Evaluating effectiveness of controlling little seed canary grass by using chemical herbicides and allelopathic synthetic community
Note by gaoch:
- Knit the Rmd document to see the results.
- Project files are now be organized as a Git repository. The remote server address is located at Gitee or GitHub. If you know Git, you may clone it from that remote repository after joining in the soilmicro organization/company [Only Gitee Required].
From:
Integrated application of synthetic community reduces consumption of herbicide in field weed control, by Amina Hadayat, Zahir Ahmad Zahir, Peng Cai, Chun-Hui Gao, submitted
library(tidyverse)
library(cowplot)
library(ggpubr)
library(pheatmap)
library(RColorBrewer)
library(vegan)
library(reshape2)
library(magrittr)
library(ggplot2)
theme_set(theme_bw())To enable reproducible study, we provided the raw-data and source codes for analysis and generating figures.
Raw data were stored in the data folder. It mainly comes from two
experiments: one from growth room study, and the other is field
experiment. The raw data is provided as in formatted form.
weed_suppression <- read_csv("data/weed suppression.csv")
wheat_promotion <- read_csv("data/wheat promotion.csv")
weed_infestation <- read_csv("data/weed infestation.csv")
infested_wheat <- read_csv("data/infested wheat.csv")
head(weed_suppression) # suppress weed growth by herbicide and SynComs
#> # A tibble: 6 × 11
#> herbicide_dose Inoculation seed_germination SPAD_value plant_height
#> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 Herbicide control Control 86.7 3.9 14
#> 2 Herbicide control Control 93.3 3.5 18
#> 3 Herbicide control Control 100 3.6 16
#> 4 Atlantis 25% Control 80 2.5 15.5
#> 5 Atlantis 25% Control 86.7 2.8 16
#> 6 Atlantis 25% Control 73.3 2.7 17
#> # ℹ 6 more variables: root_length <dbl>, fresh_biomass <dbl>, protein <dbl>,
#> # chlorophyll_a <dbl>, chlorophyll_b <dbl>, caroteniod <dbl>
head(wheat_promotion) # SynComs promote wheat growth
#> # A tibble: 6 × 10
#> Inoculations seed_germination plant_height Root_length fresh_biomass
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 control 33.3 29 10 1.25
#> 2 control 50 27 11 1.25
#> 3 control 33.3 28 8 1.22
#> 4 C1 50 30 11.5 1.52
#> 5 C1 50 32 10 1.54
#> 6 C1 66.7 31 12 1.5
#> # ℹ 5 more variables: SPAD_value <dbl>, chlorophyll_a <dbl>,
#> # chlorophyll_b <dbl>, carotenoid <dbl>, protein <dbl>
head(weed_infestation) # field trail of weed suppression
#> # A tibble: 6 × 10
#> treatments total_weed_density no_of_native_weeds shoot_length SPAD_value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 weedy control 117 89.7 109 49.4
#> 2 weedy control 137 81.8 120 45.8
#> 3 weedy control 135 76.9 107 42.6
#> 4 wheat+weed 100%… 38 42.1 98 47.3
#> 5 wheat+weed 100%… 37 39.5 103 41.2
#> 6 wheat+weed 100%… 34 38.2 102 39.2
#> # ℹ 5 more variables: grain_yield <dbl>, straw_yield <dbl>,
#> # photosynthetic_rate <dbl>, transpiration_rate <dbl>,
#> # stomatal_conductance <dbl>
head(infested_wheat) # field trail of wheat promotion
#> # A tibble: 6 × 10
#> treatments shoot_length SPAD_value no_of_tillers_per_pl…¹ biological_yield
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 weed free con… 110 48.2 14 9.95
#> 2 weed free con… 115 43.7 13 10.0
#> 3 weed free con… 126 43.7 12 10.2
#> 4 wheat+weed 10… 100 38.4 11 8.23
#> 5 wheat+weed 10… 96 40.2 10 8.12
#> 6 wheat+weed 10… 94 39.2 11 8.16
#> # ℹ abbreviated name: ¹no_of_tillers_per_plant
#> # ℹ 5 more variables: grain_yield <dbl>, straw_yield <dbl>,
#> # photosynthetic_rate <dbl>, transpiration_rate <dbl>,
#> # stomatal_conductance <dbl>The columns are:
-
herbicide_dose: herbicide_doses, indicating the name of two different chemical herbicides (Atlantis and Axial) at 25 and 50% of recommended doses: i.e. “Herbicide control”, “Atlantis 25%”, “Atlantis 50%”, “Axial 25%”, “Axial 50%”. -
Inoculation: Inoculation indicating the combinations of four different Pseudomonas strains (B11, T19, T24, T75) collected from Soil microbiology biochemistry laboratory, University of Agriculture Faisalabad. “C1”, “C2”, “C3”, “C4” represent the Pseudomonas strain combinations such as C1 (B11 x T75), C2 (T19 x T24), C3 (B11 x T24 x T75) and C4 (B11 x T19 x T24 x T75). -
treatments: Under field condition nine treatments were planned in block plot for both weed infestation and infested wheat with weedy and wheat free control respectively, in order to evaluate the selected C4 combination along with Axial herbicide at four different recommended doses such as: 25, 50, 75, and 100% Axial with respective control treatments.
