Supplementary material of Changing patterns of soil water content and relationship with national wheat and maize production in Europe

Full text

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Supplementary material of Pinke et al. Changing patterns of soil water content and
relationship with national wheat and maize production in Europe
Fig. S1 Countries and regions of the study area. SE = South Eastern Europe, S = Southern
Europe, W = Western Europe, C = Central Europe, E = Eastern Europe, N = Northern Europe

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Table S1 Annual, winter and summer half-year absolute soil water content (m3 m-3) in the
European countries in 0–28 and 28–100 cm soil layers (1981–2017). Data are means ± SD.
Country
Soil water content
0–28 cm soil layer
28–100 cm soil layer
Year
Oct–March
Apr–Sept
Year
Oct –March
Apr–Sept
Albania
0.33±0.04
0.38±0.02
0.29±0.04
0.34±0.03
0.37±0.02
0.31±0.03
Austria
0.34±0.02
0.36±0.01
0.32±0.02
0.33±0.02
0.34±0.02
0.32±0.02
Belarus
0.35±0.02
0.37±0.01
0.32±0.02
0.34±0.02
0.36±0.02
0.32±0.02
Belgium
0.31±0.02
0.34±0.01
0.28±0.02
0.30±0.03
0.33±0.02
0.28±0.02
Bosnia and Herzegovina
0.35±0.02
0.38±0.01
0.32±0.03
0.35±0.02
0.37±0.02
0.32±0.02
Bulgaria
0.31±0.03
0.34±0.02
0.27±0.03
0.29±0.03
0.30±0.03
0.28±0.03
Croatia
0.34±0.03
0.37±0.01
0.30±0.03
0.33±0.03
0.35±0.02
0.31±0.03
Czechia
0.34±0.02
0.36±0.01
0.32±0.02
0.33±0.02
0.35±0.02
0.31±0.02
Denmark
0.27±0.02
0.30±0.01
0.24±0.02
0.26±0.02
0.29±0.02
0.23±0.02
Estonia
0.35±0.02
0.37±0.01
0.32±0.02
0.34±0.02
0.36±0.02
0.32±0.02
Finland
0.42±0.02
0.43±0.01
0.41±0.02
0.41±0.02
0.43±0.01
0.40±0.02
France
0.33±0.03
0.36±0.02
0.29±0.02
0.32±0.03
0.35±0.02
0.30±0.02
Germany
0.35±0.02
0.37±0.01
0.33±0.02
0.34±0.02
0.36±0.02
0.32±0.02
Greece
0.29±0.04
0.34±0.03
0.24±0.03
0.29±0.03
0.31±0.03
0.27±0.03
Hungary
0.30±0.03
0.34±0.02
0.27±0.03
0.29±0.03
0.30±0.03
0.27±0.03
Ireland
0.37±0.02
0.39±0.01
0.35±0.02
0.36±0.02
0.39±0.01
0.34±0.02
Italy
0.33±0.03
0.37±0.02
0.29±0.03
0.32±0.03
0.35±0.02
0.30±0.02
Latvia
0.36±0.02
0.38±0.01
0.34±0.02
0.35±0.02
0.38±0.02
0.33±0.02
Lithuania
0.36±0.02
0.38±0.01
0.33±0.02
0.35±0.02
0.37±0.01
0.33±0.02
Moldova
0.30±0.03
0.34±0.03
0.26±0.03
0.29±0.03
0.30±0.03
0.28±0.03
Netherlands
0.31±0.02
0.33±0.02
0.28±0.03
0.29±0.03
0.29±0.03
0.28±0.02
North Macedonia
0.32±0.03
0.35±0.01
0.29±0.02
0.31±0.02
0.34±0.02
0.28±0.02
Norway
0.30±0.02
0.32±0.01
0.29±0.02
0.30±0.02
0.31±0.01
0.28±0.02
Poland
0.30±0.02
0.32±0.01
0.28±0.02
0.29±0.02
0.31±0.02
0.27±0.02
Portugal
0.25±0.04
0.30±0.03
0.20±0.03
0.26±0.03
0.28±0.03
0.23±0.02
Romania
0.32±0.02
0.35±0.02
0.30±0.02
0.31±0.02
0.32±0.02
0.30±0.02
Russia
0.32±0.02
0.34±0.01
0.29±0.03
0.31±0.02
0.31±0.02
0.30±0.03
Serbia
0.32±0.03
0.35±0.02
0.29±0.03
0.30±0.03
0.32±0.03
0.29±0.03
Slovakia
0.33±0.02
0.36±0.02
0.31±0.02
0.32±0.02
0.34±0.02
0.31±0.02
Slovenia
0.36±0.02
0.37±0.01
0.34±0.02
0.35±0.02
0.37±0.01
0.33±0.02
Spain
0.25±0.03
0.29±0.02
0.21±0.03
0.24±0.02
0.25±0.02
0.23±0.02
Sweden
0.30±0.02
0.32±0.01
0.28±0.02
0.30±0.02
0.32±0.01
0.28±0.02
Switzerland
0.38±0.01
0.39±0.01
0.38±0.01
0.38±0.01
0.38±0.01
0.37±0.01
Turkey
0.27±0.04
0.31±0.03
0.23±0.03
0.27±0.03
0.27±0.03
0.27±0.03
Ukraine
0.32±0.03
0.35±0.02
0.29±0.02
0.30±0.02
0.31±0.02
0.29±0.02
United Kingdom
0.36±0.02
0.38±0.01
0.33±0.02
0.35±0.02
0.37±0.02
0.32±0.02

