Agriculture Firm Project: Assessing Monthly Rainfall Variability

This project delves into the analysis of rainfall variability for an agriculture firm, focusing on the assessment of monthly precipitation patterns. It begins with background information on climate distribution and its impact, particularly in regions like Austria, and then moves on to reporting and dashboards, highlighting the significance of measuring precipitation variability for risk management and business intelligence. The project emphasizes the natural impact of climate data, competitive advantages, decision-making, and waste reduction. It also explores the use of BI frameworks for data extraction and reporting, the importance of detailed reports, and the role of marketing strategies. The project includes recommendations for the CEO, emphasizing the need for market research, SMART objectives, and a focus on competitive advantages. The project concludes with a cover letter demonstrating the application of data insights and firm recommendations.

Running Head: AGRICULTURE FIRM 1
AGRICULTURE FIRM PROJECT
NAME
INSTITUTION
DATE

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Project 3
Modelling mean monthly rainfall variability
INTRODUCTION
Task 1: Background information
The climate distribution of the Agricultural firm in Austria varies across different types
of climate. The climate is similar in East Central Europe with warm summers and other cities
especially those located in high areas. The area is notable for cold climate accompanied by
thunderstorms though at length rainfall will be experienced in all regions. The winter season is
always cold everywhere in Australia. (Chapagain, Hoekstr. & Savenije,2006).
The primary point of the examination was to create measures to evaluate the
consistency in month to month precipitation sums of Australian stations. At that point the
different qualities of the month to month and the occasional precipitation sums of Australian
stations were examined utilizing admirably fitting hypothetical likelihood dispersions.
Eventually, fitting factual models are fitted to the month to month precipitation aggregates to
reproduce and anticipate precipitation, furthermore, to locate the potential indicators of month to
month precipitation aggregates of Australian precipitation stations.

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Task 2 – Reporting / Dashboards
Australia is unique among the most factor precipitation atmospheres on the planet,
and considerable inconstancy in precipitation sums is considered as a risk to the tenants. Hence,
measuring the inconstancy in precipitation is critical to individuals living in serious variable
precipitation atmospheres, as in numerous locales of Australia (Dijk et al, 2013). The measures
used in evaluating inconstancy in precipitation is the difference and coefficient of variety.
Natural Impact: Forecasted data does not simply relate to income; it likewise
consolidates the impact your creation techniques can have on the earth; demonstrating any sad
influences that may happen if unchanged. This can enable organizations enhance their
administration discharge and search for changes in approach, fills and designing methods (Dijk
et al,2013).
Upper hand: The farming business is an aggressive market characterized by competition
in deals over a similar item thereby the importance in ably picking up possible little favorable
positions. BI arrangements help a business to advance and assume responsibility inside their
related markets.
Basic leadership: There is no space for blunder which is the thing that BI programming
can help with; giving exact information and investigation which kills any human presumptions
that prompt horrible outcomes. Administration can settle on educated choices and delegate work
appropriately.
Squander Reduction: As expressed above, with less erroneous choices come decreased
use of both time and cash. This is down to the precisely gauged information enabling

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administration distinguish what is missing from specific divisions and reasonable ranges without
additional pay and consideration. (Dijk, et al, 2013).
Task 3 – Research
The BI framework permits end-clients to remove reports when they require them as
opposed to relying upon individuals in the IT or money related office to set them up (Tessier, et
al 1996). The BI framework will even enable approved clients to plan new reports to coordinate
their necessities. To ensure specific data requirements for everybody, a wide range of detailed
reports are sent to workers. Accordingly, the data measures that don't portray a reasonable photo
of the general circumstance overpower the worker. At the point of analysis, focus shifts from
translating the numbers to assessing their differences mutilates the confidence in the information
thus no decision is made in view of the numbers (Tessier, et al 1996). .

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BI frameworks enable settling on better choices by furnishing chiefs with detailed and
exceptional data. It abbreviates the time amongst thought and activity as many people will
misplace their thought process in the event that additional data about the issue they are managing
delays.
BI is joining various information sources in like manner reports, along these lines sparing the
client from physically consolidating information in spreadsheets and so forth (Wagener, et al
2003). All reports gather their information from one source - the BI framework. Conventional
revealing frameworks mean to give clients information as indicated by a settled and predefined
structure (Wagener, et al. 2003).
Powerful promoting begins with a considered, very much educated showcasing
system (Wagener, et al. 2003). A decent showcasing methodology encourages you to
characterize your vision, mission and business objectives, and diagrams the means you have to
take to accomplish these objectives. (Wagener et al, 2003). Building up a promoting
methodology that incorporates the segments recorded beneath will enable you to capitalize on
your showcasing venture, keep your advertising centered, and measure and enhance your
business comes about.

