Risk Assessment on an Agricultural Farm
KRISTINA PAVLOVA, ELISAVETA TRICHKOVA-KASHAMOVA, STANISLAV DIMITROV
Institute of Information and Communication Technologies,
Bulgarian Academy of Sciences,
“Acad. G. Bonchev” str. bl.2, Sofia,
BULGARIA
Abstract: - Livestock production is a major economic sector concerned with the rearing, care, and production of
farm animals. Animal nutrition is an important component that has the potential to significantly impact the
profitability of livestock production. The production of own feed has many positive aspects. Producing its own
feed ensures its quality and reduces farm costs, but it also carries risks for farmers. This paper assesses the risk
on the farm, and suggests intelligent solutions to optimize the resource functioning of a livestock production
system and forecasting of management decisions and to achieve better organization of farm processes, labor
resources, etc. Based on the assessment, the ability to cover costs and service debt are determined and the
profitability of the business is assessed. The average annual yield and its standard deviation are displayed in the
paper. This statistical measure indicates the degree to which yields over a given period deviate from the average
by kg/dec. The paper analyses the Business Risk indicator, which is an assessment of the level of sales and
revenue, i.e. whether the farm can cover its costs and make a profit. It determines whether the company can
operate as a profitable enterprise. Through the computation and examination of financial and business risk
indicators, farmers may optimize their expenses and ultimately turn a profit.
Key-Words: - animal husbandry, automated feed rationing, farm management, feed costs, feeding efficiency,
livestock.
Received: September 11, 2023. Revised: April 13, 2024. Accepted: May 16, 2024. Published: May 31, 2024.
1 Introduction
According to [1], agriculture is a dangerous
business, particularly in developing nations. The
biological processes that underpin the production of
crops and animals cause considerable delays, which
are amplified in terms of "weather" because of their
significance to the production of agricultural
commodities, [2].
Nutrition is a basic prerequisite for optimal
performance on any farm. By [3], creating the right
feed balance can increase animal performance -
weight gain in beef breeds and increased milk yield
in sheep and dairy cows. Therefore, optimizing
management and feeding technology is a profitable
investment that can improve both reproduction and
the health of the whole herd.
Rising electricity, heat, and fuel prices in recent
years, together with increased technological
demands and the need to lower the cost of
production, have forced most farmers to start
creating and growing feed for their animals (Figure
1). successful farm management needs to have the
right equipment for quality milling and mixing of
forages for animal rations. Investing in in-house
feed production equipment is an effective method of
achieving efficiency in animal feeding and the
ability to comply with recipes for individual groups
of animals. Another major advantage is that it can
be upgraded at any time with grain storage silos,
textile cages for storage of finished feed, and
automated systems for precise addition of
components. Using automated systems, farmers-
breeders can use their agricultural production as
well as ration grain according to their recipes. These
systems can be programmed to give the exact
amount of feed needed to feed the animals.
Fig. 1: Growing the feed for animals
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.116
Kristina Pavlova, Elisaveta Trichkova-Kashamova,
Stanislav Dimitrov
E-ISSN: 2224-2899
1418
Volume 21, 2024
Feed costs account for more than a third of the
final consumer price. This share highlights the
important role of feed utilization as a factor for
competitiveness and its optimization through better
genetics and feeding and rearing methods.
The market activities of the farms are aimed at
obtaining maximum profit and gaining the largest
market share. There is a drive to prevent or
minimize the likelihood of failure. By using their
feed, farmers will reduce their costs significantly.
Another positive side of using your feed is that there
will always be availability. If there is a surplus of
the feed they have produced they will be able to sell
it and from there increase income.
In recent years, several companies have
provided complete design, installation, and
commissioning of feed kitchens on Bulgarian farms.
