Production cluster in the agro-industrial complex as a factor in ensuring food security

143 PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS AFACTOR IN ENSURING FOOD SECURITY ABSTRACT The research focuses onthe functioning ofgrain product cluster enterprises. The study addressed the growth rate and operational characteristics ofgrain product cluster enterprises. Sustainable development ofKazakhstan’seconomic sectors and sectors requires the exploration and implementation ofnew, more efficient forms ofproduction and business activities. Given the current complex socio-economic conditions inagricultural production, the grain product cluster isbecoming one ofthe most in-demand sectors inthe agro-industrial sector. Therefore, apriority area ofnational policy isto increase production volumes both tofully meet domestic demand and toincrease exports. This approach requires the unification ofefforts byall agricultural sector entities, coordination ofactivities, and afocus onachieving high end results. One ofthe triggers for solving this problem isthe integration ofcommodity producers, which allows for the unification ofall links inthe production cycle inthe technological chain “raw material production— finished product production” within asingle complex. Astudy ofdomestic and international experience shows that integrated entities such asgrain product clusters achieve high levels ofefficiency and competitiveness. The development ofgrain product cluster models and mechanisms, the modernization ofagricultural and processing industries, and the selection ofmethods and tools that enhance agribusiness’sresponsiveness toinnovative development require appropriate theoretical and methodological support, taking into account the specifics ofproduction ineach industry. Practical experience shows that, despite the intensification ofintegration processes ingrain product clusters, inefficiencies and the disintegration ofa number ofsuch formations are occurring. This islargely due tothe fact that, under the new economic conditions, the traditional mechanism ofthe grain product cluster inKazakhstan’sagro-industrial complex does not allow for the systematic implementation oflarge-scale innovation processes, limiting itself tominor (local) changes. Successful implementation ofprojects toform and develop grain product clusters requires in-depth study, generalization, and systematization ofthe experience ofusing such amechanism byboth national and foreign companies that have achieved high results inthis area. Currently, the grain product cluster remains inits early stages ofdevelopment, largely due tothe specific high-risk characteristics ofits industries. KEYWORDS Assessment methodology, growth rate, integral indicator, optimization model, trends, development, food security, strategy, socio-economic status, influence, efficiency, grain, trend, trigger, function, location, result. DOI: 10.15587/978-617-8360-18-4.CH5 Perizat Beisekova © The Author(s) of chapter, 2025. This is an Open Access chapter distributed under the terms of the CC BY license 5 144 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES The special role ofgrain inthe agricultural sector’scommercial output isdetermined byits strategic importance asa staple food and acrucial— and for some livestock sectors, acrucial— feed component. Furthermore, the Republic of Kazakhstan, due toits inherent natural and other characteristics, has become amajor grain producer. Inthe context ofeconomic transformation, maintaining grain production and ensuring the rational use ofits development potential islargely determined bythe functioning ofthe grain product cluster. Grain production inthe republic has always been one ofthe most important characteristics ofthe country’seconomic independence and prosperity. This highly valuable commodity isstrategic innature, which determines significant state interest ingrain production; onthe other hand, itis the foundation for the development ofthe grain product cluster inthe agricultural sector. The key feature ofthe grain product cluster’sfunctioning isthe specific nature and purpose ofthe goods itprocesses, namely grain and grain products, and their high share inthe national food consumption structure. Bread and grain products, asa vital and irreplaceable commodity, enjoy guaranteed demand from the population. They satisfy approximately athird ofthe population’sdaily food needs, upto 50% ofthe daily protein requirement, 30 to50% ofthe required energy, upto 50–60% ofB vitamins, and upto 80% ofvitamin E. Moreover, grain protein, with its high nutritional value, issignificantly cheaper than animal protein, which, toa certain extent, helps tocombine the issues ofquantity and quality into asingle whole. Grain production isa special element ofthe grain product sub-complex. Grain crops occupy approximately half ofthe area under agricultural crops and decisively determine the level and pace ofdevelopment not only offarming but ofagricultural production asa whole. Asthe largest branch ofagriculture, grain production forms the basis ofthe country’sfood supply and serves asthe raw material base for the development offlour and cereal milling, feed milling, starch production, alcohol production, brewing, and other industries. Grain production islargely responsible for the employment ofa significant portion ofthe population, aswell asthe sectoral, regional, and national economic efficiency ofthe agricultural sector[1]. Along with the social significance ofgrain asa valuable, essential, and everyday food product for the population, aswell asthe basis for livestock production, the financial aspect isalso ofconsiderable importance. Grain isone ofthe most reliable sources ofincome for commodity producers, giving them relative independence inthe reproduction process. The strategic importance ofgrain inthe country’sfood supply isalso determined bysignificant export reserves, which can become asignificant source offoreign exchange revenue for the national budget (Table 5.1). The discrepancy between per