FarmStat
Cormac Harten
08 October, 2022
Land Utilisation
My reading of this is that the average size of farms have increased with the smaller farms being consolidated or sold off.
# Census Data for Land Utilisation
AVA32 <- cso_get_data("AVA32")
AVA32 <- AVA32 %>%
pivot_longer(!1:3, names_to = "year")
AVA32$value <- as.integer(AVA32$value)
AVA32.N <- AVA32 %>%
filter(Statistic=="Farms with Crops") %>%
filter(Type.of.Crop != "Area farmed (AAU)") %>%
filter(Type.of.Crop != "All grassland") %>%
filter(Type.of.Crop != "Commonage")
AVA32.A <- AVA32 %>%
filter(Statistic=="Average Area Farmed")%>%
filter(Type.of.Crop=="Area farmed (AAU)")
AVA32.N2 <- AVA32 %>%
filter(Statistic=="Farms with Crops")%>%
filter(Type.of.Crop=="All grassland")
AVA32.N_T <- AVA32.N %>%
group_by(Statistic,Type.of.Crop,year)%>%
summarise(value=sum(value,na.rm=TRUE))
AVA32.N_T %>%
ggplot(aes(x=year,y=value,group=Type.of.Crop,colour=Type.of.Crop,fill=Type.of.Crop))+
geom_col(alpha=0.4)+
geom_text(position=position_stack(vjust=0.5),aes(label=value))+
theme_bw()+
theme(legend.position = "bottom")+
xlab("Census Year")+ylab("Number of farms")+
ggtitle("Land Utilisation - Total")+
labs(subtitle="AVA32",caption="removed categories: 'All grassland', 'Area farmed' assuming double count. removed 'Commonage due to the value being zero " )
# Map
shp <- sf::read_sf("/Users/harte/OneDrive/Experimental/Counties___OSi_National_Statutory_Boundaries_.shp")
GEO_Merge.Counties <- readxl::read_xlsx(path="C:\\Users\\harte\\OneDrive\\Experimental\\Counties.Agri.xlsx")
AVA32.A<- AVA32.A %>%
rename(County.CSO=County)
AVA32.A <- right_join(GEO_Merge.Counties,AVA32.A,by="County.CSO")
AVA32.N2<- AVA32.N2 %>%
rename(County.CSO=County)
AVA32.N2 <- right_join(GEO_Merge.Counties,AVA32.N2,by="County.CSO")
shp_AVA32.A <- merge(shp,AVA32.A,by.x="COUNTY",by.y="County")
shp_AVA32.N2 <- merge(shp,AVA32.N2,by.x="COUNTY",by.y="County")
shp_AVA32.N2 %>%
ggplot() +
geom_sf(aes(fill = value,alpha=0.9))+
theme_void()+
scale_fill_distiller(palette = "Spectral", name="NR")+
ggtitle("Land Utilisation")+theme(plot.title = element_text(face = "bold"))+
labs(subtitle="Farms with Crops: All grassland",
caption = "CSO: AVA32")+
facet_wrap(~year,ncol=3) 
shp_AVA32.A %>%
ggplot() +
geom_sf(aes(fill = value,alpha=0.9))+
theme_void()+
scale_fill_distiller(palette = "Spectral", name="HA")+
ggtitle("Land Utilisation")+theme(plot.title = element_text(face = "bold"))+
labs(subtitle="Area farmed: Average Area Farmed (Hectares)",
caption = "CSO: AVA32")+
facet_wrap(~year,ncol=3) 
# Cross reference with AVA24: Land Holdings
AVA24 <- cso_get_data("AVA24")
AVA24 <- AVA24 %>%
pivot_longer(!1:3, names_to = "year")
AVA24_T <- AVA24 %>%
group_by(Statistic,Size.of.Holding,year)%>%
summarise(value=sum(value,na.rm=TRUE))
AVA24_T <- AVA24_T %>%
filter(Size.of.Holding!="All farms")
AVA24_T %>%
ggplot(aes(x=year,y=value,group=Size.of.Holding,colour=Size.of.Holding,fill=Size.of.Holding))+
geom_col(alpha=0.4)+
geom_text(position=position_stack(vjust=0.5),aes(label=value))+
theme_bw()+
theme(legend.position = "bottom")+
xlab("Census Year")+ylab("Holdings (Number)")+
ggtitle("Agricultural Holdings - Total")+
labs(subtitle="AVA24",caption="" )
AVA24.100 <- AVA24 %>%
filter(Size.of.Holding=="100 ha or more")
AVA24.50 <- AVA24 %>%
filter(Size.of.Holding=="50 ha or more but less than 100 ha")
#AVA24$value <- as.integer(AVA24$value)Cork, Tipperary and Meath stand out.
