**Annotation**

Tse yu is to develop a fuzzy-multiple methodology to assess the dynamics of development of agribusiness in the region on the basis of a set of diverse indicators and carry out comparisons (ranking) of agricultural facilities or regions. The technique is based on the use of standard multilevel fuzzy [0,1] -classifiers. It makes it possible to calculate a complex numerical estimate for the level of agricultural intensification by the criteria of two groups for any number of years under study: the level of intensification of production in agriculture and the level of economic efficiency of intensification of production in agriculture, and also to give practical recommendations for the further development of agriculture in the region.

** Abstract **

The work aims to develop a fuzzy-multiple methodology that allows assessing the dynamics of the development of the agro-industrial complex agricultural objects or regions. The technique is based on the use of standard multilevel fuzzy [0,1] – classifiers. This makes it possible to calculate a complex level estimate for the level of agricultural intensification of agricultural products in agriculture, and also to give practical recommendations for the further development of agriculture in the region.

** Keywords: ** methodology, integrated assessment, the intensity of agricultural production, indicators, the theory of fuzzy sets.

** Introduction **

The essence of the criterion of the effectiveness of agricultural production is the maximum production of agricultural products necessary for society, given the costs and the amount of resources per unit products that ensure high quality products and the rational use of labor, material and land resources [1]. In the aspect of optimizing agricultural production, the development of techniques that make it possible to produce a comprehensive estimate of the intensity of agricultural production on the basis of a set of ranked indicators that objectively reflects the efficiency of the use of material and financial resources by the agroindustrial complex industries, as well as the influence of agricultural production on the ecology of the region, is of practical importance.

Currently there are methods for assessing the intensity of agricultural production on its individual indicators [2]. However, they do not allow to evaluate and rank agro-industrial enterprises, agribusiness sectors and entire regions on the basis of a comprehensive analysis of many indicators. To rank in the agribusiness in practice, ratings are used, for example, the annual evaluation of the Agro-300 club [3]. The standard methodology for calculating the general economic rating provides for the use of only two indicators: revenues and profits from the sale of agricultural products [4]. Industry ratings are calculated rather difficult and are constructed using only the three most important indicators for the industry: the volume of the gross output of the industry, the value of marketable products, the profit from sales.

The wide practical application of these methods is difficult due to the following factors: 1) the complexity of calculating rating ratings ; 2) accounting for the construction of rating estimates of a small number of significant indicators; 3) the use of indicators directly dependent on soil and weather conditions, resulting in, for example, the Agro-300 club usually includes participants with the most favorable conditions of agricultural production, which do not require additional costs for agrotechnical measures. Thus, for a fair assessment of the dynamics of the intensity of agricultural production, methods that objectively reflect the efficiency of the use of material and financial resources of the agroindustrial complex are needed.

In this paper, we propose a technique based on the methods of the theory of fuzzy sets and aimed at obtaining an objective complex quantitative assessment of the intensity of agricultural production by a set of criteria for two groups: the level of production intensification and the level of economic efficiency NOSTA intensification of production in agriculture.

The novelty of the proposed method, and also its contrast to similar developments is that for each of the indicators on the basis of time series values by normalizing its integrated evaluation formulas are calculated. The subsequent application to them of the standard five-level fuzzy [0,1] -classifier (previously used in financial analysis and not used in production intensity estimation methods [5]) allows to calculate the normalized complex estimates of the levels of production intensification and economic efficiency of intensification of production in agriculture, a comprehensive assessment of the intensity of its production. Aggregation of the formed estimates on the basis of standard five-level fuzzy [0,1] -classifiers allowed to obtain the final complex assessment of the intensity of agricultural production in the region by the example of the Rostov region.

** General principles of the methodology for assessing the intensity of agricultural production **

The general principles of the author’s methodology for estimating the intensity of agricultural production are described in detail in [6][7]. The mathematical apparatus underlying the methodology and representing a modification of the standard multilevel fuzzy [0,1] -classifiers was disclosed in [8][9].

