Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну
This paper examines the effectiveness of grouping agricultural enterprises according to the wheat harvested area and assesses their profitability. We have developed linear and non-linear regression equations to predict the income for said groups of enterprises. The methodology is designed for cases...
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| author | Tsesliv, Olga Dunaieva, Tamara Yereshko, Julia Tsesliv, Oleksandr |
| author_facet | Tsesliv, Olga Dunaieva, Tamara Yereshko, Julia Tsesliv, Oleksandr |
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| description | This paper examines the effectiveness of grouping agricultural enterprises according to the wheat harvested area and assesses their profitability. We have developed linear and non-linear regression equations to predict the income for said groups of enterprises. The methodology is designed for cases when future market prices are probabilistic in nature. With the help of the developed methodology, it is possible to calculate the necessary production volumes in the conditions of price fluctuations. We have used the Goldfeld–Quandt parametric test to test the model for heteroscedasticity. Calculations show that agricultural holdings are indeed inefficient, and preference should be given to enterprises with medium crop areas. Application of the Lagrange multipliers method when solving the problem of agricultural enterprise optimization makes it possible to increase profitability. The case of price risk, when future market prices are not deterministic, is considered. Therefore, it is necessary to be guided by two criteria when making managerial decisions: to maximize the expected total net income and to minimize the variance of the total net income. |
| doi_str_mv | 10.20535/SRIT.2308-8893.2024.1.05 |
| first_indexed | 2025-07-17T10:28:30Z |
| format | Article |
| fulltext |
Olga Tsesliv, Tamara Dunaieva, Julia Yereshko, Oleksandr Tsesliv, 2024
62 ISSN 1681–6048 System Research & Information Technologies, 2024, № 1
UDC 338.3:330.46
DOI: 10.20535/SRIT.2308-8893.2024.1.05
STUDY ON THE PROFITABILITY OF AGRICULTURAL
ENTERPRISES IN UKRAINE DURING
THE RUSSIAN MILITARY INVASION OF UKRAINE
OLGA TSESLIV, TAMARA DUNAIEVA, JULIA YERESHKO,
OLEKSANDR TSESLIV
Abstract. This paper examines the effectiveness of grouping agricultural enterprises
according to the wheat harvested area and assesses their profitability. We have de-
veloped linear and non-linear regression equations to predict the income for said
groups of enterprises. The methodology is designed for cases when future market
prices are probabilistic in nature. With the help of the developed methodology, it is
possible to calculate the necessary production volumes in the conditions of price
fluctuations. We have used the Goldfeld–Quandt parametric test to test the model
for heteroscedasticity. Calculations show that agricultural holdings are indeed ineffi-
cient, and preference should be given to enterprises with medium crop areas. Appli-
cation of the Lagrange multipliers method when solving the problem of agricultural
enterprise optimization makes it possible to increase profitability. The case of price
risk, when future market prices are not deterministic, is considered. Therefore, it is
necessary to be guided by two criteria when making managerial decisions: to
maximize the expected total net income and to minimize the variance of the total net
income.
Keywords: economic and mathematical models, heteroscedasticity, models of re-
gression analysis, profitability, income, linear regression, nonlinear model, full-scale
russian invasion of Ukraine.
INTRODUCTION
Wheat is one of the most important crops for food security worldwide. Growing
wheat is also a source of income for the considerable part of Ukraine's population.
Among the agricultural crops in Ukraine, wheat occupies more than half of the
sown area. In the recent years, nation had entered the top ten major grain produc-
ing countries and became one of the world's leading exporters of wheat (Fig. 1).
Moreover, wheat exports to Africa, Southeast Asia and the Western Hemisphere
Fig. 1. Leading 10 wheat producers worldwide in 2022/2023 (in 1.000 metric tons)
17.25
21.00
26.40
30.14
33.82
38.00
44.90
92.00
134.70
137.72
103.00
Study on the profitability of agricultural enterprises in Ukraine during the russian military …
Системні дослідження та інформаційні технології, 2024, № 1 63
were expected to increase in 2022/23. Unfortunately, as of May 2023 we can ob-
serve negative effects of war, as the economy exported 41.6 million tons of grain
in the 2022/23 July-June season within the Black Sea Grain Initiative and overall
wheat export is predicted to peak at 24 million tons. The negative trend is ex-
pected to remain, as the prognosis for the following period 2023/24 season stands
at the threshold of 26 million tons [1].