Livestock farmers are increasingly turning to their
automated feed production equipment. There are
several reasons for this - the higher cost of animal
feed produced in large factories and transport costs,
independence due to the possibility of self-
production, or the urgent need for specialized feed
prepared according to a special recipe. Using
automated systems (Figure 2), they will be able to
compile the exact animal ration corresponding to the
required amounts of nutrients that the animals must
take by Regulation (EC) No 178/2002 of the
European Parliament and the Council of 28 January
2002 laying down the general principles and
requirements of food law, establishing the European
Food Safety Authority and laying down procedures
in matters of food safety.
Fig. 2: Automated systems to compile the animal
ration
The objectives of this document relate to the
practice of providing individual or group feeds
tailored to changing nutritional needs over time and
individual differences in nutritional requirements.
This practice aims to and optimize animal health
and performance while reducing feed wastage and
environmental impact. It is defined as the accurate
assessment of the nutrients contained in feed and
feed ingredients, the precise formulation of diets and
the assessment of the nutritional needs of individual
animals or groups of animals, [4]. Implementing
precision animal nutrition on farms requires the
integration of three important activities: automated
data collection, data processing, and actions related
to the control and management of the system on the
farm, [5], [6] , [7]. For precision animal feeding to
be applied at a personalized level, measurements,
data processing, and control actions must be applied
to individual animals according to [8].
Banks and farm economists are still faced with
the challenge of evaluating and forecasting the
financial performance of agricultural enterprises.
However, even though a sizable number of studies
analyzing the financial determinants of business
performance have been conducted in the United
States, there hasn't been much formal analysis in a
European farming context, except growth and
survival studies that are more focused on physical
and social determinants, [9], [10], [11] work from
the 1980s and 1990s, respectively. Furthermore,
rather than continuous variables evaluating
"success" from the owner's perspective, a large
portion of the work utilizing financial determinants
has been based on performance categories, defined
according to creditworthiness or default risk.
In [12], examines a self-insurance strategy used
by farmers for risk protection. This paper examines
the impact of various farm, operator, and household
characteristics on the level of on-farm
diversification. Additionally, results also show a
significant positive relationship between
diversification and farm/crop insurance and sole
proprietorships. Finally, there is also evidence that
farms that received government payments are more
diversified than their counterparts.
Optimization techniques are used within a
simulation framework, this study demonstrates the
synergy between balancing risk and alternative
strategies to effectively mitigate risk under changing
farm conditions. Farmers with high-risk aversion
tend to prefer integrated risk management plans
based on the principle of diversification. The greater
attractiveness of a more diversified plan usually
reduces the importance of the risk balancing
strategy as the farm uses credit reserves to
implement other production and marketing plans
considered essential for overall risk reduction, [13].
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Kristina Pavlova, Elisaveta Trichkova-Kashamova,
Stanislav Dimitrov
E-ISSN: 2224-2899
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Farmers must make choices to minimize their
effects or be ready for such risky circumstances to
preserve vital operations. A data-driven viewpoint is
becoming more and more necessary, with machine
learning (ML) emerging as a crucial instrument for
the automatic extraction of valuable data to assist in
risk and farm management decision-making. With
recent technological advancements and
digitalization, machine learning's (ML) importance
in farm risk management (FRM) has grown, [14]. In
many nations, risk is central to new paradigms and
techniques that guide risk management programs
and influence investment decisions according to
[15].
The main task of farmers is to make decisions
related to their daily activities. Many factors
influence their decisions and they cannot be
predicted with 100% accuracy. Farming is
becoming increasingly risky because farmers are
striving to have higher profits. This makes it
necessary for farmers to analyze and identify the
risk they face and have the skills to manage them, in
order to better anticipate possible problems and
mitigate their consequences according to [16].
Risk is the impact on production, this can be the
change in weather, the emergence of pests and
diseases in the fields, damage to equipment, and
fluctuations in market prices. Borrowing can also be
risky due to unexpected changes in interest rates.
