chao$Estimator
}
tax_res <- read_csv("taxon_table_Rader.csv")
#Mutate pollinator labels to match those of taxon table
tax_estimation <- insect_sampling %>% mutate(pollinator=str_replace(pollinator,"_"," ")) %>%
left_join(tax_res, by="pollinator")
tax_estimation %>% group_by(rank) %>% count()
percentage_species_morphos <-
sum(tax_estimation$rank %in% c("morphospecies","species"))/nrow(tax_estimation)
richness_aux <- abundace_field %>% select(site_id,r_obser,r_chao)
richness_aux <- richness_aux %>% rename(observed_pollinator_richness=r_obser,
other_pollinator_richness=r_chao) %>%
mutate(other_richness_estimator_method="Chao1")
if (percentage_species_morphos < 0.8){
richness_aux[,2:ncol(richness_aux)] <- NA
}
data.site <- data.site %>% left_join(richness_aux, by = "site_id")
field_level_data <- tibble(
study_id = data.site$study_id,
site_id = data.site$site_id,
crop = data.site$crop,
variety = data.site$variety,
management = data.site$management,
country = data.site$country,
latitude = data.site$latitude,
longitude = data.site$longitude,
X_UTM=data.site$X_UTM,
Y_UTM=data.site$Y_UTM,
zone_UTM=data.site$zone_UTM,
sampling_start_month = data.site$sampling_start_month,
sampling_end_month = data.site$sampling_end_month,
sampling_year = data.site$sampling_year,
field_size = data.site$field_size,
yield=data.site$yield,
yield_units=data.site$yield_units,
yield2=data.site$yield2,
yield2_units=data.site$yield2_units,
yield_treatments_no_pollinators=data.site$yield_treatments_no_pollinators,
yield_treatments_pollen_supplement=data.site$yield_treatments_no_pollinators,
yield_treatments_no_pollinators2=data.site$yield_treatments_no_pollinators2,
yield_treatments_pollen_supplement2=data.site$yield_treatments_pollen_supplement2,
fruits_per_plant=data.site$fruits_per_plant,
fruit_weight= data.site$fruit_weight,
plant_density=data.site$plant_density,
seeds_per_fruit=data.site$seeds_per_fruit,
seeds_per_plant=data.site$seeds_per_plant,
seed_weight=data.site$seed_weight,
observed_pollinator_richness=data.site$observed_pollinator_richness,
other_pollinator_richness=data.site$other_pollinator_richness,
other_richness_estimator_method=data.site$other_richness_estimator_method,
richness_restriction = NA,
abundance = data.site$total,
ab_honeybee = data.site$honeybees,
ab_bombus = data.site$bumblebees,
ab_wildbees = data.site$other_wild_bees,
ab_syrphids = data.site$syrphids,
ab_humbleflies= data.site$humbleflies,
ab_other_flies= data.site$other_flies,
ab_beetles=data.site$beetles,
ab_lepidoptera=data.site$lepidoptera,
ab_nonbee_hymenoptera=data.site$non_bee_hymenoptera,
ab_others = data.site$other,
total_sampled_area = 2*150,
total_sampled_time = 2*15,
visitation_rate_units = NA,
visitation_rate = NA,
visit_honeybee = NA,
visit_bombus = NA,
visit_wildbees = NA,
visit_syrphids = NA,
visit_humbleflies = NA,
visit_other_flies = NA,
visit_beetles = NA,
visit_lepidoptera = NA,
visit_nonbee_hymenoptera = NA,
visit_others = NA,
Publication = data.site$Publication,
Credit = data.site$Credit,
Email_contact = data.site$Email_contact
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(field_level_data, "field_level_data_Maj_Rundlof_Brassica_napus_Sweden_2011.csv")
setwd(dir_ini)
View(field_level_data)
library(tidyverse)
library(sp) #Transforming latitude and longitude
library("iNEXT")
dir_ini <- getwd()
data_raw <- read_csv("Individual CSV/Rundlof_2012.csv")
# Remove columns full of NA's
data_raw_without_NAs <-
data_raw[,colSums(is.na(data_raw))<nrow(data_raw)]
data.site_aux <- tibble(
study_id = "Maj_Rundlof_Brassica_napus_Sweden_2012",
