Unit 3: Biodiversity and the interconnectedness of life

Topic 1: Describing biodiversity Topic

2: Ecosystem dynamics.
Angie Ton
Rationale
Biomass is renewable organic material that comes from plants and animals. Biomass contains
stored chemical energy from the sun that is produced by plants through photosynthesis (EIA,
2023). With increasing concerns about fossil fuels as a finite resource, aquatic plants are being
investigated as a potential source of renewable, biomass fuel. Their ability to rapidly sequester
carbon and grow quickly makes them a potential sustainable alternative (Ghayal & Pandya,
2012).

Duckweed is an aquatic plant that has a fast growth rate, is a multicellular eukaryotes and lives
in freshwater (Barbara, et al, 2015). It is easy to cultivate, contains rich, organic substances
which are suitable to use in anaerobic digestion and fermentation process to concert biomass
into biogas and ethanol (Nahar, 2019). Similar to most aquatic plants, insufficient supplies of
mineral salts, and lack of sunlight can limit duckweed's growth (Ziegler, 2023). They also
require levels of phosphorus, potassium, and sulphate for optimum growth (Pondeljiak, 2021).

Green onion is a vegetable is comparable to duckweed in terms of growth, cultivation, and

organic substances. Furthermore, it has a low carbon footprint, allowing the agriculture of green
onion to be sustainable (Shlosberg, 2022). Studies has shown that green onion roots in a bio -
electrochemical cell can produce continuous bias - free electric current for more than 24 hours
(Shlosberg, 2022).

Organic soil is often use for household vegetables as it supplies important sources of nitrogen,
phosphorus, and sulphur (NSW - DIP, 2024). With the use of potash fertiliser, this can also
exhibit advantageous growth as it allows the plant to resist pests and diseases (BBC
Gardenersworlds, 2021). Consequently, this consideration led to the question could organic
soil, with the addition of potash fertiliser be used to grow green onion?

Research Question
How does changing the concentration (0.10g, 0.15g, 0.20g, 0.25g, 0.30g) of the sulphate
potassium fertiliser affect the growth rate of green onion that are grown from organic potting
mix over the course of 14 days?

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Original experiment
The original experiment (Flinn Biostudy, 2016) was utilised by growing duckweed for two weeks and
count fronds to model population growth curve, carrying capacity may or may not be reached, discuss
results in terms of actual/eventual limiting factors. The original experiment was conducted using
four different concentrations (1,2,3,4 drops) of SeaSol liquid fertiliser mixed with pond water.
Duckweed was used as the aquatic plant for the experiment. In each concentration, twenty
duckweed buds were placed into the container and covered with cling wrap for humidity. The
experiment was over the course of fourteen days to see if there is a correlation between the
fertiliser and the growth of duckweed. The experiment was then modified by the following steps.

Modifications

Modification Modification Justification

criteria
Redirection Instead of analysing the growth rate of A before-hand research indicated
duckweed, green onion has been used that green onion is more suitable
instead. than duckweed in producing
electrical energy and is low in
carbon footprint.
Refinement Instead of doing one trial, three trials will To increase the accuracy of the
be utilised for each condition of the results as doing one trial is not
fertiliser. sufficient to validate the trend
between concentration and growth
rate.
Refinement The original experiment used pond water When initiating the original
to investigate the growth rate of experiment, the pond water did not
duckweed. This experiment will use have a reliable source. Therefore,
organic soil to investigate the chosen by using soil that has an ingredient
vegetable. list can allow the interpretations of
the results to be more reliable.
Refinement Different concentrations of the fertiliser The original experiment measured
(0.10,0.15,0.20,0.25,0.30), measured in fertiliser in drops, this was not
grams, will be used to investigate if accurate. By implementing the
fertiliser affect plant growth. accurate amount of fertiliser in
grams, this allows the results to be
more reliable.