Risk can also arise as a result of changes in
government policies. These risks often have a major
impact on the income generated on the farm and on
the health and physical condition of the farmer and
also of the farm workforce according to [17].
Decision-making is the main activity related to
farm management. From all decisions made, there
are either results or consequences. The outcome of
decisions made cannot be fully predicted even with
information. It is important to determine whether
producers consider the risks associated with the cost
of production and animal health separately or jointly
by [18]. The more complex the risk, the harder it is
for farmers to make an informed decision. To make
effective decisions, farmers need to gather
information from a variety of sources and need
information about many aspects of the farming
business. Farmers need to find ways to manage risk
and protect themselves from the uncertainty of the
future by [19].
2 Material and Methods
The management of the farm is carried out in such a
way as to maintain the animals in good health, to
provide adequate and non-contagious feed and
water, and to ensure optimum living conditions.
Animals are reared based on risk analysis and these
risks are controlled to produce food safely.
Appropriate records are kept for easy traceability
according to [20].
The paper analyses the financial performance of
a farm. The analysis presented allows farmers to
gain skills and knowledge to manage risk, identify
and understand their problems, and help them make
better farm management decisions. For every
farmer, it matters where the animal's food will come
from, whether the farmer produces it or buys it from
producers. Each farmer should source it himself, as
this way he will get it cheaper and know the quality
of the food. The most important thing for all animals
is the quality of the food they take. Because the
productivity of the animals - meat, milk, eggs -
depends on good food according to [20].
The quality of crops and livestock depends on
biological processes that are influenced by weather,
pests, and diseases. For example, low rainfall or
drought leads to lower yields. Heavy rains damage
or destroy crops. Pest or disease outbreaks cause
large losses in terms of crop and livestock yields.
When farmers plant seeds and fertilize the soil,
they do not know for sure how much rain will fall,
or whether there will be storms and hailstorms.
They don't know if there will be a pest or disease
problem, but they have to decide whether to plant
their crops or raise their livestock. Resources used
such as financial capital, time and labor to plow,
plant, and fertilize crops or to care for, feed, and
medicate livestock may not be recovered. These are
all examples of different factors that are risk factors
for the activities developed on the farm. This means
that farmers produce without complete certainty
about what will happen to their produce according
to [21].
The supply of a product is influenced by a
combination of decisions made by farmers to grow
that product, the weather, and other factors that
affect yields.
The unit cost of production depends on inputs
and yield. The influence of both factors makes them
highly variable. Input costs are generally less
variable than output prices. The combination of
yield variability and production cost variability
makes production a serious source of risk. In this
article, an analysis will be carried out on two
indicators - the price of the final output and the yield
of the product produced.
Financial risk for the farm. Financial risk arises
when a loan is taken out to finance the farming
business. This risk can be caused by uncertainty
about future interest rates, the willingness and
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Stanislav Dimitrov
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ability of the lender to provide funds to the farm,
and the farmer's ability to generate the income
needed to repay the loan.
Small farmers who borrow at high-interest rates
may have difficulty repaying the debt. Lower than-
expected farm gate prices for produce, combined
with low yields, can make it difficult to repay the
debt and even lead to the sale of the farm. The paper
uses a methodology that examines the farm's ability
to repay its loan as well.
Subsidies, food import and export control
regulations, animal waste disposal rules, and farm
income support payments are examples of
government decisions that have a strong impact on
agribusiness.
Data Input. Data from the operation of an
animal farm also engaged in crop production are
collected in Tab. 1. The data in the table are for
wheat production. Feed grains are used to feed
sheep, pigs, cows, rabbits, chickens, geese, ducks,
and other animals and birds. The data are organized
in four columns the first for the year recorded, in the
second the yield in kg/dec, in the third the price per
kg is entered for the final output during the year,
and in the last column, the gross income is
presented, which is the product of the data in
columns two and three.
Table 1. Input data.