A starting point in understanding the Ukraine's grain producing sector, is to
analyse and group the enterprises by the size of the harvested area of wheat in
2021. In percentage terms, the ratio is as follows (Fig. 2). There are 24.016 wheat-
growing enterprises in total. 61.6% of those are the small enterprises, in particular
those with harvesting area of up to 100 hectares. Their aggregated volume of pro-
duction is 1.986 thousand tons of wheat, which is accordingly 7.7% of the total
volume of wheat production in Ukraine. There are only 123 enterprises with a
total area of more than 3.000 hectares, the volume of production of which is
2.852.1 thousand tons, which is 11.1% of the total volume of wheat production in
Ukraine [2].
The yield of wheat differs according to various groups of enterprises as fol-
lows: enterprises with an area of up to 100 hectares have a yield of 35 t/ha; with
an area of 500-1000, respectively, 50.3 cwt/ha; for the enterprises with more than
3.000 hectares, the yield is 65.4 t/ha. By volume of harvested wheat to the total
volume of production: enterprises with an area of up to 100 hectares collect 4%;
with an area of 500–1000, respectively, 16.6%; with an area of 1000–2000 —
23.4%; with an area of 2.000–3.000 hectares — 12.8%; enterprises over 3.000
hectares 28.7%.
And the trend of overall wheat production in the previous years shows con-
stant increase up to 2022 (Fig. 3), that is explained by the consequences of russian
aggression towards Ukraine. As war escalates, under constant bombing and shell-
ing, Ukrainian farmers are not able to harvest the grain. Moreover, fields, that are
Fig. 2. Enterprises grouping by wheat production (thousand tons) in 2021
Source: Source: authors’ elaboration on the data of [3]
1
2
3
4
6
5
7 1 < 100
2 — 100–200
3 — 200–500
4 — 500–1000
5 — 1000–2000
6 — 2000–3000
7 — >3000
Olga Tsesliv, Tamara Dunaieva, Julia Yereshko, Oleksandr Tsesliv
ISSN 1681–6048 System Research & Information Technologies, 2024, № 1 64
under russian occurrence are unreachable and inaccessible to them. Those crops,
if being harvested at all, are at the disposal of occupiers and are being expropri-
ated by them and consequently, their sale bypasses Ukraine. Hence, 25687.2
thousand tons of wheat were grown in 2021. Ukrainians consume about 20% of
local wheat — the rest is being exported. That is, 20549.76 thousand tons were
exported in 2021.
Due to the use of the higher-quality seeds, modern technology and plant pro-
tection products, this year, despite the war, farmers managed to harvest a quite
proficient harvest. All this contributed to the increase in productivity. For the first
time in 20 years, there was no drought in Ukraine. The best harvesting results
were observed in Vinnytsia (6.7 million tons), Chernihiv (6.2 million tons), and
Poltava regions (5.7 million tons) [4]. According to the groupings of agricultural
producers, the productivity is as follows (Fig. 4): with an area of 100 hectares, as
a percentage of the total 61%: with an area of 200–-500 hectares — 11.8%; with
an area of 500–1000, respectively, 8%; with an area of 1000-2000 — 5.5%; with
an area of 2.000–3.000 hectares — 1.6%; enterprises over 3.000 hectares 1.5%.
Compared to 2019, in 2022 the overall grain production trends were as fol-
lows: soybean harvest increased by 17; 40 million tons of corn were harvested; as
well as 32 million tons of wheat (8 million tons more than in 2020). According to
the press service of the Ukrainian Grain Association, the record wheat harvest
guarantees the country's food security. By the end of 2021, all countries of the
0
5
10
15
20
25
30
35
40
2017 2018 2019 2020 2021 2022
Fig. 3. Wheat harvest in Ukraine in previous years (1 cwt per hectare)
Source: authors’ elaboration on the data of [4]
Fig. 4. Enterprises grouping by wheat yield in 2021 (centner per 1 hectare)
Source: authors’ elaboration on the data of [3]
Study on the profitability of agricultural enterprises in Ukraine during the russian military …
Системні дослідження та інформаційні технології, 2024, № 1 65
world expect to harvest a record 2.8 billion tons of grain, the Food and Agricul-
ture Organization of the United Nations (FAO) predicts.
