Introduction:

Background:

The global population rate is predicted to reach 9.8 billion by the year 2050 (UN, 2017). The increased population will place an enormous strain on the agricultural sector who’s current farming practises are already detrimental to the natural environment (Karancsi, Z. et al., 2010). Over-cultivation can lead to permanent damage to the productivity of the land (Tiessen, H. et al., 1982) and failing yields place a greater threat to food security. Fertilisers offer a solution to this problem and can have a significant effect on plant growth through its ability to replenish the topsoil with vital nutrients. Radishes are fast growing crops and have been used to test the variability of growth caused by the usage of fertilisers.

Research hypothesis:

Radishes subjected to fertiliser, both chemical and organic, will demonstrate accelerated growth, measured by height grown in centimetres, in comparison to radishes grown in regular soil. Moreover, radishes grown with chemical fertiliser will demonstrate the fastest growth compared to organic fertiliser.

Aim:

To determine the effects of varying fertilisers on the growth of Radish sprouts.

Hypothesis- null and alternate:

H0:There is no statistically significant relationship between the application of fertiliser and accelerated growth rate in relevance to radish growth.

H1: If plants are subject to the application of fertiliser, then plants grown with chemical fertiliser will have accelerated growth compared to those grown in regular soil and grown with organic fertiliser.

Materials and Methods:

A manipulative experiment was conducted to measure how different types of soil and fertiliser affect the growth rate of radishes in a controlled environment. The independent variable in the experiment is the soil type the radishes are grown in. The independent variable consists of three treatments of soil; regular soil containing no fertiliser, soil and organic compost and soil with a chemical fertiliser. The dependent variable of the experiment is the final height in cm of the radishes.

To reduce the risk of confounding variables, blind randomisation was used. Seed distribution was delegated to an assistant unaware of the experimental actions. Replication was ensured by designating each treatment and control with 10 pots containing one seed each. Each seedling represents one datapoint with ten replicates allowing for more reliable results. In order to ensure that the results obtained are valid, the pots, seeds, seed depth, environment and water were kept the same. The plants were all kept in the same room, ensuring equal light exposure, humidity and temperature.

Equipment:

Method:

  1. Label each pot within each planting tray with numbers 1-10. This will ensure that data for each plant can be recorded accurately.
  2. Place 100g of soil in each of the 10 pots in all 3 planting trays.
  3. One of the planting trays will receive the organic compost treatment. 30g of compost must be added to each of the 10 pots in one of the planting trays.
  4. The other planting tray will receive the chemical fertiliser. 10ml of the liquid fertiliser must be added to each of the 10 pots in one of the planting trays. (This should be repeated every fortnight).
  5. To ensure all the seeds are at the same depth mark the blunt end of a pencil at 2cm and create an incision in each pot.
  6. The assistant will randomly assign one seed to each pot. Cover the seed with soil and compact the soil slightly.
  7. The planting trays must be placed in a location which will receive sufficient and adequate lighting.
  8. Each seedling will receive 15ml of water each morning at the same time every day.
  9. After 10 days of the first sprouting detach each seedling from the root. Take a ruler and measure the length of the plant. Record each individual data point.

Results:

Looking at the raw data:

The dataset has three variables, no fertiliser, chemical fertiliser and organic fertiliser which have been assigned to different plots. The growth rate is assessed by recording the height(cm) after 10 days of the first sprouting. Before conducting any statistical analysis it is evident that there are a few 0 values across the groups (T1). This may cause skewing and suggests that the data may not be normal.

Table 1: Table of the raw data results split into the three variables.

figure

library(emmeans)
library(agricolae)
library(readxl)
Radish <- read_excel("~/Documents/ENV2001/Report2.xlsx")
Radish$Fertilizer<-as.factor(Radish$Fertilizer)
head(Radish)
## # A tibble: 6 x 3
##   Fertilizer     Plot `Height(cm)`
##   <fct>         <dbl>        <dbl>
## 1 No Fertilizer     1          0  
## 2 No Fertilizer     2          8.6
## 3 No Fertilizer     3          3.2
## 4 No Fertilizer     4         11.6
## 5 No Fertilizer     5          6.2
## 6 No Fertilizer     6         10.6
str(Radish)
## tibble[,3] [30 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Fertilizer: Factor w/ 3 levels "Chemical Fertilzer",..: 2 2 2 2 2 2 2 2 2 2 ...
##  $ Plot      : num [1:30] 1 2 3 4 5 6 7 8 9 10 ...
##  $ Height(cm): num [1:30] 0 8.6 3.2 11.6 6.2 10.6 7.3 9.3 12.6 0 ...

