library(tidyverse)
library(gsheet)
library(broom)
library(metafor)
library(dplyr)
library(ggthemes)
library(plyr)
library(janitor)
library(metafor)
library(cowplot)Performance of dual and triple fungicide premixes for the control of soybean target spot after seven years
Packages
ma <- gsheet2tbl("https://docs.google.com/spreadsheets/d/151DE26uSMN4WBS0PTQcjdw7BO56gXy_VilFZS3b9AAM/edit?usp=sharing")
head(ma)# A tibble: 6 × 15
study trial_name year cultivar location lat lon state commercial group
<dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 1 Embrapa Sinop 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
2 1 Embrapa Sinop 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
3 1 Embrapa Sinop 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
4 1 Embrapa Sinop 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
5 2 AgroCarregal 2017 CD 2728… Rio Ver… -17.8 -50.9 GO ATIVUM + … tria…
6 2 AgroCarregal 2017 CD 2728… Rio Ver… -17.8 -50.9 GO ATIVUM + … tria…
# ℹ 5 more variables: ai <chr>, block <dbl>, sev <dbl>, yld <dbl>, rust <dbl>Descriptive
Yield
yield = ma %>%
filter(ai %in% c("BIX+PROT+TRIFL", "_CHECK", "FLUX+PYRA")) %>%
ggplot(aes(yld))+
geom_histogram(color = "white", fill = "darkred")+
facet_wrap(~ai)+
theme_few()+
labs(x = "Yield (kg/ha)",
y = "Frequency")+
theme(text = element_text(face = "bold",size = 14))
ggsave("fig/yield.png", bg = "white", width = 10, height = 8)Yield x Year
yield_year = ma %>%
filter(ai %in% c("BIX+PROT+TRIFL", "_CHECK", "FLUX+PYRA")) %>%
ggplot(aes(as.factor(year),yld))+
#geom_histogram(color = "white", fill = "darkgreen")+
geom_boxplot(fill = NA, color = "black", size = 1)+
facet_wrap(~ai)+
theme_few()+
labs(x = "Year",
y = "Yield (kg/ha)")+
#coord_flip()+
theme(text = element_text(face = "bold",size = 14),
strip.text = element_blank())
ggsave("fig/yield_year.png", bg = "white", width = 10, height = 8)Plot
plot_grid(yield,yield_year, labels = c("AUTO"), ncol = 1)
ggsave("fig/frequency_boxplot_yield.png", bg = "white", height = 8, width = 10)Severity
severity = ma %>%
filter(ai %in% c("BIX+PROT+TRIFL", "_CHECK", "FLUX+PYRA")) %>%
ggplot(aes(sev))+
geom_histogram(color = "white", fill = "darkgreen")+
facet_wrap(~ai)+
theme_few()+
labs(x = "Severity (%)",
y = "Frequency")+
theme(text = element_text(face = "bold",size = 14))
ggsave("fig/severity.png", bg = "white", width = 10, height = 8)Severity x Year
severity_year = ma %>%
filter(ai %in% c("BIX+PROT+TRIFL", "_CHECK", "FLUX+PYRA")) %>%
ggplot(aes(as.factor(year),sev))+
#geom_histogram(color = "white", fill = "darkgreen")+
geom_boxplot(fill = NA, color = "black", size = 1)+
facet_wrap(~ai)+
theme_few()+
labs(x = "Year",
y = "Severity (%)")+
#coord_flip()+
theme(text = element_text(face = "bold",size = 14),
strip.text = element_blank())
ggsave("fig/severity_year.png", bg = "white", width = 10, height = 8)Plot
plot_grid(severity,severity_year, labels = c("AUTO"), ncol = 1)
ggsave("fig/frequency_boxplot_severity.png",
bg = "white", height = 8,width = 10)Meta-analysis
Severity and yield
ma# A tibble: 6,582 × 15
study trial_name year cultivar location lat lon state commercial group
<dbl> <chr> <dbl> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
1 1 Embrapa Sin… 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
2 1 Embrapa Sin… 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
3 1 Embrapa Sin… 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
4 1 Embrapa Sin… 2017 M8210 I… Sinop -11.9 -55.5 MT ATIVUM + … tria…
5 2 AgroCarregal 2017 CD 2728… Rio Ver… -17.8 -50.9 GO ATIVUM + … tria…
6 2 AgroCarregal 2017 CD 2728… Rio Ver… -17.8 -50.9 GO ATIVUM + … tria…
7 2 AgroCarregal 2017 CD 2728… Rio Ver… -17.8 -50.9 GO ATIVUM + … tria…
8 2 AgroCarregal 2017 CD 2728… Rio Ver… -17.8 -50.9 GO ATIVUM + … tria…
9 3 Fundação MS… 2017 BMX Pot… Bonito -21.1 -56.5 MS ATIVUM + … tria…
10 3 Fundação MS… 2017 BMX Pot… Bonito -21.1 -56.5 MS ATIVUM + … tria…
# ℹ 6,572 more rows
# ℹ 5 more variables: ai <chr>, block <dbl>, sev <dbl>, yld <dbl>, rust <dbl>ma1 <- ma %>%
#filter(ai %in% c("BIX+PROT+TRIFL", "_CHECK", "FLUX+PYRA")) %>%
dplyr::group_by(study, year, location, state, ai) %>%
dplyr::summarise(mean_sev = mean(sev),
mean_yld = mean(yld))Severity regression
ma_sev <- ma %>%
dplyr::filter(!is.na(sev)) %>%
dplyr::filter(!is.na(yld)) %>%
dplyr::filter(yld>0) %>%
dplyr::group_by(study, year) %>%
dplyr::select(ai, block, sev) %>%
dplyr::group_by(study, year) %>%
do(tidy(aov(.$sev ~ .$ai + factor(.$block)))) %>%
dplyr::filter(term == "Residuals") %>%
dplyr::select(1,2,6) %>%
set_names(c("study", "year", "v_sev"))Yield regression
ma_yld <- ma %>%
dplyr::filter(!is.na(sev)) %>%
dplyr::filter(!is.na(yld)) %>%
dplyr::filter(yld>0) %>%
dplyr::group_by(study, year) %>%
dplyr::select(ai, block, yld) %>%
dplyr::group_by(study, year) %>%
do(tidy(aov(.$yld ~ .$ai + factor(.$block)))) %>%
dplyr::filter(term == "Residuals") %>%
dplyr::select(1,2,6) %>%
set_names(c("study", "year", "v_yld"))Joining
qmr = left_join(ma_sev, ma_yld)
ma_trial = dplyr::full_join(ma1, qmr)A.I. selection
ma3 = ma_trial %>%
filter(!is.na(mean_sev)) %>%
filter(!is.na(mean_yld)) %>%
filter(!is.na(v_sev)) %>%
filter(!is.na(v_yld)) %>%
filter(ai %in% c("BIX+PROT+TRIFL", "_CHECK", "FLUX+PYRA"))
summary(ma3) study year location state
Min. : 1.00 Min. :2017 Length:340 Length:340
1st Qu.: 5.00 1st Qu.:2018 Class :character Class :character
Median :10.00 Median :2020 Mode :character Mode :character
Mean :10.27 Mean :2020
3rd Qu.:15.00 3rd Qu.:2022
Max. :22.00 Max. :2023
ai mean_sev mean_yld v_sev
Length:340 Min. : 0.10 Min. :1558 Min. : 0.0000
Class :character 1st Qu.:10.00 1st Qu.:3282 1st Qu.: 0.9804
Mode :character Median :21.49 Median :3762 Median : 2.5969
Mean :25.27 Mean :3747 Mean : 6.8977
3rd Qu.:38.82 3rd Qu.:4224 3rd Qu.: 8.4684
Max. :86.50 Max. :6078 Max. :64.2031
v_yld
Min. : 4252
1st Qu.: 30921
Median : 51681
Mean : 62881
3rd Qu.: 83078
Max. :254728 Rename
ma3$ai <- revalue(ma3$ai, c("_CHECK" = "AACHECK"))
ma3$ai <- revalue(ma3$ai, c("BIX+PROT+TRIFL" = "BIX + PROT + TRIFL"))
ma3$ai <- revalue(ma3$ai, c("FLUX+PYRA" = "FLUX + PYRA"))
ma3$study = as.factor(ma3$study)#ma3_unique <- ma3 %>%
# dplyr::distinct(study, .keep_all = TRUE)
ma3 = ma3 %>% # REVISAR
dplyr::group_by(ai,study,year) %>%
dplyr::summarise(
mean_yld = mean(mean_yld),
v_yld = mean(v_yld),
mean_sev = mean(mean_sev),
v_sev = mean(v_sev)
)
ma_check = ma3 %>%
ungroup() %>%
dplyr::filter(ai == "AACHECK")%>%
group_by(study) %>%
dplyr::mutate(check = ai, sev_check = mean_sev,
v_sev_check = v_sev, yld_check = mean_yld, v_yld_check = v_yld ) %>%
dplyr::select(study, yld_check, v_yld_check, sev_check, v_sev_check)
ma_check = ma_check %>%
filter(!is.na(yld_check)) %>%
filter(!is.na(v_yld_check)) %>%
filter(!is.na(sev_check)) %>%
filter(!is.na(v_sev_check))
ma_check = ma_check %>% # REVISAR
dplyr::group_by(study) %>%
dplyr::summarise(
yld_check = mean(yld_check),
v_yld_check = mean(v_yld_check),
sev_check = mean(sev_check),