Exports of agricultural products, during the 11 months of the war, amounted
to $24.4 billion, which is 22% higher than last year, according to customs data.
Ukrainian products are mostly imported to China, India and the Netherlands [5].
For 2023, the National Bank of Ukraine (NBU) forecasts consumer inflation (De-
cember to December) at the level of 18.7% and an average annual rate of 20.3%
(in 2022 it was 26.6% and 20.2%, respectively, in 2021 — 10% and 9.4%, in
2020 — 5% and 2.7%). According to NBU estimates, the average unemployment
rate in Ukraine in 2023 will be 26.1% (in 2022 — 25.8%, in 2021 — 9.8%, in
2020 — 9.5%) [21].
Optimizing in the war time
Theoretical issues of land management were developed in the works of: Schluter G.
[6, p. 747], who investigated the extent to which an increase in the minimum
wage will affect food prices; Sánchez-Fung J. [7], who deals with the stability of
macro indicators; Rasool H. [11, p. 87], who investigates the relationship between
rural wages and food inflation; Dorward A. [12, p. 633], food safety specialist; as
well as Sitikantha P. [10, p. 244], Bhattacharya R. [11, p.144], Duckett T. [12, p. 17],
Bhattacharya R. [13, p. 146], Nwaolisa E. [17] Wilson I. [19, p. 505].
During the war, the problem of optimizing the groupings of agricultural pro-
ducers is relevant both at the micro and macro level, since a significant part of the
Ukraine's agrarian-industrial complex products is being exported. Moreover, agri-
cultural production is characterized by seasonality. The manufacturer periodically
builds up stocks of products that will possibly be sold in the future. The proposed
methodology is designed for cases when the price of agricultural products is
probabilistic. Its use will allow the owner of agricultural products to protect his
economic interests to the maximum possible extent. The methods of substantiat-
ing management decisions in conditions of risk and uncertainty are to be consid-
ered from Taha H. [10].
Literature review. Models and metods
Since enterprises growing wheat have different areas, and accordingly will re-
ceive different income, it is advisable to monitor their income. To do this, to be-
gin with, the variance of the residuals for different groups of enterprises was cal-
culated, that is, it was checked for heteroskedasticity. The presence of
heteroskedasticity causes a violation of the properties of model parameter esti-
mates when calculating them using the least squares method. Therefore, it is al-
ways necessary to study this phenomenon and, if it exists, to use the generalized
least squares method (Aitken's method) to estimate the model parameters. Here, in
order to determine heteroskedasticity we have used the Goldfeld–Quandt para-
metric test.
In an econometric model that characterizes the dependence of consumption
costs on income, the variance of the residuals may change for observations that
belong to different groups of the population in terms of income.
We have developed a technique for optimizing the grouping of enterprises in
wartime. Since future market prices are not deterministic, the decision must be
guided by two criteria: maximizing expected total net income and minimizing the
Olga Tsesliv, Tamara Dunaieva, Julia Yereshko, Oleksandr Tsesliv
ISSN 1681–6048 System Research & Information Technologies, 2024, № 1 66
variance of total net income. The Lagrange method is used to solve the problem.
The developed methodology is designed for the probabilistic nature of future
market prices for products and makes it possible to take inflation into account.
Let us consider the method for optimizing the grouping of enterprises in the
conditions of the price risk and price fluctuations, in a wartime. Since future mar-
ket prices are not deterministic, the decision must be guided by two criteria:
maximizing expected total net income and minimizing the variance of total net
income. To solve the problem, the Lagrange method is used. Hence, using the
Lagrange method we develop a methodise, designed for cases when the price of
agricultural products has a probabilistic nature. We find the maximum income
according to formula:
ttt
T
t
xcpz )(
1
max
,
maxz — maximum income; tp — the sale price of a product unit at the given
time t ; tc — costs associated with the storage of a product unit until the time
Tt ,1 ; min)(z — minimal dispersion; a — production volume; T — planning
period. Then the maximum dispersion is:
*
max)( taz .
Next, the worst values of the criterion indicators are calculated for a set of
effective variants of the calendar plan:
21
min
1
)(
t
T
t
a
z
;
21
21
min
)(
1 t
ttT
t
t
T
t
cpa
z
.
Therefore, we calculate the range of variation of criterion indicators:
max0minmax0min )(; zzzz .