capita grain production and consumption across the country, aswell asthe local nature ofproduction ofcertain types ofgrain, necessitate the transportation ofgrain from one region toanother. Akey feature ofgrain, distinguishing itfrom other agricultural products, isits high transportability and suitability for long-term storage, allowing for the rapid implementation oflong-distance interregional grain shipments. Indetermining the principles offormation and operation ofa grain product cluster, amethodological approach isadopted that views the cluster asa complex system ofmarket relations through which the production structure spontaneously adapts tothe volume and structure ofsocial needs, distributing production factors among various industries[2]. Alternatively, acluster can beconceptualized asa collection CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 145 ofmarkets ofvarious types, interconnected bya common goal— ensuring the normal functioning ofthe reproduction process inthe region. Thus, agrain product cluster isa geographically distinct, complex economic system comprising aset ofcommodity relations and connections between its entities, which include rural producers, enterprises and organizations involved inthe storage, processing, and drying ofgrain, and its processing, aswell asinfrastructure facilities that facilitate the accelerated flow ofmaterials, financial resources, and information. Table 5.1 Grain requirements for the production ofprocessed products Name Potential capacity ofenterprises, tons Domestic demand, tons Required amount ofgrain for potential processing, tons Required amount ofgrain for domestic consumption, tons Required area for potential production, thousand hectares Flour 5,139,550 1,800,000 7,342,214 2,580,000 6,440 Compound feed 2,418,750 2,665,700 2,845,588 3,136,117 2,496 Cereals 503,337 337,400 1,198,421 1,997,368 1,051 Alcohol, thousand dal 30,189 3,719 928,892 114,418 814 Total – – 12,315,115 7,827,903 10,801 The study examined the works ofthe following authors onassessing the effectiveness ofgrain product clusters: M.R.Hagerty[3], L. Kaufman, P.J.Rousseeuw[4], T. Hastie[5]. Ananalysis ofthe methods showed that the most probabilistic assessment model ispossible using anon-stationary time series with amathematical model that allows for the assessment ofgrain cluster development trends and its key characteristics. For this purpose, analgorithm was developed that applies the theoretical principles ofmodeling two-parameter monotonic functions. The created program automates the process offinding empirical approximating functions for the trend. The described algorithm uses the least-squares method and atheorem based onthe general property oflinear dependence ofparameters ina class offunctions. This program iscapable ofapproximating trends not only for linear functions but also for nonlinear ones (e.g., quadratic, logarithmic, hyperbolic, exponential, and others). This method for determining anempirical approximating function inthe class ofmonotone two-parameter functions ismore efficient than other methods, requiring less execution time. Atime series isa set ofobservations measured over specified temporal orspatial intervals and arranged chronologically. Examples ofsuch series include annual demand for acommodity, weekly prices for acommodity, food production, etc. Different economists and statisticians define time series using different terms. Some ofthe definitions are given below: –W. N. van Wieringen [6] describes atime series asa set ofquantitative data arranged inthe order oftheir occurrence; 146 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES — H. Morris[7] defines atime series asa set ofstatistical observations arranged chronologically; — K.D.Patterson[8] considers atime series tobe statistical data collected, recorded, orobserved insuccessive increments; — C.Y.Chen etal.[9] characterize atime series asa set ofquantities relating todifferent periods oftime orto variables such assteel production, per capita income, gross national product, tobacco prices, orthe industrial production index; — C.H.Meyers[10] defines atime series asa sequence ofrepeated measurements ofa variable, made periodically over time; — W.Z.Hirsch[11] describes atime series asa sequence ofvalues ofthe same variable corresponding tosuccessive points intime; — S. Spiegel[12] defines atime series asa set ofobservations made ata specific time, usually atequal time intervals. Intime series analysis, special attention ispaid toidentifying patterns intheir dynamics over along period. The goal ofstatistics isto provide acharacterization ofchanges instatistical indicators over time. How does acountry’sgross national product and national income change from year toyear? What are the trends inincreasing ordecreasing unemployment and wages? Are there significant fluctuations ingrain yields, and can atrend toward their increase beidentified? These questions can only beanswered using specialized statistical methods designed toanalyze development and change over time, or, asis customary instatistics, tostudy dynamics. Studying patterns ofchange over time isa complex and labor-intensive research process, asany phenomenon under study isinfluenced bynumerous factors acting invarious directions. Instatistical analysis ofdynamics, itis necessary toclearly distinguish between two main elements— trend and variability— inorder toprovide aquantitative characterization ofeach using specific indicators. Toconstruct aclassical mathematical model ofa time series, its components must beanalyzed. These components include the trend ortendency, periodic fluctuations, and random fluctuations. Inother words, this can besymbolically described asfollows ( , , ), t y fTPE= where ytrepresents the level ofthe time series, i.e., the value ofa specific indicator attime t; Tdenotes the trend ortendency that determines the underlying dynamics ofthe series over asignificant time interval; Prepresents periodic fluctuations that exhibit asimilar pattern ofdevelopment over specific