INFLUENCE OF ECOLOGICAL SITUATION ON THE FOOD SECURITY ON EXAMPLE OF AZERBAIJAN DATAS Текст научной статьи по специальности «Сельское хозяйство, лесное хозяйство, рыбное хозяйство»

UOT 338.43, 57.04

INFLUENCE OF ECOLOGICAL SITUATION ON THE FOOD SECURITY ON EXAMPLE OF AZERBAIJAN DATAS

PUR RIZA SAMIR MASUDOVICH

senior researcher at the Institute of Control Systems of the National Academy of Sciences of

Azerbaijan, Baku, Azerbaijan

MURTUZAEVA MALAHAT MUSTAFAEVNA senior researcher at the Institute of Control Systems of the National Academy of Sciences of

Azerbaijan, Baku, Azerbaijan

Abstract. The global economic situation associated with the COVID-19 pandemic has once again highlighted how risky globalization is when countries are very mutually dependent, especially in industries such as food production. This circumstance is further aggravated by the outbreak of war between Russia and Ukraine, which had strong trade and economic relations in the field of agricultural production in the structure of foreign trade. Given this and the heterogeneity of the factors taken into account, in order to analyze and identify the levers of food security, we will construct a model that uses the apparatus of the theory of intuitionistic fuzzy sets (IFS). In order to solve the problem of analyzing the state of food security in Azerbaijan, we use the statistical values of eleven climatic and production factors with a period covering 2010-2020-s years.

Key words: food security, intuitionistic fuzzy sets (IFS), membership and non-membership functions, linguistic values.

Introduction

In the late 90s of the XX century, a number of world powers also paid considerable attention to food security issues, fixing this in the "Rome Declaration on World Food Security" [1]. At the same time, emphasis was placed on the need for state support for agricultural producers, which was successfully carried out in developed countries with existing agricultural potential. Food security is an integral and most important part of national security, because it ensures the sustainable production of basic foodstuffs and their availability to the population. Ensuring food security contributes to a sustainable social climate in society. In the absence of the necessary stocks and reserves in the regions, dissatisfaction may arise among the population, which allows us to consider the food problem as the most important structural element that ensures the national security of the country. The basis for ensuring food security is the organization of the entire agro-industrial complex (AIC) - from growing plants and animals to providing it with the means of production and sale of final products. These are problems of labor resources, raw materials, materials, etc., covering major intersectoral, and in fact, national problems [2].

The developed countries of the world currently have an established and efficient food system. An example is the experience of such states as Germany, France, Sweden, the United States of America, etc.

In practice, the founder of the development of the legal field in terms of the formation of food security and regulation of the development of the agricultural industry was Sweden, which already in 1974 adopted the Law "On Rationalization of Agriculture". The key element of this document was to ensure the competitiveness of agricultural production by establishing cost-effective support measures for farmers.

Present time food security becomes one of the priority problems in the World. With an estimated 600 million cases of food-related illness each year, unsafe food poses a threat to human

health as well as economies around the world. Ensuring food security is a public health priority and an important step towards achieving food security.

The Food and Agriculture Organization of the United Nations (FAO) is the only international organization that oversees all aspects of the food chain, providing a comprehensive and integrated approach to food safety.

Food security of food products is a set of measures and actions designed to check the food that reaches the consumer as much as possible. There are several risk factors here, and many in our country not taken into account yet. The product must be in good condition and meet the requirements of sanitary services; its production must be carry out taking into account the ecology of the area. One of the innovations that have entered the system relatively recently has been the prediction of human exposure to various food additives or chemicals contained in products. Indeed, there is little information about the harm or benefit of additives for the human body, because have been consumed in recent years.

Problem statement

The purpose of our work is to calculate the values of food security and assess the present situation in Azerbaijan.

Next, we will provide statistical data for the Republic of Azerbaijan to calculate food security values for 2010-2020, which consist of 11 factors, which was take based on the Report of the Food and Agriculture Organization (FAO).

