Production Capacities of Russian Agricultural Organizations: Assessment and Forecast

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Production Capacities of Russian Agricultural

Organizations: Assessment and Forecast

Rafik M. Sagatgareev , Evgenii N. Mazhara , Elena A. Fomina , Oleg A. Zykov , Andrei N.

Chernov⁵

¹Candidate of Economics, Banking and Finance Department, e-mail: rafik-sagatgareev@yandex.ru

Financial University under the Government of the Russian Federation, (Ufa branch),

Candidate of Economics, Banking and Finance Department, Financial University under the Government of the Russian Federation

(

Ufa branch), e-mail: maschara102@mail.ru

Candidate of Economics, Banking and Finance Department, Financial University under the Government of the Russian Federation

Ufa branch), e-mail: kaffba@list.ru

(

Candidate of Economics, Department of Economics, Management, and Marketing, Financial University under the Government of

the Russian Federation (Ufa branch), e-mail: OLEG-Zykov@mail.ru

Candidate of Economics, Department of Economics, Management, and Marketing, Financial University under the Government of

the Russian Federation (Ufa branch), e-mail: dru175@rambler.ru Financial University under the Government of the Russian

Federation (Ufa branch), 69/1, M. Karima St., Ufa 450015, Republic of Bashkortostan, Russian Federation

Received: 05/04/2019

Accepted: 22/08/2019

Published: 29/08/2019

Abstract

Russia currentlyleads in individual branches of the industry(in particular,in 2016, it was the world's third-largestwheat producer).

However, in essential food products (like meat, meat products, milk, dairy products, or fruits) Russia fails to meet even threshold

requirementsaccording to the Russian Federation Food Security Doctrine, and their production levels are lower than that of the USSR.

Anti-Russia sanctions restricting imports of agricultural products into Russia make things worse and pose a certain threat to national

food security. This article reviews the body of literature on the topic, refines the key factors of intensification of production growth of

agricultural products in Russia, develops an economic and mathematical model for assessment and making predictions of production

capacity (the monetary volume of agricultural output) of agricultural organizations (the core category of agricultural producers) in the

Russian Federation. A correlation and regression analysis revealed that the resultant indicator is formed mainly by two factors: (1)

productivity of grains and grain legumes, and (2) the average monthly nominal job compensation at agricultural organizations. Factor

(

2) has a much greaterimpact on the output of agriculturalorganizations in Russia. If the tendency of the factors' changing is maintained

in 2018–2021, in the medium term horizon, they are expected to grow. And this, in turn, should increase the resultantindicator. Despite

the optimistic forecasts, Russian agricultural producers still have significant potential of increasing agricultural production output. It

should be noted that agricultural economic growth in Russia is impossible without solving social problems.

Keywords: Economic and mathematical model, correlation and regression analysis, agriculturalproduce, agricultural organizations of

Russia, production capacity, assessment, growth factors, forecast, trend extrapolation, multiple regression.

according to A. G. Aganbegyan, a member of the Academy of

Introduction

Sciences, Russian agricultural organizations cannot fully meet

the needs of the Russian population in milk, beef, oil, and

fruits. At the same time, the actual current self-sufficiency in

milk, dairy products, meat, and meat products in Russia is

significantly below not only the threshold requirements set by

the RussianFederation Food Security Doctrine, but also below

that of the USSR (1). Therefore, the issue of food security in

Russia still remains pressing.

In 2016, wheat production in Russia had grown to record-

breaking 73.3 million tonnes (the year-to-year increase of

8.6%). That year, Russia was ahead of the US and ranked 3rd

in the world after China and India (18). However, research

performed by leading Russian scholars revealed that Russian

agricultural producers currently do not fully meet public

demand for certain essential food products (21). For example,

Corresponding author: Rafik M. Sagatgareev, Candidate of Economics, Banking and Finance Department, E-mail: rafik-

sagatgareev@yandex.ru.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

Figure 1: Structure of agricultural produce by categories of entities in the Russian Federation.