periods oftime, associated with seasons. Inother words, these are deviations from the mean that occur periodically and are characterized byseasonality; Edenotes random fluctuations, which represent deviations from the mean ofthe series atspecific points intime and are caused byexternal factors. Obviously, not all time series have the same set ofcomponents. For example, the stationary series mentioned earlier inthe introduction isdescribed asfollows . t y yE = + CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 147 Inother words, the time series depends onthe mean value ofthe level, i.e., the mathematical expectation , y which remains unchanged and isconstant over the entire time interval. Furthermore, the series depends onrandom fluctuations ora random component, which can beexpressed as . t Ey y = − However, most time series are characterized bynon-stationarity and can bedescribed asfollows () , t y ft E= + where the function f(t) = T represents a time dependence describing the patterns ofchanges inthe levels ofthe time series over the entire time interval under consideration; inother words, itis atrend. Incontrast, Erepresents random fluctuations[13]. Functions describing atrend can bedivided into two groups: 1. The first group includes monotonic functions and functions that donot exhibit limiting growth. In other words, these functions continue togrow over time. 2. The second group, incontrast, exhibits limiting growth, orin other words, reaches asaturation level. Tomore accurately identify the type offunction describing atrend, the following preparatory steps are carried out: 1. Identifying the type oftime series todetermine its components. Anassessment ismade ofwhether the time series isstationary, consisting only ofrandom fluctuations and amean, ornon-stationary with atrend orperiodicity, orboth. 2. Given that periodic and random fluctuations are not considered, excess noise isremoved using smoothing methods. Todetermine the trend ofstationarity ornon-stationarity ina time series, methods for identifying the type oftime series are used. The following methods are used toachieve this goal. Before selecting amodel for atime series trend, itis important toestablish the presence ofa trend inthe series. Ifa time series follows atrend, the levels ofthe series are correlated with each other, meaning that each subsequent level depends onthe previous one. This relationship between the values ofthe series isknown asthe autocorrelation ofthe series levels. The following formula isused tomeasure the degree ofautocorrelation , tt tt tt tt yy yy yy yy r−τ −τ −τ −τ − − =σσ where 1, n tt t tt yy yy n −τ =τ+ −τ =−τ ∑ 148 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES 1, n t t t y yn =τ+ =−τ ∑ 1. n t t t y yn −τ =τ+ −τ =−τ ∑ τis the magnitude ofthe time shift and takes values asnatural numbers. For τ = 1, the first-order autocorrelation coefficient iscalculated, τ = 2 the second, and soon. This coefficient isbetween -1 and +1, and the closer itis tothem, the more pronounced the correlation is. Thus, ifthe time series has atrend, the absolute value ofthe first-order coefficient will beclose toone. Whereas for astationary time series with small fluctuations inlevels, this coefficient will beclose tozero. For further shifts τ = 2, 3,..., the periodicity ofthe time series isstudied, namely, the period ofoscillations, which isequal tothe shift atwhich the coefficient isclosest to±1. For example, amonotonically increasing ordecreasing time series will have apositive correlation coefficient close toone; however, the greater the magnitude ofthe shift, the further itwill move away from one[14]. Thus, ifthe time series has atrend, the absolute value ofthe first-order autocorrelation coefficient will beclose toone. 5.1 DEVELOPMENT TRENDS AND ANALYSIS OFTHE GRAIN PRODUCT CLUSTER The regional grain product cluster isbased onits territorial isolation and close ties between enterprises from various industries within the cluster, including those involved inthe production ofthe final product. Participants inthe regional grain product cluster include: agricultural enterprises; agricultural machinery enterprises; food processing enterprises; integrated agro-industrial complexes (corporations); consulting organizations; research institutes; educational institutions; government agencies; and financial institutions. The cluster core may include enterprises specializing ingrain production, storage, and processing, around which infrastructure organizations are concentrated. The regional cluster was formed inthree stages: the preliminary stage, during which the clustering potential isdetermined and aprogram for implementing cluster projects isdeveloped; the main stage involves activating clustering processes inthe region and determining the composition ofparticipants incluster schemes; and the final stage involves assessing the cluster’sperformance based onindicators characterizing economic development[15]. Cluster development asa tool for enhancing regional competitiveness and innovative economic development isa new approach tothe country’sregional development. The objectives ofthe Kazakhstan cluster initiative are tocreate conditions for maximizing Kazakhstan’scompetitive advantages indeveloping the non-resource sector ofthe economy byengaging private businesses inthe industry. The main grain producer inthe Republic of Kazakhstan isthe northern region: Akmola, Kostanay, and North Kazakhstan regions, which account for approximately 66% ofthe country’sgross harvest. The total area under grain crops isshown inTable 5.2. CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 149 Table 5.2 Dynamics ofgrain crop area inthe Republic of Kazakhstan, 2020–2024, thousand hectares Crop Name 2020 2021 2022 2023 2024 Specific gravity, 2024, % Wheat 11,354.4 11,296.6 12,057.1 12,719.4 12,810.6 81.4 Barley 2,517.0 2,976.8 2,728.8 2,157.5 2,175.6 13.8 Buckwheat 95.8 67.5 55.1 87.1 119.9 0.8 Grain corn 150.1 156.3 162.8 188.7 188.4 1.2 Rye 21.5 21.2 23.9 43.9 34.3 0.2 Oats 235.2 243.5 228.9 