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Task4 – Recommendations for CEO
To build up your promoting technique, recognize your larger business objectives, with the goal
that you would then be able to characterize an arrangement of advertising objectives to help
them. (Wagener, et al 2003) When defining objectives, apply the SMART criteria.
Ensure your general techniques are additionally down to earth and quantifiable. A decent
promoting technique won't be changed each year, yet amended when your methodologies have
been accomplished or you're advertising objectives have been met or if outside market changes.
(Wagener, et al. 2003).
Research: You have to assemble data about your market e.g. its size, development, social
patterns and demographics. Watch out for your market so you know about any progressions after
some time, so your procedure stays pertinent and focused on. Utilize your statistical surveying to
build up a profile of the clients you are focusing on and recognize their requirements.
Ensure your showcasing system likewise enables you to keep up associations with your current
clients. Thus, your showcasing procedure you ought to build up a profile of your rivals by
distinguishing their items, supply chains, estimating and advertising strategies. Utilize this to
recognize your upper hand - what separates your business from your rivals. (Strauss, et al 2004).

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Task 5 – Cover letter
Name of a manger
Client Service Manager
Agricultural Inc.
123 Corporate Blvd.
Some town, CO 50802
Tel: (cell) 555-555-5500/ (home) 555-555-5555
RE: IMPORTANT OF THE DATA INSIGHT AND FIRM RECOMMENDATION
Dear Ms. West:
I was eager to see your opening for a client benefit rep, and I plan to be welcomed for a meeting.
My experience incorporates filling in as a client benefit relate inside both call-focus and retail
conditions.
Already, I worked inside two high-volume client bolster call habitats for a noteworthy broadcast
communications transporter and a satellite TV administrations supplier. In these positions, I
exhibited the capacity to determine an assortment of issues and dissensions such as charging
question, benefit interferences or shorts and repair expert postponements/no-shows. I reliably
met my call-volume objectives, taking care of a normal of 56 to 60 calls for every day.
Notwithstanding this experience, I increased extensive client benefit abilities amid my low
maintenance work as a server and eatery leader while in secondary school. I additionally convey

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to the table solid PC proficiencies in MS Word, MS Excel and CRM database applications and a
time of school (business major). If it's not too much trouble, see the going with continue for
points of interest of my experience and training. I am sure that I can offer you the client
administration, correspondence and critical thinking abilities you are looking for.
I look forward to your response.
Sincerely,
Harun.

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References
Chapagain, A. K., Hoekstra, A. Y., & Savenije, H. H. G. (2006). Water saving through
international trade of agricultural products. Hydrology and Earth System Sciences
Discussions, 10(3), 455-468.
Dijk, A. I., Beck, H. E., Crosbie, R. S., Jeu, R. A., Liu, Y. Y., Podger, G. M., ... & Viney, N. R.
(2013). The Millennium Drought in southeast Australia (2001–2009): Natural
and human causes and implications for water resources, ecosystems, economy, and
society. Water Resources Research, 49(2), 1040-1057.
Tessier, Y., Lovejoy, S., Hubert, P., Schertzer, D., & Pecknold, S. (1996). Multifractal analysis
and modeling of rainfall and river flows and scaling, causal transfer functions. Journal of
Geophysical Research: Atmospheres, 101(D21), 26427-26440.
Ramana, R. V., Krishna, B., Kumar, S. R., & Pandey, N. G. (2013). Monthly rainfall prediction
using wavelet neural network analysis. Water resources management, 27(10),
3697-3711.
Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., & Gupta, H. V. (2003). Towards reduced
uncertainty in conceptual rainfall‐runoff modelling: Dynamic identifiability
analysis. Hydrological Processes, 17(2), 455-476.
Onof, C., & Wheater, H. S. (1993). Modelling of British rainfall using a random parameter
Bartlett- Lewis rectangular pulse model. Journal of Hydrology, 149(1-4), 67-95.

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Rowell, D. P., Folland, C. K., Maskell, K., & Ward, M. N. (1995). Variability of summer rainfall
over tropical North Africa (1906–92): Observations and modelling. Quarterly Journal of
the Royal Meteorological Society, 121(523), 669-704.
Simmons, R. K., Alberti, K. G. M. M., Gale, E. A. M., Colagiuri, S., Tuomilehto, J., Qiao, Q., ...
& Reaven, G. (2010). The metabolic syndrome: useful concept or clinical tool?
Report of a WHO Expert Consultation. Diabetologia, 53(4), 600-605.
Strauss, G. M., Herndon, J., Maddaus, M. A., Johnstone, D. W., Johnson, E. A., Watson, D. M.,
... & Green, M. R. (2004). Randomized clinical trial of adjuvant chemotherapy
with paclitaxel and carboplatin following resection in stage IB non-small cell lung
cancer (NSCLC): report of Cancer and Leukemia Group B (CALGB) Protocol
9633. Journal of Clinical Oncology, 22(14_suppl), 7019-7019.