Data Analysis and Modeling: Mean Monthly Solar Exposure Report

This report presents an analysis of mean monthly solar exposure data, focusing on its relevance to cotton farming for Brookstead Farming Company. The study utilizes secondary data from the Australian Bureau of Meteorology, specifically data from the Ballarat airdrome, to model solar exposure trends. The report includes data tables, graphical representations of the data, and a discussion of the implications for cotton production, considering the crop's sensitivity to temperature and solar radiation. The research culminates in recommendations to the CEO, emphasizing the importance of monitoring solar exposure fluctuations and implementing strategies like heat-tolerant cotton varieties and adjusted planting dates to mitigate risks. Furthermore, the report advocates for the adoption of an analytical dashboard to facilitate data-driven decision-making by the operational manager, providing real-time insights into weather patterns and their impact on productivity.

Modelling mean monthly solar exposure
Student name
Analytics
29th September 2017

Modelling mean monthly solar exposure
Contents
Introduction...........................................................................................................................................2
Background information........................................................................................................................2
Reporting...............................................................................................................................................3
Research................................................................................................................................................5
Recommendations to CEO....................................................................................................................6
Cover letter............................................................................................................................................7
References.............................................................................................................................................9
Introduction
Solar radiation is the power obtained from the sun per unit surface, it is in form of
electromagnetic radiation. This energy can be measured in space or on the earth surface after
undergoing atmospheric absorption and scattering. Plants on the earth's surface rely on the
UV light derived from the sunlight to photosynthesis and so make food. The problem is that
the sunlight's effect is not only the UV light but there is an accompanying issue of
temperature (Scafetta & Willson, 2014).
This project will involve the analysis of sunlight exposure as measured in Ballarat airdrome.
The data used for analysis will purely be secondary, derived from the Australian website with
journals being used for purposes of analysis.
Being that cotton is one of the crops which is very sensitive to temperatures the data
generated from the analysis will be used to model a dashboard for use by the operational
manager of Brookstead farming company. The result and findings will be communicated to
the Chief Executive officer of the company to make relevant adjustments.
Background information
The selected project is modelling of the monthly solar exposure. Crops need exposure to UV
solar radiation for them to carry photosynthesis. This makes tropical climates to have a bigger
impact on plants developments as the solar radiation access the plants for a long time. In this
study, our company of focus will be Brookstead Farming Company Pty Ltd. This is a
company which operates in Queensland Australia and majors in cotton farming. Having
considers the influence of UV light on plants maturity the data set selected will be of
importance to the company. Cotton farming is an agricultural activity which relies on
effective prediction of future climatic patterns to prosper. Since cotton once planted cannot be
shifted geographically to the climatic friendly areas the decision of when to plant then

Modelling mean monthly solar exposure
becomes a heavy task to the operational manager of the company. By analysing the monthly
mean daily global solar exposure data, the company officials can analyse the situation and
have an effective decision on when to plant and harvest the cotton (Priti Dehariya, 2011).
The monthly mean daily global solar exposure is the mean of all the daily solar energy on a
surface from midnight to midnight for a period of one month. Daily solar energy values
normally have a range of 1 to 35 megajoules per square meter(Australian Government
Bureau of Meteorology, 2012).
Reporting
Table 1: solar exposure data in MJm-2 (Australian government Bureau of Meteorology, 2017)
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Modelling mean monthly solar exposure
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Modelling mean monthly solar exposure
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Summary statistics
for all years

Modelling mean monthly solar exposure
Stati
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Figure 1: A line graph of monthly average, maximum and minimum from 1990-2017
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
0
5
10
15
20
25
30
35
Trend in Solar exposure
Series1 Series2 Series3
Series: 1 mean
2 lowest
3 highest

Modelling mean monthly solar exposure
Figure 2: A line graph of annual data from 1990-2017
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
0
2
4
6
8
10
12
14
16
18
20
Annual
An analytical dashboard is for reporting that is useful in analysis of enormous data so that the
users can easily analyse trend, predict future expectations and gain insights. The operation
manager of Brookstead farming company ltd is more interested with the analyst valuation of
the trends in solar exposure data for decision making, this dashboard will thereby be more
effective to him.
Research
The dashboard will avail critical information at a glance to the operational manager. In other
words, a dashboard can be regarded as a progress report. Its displays information to a
database linked to a web page this way the report viewed from the dashboard is updated
frequently. The analytical dashboard will show trend information of the solar exposure, as
more verify information is fed into the website any changes in trend are reflected in the
dashboard report.
The use of digital dashboard gives the operational manager capacity of monitoring the
weather changes and relative improvements in productivity. As result, he will be able to
identify how cotton production is affected by the weather pattern at a glance (Briggs, 2013).
There are several advantages derived from the use of analytical dashboard some of them
being the performance measured is presented visually which makes interpretation of results
easy. The dashboard information is updated frequently and trend well presented the
operational manager is, therefore, able to detect any negative trends and take the appropriate