The optimal calendar plan for the sale of stocks of agricultural products is
determined. This plan ),,( **
1
*
Txxx is computed by solving the convex pro-
gramming problem, given that s — development criteria:
maxs ,
)()( 0max0
1
zzszxcp ttt
T
t
, (1)
),)(( min
22
0
2
0
22
1
zsxtt
t
(2)
,
1
axt
T
t
(3)
Study on the profitability of agricultural enterprises in Ukraine during the russian military …
Системні дослідження та інформаційні технології, 2024, № 1 67
Ttxt ,1 ,0 . (4)
It should be added that the optimal value *s will show whether the accept-
able levels of criterion indicators chosen by the product owner were true (at
0* s ) or not (at 0* s ).
MODELLING THE DEPENDENCE OF ENTERPRISE’S INCOME ON THE
HARVESTED AREA
To build this model, the original data set, which includes 7 observations, is used.
These data and calculations based on them are given in Table 1. Based on the na-
ture of the relationship between the value of income of enterprises from the har-
vested area, it can be assumed that the variance of the residuals is not constant for
each observation, that is, there may be a phenomenon of heteroscedasticity.
Therefore, in order to choose the right method for estimating the parameters of
the model, it is necessary to check whether heteroscedasticity is inherent in the
given input data.
T a b l e 1 . Input data and calculations
Enterprises
by area,
thousand hectares
Volume of
production, thou-
sand centners
Crop yields,
1 centner
per hectare
Production
expenditures
per ton, UAH
Price per ton
including
costs, UAH
Revenue,
UAH
billion
up to 100.00 1986.0 39.2 630.37 7299.63 14.498
10001–200.00 2010.3 45.2 539.21 7390.78 14.858
200.01–500.00 5044.5 47.0 508.08 7421.92 37.44
500.01–1000.00 6000.8 48.3 494.38 7435.62 44.62
1000.01–2000.00 5806.4 49.4 493.27 7436.73 43.181
2000.01–3000.00 1987.1 49.6 489.98 7440.02 14.785
over 3000.00 2852.1 49.1 464.14 7465.86 21.294
Source: authors’ developments.
We consider the price per ton of wheat to be a constant value, which in 2022
is 7930 UAH. The cost of wheat production on one hectare of land is 22 thousand
UAH. Thus, with an average yield of 5.37 centners/hectare (Fig. 4), the cost of 1
ton of production will be equal to 4.1 thousand UAH. At the selling price of
wheat of 7931 UAH/ton, the profit per hectare will be 20 thousand UAH, which
will provide the profitability of more than 90%. Data for the Goldfeld–Quandt
parametric test is given in Table 2.
T a b l e 2 . Data for the Goldfeld–Quandt parametric test
Y X 2X XY Ŷ )ˆ( YY 2)ˆ( YY
14.498 1986 3944196 28793.028 13.57 0.928 0.861184
14.858 2010.3 4041306.09 29869.0374 13.74 1.118 1.249924
37.44 5044.5 25446980.25 188866.081 34.98 2.46 6.0516
44.62 6000.8 36009600.64 267755.696 41.67 2.95 8.7025
43.181 5806.4 33714280.96 250726.158 40.31 2.871 8.242641
14.785 1987.1 3948566.41 29379.2735 13.57 1.215 1.476225
21.294 2852.1 8134474.41 60732.6174 19.63 1.664 2.768896
Source: authors’ developments.
Olga Tsesliv, Tamara Dunaieva, Julia Yereshko, Oleksandr Tsesliv
ISSN 1681–6048 System Research & Information Technologies, 2024, № 1 68
Identification of variables: ),,( uXfY where Y — dependent variable
(revenue); X — independent variable (area size); u — stochastic component.
Model specification:
uXaaY 10 , XaaY 10 ˆˆˆ , YYu ˆ .
Using the Goldfeld–Kwandt algorithm, we determine the presence of hetero-
scedasticity. We find C observations that are in the middle of the population:
,
15
4
n
C
,
15
4
7
C
15
7*4
C , 2C .
Then 31 n , 32 n = 3. Let us estimate an econometric model for the popu-
lation 1n = 3. Let us quantitatively estimate the model parameters based on OLS:
.ˆˆ
,ˆˆ
2
10
10
xyxaxa
yxaan
For each model we find the sum of squares of residuals:
2
11111 )ˆ( YYuuS , 2
22222 )ˆ( YYuuS , 1S =8.16, 2S =12.48.