v_sev_check = mean(v_sev_check)
)
ma_data = ma3 %>%
full_join(ma_check)Severity
ma_sev <- ma_data %>%
filter(mean_sev != "NA") %>%
filter(mean_sev>0)
hist(ma_sev$mean_sev)
ma_sev <- ma_sev %>%
mutate(log_sev = log(mean_sev))
hist(ma_sev$log_sev)
ma_sev$vi_sev <- with(ma_sev, v_sev / (4 * mean_sev^2))
summary(ma_sev$vi_sev) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.000000 0.000399 0.001960 0.043301 0.009596 6.681174 ma_sev = ma_sev %>%
filter(!is.na(mean_yld))ma_sev <- ma_sev %>%
filter(!is.na(mean_yld)) %>%
filter(!is.na(mean_sev)) %>%
group_by(study) %>%
dplyr::mutate(n2 = n()) %>%
filter(n2 != 1)
unique(ma_sev$n2)[1] 17 18 15 14 7 4summary(ma_sev) ai study year mean_yld
Length:340 2 : 18 Min. :2017 Min. :1558
Class :character 3 : 18 1st Qu.:2018 1st Qu.:3282
Mode :character 4 : 18 Median :2020 Median :3762
5 : 18 Mean :2020 Mean :3747
6 : 18 3rd Qu.:2022 3rd Qu.:4224
7 : 18 Max. :2023 Max. :6078
(Other):232
v_yld mean_sev v_sev yld_check
Min. : 4252 Min. : 0.10 Min. : 0.0000 Min. :2506
1st Qu.: 30921 1st Qu.:10.00 1st Qu.: 0.9804 1st Qu.:3248
Median : 51681 Median :21.49 Median : 2.5969 Median :3405
Mean : 62881 Mean :25.27 Mean : 6.8977 Mean :3370
3rd Qu.: 83078 3rd Qu.:38.82 3rd Qu.: 8.4684 3rd Qu.:3598
Max. :254728 Max. :86.50 Max. :64.2031 Max. :4079
v_yld_check sev_check v_sev_check log_sev
Min. : 33766 Min. :22.85 Min. : 1.515 Min. :-2.303
1st Qu.: 45618 1st Qu.:35.97 1st Qu.: 5.254 1st Qu.: 2.303
Median : 60818 Median :40.21 Median : 7.025 Median : 3.067
Mean : 63959 Mean :40.85 Mean : 6.838 Mean : 2.873
3rd Qu.: 76331 3rd Qu.:46.66 3rd Qu.: 8.461 3rd Qu.: 3.659
Max. :111502 Max. :52.68 Max. :13.581 Max. : 4.460
vi_sev n2
Min. :0.000000 Min. : 4.00
1st Qu.:0.000399 1st Qu.:17.00
Median :0.001960 Median :18.00
Mean :0.043301 Mean :16.68
3rd Qu.:0.009596 3rd Qu.:18.00
Max. :6.681174 Max. :18.00
Model fitting
Overall
# Overall
mv_sev <- rma.mv(log_sev, vi_sev,
mods = ~ai,
random = list(~ai | factor(study)),
struct = "HCS",
method = "ML",
#control = list(optimizer = "nlm"),
data = ma_sev)
summary(mv_sev)
Multivariate Meta-Analysis Model (k = 340; method: ML)
logLik Deviance AIC BIC AICc
-24040.6371 49724.4605 48095.2742 48122.0768 48095.6116
Variance Components:
outer factor: factor(study) (nlvls = 22)
inner factor: ai (nlvls = 3)
estim sqrt k.lvl fixed level
tau^2.1 0.0389 0.1971 135 no AACHECK
tau^2.2 0.1082 0.3289 132 no BIX + PROT + TRIFL
tau^2.3 0.1399 0.3741 73 no FLUX + PYRA
rho 0.5527 no
Test for Residual Heterogeneity:
QE(df = 337) = 172868.5215, p-val < .0001
Test of Moderators (coefficients 2:3):
QM(df = 2) = 272.0872, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 3.7415 0.0421 88.9572 <.0001 3.6591 3.8240 ***
aiBIX + PROT + TRIFL -0.9501 0.0587 -16.1722 <.0001 -1.0652 -0.8349 ***
aiFLUX + PYRA -0.5919 0.0695 -8.5192 <.0001 -0.7281 -0.4557 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#anova(mv_sev, btt = 5:6) mv_sev_means <- emmprep(mv_sev)
library(emmeans)
mv_sev_emmeans <- emmeans(mv_sev_means, ~ ai)
pwpm(mv_sev_emmeans) AACHECK BIX + PROT + TRIFL FLUX + PYRA
AACHECK [3.74] <.0001 <.0001
BIX + PROT + TRIFL 0.950 [2.79] <.0001
FLUX + PYRA 0.592 -0.358 [3.15]
Row and column labels: ai
Upper triangle: P values adjust = "tukey"
Diagonal: [Estimates] (emmean)
Lower triangle: Comparisons (estimate) earlier vs. lateremmeans_summary <- summary(mv_sev_emmeans)
emmeans_df <- as.data.frame(emmeans_summary)
colnames(emmeans_df) <- c("ai", "emmeans", "SE", "df", "lower.CL", "upper.CL")
emmeans_df$emmeans = exp(emmeans_df$emmeans)
emmeans_df$SE = exp(emmeans_df$SE)
emmeans_df$lower.CL = exp(emmeans_df$lower.CL)
emmeans_df$upper.CL = exp(emmeans_df$upper.CL)
library(multcomp)
cld(mv_sev_emmeans) ai emmean SE df asymp.LCL asymp.UCL .group
BIX + PROT + TRIFL 2.79 0.0703 Inf 2.65 2.93 1
FLUX + PYRA 3.15 0.0822 Inf 2.99 3.31 2
AACHECK 3.74 0.0421 Inf 3.66 3.82 3
Confidence level used: 0.95
P value adjustment: tukey method for comparing a family of 3 estimates
significance level used: alpha = 0.05
NOTE: If two or more means share the same grouping symbol,
then we cannot show them to be different.
But we also did not show them to be the same. Estimated
efficacy_sev <- data.frame(cbind(
(1 - exp(mv_sev$b)) * 100,
(1 - exp(mv_sev$ci.lb)) * 100,
(1 - exp(mv_sev$ci.ub)) * 100
))
efficacy_sev X1 X2 X3
intrcpt -4116.27129 -3782.63928 -4478.57204
aiBIX + PROT + TRIFL 61.32943 65.53536 56.61022
aiFLUX + PYRA 44.67358 51.71744 36.60210Year
# Year
mv_sev_year <- rma.mv(log_sev, vi_sev,
mods = ~ai*year,
random = list(~ai | factor(study)),
struct = "HCS",
method = "ML",
#verbose = TRUE,
#control=list(optimizer="Nelder-Mead"),
data = ma_sev%>% mutate(year= year - 2017))
mv_sev_year
Multivariate Meta-Analysis Model (k = 340; method: ML)
Variance Components:
outer factor: factor(study) (nlvls = 22)
inner factor: ai (nlvls = 3)
estim sqrt k.lvl fixed level
tau^2.1 0.0382 0.1955 135 no AACHECK
tau^2.2 0.1087 0.3297 132 no BIX + PROT + TRIFL
tau^2.3 0.1402 0.3744 73 no FLUX + PYRA
rho 0.5581 no
Test of Moderators (coefficients 2:6):
QM(df = 5) = 2195.6131, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 3.8521 0.0418 92.1599 <.0001 3.7702 3.9341
aiBIX + PROT + TRIFL -1.0060 0.0598 -16.8307 <.0001 -1.1231 -0.8888
aiFLUX + PYRA -0.8120 0.0708 -11.4755 <.0001 -0.9507 -0.6733
year -0.0307 0.0007 -42.8965 <.0001 -0.0321 -0.0293
aiBIX + PROT + TRIFL:year 0.0175 0.0029 6.1140 <.0001 0.0119 0.0232
aiFLUX + PYRA:year 0.0576 0.0036 16.1787 <.0001 0.0506 0.0646
intrcpt ***
aiBIX + PROT + TRIFL ***
aiFLUX + PYRA ***
year ***
aiBIX + PROT + TRIFL:year ***
aiFLUX + PYRA:year ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(mv_sev_year, btt = 5:6)
Test of Moderators (coefficients 5:6):
QM(df = 2) = 289.8600, p-val < .0001anova(mv_sev_year,mv_sev)
df AIC BIC AICc logLik LRT pval
Full 10 46179.0644 46217.3539 46179.7331 -23079.5322
Reduced 7 48095.2742 48122.0768 48095.6116 -24040.6371 1922.2098 <.0001
QE
Full NA
Reduced 172868.5215 Declining
FLUX_PYRA_dc = (1-exp(0.0576))*100
BIX_PROT_TRIFL_dc = (1-exp(0.0175))*100Estimated
reg1 = data.frame(mv_sev_year$beta, mv_sev_year$ci.lb, mv_sev_year$ci.ub) %>%
rownames_to_column("trat") %>%
tidyr::separate(trat, into = c("lado1", "lado2"), sep = ":") %>%
tidyr::separate(lado1, into = c("lixo", "lado3"),sep = "ai") %>%
dplyr::select(-lixo) %>%
filter(lado3 != "NA") %>%
mutate(mod = c(rep("intercept", 2), rep("slope", 2))) %>%
dplyr::select(-lado2)
names(reg1) = c("fungicide", "mean", "ci.lb", "ci.ub", "mod")
mean = reg1 %>%
group_by(fungicide) %>%
dplyr::select(1:2,5) %>%
spread(mod, mean)
names(mean) = c("fungicide", "intercept_mean", "slope_mean")
upper = reg1 %>%
group_by(fungicide) %>%
dplyr::select(1,3,5) %>%
spread(mod, ci.lb)
names(upper) = c("fungicide", "intercept_upper", "slope_upper")
lower = reg1 %>%
group_by(fungicide) %>%
dplyr::select(1,4:5) %>%
spread(mod, ci.ub)
names(lower) = c("fungicide", "intercept_lower", "slope_lower")
data_model = left_join(mean, lower, by= c("fungicide")) %>%