The general principles of the methodology for estimating the intensity of agricultural production are reduced to the following algorithm.

** Stage 1 . ** Forming a list of significant indicators of the level of intensification of production in agriculture during the period n years (hereinafter: the first group of indicators), as well as significant indicators of the level of economic efficiency of intensification of production in agriculture for the same period (second group of indicators).

** Stage 2. ** Ranking the importance of the studied indicators for assessing the intensity of agriculture, calculating their weight coefficients, on the basis of expert estimates.

** Stage 3. ** Calculation of normalized (that is, belonging to the interval [0,1]) of the numerical values of the investigated indicators of the first and second groups over the considered period of n years, for example, on the basis of formulas determined by the meaning of the problem.

** Stage 4. ** Setting of linguistic variables. The normalized values of the indicators defined in Step 3 are numerical values of fuzzy variables with a universal set (carrier) in the form of a segment . They are compared linguistic variables with term sets, consisting of five terms: “very low level of the indicator”; “Low level of the indicator”; “Average level of the indicator”; “High level of the indicator”; “Very high level of the indicator.” The functions of belonging to linguistic variables are defined by trapezoidal functions.

In addition, we introduce linguistic variables: * γ * = “integrated assessment of the intensity of agricultural production”; * γ _{ 1 }* = “assessment of the level of intensification of production in agriculture”;

*γ*= “Estimation of economic efficiency of intensification of production in agriculture”. The universal set for each linguistic variable is the numerical segment and the set of values of all three variables – term – set where

_{ 2 }*G*– “a steady tendency to decrease in growth”;

_{ 1 }*G*– “tendency to decrease in growth”;

_{ 2 }*G*– “the tendency to stagnation”;

_{ 3 }*G*– “the tendency to growth”;

_{ 4 }*G*– “a steady trend towards growth.” The membership functions also have a trapezoidal shape.

_{ 5 }** Stage 5. ** The transition from numerical values of indicators to numerical values of estimates based on the general algorithm of the operation of standard five-level fuzzy [0,1] -classifiers.

** Stage 6. ** Linguistic recognition of the numerical estimates obtained in accordance with the definition of the term set as well as analysis of the obtained intensity estimates based on numerical values of indicators and recommendations for correcting the current situation.

** th dynamics for 18 years. ** The assessment of the level of intensity of agricultural production in the Rostov Region was carried out on the basis of statistical data provided by the Ministry of Agriculture and Food of the Rostov Region for 18 years, taking into account the positive and negative dynamics of their change.

The investigated indicators of the level of production intensification form four groups: the first group reflects the cost of production assets by 1 ha of agricultural land, the second group – energy resources; the third group – characteristics of the basic production assets; the fourth group is the current production costs. The role of aggregate costs and the cost of fixed production assets in assessing the level of intensification of production in agriculture is revealed through more detailed indicators: in the second group – through the indicators of energy efficiency and power-to-weight ratio; in the third group – through the renewal rates of agricultural machinery, the proportion of breed animals in the total number of livestock, and the density of cattle per 100 hectares of farmland (heads); in the third group – through indicators of the current production costs of crop production and livestock. In general, to assess the level of production intensification, there are statistical data on 14 indicators submitted by the Ministry of Agriculture and Food of the Rostov Region for 1996-2013

The indicators of the economic efficiency of the intensification of production in the agricultural sector of the Rostov Region are formed by six groups: the volume of gross income, the level of profitability, the return on assets , labor productivity, crop yields by groups, productivity of farm animals by groups.

Thus, economically th effectiveness of intensification of production in agriculture Rostov region it is necessary to assess the 10 indicators based on statistical data za18 years. It is required on the basis of the received estimates of the level of production intensification in agriculture in the Rostov Region and the economic efficiency of the intensification of production in the agriculture of the Rostov region, to form the final comprehensive assessment of the intensification of production in agriculture in the Rostov Region.