The situation (the solution to the problem of ensuring the

country's food security) is exacerbatedby the fact that the USA

and EU member states initiatedand imposed sanctions against

Russia restricting the import of foreign agricultural products

into Russia (5). Therefore, the Russian authorities face the

challenge of increasing the volume of agricultural output,

primarily by agricultural organizations. The structure of

agriculturalproduce by categories of entitiesin Russia (Fig. 1)

shows that, since 2011, it is mainly produced by agricultural

organizations. At the same time, over the past three years,

agriculturalorganizations have been producing more than 50%

of the total agricultural output in the Russian Federation. The

steady growth trend in the share of agricultural organizations

in the agricultural output in Russia in 2013–2017 is also worth

pointing out. This trend was due to the outperforming growth

of agricultural output produced by Russian agricultural

organizations as compared with the other categories of entities.

In our opinion, the faster growth of agricultural output

produced by Russian agriculturalorganizations was a response

to external pressure on the Russian agricultural sector (in the

form of restricted import of foreign agricultural products from

the EU into Russia).

economic and mathematical model, we reviewed the body of

literature on the topic.

Literature review; materials and methods

Despite the Doctrine establishes the official definition of

national food security, there is no generally accepted

interpretation of this concept even among the leading Russian

researches. Therefore, we will begin the literature review by

clarifying the concept of national food security. Our view on

this matter is closest to the opinion of Ya. Sh. Pappe,

N. S. Antonenko, and D. A. Polzikov, who believe, from the

economic standpoint, that the concept of food security “is

equivalent to physical and economic availability of food for

people and should not a priori include other conditions” (15).

Developing the idea, the authors made a number of rightful

important conclusions:

. The need for food independence (or self-sufficiency)

requires justification, whatever definition is used.

. The successful development of Russian internal

agricultural production is not always the necessary and

sufficient condition for ensuring the physical and economic

availability of food.

In view of the above, the purpose of the study was to

addressthe increasinglyurgent issueof improving the tools for

assessingand forecasting production capacity, above all, of the

Russian agricultural organizations. Before developing the

. The successful agricultural industry is neither sufficient

nor necessary condition for national self-sufficiency in

agricultural products (15).

The above does not mean that the government should step

back and not support domestic agricultural producers.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

However, not all issuesare relatedto the national food security

policy, indeed. For example, the work toward a developed

agricultural industry and protection of agricultural producers

from current and potential threats are not related to food

security but rather agrarian policy within the general national

industrial policy. And the issue of sustainable development of

rural areas in Russia should be handled as part of the national

social policy.

Further, the literature review allowed to identify the

leading obstacles to the intensive growth of the national

agricultural output, including by subsectors. After that, we will

briefly describe economic and mathematical models for

accurate assessment and forecasting the production

capabilities under study.

In our opinion, one of the leading problems of Russian

agriculture is labor productivity considerably lagging behind

countries like Denmark, Germany, Norway, Poland, and

Sweden, whose climatic conditions are almost equivalent with

Russia's (11). In turn, this is due to the relatively unfavorable

situation in the availability of technology and relatively low

labor quality in the Russian agricultural sector. For example,

according to the long-term average annual data (2010–2016),

Russian farmers have significantly higher yield losses

compared to a number of countries (18% versus 2.3% in

Germany, 1.6% in Denmark, and 3.52% in Sweden), higher

losses of cattle (18% versus almost none in Germany and

Denmark, 0.63% in Sweden), while the share of elite cultivars

is considerably lower (9.5% versus 95.6% in Germany, 98.1 in

Denmark, and 95.27% in Sweden), and elite livestock breeds

sphere available for them, and improving the environmental

situation in the Russian rural areas. Such closely related

challenges cannot be solved without a systematic country-

specific approach.