left_join(upper, by = c("fungicide"))sbr_effic <- ma_sev %>%
mutate(efficacy = (1-(mean_sev/sev_check))) %>%
mutate(efficacy1 = efficacy*100) %>%
filter(ai!= "AACHECK") %>%
filter(!efficacy1 <0)
year = seq(0,7, by = 0.1)
fungicide = NULL
year_col = NULL
for(i in 1:length(data_model$fungicide)){
data_cache = sbr_effic %>%
filter(ai == data_model$fungicide[i])
years = unique(data_cache$year)-2017
year = sort(years)
year = seq(first(year), last(year), by = 0.1)
year_col = c(year_col,year)
fungicide = c(fungicide, rep(data_model$fungicide[i], length(year)))
}
predicted = data.frame(year_col, fungicide) %>%
mutate(year = year_col) %>%
right_join(data_model, by = "fungicide") %>%
mutate(mean_efficacy = (1-exp(intercept_mean + slope_mean*year))*100,
CIL = (1-exp(intercept_lower + slope_lower*year))*100,
CIU = (1-exp(intercept_upper + slope_upper*year))*100,
year = year+2017) %>%
mutate(brand_name = fungicide) %>%
filter(year <2023.2) %>%
dplyr::select(-fungicide)
predicted year_col year intercept_mean slope_mean intercept_lower slope_lower
1 0.0 2017.0 -1.0059816 0.01753810 -0.8888332 0.02316033
2 0.1 2017.1 -1.0059816 0.01753810 -0.8888332 0.02316033
3 0.2 2017.2 -1.0059816 0.01753810 -0.8888332 0.02316033
4 0.3 2017.3 -1.0059816 0.01753810 -0.8888332 0.02316033
5 0.4 2017.4 -1.0059816 0.01753810 -0.8888332 0.02316033
6 0.5 2017.5 -1.0059816 0.01753810 -0.8888332 0.02316033
7 0.6 2017.6 -1.0059816 0.01753810 -0.8888332 0.02316033
8 0.7 2017.7 -1.0059816 0.01753810 -0.8888332 0.02316033
9 0.8 2017.8 -1.0059816 0.01753810 -0.8888332 0.02316033
10 0.9 2017.9 -1.0059816 0.01753810 -0.8888332 0.02316033
11 1.0 2018.0 -1.0059816 0.01753810 -0.8888332 0.02316033
12 1.1 2018.1 -1.0059816 0.01753810 -0.8888332 0.02316033
13 1.2 2018.2 -1.0059816 0.01753810 -0.8888332 0.02316033
14 1.3 2018.3 -1.0059816 0.01753810 -0.8888332 0.02316033
15 1.4 2018.4 -1.0059816 0.01753810 -0.8888332 0.02316033
16 1.5 2018.5 -1.0059816 0.01753810 -0.8888332 0.02316033
17 1.6 2018.6 -1.0059816 0.01753810 -0.8888332 0.02316033
18 1.7 2018.7 -1.0059816 0.01753810 -0.8888332 0.02316033
19 1.8 2018.8 -1.0059816 0.01753810 -0.8888332 0.02316033
20 1.9 2018.9 -1.0059816 0.01753810 -0.8888332 0.02316033
21 2.0 2019.0 -1.0059816 0.01753810 -0.8888332 0.02316033
22 2.1 2019.1 -1.0059816 0.01753810 -0.8888332 0.02316033
23 2.2 2019.2 -1.0059816 0.01753810 -0.8888332 0.02316033
24 2.3 2019.3 -1.0059816 0.01753810 -0.8888332 0.02316033
25 2.4 2019.4 -1.0059816 0.01753810 -0.8888332 0.02316033
26 2.5 2019.5 -1.0059816 0.01753810 -0.8888332 0.02316033
27 2.6 2019.6 -1.0059816 0.01753810 -0.8888332 0.02316033
28 2.7 2019.7 -1.0059816 0.01753810 -0.8888332 0.02316033
29 2.8 2019.8 -1.0059816 0.01753810 -0.8888332 0.02316033
30 2.9 2019.9 -1.0059816 0.01753810 -0.8888332 0.02316033
31 3.0 2020.0 -1.0059816 0.01753810 -0.8888332 0.02316033
32 3.1 2020.1 -1.0059816 0.01753810 -0.8888332 0.02316033
33 3.2 2020.2 -1.0059816 0.01753810 -0.8888332 0.02316033
34 3.3 2020.3 -1.0059816 0.01753810 -0.8888332 0.02316033
35 3.4 2020.4 -1.0059816 0.01753810 -0.8888332 0.02316033
36 3.5 2020.5 -1.0059816 0.01753810 -0.8888332 0.02316033
37 3.6 2020.6 -1.0059816 0.01753810 -0.8888332 0.02316033
38 3.7 2020.7 -1.0059816 0.01753810 -0.8888332 0.02316033
39 3.8 2020.8 -1.0059816 0.01753810 -0.8888332 0.02316033
40 3.9 2020.9 -1.0059816 0.01753810 -0.8888332 0.02316033
41 4.0 2021.0 -1.0059816 0.01753810 -0.8888332 0.02316033
42 4.1 2021.1 -1.0059816 0.01753810 -0.8888332 0.02316033
43 4.2 2021.2 -1.0059816 0.01753810 -0.8888332 0.02316033
44 4.3 2021.3 -1.0059816 0.01753810 -0.8888332 0.02316033
45 4.4 2021.4 -1.0059816 0.01753810 -0.8888332 0.02316033
46 4.5 2021.5 -1.0059816 0.01753810 -0.8888332 0.02316033
47 4.6 2021.6 -1.0059816 0.01753810 -0.8888332 0.02316033
48 4.7 2021.7 -1.0059816 0.01753810 -0.8888332 0.02316033
49 4.8 2021.8 -1.0059816 0.01753810 -0.8888332 0.02316033
50 4.9 2021.9 -1.0059816 0.01753810 -0.8888332 0.02316033
51 5.0 2022.0 -1.0059816 0.01753810 -0.8888332 0.02316033
52 5.1 2022.1 -1.0059816 0.01753810 -0.8888332 0.02316033
53 5.2 2022.2 -1.0059816 0.01753810 -0.8888332 0.02316033
54 5.3 2022.3 -1.0059816 0.01753810 -0.8888332 0.02316033
55 5.4 2022.4 -1.0059816 0.01753810 -0.8888332 0.02316033
56 5.5 2022.5 -1.0059816 0.01753810 -0.8888332 0.02316033
57 5.6 2022.6 -1.0059816 0.01753810 -0.8888332 0.02316033
58 5.7 2022.7 -1.0059816 0.01753810 -0.8888332 0.02316033
59 5.8 2022.8 -1.0059816 0.01753810 -0.8888332 0.02316033
60 5.9 2022.9 -1.0059816 0.01753810 -0.8888332 0.02316033
61 6.0 2023.0 -1.0059816 0.01753810 -0.8888332 0.02316033
62 0.0 2017.0 -0.8120024 0.05763108 -0.6733162 0.06461280
63 0.1 2017.1 -0.8120024 0.05763108 -0.6733162 0.06461280
64 0.2 2017.2 -0.8120024 0.05763108 -0.6733162 0.06461280
65 0.3 2017.3 -0.8120024 0.05763108 -0.6733162 0.06461280
66 0.4 2017.4 -0.8120024 0.05763108 -0.6733162 0.06461280
67 0.5 2017.5 -0.8120024 0.05763108 -0.6733162 0.06461280
68 0.6 2017.6 -0.8120024 0.05763108 -0.6733162 0.06461280
69 0.7 2017.7 -0.8120024 0.05763108 -0.6733162 0.06461280
70 0.8 2017.8 -0.8120024 0.05763108 -0.6733162 0.06461280
71 0.9 2017.9 -0.8120024 0.05763108 -0.6733162 0.06461280
72 1.0 2018.0 -0.8120024 0.05763108 -0.6733162 0.06461280
73 1.1 2018.1 -0.8120024 0.05763108 -0.6733162 0.06461280
74 1.2 2018.2 -0.8120024 0.05763108 -0.6733162 0.06461280
75 1.3 2018.3 -0.8120024 0.05763108 -0.6733162 0.06461280
76 1.4 2018.4 -0.8120024 0.05763108 -0.6733162 0.06461280
77 1.5 2018.5 -0.8120024 0.05763108 -0.6733162 0.06461280
78 1.6 2018.6 -0.8120024 0.05763108 -0.6733162 0.06461280
79 1.7 2018.7 -0.8120024 0.05763108 -0.6733162 0.06461280
80 1.8 2018.8 -0.8120024 0.05763108 -0.6733162 0.06461280
81 1.9 2018.9 -0.8120024 0.05763108 -0.6733162 0.06461280
82 2.0 2019.0 -0.8120024 0.05763108 -0.6733162 0.06461280
83 2.1 2019.1 -0.8120024 0.05763108 -0.6733162 0.06461280
84 2.2 2019.2 -0.8120024 0.05763108 -0.6733162 0.06461280
85 2.3 2019.3 -0.8120024 0.05763108 -0.6733162 0.06461280
86 2.4 2019.4 -0.8120024 0.05763108 -0.6733162 0.06461280
87 2.5 2019.5 -0.8120024 0.05763108 -0.6733162 0.06461280
88 2.6 2019.6 -0.8120024 0.05763108 -0.6733162 0.06461280
89 2.7 2019.7 -0.8120024 0.05763108 -0.6733162 0.06461280
90 2.8 2019.8 -0.8120024 0.05763108 -0.6733162 0.06461280
91 2.9 2019.9 -0.8120024 0.05763108 -0.6733162 0.06461280
92 3.0 2020.0 -0.8120024 0.05763108 -0.6733162 0.06461280
93 3.1 2020.1 -0.8120024 0.05763108 -0.6733162 0.06461280
94 3.2 2020.2 -0.8120024 0.05763108 -0.6733162 0.06461280
95 3.3 2020.3 -0.8120024 0.05763108 -0.6733162 0.06461280
96 3.4 2020.4 -0.8120024 0.05763108 -0.6733162 0.06461280
97 3.5 2020.5 -0.8120024 0.05763108 -0.6733162 0.06461280
98 3.6 2020.6 -0.8120024 0.05763108 -0.6733162 0.06461280
99 3.7 2020.7 -0.8120024 0.05763108 -0.6733162 0.06461280
100 3.8 2020.8 -0.8120024 0.05763108 -0.6733162 0.06461280
101 3.9 2020.9 -0.8120024 0.05763108 -0.6733162 0.06461280