weed_suppression indicates the effect of SynComs and herbicides on the
growth and physiology of P. minor under axenic condition. Nine
parameters were obtained.
par(mfrow=c(3,3))
hist(weed_suppression$seed_germination, main = "Seed germination")
hist(weed_suppression$SPAD_value, main = "SPAD value")
hist(weed_suppression$plant_height, main = "Plant height")
hist(weed_suppression$root_length, main = "Root length")
hist(weed_suppression$fresh_biomass, main = "Fresh biomass")
hist(weed_suppression$protein, main = "Protein")
hist(weed_suppression$chlorophyll_a, main = "Chlorophyll A")
hist(weed_suppression$chlorophyll_b, main = "Chlorophyll B")
hist(weed_suppression$caroteniod, main = "Caroteniod")
Disign a graph to show the results more clearly. This plot has three parts.
- boxplot shows the value of data, and statistical results of comparision;
- a dotplot shows the dose of herbicide;
- another dotplot shows the applying inoculation.
- seed germination rate of weed
The less of seed germination rate of weed, the better of the suppression of a recipe.
df = weed_suppression %>%
mutate(herbicide_dose = fct_rev(as_factor(herbicide_dose)),
Inoculation = fct_rev(as_factor(Inoculation))) %>%
group_by(herbicide_dose, Inoculation) %>%
mutate(group = factor(cur_group_id()), .before = 3)
df$group = fct_reorder(df$group, df$seed_germination)
symnum.args<-list(cutpoints= c(0, 0.01, 0.05, Inf),
symbols = c("**","*","ns"))
# find the group id by groups
groups = distinct(df, herbicide_dose, Inoculation, group) %>%
filter(herbicide_dose == "Herbicide control", Inoculation == "Control")
ref_group = groups$group %>% as.character()
# boxplot
p1 = df %>%
ggplot(aes(group, seed_germination)) +
geom_boxplot(outlier.shape = NA) +
stat_compare_means(ref.group = ref_group,
method = "t.test",
hide.ns = FALSE,
label = "p.signif",
symnum.args = symnum.args) +
# geom_jitter(width = 0.01) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
# the herbicide dose
p2 = df %>%
ggplot(aes(group, herbicide_dose)) +
geom_point(size = 3, shape = 15) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt"))
# the inoculation
p3 = df %>%
ggplot(aes(group, Inoculation)) +
geom_point(size = 3, shape = 15) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt"))
aplot::plot_list(p1,p2,p3, ncol = 1, heights = c(1,.2,.2))
Control is located on the most right side, so that all the combinations of herbicide and SynComs have better effect in suppressing weed seed germination. Among them, who have the best performance are Axial 25%-50% plus C1/C3/C4.
Now, every parameter can generate a similar plot.
# reuse code with a function
plot_recipe_effect = function(df, value){
df$group = fct_reorder(df$group, df[[value]])
# symnum.args
symnum.args<-list(cutpoints= c(0, 0.01, 0.05, Inf),
symbols = c("**","*","ns"))
# find the group id by groups
groups = distinct(df, herbicide_dose, Inoculation, group) %>%
filter(herbicide_dose == "Herbicide control", Inoculation == "Control")
ref_group = groups$group %>% as.character()
p1 = df %>%
ggplot(aes_string("group", value)) +
geom_boxplot(outlier.shape = NA) +
stat_compare_means(ref.group = ref_group,
method = "t.test",
hide.ns = FALSE,
label = "p.signif",
symnum.args = symnum.args) +
# geom_jitter(width = 0.01) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank())
p2 = df %>%
ggplot(aes(group, herbicide_dose)) +
geom_point(size = 3, shape = 15) +
theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt"))
p3 = df %>%
ggplot(aes(group, Inoculation)) +
geom_point(size = 3, shape = 15) +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
plot.margin = margin(t = 0, r = 0, b = 0, l = 0, unit = "pt"))
# combined graph
aplot::plot_list(p1,p2,p3, ncol = 1, heights = c(1,.2,.2))
}Firstly, we investigated the influence of AB combinations and chemical herbicides on P. minor growth and phenological parameter reduction. The experiments were carried out in green house, and only weed were planted.