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Risk Assessment

Risk Hazard Risk control Disposal

Handling of  Eye  Appropriate gloves  Incinerated either
green onion contact when handling seeds. in a permitted
seed  Skin  Wear face mask if hazardous waste
contact possible. incinerator or
 Inhalation  Ensure appropriate municipal solid
 Viruses eyewear. waste incinerator
 Ensure appropriate with appropriate air
footwear. emissions control
equipment.
Fertiliser  Repertory  If inhaled, remove from  Do not dispose
chemicals harms contaminated area to solutions down the
(potassium,  Ingestion fresh air. Apply sink, as sulphur is
sulphur)  Skin artificial respiration if toxic to aquatic
irritations not breathing. life.
 Rinse mouth with  Place into sealed
water until all traces of containers for
product have been disposal.
removed.  Potassium nitrate
 Immediately remove can be disposed by
contaminated clothing the drains.
and wash affected area
with water for at least
15 minutes (seek
medical attention
depending on the
severity)
Plastic  Cuts  Avoid sharp corners of  Dispose in regular
seedling tray seedling tray. waste
 Rinse cuts with alcohol
and apply bandages if
hazard was befallen.

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Results
Table 1: Raw Data

Concentration of fertiliser (g) ± 0.05- cm

0.10g 0.15g 0.20g 0.25g 0.30g
Da 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3
ys
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
6 0. 0. 0. 0. 1 0. 0.7 1 0.4 0.5 0.3 0.5 0.5 0.5 0
8 7 8 5 6
8 2. 2. 3 3 4 3. 3 3.5 2.8 3 2.5 2.8 2.2 2.7 2
7 8 5
10 5. 5. 5. 5 5. 6 6.2 5.4 5.5 5.2 5.7 5.1 4.6 5 4.8
9 4 9 9
12 7 6. 6. 6. 7. 7 6.5 6.2 6.9 6.2 6.9 7 6 6.1 6
5 8 7 1
14 7. 7. 7. 7. 7. 7. 7.7 7.2 7.3 7.1 8.1 7.8 6.5 7.2 6.5
3 2 4 3 5 8

Table 2: Sample Calculation

Formula: Sample Calculation: day 6, 0.10g

Calculating Mean: 0.8+0.7+ 0.8
Value1+Value 2+ValueN =0.767 g
3
N
Standard Deviation: Standard deviation was calculated using the following excel

√
2 function:
∑ 2 ( Value−Mean)2 ==STDEV.S(B19:F19) =0.191
3
¿
Sample ¿ ¿ ¿
Standard Error: Standard error was calculated using the following excel function:
Standard Deviation G19
¿ =0.110
√ Sample ¿ ¿ ¿ ¿ √( 3 )
T-Test Value T – test values was calculated using the following excel function:
¿ TTest ( Array 1 , Array 2 , Array 2 ) , tail , type

=TTEST(B16:F16,B23:F23,2,3)

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 Table 3: Processed Data, Mean Values

Mean Height of Green onions ± 0.05(cm)

Concentration of fertiliser ± 0.05(g)
Days 0.10g 0.15g 0.20g 0.25g 0.30g Standard Standard T-test
Deviation Error ±0.02
±0.02
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
2 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
4 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
6 0.767 0.700 0.700 0.433 0.333 0.191 0.110 0.000
8 2.833 3.500 3.100 2.767 2.300 0.442 0.255 0.000
10 5.733 5.633 5.700 5.333 4.800 0.391 0.226 0.000
12 6.767 6.933 6.533 6.700 6.033 0.344 0.199 0.011
14 7.300 7.533 7.400 7.667 6.733 0.359 0.207 1.000

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Trends, patterns, and relationships
Graph 1: Mean height of green onions across five concentrations with standard deviation as
error bars.

Mean height of green onions across five different concentra-

tions
9.000

8.000
Mean height of green onions (cm)

7.000

6.000

5.000

4.000

3.000

2.000

1.000

0.000
0 2 4 6 8 10 12 14

Days (24Hr)

Concentration of fertiliser (g) 0.10g Concentration of fertiliser (g) 0.15g

Concentration of fertiliser (g) 0.20g Concentration of fertiliser (g) 0.25g
Concentration of fertiliser (g) 0.30g

Interpretation and Analysis

Table 3 displayed that at day six of growing day, the 0.10g concentration had the highest mean
height at 0.767cm. Evidently, graph 1 also display this trend, with 0.15g at 0.700, 0.20g at
0.700, 0.25g at 0.433cm and 0.30g at 0.191cm. The graph suggests that 0.10g of fertilizer was
the optimal amount for green onion to grow effectively, and increasing the fertilizer would
inhibit its’ growth (graph 1). However, this trend is not consistent. Seen in table 2, at day 8,
0.15g and 0.20g increased more than other concentrations, having the mean height at 3.500cm
and 3.100cm respectively. The inconsistency continued until day 14. Evidently, this can be seen
visually from graph 1 that the hypothesis of as the concentration of fertilizer increase so does
the growth rate of the green onion.