202 197.9 1.3 Millet 43.4 50.9 50.5 38.2 37.4 0.2 Sorghum (dzhugara) 3 8.3 7.8 9 17.4 0.1 Corn mixture 86.4 91.8 85.9 69.6 61.3 0.4 Triticale 1.5 0.8 1.6 5.6 6.3 0.04 Rice 101.5 102.0 102.3 99.6 87.9 0.6 Total grains 14,609.8 15,015.7 15,504.7 15,620.6 15,737 100 Source: Office for National Statistics[16, 17] Analyzing Table 5.2, the dynamics ofsown areas ofgrain crops inthe republic of Kazakhstan, wheat in2018 amounted to11,354.4 thousand hectares, and in2022 itincreased to12,810.6 thousand hectares, which isan increase of81.4% ofthe share, also showed anincrease ingrain crops buckwheat from 95.8 thousand hectares to119.9 thousand hectares, grain corn 150.1 thousand hectares to188.4 thousand hectares, rye 21.5 thousand hectares to34.3 thousand hectares, sorghum (dzhugara) 3 thousand hectares to 17.4 thousand hectares, triticale 1.5 thousand hectares to6.3 thousand hectares. The area sown tobarley decreased from 2,517,000hectares in2018 to2,175,600 hectares in2018, accounting for 13.8% ofthe total. Oats, millet, mixed cereals, and rice also saw their area sown todecrease by0.6% to1.3%. In2024, wheat will account for the largest share ofgrain sown area across all farm categories inthe the Republic of Kazakhstan (81.4%), followed bybarley (13.8%), followed byother crops, which range from 0.1% to1.3%. Fig. 5.1 shows the share ofgrain crop area across all farm categories. In2022, wheat (81.4%) accounted for the largest share ofgrain sown area across all farm categories inthe republic, followed by barley (13.8%), followed byother crops, which ranged from 0.1% to1.3%. Table 5.3 shows the dynamics of grain crops byfarm category. 150 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES 81.4 1.2 0.8 13.8 0.2 0.2 0.6 0.1 0.4 1.3 Wheat Rye SorghumBarley Oats Buckwheat Millet Grain corn Rice Corn mixture Fig. 5.1 Proportion ofgrain crop acreage inall farm categories inthe republic ofKazakhstan in2024, % Table 5.3 Dynamics ofgrain crop acreage byfarm category inthe Republic ofKazakhstan, 2020–2024, thousand hectares Category offarms 2020 2021 2022 2023 2024 2024/2020, % 2024/2023, % 1 2 3 4 5 6 7 8 Wheat All categories offarms 11,354.4 11,296.6 12,057.1 12,719.4 12,810.6 112.8 100.7 Agricultural enterprises 7,685.1 7,516.6 7,998.9 8,443.8 8,525.7 110.9 101.0 Individual entrepreneurs and peasant (farm) households 3,669.3 3,780.1 4,058.1 4,275.6 4,284.8 116.8 100.2 Barley All categories offarms 2,517.0 2,976.8 2,728.8 2,157.5 2,175.6 86.4 100.8 Agricultural enterprises 1,330.7 1,632.4 1,463.7 1,100.3 1,141.9 85.8 103.8 Individual entrepreneurs and peasant (farm) households 1,186.3 1,344.4 1,265.1 1,057.2 1,033.7 87.1 97.8 Buckwheat All categories offarms 95.8 67.5 55.1 87.1 119.9 125.2 137.7 Agricultural enterprises 38.7 24.9 26.9 47.2 61.5 158.9 130.3 Individual entrepreneurs and peasant (farm) households 57.1 42.6 28.2 39.9 58.4 102.3 146.4 Grain corn All categories offarms 150.1 156.3 162.8 188.7 188.4 125.5 99.8 CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 151  Continuation of Table 5.3 1 2 3 4 5 6 7 8 Agricultural enterprises 30.4 36.3 35.7 54.6 60.5 199.0 110.8 Individual entrepreneurs and peasant (farm) households 113.8 114.7 122 128.9 122.9 108.0 95.3 Households ofthe population 5.9 5.3 5.2 5.2 5 84.7 96.2 Rye All categories offarms 21.5 21.2 23.9 43.9 34.3 159.5 78.1 Agricultural enterprises 7.6 7.1 10.1 25.3 22.9 301.3 90.5 Individual entrepreneurs and peasant (farm) households 13.9 14.1 13.8 18.5 11.3 81.3 61.1 Oats All categories offarms 235.2 243.5 228.9 202 197.9 84.1 98.0 Agricultural enterprises 152.5 161.7 151.3 134.6 128.8 84.5 95.7 Individual entrepreneurs and peasant (farm) households 82.7 81.7 77.4 67.4 69.1 83.6 102.5 Households ofthe population – 0.1 0.2 – – – – Millet All categories offarms 43.4 50.9 50.5 38.2 37.4 86.2 97.9 Agricultural enterprises 20.0 22.4 26.5 18.0 18.3 91.5 101.7 Individual entrepreneurs and peasant (farm) households 22.8 28.1 23.5 19.8 18.5 81.1 93.4 Households ofthe population 0.6 0.4 0.5 0.4 0.6 100.0 150.0 Sorghum (dzhugara) All categories offarms 3 8.3 7.8 9 17.4 580.0 193.3 Agricultural enterprises 1.9 3.1 3.5 4.5 10.8 568.4 240.0 Individual entrepreneurs and peasant (farm) households 1.1 5.1 4.3 4.5 6.6 600.0 146.7 Corn mixture All categories offarms 86.4 91.8 85.9 69.6 61.3 70.9 88.1 Agricultural enterprises 53.3 61.5 57 43.1 46.9 88.0 108.8 158 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES x3(i)— production quantity ofmowers, including mowers mounted ona tractor, not included inother groups; x4(i)— production quantity ofrow headers; x5(i)— quantity ofproduction ofgrain harvesters. According to(5.1), the dynamics ofchanges inthe indicators ofthe production ofmaterial and technical resources for the grain product sub-complex can bepresented inthe form ofTables 5.8 and 5.9. Table 5.8 Dynamics ofchanges inthe production ofmaterial and technical resources for the grain product subcomplex inthe Republic of Kazakhstan in2016–2024 Years, i 2016 2017 2018 2019 2020 2021 2022 2023 2024 x1(i) 53.55 48.47 47.02 50.37 43.40 23.68 54.52 74.95 69.61 x2(i)52.80 54.79 50.00 2.83 76.00 90.69 56.06 48.58 46.90 x3(i)Nodata Nodata Nodata 96.00 79.49 42.59 69.20 60.56 61.67 x4(i) 55.16 39.25 56.41 55.45 45.48 57.45 53.26 52.84 67.80 x5(i) 68.65 48.12 48.37 49.90 52.66 27.85 59.06 56.59 70.05 Source: Bureau ofNational Statistics[16, 17] Table 5.9 Grain production and grain processing products inthe Republic of Kazakhstan in2014–2024, thousands oftons Years, i 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 Grain production, y(i) 12,864.8 18,231.1 17,162.2 18,673.7 20,634.4 20,585.1 20,273.7 17,428.6 20,065.3 16,375.9 Production ofego processing products 4,163.1 4,073.9 4,093.7 3,955.9 4,205.5 4,129.2 4,032.1 3,533.9 3,642.0 3,588.0 Source: Bureau ofNational Statistics[16, 17] The materials ofTable 5.9 show that grain production inthe Republic of Kazakhstan fluctuated greatly. Thus, in2018, growth was noted— by5366.3 thousand tons, or1.42 times. In2017— adecrease, in2018— growth, which lasted for the next