Statistical data of the indicators of food securities in Azerbaijan*

Table 1

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

CO2 128,2' 139,4! 150,4' 161,48 183,0: 181,3! 190,1 186,38 170,61 164,92 147,17

NO2 82,1 86,7 95 110,1 110,6 111,1 123,8 114,7 107,8 131,1 96,2

TSP 19,3 18 9,9 11,6 9 6,6 6,1 6,2 6,5 7,4 3,8

CH4 236,2 243,3 259,2 228,6 268,2 262,6 286,6 275,2 271,5 265,8 222,2

SO2 530,5 557.4 608.0 661.2 701.9 711.9 687.5 700.5 688 666.9 446.8

AAT +14.4 +12.1 +13.3 +13.4 +13.4 +13.6 +13.0 +13.5 +15.0 +14.4 +13.8

AAP 499.8 605.8 457.9 453 387.7 472.6 570.7 393.6 436.1 391.4 445.5

PRO 20,7 25,4 27,2 27,5 24 31,5 30,6 29,8 30 32,1 31,8

PRF 1924.6 2107.6 2574.8 2286.< 2422 2307.< 2964.7 2999.8 3050.8 3584.8 3884.3

IFP 1233.' 1630.« 1442. 4 1924. 7 1553. 7 1584. 4 1574. 6 2116. 6 1703. 4 2794, 4 1903. 8

CFP 896.9 911.2 893.8 914.3 932 944.6 949.5 960.9 963.5 1032.7 1070.1

(*The State Statistical Committee of the Republic of Azerbaijan)[3]

Where CO2 - Carbon dioxide (thsd tonn); NO2 - Nitrogen dioxide (thsd tonn); TSP - Total suspended particles (thsd tonn); CH4 - Methane (thsd tonn); SO2 - SulfUr dioxide (thsd tonn)AAT - Average annual temperature (0C); AAP - Average annual precipitation (mm); PRO - Productivity c/ha; PRF -Production of food products per capita (mln AZN); IFP - Import of food products (mln USD); CFP -Consumption of food products, kg. The environmental pollution factors (CO2, NO2, CH4, SO2 and TSP) include pollution from stationary and mobile sources together.

The datas presented in above tables, have the different characteristic. Therefore, we apply here the methods and tools of fuzzy sets, which make it easier and faster to work with data of various nature and allow us to obtain the best result in the calculation processes.

We use an intuitionistic linguistic number (ILN) A in X, which defined [4] as (1):

ОФ "Международный научно-исследовательский центр "Endless Light in Science"

A = «x[he(x), (nA(x),vA(x))])\x G X} (1)

here h$(X) G S, and рл(х) and va(x) represent the membership degree and non-membership degree of the element x related to linguistic index hex), respectively.

0 < дA(x) + vA(x) < 1, for all x G X. For each ILNA in X, if

nA(x) = 1 — дA(x) — vA(x), Ух G X (2)

where nA(x) is called the indeterminacy degree or hesitation degree of x to linguistic index h$(X).

For further calculations, we bring these data into one system of calculations - normalized values. Normalized indicators are converted into intuitionistic fuzzy numbers using the intuitionistic fuzzy triangular functions iftrif [5] by next formula (3):

Norm =- Positive

Norm = Xi-Xmin Negative (3)

Below we present these values in table 2.

Normalized values

Table 2

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

CO2 0.00 0,18 0,36 0,54 0,89 0,86 1.00 0,94 0,68 0,59 0,31

NO2 0.00 0,09 0,26 0,57 0,58 0,59 0,85 0,67 0,52 1.00 0,29

TSP 1.00 0,92 0,39 0,50 0,34 0,18 0,15 0,15 0,17 0,23 0.00

CH4 0,22 0,33 0,57 0,10 0,71 0,63 1.00 0,82 0,77 0,68 0.00

SO2 0,32 0,42 0,61 0,81 0,96 1.00 0,91 0,96 0,91 0,83 0.00

AAT 0,21 1.00 0,59 0,55 0,55 0,48 0,69 0,52 0.00 0,21 0,41

AAP 0,49 0.00 0,68 0,70 1.00 0,61 0,16 0,97 0,78 0,98 0,73

PRO 1.00 0,59 0,43 0,40 0,71 0,05 0,13 0,20 0,18 0.00 0,03

PRF 1.00 0,91 0,67 0,82 0,75 0,80 0,47 0,45 0,43 0,15 0.00

IFP 1.00 0,75 0,87 0,56 0,79 0,78 0,78 0,43 0,70 0.00 0,57

CFP 0,98 0,90 1.00 0,88 0,78 0,71 0,68 0,62 0,61 0,21 0.00

After normalizing the data, we define the term-sets (tab.3) that will be used in the next calculations.

Term-sets

Table 3

Low Medium High

0.00 0,18 0,36 0,32 0,50 0,68 0,64 0,82 1.00

At the next stage, we proceed to the calculation of the Intuitionistic fuzzy triangular membership and non-membershipVx(x) functions of A, in corresponded term-set by the

formulas (4), (5) [6], given below:

( y-x(x-t)

ßxM = 4

t-t

ux(t-x) t-t

ift<x<t

if X = t

ift <x<t ifx < tor x>t

(4)

0

V

[t-x+Wx(x-t)} t-t

f Л

Vx(x) = r Î-

t-t 1

ift<x<t

if X = t

if t<x<t if X < t or X >t

(5)

where t, t, t are the vertices of a triangular fuzzy number.

