Dear list members.
We are trying see effect of N on yield of three wheat genotypes under late
and early planting conditions in saline and non-saline environments. Our
experimental structure as follows:
Districts (2: DIST01 and DIST02) - Not randomly selected
ENV (Saline and Non-Saline) - One saline and non-saline environment (eg.
village) were selected in each district (not random).
*SOWING Time (Late and Early):* 8 farmers' fields were selected randomly
in each environment or village. These are spatially distributed in a
village. Among them, 4 were grouped as early and 4 were grouped as a late
sowing group.
*N_TREAT (N0 and N100)* - Then, each farmer field was splitted into
two main
plots where two N-treatment were assigned randomly.
*GENOTYPE ( G1, G2, G3*) - Then, N-main plots were further splited into
three subplots here 3 wheat varieties were assigned randomly.
This experiment was repeated for two years. Farmers' fields in Year 1 and
Year 2 were not same. We are not interested to see the District effect on
wheat yield here. Rather, we like to see the effect of saline environment,
sowing time, N and genotype and their interaction on wheat yield for year
1 and year 2 separately.
We are using the following ANOVA model. We do not know whether we are
missing something here. Help will be highly arreciated.
Regards
Zia Ahmed, CIMMYT
model<-aov(YIELD~ENV*SOWING*N_RATE*GEN+Error(FARMERS/N_RATE/GEN),
data=mydata)
summary(model)
# District: two
DIST<-as.factor(rep(c("DIST01","DIST02"),each=96))
# ENV: Saline and Non-saline environment
ENV<-as.factor(rep(rep(c("Saline","Non-saline"),each=48),2))
# Farmers 16
FARMERS<-as.factor(rep(c("F1","F2","F3","F4","F5","F6","F7","F8",
"F9","F10","F11",
"F12","F13","F14","F15","F16"),each=12))
# Showing Date: two
SOWING<-as.factor(rep(rep(c("Early","Late"),each=6),16))
# Nitrogen treatments: N0 and N100
N_RATE<-as.factor(rep(rep(c("N0","N100"),each=3),32))
# Genotype
GEN<-as.factor(rep(rep(c("V1","V2","V3"),each=1),64))
# Response: Wheat Yield
set.seed(1234)
YIELD <- rnorm(n=192, mean=3.0, sd=0.5)
# Create Data Frame
mydata<-data.frame(DIST,ENV, FARMERS,SOWING,N_RATE,GEN,YIELD)
mydata
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