Though, the data does consistently display that for 0.30g concentration, the green onion has the
slowest growth rate (graph 1). Evidently, table 3 supports this, highlighting that at day 14, the
0.30g concentration only grew to 6.733cm, while the other concentrations grew passed 7cm.

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This trend suggests that the 0.30g concentration was a substantial amount for green onion and
did hinder its’ growth. This often occurs as too much soluble salts can cause the leaves to wilt
and turn yellow, effecting the photosynthesis rate as pigment is essential for photosynthesis.
The chlorophyl within the leaves are unable to intake the amount of sunlight needed, therefore,
causing the green onion to develop plant stress and weakens them – hindering its’ growth.

At the end of day 14, the 0.25g concentration shows the fastest growth rate with the mean
height at 7.667cm (Table 3). This highlights that the 0.25g concentration is the most optimal
amount for effective green onion growth. Additionally, standard deviation value was 0.359 at
day 14 (Table 3). This suggests that individual values at day 14 across all concentrations are
relatively clustered together. Demonstrating that the day at day 14 is high in reliability and
validity. The highest standard deviation value is at day 8, at 0.442 (Table 3, graph 1). This
suggests that the individual values across 3 different trials and five different concentrations are
disperse at day 8. Factors that can contribute to this dispersion are the amount of sunlight, the
accuracy of the concentration of the fertilizer, the compactness of the soil, and the germination
of the seeds.

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Graph 2: Mean height of green onions across five concentrations with standard error as error
bars.

Mean height of green onions across five different concentra-

tions
9.000
Mean height of green onions (CM)

8.000
7.000
6.000
5.000
4.000
3.000
2.000
1.000
0.000
0 2 4 6 8 10 12 14

Days (24hr)

Concentration of fertiliser (g) 0.10g Concentration of fertiliser (g) 0.15g

Concentration of fertiliser (g) 0.20g Concentration of fertiliser (g) 0.25g
Concentration of fertiliser (g) 0.30g

Interpretation and Analysis

Graph 2 illustrates the same interpretation made above. However, the highest standard error
calculated is 0.255 at day 8 (table 3, graph 1). This highlights that the sample data collected at
day 8, is accurately representing the overall growth rate of the green onions in the experiment,
increasing the reliability and validity of the data. Therefore, this shows that the while there are
no trends, it can be identified that 0.25g of fertilizer is the most suitable amount across all
concentrations to exhibit optimal growth of green onion. Additionally, the overlapping error
bars (graph 2) indicate that there is no statistically significant difference between the means
being compared. This suggests that any observed differences could likely be due to random
variation rather than a true difference in the populations being studied. Additionally, the
calculated p value of 0.011 and 0.00 across 14 days supports this (table 3). Table 3 shows p-
value of 0.011 at day 12 and p values of 0.00 on other days. This indicates that if the null
hypothesis were true, there would be a probability of 0.011 (or 1.1%) of observing the data or a
more extreme result by random chance alone. While the error bars in standard deviation and
standard errors were not significant, the p - value of 0.011 suggests that the data collected were
not sufficient to be applied to the whole population.

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 Evaluation
Limitations of the evidence

Standard error, and standard deviation, overlapping error bars and confidence intervals are all
examples of the uncertainty and limitations observed from an analysis of the evidence. This can
be explained by a lack of reliability and validity in the experimental process.

The height recorded for the scallions were inconsistent (Table 1 and 3), therefore, the calculated
standard deviation and standard error shows a lack of reliability. This suggests that not all
variables were fully controlled.

Also, the varying standard error across five concentrations suggests low precision in the
measuring instrument or high random biological variation in the samples. Additionally, the low
sample size of this experiment, as well as the shorted length, is a major factor in determining
the full growth rate of the scallion and its’ potential height (Table 1). Consequently, the
evidence is limited in its ability to be used to extrapolate the findings of the experiment to the
population of scallions.

