Clone Performance Test - Exp I


source: BBC

The first experiment will be a clone performance experiment. The data used is the ‘popdata’ which is accessible here.

This analysis will seek to answer the question: - Is the treatment having an influence - Which clone is performing best

library(doBy)
library(ggplot2)

Importing the data

pop <- read.table('Data/Lab1/popdata.txt', header = T)

head(pop)
##   block cutw height dia clone fert
## 1     1  2.4     71 0.6     A    3
## 2     1  0.7     67 1.4     A    3
## 3     1  6.5    211 3.5     A    3
## 4     1  1.1     69 1.0     A    3
## 5     2  2.0    116 1.4     A    3
## 6     2  4.9    123 3.2     A    3

Data description

We can create a column now and assign the names to the different values.

Creating a column to give name to the values of the fert

pop$fert_value <- ifelse(pop$fert==1, 'fertilized', 'control')
pop$fert <- as.factor(pop$fert)
head(pop)
##   block cutw height dia clone fert fert_value
## 1     1  2.4     71 0.6     A    3    control
## 2     1  0.7     67 1.4     A    3    control
## 3     1  6.5    211 3.5     A    3    control
## 4     1  1.1     69 1.0     A    3    control
## 5     2  2.0    116 1.4     A    3    control
## 6     2  4.9    123 3.2     A    3    control

Questions

-   Plot the height diameter relationship of different treatment of seedlings

- estimate the index of slenderness of the stand

-   Plot the performance of the seedlings and state which of the clones performing the best

Height diameter relationship of the control and fertilized seedlings

The first step of the analysis is visualize the effect of treatment on diameter and height

ggplot(pop, aes(dia, height, col = fert_value)) +
  geom_point()+ 
  labs(title = 'Height vs Diameter',
       col = 'Treatment',
       x = 'diameter (mm)',
       y = 'height (mm)')+
  theme(plot.title = element_text(face = 'bold'),
        axis.title.x = element_text(face = 'bold'),
        axis.title.y = element_text(face = 'bold'),
        legend.title =  element_text(face = 'bold'))

Index of Slenderness

The height diameter ratio or index of slenderness is an important measure as it can be used to evaluate a tree stability. To read more on height diameter relationship click [here](https://www.mdpi.com/1999-4907/10/1/70/htm#:~:text=Height%2Dto%2Ddiameter%20ratio%20(,of%20tree%20and%20stand%20stability.). The formula for HDr is given below

\[HDr = height/diameter\] Where HDr =height diameter ratio

pop$hd <- pop$height/pop$dia

Given the data we can estimate the average height and diameter for the clones and treatments

pop_summary <- summaryBy(height + dia ~ fert + clone,
                         data = pop, FUN=mean)
head(pop_summary)
##   fert clone height.mean dia.mean
## 1    1     A    325.5926 3.281481
## 2    1     B    361.3243 3.418919
## 3    1     C    364.7941 3.788235
## 4    3     A    118.7917 2.070833
## 5    3     B    165.8387 2.293548
## 6    3     C    124.7222 2.108333

The mean height to diameter of the different treatment as given with the formula above can be estimated.

ggplot(pop_summary, aes(clone, height.mean, fill = fert))+
  geom_bar(stat = 'identity',
           position = 'dodge')+
  labs(title = 'Seedlings clone performance',
       y = 'height diameter ratio',
       x = 'clone')+
  scale_fill_discrete(name = 'Treatment', labels = c('Control', 'Fertilized'))+
  theme(plot.title = element_text(face = 'bold'),
        axis.title = element_text(face = 'bold'),
        legend.title = element_text(face = 'bold'))

It is evident that the fertilized are performing than the control, with the B clone class being the highest performing either fertilized or not.

Thinning Intensity and Frequency Experiment

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