AVA24.100<- AVA24.100 %>%
rename(County.CSO=County)
AVA24.100 <- right_join(GEO_Merge.Counties,AVA24.100,by="County.CSO")
AVA24.50<- AVA24.50 %>%
rename(County.CSO=County)
AVA24.50 <- right_join(GEO_Merge.Counties,AVA24.50,by="County.CSO")
shp_AVA24.100 <- merge(shp,AVA24.100,by.x="COUNTY",by.y="County")
shp_AVA24.50 <- merge(shp,AVA24.50,by.x="COUNTY",by.y="County")
shp_AVA24.100 %>%
ggplot() +
geom_sf(aes(fill = value,alpha=0.9))+
theme_void()+
scale_fill_fermenter(n.breaks = 9, palette = "PuOr",name="NR")+
ggtitle("Agricultural Holdings")+theme(plot.title = element_text(face = "bold"))+
labs(subtitle="Holdings with 100 ha or more",
caption = "CSO: AVA24")+
facet_wrap(~year,ncol=2) 
shp_AVA24.50 %>%
ggplot() +
geom_sf(aes(fill = value,alpha=0.9))+
theme_void()+
scale_fill_fermenter(n.breaks = 9, palette = "PuOr",name="NR")+
ggtitle("Agricultural Holdings")+theme(plot.title = element_text(face = "bold"))+
labs(subtitle="Holdings with 50 to 100 ha",
caption = "CSO: AVA24")+
facet_wrap(~year,ncol=2) 
Milk
AKM01<-cso_get_data("AKM01")
AKM02<-cso_get_data("AKM02")
AKM03<-cso_get_data("AKM03")
AKM01 <- AKM01%>%
pivot_longer(!1:2,names_to = "year_month")
AKM02 <- AKM02%>%
pivot_longer(!1:2,names_to = "year_month")
AKM03 <- AKM03%>%
pivot_longer(!1:2,names_to = "year_month")
AKM01$Year <-substr(AKM01$year_month,1,4)
AKM01$Month <- sub(".* ", "", AKM01$year_month)
AKM01$Month_NR <- as.integer(factor(AKM01$Month, levels=month.name))
AKM01$Date <- as.yearmon(paste(AKM01$Year, AKM01$Month_NR), "%Y %m")
AKM02$Year <-substr(AKM02$year_month,1,4)
AKM02$Month <- sub(".* ", "", AKM02$year_month)
AKM02$Month_NR <- as.integer(factor(AKM02$Month, levels=month.name))
AKM02$Date <- as.yearmon(paste(AKM02$Year, AKM02$Month_NR), "%Y %m")
AKM03$Year <-substr(AKM03$year_month,1,4)
AKM03$Month <- sub(".* ", "", AKM03$year_month)
AKM03$Month_NR <- as.integer(factor(AKM03$Month, levels=month.name))
AKM03$Date <- as.yearmon(paste(AKM03$Year, AKM03$Month_NR), "%Y %m")
AKM01$value<-as.numeric(AKM01$value)
AKM02$value<-as.numeric(AKM02$value)
AKM03$value<-as.numeric(AKM03$value)Intake of Cows Milk
Domestic
# AKM01
## Statistics included: 1.Intake (ltr), 2.Fat (%), 3.Protein (%)
AKM01.1 <- AKM01 %>%
filter(Statistic=="Intake of Cows Milk by Creameries and Pasteurisers")
AKM01.1.1 <- AKM01.1 %>%
filter(Domestic.or.Import.Source=="Domestic")
# T_AKM01.1.1_1 <- AKM01.1.1 %>%
# group_by(Year)%>%
# summarise(Average_Intake = mean(value),
# Total_Intake = sum(value))
#
# ggplot(data=T_AKM01.1.1_1, aes(x=Year,y=Average_Intake,group=1))+
# geom_line(colour="#00B700")+
# theme_bw()+
# geom_text_repel(aes(label=round(Average_Intake, digits=2)),data = T_AKM01.1.1_1, size = 2.5)+
# ggtitle("Average - Intake of Cows Milk by Creameries and Pasteurisers")+
# labs(subtitle = "AKM01-Domestic",y="Million Litres")+
# theme(plot.title=element_text(face="bold"))
AKM01.1.1$SMA.12 <- round(SMA(AKM01.1.1$value,n=12),digits=2)