Finding the criterion R:
2
1
S
S
R ;
16.8
48.12
R =1,53; 67.6tabl F .
Because of tablFR grouping of enterprises by the size of the harvested
area of wheat heteroscedasticity is absent. If there is no heteroscedasticity, the
least squares method can be applied (Table 2).
Calculating the coefficients of linear pairwise correlation ( xyr ) and determi-
nation ( 2
1R ): 22
1 xyrR 9999.012 .
Most often, in case of a system of linear equations, the linear method of least
squares is used. For our case, the linear regression formula is obtained: Y
1215.0*0075.0 3 C . Using this formula, we can calculate exactly how much
the income will increase when the area increases by one unit. In our case, an increase
in area by 1 hectare will lead to an increase in income by 0.13 (billion UAH).
Further on, we find and analyse the power regression equation baxy ˆ , for
the inputs ix and iy from Table 3.
Source: authors’ developments.
T a b l e 3 . Additional values for calculating linear pairwise correlation ( xyr )
and determination ( 2
1R ) coefficient
i ix iy iŷ ))(( xxi 2))(( xxi i 2
i iA c 2
i
1 1986 14.4 14.6 -1683.5 2834364.65 -0.188 0.035 0.001 – –
2 2010.3 14.8 14.8 -1659.5 2754129.91 -0.007 0.0001 0.0005 0.1811 0.0328
3 5044.5 37.4 37.4 1374.9 1890467.86 -0.051 0.002 0.0014 0.0438 0.0019
4 6000.8 44.6 44.6 2331.2 5434693.25 -0.001 0 0 0.0496 0.0025
5 5806.4 43.1 43.1 2136.8 4566097.39 0.008 0.0001 0.0002 0.0105 0.0001
6 1987.1 14.7 14.6 -1682.4 280662.037 0.090 0.008 0.006 0.081 0.006
7 2852.1 21.2 21.1 -817.45 668236.18 – 0.068 0.028 – 0.047
Study on the profitability of agricultural enterprises in Ukraine during the russian military …
Системні дослідження та інформаційні технології, 2024, № 1 69
Assessing the significance of regression and correlation parameters
In order to estimate the significance of regression and correlation parameters,
let's find the average value ;56,3669x make a table of additional values,
where iii yy ˆ ; iii yy ˆ ;
i
ii
i y
yy
A
ˆ
.
The average approximation error (Table 3):
%43,0%100
)ˆ(
А
iy
ii yy
n ,
F — Fisher criteria 67.6tabl F .
As it is known, the least squares method is a method of finding an approxi-
mate solution of an over determined system, which is used in regression analysis.
The most commonly linear least squares method is used in the case of a system of
linear equations. For our case, we obtain the linear regression formula:
1215.03*0075.0 CY .
Using this formula, we can calculate, how much income will increase with
an increase in area per unit. In our case, an increase in area by 1 hectare will lead
to an increase in income by 0.13(billion UAH).
Let us find and analyse the power regression equation baxy ˆ , for data ix
and iy for Table 4.
T a b l e 4 . Auxiliary variables for calculating power regression
i ix iy ln ix 2ln ix ln iy ln lni ix y
1 1986 14.498 7.59 57.66 2.67 20.30
2 2010.3 14.858 7.61 57.84 2.69 20.52
3 5044.5 37.44 8.52 72.69 3.62 30.88
4 6000.8 44.62 8.69 75.68 3.79 33.04
5 5806.4 43.181 8.66 75.11 3.76 32.63
6 1987.1 14.785 7.59 57.67 2.69 20.45
7 2852.1 21.294 7.95 63.29 3.06 24.33
Total 25686.9 190.676 56.64 459.97 22.31 182.18
Source: authors’ developments.
Let us calculate the coefficients a and b of the power regression equation by
the known formulas:
)ln(ln
ln*ln)ln*(ln
2
ii
iiii
xxn
yxyxn
b
= 0064.1
64.5697.459*7
31.22*64.5619.182*7
2
;
007.064.55*7
0064.1
31.22*7
1
explnln
1
exp
ii x
n
b
y
n
a .
Nonlinear regression equation 0064,1
*007,0ˆ xY .
Let’s compare the calculations with linear and power regression.
The data obtained by calculations, linear and power regression actually
match. Thus, both relational models can be used.