In the context of the study, rather interesting is the work

O. A. Cherednichenko,

N. A. Dovgot'ko,

and

N. N. Yashalova, who further defined national priorities and

guidelines for sustainable development of the national agri-

food sector through systematization of key problems in the

industry in Russia (27). For example, authors believe it

possible to solve a wide arrayof goals of the Russianagri-food

sector through achieving 14 closely related goals of the global

agenda (in line with the UN Sustainable Development Goals):

1) No poverty; 2) Zero hunger and sustainable agriculture; 3)

Good healthand well-being;4) gender equality; 5) Clean water

and sanitation;6) Affordable and clean energy; 7) Decent work

and economic growth; 8) Industry, innovation and

infrastructure; 9) Reduced inequalities; 10) Sustainable cities

and communities; 11) Responsible consumption and

production; 12) Climate action; 13) Conservation of marine

ecosystems; and 14) conservation of terrestrial ecosystems.

Based on these goals (to achieve them), the authors

consider it expedient to propose and solve 78 objectives,

making a fair point that no goal can be achieved in isolation

from the other ones, and all the goals are related to the

proposed objectives. At the same time, ensuring the balance

and interrelation between different dimensions of sustainable

development is reflected not only at the level of goals, but also

at the level of objectives (27). For example, “doubling of

agricultural performance and the income of small food

producers by 2030 can be achieved through a substantial

increase in productivity of crops by a more extensive use of

methods to increase fertility, including biological methods,

and the introduction of better performing agricultural

technologies and equipment” (27).

(

8% versus 98.5% in Germany, 99.5 in Denmark, and 94.47%

in Sweden) (11). The seizure of land from producers for

construction of housing and industrial facilities also are not

conducive to increasing agricultural output. For instance, in

1995–2016, the seizure of agricultural land in Russia

amounted to about 17% (10).

In view of the trend of depopulation of the country's rural

areas (by 4.5% among people below working age and 15.7%

among working age people by 2040) (4), the labor productivity

in the industryand, above all, in the depressedRussian regions

of the North-West, the center of the European part, and the Far

East can be increased through active adoption of digital,

intelligent and robotic technologies. E. A. Skvortsov,

E. G. Skvortsova, E. S. Sandu, and G. A. Iovlev (22) assessed

the current dynamics in implementation of robotic

technologies and robotization density in Russia from the mid-

It should be noted that while a number of important

national agricultural subsectors are dominated by foreign

producers (about 60% of milk processing, 70% of juices

production, 80% and 90% of frozen and canned vegetables and

fruits respectively), in the meat subsector, Russian producers

provide the bulk of the agricultural output (29, 30). In our

opinion, foreign investors are unwilling to invest in the

production of meat and meat products in Russia, among other

reasons, because of the lack of national legislation on holding

companies (the most common form of business in this

agricultural subsector both in Russia and worldwide) (16).

Based on findings of an empirical research, E. V. Rodionova

concluded that the activity of integrated forms of Russia meat

subsector is a good demonstration of the advantages of large-

scale production and agricultural and industrial integration (in

particular, increasing agricultural performance and financial

resources for purchasingmodern technologies and equipment).

However, it has negative social and economic impacts (e.g.

market monopolization and reduced competition, lower

development opportunities and ousting small and medium-

sized businesses, barriers to entry to the market). Therefore,

the author suggests vectors of further development of the

integration processes for the government, business, industry

associations and the scientific community should focus their

action on (in particular, ensuring the effective entry of

2000s to 2016 inclusive. Based on the assessment, the authors

developed an effective mechanism of transition of the Russian

agricultural sector (considering its specific features) to

robotics. In addition to increasing labor productivity, the

authors believe, robotization of Russian agriculture will also:

(

1) improve safety and working conditions of agriculture

employees, (2) improve thequalityof agriculturalproducts, (3)

create more jobs in adjacent sectors, and (4) lead to work

enrichment in the agricultural sector.