Year
Yield
[kg/dec]
Price
lv/kg
1
285,813
0,485
2
423,675
0,347
3
154,675
0,405
4
104,238
0,443
5
346,338
0,337
6
427,038
0,380
7
541,363
0,384
8
252,188
0,494
9
312,713
0,369
10
423,675
0,468
11
386,688
0,405
12
467,388
0,435
13
282,450
0,406
14
215,200
0,350
15
312,713
0,446
16
279,088
0,378
17
504,375
0,488
18
171,488
0,412
19
460,663
0,420
20
406,863
0,416
Check for normal data distribution. The data
from Tab. 1 are checked for normal distribution by
the Jarque-Bera Test, from the applied data
evaluation approach it is found that the data for all
the indicators are normally distributed.
Descriptive statistics. Initially, the data were
processed with the MS Excel 2016 data analysis tool
Descriptive Statistics. The results are presented in
Table 2.
Table 2. Descriptive statistics.
Table 2 shows that the average annual yield is
337.93 kg/dec and its standard deviation is 121.31
kg/dec. This statistical parameter indicates how
much kg/dec the yields over the years differ from
the average over the research period. The smallest
yield was 104.24 kg/dec and was obtained in the
fourth year of the study period and the largest yield
was 541.36 kg/dec and was obtained in the
seventeenth year. Furthermore, it can be stated with
95% confidence that the yield will be between 281
and 394 kg/dec, and that 68% of the data in the
interval are between 216 and 459 kg/dec.
For the other two indicators, it can be seen that
the average price for the study period was 41.3 cents
and the average gross revenue was 139.69 lv/dec.
Furthermore, the highest gross revenue was
Yield
[kg]
Price
lv/kg
Gross
[lv/dec]
Mean
337,931
0,413
139,69
Standard Error
27,125
0,010
12,01
Median
329,525
0,409
139,18
Mode
423,675
0,405
Standard
Deviation
121,308
0,047
53,70
Sample
Variance
14715,585
0,002
2883,60
Kurtosis
-0,745
-0,805
-0,58
Skewness
-0,232
0,187
0,08
Range
437,125
0,157
199,86
Minimum
104,238
0,336
46,17
Maximum
541,363
0,493
246,03
Sum
6758,625
8,267
2793,85
Count
20,000
20
20,00
Confidence
Level(95,0%)
56,774
0,022
25,13
Upper Level
394,705
0,435
164,82
Lower Level
281,157
0,391
114,56
Mean + SD
459,239
0,461
193,39
Mean – SD
216,623
0,366
85,99
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obtained in the seventeenth year of the study period
and was 246.17 lv/dec and the lowest revenue was
46.17 lv/dec obtained in the fourth year.
Analysis of the yield indicator. According to
experts, yields below 250 kg are considered low,
yields around 350 kg are considered medium, and
yields above 450 kg are considered high. Their
claim is verified by sorting the measured yield data
in ascending order and dividing them into three
groups, the first group recording the five years with
the highest yield, the second group the next ten in
order of magnitude, and the third group the five
years with the lowest yield. The average yield for
the first group was 480.17 kg/dec, for the second
group it was 346 kg/dec, and for the third group the
average yield was 179.59 kg/dec. These data
confirm the experts' statement. When analyzing the
data and presenting them in Table 1 and Table 2, the
results of the analysis were in line with the results of
the present study. In Table 3 it is seen that 25% of
the years are high, 25% low, and 50% average.
Table 3. Analysis of the indicator "Yield"
3 Results and Discussion
The cumulative distribution function (CDF) of a
random variable is a method of describing the
distribution of a continuous, discrete, and mixed
random variable. In farming and agriculture farm
performance is measured by continuous variables,
and using a cumulative distribution function allows
one to estimate the probability of a random variable
having a value less than or equal to a number X
(random event).