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Table S1 Annual, winter and summer half-year available soil water content (m3 m-3) in the
European countries in 0–28 and 28–100 cm soil layers (1981–2017). Data are means ± SD.
Country
Soil water content
0–28 cm soil layer
28–100 cm soil layer
Year
Oct–March
Apr–Sept
Year
Oct –March
Apr–Sept
Albania
0.19±0.04
0.24±0.02
0.15±0.04
0.20±0.03
0.23±0.02
0.17±0.03
Austria
0.19±0.02
0.21±0.01
0.17±0.02
0.18±0.02
0.19±0.01
0.17±0.02
Belarus
0.19±0.02
0.22±0.01
0.17±0.02
0.19±0.02
0.20±0.02
0.17±0.02
Belgium
0.18±0.02
0.21±0.01
0.15±0.02
0.17±0.03
0.20±0.02
0.15±0.02
Bosnia and Herzegovina
0.20±0.02
0.23±0.01
0.18±0.03
0.20±0.02
0.22±0.02
0.18±0.02
Bulgaria
0.16±0.03
0.19±0.02
0.13±0.03
0.14±0.03
0.15±0.03
0.14±0.03
Croatia
0.19±0.03
0.22±0.01
0.16±0.03
0.18±0.03
0.21±0.02
0.16±0.03
Czechia
0.19±0.02
0.21±0.01
0.17±0.02
0.18±0.02
0.20±0.02
0.16±0.02
Denmark
0.17±0.02
0.20±0.01
0.14±0.02
0.16±0.02
0.19±0.02
0.13±0.02
Estonia
0.20±0.02
0.22±0.01
0.17±0.02
0.19±0.02
0.21±0.02
0.17±0.02
Finland
0.24±0.02
0.25±0.01
0.23±0.02
0.24±0.02
0.25±0.01
0.23±0.02
France
0.19±0.03
0.22±0.02
0.15±0.02
0.18±0.03
0.20±0.02
0.16±0.02
Germany
0.21±0.02
0.23±0.01
0.19±0.02
0.20±0.02
0.23±0.02
0.18±0.02
Greece
0.15±0.04
0.20±0.03
0.10±0.03
0.15±0.03
0.17±0.03
0.13±0.03
Hungary
0.17±0.03
0.21±0.02
0.14±0.03
0.16±0.03
0.17±0.03
0.14±0.02
Ireland
0.23±0.02
0.25±0.01
0.21±0.02
0.22±0.02
0.24±0.01
0.20±0.02
Italy
0.18±0.03
0.22±0.02
0.14±0.03
0.18±0.03
0.21±0.02
0.16±0.02
Latvia
0.20±0.02
0.22±0.01
0.18±0.02
0.20±0.02
0.22±0.02
0.17±0.02
Lithuania
0.21±0.02
0.23±0.01
0.19±0.02
0.20±0.02
0.23±0.01
0.18±0.02
Moldova
0.16±0.03
0.18±0.02
0.13±0.03
0.14±0.03
0.14±0.03
0.13±0.03
Netherlands
0.20±0.02
0.23±0.01
0.17±0.02
0.19±0.03
0.22±0.03
0.17±0.02
North Macedonia
0.15±0.03
0.19±0.03
0.12±0.03
0.14±0.03
0.15±0.03
0.13±0.03
Norway
0.20±0.02
0.21±0.01
0.18±0.02
0.19±0.02
0.21±0.01
0.18±0.02
Poland
0.19±0.02
0.21±0.01
0.16±0.02
0.18±0.02
0.20±0.02
0.16±0.02
Portugal
0.13±0.04
0.18±0.03
0.08±0.03
0.13±0.03
0.15±0.03
0.11±0.02
Romania
0.18±0.02
0.21±0.02
0.16±0.02
0.17±0.02
0.18±0.02
0.16±0.02
Russia
0.17±0.02
0.19±0.01
0.14±0.03
0.16±0.02
0.16±0.02
0.15±0.02
Serbia
0.18±0.03
0.21±0.02
0.15±0.03
0.16±0.02
0.18±0.03
0.15±0.03
Slovakia
0.19±0.02
0.21±0.02
0.16±0.02
0.18±0.02
0.19±0.02
0.16±0.02
Slovenia
0.21±0.02
0.22±0.01
0.19±0.02
0.20±0.02
0.22±0.01
0.18±0.02
Spain
0.11±0.03
0.15±0.02
0.07±0.02
0.10±0.02
0.11±0.02
0.09±0.02
Sweden
0.21±0.02
0.24±0.01
0.19±0.02
0.21±0.02
0.23±0.01
0.19±0.02
Switzerland
0.23±0.01
0.24±0.01
0.23±0.01
0.23±0.01
0.24±0.01
0.23±0.1
Turkey
0.13±0.04
0.17±0.03
0.09±0.03
0.13±0.03
0.13±0.03
0.12±0.03
Ukraine
0.16±0.03
0.19±0.02
0.14±0.02
0.15±0.02
0.15±0.02
0.14±0.02
United Kingdom
0.22±0.02
0.24±0.01
0.19±0.02
0.21±0.02
0.23±0.02
0.18±0.02