two years, then again adecrease, etc. Ifto compare grain production in2024, then its volume was 16,375.9 thousand tons, which is10,584.6 less than in2015, or1.65 times. The 2024 indicator ranks second from the bottom after 2015 interms ofthe lowest volumes ofgrain production inthe Republic of Kazakhstan for 2015–2024. The indicator ofgrain processing production also fluctuates in the analyzed period, but not sosignificantly. Its minimum volumes were recorded in2022— 3,533.9 thousand tons, and the maximum in2019— 4,205.5, which is671.6 thousand tons less, or1.19 times. CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 159 Let’spresent the characteristics ofchanges ingrain production indicators, expressed bythe formula 100 ( 1) () , ( ) ( 1) yi yi yi yi + = ++  (5.2) where y(i) and y(i+1)— indicators ofi and i+1 consecutive years. According to(5.2), the dynamics ofchanges ingrain production indicators can bepresented inthe form ofTable 5.10. Table 5.10 Dynamics ofchanges inthe production ofmaterial and technical resources for the grain product subcomplex inthe Republic of Kazakhstan in2016–2024 Years, i 2016 2017 2018 2019 2020 2021 2022 2023 2024 ()yi  58.63 48.49 52.11 52.49 49.94 49.62 46.23 53.52 44.94 Thus, Table 5.10 received processed statistical data describing the dynamics ofchanges inthe production ofmaterial and technical resources for the grain product cluster inthe Republic of Kazakhstan in2016–2024. The purpose ofthis stage isthe analysis ofpaired regression dependencies ofchanges ingrain production indicators ( 1) yi +  ofthe (i+1)-thyear, that is, changes inthe indicators () k xi  ofmaterial and technical resources for the grain product cluster ofthe i-thyear, constructed according tothe algorithm. 5.3 REGRESSION ANALYSIS OFTHE DEPENDENCE OFGRAIN PRODUCTION INDICATORS ONTRACTOR PRODUCTION INDICATORS Thus, the regression dependence ofthe change f1(i+1) ingrain production onthe change 1 ()xi  inthe production oftractors for agriculture and forestry has the following form 2 1 12 1 () ( 1) . 0.02 ( ) 0.06 xi fi xi += +   (5.3) Differentiating the empirical function f1(i+1) on 1 ()xi  , itis possible toobtain the elasticity coefficient 112 11 1 ( 1) 0.12 () . ( 1) ( ) 0.02 ( ) 0.06 e fi K xi fi x i xi ∂+ = = +∂ +   160 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES Based onthe statistical data 1 ()xi  ofTable 5.2, the elasticity coefficient Ketakes values less than1. Therefore, if 1 ()xi  changes by1%, f1(i+1) will change byless than 1%. Itis possible topresent the data ofTable 5.2 inthe form ofa variation series. The graphical representation ofthe regression dependence can bepresented inthe form ofFig. 5.3. From Fig. 5.3 itfollows that a15-percent increase inthe production ofagricultural and forestry tractors contributes tothe growth ofgrain production. The coefficient ofcorrelation and analytical values ofpaired regression is –0.0018, which indicates the fact ofnon-correlation. Non-correlation isexplained bythe absence oflinear dependence. Atthe same time, the error relative tothe method ofaverages is0.238, that is, there are insignificant differences between the actual data and the values determined byformula (5.3). Change in the production volume of tractors for griculture and forestry 50 55 60 65 70 75 80 85 90 955 10 15 20 25 30 35 40 45 52 50 48 46 44 42 Changes in grain production indicators  Fig. 5.3 Regression dependence ofchange inf1(i+1) ofgrain production onchange 1 ()xi  inagricultural tractor production Let’sconsider the differences d1(i) between the empirical values off1(i+1), calculated according toformula (5.3), and actual grain production data () yi  11 () () (), d i fi yi= −  presented inTable 5.11. Table 5.11 Comparison ofempirical values off1(i) and actual grain production data Years, i 2016 2017 2018 2019 2020 2021 2022 2023 2024 1()fi 50.32 50.31 50.31 50.32 50.29 50.11 50.32 50.35 50.34 ()yi  58.63 48.49 52.11 52.49 49.94 49.62 46.23 53.52 44.94 1()di –8.31 1.82 –1.80 –2.18 0.35 0.49 4.10 –3.17 5.41 CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 161 Totest the hypothesis for the adequacy ofthe proposed model, itis possible toassume that the difference d1(i) isa random variable obeying aGaussian distribution with amean ofµ = –0.37 and astandard deviation ofσ = 4.12. Inother words, the probability that, for any indicator 1 ()xi  , the probability ofdeviation ofthe empirical values off1(i), calculated according toformula (5.3), from the actual grain production data ( ),yi  isdetermined bythe formula 11 1 0.37 0.37 () 1 . 4.12 4.12 dd P d erf erf −+ −−    =−+       Ina study based onPearson’sχ2, due tothe fact that the critical region for this statistic isrightsided:[Kkp;+∞), where the boundary value 2( 1; ). kp K kr=χ −−α According tothe χ2 distribution tables and the values ofσ, k = 5, r = 2 (parameters μand σare estimated from the sample), this corresponds toKkp(0.05; 2) = 5.95 for asignificance level ofα = 0.05. Due tothe fact that 0.123 (0.05;2), obs kp KK = < this confirms the adequacy ofthe hypothesis for applying the proposed model. 5.4 REGRESSION ANALYSIS OFTHE RELATIONSHIP BETWEEN GRAIN PRODUCTION INDICATORS AND THE PRODUCTION OFTRANSPLANTING EQUIPMENT The regression relationship between the change inf2(i+1) ofgrain production and the change 2 ()xi  inthe production ofseeders, planters, and transplanting machines isas follows 2 2 2 () ( 1) . 0.003 ( ) 0.0008 xi fi xi += −   (5.4) Differentiating with respect tothe empirical function f2(i+1), itis possible toobtain the elasticity coefficient ( ) 2 222 2 21 ( 1) 44.44(0.003 ( ) 0.0008) () . ( 1) ( ) ( ) 0.27 e fi xi K xi fi x i xi ∂+ − = = +∂ −    162 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES Based onstatistical data 2 ()xi  , the elasticity coefficient Ketakes values less than1. Therefore, for a1% change in 2 ()xi  , f2(i+1) will change byless than 1%. Byrepresenting the data 2 ()xi  asa variation series, agraphical representation ofthe regression relationship can beshown inFig. 