102 4.0 2021.0 -0.8120024 0.05763108 -0.6733162 0.06461280
103 4.1 2021.1 -0.8120024 0.05763108 -0.6733162 0.06461280
104 4.2 2021.2 -0.8120024 0.05763108 -0.6733162 0.06461280
105 4.3 2021.3 -0.8120024 0.05763108 -0.6733162 0.06461280
106 4.4 2021.4 -0.8120024 0.05763108 -0.6733162 0.06461280
107 4.5 2021.5 -0.8120024 0.05763108 -0.6733162 0.06461280
108 4.6 2021.6 -0.8120024 0.05763108 -0.6733162 0.06461280
109 4.7 2021.7 -0.8120024 0.05763108 -0.6733162 0.06461280
110 4.8 2021.8 -0.8120024 0.05763108 -0.6733162 0.06461280
111 4.9 2021.9 -0.8120024 0.05763108 -0.6733162 0.06461280
112 5.0 2022.0 -0.8120024 0.05763108 -0.6733162 0.06461280
intercept_upper slope_upper mean_efficacy CIL CIU
1 -1.1231300 0.01191587 63.43145 58.88648 67.47399
2 -1.1231300 0.01191587 63.36726 58.79115 67.43520
3 -1.1231300 0.01191587 63.30296 58.69560 67.39638
4 -1.1231300 0.01191587 63.23854 58.59983 67.35750
5 -1.1231300 0.01191587 63.17401 58.50383 67.31859
6 -1.1231300 0.01191587 63.10937 58.40761 67.27962
7 -1.1231300 0.01191587 63.04461 58.31117 67.24061
8 -1.1231300 0.01191587 62.97974 58.21451 67.20155
9 -1.1231300 0.01191587 62.91476 58.11762 67.16244
10 -1.1231300 0.01191587 62.84966 58.02051 67.12329
11 -1.1231300 0.01191587 62.78445 57.92317 67.08409
12 -1.1231300 0.01191587 62.71912 57.82560 67.04485
13 -1.1231300 0.01191587 62.65368 57.72781 67.00555
14 -1.1231300 0.01191587 62.58813 57.62979 66.96621
15 -1.1231300 0.01191587 62.52246 57.53155 66.92683
16 -1.1231300 0.01191587 62.45667 57.43308 66.88740
17 -1.1231300 0.01191587 62.39077 57.33438 66.84791
18 -1.1231300 0.01191587 62.32475 57.23545 66.80839
19 -1.1231300 0.01191587 62.25862 57.13629 66.76881
20 -1.1231300 0.01191587 62.19237 57.03690 66.72919
21 -1.1231300 0.01191587 62.12600 56.93728 66.68952
22 -1.1231300 0.01191587 62.05952 56.83743 66.64981
23 -1.1231300 0.01191587 61.99292 56.73735 66.61004
24 -1.1231300 0.01191587 61.92621 56.63703 66.57023
25 -1.1231300 0.01191587 61.85937 56.53649 66.53038
26 -1.1231300 0.01191587 61.79242 56.43571 66.49047
27 -1.1231300 0.01191587 61.72536 56.33470 66.45052
28 -1.1231300 0.01191587 61.65817 56.23345 66.41052
29 -1.1231300 0.01191587 61.59087 56.13197 66.37047
30 -1.1231300 0.01191587 61.52345 56.03025 66.33037
31 -1.1231300 0.01191587 61.45591 55.92829 66.29023
32 -1.1231300 0.01191587 61.38825 55.82610 66.25003
33 -1.1231300 0.01191587 61.32047 55.72368 66.20979
34 -1.1231300 0.01191587 61.25257 55.62101 66.16951
35 -1.1231300 0.01191587 61.18456 55.51811 66.12917
36 -1.1231300 0.01191587 61.11642 55.41497 66.08879
37 -1.1231300 0.01191587 61.04817 55.31159 66.04835
38 -1.1231300 0.01191587 60.97980 55.20797 66.00787
39 -1.1231300 0.01191587 60.91130 55.10411 65.96734
40 -1.1231300 0.01191587 60.84269 55.00001 65.92677
41 -1.1231300 0.01191587 60.77395 54.89567 65.88614
42 -1.1231300 0.01191587 60.70510 54.79108 65.84547
43 -1.1231300 0.01191587 60.63612 54.68626 65.80475
44 -1.1231300 0.01191587 60.56702 54.58119 65.76397
45 -1.1231300 0.01191587 60.49780 54.47587 65.72316
46 -1.1231300 0.01191587 60.42846 54.37032 65.68229
47 -1.1231300 0.01191587 60.35900 54.26451 65.64137
48 -1.1231300 0.01191587 60.28942 54.15847 65.60040
49 -1.1231300 0.01191587 60.21971 54.05217 65.55939
50 -1.1231300 0.01191587 60.14988 53.94563 65.51833
51 -1.1231300 0.01191587 60.07993 53.83885 65.47721
52 -1.1231300 0.01191587 60.00986 53.73181 65.43605
53 -1.1231300 0.01191587 59.93966 53.62453 65.39484
54 -1.1231300 0.01191587 59.86934 53.51700 65.35358
55 -1.1231300 0.01191587 59.79890 53.40922 65.31227
56 -1.1231300 0.01191587 59.72833 53.30118 65.27092
57 -1.1231300 0.01191587 59.65764 53.19290 65.22951
58 -1.1231300 0.01191587 59.58683 53.08437 65.18805
59 -1.1231300 0.01191587 59.51589 52.97559 65.14655
60 -1.1231300 0.01191587 59.44482 52.86655 65.10499
61 -1.1231300 0.01191587 59.37364 52.75726 65.06338
62 -0.9506886 0.05064936 55.60318 48.99855 61.35252
63 -0.9506886 0.05064936 55.34658 48.66795 61.15628
64 -0.9506886 0.05064936 55.08850 48.33521 60.95904
65 -0.9506886 0.05064936 54.82892 48.00031 60.76080
66 -0.9506886 0.05064936 54.56784 47.66323 60.56155
67 -0.9506886 0.05064936 54.30526 47.32398 60.36129
68 -0.9506886 0.05064936 54.04115 46.98252 60.16001
69 -0.9506886 0.05064936 53.77552 46.63885 59.95771
70 -0.9506886 0.05064936 53.50835 46.29295 59.75439
71 -0.9506886 0.05064936 53.23964 45.94481 59.55003
72 -0.9506886 0.05064936 52.96938 45.59442 59.34463
73 -0.9506886 0.05064936 52.69756 45.24175 59.13819
74 -0.9506886 0.05064936 52.42416 44.88680 58.93070
75 -0.9506886 0.05064936 52.14918 44.52954 58.72216
76 -0.9506886 0.05064936 51.87262 44.16997 58.51256
77 -0.9506886 0.05064936 51.59445 43.80807 58.30190
78 -0.9506886 0.05064936 51.31468 43.44382 58.09017
79 -0.9506886 0.05064936 51.03329 43.07721 57.87736
80 -0.9506886 0.05064936 50.75028 42.70823 57.66347
81 -0.9506886 0.05064936 50.46563 42.33685 57.44849
82 -0.9506886 0.05064936 50.17933 41.96307 57.23242
83 -0.9506886 0.05064936 49.89138 41.58686 57.01526
84 -0.9506886 0.05064936 49.60177 41.20821 56.79699
85 -0.9506886 0.05064936 49.31048 40.82711 56.57762
86 -0.9506886 0.05064936 49.01750 40.44354 56.35713
87 -0.9506886 0.05064936 48.72284 40.05749 56.13552
88 -0.9506886 0.05064936 48.42647 39.66893 55.91278
89 -0.9506886 0.05064936 48.12839 39.27785 55.68892
90 -0.9506886 0.05064936 47.82858 38.88424 55.46392
91 -0.9506886 0.05064936 47.52704 38.48807 55.23777
92 -0.9506886 0.05064936 47.22376 38.08934 55.01048
93 -0.9506886 0.05064936 46.91873 37.68802 54.78203
94 -0.9506886 0.05064936 46.61193 37.28410 54.55242
95 -0.9506886 0.05064936 46.30337 36.87757 54.32165
96 -0.9506886 0.05064936 45.99301 36.46839 54.08971
97 -0.9506886 0.05064936 45.68087 36.05657 53.85658
98 -0.9506886 0.05064936 45.36691 35.64208 53.62228
99 -0.9506886 0.05064936 45.05115 35.22490 53.38678
100 -0.9506886 0.05064936 44.73356 34.80501 53.15009
101 -0.9506886 0.05064936 44.41413 34.38240 52.91219
102 -0.9506886 0.05064936 44.09286 33.95706 52.67309
103 -0.9506886 0.05064936 43.76973 33.52895 52.43278
104 -0.9506886 0.05064936 43.44473 33.09807 52.19124
105 -0.9506886 0.05064936 43.11786 32.66440 51.94848
106 -0.9506886 0.05064936 42.78910 32.22792 51.70448
107 -0.9506886 0.05064936 42.45843 31.78861 51.45925
108 -0.9506886 0.05064936 42.12585 31.34645 51.21277
109 -0.9506886 0.05064936 41.79136 30.90142 50.96504
110 -0.9506886 0.05064936 41.45493 30.45351 50.71605
111 -0.9506886 0.05064936 41.11655 30.00270 50.46580
112 -0.9506886 0.05064936 40.77622 29.54896 50.21427
brand_name
1 BIX + PROT + TRIFL
2 BIX + PROT + TRIFL
3 BIX + PROT + TRIFL
4 BIX + PROT + TRIFL
5 BIX + PROT + TRIFL
6 BIX + PROT + TRIFL
7 BIX + PROT + TRIFL
8 BIX + PROT + TRIFL
9 BIX + PROT + TRIFL
10 BIX + PROT + TRIFL
11 BIX + PROT + TRIFL
12 BIX + PROT + TRIFL
13 BIX + PROT + TRIFL
14 BIX + PROT + TRIFL
15 BIX + PROT + TRIFL
16 BIX + PROT + TRIFL
17 BIX + PROT + TRIFL
18 BIX + PROT + TRIFL
19 BIX + PROT + TRIFL
20 BIX + PROT + TRIFL