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Fig. S2 The annual country-scale average of 0–28 cm available soil water contain in the ten
biggest European wheat and maize producer countries for 1981–2017.

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Fig. S3 The annual country-scale average of 28–100 cm available soil water contain in the ten
biggest European wheat and maize producer countries for 1981–2017.

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Table S3 Significant Mann-Kandell test results of the country-scale seasonal average of 0–28
cm available soil water contain for 1981–2017.
Decrease significant
Increase significant
Country
Test result
Country
Test result
Winter
Bulgaria
tau = 0.10; p = 0.02
Croatia
tau = 0.10; p = 0.02
Hungary
tau = 0.10; p = 0.03
Italy
tau = 0.12; p = 0.01
Serbia
tau = 0.11; p = 0.01
Summer
Belarus
tau = -0.15; p < 0.01
Czech
tau = -0.09; p = 0.04
France
tau = -0.11; p = 0.02
Latvia
tau = -0.09; p = 0.04
Lithuania
tau = -0.11; p = 0.02
Moldova
tau = -0.24; p < 0.01
Poland
tau = -0.11; p = 0.01
Portugal
tau = -0.09; p = 0.05
Romania
tau = -0.16; p < 0.01
Spain
tau = -0.10; p = 0.02
Ukraine
tau = -0.23; p < 0.01
April–July
Belarus
tau = -0.12; p = 0.02
Belgium
tau = -0.16; p < 0.01
France
tau = -0.12; p = 0.03
Germany
tau = -0.13; p = 0.02
Lithuania
tau = -0.13; p = 0.02
Moldova
tau = -0.24; p < 0.01
Poland
tau = -0.11; p = 0.04
Romania
tau = -0.16; p < 0.01
Slovakia
tau = -0.12; p = 0.03
Slovenia
tau = -0.14; p = 0.01
Ukraine
tau = -0.21; p < 0.01
July August
Belarus
tau = -0.19; p = 0.02
Britain
tau = 0.16; p = 0.05
Bosnia
tau = -0.16; p = 0.05
Ireland
tau = 0.19; p = 0.02
Moldova
tau = -0.37; p < 0.01
Denmark
tau = 0.19; p = 0.02
Portugal
tau = -0.21; p = 0.01
Norway
tau = 0.22; p = 0.01
Romania
tau = -0.27; p < 0.01
Spain
tau = -0.29; p < 0.01
Turkey
tau = -0.18; p = 0.03
Ukraine
tau = -0.41; p < 0.01