plot_recipe_effect(df, value = "seed_germination")
#> Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
#> ℹ Please use tidy evaluation idioms with `aes()`.
#> ℹ See also `vignette("ggplot2-in-packages")` for more information.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
plot_recipe_effect(df, value = "plant_height")
plot_recipe_effect(df, value = "root_length")
plot_recipe_effect(df, value = "fresh_biomass")
plot_recipe_effect(df, value = "protein")
Plants contain several different pigments, including chlorophylls, carotenoids, and flavonoids. Chlorophylls are the primary pigments responsible for the green color of plants and are essential for photosynthesis. Carotenoids, which include beta-carotene, lycopene, lutein, and zeaxanthin, are responsible for the yellow, orange, and red colors of many fruits and vegetables, and also serve as antioxidants. Flavonoids, which include anthocyanins, flavonols, and flavones, are responsible for the red, blue, and purple colors of many fruits and flowers, and also play a role in plant defense against environmental stresses.
SPAD value is a measure provided by the SPAD-502Plus, which is a portable, non-destructive measuring device for the chlorophyll content of leaves.
Product of SPAD-502+
plot_recipe_effect(df, value = "SPAD_value")
There are several types of chlorophyll, including chlorophyll a, chlorophyll b, chlorophyll c, chlorophyll d, and chlorophyll e, each with their own unique molecular structure and light absorption properties. Chlorophyll is responsible for giving plants their green color, and it is also used as a natural food coloring agent and in some medicinal and industrial applications.
Chlorophyll a is the primary pigment involved in photosynthesis and is found in all photosynthetic organisms, including plants, algae, and cyanobacteria. It absorbs light most efficiently at wavelengths of 430-660 nm (blue and red light), and it plays a critical role in the initial stages of photosynthesis by absorbing light and passing on the energy to other molecules.
plot_recipe_effect(df, value = "chlorophyll_a")
Chlorophyll b, on the other hand, is an accessory pigment that is found only in higher plants and green algae. It absorbs light most efficiently at wavelengths of 450-650 nm (blue and orange light) and transfers this energy to chlorophyll a. Chlorophyll b also helps to broaden the spectrum of light that can be absorbed by the plant, allowing it to capture more energy from the sun and carry out photosynthesis more efficiently.
plot_recipe_effect(df, value = "chlorophyll_b")
Carotenoids are a group of natural pigments that are found in many plants, algae, and some bacteria. They are responsible for the yellow, orange, and red colors of many fruits and vegetables, as well as the bright colors of many flowers. Carotenoids are important antioxidants that help protect plants and other organisms from damage caused by harmful molecules known as free radicals. In addition to their role in providing color to plants and other organisms, carotenoids also have important health benefits for humans, including promoting eye health, supporting the immune system, and reducing the risk of certain chronic diseases. Some common carotenoids include beta-carotene, lycopene, lutein, and zeaxanthin.