The Strategy of Sustainable Development of Rural Areas

of the Russian Federation through to 2030 (23) lists an

intensive growth of Russian agricultural output as only one of

the core goals. Other equally important goals include

improving the quality and standard of living of the Russian

rural population, making the services of organizations in the

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

integrated structures to the international agri-food market and

creating a national regulatory framework for property and

administrative relations between members of such structures)

and by industry. The work by N. V. Suvorov, in our opinion,

deserves special attention: the author developed and tested the

alternative method of linear regression (AMLR) on the data of

industrystatistics of the USSR and Russia. The method allows

accurate calculation of the dynamic parameters of the Cobb–

Douglas production function (24, 25, 26). In addition to the

high accuracy of verification of the model’s parameters

(calculations are carried out on growth rates of the variables,

i.e. on small numbers), his method guarantees their positive

economic outcome under all the factors. Of equal interest, in

our opinion, is the joint study by V. K. Gorbunov and

A. K. L’vov that presents an authorial method of assessing the

value of effective funds (formed in the process of utilization of

business investments) and simultaneously creating capital

production function (8). The researchers achieved the high

accuracy of economic and mathematical modeling of the

object's production capacity through using a special variation

of the parameter continuation method, a known effective

method of solving systems of nonlinear equations. At the same

time, case studies suggest that not all attempts to develop the

(

16, 31).

Another key agricultural subsector in Russia, dairy cattle

breeding, still faces the problems of low profitability, growing

production costs, shortage of proprietory funds, the annual

reduction in cow numbers and milk production, unbalanced

ration, shortage of forage and its poor quality, and other

negative trends. One of the leading causes of the current

situation in the subsector, according to K. A. Zadumkin, A. N.

Anishchenko, V. V. Vakhrusheva, and N. Yu. Konovalov, and

we share their opinion, is an unsatisfactory condition of the

forage resources (12).

Another team of authors that included A. A. Kuzina,

N. A. Medvedeva, K. A. Zadumkina, and V. V. Vakhrusheva

concluded from their empirical study that the effective

development of the Russian dairy industry is only possible

through balanced measures of public policy that would take

into consideration both the challenges facing the industry and

internationalexperience (13, 32). The authors analyzed the use

of the best available techniques (BAT) and proposed a model

for creating the concept of development of the subsector using

such techniques. In our opinion, of practical interest are

suggested scenarios of development of the Russian dairy

industry and the conclusion that the public policy for its

development should be based on an innovative scenario

involving its systemic modernization to ensure the national

food and environmental safety, and on exporting dairy

products.

method were successful. For example,

a number of

publications incorrectly applied the method of spatial

regression for creating a universal investment production

function of any Russian region based on regional statistical

data for one year or more. V. K. Gorbunov and

V. G. Derevenskii published a critical analysis of such studies

and refuted the validity of using the method for such purposes

(7).

Let's proceed to the methodological aspect of the study.

The study employed a number of methods of economic and

mathematical analysis (graphical, tabular, comparisons

analysis, etc.) The key role was played by well-known

methods of economic and mathematical modeling, namely:

correlation and regression analysis and trend extrapolation

method.

Concluding the literature review, we will briefly describe

the known economic-mathematical models thatcan be used for

estimating and forecasting production capacity with the

necessary degree of accuracy.

Up to now, an effective assessment tool for production

capacities of the country, region, industry sector (including

agriculture), and enterprise, both in Russia and abroad, have

been the Cobb–Douglas production function (14). The classic

version of this function allows to estimate and forecast the

output depending on two factors (labor and capital) (28):

Based on the literature review on the topic, the objective

was set for this study to develop an economic and

mathematical model that would allow assessment and

forecasting the agricultural output of Russian agricultural

organizations with the required accuracy.