Membership and non-membership functions reduction coefficients (%, ware used, which take into account accuracy of statistical information. Using formulas (4) and (5), we calculated intuitionistic fuzzy sets (IFS) for data (tab.2), and introduce in (tab.4).

The values of membership and non-membership function

Table 4

2010 2011 2012 2013 2014

ß V л ß V л ß V л ß V л ß V л

CO2 0.00 1.00 0.00 0.84 0.06 0.10 0.18 0.80 0.02 0.67 0.25 0.08 0.54 0.40 0.06

NO2 0.00 1.00 0.00 0.44 0.51 0.05 0.46 0.49 0.05 0.51 0.43 0.06 0.46 0.48 0.06

TSP 0.00 1.00 0.00 0.40 0.56 0.04 0.35 0.61 0.04 0.83 0.07 0.10 0.12 0.87 0.01

CH4 0.67 0.25 0.08 0.15 0.83 0.02 0.50 0.44 0.06 0.47 0.48 0.05 0.35 0.61 0.04

SO2 0.21 0.45 0.34 0.46 0.49 0.05 0.34 0.62 0.04 0.80 0.11 0.09 0.18 0.80 0.02

AAT 0.63 0.24 0.13 0.00 1.00 0.00 0.40 0.56 0.04 0.56 0.38 0.06 0.56 0.38 0.06

AAP 0.83 0.07 0.10 0 0.94 0.06 0.18 0.80 0.02 0.29 0.68 0.03 0.00 1 0.00

PRO 0.00 1 0.00 0.39 0.57 0.04 0.56 0.37 0.07 0.44 0.51 0.05 0.33 0.63 0.04

PRF 0.00 1 0.00 0.44 0.51 0.05 0.13 0.85 0.02 0.83 0.07 0.10 0.50 0.44 0.06

IFP 0.00 1 0.00 0.50 0.44 0.06 0.63 0.29 0.08 0.53 0.41 0.06 0.73 0.18 0.09

CFP 0.08 0.91 0.01 0.47 0.48 0.05 0.00 1 0.00 0.55 0.39 0.05 0.68 0.24 0.08

Table 4 (continue

2015 2016 2017 2018 2019 2020

ß V л ß V n ß V n ß V n ß V n ß V n

CO2 0.67 0.25 0.08 0.00 1.00 0.00 0.29 0.68 0.03 0.21 077 0.02 0.41 0.54 0.05 0.26 0.71 0.03

NO2 0.42 0.53 0.05 0.70 0.16 0.14 0.12 0.87 0.01 0.73 0.18 0.09 0.00 1.00 0.00 0.34 0.62 0.04

TSP 0.85 0.05 0.10 0.70 0.22 0.08 0.73 0.18 0.09 0.82 0.08 0.10 0.60 0.33 0.07 0.00 1.00 0.00

CH4 0.25 0.72 0.03 0.00 1.00 0.00 0.77 0.08 0.15 0.50 0.39 0.11 0.06 0.93 0.01 0.00 1.00 0.00

SO2 0.00 1.00 0.00 0.44 0.51 0.05 0.20 0.77 0.03 0.43 0.52 0.05 0.80 0.05 0.15 0.00 1.00 0.00

AAT 0.81 0.09 0.10 0.23 0.74 0.03 0.72 0.19 0.09 0.00 0.94 0.06 0.63 0.24 0.13 0.49 0.45 0.06

AAP 0.28 0.69 0.03 0.76 0.10 0.14 0.13 0.86 0.01 0.65 0.27 0.08 0.08 0.91 0.01 0.45 0.50 0.05

PRO 0.25 0.14 0.61 0.62 0.25 0.13 0.65 0.22 0.13 0.74 0.12 0.14 0.00 0.94 0.06 0.12 0.81 0.07

PRF 0.78 0.13 0.09 0.75 0.16 0.09 0.67 0.25 0.08 0.55 0.39 0.06 0.72 0.14 0.14 0.00 0.94 0.06

IFP 0.64 0.29 0.07 0.67 0.25 0.08 0.59 0.34 0.07 0.28 0.69 0.03 0.00 0.94 0.06 0.47 0.48 0.05

CFP 0.34 0.62 0.04 0.21 0.76 0.03 0.23 0.74 0.03 0.30 0.66 0.04 0.59 0.28 0.13 0.00 0.94 0.06

In the next step, we calculated the weights of criteria factors on yearly basis. Weights of indicators are estimated as follow as proposed by Boran, and et.al formula (6) [7] and demonstrated in the table 5.