plot_recipe_effect(df, value = "caroteniod")
wheat_promotion
#> # A tibble: 15 × 10
#> Inoculations seed_germination plant_height Root_length fresh_biomass
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 control 33.3 29 10 1.25
#> 2 control 50 27 11 1.25
#> 3 control 33.3 28 8 1.22
#> 4 C1 50 30 11.5 1.52
#> 5 C1 50 32 10 1.54
#> 6 C1 66.7 31 12 1.5
#> 7 C2 50 28 9 1.23
#> 8 C2 66.7 29 12 1.22
#> 9 C2 66.7 26 11 1.24
#> 10 C3 50 31 13 1.44
#> 11 C3 83.3 35 15 1.42
#> 12 C3 66.7 32 12.5 1.41
#> 13 C4 66.7 34 15.5 1.6
#> 14 C4 83.3 35.5 14 1.59
#> 15 C4 66.7 36 15.5 1.58
#> # ℹ 5 more variables: SPAD_value <dbl>, chlorophyll_a <dbl>,
#> # chlorophyll_b <dbl>, carotenoid <dbl>, protein <dbl>compare_means(seed_germination~Inoculations,data = wheat_promotion,ref.group="control",
method = "t.test")
#> # A tibble: 4 × 8
#> .y. group1 group2 p p.adj p.format p.signif method
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 seed_germination control C1 0.101 0.16 0.101 ns T-test
#> 2 seed_germination control C2 0.0474 0.14 0.047 * T-test
#> 3 seed_germination control C3 0.0824 0.16 0.082 ns T-test
#> 4 seed_germination control C4 0.0132 0.053 0.013 * T-test
compare_means(plant_height~Inoculations,data = wheat_promotion,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 plant_height 0.000277 0.00028 0.00028 *** Anova
compare_means(Root_length~Inoculations,data = wheat_promotion,ref.group="control",
method = "t.test")
#> # A tibble: 4 × 8
#> .y. group1 group2 p p.adj p.format p.signif method
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 Root_length control C1 0.242 0.48 0.242 ns T-test
#> 2 Root_length control C2 0.468 0.48 0.468 ns T-test
#> 3 Root_length control C3 0.0313 0.094 0.031 * T-test
#> 4 Root_length control C4 0.0117 0.047 0.012 * T-test
compare_means(fresh_biomass~Inoculations,data = wheat_promotion,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 fresh_biomass 1.08e-10 0.00000000011 1.1e-10 **** Anova
compare_means(SPAD_value~Inoculations,data = wheat_promotion,ref.group="control",
method = "t.test")
#> # A tibble: 4 × 8
#> .y. group1 group2 p p.adj p.format p.signif method
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 SPAD_value control C1 0.431 0.43 0.4313 ns T-test
#> 2 SPAD_value control C2 0.00915 0.027 0.0091 ** T-test
#> 3 SPAD_value control C3 0.0240 0.048 0.0240 * T-test
#> 4 SPAD_value control C4 0.00319 0.013 0.0032 ** T-test
compare_means(chlorophyll_a~Inoculations,data = wheat_promotion,ref.group="control",
method = "t.test")
#> # A tibble: 4 × 8
#> .y. group1 group2 p p.adj p.format p.signif method
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 chlorophyll_a control C1 0.00353 0.014 0.0035 ** T-test
#> 2 chlorophyll_a control C2 0.128 0.26 0.1280 ns T-test
#> 3 chlorophyll_a control C3 0.610 0.61 0.6104 ns T-test
#> 4 chlorophyll_a control C4 0.0110 0.033 0.0110 * T-test
compare_means(chlorophyll_b~Inoculations,data = wheat_promotion,ref.group="control",
method = "t.test")
#> # A tibble: 4 × 8
#> .y. group1 group2 p p.adj p.format p.signif method
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 chlorophyll_b control C1 0.0184 0.032 0.0184 * T-test
#> 2 chlorophyll_b control C2 0.00494 0.019 0.0049 ** T-test
#> 3 chlorophyll_b control C3 0.0162 0.032 0.0162 * T-test
#> 4 chlorophyll_b control C4 0.00483 0.019 0.0048 ** T-test
compare_means(carotenoid~Inoculations,data = wheat_promotion,ref.group="control",
method = "t.test")
#> # A tibble: 4 × 8
#> .y. group1 group2 p p.adj p.format p.signif method
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 carotenoid control C1 0.0486 0.097 0.049 * T-test
#> 2 carotenoid control C2 0.303 0.3 0.303 ns T-test
#> 3 carotenoid control C3 0.0324 0.097 0.032 * T-test
#> 4 carotenoid control C4 0.0187 0.075 0.019 * T-test
compare_means(protein~Inoculations,data = wheat_promotion,ref.group="control",
method = "t.test")
#> # A tibble: 4 × 8
#> .y. group1 group2 p p.adj p.format p.signif method
#> <chr> <chr> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 protein control C1 0.000661 0.0026 0.00066 *** T-test
#> 2 protein control C2 0.356 0.36 0.35581 ns T-test
#> 3 protein control C3 0.00480 0.0096 0.00480 ** T-test
#> 4 protein control C4 0.00185 0.0056 0.00185 ** T-testWhether strains combinations have ability to enhance the growth of wheat or not.