Results and discussion

In this study, the modeling was carried out through the

Y  A K  L

(1)

correlation and regression analysis that allows not only to

deepen the factor analysis of the effective indicator but also to

realize the forecast function. The multifactor correlation and

regression model is created through a number of steps (3): (1)

a-priori study of the economic problem, (2) listing factors,

their logical analysis, 3) collection of initial data and their

original processing, (4) specification of the regression

equation, (5) assessment of the regression equation, (6)

selection of the main factors, (7) verification of the model

vaildity, (8) economic interpretation, and (9) forecasting of

unknown values of the dependent variable. Table 1 shows the

initialdata for the economic and mathematical modeling of the

agricultural output of Russian agricultural organizations over

the last 12 years.

where A — production coefficient; L, K — production

factors, labor (average number of employees) and capital

average fixed assets value for the period) respectively; α, β —

output elasticity coefficients by capital and labor.

The development of economic and mathematicalmodeling

of the production capacity is currently actively researched,

precisely on the basis of the Cobb–Douglas production

function. In our view, they can be categorized into two groups:

(

) modification of functional specification by including (apart

from the classic factors) a number of alternative independent

variables; 2) development of authorial econometric methods

allowing to correctly determine both static (unchanging over

time) and dynamic (varying by periods) parameters of the

Cobb–Douglas production function. Most interesting are

studies of the second variety, as they are generally intended to

improve the accuracy of assessment and forecasting of

production capacities at the macro-, meso-, and microlevel,

This information, in turn, was obtained from the Russian

state statistical monitoring agency (17). In our case, the

resultant indicator (dependent variable) is the monetary

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

agricultural output of agriculturalorganizations (actual prices)

in billion rubles. Previously, on the basis of the system

analysis, a set of 7 factors (independent variables) was

obtained that, according to the author, play the biggest role in

the resultant indicator. They included variables that describe

key indicators of the lines of business of agricultural

organizations (crop farming and animal husbandry), assess the

condition of infrastructure and facilities, utilization efficiency

of the basic resources (labor and capital), and the level of job

compensation in Russian agricultural sector.

Table 1: Initial data for the economic and mathematical modeling of the agricultural output of Russian agricultural organizations, 2006–2017.

Indicator

Agricultural

output

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

2017

agricultural

organizations

(

actual prices)

Y), billion

rubles.

704.5

918.5

1183.7

1141.5

1150.0

1540.6

1600.8

1756.0

2139.0

2657.1

2890.4

2978.0

Cultivation

areas of grains

and

grain

legumes crops

belonging

agricultural

organizations

(

), thousand

hectares

33,632

33,754

35,363

35,713

32,048

32,114

32,120

32,644

32,147

32,052

31,933

31,618

Productivity of

grains and grain

legumes

agricultural

organizations

(

metric

per

centners

hectare

harvested area

Сattle numbers

in agricultural

organizations

19.2

20.5

24.6

23.6

19.0

23.3

19.3

23.1

25.4

25.0

27.6

31.0

(

), thousand 10,840. 10,456. 10,079. 9,709.

9,405.

9,210.

9,112.

8,930.

8,661.

8,485.

8,401.

8,304.

heads

Milk yield per

cow

agricultural

organizations

(

), kilograms

3,564

187

3,758

197

3,892

210

4,089

226

4,189

236

4,306

247

4,521

258

4,519

274

4,841

290

5,140

307

5,370

318

5,660

327

Load of land per

tractor

agricultural

organizations

(

), hectare

Combine

harvesters per

thousand

hectares

planted areas of

crops

agricultural

organizations

(

), pcs

Average

monthly

nominal

job

compensation of

agricultural

organizations

employees (X ),

RUB

4,569

6,144

8,475

9,619

10,668

12,464

14,129

15,724

17,724

19,721

21,445

25,156

Source: the author's compilation.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

Table 2: Matrix of Pearson's paired correlation coefficients.

Indicator

-0.633

0.822

-0.918

0.986

-0.21

0.69

-0.67

0.79

-0.95

0.977

-0.69

0.75

-0.97

0.99

-0.942

0.980

0.57

-0.67

-0.78

0.79

0.91

-0.96

-0.94

0.99

-0.94

0.99

-0.93

Source: the author's compilation.