(Mk+^kfir))

Ak =-^^ (6)

k sU(^g) ( )

Weights of the indicators - Ak

Table 5

Ak 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

CO2 0.00 0,39 0,05 0,09 0,11 0,10 0.00 0,04 0,02 0,08 0,12

NO2 0.00 0,08 0,12 0,06 0,09 0,06 0,14 0,01 0,13 0.00 0,16

TSP 0.00 0,07 0,09 0,20 0,02 0,29 0,14 0,13 0,22 0,13 0.00

CH4 0,25 0,03 0,14 0,05 0,07 0,03 0.00 0,26 0,09 0.00 0.00

SO2 0,06 0,09 0,09 0,14 0,03 0.00 0,07 0,03 0,05 0,26 0.00

AAT 0,29 0.00 0,12 0,07 0,14 0,15 0,04 0,16 0.00 0,18 0,21

AAP 0,38 0.00 0,05 0,03 0.00 0,04 0,18 0,01 0,10 0,01 0,21

PRO 0.00 0,08 0,14 0,04 0,07 0,03 0,11 0,14 0,23 0.00 0,05

PRF 0.00 0,08 0,01 0,19 0,10 0,16 0,15 0,10 0,07 0,18 0.00

IFP 0.00 0,09 0,19 0,07 0,20 0,10 0,13 0,08 0,04 0.00 0,25

CFP 0,02 0,09 0.00 0,06 0,17 0,04 0,04 0,04 0,05 0,16 0.00

Based on the weights for 2010-2020s years let us analyze the factors of the system as follows. To pass to linguistic variables, we divide the interval between the maximum and minimum values ([0.0,...,0.39]) into the following 3 subsets, respectively: L (Law) - [0,..,0.13], M (Medium) -[0.13,..,0.26], H (High)- [0.26,..0.39].

Then determine the values of 6, based on decision above, we obtain the values of each factor and

give them the following values (tab.6):

Law - 1,

Medium - 2:

High - 3

6- values of the variables

Table 6

Ak 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

CO2 1 3 1 1 1 1 1 1 1 1 1

NO2 1 1 1 1 1 1 2 1 1 1 2

TSP 1 1 1 2 1 2 1 1 1

CH4 2 1 2 1 1 1 1 3 1 1 1

SO2 1 1 1 2 1 1 1 1 1 3 1

AAT 3 1 1 1 1 2 1 2 2

AAP 3 1 1 1 1 1 2 1 1 1 2

PRO 1 1 2 1 1 1 1 2 1 1

PRF 1 1 1 2 1 2 1 1 2 1

IFP 1 1 2 L 2 1 1 1 1 1 2

CFP 1 1 1 1 2 1 1 1 1 2 1

Using formulas Sv t a( k\ we calculated resulting values for total period by every factor (tab.7).

Lk=i Aka(aij)

Resulting values for total period

Tab

01 02 03 04 05 06 07 08 09 010 0ii

0.61 0.596 0.474 0.751 0.927 0.709 0.815 0.412 0.79 0.86 0.76

e 7

Conclusion

Over the past 2 years, the world economy has been affected by the global pandemic due to the coronavirus significantly negatively, which naturally affected the behavior of the model, but since the situation has stabilized in a short time, the results can be considered reliable.

Thus, the maximum value of the impact on food security is positive beyond the Sulfur dioxide (SO2) factor. Factors Average annual precipitation (AAP), Import of food products (IFP), Production of food products per capita (PRF) also have a relatively strong influence. In results, obtain that implicating to the values of Sulfur dioxide (SO2), Import of food products (IFP), and Production of food products per capita (PRF) factors it is possible to improve the statement of Food Security.

REFERENCE

1. FAO, Rome Declaration on world food security and world food summit plan of action 1996 (Rome, Italy). URL:http://www.fao.org/3/w3613e/w3613e00.htm. Accessed 26 November 2019

2. I. V. Efimov, M. I. Bukiya, The Level of Food Security of the Russian Federation and Ways of Its Increase. // Economy. Right. Society. 2017. №.3 (11), Moscow, Russian Federation. pp. 118-125.

3. The State Statistical Committee of the Republic of Azerbaijan. URL: https://www. stat. gov. az

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5. Radhika C., Parvathi R. Intuitionistic fuzzification functions // Global Journal of Pure and Applied Mathematics. 2016. № 2. pp. 1211-1227.

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8. The State agency on mandatory health insuarence: URL: https://its.gov.az/page/general-situation-in-the-country.