5.4. Change in the production volume of seeders, planters and transplanting machines 50 55 60 65 70 75 80 85 90 9510 15 20 25 30 35 40 45 51 50.8 50.6 50.4 50.2 50 Changes in grain production indicators  Fig. 5.4 Regression dependence ofchange inf2(i+1) ingrain production onchange 2() xi  inproduction ofseeders, planters, and transplanters From Fig. 5.4, itfollows that changes inthe production ofseeders, planters, and transplanters donot contribute togrowth ingrain production. The correlation coefficient and analytical values ofthe paired regression are -0.503, indicating alack ofcorrelation. This lack ofcorrelation isexplained bythe lack ofa linear relationship. Moreover, the error relative tothe mean method is0.373, indicating minor discrepancies between the actual data and the values determined byformula (5.4). Let’sconsider the differences d2(i) between the empirical values off2(i), calculated according toformula(5.3), and the actual grain production data ()yi  22 () () (), d i f i yi= −  presented inTable 5.12. Table 5.12 Comparison ofempirical values off2(i) and actual grain production data Years, i 2016 2017 2018 2019 2020 2021 2022 2023 2024 f2(i)50.33 50.33 50.34 52.34 50.30 50.29 50.33 50.34 50.35 ()yi  58.63 48.49 52.11 52.49 49.94 49.62 46.23 53.52 44.94 d2(i)-8.30 1.84 -1.77 -0.16 0.36 0.67 4.10 -3.18 5.41 CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 163 Totest the hypothesis for the adequacy ofthe proposed model, itis possible toassume that the difference d2(i) isa random variable obeying aGaussian distribution with amean ofµ = –0.11 and astandard deviation ofσ = 4.06. Inother words, the probability that, for any indicator 2 ()xi  , the probability ofdeviation ofthe empirical values off2(i), calculated according toformula (5.4), from the actual grain production data ()yi  isdetermined bythe formula 22 2 0.11 0.11 () 1 . 4.06 4.06 dd P d erf erf −+ −−    =−+       Ina study based onPearson’sχ2, due tothe fact that the critical region for this statistic isright-sided: [Kkp;+∞), where the boundary value 2( 1; ). kp K kr=χ −−α According tothe χ2 distribution tables and the values ofσ, k = 5, r = 2 (parameters μand σare estimated from the sample), this corresponds toKkp(0.05;2) = 5.95 for asignificance level ofα = 0.05. Due tothe fact that 0.117 (0.05;2), obs kp KK = < this confirms the adequacy ofthe hypothesis for applying the proposed model. 5.5 REGRESSION ANALYSIS OFTHE DEPENDENCE OFGRAIN PRODUCTION INDICATORS ONCHANGES INMOWER PRODUCTION The regression dependence ofthe change inf3(i+1) ingrain production onthe change inmower production, including tractor-mounted mowers not included inother groupings, isas follows 3 3 1 ( 1) . 0.002 ( ) 0.004 fi xi += +  (5.5) Differentiating the empirical function f3(i+1) with respect to 3 ()xi  , itis possible toobtain the elasticity coefficient 33 2 3 ( 1) ( ). ( 1) ( ) e fi K xi fi x i ∂+ =+∂   164 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES Based onstatistical data 3 ()xi  , the elasticity coefficient Ketakes values less than 1. Therefore, with a1% change in 3 ()xi  , f3(i+1) will change byless than 1%. Byrepresenting the data 3 ()xi  asa variation series, agraphical representation ofthe regression dependence can beshown inFig. 5.5. Change in the production quantity of mowers, including tractor-mounted mowers, not included in other groups 50 55 60 65 70 75 80 85 90 9510 15 20 25 30 35 40 45 0.0760877 0.0760877 0.0760877 0.0760877 0.0760877 0.0760877 0.0760877 0.0760877 Changes in grain production indicators  Fig. 5.5 Regression dependence of change in f3(i + 1) of grain production on change 3 ()xi  in mower production, including tractor-mounted mowers not included in other groupings Fig. 5.5 shows that the change inmower production, including tractor-mounted mowers not included inother groupings, is0.603. The correlation coefficient and analytical values ofthe paired regression are 0.603, indicating alack ofcorrelation. This lack ofcorrelation isexplained bythe lack ofa linear relationship. Moreover, the margin oferror relative tothe mean method is1.88, indicating significant discrepancies between the actual data and the values determined byformula (5.5), which are explained bythe small sample size ofthe observed indicator. Let’sconsider the differences d3(i) between the empirical values off3(i), calculated according toformula (5.3), and the actual grain production data ()yi  33 () () (), d i f i yi= −  presented inTable 5.13. Table 5.13 Comparison ofempirical values off3(i) and actual grain production data Years, i2019 2020 2021 2022 2023 2024 f3(i) 47.65 47.65 47.65 47.65 47.65 47.65 ()yi  52.49 49.94 49.62 46.23 53.52 44.94 d3(i)-4.85 -2.29 -1.97 1.42 -5.87 2.71 CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 165 Totest the hypothesis for the adequacy ofthe proposed model, itis possible toassume that the difference d3(i) isa random variable obeying aGaussian distribution with amean ofµ = –1.81 and astandard deviation ofσ = 3.37. Inother words, the probability that, for any indicator 3 ()xi  , the probability ofdeviation ofthe empirical values off3(i), calculated according toformula (5.5), from the actual grain production data ()yi  isdetermined bythe formula 33 3 0.81 0.81 () 1 . 3.37 3.37 dd P d erf erf −+ −−    =−+       Ina study based onPearson’sχ2, due tothe fact that the critical region for this statistic isrightsided:[Kkp;+∞), where the boundary value 2( 1; ). kp K kr=χ −−α According tothe χ2 distribution tables and the values ofσ, k = 5, r = 2 (parameters μand σare estimated from the sample), this corresponds toKkp(0.05; 2) = 5.95 for asignificance level ofα = 0.05. Due tothe fact that 0.551 (0.05;2), obs kp KK = < this confirms the adequacy ofthe hypothesis for applying the proposed model. 5.6 REGRESSION ANALYSIS OFTHE DEPENDENCE OFCHANGES INGRAIN PRODUCTION INDICATORS ONCHANGES INROW HEADER PRODUCTION INDICATORS The regression dependence ofchanges inf4(i+1) ingrain production onchanges in 4 ()xi  ofrow header production isas follows 4 4 1 ( 1) . 