AKM01.1.1_Max <- AKM01.1.1 %>%
slice(which.max(Date))
AKM01.1.1 %>%
ggplot(aes(x=Date,y=SMA.12,group=1))+
geom_line(colour="#00B700")+
theme_bw()+
geom_text_repel(aes(label=round(value, digits=2)),size = 2.5,colour="black",alpha=0.6)+
geom_text(data=AKM01.1.1_Max,aes(label=round(value,digits=2)),colour="black")+
ggtitle("Intake of Cows Milk by Creameries and Pasteurisers")+
labs(subtitle = "AKM01-Domestic: 12 Month Moving Average",y="Million Litres")+
theme(plot.title=element_text(face="bold"))
Import
# AKM01
## Statistics included: 1.Intake (ltr), 2.Fat (%), 3.Protein (%)
# AKM01.1 <- AKM01 %>%
# filter(Statistic=="Intake of Cows Milk by Creameries and Pasteurisers")
# AKM01.1.2 <- AKM01.1 %>%
# filter(Domestic.or.Import.Source=="Import")
# T_AKM01.1.2_1 <- AKM01.1.2 %>%
# group_by(Year)%>%
# summarise(Average_Intake = mean(value),
# Total_Intake = sum(value))
# ggplot(data=T_AKM01.1.2_1, aes(x=Year,y=Average_Intake,group=1))+
# geom_line(colour="#0B00B7")+
# theme_bw()+
# geom_text_repel(aes(label=round(Average_Intake, digits=2)),data = T_AKM01.1.2_1, size = 2.5)+
# ggtitle("Average - Intake of Cows Milk by Creameries and Pasteurisers")+
# labs(subtitle = "AKM01-Import",y="Million Litres")+
# theme(plot.title=element_text(face="bold"))
#
# ggplot(data=T_AKM01.1.2_1, aes(x=Year,y=Total_Intake,group=1))+
# geom_col(fill="#0B00B7",alpha=0.5)+
# theme_bw()+
# geom_text_repel(aes(label=round(Total_Intake, digits=2)),data = T_AKM01.1.2_1, size = 2.5)+
# ggtitle("Total - Intake of Cows Milk by Creameries and Pasteurisers")+
# labs(subtitle = "AKM01-Import",y="Million Litres")+
# theme(plot.title=element_text(face="bold"))Dairy Sales for Human Consumption
AKM02.1 <- AKM02 %>%
filter(Type.of.Milk != "All Milk")
AKM02.1 %>%
ggplot(aes(x=Date,y=value,group=Type.of.Milk,colour=Type.of.Milk))+
geom_line()+
geom_text_repel(aes(label=value),alpha=0.7,max.overlaps = 8)+
theme_bw()+
theme(legend.position = "none")+
ggtitle("Milk Sales for Human Consumption")+
ylab("Million Litres")+
facet_wrap(~Type.of.Milk,ncol=2,labeller = labeller(Type.of.Milk = label_wrap_gen(width = 45)))
Production of Dairy Products
AKM03 %>%
ggplot(aes(x=Date,y=value,group=Product,colour=Product))+
geom_line()+
geom_text_repel(aes(label=value),alpha=0.7,max.overlaps = 8)+
theme_bw()+
theme(legend.position = "none")+
ggtitle("Production of Dairy Products")+
ylab("Tonnes")+
facet_wrap(~Product,ncol=1)
Removing Skimmed Milk Powder as it is a broken series and run a 12 month moving average