21 BIX + PROT + TRIFL
22 BIX + PROT + TRIFL
23 BIX + PROT + TRIFL
24 BIX + PROT + TRIFL
25 BIX + PROT + TRIFL
26 BIX + PROT + TRIFL
27 BIX + PROT + TRIFL
28 BIX + PROT + TRIFL
29 BIX + PROT + TRIFL
30 BIX + PROT + TRIFL
31 BIX + PROT + TRIFL
32 BIX + PROT + TRIFL
33 BIX + PROT + TRIFL
34 BIX + PROT + TRIFL
35 BIX + PROT + TRIFL
36 BIX + PROT + TRIFL
37 BIX + PROT + TRIFL
38 BIX + PROT + TRIFL
39 BIX + PROT + TRIFL
40 BIX + PROT + TRIFL
41 BIX + PROT + TRIFL
42 BIX + PROT + TRIFL
43 BIX + PROT + TRIFL
44 BIX + PROT + TRIFL
45 BIX + PROT + TRIFL
46 BIX + PROT + TRIFL
47 BIX + PROT + TRIFL
48 BIX + PROT + TRIFL
49 BIX + PROT + TRIFL
50 BIX + PROT + TRIFL
51 BIX + PROT + TRIFL
52 BIX + PROT + TRIFL
53 BIX + PROT + TRIFL
54 BIX + PROT + TRIFL
55 BIX + PROT + TRIFL
56 BIX + PROT + TRIFL
57 BIX + PROT + TRIFL
58 BIX + PROT + TRIFL
59 BIX + PROT + TRIFL
60 BIX + PROT + TRIFL
61 BIX + PROT + TRIFL
62 FLUX + PYRA
63 FLUX + PYRA
64 FLUX + PYRA
65 FLUX + PYRA
66 FLUX + PYRA
67 FLUX + PYRA
68 FLUX + PYRA
69 FLUX + PYRA
70 FLUX + PYRA
71 FLUX + PYRA
72 FLUX + PYRA
73 FLUX + PYRA
74 FLUX + PYRA
75 FLUX + PYRA
76 FLUX + PYRA
77 FLUX + PYRA
78 FLUX + PYRA
79 FLUX + PYRA
80 FLUX + PYRA
81 FLUX + PYRA
82 FLUX + PYRA
83 FLUX + PYRA
84 FLUX + PYRA
85 FLUX + PYRA
86 FLUX + PYRA
87 FLUX + PYRA
88 FLUX + PYRA
89 FLUX + PYRA
90 FLUX + PYRA
91 FLUX + PYRA
92 FLUX + PYRA
93 FLUX + PYRA
94 FLUX + PYRA
95 FLUX + PYRA
96 FLUX + PYRA
97 FLUX + PYRA
98 FLUX + PYRA
99 FLUX + PYRA
100 FLUX + PYRA
101 FLUX + PYRA
102 FLUX + PYRA
103 FLUX + PYRA
104 FLUX + PYRA
105 FLUX + PYRA
106 FLUX + PYRA
107 FLUX + PYRA
108 FLUX + PYRA
109 FLUX + PYRA
110 FLUX + PYRA
111 FLUX + PYRA
112 FLUX + PYRAPlot
colnames(sbr_effic)[colnames(sbr_effic) == "ai"] <- "brand_name"
plot_sev = predicted %>%
mutate(brand_name = factor(brand_name,
levels = c("BIX + PROT + TRIFL","FLUX + PYRA"))) %>%
ggplot()+
geom_jitter(data = sbr_effic, aes(year, efficacy1, size = vi_sev), alpha= 0.13, width = .2)+
geom_line(data = predicted, aes(year, mean_efficacy), size = 1.7, color = "black")+
geom_line(data = predicted, aes(year, CIL), linetype="dashed", size = 1, alpha = 1)+
geom_line(data = predicted, aes(year, CIU), linetype="dashed", size = 1, alpha = 1)+
theme_minimal_hgrid(font_size = 10)+
scale_size_continuous(range = c(3,10), breaks = c(1,10,100))+
guides(color = guide_legend(override.aes = list(size=2.5)))+
theme(legend.position = "none",
legend.justification = "top",
legend.direction = "horizontal",
legend.key.height = unit(1, "cm"),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 12),
panel.grid = element_blank(),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size=14, face = "bold"),
axis.title.y = element_text(size=14, face = "bold"),
strip.text = element_text(size = 12,
color = "black", face = "bold"),
strip.background = element_rect(colour="white", fill="white"),
panel.border = element_rect(color = "gray60", size=1))+
scale_x_continuous(breaks=c(2017, 2018, 2019, 2020, 2021,2022,2023), limits=c(2017,2023))+
scale_y_continuous(breaks=c(0, 10, 20, 30,40,50,60,70,80,90,100), limits=c(0,100))+
labs(y = "Efficacy (%)", x = "Crop Season")+
facet_wrap(~factor(brand_name), ncol = 2)+
coord_cartesian(ylim=c(0,100))+
labs(y = "Efficacy (%)", x = "", size = "Sampling Variance", color = "Region")
plot_sev
ggsave("fig/decline_efficacy.png", width = 8, height = 6, dpi = 600, bg = "white")Yield
ma_yld <- ma_data %>%
filter(mean_yld != "NA")
hist(ma_yld$mean_yld)
# Sampling variance for yield
ma_yld$vi_yld <- with(ma_yld, v_yld/4)Model fitting
Overall
mv_yld <- rma.mv(mean_yld, vi_yld,
mods = ~ai,
random = list(~ai | study),
struct = "UN",
method = "ML",
control = list(optimizer = "nlm"),
data = ma_yld)
summary(mv_yld)
Multivariate Meta-Analysis Model (k = 340; method: ML)
logLik Deviance AIC BIC AICc
-7527.7420 11231.3446 15073.4841 15107.9446 15074.0296
Variance Components:
outer factor: study (nlvls = 22)
inner factor: ai (nlvls = 3)
estim sqrt k.lvl fixed level
tau^2.1 170810.2081 413.2919 135 no AACHECK
tau^2.2 176466.8105 420.0795 132 no BIX + PROT + TRIFL
tau^2.3 240230.6256 490.1333 73 no FLUX + PYRA
rho.AACH rho.B+P+T rho.FL+P AACH B+P+T FL+P
AACHECK 1 - 22 20
BIX + PROT + TRIFL 0.8315 1 no - 20
FLUX + PYRA 0.6546 0.5783 1 no no -
Test for Residual Heterogeneity:
QE(df = 337) = 17506.4841, p-val < .0001
Test of Moderators (coefficients 2:3):
QM(df = 2) = 157.0300, p-val < .0001
Model Results:
estimate se zval pval ci.lb ci.ub
intrcpt 3256.7265 88.6126 36.7524 <.0001 3083.0489 3430.4041
aiBIX + PROT + TRIFL 647.1882 53.2624 12.1509 <.0001 542.7959 751.5806
aiFLUX + PYRA 414.8797 86.6221 4.7895 <.0001 245.1036 584.6559
intrcpt ***
aiBIX + PROT + TRIFL ***
aiFLUX + PYRA ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Estimated
yield_res<- data.frame(cbind(mv_yld$b,
mv_yld$ci.lb,
mv_yld$ci.ub)) %>%
mutate(fungicide = c("check", "BIX + PROT + TRIFL","FLUX + PYRA")) %>%
filter(fungicide != "check")
names (yield_res) = c("yld", "yld_lower", "yld_upper", "fungicide")
yield_res yld yld_lower yld_upper fungicide
aiBIX + PROT + TRIFL 647.1882 542.7959 751.5806 BIX + PROT + TRIFL
aiFLUX + PYRA 414.8797 245.1036 584.6559 FLUX + PYRAmv_yld_means <- emmprep(mv_yld)
library(emmeans)
mv_yld_emmeans <- emmeans(mv_yld_means, ~ ai)
pwpm(mv_yld_emmeans) AACHECK BIX + PROT + TRIFL FLUX + PYRA
AACHECK [3257] <.0001 <.0001
BIX + PROT + TRIFL -647 [3904] 0.0382
FLUX + PYRA -415 232 [3672]
Row and column labels: ai
Upper triangle: P values adjust = "tukey"
Diagonal: [Estimates] (emmean)
Lower triangle: Comparisons (estimate) earlier vs. laterYear
mv_yld_year <- rma.mv(mean_yld, vi_yld,
mods = ~ai * as.numeric(year),
random = list(~ai | study),
struct = "UN",
method = "ML",
control = list(optimizer = "nlm"),
data = ma_yld %>% mutate(year= year - 2017))
mv_yld_year
Multivariate Meta-Analysis Model (k = 340; method: ML)
Variance Components:
outer factor: study (nlvls = 22)
inner factor: ai (nlvls = 3)
estim sqrt k.lvl fixed level
tau^2.1 170898.0890 413.3982 135 no AACHECK
tau^2.2 173288.8622 416.2798 132 no BIX + PROT + TRIFL
tau^2.3 232776.2032 482.4689 73 no FLUX + PYRA
rho.AACH rho.B+P+T rho.FL+P AACH B+P+T FL+P
AACHECK 1 - 22 20
BIX + PROT + TRIFL 0.8264 1 no - 20
FLUX + PYRA 0.6792 0.6104 1 no no -
Test for Residual Heterogeneity:
QE(df = 334) = 17179.6534, p-val < .0001
Test of Moderators (coefficients 2:6):
QM(df = 5) = 369.2872, p-val < .0001
Model Results:
estimate se zval pval
intrcpt 3103.9647 89.6533 34.6219 <.0001
aiBIX + PROT + TRIFL 679.9106 57.0613 11.9154 <.0001
aiFLUX + PYRA 614.1169 85.1917 7.2086 <.0001
as.numeric(year) 51.6683 4.5580 11.3357 <.0001
aiBIX + PROT + TRIFL:as.numeric(year) -11.0674 6.4685 -1.7110 0.0871
aiFLUX + PYRA:as.numeric(year) -71.3313 7.7070 -9.2554 <.0001
ci.lb ci.ub
intrcpt 2928.2474 3279.6820 ***
aiBIX + PROT + TRIFL 568.0724 791.7487 ***
aiFLUX + PYRA 447.1443 781.0895 ***
as.numeric(year) 42.7348 60.6018 ***
aiBIX + PROT + TRIFL:as.numeric(year) -23.7456 1.6107 .