Based on the analysis of the input data
presented in tab. 2, intervals are defined in which
the real data values of the three indicators yield,
price, and gross profit may fall. Based on these data,
the probability of occurrence of a random event (the
number X) is calculated about indicators used to
evaluate the financial efficiency of the farm. In MS
Excel, the cumulative function for the data is
calculated using the function =NORM.DIST(X;
Mean;Standard_dev;TRUE), the obtained results are
presented in the Cumulative value column of Table
4, for each indicator. The results show how likely it
is that the value for the metric will be less than or
equal to a set critical value.
Table 4 also presents results for values that are
of interest to experts, which are the probability that
the yield is below 500 kg/dec, the price is below 43
cents and the gross income per hectare is below
BGN 120. For the first indicator, the probability is
91%, for the second it is 64%, and for the third 36%.
Table 4. Critical values
Graphically obtained results from the Table 4
for all indicators are presented in Figure 3, Figure 4
and Figure 5.
Fig. 3: Cumulative function for the Yield indicator
2% 9% 23% 46% 70% 87% 91% 96%
0%
50%
100%
150%
Risk
Yield [kg/dec]
Cumulative function of
Yield [kg/dec]
Year
Yield
[kg]
Point
Yield
[kg]
Rank
Percent
1
285,813
7
541,363
1
100,00%
2
423,675
17
504,375
2
94,70%
3
154,675
12
467,388
3
89,40%
4
104,238
19
460,663
4
84,20%
5
346,338
6
427,038
5
78,90%
6
427,038
2
423,675
6
68,40%
7
541,363
10
423,675
6
68,40%
8
252,188
20
406,863
8
63,10%
9
312,713
11
386,688
9
57,80%
10
423,675
5
346,338
10
52,60%
11
386,688
9
312,713
11
42,10%
12
467,388
15
312,713
11
42,10%
13
282,450
1
285,813
13
36,80%
14
215,200
13
282,450
14
31,50%
15
312,713
16
279,088
15
26,30%
16
279,088
8
252,188
16
21,00%
17
504,375
14
215,200
17
15,70%
18
171,488
18
171,488
18
10,50%
19
460,663
3
154,675
19
5,20%
20
406,863
4
104,238
20
0,00%
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Fig. 4: Cumulative function for the indicator Price
Fig. 5: Cumulative function for the indicator "Gross
revenue"
Assessment of financial indicators of the farm.
With the results obtained so far, it is not possible to
assess the financial status of the farm. For this
reason, the available data is also used to analyze the
financial status of the farm.
Determination of average annual income. The
average annual income is calculated by multiplying
the value for cultivated areas on the farm and the
average income per hectare ( 4625.607 [dec] *
139.69 [lv/dec] = 646162 lv.)
Calculation of annual deductions. Annual
deductions are calculated using formula (1).
Annually deductions = (Annually expenses +
Household costs) – Debt (1)
Annually deductions = (539340 + 96000) - 100000
= 535340 lv
The calculations were obtained from the input
data for the financial indicators of the farm
presented in the Table 5. In the last column, the
income and expenditure data on the farm are
converted per hectare of cultivated land.
Table 5. Financial data
Calculation of financial and business risk. A
variety of risks and uncertainties are associated with
agricultural activities because of the ever-changing
economic and biophysical environment. Firstly,
there is business risk, which encompasses risk
related to production, markets, institutions, and
individuals. The second is the financial risk
associated with the various ways that agricultural
activities are financed. The geographical location,
governmental laws, and policies, the availability of
formal (government) and/or traditional risk
management tools, the kind of agricultural product,
etc., may all have an impact on how important
various risk sources are about one another. To
control their risks at the farm level, farmers have
access to a variety of risk management instruments,
[22]. Financial and business risk are two estimates
of the economic efficiency of the farm. Financial
risk is an assessment of the effective use of financial
leverage and debt management in the firm. It is used
to determine the statistical probability that the
company cannot repay its debt to creditors.