0.002 ( ) 0.004 fi xi += +  (5.6) Differentiating the empirical function f4(i+1) with respect to 4 ()xi  , itis possible toobtain the elasticity coefficient 44 4 4 ( 1) ( ). ( 1) ( ) e fi K xi fi x i ∂+ =+∂   166 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES Based onstatistical data 4 ()xi  , the elasticity coefficient Ketakes values less than1. Therefore, with achange in 4 ()xi  by1%, f4(i+1) will change byless than 1%. Byrepresenting the data 4 ()xi  asa variation series, agraphical representation ofthe regression dependence can beshown inFig. 5.6. Changes in the production of row headers 50 55 60 65 70 75 80 85 90 9510 15 20 25 30 35 40 45 50.45 50.40 50.35 50.30 50.25 50.20 50.15 50.15 Changes in grain production  Fig. 5.6 Regression dependence ofchange inf4(i+1) ofgrain production onchange 4 ()xi  inrow header production The correlation coefficient and analytical values ofthe paired regression are 0.11, indicating alack ofcorrelation. This lack ofcorrelation isexplained bythe lack ofa linear relationship. Moreover, the error relative tothe average method is1.29, indicating minor discrepancies between the actual data and the values determined byformula (5.6). Let’sconsider the differences d4(i) between the empirical values off4(i), calculated according toformula(5.6), and the actual grain production data ()yi  44 () () (), d i f i yi= −  presented inTable 5.14. Table 5.14 Comparison ofempirical values off4(i) and actual grain production data Years, i2016 2017 2018 2019 2020 2021 2022 2023 2024 f4(i)50.14 50.17 50.14 50.14 50.16 50.14 50.14 50.14 50.12 ()yi  58.63 48.49 52.11 52.49 49.94 49.62 46.23 53.52 44.94 d4(i)-8.49 1.68 -1.97 -2.35 0.22 0.52 3.92 -3.37 5.18 Totest the hypothesis for the adequacy ofthe proposed model, itis possible toassume that the difference d4(i) isa random variable obeying aGaussian distribution with amean ofµ = –0.52 and astandard deviation ofσ = 4.12. CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 167 Inother words, the probability that, for any indicator 4 ()xi  , the probability ofdeviation ofthe empirical values off4(i), calculated according toformula (5.6), from the actual grain production data ()yi  isdetermined bythe formula ( ) 00 0 0.52 0.52 1. 4.12 4.12 dd P d d erf erf −+ −−    >=− +       When studying using the Pearson criterion, due tothe fact that the critical region for this statistic isright-sided:[Kkp;+∞), where the boundary value 2( 1; ). kp K kr=χ −−α According tothe χ2 distribution tables and the values ofσ, k = 5, r = 2 (parameters μand σare estimated from the sample), this corresponds toKkp(0.05;2) = 5.95 for asignificance level ofα = 0.05. Due tothe fact that 0.134 (0.05;2), obs kp KK = < this confirms the adequacy ofthe hypothesis for applying the proposed model. 5.7 REGRESSION ANALYSIS OFTHE DEPENDENCE OFCHANGES INGRAIN PRODUCTION INDICATORS ONCHANGES INCOMBINE HARVESTER PRODUCTION INDICATORS The regression dependence ofchanges inf5(i+1) ingrain production onchanges in 5 ()xi  ofcombine harvester production isas follows 4 1 ( 1) . 0.002 ( ) 0.004 yi xi += +   (5.7) Differentiating with respect tothe empirical function f5(i+1), itis possible toobtain the elasticity coefficient 55 5 5 ( 1) ( ). ( 1) ( ) e fi K xi fi x i ∂+ =+∂   Based onstatistical data 5 ()xi  , the elasticity coefficient Ketakes values less than 1. Therefore, with a1% change in 5 ()xi  , f5(i+1) will change byless than 1%. 174 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES — the grain product cluster lacks the necessary methods and information toaccurately determine the range and volume ofagricultural crop production; — state agricultural management bodies use ineffective forecasting and strategic marketing methods. Animportant objective ofstate agricultural policy isto improve the quality and competitiveness ofagricultural products and improve the well-being ofrural residents. The effective functioning ofthe grain product cluster isimpossible without active government intervention. Inthe conditions ofthe functioning ofa socially-oriented market economy, the possibility ofstate regulation isobjectively determined bythe nature ofa mixed economy, which ischaracterized bya combination ofcompetition, freedom ofchoice ofbuyer and seller with the need for the state toensure equal “rules ofthe game” for all economic entities inthe grain product cluster and social protection for the low-income part ofthe population. Under these conditions, the development ofeconomic relations inthe grain product cluster isassociated with the emergence ofa number ofcontradictions: — the desire ofeconomic entities inthe grain sector toachieve leadership positions, which leads tothe replacement ofperfect competition with monopoly, which isunacceptable ina market economy; — the differentiation ofeconomic actors, the ruin ofsome ofthem resulting from fierce competition, and the need for social protection for low-income groups; — the limited regulatory impact ofthe market mechanism onthe reproduction process, which fails toensure environmental safety, the development offundamental science, education, healthcare, etc.; — the consolidation ofcapital for the development ofscientific and technological progress, the implementation ofits most significant achievements inthe form ofvarious innovations. The elimination ofthese contradictions cannot beachieved through market self-regulation. They require appropriate action from society, represented bythe state. Creating favorable conditions for the production and promotion ofagricultural products onthe market and providing after-sales service tocustomers contribute toincreasing their competitiveness. It’sclear that, given the same prices, the highest-quality product will bein greatest demand. Therefore, agro-industrial enterprises should pay significant attention toanalyzing and assessing their competitiveness. State support for the development ofeconomic entities within the grain product cluster atthe regional level serves two main functions: compensatory (reimbursement ofa portion ofacquisition and construction costs) and incentive (reimbursement ofa portion