Olga Tsesliv, Tamara Dunaieva, Julia Yereshko, Oleksandr Tsesliv
ISSN 1681–6048 System Research & Information Technologies, 2024, № 1 70
Needless to say, that in order to create economic and mathematical models
of agricultural enterprises in market conditions, it is necessary to take into account
all factors, in particular: land resources, labour resources, fixed assets, movable
assets, financial resources, information resources, costs. The combination of these
indicators makes it possible to forecast income more accurately.
Thus, the study showed the efficiency of different groups of enterprises in
terms of the size of the harvested area of wheat. Their profitability and income
were estimated. Since the best results are obtained from enterprises with a har-
vested area of 200-2000 thousand hectares (Fig. 5), this is obviously the best op-
tion for an agricultural enterprise. Nevertheless, in a war time and respective post-
war times, small businesses become more manoeuvrable and accessible to the
population.
General problem of conditional optimization
The general problem of conditional optimization with equality constraints is re-
duced to the problem of unconditional minimization using the Lagrange function,
which is written in the form:
),,,,,,(),,,,,,,,( 7654321217654321 xxxxxxxfxxxxxxxF
)),,,,,,((),,,,,,(( max7654321276543211 zxxxxxxxuxxxxxxxu .
An indispensable condition for using expression (2) is that the number of re-
strictions must be less than the number of variables. If this condition is not ful-
filled, then there is no optimization problem, since the number of connections of
variables exceeds their number. Thus, the task is reduced to finding the minimum
of the function:
0
)(
j
kj
x
xF
.
A necessary condition for the minimum of the function (2) is the equality of
its gradient to zero, which leads to the system of equations. Consequently, solving
this system of equations leads to finding unknown quantities.
Fig. 5. Comparison of linear and power regression calculations
Source: authors’ developments
Study on the profitability of agricultural enterprises in Ukraine during the russian military …
Системні дослідження та інформаційні технології, 2024, № 1 71
The calculations were conducted in MathCAD, using the following formula
for the maximum income:
),,,,,,,,( 217654321 xxxxxxx
7228006273352566425163280224901212800
variance:
),,,,,,( 7654321 xxxxxxxu
77465674406744057436374352742117290 .
Solving the equation, we obtain optimal production volumes. We observe
that there has been a shift towards enterprises with an area of 200.01-500.00 thou-
sand hectares. Taking into account inflation, in order to achieve the received in-
come, it is necessary to obtain the production volumes indicated in Table 5.
T a b l e 5 . Auxiliary variables for calculating power regression
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fl
at
io
n
1 up to 100.00 1986.0 7299.63 14.498 –113.3 12838.5 920 1220
2 100.01–200.00 2010.3 7390.78 14.858 –22.2 490.9 2550 560
3 200.01–500.00 5044.5 7421.92 37.44 8.9 80.7 16500 18000
4 500.01–1000.00 6000.8 7435.62 44.62 22.7 514.5 2530 2800
5 1000.01–2000.00 5806.4 7436.73 43.181 23.8 566.1 1940 1940
6 2000.01–3000.00 1987.1 7440.02 14.785 27.1 733.5 1200 630
7 over 3000.00 2852.1 7465.86 21.294 52.9 2800.8 0.18 1
Source: authors’ developments
Solving the system of partial differential equations (1)–(4), we obtain the op-
timal volumes of production, with the inflation taken into account. The results are
shown in Table 5. We observe that there has been a shift towards enterprises with
an area of 200.01–500.00 thousand hectares. That is, enterprises with an area of
200.000–500.000 hectares are the most efficient and provide the optimal volumes
of wheat production. It becomes clear, considering this study, that enterprises
with an area of 200–500, 500–1000 and 1000–2000 thousand hectares are effi-
cient and competitive, and it is most expedient to develop precisely them.
The problem was solved using the Lagrange method and the probabilistic na-
ture of prices was taken into account. The developed technique makes it possible
to calculate the necessary volumes of production during the period of inflation.
Calculations show that agricultural holdings are not efficient, and preference
should be given to enterprises with average cultivated areas.
CONCLUSIONS
In a current paper we study the grouping of enterprises by the size of the har-
vested area of wheat. Wheat is one of the most important sources of income for
Olga Tsesliv, Tamara Dunaieva, Julia Yereshko, Oleksandr Tsesliv
ISSN 1681–6048 System Research & Information Technologies, 2024, № 1 72
the part of the population of Ukraine. Over the years, Ukraine has had agricultural
land (71%), 78% of which is arable land; 97.2% of agricultural land is systemati-
cally used for economic purposes.