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Table S4 Significant Mann-Kandell test results of the country-scale seasonal average of 28–
100 cm available soil water contain for 1981–2017.
Decrease significant
Increase significant
Country
Test result
Country
Test result
Winter
Belarus
tau = -0.08, p = 0.06
Austria
tau = 0.13, p < 0.01
Ukraine
tau = -0.11, p = 0.02
Bosnia
tau = 0.10, p = 0.03
Croatia
tau = 0.13, p = 0.01
Hungary
tau = 0.12, p = 0.01
Italy
tau = 0.12, p = 0.01
Serbia
tau = 0.12, p = 0.01
Summer
Belarus
tau = -0.13, p < 0.01
Norway
tau = 0.10, p = 0.03
Belgium
tau = -0.12, p = 0.01
Czech
tau = -0.11, p = 0.02
France
tau = -0.12, p = 0.01
Germany
tau = -0.10, p = 0.03
Latvia
tau = -0.09, p = 0.04
Lithuania
tau = -0.10, p = 0.03
Moldova
tau = -0.25, p < 0.01
Poland
tau = -0.09, p = 0.05
Romania
tau = -0.15, p < 0.01
Slovakia
tau = -0.10, p = 0.02
Slovenia
tau = -0.11, p = 0.02
Turkey
tau = -0.09, p = 0.04
Ukraine
tau = -0.22, p < 0.01

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Fig. S4 Seasonal trends of 0–28 cm soil moisture content change on the European croplands
between 1981 and 2017. Calculation are performed for 0.1° × 0.1° grid cells. N: negative
direction; P: positive direction; S: significant; NS: non-significant.

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Fig. S5 Overlapping significant negative trends of (i) 0–28 cm soil moisture content change in
summer and winter hydrological half year time windows (1981–2017) and (ii) the
coefficients of determination (R2) of positive relationship between 0–28 cm soil moisture
and wheat and maize yields in Europe 1993–2017.

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Fig. S6 Overlapping significant negative trends of (i) 28–100 cm soil moisture content change
in different seasons (1981–2017) and (ii) the coefficients of determination (R2) of positive

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relationship between 0–28 cm soil moisture and wheat and maize yields in Europe 1993–
2017. N: negative direction; P: positive direction; S: significant; NS: non-significant.
Table S5 Sowing and harvesting periods of winter wheat and maize in Europe. Source: USDA, 2021
Winter wheat
Sowing period
Harvesting period
Country
Aug
Sep
Oct
Nov
Dec
May
Jun
Jul
Aug
Albania
Austria
Belarus
Belgium
Bosnia
Bulgaria
Croatia
Czechia
Denmark
Estonia
Finland
France
Germany
Greece
Hungary
Ireland
Italy
Latvia
Lithuania
Moldova
Netherlands
North Macedonia
Norway
Poland
Portugal
Romania
Russia
Serbia
Slovakia
Slovenia
Spain
Sweden
Switzerland
Turkey
Ukraine
United Kingdom
On the basis of Crop Calendars for Europe (U.S. Department of Agriculture, 2021).

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Maize
Sowing period
Harvesting period
Country
Feb
March
Apr
May
Jun
Aug
Sep
Oct
Nov
Albania
Austria
Belarus
Belgium
Bosnia
Bulgaria
Croatia
Czechia
France
Germany
Greece
Hungary
Italy
Moldova
Netherlands
North
Macedonia
Poland
Portugal
Romania
Russia
Serbia
Slovakia
Slovenia
Spain
Switzerland
Turkey
Ukraine
On the basis of Crop Calendars for Europe (USDA).
Table S6 LOESS R-script for Fig1.
# LOESS SM lineplot
library(ggplot2)
library(gridExtra)
library(ggthemes)
per = factor(data$per, labels=c("a", "b"))
plotl1 = ggplot(data, aes(x = year, y = centraleurope, colour = per)) +