cols <- setdiff(names(wheat_promotion), 'Inoculations')
y_labels <- c("seed germination (%)","plant height (cm)", "root length (cm)","fresh biomass (g)","SPAD value","chlorophyll a (ppm)","chlorophyll b (ppm)","carotenoid (ppm)","protein (ppm)")
#symnum.args to adjust p-value
symnum.args<-list(cutpoints= c(0, 0.01, 0.05, Inf),
symbols = c("**","*","ns"))
list_plots = Map(function(x, y) {
ggboxplot(data = wheat_promotion, x = "Inoculations", y = x,
ylab = y, xlab = "Inoculations",
add = "jitter")+
stat_compare_means(label = "p.signif",
method = "t.test",
ref.group = "control",
symnum.args = symnum.args)+
rotate_x_text(angle = 40)+
labs(x="") +
theme(legend.position = "right",
legend.title = element_text(color="black",size = 10,
face = "bold"),
legend.text = element_text(color = "black",size = 8),
plot.margin = margin(t = 2, r = 0, b = 0, l = 2, unit = "pt")) +
scale_y_continuous(expand = expansion(0.2))
}, cols, y_labels)
cowplot::plot_grid(plotlist = list_plots[1:3],
labels = "AUTO",
ncol = 2)
C4 has the best performance in increasing seed germination, plant height, root length and fresh biomass of wheat, followed by C3 or C1.
cowplot::plot_grid(plotlist = list_plots[6:9],
labels = "AUTO",
align = "hv")
compare_means(total_weed_density~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 total_weed_density 3.63e-15 3.60e-15 3.6e-15 **** Anova
compare_means(no_of_native_weeds~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 no_of_native_weeds 0.0000000965 0.000000097 9.7e-08 **** Anova
compare_means(shoot_length~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 shoot_length 0.000334 0.00033 0.00033 *** Anova
compare_means(SPAD_value~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 SPAD_value 0.00106 0.0011 0.0011 ** Anova
compare_means(grain_yield~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 grain_yield 6.42e-13 6.40e-13 6.4e-13 **** Anova
compare_means(straw_yield~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 straw_yield 0.00535 0.0053 0.0053 ** Anova
compare_means(photosynthetic_rate~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 photosynthetic_rate 0.000230 0.00023 0.00023 *** Anova
compare_means(transpiration_rate~treatments,data = weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 transpiration_rate 0.000144 0.00014 0.00014 *** Anova
compare_means(stomatal_conductance~treatments,data =weed_infestation,
method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 stomatal_conductance 0.0000187 0.000019 1.9e-05 **** AnovaIn subsequent comparisons, we aimed to analysis whether the presence of C4 can enhance the ability to a herbicide Axial.
Axial is a brand name of a herbicide produced by the company BASF. The active ingredient in Axial is pinoxaden, which is a selective herbicide used to control grass weeds in a variety of crops, including cereals, rice, and grass seed crops. Axial works by inhibiting the growth of the targeted grass weeds, which eventually leads to their death. It is typically applied to crops as a post-emergence herbicide, meaning it is sprayed directly onto the weeds after they have emerged from the soil. As with all herbicides, it is important to follow the label instructions carefully and use Axial only as directed to ensure effective control of weeds and to minimize any potential risks to humans, animals, and the environment.
Before start, we edited the treatments and added a new column has_c4
in this data frame. The reason to do this is that we know that the using
of herbicide and C4 can suppress weed growth significantly. Now that the
question is whether C4 can play an important role.
weed_infestation %<>%
mutate(has_c4 = as_factor(if_else(str_detect(treatments, "c4"), "with C4", "without C4"))) %>%
mutate(treatments = factor(
treatments,
levels = c("weedy control",
"wheat+weed 100% axial control",
"wheat+weed 100% axial control+c4 inoculation",
"wheat+weed 75% axial control",
"wheat+weed 75% axial control+c4 inoculation",
"wheat+weed 50% axial control",
"wheat+weed 50% axial control+c4 inoculation",
"wheat+weed 25% axial control",
"wheat+weed 25% axial control+c4 inoculation"),
labels = c("wild",
"100% Axial",
"100% Axial",
"75% Axial",
"75% Axial",
"50% Axial",
"50% Axial",
"25% Axial",
"25% Axial")))Question:
- Is the grain yield in this data frame from weed or wheat?