The task is difficult to implement by classical mathematical modeling, , due to the need to take into account a significant amount of heterogeneous data. The source statistical material is a table of 24 rows and 18 columns; while the contribution of each of the indicators to the final evaluation is not equivalent. The indicators have different economic meaning, scale and dimension (for example, the yield of agricultural crops, measured in c / ha, the return on assets, rubles, the level of profitability,%, etc.). In addition, for the indicators in question, currently there are no generally accepted standards. “Positive” is the trend of a constant positive increase in indicators; “Negative” – zero or negative increment for each of the indicators.

Therefore, the calculation of the normalized values of the investigated indicators of the level of production intensification in agriculture during the period N years based on the scheme that integrates the time series of data for each of the indicators and takes into account the significance of different time periods due to the weighting factors:

where * k _{ i }* – weights determined on the basis of n Avila Fishburne, wherein the numbering of the time periods is carried out in reverse order (i.e., in this example, the first period – the years 2012-2013, and the last 17 minutes, 1996-1997). I

_{ i }– integer functions defined in such a way that the value “-1” corresponds to a negative increase in the i-th indicator; value “1” – a positive increase in the i-th indicator; value “0” – stagnation, zero increment.

Analysis of formula (1) shows that the scheme takes into account the temporal significance of each of the periods considered. If there is a positive increase in all periods, the sum in parenthesis is unity, and the final numerical value of the indicator reaches its maximum and is equal to one. In case of negative growth in all periods, the value of the indicator reaches a minimum and is equal to zero. The total weight of the time periods from 2008 to 2013 is 0,4902; from 2002 to 2007 – 0.3726; from 1996 to 2001 – only 0.1372. Thus, with a stable negative period in the last 5 years, the total numerical estimate for the i-th indicator is not higher than 0.5098 (which means “bad”).

Calculation of the normalized values is carried out in the same way of the investigated indicators of the level of economic efficiency of intensification of production in agriculture . The calculation of complex estimates was carried out in accordance with the general scheme. The weights, as well as the values of the membership functions for the indices of the first and second groups, are given in Table. 1 and 2.

For the calculations based on the above-described schemes, a software package was developed [10].

On the basis of Table. 1 computation and linguistic recognition * = “estimates of the level of intensification of production in agriculture of the Rostov region” *

. Consequently, = “Assessment of the level of intensification of production in agriculture of the Rostov region” corresponds to the term * G *_{ 4 } – “the tendency to growth.”

Based on the same table. 1 critical indicators are determined (those whose normalized value is less than 0.5). These are: 1) the density of cattle per 100 hectares of farmland (heads), ; 2) coefficient of renewal of agricultural machinery (%), harvesters of all kinds, ; 3) specific weight of breed animals in the total number of livestock (%), sheep, ; 4) energy-saving, ; 5) specific weight of breed animals in the total number of livestock (%), pigs, .

On the basis of the obtained estimates, we can conclude that in 1996 – 2013 in the agriculture of the Rostov region, there was a steady downward trend in the following significant indicators characterizing the level of intensification of agricultural production: the density of cattle per 100 hectares of farmland, the proportion of breed animals in the total number of livestock (sheep, pigs), the renewal ratio of agricultural machinery

Calculation and linguistic recognition

= * “Estimates of the economic efficiency of the intensification of production in the agricultural sector of the Rostov Region” “*

was also carried out on the basis of Table 2.

Therefore, = “Estimation of economic efficiency of production intensification in agriculture of the Rostov region” corresponds to two terms * G *_{ 3 } – “tendency to stagnation” and * G [19659065] 4 – “the tendency to growth,” and the saying “there is a tendency to stagnate” is more true than saying “there is a tendency to growth.” *

On the basis of Table 3, the indicators that lead to a decrease in the final assessment are highlighted, which include: 1) calves’ output per 100 main cows (head),; 2) the yield of cereals and legumes without corn,; 3) level of profitability (%),; 4) the volume of output per ruble of invested capital (rubles),. On the basis of the obtained estimates, it can be concluded that in 1996 – 2013 in agriculture in the Rostov region there was a stable tendency to reduce the following significant indicators characterizing the level of economic efficiency of intensification of agricultural production: the yield of calves per 100 main cows, the yield of cereals and leguminous plants without maize, the level profitability, the volume of output produced per ruble of invested capital.