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scale_colour_manual(values=c("lightseagreen", "lightseagreen")) +
geom_point(size = 1) +
theme(legend.position="none") +
geom_line() +
geom_hline(aes(yintercept=0.31), linetype="dashed") +
geom_smooth(se = FALSE, method = lm) +
ylim(0.25, 0.40) +
ggtitle('Central Europe') +
labs(
x = " ",
y = "m3 m-3"
) +
theme_gray(base_size = 18) +
theme(panel.background = element_rect(fill = "white"))+
theme(legend.position = "none")
plotl2 = ggplot(data, aes(x = year, y = easterneurope, colour = per)) +
scale_colour_manual(values=c("lightseagreen", "lightseagreen")) +
geom_point(size = 1) +
geom_line() +
geom_hline(aes(yintercept=0.32), linetype="dashed") +
geom_smooth(se = FALSE, method = lm) +
ylim(0.25, 0.40) +
ggtitle('Eastern Europe') +
labs(
x = " ",
y = " "
) +
theme_gray(base_size = 18) +
theme(panel.background = element_rect(fill = "white"))+
theme(legend.position = "none")
plotl3 = ggplot(data, aes(x = year, y = northerneurope, colour = per)) +
scale_colour_manual(values=c("lightseagreen", "lightseagreen")) +
geom_point(size = 1) +
theme(legend.position="none") +
geom_line() +
geom_hline(aes(yintercept=0.34), linetype="dashed") +
geom_smooth(se = FALSE, method = lm) +
ylim(0.25, 0.40) +
ggtitle('Northern Europe') +
labs(
x = " ",
y = "m3 m-3"
) +
theme_gray(base_size = 18) +
theme(panel.background = element_rect(fill = "white"))+
theme(legend.position = "none")

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plotl4 = ggplot(data, aes(x = year, y = southeasterneurope, colour = per)) +
scale_colour_manual(values=c("lightseagreen", "lightseagreen")) +
geom_point(size = 1) +
theme(legend.position="none") +
geom_line() +
geom_hline(aes(yintercept=0.30), linetype="dashed") +
geom_smooth(se = FALSE, method = lm) +
ylim(0.25, 0.40) +
ggtitle('Southeastern_Europe') +
labs(
x = " ",
y = " "
) +
theme_gray(base_size = 18) +
theme(panel.background = element_rect(fill = "white"))+
theme(legend.position = "none")
plotl5 = ggplot(data, aes(x = year, y = southerneurope, colour = per)) +
scale_colour_manual(values=c("lightseagreen", "lightseagreen")) +
geom_point(size = 1) +
theme(legend.position="none") +
geom_line() +
geom_hline(aes(yintercept=0.27), linetype="dashed") +
geom_smooth(se = FALSE, method = lm) +
ylim(0.25, 0.40) +
ggtitle('Southern Europe') +
labs(
x = " ",
y = "m3 m-3"
) +
theme_gray(base_size = 18) +
theme(panel.background = element_rect(fill = "white"))+
theme(legend.position = "none")
plotl6 = ggplot(data, aes(x = year, y = westerneurope, colour = per)) +
scale_colour_manual(values=c("lightseagreen", "lightseagreen")) +
geom_point(size = 1) +
geom_line() +
geom_hline(aes(yintercept=0.34), linetype="dashed") +
geom_smooth(se = FALSE, method = lm) +
ylim(0.25, 0.40) +
ggtitle('Western Europe') +
labs(
x = " ",
y = " "
) +
theme_gray(base_size = 18) +

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theme(panel.background = element_rect(fill = "white"))+
theme(legend.position = "none")
grid.arrange(plotl1, plotl2, plotl3, plotl4, plotl5, plotl6, ncol = 2, nrow = 3)