The factors for the model were selected based on the

calculation and analysis of Pearson's paired correlation

coefficients (see Table 2). The formula for calculating such

coefficients on the example of the resultantor any other factor

is below (3):

1 2

where X and X — Factors 2 and 7, respectively, of the

initial set of indicators.

Table 3: Results of verification of the initial data for consistency and

compliance with the normal distribution law

Indicator

푛

푖ꢁ1

∑

푖 푖

[(푥 −푥)(푦 − 푦ꢀ )]

Arithmetic mean

Mean-square deviation

1721.7

745.8

23.5

3.5

13,820

6,075

푟

푥푦

2^,

(2)

푛

푖ꢁ1

₎2

푛

(푦푖− 푦ꢀ )

푖ꢁ1

√

∑

(푥푖−푥

∑

Variation coefficient

Skewness

0.433

0.54

0.148

0.55

0.440

0.29

where x

and resultant indicator; ꢂ

and resultant indicators. The pairedcorrelation coefficients for

any combination of factors is calculated in the same manner.

The decision to includea factor in the model is taken according

to a number of rules (19). According to Table 2, Factors 2, 3,

, y

— empirical value, respectively, of any factor

, ꢃꢀ — arithmetic mean of the factor

Skewness error

The ratio of skewness to its error

Kurtosis

0.71

0.77

-1.06

0.71

0.78

0.14

0.71

0.40

-0.79

Kurtosis error

1.41

0.10

1.41

The ratio of kurtosis to its error

Source: the author's compilation.

-0.75

-0.56

, 5, and 7 (3 and 6) have a direct/inversestrong impact on the

resultant indicator. The moderate relationship was found

between the dependent variable and Factor 1. There is also a

strong relationship between certain factors. In view of the

calculation, analysis and the above rules, it was decided that

only two factors (Factor 2 and 7) should be included in the

model. First, both factors have a direct strong impact on the

resultant indicator. Secondly, they are loosely bound with one

another. Such indicators will hereinafter be referred to as

Factors 1 and 2. After that, the initial data are verified (in

terms of the indicators included in the model) for consistency

and compliance with the normal distribution law based on

calculation and analysis of the following indicators: the

variation coefficient and the ratio of skewness/kurtosisto their

error (see Table 3). The calculations are carried out according

to the formulas given in (19). The calculation and analysis of

the variation coefficient show that while Factor 1 is

characterized by a medium variation, for other indicators it is

considerable. However, in this case, it is inexpedient to

exclude atypical observations to reduce the variation

coefficient of the resultant indicator and Factor 2, as it would

adversely affect the adequacy of the model. Therefore, we will

assume the initialdata to be conditionally consistent. The ratio

of skewness/kurtosisto their error is significantly lower than 3

in absolute terms. This means that skewness and kurtosis are

insignificant, and therefore the initial data complies with the

normal distribution law. Therefore, the data can be used for the

correlation and regression analysis. The specification of the

model was determined through the regression analysis:

The above regression equation suggests that both factors

have a direct impact on the resultantindicator. This means that

an increase in both productivity of grains and grain legumes in

agricultural organizations and the average monthly job

compensation of their employees increases agricultural output

of Russian economic entities. The key step of the correlation

and regression analysis is verifying the model's adequacy (its

main findings are shown in Table 4). Such verification is

carried out according to the method given in (3) and (6). Table

preliminarily suggests that the model is adequate and,

therefore, the results of correlation and regression analysis can

be used in practical work. To determine the impact of each of

the factors on the resultant indicator, we calculated another

special indicator: elasticity.