site_id = data_raw_without_NAs$site,
crop = "Brassica napus",
variety = NA,
management = data_raw_without_NAs$land_management,
country = "Sweden",
latitude = NA,#"55?48'15.31''N",
longitude = NA,#"13?28'4.12''E",
X_UTM=NA,
Y_UTM=NA,
zone_UTM=NA,
sampling_start_month = 5,
sampling_end_month = 6,
sampling_year = data_raw_without_NAs$Year_of_study,
field_size = NA,
yield=NA,
yield_units=NA,
yield2= NA,
yield2_units= NA,
yield_treatments_no_pollinators= NA,
yield_treatments_pollen_supplement=NA,
yield_treatments_no_pollinators2= NA,
yield_treatments_pollen_supplement2=NA,
fruits_per_plant= NA,
fruit_weight=  NA,
plant_density= NA,
seeds_per_fruit= NA,
seeds_per_plant= NA,
seed_weight= NA
)
data.site <- data.site_aux %>%
group_by(study_id,site_id,crop,variety,management,country,
latitude,longitude,X_UTM,zone_UTM,sampling_end_month,sampling_year,yield_units) %>%
summarise_all(mean, na.rm = TRUE)
# Columns full of NAs return NaN: Set those Nan to NA
# is.nan doesn't actually have a method for data frames, unlike is.na
is.nan.data.frame <- function(x){
do.call(cbind, lapply(x, is.nan))}
data.site[is.nan(data.site)] <- NA
data.site$Publication <- NA
data.site$Credit  <- "Maj Rundlof, Henrik Smith and Riccardo Bommarco"
data.site$Email_contact <- "Maj.Rundlof@biol.lu.se"
data_raw_obs <- data_raw %>%
select(site,round,row,observation_location, names(data_raw[30:ncol(data_raw)]))
# Remove NAs
data_raw_obs <-
data_raw_obs[,colSums(is.na(data_raw_obs))<nrow(data_raw_obs)]
# Gather observations
data_raw_gather <- data_raw_obs %>% gather(-site,key = "Organism_ID", value = 'Abundance', !contains("site"))
data_raw_gather$Family <- as.character(NA)
gild_list <- read_csv("C:/Users/USUARIO/Desktop/OBservData/Thesaurus_Pollinators/Table_organism_guild_META.csv")
data_raw_gather <- data_raw_gather %>% left_join(gild_list,by=c("Organism_ID","Family"))
#Check NA's in guild
data_raw_gather %>% filter(is.na(Guild))
# Remove entries with zero abundance
data_raw_gather <- data_raw_gather %>% filter(Abundance>0)
insect_sampling <- tibble(
study_id = "Maj_Rundlof_Brassica_napus_Sweden_2012",
site_id = data_raw_gather$site,
pollinator = data_raw_gather$Organism_ID,
guild = data_raw_gather$Guild,
sampling_method = "transects",
abundance = data_raw_gather$Abundance,
total_sampled_area = 2*150*1,
total_sampled_time = 2*15,
total_sampled_flowers = NA,
Description = "Two surveys per field, and surveys were conducted in one 150 x 1 m transect per field, 15 min each"
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(insect_sampling, "insect_sampling_Maj_Rundlof_Brassica_napus_Sweden_2012.csv")
setwd(dir_ini)
data_raw_gather <-  data_raw_gather %>%
group_by(site,Organism_ID,Family,Guild) %>% summarise_all(sum,na.rm=TRUE)
abundance_aux <- data_raw_gather %>% rename(site_id=site) %>%
group_by(site_id,Guild) %>% count(wt=Abundance) %>%
spread(key=Guild, value=n)
names(abundance_aux)
abundance_aux <- abundance_aux %>% mutate(other_flies=0,lepidoptera=0,beetles=0,
non_bee_hymenoptera=0,other=0,humbleflies=0,total=0)
abundance_aux[is.na(abundance_aux)] <- 0
abundance_aux$total <- rowSums(abundance_aux[,c(2:ncol(abundance_aux))])
data.site <- data.site %>% left_join(abundance_aux, by = "site_id")
abundace_field <- data_raw_gather %>% rename(site_id=site) %>%
select(site_id,Organism_ID,Abundance)%>%
group_by(site_id,Organism_ID) %>% count(wt=Abundance)
abundace_field <- abundace_field %>% spread(key=Organism_ID,value=n)
abundace_field[is.na(abundace_field)] <- 0
abundace_field$r_obser <-  0
abundace_field$r_chao <-  0
for (i in 1:nrow(abundace_field)) {