Here we examine the income of various groups of enterprises, using The
Goldfeld–Quandt parametric test in order to determine heteroskedasticity.
Our analysis of yield, volume of production, income of each group of enter-
prises showed, that the best results are obtained by enterprises with an area of
200–2000 thousand hectares. Meaning, that large agricultural holdings proved to
be not efficient, and preference should be given to enterprises with medium areas.
Since the phenomenon of heteroscedasticity was not detected, a linear re-
gression formula was constructed using the method of least squares. For compari-
son, a power-law regression equation was found and analysed.
In order to predict the income for each group of enterprises by the size of the
harvested area of wheat, we have developed equations of linear and nonlinear re-
gression.
The scientific novelty of the work is that the equations of linear and nonlin-
ear regression were developed to predict the income of each group of enterprises
by the size of the harvested area of wheat. Application of the Lagrange method
multipliers when solving the problem of optimization of agricultural enterprises
makes it possible to increase profitability.
Developed models can be used to analyse the income and profitability of ag-
ricultural producers.
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Received 04.07.2023
INFORMATION ON THE ARTICLE
Olga V. Tsesliv, ORCID: 0000-0002-8190-2502, National Technical University
of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail:
ceslivolga@gmail.com
Olga Tsesliv, Tamara Dunaieva, Julia Yereshko, Oleksandr Tsesliv
ISSN 1681–6048 System Research & Information Technologies, 2024, № 1 74
Tamara A. Dunaieva, ORCID: 0000-0001-8104-7836, National Technical University of
Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine, e-mail: dunaeva.toma
@gmail.com
Julia O. Yereshko, ORCID: 0000-0002-9161-8820, National Technical University of
Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine and Technical University of
Munich, Germany, e-mail: julia.jereshko@gmail.com
Oleksandr S. Tsesliv, ORCID: 0000-0002-8602-4673, Taras Shevchenko National Uni-
versity of Kyiv, Ukraine, e-mail: atsesliv@gmail.com
ДОСЛІДЖЕННЯ РЕНТАБЕЛЬНОСТІ СІЛЬСЬКОГОСПОДАРСЬКИХ
ПІДПРИЄМСТВ В УКРАЇНІ ПІД ЧАС ВІЙСЬКОВОГО ВТОРГНЕННЯ
РОСІЇ В УКРАЇНУ / O.В. Цеслів, Т.А. Дунаєва, Ю.О. Єрешко, О.С. Цеслів
Анотація. Досліджено ефективність групування сільськогосподарських під-
приємств за розміром збиральної площі пшениці та дано оцінку їх прибутко-
вості. Розроблено лінійні та нелінійні рівняння регресії для прогнозування до-
ходу для зазначених груп підприємств. Методику розроблено для випадків,
коли майбутні ринкові ціни мають імовірнісний характер. За допомогою
розробленої методики можна розрахувати необхідні обсяги виробництва
в умовах коливання цін. Використано параметричний тест Голдфельда–
Квандта для перевірки моделі на гетероскедастичність. Розрахунки показують,
що агрохолдинги насправді неефективні, і перевагу слід віддавати
підприємствам із середніми посівними площами. Застосування методу множників
Лагранжа для вирішення завдання оптимізації сільськогосподарських
підприємств дає змогу підвищити рентабельність. Розглянуто випадок
цінового ризику, коли майбутні ринкові ціни не є детермінованими. Тому під
час прийняття управлінських рішень необхідно керуватися двома цілями-
критеріями: максимізувати очікуваний сукупний чистий дохід і мінімізувати
дисперсію сукупного чистого доходу.