푋ꢀ ꢀꢀꢋ

Э_Х_푖= 퐴_ꢊ_ꢌ_ꢀ

(4)

where

—

previously determined coefficients

(

parameters) of the regression equation under each factor. In

our case, the elasticity was 0.36 and 0.87, respectively for

factors 1 and 2. This means that while a 1% increase in the

productivity of grains and grain legumes in agricultural

organizations increases the resultant indicator only by 0.36%,

increasing the average nominal monthly pay for their

employees increases the resultant indicator by 0.87%.

Therefore, the calculation and analysis of the elasticityfor each

factor suggest that the greatest potential for growth of

agricultural output of Russian agricultural organizations rests

with increasing the level of job compensation for employees.

푌 = ꢄ39ꢅ.3 + ꢆ6.3ꢇ + 0.ꢅ08ꢇ ,

(3)

푋푖

ꢈ

ꢉ

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

An equally important marker of the model's adequacy is the

average approximation error:

Table 4: Assessment of the model's adequacy.

Indicator’s value

Tabulated

Hypothesis (indicator)

Note

Calculated

(standard)

1 for A

for A , and 8.66

for A

; 1.2

. Hypothesis of the statistical significance of the

regression coefficients (Student's t-test)

Only A

is a significant coefficient of the

regression equation

2.2622*

. Hypothesis of the statistical significance of the

25.18

4.26*

The regression equation is significant

regression equation (the Fischer–Snedecor F-test)

. Coefficient of determination

. Adjusted coefficient of determination

0.965

0.958

0.8–0.9**

The model allows to carry out an accurate

assessment of the phenomenon under study

Note: * and ** — data obtained from (9) and (6), respectively

Source: the author's compilation.

ꢈ

푦푖−푦푖т

푦푖

ꢍ

ꢊꢎꢈ

5). As Table 5 shows, if the trend of the two factors changing

over time is maintained (annual growth since 2017), the

resultant indicator is expected to grow in the medium-term

horizon.

퐸 =

∑

| ꢅ00%,

(5)

ꢍ

where y

empirical and obtained through the modeling, respectively; n

the number of observations. In this case, the approximation

and yiт — values of the resultant indicator,

—

error was 7.3%. Given that in economic calculations the

permissible error is within 5–8% (19), the following

conclusion can be made: the regression equation describes the

dependencies under study with sufficient accuracy.

Therefore, the model's adequacy test indicates that the

above resultsof correlation and regressionanalysis can be used

in practice, namely not only for calculatinggrowth potential of

the resultant indicator but also for forecasting it. The factors

are forecasted through the trend extrapolationmethod (see Fig.

and 3).

Figure 3: Changes in X over time

Table 5: Resultant and factorial indicators — forecast

Forecasting period

Time

1пр

2пр

пр

018

019

31.6

33.7

35.9

38.2

26,642

29,062

31,577

34,189

3,324.9

3,640.5

3,970.8

4,315.8

,020

,021

Source: the author's compilation.

The forecast for the core indicator (monetary agricultural

output) indicates an optimistic scenario for Russian

agricultural organizations for 2018–2021. Despite this

forecast, Russian agricultural organizations currently have

significant growth potential in agricultural output. Among

other things, this is shown by the analysis of job compensation

level at Russian agricultural organizations over the past 12

years (Table 6).

Figure 2: Changes in X over time

The trend type (changes over time) is selected through the

analysis of the coefficient of determination. In our case, the

trend is considered to be detected if the above indicator

exceeds 0.9. In order to fulfill this condition, a number of

atypical observations were excluded from the time series for

factor 1. Figures 2 and 3 show that the change over time in

each of the two factors is described by a polynomial trendline.

This allows to generate a forecast of the resultant and

factor indicators for the mid-term perspective (4 years) (Table

Despite in 2013–2017 the job compensation was steadily

growing for employees of agriculturalorganizations in Russia,

the average salary of Russian agricultural employees was

35.7% lower than overall in Russian economy. In our opinion,

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 522-530

this problem cannot be solved without the development and

implementationof a strategyto increasethe income of the rural

population.

identify growth potential for the resultant indicator but also to

make forecasts of it.

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