x <- as.numeric(abundace_field[i,2:(ncol(abundace_field)-2)])
chao  <-  ChaoRichness(x, datatype = "abundance", conf = 0.95)
abundace_field$r_obser[i] <-  chao$Observed
abundace_field$r_chao[i] <-  chao$Estimator
}
tax_res <- read_csv("taxon_table_Rader.csv")
#Mutate pollinator labels to match those of taxon table
tax_estimation <- insect_sampling %>% mutate(pollinator=str_replace(pollinator,"_"," ")) %>%
left_join(tax_res, by="pollinator")
tax_estimation %>% group_by(rank) %>% count()
percentage_species_morphos <-
sum(tax_estimation$rank %in% c("morphospecies","species"))/nrow(tax_estimation)
richness_aux <- abundace_field %>% select(site_id,r_obser,r_chao)
richness_aux <- richness_aux %>% rename(observed_pollinator_richness=r_obser,
other_pollinator_richness=r_chao) %>%
mutate(other_richness_estimator_method="Chao1")
if (percentage_species_morphos < 0.8){
richness_aux[,2:ncol(richness_aux)] <- NA
}
data.site <- data.site %>% left_join(richness_aux, by = "site_id")
field_level_data <- tibble(
study_id = data.site$study_id,
site_id = data.site$site_id,
crop = data.site$crop,
variety = data.site$variety,
management = data.site$management,
country = data.site$country,
latitude = data.site$latitude,
longitude = data.site$longitude,
X_UTM=data.site$X_UTM,
Y_UTM=data.site$Y_UTM,
zone_UTM=data.site$zone_UTM,
sampling_start_month = data.site$sampling_start_month,
sampling_end_month = data.site$sampling_end_month,
sampling_year = data.site$sampling_year,
field_size = data.site$field_size,
yield=data.site$yield,
yield_units=data.site$yield_units,
yield2=data.site$yield2,
yield2_units=data.site$yield2_units,
yield_treatments_no_pollinators=data.site$yield_treatments_no_pollinators,
yield_treatments_pollen_supplement=data.site$yield_treatments_no_pollinators,
yield_treatments_no_pollinators2=data.site$yield_treatments_no_pollinators2,
yield_treatments_pollen_supplement2=data.site$yield_treatments_pollen_supplement2,
fruits_per_plant=data.site$fruits_per_plant,
fruit_weight= data.site$fruit_weight,
plant_density=data.site$plant_density,
seeds_per_fruit=data.site$seeds_per_fruit,
seeds_per_plant=data.site$seeds_per_plant,
seed_weight=data.site$seed_weight,
observed_pollinator_richness=data.site$observed_pollinator_richness,
other_pollinator_richness=data.site$other_pollinator_richness,
other_richness_estimator_method=data.site$other_richness_estimator_method,
abundance = data.site$total,
ab_honeybee = data.site$honeybees,
ab_bombus = data.site$bumblebees,
ab_wildbees = data.site$other_wild_bees,
ab_syrphids = data.site$syrphids,
ab_humbleflies= data.site$humbleflies,
ab_other_flies= data.site$other_flies,
ab_beetles=data.site$beetles,
ab_lepidoptera=data.site$lepidoptera,
ab_nonbee_hymenoptera=data.site$non_bee_hymenoptera,
ab_others = data.site$other,
richness_restriction = NA,
total_sampled_area = 2*150,
total_sampled_time = 2*15,
visitation_rate_units = NA,
visitation_rate = NA,
visit_honeybee = NA,
visit_bombus = NA,
visit_wildbees = NA,
visit_syrphids = NA,
visit_humbleflies = NA,
visit_other_flies = NA,
visit_beetles = NA,
visit_lepidoptera = NA,
visit_nonbee_hymenoptera = NA,
visit_others = NA,
Publication = data.site$Publication,
Credit = data.site$Credit,
Email_contact = data.site$Email_contact
)
View(field_level_data)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(field_level_data, "field_level_data_Maj_Rundlof_Brassica_napus_Sweden_2012.csv")
setwd(dir_ini)
library(tidyverse)
library(sp) #Transforming latitude and longitude
library("iNEXT")
dir_ini <- getwd()
data_raw <- read_csv("Individual CSV/Rundlof_2012.csv")