aiFLUX + PYRA:as.numeric(year) -86.4368 -56.2258 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(mv_yld,mv_yld_year)
df AIC BIC AICc logLik LRT pval
Full 12 14863.4979 14909.4452 14864.4520 -7419.7489
Reduced 9 15073.4841 15107.9446 15074.0296 -7527.7420 215.9862 <.0001
QE
Full 17179.6534
Reduced 17506.4841 Declining
mv_yld_year
Multivariate Meta-Analysis Model (k = 340; method: ML)
Variance Components:
outer factor: study (nlvls = 22)
inner factor: ai (nlvls = 3)
estim sqrt k.lvl fixed level
tau^2.1 170898.0890 413.3982 135 no AACHECK
tau^2.2 173288.8622 416.2798 132 no BIX + PROT + TRIFL
tau^2.3 232776.2032 482.4689 73 no FLUX + PYRA
rho.AACH rho.B+P+T rho.FL+P AACH B+P+T FL+P
AACHECK 1 - 22 20
BIX + PROT + TRIFL 0.8264 1 no - 20
FLUX + PYRA 0.6792 0.6104 1 no no -
Test for Residual Heterogeneity:
QE(df = 334) = 17179.6534, p-val < .0001
Test of Moderators (coefficients 2:6):
QM(df = 5) = 369.2872, p-val < .0001
Model Results:
estimate se zval pval
intrcpt 3103.9647 89.6533 34.6219 <.0001
aiBIX + PROT + TRIFL 679.9106 57.0613 11.9154 <.0001
aiFLUX + PYRA 614.1169 85.1917 7.2086 <.0001
as.numeric(year) 51.6683 4.5580 11.3357 <.0001
aiBIX + PROT + TRIFL:as.numeric(year) -11.0674 6.4685 -1.7110 0.0871
aiFLUX + PYRA:as.numeric(year) -71.3313 7.7070 -9.2554 <.0001
ci.lb ci.ub
intrcpt 2928.2474 3279.6820 ***
aiBIX + PROT + TRIFL 568.0724 791.7487 ***
aiFLUX + PYRA 447.1443 781.0895 ***
as.numeric(year) 42.7348 60.6018 ***
aiBIX + PROT + TRIFL:as.numeric(year) -23.7456 1.6107 .
aiFLUX + PYRA:as.numeric(year) -86.4368 -56.2258 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Estimated
reg1_yld = data.frame(mv_yld_year$beta, mv_yld_year$ci.lb, mv_yld_year$ci.ub) %>%
rownames_to_column("trat") %>%
tidyr::separate(trat, into = c("lado1", "lado2"), sep = ":") %>%
separate(lado1, into = c("lixo", "lado3"),sep = "ai") %>%
dplyr::select(-lixo) %>%
filter(lado3 != "NA") %>%
mutate(mod = c(rep("intercept", 2), rep("slope", 2))) %>%
dplyr::select(-lado2)
names(reg1_yld) = c("fungicide", "mean", "ci.lb", "ci.ub", "mod")
mean_yld = reg1_yld %>%
group_by(fungicide) %>%
dplyr::select(1:2,5) %>%
spread(mod, mean)
names(mean_yld) = c("fungicide", "intercept_mean", "slope_mean")
upper_yld = reg1_yld %>%
group_by(fungicide) %>%
dplyr::select(1,3,5) %>%
spread(mod, ci.lb)
names(upper_yld) = c("fungicide", "intercept_upper", "slope_upper")
lower_yld = reg1_yld %>%
group_by(fungicide) %>%
dplyr::select(1,4:5) %>%
spread(mod, ci.ub)
names(lower_yld) = c("fungicide", "intercept_lower", "slope_lower")
data_model_yld = left_join(mean_yld, lower_yld, by= c("fungicide")) %>%
left_join(upper_yld, by = c("fungicide"))yld_gain <- ma_yld %>%
mutate(gain = mean_yld - yld_check) %>%
filter(ai!= "AACHECK") %>%
filter(!gain <0)
year = seq(0,7, by = 0.1)
fungicide = NULL
year_col = NULL
for(i in 1:length(data_model_yld$fungicide)){
data_cache = yld_gain %>%
filter(ai == data_model_yld$fungicide[i])
years = unique(data_cache$year)-2017
year = sort(years)
year = seq(first(year), last(year), by = 0.1)
year_col = c(year_col,year)
fungicide = c(fungicide, rep(data_model_yld$fungicide[i], length(year)))
}
predicted_yld = data.frame(year_col, fungicide) %>%
mutate(year = year_col) %>%
right_join(data_model_yld, by = "fungicide") %>%
mutate(mean_gain = intercept_mean + slope_mean*year,
CIL = intercept_lower + slope_lower*year,
CIU = intercept_upper + slope_upper*year,
year = year+2017) %>%
mutate(ai = fungicide) %>%
filter(year <2023.2) %>%
dplyr::select(-fungicide)
predicted_yld year_col year intercept_mean slope_mean intercept_lower slope_lower
1 0.0 2017.0 679.9106 -11.06745 791.7487 1.610664
2 0.1 2017.1 679.9106 -11.06745 791.7487 1.610664
3 0.2 2017.2 679.9106 -11.06745 791.7487 1.610664
4 0.3 2017.3 679.9106 -11.06745 791.7487 1.610664
5 0.4 2017.4 679.9106 -11.06745 791.7487 1.610664
6 0.5 2017.5 679.9106 -11.06745 791.7487 1.610664
7 0.6 2017.6 679.9106 -11.06745 791.7487 1.610664
8 0.7 2017.7 679.9106 -11.06745 791.7487 1.610664
9 0.8 2017.8 679.9106 -11.06745 791.7487 1.610664
10 0.9 2017.9 679.9106 -11.06745 791.7487 1.610664
11 1.0 2018.0 679.9106 -11.06745 791.7487 1.610664
12 1.1 2018.1 679.9106 -11.06745 791.7487 1.610664
13 1.2 2018.2 679.9106 -11.06745 791.7487 1.610664
14 1.3 2018.3 679.9106 -11.06745 791.7487 1.610664
15 1.4 2018.4 679.9106 -11.06745 791.7487 1.610664
16 1.5 2018.5 679.9106 -11.06745 791.7487 1.610664
17 1.6 2018.6 679.9106 -11.06745 791.7487 1.610664
18 1.7 2018.7 679.9106 -11.06745 791.7487 1.610664
19 1.8 2018.8 679.9106 -11.06745 791.7487 1.610664
20 1.9 2018.9 679.9106 -11.06745 791.7487 1.610664
21 2.0 2019.0 679.9106 -11.06745 791.7487 1.610664
22 2.1 2019.1 679.9106 -11.06745 791.7487 1.610664
23 2.2 2019.2 679.9106 -11.06745 791.7487 1.610664
24 2.3 2019.3 679.9106 -11.06745 791.7487 1.610664
25 2.4 2019.4 679.9106 -11.06745 791.7487 1.610664
26 2.5 2019.5 679.9106 -11.06745 791.7487 1.610664
27 2.6 2019.6 679.9106 -11.06745 791.7487 1.610664
28 2.7 2019.7 679.9106 -11.06745 791.7487 1.610664
29 2.8 2019.8 679.9106 -11.06745 791.7487 1.610664
30 2.9 2019.9 679.9106 -11.06745 791.7487 1.610664
31 3.0 2020.0 679.9106 -11.06745 791.7487 1.610664
32 3.1 2020.1 679.9106 -11.06745 791.7487 1.610664
33 3.2 2020.2 679.9106 -11.06745 791.7487 1.610664
34 3.3 2020.3 679.9106 -11.06745 791.7487 1.610664
35 3.4 2020.4 679.9106 -11.06745 791.7487 1.610664
36 3.5 2020.5 679.9106 -11.06745 791.7487 1.610664
37 3.6 2020.6 679.9106 -11.06745 791.7487 1.610664
38 3.7 2020.7 679.9106 -11.06745 791.7487 1.610664
39 3.8 2020.8 679.9106 -11.06745 791.7487 1.610664
40 3.9 2020.9 679.9106 -11.06745 791.7487 1.610664
41 4.0 2021.0 679.9106 -11.06745 791.7487 1.610664
42 4.1 2021.1 679.9106 -11.06745 791.7487 1.610664
43 4.2 2021.2 679.9106 -11.06745 791.7487 1.610664
44 4.3 2021.3 679.9106 -11.06745 791.7487 1.610664
45 4.4 2021.4 679.9106 -11.06745 791.7487 1.610664
46 4.5 2021.5 679.9106 -11.06745 791.7487 1.610664
47 4.6 2021.6 679.9106 -11.06745 791.7487 1.610664
48 4.7 2021.7 679.9106 -11.06745 791.7487 1.610664
49 4.8 2021.8 679.9106 -11.06745 791.7487 1.610664
50 4.9 2021.9 679.9106 -11.06745 791.7487 1.610664
51 5.0 2022.0 679.9106 -11.06745 791.7487 1.610664