cols <- setdiff(names(weed_infestation),c('treatments',"has_c4"))
y_labels <- c("weed density m-2",
"no of weeds (%)",
"shoot length (cm)",
"SPAD value",
"grain yield (t ha-1)",
"straw yield (t ha-1)",
"photosynthetic rate (µmol m-2 s-1) ",
"transpiration rate (µmol m-2 s-1)",
"stomatal conductance (mmol m-2 s-1)")
my_plots = Map(function(x, y) {
ggboxplot(data = weed_infestation, x = "treatments", y = x,
legend ="none",
color = "has_c4",
ylab = y, xlab = "treatments",
bxp.errorbar = FALSE,
bxp.errorbar.width = 0.4,
palette = "npg",
notch = FALSE,
add = "jitter",
repel = TRUE)+
stat_compare_means(aes(group = has_c4),
label = "p.signif",
method = "t.test")+
theme(legend.position = "top",
legend.text = element_text(face = "italic"),
legend.title = element_blank(),
axis.text.x = element_text(angle = 30, hjust = 1,vjust = 1)) +
scale_y_continuous(expand = expansion(0.1, 0))
}, cols, y_labels)In this figure, why using 25% or 50% Axial have less total weed density when compare with 75% or 100% Axial? Similar results were also found in B and C.
cowplot::plot_grid(plotlist = my_plots[c(1,3,5)],
labels = "AUTO",
ncol = 2)
Are these parameters come from weed plant?
cowplot::plot_grid(plotlist = my_plots[c(4,6:9)],
labels = "AUTO")
Could you explain the setting of weed free control in this data frame?
infested_wheat
#> # A tibble: 27 × 10
#> treatments shoot_length SPAD_value no_of_tillers_per_pl…¹ biological_yield
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 weed free co… 110 48.2 14 9.95
#> 2 weed free co… 115 43.7 13 10.0
#> 3 weed free co… 126 43.7 12 10.2
#> 4 wheat+weed 1… 100 38.4 11 8.23
#> 5 wheat+weed 1… 96 40.2 10 8.12
#> 6 wheat+weed 1… 94 39.2 11 8.16
#> 7 wheat+weed 1… 100 44.3 11 9.09
#> 8 wheat+weed 1… 109 44.1 12 9.08
#> 9 wheat+weed 1… 108 42.8 13 9.32
#> 10 wheat+weed 7… 98 38.1 8 8.32
#> # ℹ 17 more rows
#> # ℹ abbreviated name: ¹no_of_tillers_per_plant
#> # ℹ 5 more variables: grain_yield <dbl>, straw_yield <dbl>,
#> # photosynthetic_rate <dbl>, transpiration_rate <dbl>,
#> # stomatal_conductance <dbl>compare_means(shoot_length~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 shoot_length 0.000108 0.00011 0.00011 *** Anova
compare_means(SPAD_value~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 SPAD_value 0.0000430 0.000043 4.3e-05 **** Anova
compare_means(no_of_tillers_per_plant~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 no_of_tillers_per_plant 0.138 0.14 0.14 ns Anova
compare_means(biological_yield~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 biological_yield 7.54e-11 7.5e-11 7.5e-11 **** Anova
compare_means(grain_yield~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 grain_yield 0.000000114 0.00000011 1.1e-07 **** Anova
compare_means(straw_yield~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 straw_yield 0.000000147 0.00000015 1.5e-07 **** Anova
compare_means(photosynthetic_rate~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 photosynthetic_rate 0.000000490 0.00000049 4.9e-07 **** Anova
compare_means(transpiration_rate~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 transpiration_rate 0.00000117 0.0000012 1.2e-06 **** Anova
compare_means(stomatal_conductance~treatments,data = infested_wheat,method = "anova")
#> # A tibble: 1 × 6
#> .y. p p.adj p.format p.signif method
#> <chr> <dbl> <dbl> <chr> <chr> <chr>
#> 1 stomatal_conductance 0.0000000731 0.000000073 7.3e-08 **** AnovaSimplifying the group names.