Business risk is an assessment of the level
of sales and income, i.e. whether the farm can cover
its costs and make a profit. It determines whether
the company can operate as a profitable enterprise.
Measuring financial risk
Financial leverage ratio. The leverage ratio is a
financial indicator measuring the amount of capital
entering the farm in the form of debt (borrowings)
and assesses the company's ability to meet its
financial obligations. When the financial leverage
4% 13%
31%
55,57%64%
78%
92% 98%
0%
20%
40%
60%
80%
100%
120%
0,3300,3600,3900,4200,4300,4500,4800,510
Risk
Price [lv]
Cumulative function of Price [lv]
4% 13% 32% 36%
58%
80% 93% 98%
0%
50%
100%
150%
45 80 115 120 150 185 220 255
Risk
Gross[lv/dec]
Cumulative function of
Gross[lv/dec]
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ratio is less than 1, the farm is considered to be well
financial by industry standards, when the ratio is
greater than 1, it is considered to be a risky
company. While a financial leverage ratio greater
than 2 is assumed that the financial condition of the
farm is not good.
 
 
 
(2)
The studied farm has a leverage ratio of 2.22,
which means that the financial condition of the farm
is not good and risky.
Measuring business risk on a farm
A farm's business risk can be measured by the ROA
metric. The % return on assets is a reliable indicator
for assessing business risk. Based on the
calculations in the summary statistics section and
the mean and standard deviation data for the gross
income indicator, the % return on risk indicator can
be calculated very easily.
The business risk of the farm can be measured
by indicator of the return on assets. The asset on the
farm is the cultivated land and based on the
summary statistics mean and standard deviation for
gross profit of the farm, the % return on the asset
can be calculated.
To determine the % return on the asset
(revenue), the total profit per acre is calculated and
the gross profit is taxed at 15%. Annual deductions
are also calculated per acre.
The return per acre per year is calculated using
formula (3).
Return of asset [lv/dec] = Total [lv/dec] -
Annually deductions [lv/dec] (3)
where:
- Total [lv/dec] is the total profit per hectare of
cultivated land;
- Annually deductions [lv/dec] are the average
annual deductions.
The indicator % return on the asset is calculated
by formula (4)
 󰇣
󰇤
󰇣
󰇤
(4)
Where
- Return of asset [lv/dec] is the annual return;
- Total investment[lv/dec] is the total amount of
the investment.
From the obtained results, it can be concluded
that the average annual % return is 14%, and the
standard deviation is 20%, and in absolute terms, the
average return per hectare is BGN 44.91, with a
standard deviation of BGN 61.75.
The annual interest on the farm loan is 100,000
lv or 21.62 lv./dec.
The return on equity of farmers is used formula
(5).
Return of equity = Return of asset [lv/dec] - Interest
[lv/dec] (5)
Where
- Return of asset [lv/dec] is the annual return
- Interest [lv/dec] is the annual cost of the loan
The indicator % return on own funds is
calculated by formula (6).
 󰇣 
󰇤
󰇣 
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(6)
From the obtained results it can be concluded
that the average return on the farmer's funds is 24%,
and the standard deviation is 63%, in absolute
values per hectare of cultivated land the average
return is 23.29 lv/dec and the standard deviation is
61.75 lv /dec.
Table 6 summarizes and presents the results of
the farm's business risk.
WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.116
Kristina Pavlova, Elisaveta Trichkova-Kashamova,
Stanislav Dimitrov
E-ISSN: 2224-2899
1424
Volume 21, 2024
Table 6. Return on asset
Probabilistic estimate of return on an asset. The
data for probabilistic assessment of return on an
asset are presented in the Table 7.
Table 7. Probability table for return on asset
Since the gross annual income for the studied
period is between 46 and 246 [lv/dec] the
determined critical values in the first column, are
also used in the cumulative function. The data in the
second column is calculated by multiplying the
value from the first column by a coefficient of 1.15.