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Table S7 R-script for Fig. 2
# variables ----
p2 <- "p:/data/Europa_Tavg_Tmax_P_cropland/" #input path
f2 <- "cru_ts4.03.1901.2018.pre.dat_-15-60E_30-70N.nc" #input filename for pre
f2 <- "cru_ts4.03.1901.2018.tmp.dat_-15-60E_30-70N.nc" #input filename for tmp
f2 <- "cru_ts4.03.1901.2018.tmx.dat_-15-60E_30-70N.nc" #input filename for tmx
p0 <- paste0("d:/Kardos/output/") #output path
obsdata2 <- ncdf4::nc_open(paste(p2,f2,sep="")) #open input file
msv.l <- list(1:12,c(1:7,9:12),5:8) #month's vector for precipitation
msv.l <- list(1:12,5:7) #month's vector for average temperature
msv.l <- list(1:12,7:8) #month's vector for max temperature
# reading from HDD ----
# brick reads all layers #
var2 <- "tmx" #the particular environmental variable !!!
r2 <- raster::brick(paste(p2,f2,sep=""), varname = var2)
dummy.v <- as.numeric(substr(rownames(t(raster::extract(r2, matrix(c(1,1), ncol = 2)))),2,11))
require(lubridate)
dates.v <- rep(as.Date('1901-1-1'),times=length(dummy.v)) %m+% months(dummy.v)
# raster extension #
lon.v <- obsdata2[["dim"]][["lon"]][["vals"]] #vector of longitudes
lat.v <- obsdata2[["dim"]][["lat"]][["vals"]] #vector of latitudes
# cylce ----
ysv1 <- 1993:2017
empty.r <- r2[[1]]; empty.r[,] <- NA #"empty" raster
aver.l <- list(empty.r,empty.r,empty.r) #raster of averages
tau.l <- list(empty.r,empty.r,empty.r) #raster of taus
sl.l <- list(empty.r,empty.r,empty.r) #raster of sl-s
for (jx in 1:length(lon.v)){ #70:80
lon1 <- lon.v[jx]
for(jy in 1:length(lat.v)){ #30:50
lat1 <- lat.v[jy]
val.df <-
as.data.frame(matrix(data=NA,nrow=length(dates.v),ncol=2,dimnames=list(c(),c("d","val"))))
val.df$d <- dates.v
for (jz in 701:nrow(val.df)) val.df$val[jz] <- r2[[jz]][jy,jx] #!!! nem megyünk a legrégebbitol!!

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# plot(val.df$d,val.df$val,type="l")
val.df0 <- val.df
val.df <- val.df[lubridate::year(val.df$d) %in% ysv1,] #szures evekre
for (jmsv in 1:length(msv.l)){
msv1 <- msv.l[[jmsv]]
val.df1 <- val.df[lubridate::month(val.df$d) %in% msv1,] #szures honapokra
if(sum(!is.na(val.df1$val))>3){
aver.l[[jmsv]][jy,jx] <- mean(val.df1$val,na.rm=T)
mk1 <- Kendall::MannKendall(val.df1$val)
tau.l[[jmsv]][jy,jx] <- mk1$tau
sl.l[[jmsv]][jy,jx] <- mk1$sl
}#if
}#jmsv
}#jy
# export to HDD ----
for (jmsv in 1:length(msv.l)){
msv1 <- msv.l[[jmsv]]
raster::writeRaster(aver.l[[jmsv]],paste(p0,var2,"_aver_y",ysv1[1],"-
",ysv1[length(ysv1)],"_m",paste(msv1,collapse=""), ".tif",sep=""),overwrite=TRUE)
raster::writeRaster(tau.l[[jmsv]],paste(p0,var2,"_tau_y",ysv1[1],"-
",ysv1[length(ysv1)],"_m",paste(msv1,collapse=""), ".tif",sep=""),overwrite=TRUE)
raster::writeRaster(sl.l[[jmsv]],paste(p0,var2,"_sl_y",ysv1[1],"-
",ysv1[length(ysv1)],"_m",paste(msv1,collapse=""), ".tif",sep=""),overwrite=TRUE)
} #jmsv
}#jx
# trend irany es szignifikanica egyuttes megjelenitesere szolgalo raszterek legyartasa + abrazolas
####
p0 <- "d:/publications/2021-03-gabonasakktabla_gazdtört-kötet/trend_test_output/output/"
f1 <- "pre_"
f3 <- "_y1993-2017_m5678"
e1 <- ".tif"
tau.r <- raster::raster(paste0(p0,f1,"tau",f3,e1))
sl.r <- raster::raster(paste0(p0,f1,"sl",f3,e1))
rcl1 <- matrix(c(min(raster::getValues(tau.r),na.rm=T)-1,0, -1,
0,max(raster::getValues(tau.r),na.rm=T)+1,1),ncol=3,byrow=T) #reclass rules:
kivalasztott=1, everything_else=0
tau.rr <- raster::reclassify(tau.r,rcl1) # raster reclassed: negative=-1, positive=1