Sources of error
Error type Limitation Reliability or Validity Justification
Systematic No control Validity Because there was no control variable, there was no
variable guarantee that the independent variable made a
difference to the growth rate of scallions. This
affects the validity of the experiment as there might
be outstanding extraneous variables.
Random Measurement Reliability Due to human error, the uncertainty of the
of fertiliser measurement of fertiliser was ±0.05g. This affects
the reliability of the data and interpretations as the
difference in measurement could affected the data.
Random Measurement Reliability Due to human error, the uncertainty of the
of green measurement of fertiliser was ±0.05cm. This affects
onion height the reliability of the processed data as small changes
in values could affect the rounding of the actual
value. Thus, this would also affect the interpretations
and analysis.
Random The amount Reliability In the methodology of the modified experiment,
of soil in there was no actual value (g) for the soil, it was
each seedling assumed to just fill the soil up to the full tray. This
tray affects the reliability of the data as the seedlings

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 need a specific amount of soil for optimal growth, by
only assuming the amount of soil in each try, this
creates a difference in the methodology, making the
experiment unfair.
Systematic Variability in Validity It was interpreted that the little difference in each
each concentration (only 0.5g) was not enough to produce
concentration a substantial difference. This affects the validity of
the experiment as the lower the independent values,
the more error it could potentially produce.

Extensions and Improvements

Improvements

 Reducing random error would improve the reliability. This could be improved by
minimizing the random error through increasing the number of trials from three to five,
increasing the difference within each concentration e.g: 0.10g – 0.20g, and increasing
the number of repeat readings for each sample.
 Instead of using an electronic balance, the accuracy of the mass of the concentration
could be improved by using an electrical analytical balance with wind protectant. This
would reduce the random error in the uncertainty amount for concentration within each
measurement. This would improve the reliability of the experiment.
 Measuring the exact amount of soil for each seedling tray would reduce the random
errors in the experiment and improve the reliability of the data.
 To improve the reliability and validity of the experiment by reducing systematic and
random error, an ANOVA scientific calculation should be use instead of the TTEST
scientific calculation. This would improve the interpretation and analysis for the data as
an ANOVA test allow analysis of the difference between the means of each
concentration and potential show a trend between each concentration.

Extensions

 Redirect the experiment by cauclating the mass difference between each scallion to
investigate if different concentration of fertilizer affects the quality and growth of the
scallion.
 Extended by implementing a control variable in the experiment will reduce the
systematic error and improve the validity of the experiment. This can be done by
growing a scallion in the same way, except with no fertilizer present.

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Conclusion
In conclusion, the evidence suggests that increasing the concentration of fertilizer does not
increase the growth of scallions. Instead, it shows the suggested optimal amount of
concentration of fertilizer that woud exhibit the scallions’ growth. However, there are
significant limitations to the experimental design and further statistical analysis would be
required to support this conclusion.

References
Bekcan, S., Atar, H.H. and Beyaz, A. (2009). Measurement of the Effects of Liquid Fertilizers
at the Different Levels on Duckweed (Lemna MinorL.) Growth Using Image Analysis
Technique. Biotechnology & Biotechnological Equipment, 23(2), pp.1205–1209.
doi:https://doi.org/10.1080/13102818.2009.10817639.

ehrs.upenn.edu. (n.d.). Fact Sheet: Glassware Handling | PennEHRS. [online] Available at:
https://ehrs.upenn.edu/health-safety/lab-safety/chemical-hygiene-plan/fact-sheets/fact-sheet-
glassware-handling#:~:text=Injuries%20to%20the%20hand%20are.

ScienceDirect (2011). Duckweed - an overview | ScienceDirect Topics. [online]

Sciencedirect.com. Available at: https://www.sciencedirect.com/topics/earth-and-planetary-
sciences/duckweed.

Xu, J. and Shen, G. (2011). Growing duckweed in swine wastewater for nutrient recovery and
biomass production. Bioresource Technology, 102(2), pp.848–853.
doi:https://doi.org/10.1016/j.biortech.2010.09.003.

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