Calculated = “Integrated assessment of intensification and production in the agriculture of the region”. Weights of terms are defined as the arithmetic mean of the corresponding weights of the estimates и :

Consequently, = “a comprehensive assessment of the intensification of production in the agriculture of the region” for 1996-2013 corresponds to two terms G3 – “the tendency to stagnate” and G4 – “the tendency to growth,” and the saying “there is a tendency to increase” is more true , than the statement “there is a tendency to stagnation”.

Thus, the proposed methodology made it possible to carry out a comprehensive analysis of the development of agriculture in the Rostov region in 1996 – 2013 on the basis of taking into account the positive and negative dynamics of the heterogeneous indicators of the two groups: 14 indicators characterizing the intensification of production and 10 indicators – the economic efficiency of intensification of production. The analysis was carried out taking into account the weight significance of the indicators, as well as the ranking of the contribution of different time periods to the final evaluation.

It is established that the level of production intensification in agriculture in the Rostov region in the period under review can be estimated as “the trend towards growth,” while the level of economic efficiency of the intensification of production in agriculture in the Rostov region corresponds more to the “stagnation” “The tendency to growth.” The factors leading to a low final evaluation of the level of economic efficiency of intensification of production are, first of all, stable trends towards a decrease in the level of profitability, as well as the volume of output produced per ruble of invested capital. These factors, in turn, are a consequence of a steady trend towards a decrease in such important indicators that characterize the level of intensification of agricultural production, such as: the density of cattle per 100 hectares of farmland, the proportion of breed animals in the total number of livestock, the renewal ratio of agricultural machinery, and energy.

The integrated assessment of production intensification in the agricultural sector of the region corresponds to a more “upward trend” than “stagnation trends,” which indicates a relatively stable dynamics of the agricultural production in the Rostov region in 1996 – 2013. The analysis indicated the areas of agricultural production, which in the future requires the attraction of investments to ensure sustainable growth.

**Conclusions**

A methodology is proposed for assessing the ecological and economic efficiency of agricultural production in the region on the basis of standard five-level fuzzy [0,1] -classifiers. The practical importance of the methodology is that it allows to formulate a comprehensive assessment of the intensity of agricultural production in the region, as well as a comprehensive assessment of the impact of agricultural production on the ecology of the region on the basis of integrated estimates of time series of numerical statistical values of disparate indicators, reflecting both the level and growth rates for corresponding periods; and the contribution of each of the indicators is estimated using a weighting factor that reflects its significance.

Compared with the existing evaluation methods, the proposed evaluation methodology has a number of practically significant advantages: 1) a simple calculation scheme; 2) taking into account in the construction of estimates a large number of heterogeneous significant indicators that can be varied depending on the available statistical material and the particular practical problem being solved; 3) using only indicators that objectively reflect the efficiency of using the material and financial resources of the agroindustrial complex and the impact of agricultural production on the ecology of the region; 4) the possibility of varying the weight of the contribution of the studied indicators to a comprehensive assessment of the intensity of agricultural production in the region; 5) the adaptability and universality of the proposed methodology, which makes it possible to apply it to the assessment of the intensity of not only agricultural but also industrial production in various scales; 6) the possibility of applying it for ranking regions by sets of indicators, as well as forecasting their development, provided that the trend models of the indicators are constructed; 7) the possibility of analyzing on its basis the situation in the production under consideration and the formation of practical recommendations based on the calculated integrated estimates of indicators.

Conflict of Interest
None declared. |
Conflict of Interest
None declared. |