52 5.1 2022.1 679.9106 -11.06745 791.7487 1.610664
53 5.2 2022.2 679.9106 -11.06745 791.7487 1.610664
54 5.3 2022.3 679.9106 -11.06745 791.7487 1.610664
55 5.4 2022.4 679.9106 -11.06745 791.7487 1.610664
56 5.5 2022.5 679.9106 -11.06745 791.7487 1.610664
57 5.6 2022.6 679.9106 -11.06745 791.7487 1.610664
58 5.7 2022.7 679.9106 -11.06745 791.7487 1.610664
59 5.8 2022.8 679.9106 -11.06745 791.7487 1.610664
60 5.9 2022.9 679.9106 -11.06745 791.7487 1.610664
61 6.0 2023.0 679.9106 -11.06745 791.7487 1.610664
62 0.0 2017.0 614.1169 -71.33133 781.0895 -56.225845
63 0.1 2017.1 614.1169 -71.33133 781.0895 -56.225845
64 0.2 2017.2 614.1169 -71.33133 781.0895 -56.225845
65 0.3 2017.3 614.1169 -71.33133 781.0895 -56.225845
66 0.4 2017.4 614.1169 -71.33133 781.0895 -56.225845
67 0.5 2017.5 614.1169 -71.33133 781.0895 -56.225845
68 0.6 2017.6 614.1169 -71.33133 781.0895 -56.225845
69 0.7 2017.7 614.1169 -71.33133 781.0895 -56.225845
70 0.8 2017.8 614.1169 -71.33133 781.0895 -56.225845
71 0.9 2017.9 614.1169 -71.33133 781.0895 -56.225845
72 1.0 2018.0 614.1169 -71.33133 781.0895 -56.225845
73 1.1 2018.1 614.1169 -71.33133 781.0895 -56.225845
74 1.2 2018.2 614.1169 -71.33133 781.0895 -56.225845
75 1.3 2018.3 614.1169 -71.33133 781.0895 -56.225845
76 1.4 2018.4 614.1169 -71.33133 781.0895 -56.225845
77 1.5 2018.5 614.1169 -71.33133 781.0895 -56.225845
78 1.6 2018.6 614.1169 -71.33133 781.0895 -56.225845
79 1.7 2018.7 614.1169 -71.33133 781.0895 -56.225845
80 1.8 2018.8 614.1169 -71.33133 781.0895 -56.225845
81 1.9 2018.9 614.1169 -71.33133 781.0895 -56.225845
82 2.0 2019.0 614.1169 -71.33133 781.0895 -56.225845
83 2.1 2019.1 614.1169 -71.33133 781.0895 -56.225845
84 2.2 2019.2 614.1169 -71.33133 781.0895 -56.225845
85 2.3 2019.3 614.1169 -71.33133 781.0895 -56.225845
86 2.4 2019.4 614.1169 -71.33133 781.0895 -56.225845
87 2.5 2019.5 614.1169 -71.33133 781.0895 -56.225845
88 2.6 2019.6 614.1169 -71.33133 781.0895 -56.225845
89 2.7 2019.7 614.1169 -71.33133 781.0895 -56.225845
90 2.8 2019.8 614.1169 -71.33133 781.0895 -56.225845
91 2.9 2019.9 614.1169 -71.33133 781.0895 -56.225845
92 3.0 2020.0 614.1169 -71.33133 781.0895 -56.225845
93 3.1 2020.1 614.1169 -71.33133 781.0895 -56.225845
94 3.2 2020.2 614.1169 -71.33133 781.0895 -56.225845
95 3.3 2020.3 614.1169 -71.33133 781.0895 -56.225845
96 3.4 2020.4 614.1169 -71.33133 781.0895 -56.225845
97 3.5 2020.5 614.1169 -71.33133 781.0895 -56.225845
98 3.6 2020.6 614.1169 -71.33133 781.0895 -56.225845
99 3.7 2020.7 614.1169 -71.33133 781.0895 -56.225845
100 3.8 2020.8 614.1169 -71.33133 781.0895 -56.225845
101 3.9 2020.9 614.1169 -71.33133 781.0895 -56.225845
102 4.0 2021.0 614.1169 -71.33133 781.0895 -56.225845
103 4.1 2021.1 614.1169 -71.33133 781.0895 -56.225845
104 4.2 2021.2 614.1169 -71.33133 781.0895 -56.225845
105 4.3 2021.3 614.1169 -71.33133 781.0895 -56.225845
106 4.4 2021.4 614.1169 -71.33133 781.0895 -56.225845
107 4.5 2021.5 614.1169 -71.33133 781.0895 -56.225845
108 4.6 2021.6 614.1169 -71.33133 781.0895 -56.225845
109 4.7 2021.7 614.1169 -71.33133 781.0895 -56.225845
110 4.8 2021.8 614.1169 -71.33133 781.0895 -56.225845
111 4.9 2021.9 614.1169 -71.33133 781.0895 -56.225845
112 5.0 2022.0 614.1169 -71.33133 781.0895 -56.225845
intercept_upper slope_upper mean_gain CIL CIU ai
1 568.0724 -23.74556 679.9106 791.7487 568.07241 BIX + PROT + TRIFL
2 568.0724 -23.74556 678.8038 791.9098 565.69785 BIX + PROT + TRIFL
3 568.0724 -23.74556 677.6971 792.0709 563.32330 BIX + PROT + TRIFL
4 568.0724 -23.74556 676.5903 792.2319 560.94874 BIX + PROT + TRIFL
5 568.0724 -23.74556 675.4836 792.3930 558.57419 BIX + PROT + TRIFL
6 568.0724 -23.74556 674.3768 792.5541 556.19963 BIX + PROT + TRIFL
7 568.0724 -23.74556 673.2701 792.7151 553.82507 BIX + PROT + TRIFL
8 568.0724 -23.74556 672.1634 792.8762 551.45052 BIX + PROT + TRIFL
9 568.0724 -23.74556 671.0566 793.0373 549.07596 BIX + PROT + TRIFL
10 568.0724 -23.74556 669.9499 793.1983 546.70141 BIX + PROT + TRIFL
11 568.0724 -23.74556 668.8431 793.3594 544.32685 BIX + PROT + TRIFL
12 568.0724 -23.74556 667.7364 793.5205 541.95229 BIX + PROT + TRIFL
13 568.0724 -23.74556 666.6296 793.6815 539.57774 BIX + PROT + TRIFL
14 568.0724 -23.74556 665.5229 793.8426 537.20318 BIX + PROT + TRIFL
15 568.0724 -23.74556 664.4161 794.0037 534.82863 BIX + PROT + TRIFL
16 568.0724 -23.74556 663.3094 794.1647 532.45407 BIX + PROT + TRIFL
17 568.0724 -23.74556 662.2027 794.3258 530.07951 BIX + PROT + TRIFL
18 568.0724 -23.74556 661.0959 794.4869 527.70496 BIX + PROT + TRIFL
19 568.0724 -23.74556 659.9892 794.6479 525.33040 BIX + PROT + TRIFL
20 568.0724 -23.74556 658.8824 794.8090 522.95585 BIX + PROT + TRIFL
21 568.0724 -23.74556 657.7757 794.9701 520.58129 BIX + PROT + TRIFL
22 568.0724 -23.74556 656.6689 795.1311 518.20673 BIX + PROT + TRIFL
23 568.0724 -23.74556 655.5622 795.2922 515.83218 BIX + PROT + TRIFL
24 568.0724 -23.74556 654.4554 795.4533 513.45762 BIX + PROT + TRIFL
25 568.0724 -23.74556 653.3487 795.6143 511.08307 BIX + PROT + TRIFL
26 568.0724 -23.74556 652.2420 795.7754 508.70851 BIX + PROT + TRIFL
27 568.0724 -23.74556 651.1352 795.9365 506.33395 BIX + PROT + TRIFL
28 568.0724 -23.74556 650.0285 796.0975 503.95940 BIX + PROT + TRIFL
29 568.0724 -23.74556 648.9217 796.2586 501.58484 BIX + PROT + TRIFL
30 568.0724 -23.74556 647.8150 796.4197 499.21029 BIX + PROT + TRIFL
31 568.0724 -23.74556 646.7082 796.5807 496.83573 BIX + PROT + TRIFL
32 568.0724 -23.74556 645.6015 796.7418 494.46118 BIX + PROT + TRIFL
33 568.0724 -23.74556 644.4947 796.9029 492.08662 BIX + PROT + TRIFL
34 568.0724 -23.74556 643.3880 797.0639 489.71206 BIX + PROT + TRIFL
35 568.0724 -23.74556 642.2812 797.2250 487.33751 BIX + PROT + TRIFL
36 568.0724 -23.74556 641.1745 797.3861 484.96295 BIX + PROT + TRIFL
37 568.0724 -23.74556 640.0678 797.5471 482.58840 BIX + PROT + TRIFL
38 568.0724 -23.74556 638.9610 797.7082 480.21384 BIX + PROT + TRIFL
39 568.0724 -23.74556 637.8543 797.8693 477.83928 BIX + PROT + TRIFL
40 568.0724 -23.74556 636.7475 798.0303 475.46473 BIX + PROT + TRIFL
41 568.0724 -23.74556 635.6408 798.1914 473.09017 BIX + PROT + TRIFL
42 568.0724 -23.74556 634.5340 798.3525 470.71562 BIX + PROT + TRIFL