Modelling mean monthly solar exposure
cause of action. Since the dashboard gives information from interrelated data sets efficiencies
are easily measured (Eckerson, 2010).
The operational; manager using the dashboard can generate a detailed report which indicates
current issues, this has an effect in making an accurate and informed decision. The
organization's goals and strategies can be aligned according to the existing situation based
on=n the data at hand. This strategy gives the operational manager an easy task in
implementing the strategies as factored affecting them have already been highlighted.
A quality dashboard can present communication in an uncomplicated way which is easy to
understand. In addition, the distraction and confusion in the dashboard are minimised. All
these together with human visual perception skills make organization of the business easy
(Hetherington, 2009).
The data presented in line graphs gives a visual outlook of the trend. Since cotton production
depends on the intensity of solar exposure by looking at the trend the operations manager will
be able to gauge which areas will need to be worked on during a specific month.
Recommendations to CEO
The quality of cotton and quantity harvested changes depending on the weekly temperature
fluctuations. This situation is more reflected in dryland cotton in which hot temperature
represent high water consumption with little rainfall. Temperature effect production of cotton
even in irrigation cases, for this reason, it's recommended that the production manager makes
adequate plans rectify the effect as per the predicted temperature variation (Wang, et al.,
2009).
From the analyzed data, the magnitude of solar exposure is high around the month of January
it does, however, lowers as we move towards June. The month of June experiences the
minimum solar exposure before it starts to arise towards December, January February then
began lowering again. This is a periodic sequence which the operational manager should
prepare for. High solar exposure affects both the temperatures and UV light which are all
factors in the production of cotton.
Hot temperature decreases photosynthesis and triggers high respiration rate this make the
seed production process to slow down, also the lint development is hindered. All this add to
lead to poor lower quantity produced. Despite cotton having the ability to retain canopy
temperature lower than the surrounding air temperature increased humidity combined with
high air temperature could result in canopy temperature which is above the optimum level.

Modelling mean monthly solar exposure
When the temperature is high during the day the rate of photosynthesis is reduced at night
respiration will be high combining to reduce productivity. Plants manufacture the same
number of bolls but with fewer seeds. This means smaller bolls and reduced lint production
(Chapagain, Hoekstra, Savenije, & Gautam, 2006).
When growing cotton without irrigation there is a potentiality of hedging lower yield
production through a selection of more heat tolerant cotton varieties. Though the company
may need to do research on information of cotton species regarded as heat tolerant, also, the
planting dates can be stretched or even planting varieties which may mature faster before the
temperatures heat their peak. When these factors are accounted for then the firm suffers less
risk in terms of cotton production quantity and quality (Loka, 2016).
Being that the manager will have an influence on those who access the data from the
dashboard the operations manager will be able to communicate his position to the employees
in a way which can be easily be illustrated and understood, this way the employees will be
able to operate in a uniform platform having sufficient information to pursue the firm's
targets.
Cover letter
James Milwork
Po Box 234
Warren, Australia
29/9/2017
Chief Executive Officer
Brookstead Farming Company Pty Ltd
Branchview
Brookstead, QLD, Australia
Dear Mr/ Ms

Modelling mean monthly solar exposure
RE: Production data insight
Having undertaken a research work on solar exposure and its effects in cotton production
there are issues that I would like to bring your attention to.
Data analysis noted that the intensity of the sunlight radiation was very high from the months
of December to March, subsequently, there is a general low solar exposure around the month
of June. I would wish to take into account those factors when organizing for the planting
session. Furthermore, efforts should be put in place to ensure that the current crops in the
field are adequately watered during the months of December to March to try minimising the
hot temperatures dangers on the crops.
Finally, to assist in your future decision making I recommend your farm adopt the use of
analytical dashboard in accessing and analyzing relevant information needed for decision-
making. The tool is efficient in availing information instantly at your request since it can be
attached to several websites and the firm's database the information gained from it is
frequently updated based on the occurring changes the outcome is efficient, reliable and up to
date data which gives you an opportunity to make very informed decisions
Respectfully yours,
James Milwork
References
Australian Government Bureau of Meteorology. (2012). Solar Radiation Definitions. Retrieved
September 29, 2017, from http://www.bom.gov.au/climate/austmaps/solar-radiation-
glossary.shtml#globalexposure
Australian government Bureau of Meteorology. (2017, September 29). Australian government Bureau
of Meteorology. Retrieved from Monthly mean daily global solar exposure:
http://www.bom.gov.au/jsp/ncc/cdio/weatherData/av?
p_nccObsCode=203&p_display_type=dataFile&p_startYear=&p_c=&p_stn_num=089002
Briggs, J. (2013). Management Reports & Dashboard Best Practice. Target Dashboard.

Modelling mean monthly solar exposure
Chapagain, A. K., Hoekstra, A. Y., Savenije, H. H., & Gautam, R. (2006). The water footprint of
cotton consumption: An assessment of the impact of worldwide consumption of cotton
products on the water resources in the cotton producing countries. Ecological Economics,
196–203.
Eckerson, W. W. (2010). Performance Dashboards: Measuring, Monitoring, and Managing Your
Business. Wiley.
Hetherington, V. (2009). Dashboard Demystified: What is a Dashboard? Hetherington.
Loka, D. a. (2016). Increased night temperatures during cotton's early reproductive stage affect leaf
physiology and flower bud carbohydrate content decreasing. Journal of Agronomy and Crop
Science, 900-2100.
Priti Dehariya, S. K. (2011). NCBI: Assessment of impact of solar UV components on growth and
antioxidant enzyme activity in cotton plant. Retrieved September 28, 2017, from
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3550570/
Scafetta, N., & Willson, R. C. (2014). ACRIM total solar irradiance satellite composite validation
versus TSI proxy models. Astrophysics and Space Science, 400-441.
Wang, Z., Lin, H., Huang, J., Hu, R., Rozelle, S., & Pray, C. (2009). Bt Cotton in China: Are
Secondary Insect Infestations Offsetting the Benefits in Farmer Fields?". Agricultural
Sciences in China, 73–90.