The variable of no fertiliser had the largest mean value of height(cm) (6.94) and organic had the lowest (4.10). Chemical fertiliser had the largest standard deviation (+/- 5.28).

summary(Radish)
##               Fertilizer      Plot        Height(cm)   
##  Chemical Fertilzer:10   Min.   : 1.0   Min.   : 0.00  
##  No Fertilizer     :10   1st Qu.: 3.0   1st Qu.: 0.00  
##  Organic Fertilzer :10   Median : 5.5   Median : 5.90  
##                          Mean   : 5.5   Mean   : 5.68  
##                          3rd Qu.: 8.0   3rd Qu.: 9.15  
##                          Max.   :10.0   Max.   :12.90
tapply(Radish$`Height(cm)`, Radish$Fertilizer, mean)
## Chemical Fertilzer      No Fertilizer  Organic Fertilzer 
##               6.00               6.94               4.10
tapply(Radish$`Height(cm)`, Radish$Fertilizer, sd)
## Chemical Fertilzer      No Fertilizer  Organic Fertilzer 
##           5.282255           4.551727           3.937286

Assumptions: Testing for normality

Figure 2 plots the data as three boxplots with the fertilisier application on the x-axis and the height (cm) on the y-axis. It is evident that the variable of organic fertiliser is skewed. The distribution of height amongst the three variables is displayed in Figure 1. The histogram is bi-modal and skewed to the right (F1). This may be the causation for non normally distributed data, which can be confirmed by the Shapiro-Wilks test.

hist(Radish$`Height(cm)`,main="Radish growth (cm) with or without the application of Fertilisers  ",xlab="Height (cm)")

Figure 1: Histogram of radish growth (cm) among the three variables of fertiliser.

boxplot(Radish$`Height(cm)` ~ Radish$Fertilizer,main="Radish Height under different Fertilizer",
xlab="Fertlizer Type",ylab="Height (cm)")

Figure 2: Boxplot of the fertilizer types and the growth of radishes recorded as height(cm).

The p-value is <0.05 (p=0.005) concluding that the dataset is significantly different and is not normally distributed.

shapiro.test(Radish$`Height(cm)`)
## 
##  Shapiro-Wilk normality test
## 
## data:  Radish$`Height(cm)`
## W = 0.89076, p-value = 0.005029

Assumptions: Testing for constant variance:

Examining the residuals shows that the height data is J-shaped meaning it is skewed to the right and is abnormal(F3). The data scatters evenly around the group mean meaning the data possesses equal variance(F3). It is unclear if fanning is present in the fitted graph meaning further variance testing is necessary to make an appropriate judgement. This can be achieved using the Bartlett Test of Homogeneity of Variances.

Height.aov <- aov(Radish$`Height(cm)` ~ Radish$Fertilizer)
par(mfrow = c(2,2))
hist(rstandard(Height.aov))
qqnorm(rstandard(Height.aov))
qqline(rstandard(Height.aov))

plot(fitted(Height.aov),rstandard(Height.aov))
abline(0,0)
plot(fitted(Height.aov),resid(Height.aov))
abline(0,0)

Figure 3: Test assumptions of ANOVA conducted testing significance between the height(cm) of radishes grown under the use of chemical, organic or no fertiliser.