Ключові слова: економіко-математичні моделі, гетероскедастичність, моделі
регресійного аналізу, прибутковість, дохід, лінійна регресія, нелінійна модель,
повномасштабне вторгнення росії в Україну.
|
| id | journaliasakpiua-article-304340 |
| institution | System research and information technologies |
| keywords_txt_mv | keywords |
| language | English |
| last_indexed | 2025-07-17T10:28:30Z |
| publishDate | 2024 |
| publisher | The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" |
| record_format | ojs |
| resource_txt_mv | journaliasakpiua/93/a6e81532cb2b945570efdab4d9f0e293.pdf |
| spelling | journaliasakpiua-article-3043402024-05-23T07:09:36Z Study on the profitability of agricultural enterprises in Ukraine during the russian military invasion of Ukraine Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну Tsesliv, Olga Dunaieva, Tamara Yereshko, Julia Tsesliv, Oleksandr economic and mathematical models heteroscedasticity models of regression analysis profitability income linear regression nonlinear model full-scale russian invasion of Ukraine економіко-математичні моделі гетероскедастичність моделі регресійного аналізу прибутковість дохід лінійна регресія нелінійна модель повномасштабне вторгнення росії в Україну This paper examines the effectiveness of grouping agricultural enterprises according to the wheat harvested area and assesses their profitability. We have developed linear and non-linear regression equations to predict the income for said groups of enterprises. The methodology is designed for cases when future market prices are probabilistic in nature. With the help of the developed methodology, it is possible to calculate the necessary production volumes in the conditions of price fluctuations. We have used the Goldfeld–Quandt parametric test to test the model for heteroscedasticity. Calculations show that agricultural holdings are indeed inefficient, and preference should be given to enterprises with medium crop areas. Application of the Lagrange multipliers method when solving the problem of agricultural enterprise optimization makes it possible to increase profitability. The case of price risk, when future market prices are not deterministic, is considered. Therefore, it is necessary to be guided by two criteria when making managerial decisions: to maximize the expected total net income and to minimize the variance of the total net income. Досліджено ефективність групування сільськогосподарських підприємств за розміром збиральної площі пшениці та дано оцінку їх прибутковості. Розроблено лінійні та нелінійні рівняння регресії для прогнозування доходу для зазначених груп підприємств. Методику розроблено для випадків, коли майбутні ринкові ціни мають імовірнісний характер. За допомогою розробленої методики можна розрахувати необхідні обсяги виробництва в умовах коливання цін. Використано параметричний тест Голдфельда–Квандта для перевірки моделі на гетероскедастичність. Розрахунки показують, що агрохолдинги насправді неефективні, і перевагу слід віддавати підприємствам із середніми посівними площами. Застосування методу множників Лагранжа для вирішення завдання оптимізації сільськогосподарських підприємств дає змогу підвищити рентабельність. Розглянуто випадок цінового ризику, коли майбутні ринкові ціни не є детермінованими. Тому під час прийняття управлінських рішень необхідно керуватися двома цілями-критеріями: максимізувати очікуваний сукупний чистий дохід і мінімізувати дисперсію сукупного чистого доходу. The National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute" 2024-03-29 Article Article application/pdf https://journal.iasa.kpi.ua/article/view/304340 10.20535/SRIT.2308-8893.2024.1.05 System research and information technologies; No. 1 (2024); 62-74 Системные исследования и информационные технологии; № 1 (2024); 62-74 Системні дослідження та інформаційні технології; № 1 (2024); 62-74 2308-8893 1681-6048 en https://journal.iasa.kpi.ua/article/view/304340/296329 |
| spellingShingle | економіко-математичні моделі гетероскедастичність моделі регресійного аналізу прибутковість дохід лінійна регресія нелінійна модель повномасштабне вторгнення росії в Україну Tsesliv, Olga Dunaieva, Tamara Yereshko, Julia Tsesliv, Oleksandr Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну |
| title | Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну |
| title_alt | Study on the profitability of agricultural enterprises in Ukraine during the russian military invasion of Ukraine |
| title_full | Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну |
| title_fullStr | Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну |
| title_full_unstemmed | Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну |
| title_short | Дослідження рентабельності сільськогосподарських підприємств в Україні під час військового вторгнення росії в Україну |
| title_sort | дослідження рентабельності сільськогосподарських підприємств в україні під час військового вторгнення росії в україну |
| topic | економіко-математичні моделі гетероскедастичність моделі регресійного аналізу прибутковість дохід лінійна регресія нелінійна модель повномасштабне вторгнення росії в Україну |
| topic_facet | economic and mathematical models heteroscedasticity models of regression analysis profitability income linear regression nonlinear model full-scale russian invasion of Ukraine економіко-математичні моделі гетероскедастичність моделі регресійного аналізу прибутковість дохід лінійна регресія нелінійна модель повномасштабне вторгнення росії в Україну |
| url | https://journal.iasa.kpi.ua/article/view/304340 |
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