# Remove columns full of NA's
data_raw_without_NAs <-
data_raw[,colSums(is.na(data_raw))<nrow(data_raw)]
data.site_aux <- tibble(
study_id = "Maj_Rundlof_Brassica_napus_Sweden_2012",
site_id = data_raw_without_NAs$site,
crop = "Brassica napus",
variety = NA,
management = data_raw_without_NAs$land_management,
country = "Sweden",
latitude = NA,#"55?48'15.31''N",
longitude = NA,#"13?28'4.12''E",
X_UTM=NA,
Y_UTM=NA,
zone_UTM=NA,
sampling_start_month = 5,
sampling_end_month = 6,
sampling_year = data_raw_without_NAs$Year_of_study,
field_size = NA,
yield=NA,
yield_units=NA,
yield2= NA,
yield2_units= NA,
yield_treatments_no_pollinators= NA,
yield_treatments_pollen_supplement=NA,
yield_treatments_no_pollinators2= NA,
yield_treatments_pollen_supplement2=NA,
fruits_per_plant= NA,
fruit_weight=  NA,
plant_density= NA,
seeds_per_fruit= NA,
seeds_per_plant= NA,
seed_weight= NA
)
data.site <- data.site_aux %>%
group_by(study_id,site_id,crop,variety,management,country,
latitude,longitude,X_UTM,zone_UTM,sampling_end_month,sampling_year,yield_units) %>%
summarise_all(mean, na.rm = TRUE)
# Columns full of NAs return NaN: Set those Nan to NA
# is.nan doesn't actually have a method for data frames, unlike is.na
is.nan.data.frame <- function(x){
do.call(cbind, lapply(x, is.nan))}
data.site[is.nan(data.site)] <- NA
data.site$Publication <- NA
data.site$Credit  <- "Maj Rundlof, Henrik Smith and Riccardo Bommarco"
data.site$Email_contact <- "Maj.Rundlof@biol.lu.se"
data_raw_obs <- data_raw %>%
select(site,round,row,observation_location, names(data_raw[30:ncol(data_raw)]))
# Remove NAs
data_raw_obs <-
data_raw_obs[,colSums(is.na(data_raw_obs))<nrow(data_raw_obs)]
# Gather observations
data_raw_gather <- data_raw_obs %>% gather(-site,key = "Organism_ID", value = 'Abundance', !contains("site"))
data_raw_gather$Family <- as.character(NA)
gild_list <- read_csv("C:/Users/USUARIO/Desktop/OBservData/Thesaurus_Pollinators/Table_organism_guild_META.csv")
data_raw_gather <- data_raw_gather %>% left_join(gild_list,by=c("Organism_ID","Family"))
#Check NA's in guild
data_raw_gather %>% filter(is.na(Guild))
# Remove entries with zero abundance
data_raw_gather <- data_raw_gather %>% filter(Abundance>0)
insect_sampling <- tibble(
study_id = "Maj_Rundlof_Brassica_napus_Sweden_2012",
site_id = data_raw_gather$site,
pollinator = data_raw_gather$Organism_ID,
guild = data_raw_gather$Guild,
sampling_method = "transects",
abundance = data_raw_gather$Abundance,
total_sampled_area = 2*150*1,
total_sampled_time = 2*15,
total_sampled_flowers = NA,
Description = "Two surveys per field, and surveys were conducted in one 150 x 1 m transect per field, 15 min each"
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(insect_sampling, "insect_sampling_Maj_Rundlof_Brassica_napus_Sweden_2012.csv")
setwd(dir_ini)
data_raw_gather <-  data_raw_gather %>%
group_by(site,Organism_ID,Family,Guild) %>% summarise_all(sum,na.rm=TRUE)
abundance_aux <- data_raw_gather %>% rename(site_id=site) %>%
group_by(site_id,Guild) %>% count(wt=Abundance) %>%
spread(key=Guild, value=n)
names(abundance_aux)
abundance_aux <- abundance_aux %>% mutate(other_flies=0,lepidoptera=0,beetles=0,
non_bee_hymenoptera=0,other=0,humbleflies=0,total=0)
abundance_aux[is.na(abundance_aux)] <- 0
abundance_aux$total <- rowSums(abundance_aux[,c(2:ncol(abundance_aux))])
data.site <- data.site %>% left_join(abundance_aux, by = "site_id")
abundace_field <- data_raw_gather %>% rename(site_id=site) %>%
select(site_id,Organism_ID,Abundance)%>%
group_by(site_id,Organism_ID) %>% count(wt=Abundance)
abundace_field <- abundace_field %>% spread(key=Organism_ID,value=n)