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rcl1 <- matrix(c(-1,0.05, 2, # <0.05 --> 2-es szorzo
0.05,9,1),ncol=3,byrow=T) #>0.05 --> 1-es szorzo
sl.rr <- raster::reclassify(sl.r,rcl1) #
out.r <- tau.rr*sl.rr
raster::writeRaster(out.r,filename=paste0(p0,f1,"tau+sl",f3,e1))
raster::plot(out.r,main=paste0(f1,f3))
raster::plot(u,add=T)
# trend irany es szignifikancia kategoriak orszagonkent: zonal stat ####
#zonak shp fájlja
p0 <- "d:/publications/2021-03-gabonasakktabla_gazdtört-kötet/gis"
f0 <- "ne_countries"
u <- rgdal::readOGR(p0,f0,stringsAsFactors = FALSE);
#raszter
p0 <- "d:/publications/2021-03-gabonasakktabla_gazdtört-kötet/trend_test_output/output/"
f1 <- "tmx_"
f3 <- "_y1993-2017_"
f4 <- "m78"
f0 <- paste0(f1,"tau+sl",f3,f4)
e0 <- ".tif"
in.r <- raster::raster(paste0(p0,f0,e0)) #
for (j in c(-2:2,NA)){
rcl1 <- matrix(c(j-.1,j+.1,1, -9,9,0),ncol=3,byrow=T) #reclass rules: kivalasztott=1, everything_else=0
in.rr <- raster::reclassify(in.r,rcl1) # raster reclassed: kivalasztott=1, everything_else=0
newcol <- raster::extract(in.rr,u,fun=sum,na.rm=T) #zonal statistics #TAKES LONG TIME!!!
u$newcol <- newcol[,1]
colnames(u@data)[ncol(u@data)] <- paste(f1,f4,sprintf("%02d",j),sep="")
} ;write.csv(u@data,paste0(p0,"/_count.csv"))
> library(Kendall) > library(Kendall)
> southeasterneurope = ts(mann_kendall$southeasterneurope, frequency=1, start=c(1981,1)) >
easterneurope = ts(mann_kendall$easterneurope, frequency=1, start=c(1981,1))
> MK = MannKendall(southeasterneurope) > MK = MannKendall(easterneurope)
> summary(MK) > summary(MK)
Score = -116 , Var(Score) = 5846 Score = -326 , Var(Score) = 5846
denominator = 666 denominator = 666
tau = -0.174, 2-sided pvalue =0.13256 tau = -0.489, 2-sided pvalue =0.000021316
> library(Kendall) > library(Kendall)
> centraleurope = ts(mann_kendall$centraleurope, frequency=1, start=c(1981,1)) >
southerneurope = ts(mann_kendall$southerneurope, frequency=1, start=c(1981,1))
> MK = MannKendall(centraleurope) > MK = MannKendall(southerneurope)
> summary(MK) > summary(MK)
Score = -42 , Var(Score) = 5846 Score = -22 , Var(Score) = 5846
denominator = 666 denominator = 666

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tau = -0.0631, 2-sided pvalue =0.5918 tau = -0.033, 2-sided pvalue =0.78358
> library(Kendall) > library(Kendall)
> northerneurope = ts(mann_kendall$northerneurope, frequency=1, start=c(1981,1)) >
westerneurope = ts(mann_kendall$westerneurope, frequency=1, start=c(1981,1))
> MK = MannKendall(northerneurope) > MK = MannKendall(westerneurope)
> summary(MK) > summary(MK)
Score = -10 , Var(Score) = 5846 Score = -84 , Var(Score) = 5846
denominator = 666 denominator = 666
tau = -0.015, 2-sided pvalue =0.9063 tau = -0.126, 2-sided pvalue =0.27768
Table S10 Bootstrap codes for testing deterministic relationships
library (boot)
head(gdp_yield_tesztek)
foo = function (formula, data, indices) {
d <- data[indices, ] # allows boot to select sample
fit <- lm(formula, data=d)
return(summary(fit)$r.square)
}
sample_mean = function(data, indices){
sample = data[indices, ]
}
estimate = function(data, indices){
d = data[indices, ]
dw_relationship = lm(d$ Ukraine_wheat~d$ Ukraine_moisture, data = d)
dw_sq = summary(dw_relationship)$r.square
}
results = boot(data = gdp_yield_tesztek, statistic = estimate, R = 5000)
print(results)
confidence_interval_dw = boot.ci(results, index = 1, conf = 0.95, type = 'bca')
print(confidence_interval_dw)
ci_dw = confidence_interval_dw$bca[ , c(4, 5)]
print(ci_dw)

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