Nail IB's App Icon
Biology SL
Biology SL
Sample Internal Assessment
Sample Internal Assessment

Skip to

Table of content
Introduction
Research question
Method
Results
Conclusion

The effect of sunlight on biomass

The effect of sunlight on biomass Reading Time
22 mins Read
The effect of sunlight on biomass Word Count
4,296 Words
Candidate Name: N/A
Candidate Number: N/A
Session: N/A
Personal Code: N/A
Word count: 4,296

Table of content

Introduction

The mass of living things in a space is known as biomass. Different biomass can exist depending on a number of variables, including solar exposure, closeness to populated areas, nutrient levels in the form of minerals, and water accessibility. The first will be the focus of our inquiry, sunshine exposure.

 

Sun exposure is the experiment's independent variable, and dry biomass, measured in grammes (g), is the dependent variable.

 

In addition to solar exposure, there are other factors that affect the biomass of grass. These factors include the quantity and mass of water in the grass, the amount of water each area of grass receives, its proximity to pavements and walkways. Both sampled locations were the same distance from the main pavement and were allowed to dry before being measured in order to control the final two variables.

Research question

How does sunlight affect the dry above-ground biomass of grass? 

Materials

  • 1 meter squared quadrat
  • 10 centimeter squared quadrat
  • 10 plastic resealable bags
  • Scale

Method

  • The school's campus was divided into two 1-meter-square grassy areas, one of which was exposed to the sun all day and the other of which was shaded by a tree. The distance from the front sidewalk of the school was 5 metres for each square metre, managing the proximity variable.
  • A 1 metre by 1 metre square quadrat was then placed in each of the selected regions.
  • Each meter2 was divided into 100 10 cm × 10 cm quadrats. It was then divided into 100 quadrats and numbered from 1 to 100 starting in the upper left corner, left to right.
  • Five numbers—five for the meter2 in the sun and five for the meter2 in the shade—were chosen at random for each location.
  • The generated numbers corresponded to the numbered quadrats. Grass samples were collected from the randomly generated quadrat numbers and put in resealable plastic bags. A procedure was created for gathering the sample; the grass was pinched at the stem directly above the ground, then washed, leaving the roots in the soil unharmed.
  • The grass was then allowed to dry for two days after each square metre had five samples taken from it. The dry biomass can be extracted once the grass has been dried. This regulates the variable of various water content levels in the grass.
  • Each grass sample was given two days to dry before the mass was measured.
  • The t-test was used to calculate the average mass of grass in each region and to assess if the difference in masses was statistically significant or the result of chance after the masses had been recorded.

Results

1

2

3
4

5

6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

21

22
23
24
25
26
27
28
29
30
31
32
33

34

35
36
37
38
39
40

41

42
43
44
45
46
47
48
49
50
51

52

53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73

74

75
76
77
78
79

80

81
82
83
84
85
86
87
88
89
90
91
92
93
94
95

96

97
98
99

100

Figure 1 - Table On 1 Meter Squared Quadrat Divided Into 100 Ten Meter Squared Quadrats. Yellow Highlighted Quadrats Represent Location Of Sun Samples And The Blue Highlighted Quadrats Represent The Location Of Shade Samples
Figure 2 - Table On Biomass Of Grass In The Shade
Additional Observations
  • Grass in the shade was more patchy
  • More dead grass in the sun exposed area
  • Grass in the sun exposed area was wetter than the grass in the shade
Figure 3 - Table On Observations

Statistical analysis

P value: 0.058

 

P > 0.050

 

Accept null hypothesis (difference is due to chance and is not statistically significant)

Figure 4 - Average Biomass Of Grass In Sun And Shaded Areas. The Error Bars Represent ± 1 Standard Deviation

Conclusion

The evidence is in favour of the null hypothesis, which states that there is no difference between grass biomass in the sun and the shade.

 

This is one explanation that might apply. Due to its ability to fix inorganic substances (such as carbon dioxide), grass is a major source of biomass. Therefore, biomass is a proxy for a region's production. More sunlight is absorbed by grass in the sun for photosynthesis. Light energy is changed into chemical energy during photosynthesis. More light energy is absorbed and used to create more chemical energy when there is more light. Consequently, it can be claimed that productivity is higher where there is a higher biomass. The results of this experiment did not reveal a statistically significant biomass difference. Despite the fact that (tables 1 and 2), the average biomass of the grass in the sun was higher than that of the shaded region, this difference was not significant. This might be as a result of other factors, such the quantity of water and the small sample size.

Limitations

The biomass of the grass in each location may have been impacted by additional factors. It was impossible to regulate the amount of water that each region received. The grass is frequently watered by sprinkles. Each area's water intake can vary. the rate of photosynthesis, which in turn will have an impact on grass growth. The results could be inflated if the grass in either location had received more water. The sample size used in my process was adequate but not big enough to yield meaningful findings.

 

In comparison to the data in the sun-exposed area, the data in the shaded area were more varied (Figure 2). If the sample size were expanded, the variation might be reduced. The 10cm2 quadrats were also occasionally challenging to identify and properly measure. The location of the shade sample site was close to a park with a 4-square cement court. Less grass may grow in the shade region because of more direct human contact and trampling. The grass was more uneven. This can cause the biomass of the grass growing in the shade to be underestimated. Additionally, the shaded portion may be used more frequently for shade than the sun-exposed section due to the warm tropical temperature and frequent sun.

Modifications

I would create a meter-squared quadrat that is already divided into 100 quadrats of ten centimetres squared to increase measuring accuracy. As a result, there would be a significant reduction in human error. Some grass samples were still wet when the biomass was collected, and they did not completely dry. If there had been more time, the grass would have been allowed to dry for a longer period of time to make sure that water mass wasn't a factor impacting grass biomass.

 

It would be ideal and more accurate to determine whether there is a difference in grass biomass in the sun and the shade through an experiment that regulates the quantity of water each region receives as well as human touch and uses more exact measuring techniques.