AKM03.1 <- AKM03 %>%
filter(Product != "Skimmed Milk Powder")
AKM03.1 <- na.omit(AKM03.1)
AKM03.1$SMA.12 <- round(SMA(AKM03.1$value,n=12),digits=2)
AKM03.1$SMA.12[AKM03.1$Date=="Jan 1980" | AKM03.1$Date=="Feb 1980" | AKM03.1$Date=="Mar 1980"|AKM03.1$Date=="Apr 1980" | AKM03.1$Date=="May 1980"| AKM03.1$Date=="Jun 1980" | AKM03.1$Date=="Jul 1980"|AKM03.1$Date=="Aug 1980" | AKM03.1$Date=="Sep 1980"| AKM03.1$Date=="Oct 1980"|AKM03.1$Date=="Nov 1980" | AKM03.1$Date=="Dec 1980"] <- NA
AKM03.1_Max <- AKM03.1 %>%
group_by(Product)%>%
slice(which.max(Date))
AKM03.1 %>%
ggplot(aes(x=Date,y=SMA.12,group=Product,colour=Product))+
geom_line(size=1.25,alpha=0.8)+
geom_line(data=AKM03.1,aes(x=Date,y=value,group=Product,colour=Product),alpha=0.2)+
geom_text_repel(aes(label=value),alpha=0.7,max.overlaps = 8)+
geom_text(data=AKM03.1_Max,aes(label=SMA.12),colour="black",alpha=0.7)+
theme_bw()+
theme(legend.position = "none")+
ggtitle("Production of Dairy Products")+
labs(subtitle="12 month moving average")+
ylab("Tonnes")+
facet_wrap(~Product,ncol=1)
Livestock
ADM01 <- cso_get_data("ADM01")
ADM01 <- ADM01 %>%
pivot_longer(!1:2, names_to = "yearm")
ADM01$Month <- as.Date(paste(ADM01$yearm, "01", sep = "-"), "%YM%m-%d")
ADM01$Year <- year(ADM01$Month)
ADM01 <- na.omit(ADM01)
ADM01.N <- ADM01 %>%
filter(Statistic == "Number of Livestock Slaughtered")
ADM01.N$SMA.12 <- round(SMA(ADM01.N$value,n=12),digits=2)
ADM01.N <- ADM01.N %>%
filter(Month >= "1980-12-01")
ADM01.N_Max <- ADM01.N %>%
group_by(Type.of.Animal)%>%
slice(which.max(Month))
ADM01.N %>%
ggplot(aes(x=Month,y=value,group=Type.of.Animal,colour=Type.of.Animal))+
geom_line(alpha=0.4)+
geom_line(aes(x=Month,y=SMA.12,group=Type.of.Animal,colour=Type.of.Animal))+
geom_text(data=ADM01.N_Max,aes(label=SMA.12),colour="black",size=2.5,alpha=0.7)+
theme_bw()+
ylab("thousands")+
ggtitle("Livestock Slaughterings")+
labs(subtitle="Number Slaughtered")+
theme(legend.position="none")+
facet_wrap(~Type.of.Animal,ncol=2)
ADM01.W <- ADM01 %>%
filter(Statistic != "Number of Livestock Slaughtered")
ADM01.W$SMA.12 <- round(SMA(ADM01.W$value,n=12),digits=2)
ADM01.W <- ADM01.W %>%
filter(Month >= "1980-12-01")
ADM01.W_Max <- ADM01.W %>%
group_by(Type.of.Animal)%>%
slice(which.max(Month))
ADM01.W %>%
ggplot(aes(x=Month,y=value,group=Type.of.Animal,colour=Type.of.Animal))+
geom_line(alpha=0.4)+
geom_line(aes(x=Month,y=SMA.12,group=Type.of.Animal,colour=Type.of.Animal))+
geom_text(data=ADM01.W_Max,aes(label=SMA.12),colour="black",size=2.5,alpha=0.7)+
theme_bw()+
ylab("thousands (tonnes)")+
ggtitle("Livestock Slaughterings")+
labs(subtitle="Weight Slaughtered")+
theme(legend.position="none")+
facet_wrap(~Type.of.Animal,ncol=2)