In the third column, data on average annual
deductions are entered.
The significant data in this table are presented in
the last three columns. The data in the Return to
Assets column are obtained by applying formula (3),
and the data in the Return to Asset percent column
by formula (4). The data in the last column
Probability is determined by a function of MS Excel
- NORM.DIST. A point is determined at which the
total gross pass is 0 [lv/dec], i.e. the value at which
the costs of the farm are covered. The probability
that the gross income is below 100.6 [lv/dec], i.e.
that all annual deductions cannot be covered is 23%,
which means once every 4 years.
Probabilistic assessment of return on equity.
Critical values for the gross annual profit are entered
in the first column of Table 8. In the second column,
they are calculated by multiplying the value from
the first column by a coefficient of 1.15, and the
total annual gross profit is determined. In the third
column, data is entered on average annual
deductions plus annual loan costs.
Table 8. Probability table for return on equity
The significant data in this table are presented in
the last three columns. The data in the Return to
equity column are obtained by applying formula (5),
and the data in the Return to equity percent column
by formula (6). The data in the last column
Probability is determined by a function of MS Excel
- NORM.DIST. A point is defined where the total
gross profit is 0 [lv/dec]. The probability that the
gross income is below 119.44 [lv/dec], i.e. that the
annual deductions and the interest on the loan
cannot be covered is 35%, which means once every
3 years.
In the last line marked in yellow, the probability
that the farm will have an annual profit of 10% is
calculated. This level of profit is determined
because the interest on the loan is 10%. The data
shows that generating a profit of less than 10% of
the farm is 44%, which means that there is a 44%
chance that the farm will have less profit than the
bank.
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Kristina Pavlova, Elisaveta Trichkova-Kashamova,
Stanislav Dimitrov
E-ISSN: 2224-2899
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4 Conclusion
The market activities of the farms are aimed at
obtaining maximum profit and gaining the largest
market share. There is a drive to prevent or
minimize the likelihood of failure. On the other
hand, to maximize farm income, intelligent
solutions must be sought to reduce costs and to
better organize farm processes, labor resources, etc.
Sheep farms apply various modern solutions to
achieve better economic efficiency. The high cost of
feed requires its optimal use. For this reason, farm
management is a decisive factor for overall profit.
The paper uses mean estimates for expected
gross revenue, yield, price, and standard deviation to
determine their risk of occurrence. Based on the
analysis, their values can be used to determine farm
management strategies.
The relationship between the standard deviation
and the mean describes the frequency with which
adverse events occur and what the consequences
are.
Based on this assessment, the ability to cover
costs and service debt is determined and the
profitability of a business is assessed.
Knowledge of the frequency of occurrence and
financial severity of adverse events is vital to
determining whether:
- take a particular risk
- find ways by which to control
- or to transfer it to insurers
- or to eliminate it.
In this way, the farmer is presented with a
quantitative assessment and receives a summary of
the information with which to make his decision. On
the other hand, his decision depends on his attitude
to risk.
Making intelligent decisions to optimize the
resource performance of the livestock production
system and forecasting management decisions leads
to improved system performance and increased
revenues. After calculating and analyzing business
and financial risk indicators, farmers will be able to
optimize their costs and thus make a profit.
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Stanislav Dimitrov
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
The authors equally contributed to the present
research, at all stages from the formulation of the
problem to the final findings and solution
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
The research leading to these results has received
funding from the Ministry of Education and Science
under the National Science program
INTELLIGENT ANIMAL HUSBANDRY, grant
agreement N Д01- 62/18.03.2021).
Conflict of Interest
The authors have no conflicts of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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WSEAS TRANSACTIONS on BUSINESS and ECONOMICS
DOI: 10.37394/23207.2024.21.116
Kristina Pavlova, Elisaveta Trichkova-Kashamova,
Stanislav Dimitrov
E-ISSN: 2224-2899
1427
Volume 21, 2024