abundace_field[is.na(abundace_field)] <- 0
abundace_field$r_obser <-  0
abundace_field$r_chao <-  0
for (i in 1:nrow(abundace_field)) {
x <- as.numeric(abundace_field[i,2:(ncol(abundace_field)-2)])
chao  <-  ChaoRichness(x, datatype = "abundance", conf = 0.95)
abundace_field$r_obser[i] <-  chao$Observed
abundace_field$r_chao[i] <-  chao$Estimator
}
tax_res <- read_csv("taxon_table_Rader.csv")
#Mutate pollinator labels to match those of taxon table
tax_estimation <- insect_sampling %>% mutate(pollinator=str_replace(pollinator,"_"," ")) %>%
left_join(tax_res, by="pollinator")
tax_estimation %>% group_by(rank) %>% count()
percentage_species_morphos <-
sum(tax_estimation$rank %in% c("morphospecies","species"))/nrow(tax_estimation)
richness_aux <- abundace_field %>% select(site_id,r_obser,r_chao)
richness_aux <- richness_aux %>% rename(observed_pollinator_richness=r_obser,
other_pollinator_richness=r_chao) %>%
mutate(other_richness_estimator_method="Chao1")
if (percentage_species_morphos < 0.8){
richness_aux[,2:ncol(richness_aux)] <- NA
}
data.site <- data.site %>% left_join(richness_aux, by = "site_id")
field_level_data <- tibble(
study_id = data.site$study_id,
site_id = data.site$site_id,
crop = data.site$crop,
variety = data.site$variety,
management = data.site$management,
country = data.site$country,
latitude = data.site$latitude,
longitude = data.site$longitude,
X_UTM=data.site$X_UTM,
Y_UTM=data.site$Y_UTM,
zone_UTM=data.site$zone_UTM,
sampling_start_month = data.site$sampling_start_month,
sampling_end_month = data.site$sampling_end_month,
sampling_year = data.site$sampling_year,
field_size = data.site$field_size,
yield=data.site$yield,
yield_units=data.site$yield_units,
yield2=data.site$yield2,
yield2_units=data.site$yield2_units,
yield_treatments_no_pollinators=data.site$yield_treatments_no_pollinators,
yield_treatments_pollen_supplement=data.site$yield_treatments_no_pollinators,
yield_treatments_no_pollinators2=data.site$yield_treatments_no_pollinators2,
yield_treatments_pollen_supplement2=data.site$yield_treatments_pollen_supplement2,
fruits_per_plant=data.site$fruits_per_plant,
fruit_weight= data.site$fruit_weight,
plant_density=data.site$plant_density,
seeds_per_fruit=data.site$seeds_per_fruit,
seeds_per_plant=data.site$seeds_per_plant,
seed_weight=data.site$seed_weight,
observed_pollinator_richness=data.site$observed_pollinator_richness,
other_pollinator_richness=data.site$other_pollinator_richness,
other_richness_estimator_method=data.site$other_richness_estimator_method,
richness_restriction = NA,
abundance = data.site$total,
ab_honeybee = data.site$honeybees,
ab_bombus = data.site$bumblebees,
ab_wildbees = data.site$other_wild_bees,
ab_syrphids = data.site$syrphids,
ab_humbleflies= data.site$humbleflies,
ab_other_flies= data.site$other_flies,
ab_beetles=data.site$beetles,
ab_lepidoptera=data.site$lepidoptera,
ab_nonbee_hymenoptera=data.site$non_bee_hymenoptera,
ab_others = data.site$other,
total_sampled_area = 2*150,
total_sampled_time = 2*15,
visitation_rate_units = NA,
visitation_rate = NA,
visit_honeybee = NA,
visit_bombus = NA,
visit_wildbees = NA,
visit_syrphids = NA,
visit_humbleflies = NA,
visit_other_flies = NA,
visit_beetles = NA,
visit_lepidoptera = NA,
visit_nonbee_hymenoptera = NA,
visit_others = NA,
Publication = data.site$Publication,
Credit = data.site$Credit,
Email_contact = data.site$Email_contact
)
setwd("C:/Users/USUARIO/Desktop/OBservData/Datasets_storage")
write_csv(field_level_data, "field_level_data_Maj_Rundlof_Brassica_napus_Sweden_2012.csv")
setwd(dir_ini)