43 568.0724 -23.74556 633.4273 798.5135 468.34106 BIX + PROT + TRIFL
44 568.0724 -23.74556 632.3205 798.6746 465.96650 BIX + PROT + TRIFL
45 568.0724 -23.74556 631.2138 798.8357 463.59195 BIX + PROT + TRIFL
46 568.0724 -23.74556 630.1071 798.9967 461.21739 BIX + PROT + TRIFL
47 568.0724 -23.74556 629.0003 799.1578 458.84284 BIX + PROT + TRIFL
48 568.0724 -23.74556 627.8936 799.3189 456.46828 BIX + PROT + TRIFL
49 568.0724 -23.74556 626.7868 799.4799 454.09372 BIX + PROT + TRIFL
50 568.0724 -23.74556 625.6801 799.6410 451.71917 BIX + PROT + TRIFL
51 568.0724 -23.74556 624.5733 799.8021 449.34461 BIX + PROT + TRIFL
52 568.0724 -23.74556 623.4666 799.9631 446.97006 BIX + PROT + TRIFL
53 568.0724 -23.74556 622.3598 800.1242 444.59550 BIX + PROT + TRIFL
54 568.0724 -23.74556 621.2531 800.2852 442.22094 BIX + PROT + TRIFL
55 568.0724 -23.74556 620.1464 800.4463 439.84639 BIX + PROT + TRIFL
56 568.0724 -23.74556 619.0396 800.6074 437.47183 BIX + PROT + TRIFL
57 568.0724 -23.74556 617.9329 800.7684 435.09728 BIX + PROT + TRIFL
58 568.0724 -23.74556 616.8261 800.9295 432.72272 BIX + PROT + TRIFL
59 568.0724 -23.74556 615.7194 801.0906 430.34817 BIX + PROT + TRIFL
60 568.0724 -23.74556 614.6126 801.2516 427.97361 BIX + PROT + TRIFL
61 568.0724 -23.74556 613.5059 801.4127 425.59905 BIX + PROT + TRIFL
62 447.1443 -86.43681 614.1169 781.0895 447.14429 FLUX + PYRA
63 447.1443 -86.43681 606.9837 775.4669 438.50060 FLUX + PYRA
64 447.1443 -86.43681 599.8506 769.8443 429.85692 FLUX + PYRA
65 447.1443 -86.43681 592.7175 764.2217 421.21324 FLUX + PYRA
66 447.1443 -86.43681 585.5843 758.5991 412.56956 FLUX + PYRA
67 447.1443 -86.43681 578.4512 752.9765 403.92588 FLUX + PYRA
68 447.1443 -86.43681 571.3181 747.3540 395.28220 FLUX + PYRA
69 447.1443 -86.43681 564.1849 741.7314 386.63852 FLUX + PYRA
70 447.1443 -86.43681 557.0518 736.1088 377.99483 FLUX + PYRA
71 447.1443 -86.43681 549.9187 730.4862 369.35115 FLUX + PYRA
72 447.1443 -86.43681 542.7855 724.8636 360.70747 FLUX + PYRA
73 447.1443 -86.43681 535.6524 719.2410 352.06379 FLUX + PYRA
74 447.1443 -86.43681 528.5193 713.6185 343.42011 FLUX + PYRA
75 447.1443 -86.43681 521.3861 707.9959 334.77643 FLUX + PYRA
76 447.1443 -86.43681 514.2530 702.3733 326.13275 FLUX + PYRA
77 447.1443 -86.43681 507.1199 696.7507 317.48906 FLUX + PYRA
78 447.1443 -86.43681 499.9867 691.1281 308.84538 FLUX + PYRA
79 447.1443 -86.43681 492.8536 685.5055 300.20170 FLUX + PYRA
80 447.1443 -86.43681 485.7205 679.8829 291.55802 FLUX + PYRA
81 447.1443 -86.43681 478.5873 674.2604 282.91434 FLUX + PYRA
82 447.1443 -86.43681 471.4542 668.6378 274.27066 FLUX + PYRA
83 447.1443 -86.43681 464.3211 663.0152 265.62698 FLUX + PYRA
84 447.1443 -86.43681 457.1880 657.3926 256.98329 FLUX + PYRA
85 447.1443 -86.43681 450.0548 651.7700 248.33961 FLUX + PYRA
86 447.1443 -86.43681 442.9217 646.1474 239.69593 FLUX + PYRA
87 447.1443 -86.43681 435.7886 640.5249 231.05225 FLUX + PYRA
88 447.1443 -86.43681 428.6554 634.9023 222.40857 FLUX + PYRA
89 447.1443 -86.43681 421.5223 629.2797 213.76489 FLUX + PYRA
90 447.1443 -86.43681 414.3892 623.6571 205.12121 FLUX + PYRA
91 447.1443 -86.43681 407.2560 618.0345 196.47752 FLUX + PYRA
92 447.1443 -86.43681 400.1229 612.4119 187.83384 FLUX + PYRA
93 447.1443 -86.43681 392.9898 606.7893 179.19016 FLUX + PYRA
94 447.1443 -86.43681 385.8566 601.1668 170.54648 FLUX + PYRA
95 447.1443 -86.43681 378.7235 595.5442 161.90280 FLUX + PYRA
96 447.1443 -86.43681 371.5904 589.9216 153.25912 FLUX + PYRA
97 447.1443 -86.43681 364.4572 584.2990 144.61544 FLUX + PYRA
98 447.1443 -86.43681 357.3241 578.6764 135.97175 FLUX + PYRA
99 447.1443 -86.43681 350.1910 573.0538 127.32807 FLUX + PYRA
100 447.1443 -86.43681 343.0578 567.4313 118.68439 FLUX + PYRA
101 447.1443 -86.43681 335.9247 561.8087 110.04071 FLUX + PYRA
102 447.1443 -86.43681 328.7916 556.1861 101.39703 FLUX + PYRA
103 447.1443 -86.43681 321.6584 550.5635 92.75335 FLUX + PYRA
104 447.1443 -86.43681 314.5253 544.9409 84.10967 FLUX + PYRA
105 447.1443 -86.43681 307.3922 539.3183 75.46599 FLUX + PYRA
106 447.1443 -86.43681 300.2590 533.6957 66.82230 FLUX + PYRA
107 447.1443 -86.43681 293.1259 528.0732 58.17862 FLUX + PYRA
108 447.1443 -86.43681 285.9928 522.4506 49.53494 FLUX + PYRA
109 447.1443 -86.43681 278.8596 516.8280 40.89126 FLUX + PYRA
110 447.1443 -86.43681 271.7265 511.2054 32.24758 FLUX + PYRA
111 447.1443 -86.43681 264.5934 505.5828 23.60390 FLUX + PYRA
112 447.1443 -86.43681 257.4602 499.9602 14.96022 FLUX + PYRAPlot
plot_yld = predicted_yld %>%
# mutate(brand_name = factor(ai, levels = c("BIX + PROT + TRIFL","FLUX + PYRA"))) %>%
ggplot()+
geom_jitter(data = yld_gain, aes(year, gain, size = vi_yld), alpha= 0.13, width = 0.2)+
geom_line(data = predicted_yld, aes(year, mean_gain), size = 1.7, color = "black")+
geom_line(data = predicted_yld, aes(year, CIL), linetype="dashed", size = 1, alpha = 1)+
geom_line(data = predicted_yld, aes(year, CIU), linetype="dashed", size =1, alpha = 1)+
theme_minimal_hgrid(font_size = 10)+
guides(color = guide_legend(override.aes = list(size=2.5)))+
theme(legend.position = "none",
legend.justification = "top",
legend.direction = "horizontal",
legend.key.height = unit(1, "cm"),
legend.title = element_text(size = 12, face = "bold"),
legend.text = element_text(size = 12),
panel.grid = element_blank(),
axis.text.x = element_text(size = 10),
axis.text.y = element_text(size = 10),
axis.title.x = element_text(size=14, face = "bold"),
axis.title.y = element_text(size=14, face = "bold"),
strip.text = element_text(size = 12, color = "black"),
#strip.background = element_rect(colour="white", fill="white"),
panel.border = element_rect(color = "gray60", size=1),
strip.background = element_blank(),
strip.text.x = element_blank())+
scale_y_continuous(breaks=c(0, 250, 500, 750, 1000, 1250,1500,1750,2000,2250,2500), limits=c(0,2500))+
scale_x_continuous(breaks=c(2017, 2018, 2019, 2020, 2021, 2022,2023), limits=c(2017,2023))+
labs(y = "Yield response (kg/ha)", x = "Harvest Season",size = "Sampling Variance")+
facet_wrap(~factor(ai), ncol = 2)
plot_yld
plot_grid(plot_sev,plot_yld, ncol = 1, labels = c("AUTO"))
ggsave("fig/decline_efficacy_sev_yld.png", width = 8, height = 10, dpi = 600, bg = "white")