Catch crop diversity increases rhizosphere carbon input and soil microbial biomass

NormanGentsch
JensBoy
Juan DanielKennedy Batalla
DianaHeuermann
Nicolausvon Wirén
DörteSchweneker
UlfFeuerstein
JonasGroß
BernahrdBauer
BarbaraReinhold-Hurek
ThomasHurek
FabricioCamacho Céspedes
GeorgGuggenberger

2020-05-23

Abstract

Introduction

Material and methods

Results

Plant biomass and net ecosystem exchange

The NEE decreased significantly with increasing catch crop diversity (Fig. 1), suggesting increasing $C O_{2}$ -C uptake from the atmosphere.

Figure 1

Net ecosystem exchange (NEE) of C between catch crop treatments. Bars represent means ± SE; lowercase letters denote significant differences (p < 0.01) between treatments ```r library(tidyverse) library(lme4) library(emmeans) library(multcomp)

theme_set(theme_bw()) theme_myBW <- theme(axis.title.x = element_text(size = 10, color = "black"), axis.title.y = element_text(angle = 90, vjust = 1.5, size = 10, color = "black"), axis.text.x = element_text(size = 10, color = "black"), axis.text.y = element_text(size = 10, color = "black"), axis.ticks =element_line(colour="black"), strip.text.x = element_text(size = 10, color = "black"), strip.background = element_blank(), panel.border =element_rect(colour="black", fill=NA), panel.grid.major = element_blank(), panel.grid.minor = element_blank(), plot.title = element_text(size = 12, hjust=0.5), #legend.position=c(0.0,1.0), #legend.justification=c(0,1), legend.text = element_text(size = 10), legend.text.align=0, legend.title = element_text(size = 10), legend.key = element_rect(colour="white", fill = "white"), legend.key.size = unit(5, "mm"), legend.background = element_blank())

set vector with colors and label

COL <- c("Fallow" = "slategray", "Mustard" = "red3" , "Mix4" = "orchid3", "Mix12"= "orange4") SHP <- c("Fallow"=21,"Mustard"=22,"Mix4"=23, "Mix12"=24)

generate data frame with original data

data <- data.frame(cc_variant = structure(c(1L, 1L, 1L, 3L, 4L, 2L, 3L, 4L, 2L, 2L, 4L, 3L), .Label = c("Fallow", "Mustard", "Mix4", "Mix12"), class = "factor"), Date = structure(c(17092, 17093, 17098, 17092, 17092, 17092, 17093, 17093, 17093, 17098, 17098, 17098), class = "Date"), NEE = c(52.3186092, 36.752742, 34.590816, -516.868370737168, -617.110003978854, -182.24567563611, -102.63776100067, -431.558870280712, -139.041211720174, -114.099387563412, -400.212603947375, -175.332083704246) )

lm_NEE <- lmer(NEE ~ cc_variant + (1|Date), data=data) df_NEE <- cld(emmeans(lm_NEE, specs ="cc_variant"), Letters=letters, sort=FALSE)

Plot for BFS

fig1 <- ggplot(data, aes(x= cc_variant, y=NEE, fill= cc_variant))+ geom_boxplot()+ scale_fill_manual(values = COL, guide=FALSE)+ geom_text(data= df_NEE ,aes(y=-600,x=cc_variant, label=.group))+ labs(x="Catch crop variant", y=expression("NEE (mg CO"2_{"- C"}m^{-2}~h{-1}~")"), fill="")+ theme_myBW

ggsave("Fig1.png", width = 84, height = 70, units = "mm", dpi = 600)

fig1 ```

Discussion

NEE is linked to plant diversity

... The NEE in our study showed a remarkably strong negative gradient from mustard to mix 4 to mix 12 (Fig. 1), which suggested higher photosynthetic $C O_{2}$ -C fixation rates with increasing catch crop diversity.