infested_wheat %<>%
mutate(has_c4 = as_factor(if_else(str_detect(treatments, "c4"), "with C4", "without C4")),
.before = 2) %>%
mutate(treatments = factor(
treatments,
levels = c("weed free control",
"wheat+weed 100% axial control",
"wheat+weed 100% axial control+c4 inoculation",
"wheat+weed 75% axial control",
"wheat+weed 75% axial control+c4 inoculation",
"wheat+weed 50% axial control",
"wheat+weed 50% axial control+c4 inoculation",
"wheat+weed 25% axial control",
"wheat+weed 25% axial control+c4 inoculation"),
labels = c("weed-free",
"100% Axial",
"100% Axial",
"75% Axial",
"75% Axial",
"50% Axial",
"50% Axial",
"25% Axial",
"25% Axial")))
infested_wheat
#> # A tibble: 27 × 11
#> treatments has_c4 shoot_length SPAD_value no_of_tillers_per_plant
#> <fct> <fct> <dbl> <dbl> <dbl>
#> 1 weed-free without C4 110 48.2 14
#> 2 weed-free without C4 115 43.7 13
#> 3 weed-free without C4 126 43.7 12
#> 4 100% Axial without C4 100 38.4 11
#> 5 100% Axial without C4 96 40.2 10
#> 6 100% Axial without C4 94 39.2 11
#> 7 100% Axial with C4 100 44.3 11
#> 8 100% Axial with C4 109 44.1 12
#> 9 100% Axial with C4 108 42.8 13
#> 10 75% Axial without C4 98 38.1 8
#> # ℹ 17 more rows
#> # ℹ 6 more variables: biological_yield <dbl>, grain_yield <dbl>,
#> # straw_yield <dbl>, photosynthetic_rate <dbl>, transpiration_rate <dbl>,
#> # stomatal_conductance <dbl>cols <- setdiff(names(infested_wheat),c('treatments',"has_c4"))
y_labels <- c("shoot length (cm)",
"SPAD value",
"no of tillers plant-1",
"biological yield",
"grain yield (t ha-1)",
"straw yield (t ha-1)",
"photosynthetic rate (µmol m-2 s-1) ",
"transpiration rate (µmol m-2 s-1)",
"stomatal conductance (mmol m-2 s-1)")
Map(function(col_name, y_label) {
ypos = infested_wheat %>%
group_by(treatments) %>%
summarise(yposition = max(get(col_name)) * 1.01)
stat_by_group = compare_means(as.formula(paste(col_name, "~ has_c4")),
data = infested_wheat,
method = "t.test",
group.by = "treatments") %>%
mutate(xpos = as.numeric(get("treatments"))) %>%
mutate(xmin = xpos - 0.2,
xmax = xpos + 0.2) %>%
# filter(p < 0.05) %>%
left_join(ypos)
ggboxplot(data = infested_wheat,
x = "treatments",
y = col_name,
ylab = y_label,
xlab = "treatments",
color = "has_c4",
bxp.errorbar = FALSE,
bxp.errorbar.width = 0.4,
notch = FALSE,
palette = "npg",
add = "jitter") +
geom_signif(xmin = stat_by_group$xmin, xmax = stat_by_group$xmax,
annotation = stat_by_group$p.signif,
y_position = stat_by_group$yposition,
tip_length = 0) +
theme(legend.position = "top",
legend.text = element_text(face = "italic"),
legend.title = element_blank(),
axis.text.x = element_text(angle = 30, hjust = 1,vjust = 1)) +
scale_y_continuous(expand = expansion(0.1, 0))
}, cols, y_labels) -> list_of_plotsFirstly, similar questions can be raised as I did in discussing the weed_infestation results. On this basis, the conclusions are:
- The application of herbicide also have significant suppressing effect on wheat growth;
- The addition of C4 SynComs neutralize such a toxic effect.
- 75% of Axial and C4 has the best performance in maintaining wheat growth. In such condition, all the four parameters showed in this figure are comparable to the weed-free control.
cowplot::plot_grid(plotlist = list_of_plots[c(1,4:6)],
labels = "AUTO",
ncol = 2)
cowplot::plot_grid(plotlist = list_of_plots[c(2,7:9)],
labels = "AUTO",
ncol = 2)
The main questions of this study are:
- Whether we can use the combination of herbicide and SynComs to achieve environment-friendly weed control?
- If yes, how could we find the best solution, i. e. the recipe for the best? The best solution has its application value. It can be directly used to improve agricultural production.
From the data showed on the above, we now can found:
-
Yes, we can achieve a better control of weed in wheat field with both herbicide and bacteria communities. The addition of bacteria community can not only enhance the suppression effect of herbicide, but also promote the growth of wheat.
-
While combining the herbicide and bacteria community, using 75% or 50% dose of Axial and C4 is sufficient in inhibiting weed growth.