The Bartlett Test of Homogeneity of Variances gives a non-significant p-value of 0.692. Meaning the p-value is greater than (p= >0.05) significance level. Further investigation is conducted by finding the ration between the highest and lowest standard deviation. The result (1.34) was lower than 2 indicating that there is no need to transform the data.

bartlett.test(Radish$`Height(cm)` ~ Radish$Fertilizer)
## 
##  Bartlett test of homogeneity of variances
## 
## data:  Radish$`Height(cm)` by Radish$Fertilizer
## Bartlett's K-squared = 0.73632, df = 2, p-value = 0.692
outsd <- tapply((Radish$`Height(cm)`),Radish$Fertilizer,sd)
outsd
## Chemical Fertilzer      No Fertilizer  Organic Fertilzer 
##           5.282255           4.551727           3.937286
outsd[1]/outsd[3]
## Chemical Fertilzer 
##           1.341598

ANOVA

Test statistic: F=0.979 P value: 0.389

The ANOVA test resulted that the P value is greater than 0.05 (p=0.389) and the F-stat (0.979).

summary(Height.aov)
##                   Df Sum Sq Mean Sq F value Pr(>F)
## Radish$Fertilizer  2   41.9   20.93   0.979  0.389
## Residuals         27  577.1   21.37

Post hoc:

TukeyHSD(Height.aov)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Radish$`Height(cm)` ~ Radish$Fertilizer)
## 
## $`Radish$Fertilizer`
##                                       diff       lwr      upr     p adj
## No Fertilizer-Chemical Fertilzer      0.94 -4.186369 6.066369 0.8927688
## Organic Fertilzer-Chemical Fertilzer -1.90 -7.026369 3.226369 0.6332368
## Organic Fertilzer-No Fertilizer      -2.84 -7.966369 2.286369 0.3684872

Running a post-hoc test allows us to find differences between each treatment.There is no significant difference to be reported in the present test as the p-values are not significant between the means of the groups.

Discussion:

Statistical conclusion:

During the preliminary analysis, the assumption of normality was assessed using the Shapiro-Wilks test which resulted in a p-value of 0.005029. This value confirmed that the dataset is not normally distributed. Constant variance was assessed using the Bartlett Test of Homogeneity of Variances which resulted in a p-value of 0.692. The p-value is greater the 0.05 indicating that the dataset possesses equal variance and thus, we accept the null hypothesis that this dataset is confirmed to have equal variance.The p-value calculated from the ANOVA (p= 0.389) indicated the results were not significantly different. Furthermore, this was supported by the F-value (F= 0.979) which is close to 1.0 therefore, we retain the null hypothesis. The post-hoc test concluded that there was no significant difference between any of the treatments. We therefore conclude that there is no statistically significant relationship between the application of fertiliser and accelerated growth rate of the plants, thus supporting the null hypothesis.

Scientific conclusion:

The application of fertilisers to soil did not accelerate the growth rate of radishes. The planting trays with the adjunction of organic and chemical fertiliser did not demonstrate any different growth rates, therefore organic and chemical fertiliser can be said to not improve crop yields. The findings are important as they reflect the possibility of harvesting crops without the usage of fertilisers, which often possess sequential issues and cause greater environmental strains.

Limitations:

The greatest limitation in the experimental design is that true randomisation has not been taken into consideration. The randomisation of the seed does not ensure true randomisation as the treatments have not been considered. Furthermore, the lack of significance between the application of fertiliser and growth may have been attributed to the variability of constants. It is probable with further investigation into varying conditions such as temperature, CO2 levels and wavelength the effect on growth rate would become more evident.

Improvements:

By randomising the treatments within each tray, the experiment would represent true randomisation and ensure the minimisation of tray effect.

Refrences:

Huett, D (1997) Fertiliser use effciency by containerised nursery plants 2. Nutrient leaching. Australian Journal of Agricultural Research 48(2) 259 - 265

Hasnain, M (2020) The Effects of Fertilizer Type and Application Time on Soil Properties, Plant Traits, Yield and Quality of Tomato. Sustainable Agriculture.

Karancsi, Z. et al., (2010), Agriculture: Deforestation. In Anthropogenic Geomorphology: A Guide to Man-Made Landforms. Dordrecht: Springer Netherlands, pp. 95–112.

Tiessen, H (1982). Cultivation Effects on the Amounts and Concentration of Carbon, Nitrogen, and Phosphorus in Grassland Soils. Volume74, Issue5. Pages 831-835

UN, (2017), Revision of World Population Prospects Report. Accessed on: https://www.un.org/development/desa/en/news/population/world-population-prospects-2017.html

Yates, (2020) How to Grow: Radishes. Accessed on: https://www.yates.com.au/plants/vegetable/radishes/how-to-grow-radishes/