ofproduction costs). Clusters are recognized asan important tool for promoting innovation, industrial development, competitiveness, and economic efficiency. The main goal ofcluster support isto increase the competitiveness ofcluster participants and the regional economies asa whole. The development ofcluster initiatives inKazakhstan can bedivided into three stages. Inthe first stage, from 2006 to2012, clusters were formed inpriority economic sectors. Inthe second stage, from 2014 to2020, territorial clusters were formed inthe regions. Asa cluster development operator, QazIndustry facilitated the consolidation ofregional enterprise groups into territorial clusters toenhance the competitiveness ofenterprises and their products. Together with cluster participants, project pools were developed for further financing with the participation ofboth the state and the clusters. The third stage began in2020 and iscurrently ongoing. This stage ischaracterized bythe formation ofa methodological and legal platform for the operation ofterritorial CHAPTER 5. PRODUCTION CLUSTER IN THE AGRO-INDUSTRIAL COMPLEX AS A FACTOR IN ENSURING FOOD SECURITY 175 clusters and the provision ofstate incentives. InJune ofthis year, the Rules for the Competitive Selection ofTerritorial Clusters, aswell asthe Rules for the Formation and Maintenance ofa Register ofTerritorial Clusters, were approved within the framework ofthe Law “OnIndustrial Policy”. The rules provide for co-financing ofthe costs ofjoint projects byterritorial cluster participants, with upto 50% ofcosts (upto 30,000 MCI) reimbursed bythe state. Cluster policy participants are entitled toco-financing ofup to50% ofcosts (upto 3,000 MCI) tosupport the functioning ofthe cluster organization. Funding isalso planned for the implementation ofa project tomodernize shared laboratories for testing and evaluating products from regional cluster participants (upto 40,000 MCI). QazIndustry regularly provides analytical, informational, consulting, and technical support tothe pilot regional clusters. CONCLUSIONS Astudy ofthe grain product cluster inthe Republic of Kazakhstan using mathematical modeling aims toprovide a high-quality forecast tosubstantiate effective development scenarios. Todetermine amethodology for assessing the state ofthe grain product cluster, statistical processing ofactual data onthe components ofthe grain product cluster inthe agro-industrial complex and approximation ofa functional relationship smoothing the actual data were conducted. The tasks are solved using probabilistic statistical research methods. Ofall existing methods, two-parameter regression modeling ineconomic research was selected. Ingeneral, two-parameter regression isa simple and effective tool for analyzing and evaluating economic data, which can beparticularly useful insituations oflimited resources. The obtained research results are based onanalytical functions representing paired nonlinear regressions ofthe relationships between changes ingrain production indicators and changes inmaterial and technical resource indicators for the grain product cluster inthe Republic of Kazakhstan. Consequently, the functions determine trends and, thereby, provide aset offorecasts for changes ingrain production. The value ofeach ofthese atthe point corresponding tothe generalized average for argument xis determined using econo mic and mathematical modeling for processing observation results. The developed grain production optimization model, based onmodern mathematical modeling technologies, represents animportant and effective analytical method. Its advantage lies inits ability toprovide adetailed and in-depth assessment ofthe grain product sector’sperformance, taking into account numerous key aspects. The model not only accurately assesses the current state ofthe grain product cluster but also provides apowerful forecasting tool. Its versatility allows for the successful application ofthe method across various industries and fields ofactivity, opening upnew opportunities for additional research and analysis. Inconclusion, itshould benoted that inAddress tothe Nation “AFair Kazakhstan: Law and Order, Economic Growth, and Social Optimism”, the President ofthe Republic ofKazakhstan emphasized the need for systemic efforts tounlock the country’sindustrial potential. K.-J. Tokayev highlighted alist of17 major projects compiled bythe government, with aparticular emphasis onthe development ofhigh-value added value. Animportant point isthe maximum use ofdomestic raw materials and components, aswell asthe development ofrelated industries around large enterprises. 176 INTEGRATIVE OPPORTUNITIES OF NATIONAL ECONOMIES The construction ofa deep wheat processing plant inKostanay isa large-scale undertaking, designed toprocess 415,000 tons ofwheat per year. The project operator isKostanay Grain Industry LLP. Completion isscheduled for 2027, and the total investment is70 billion tenge. The plant will produce several types ofproducts: lysine (40,000 tons per year), gluten (35,300 tons per year), bioethanol (60,000 tons per year), carbon dioxide (56,000 tons per year), feed vinasse (99,000 tons per year), and bran (28,000 tons per year). Upon completion ofconstruction and reaching design capacity, 650 permanent jobs are planned tobe created. Gluten, ahigh concentration ofwhich isfound inKazakh grain, has awide range ofglobal applications inits pure form. The products are sold inEurope and the Americas. Inaddition togluten, deep grain processing will yield glucose-fructose syrup, wheat starch, modified starch, and bran. This isa virtually wastefree process. The project concept has already been developed, technology and equipment suppliers have been identified, and negotiations are underway. The design and integration ofthe basic designs will becarried out with the participation ofthe Austrian company Vogelbusch. Process engineering isalso actively underway with local and international equipment suppliers. Amemorandum ofcooperation has been signed between the KazFoodProducts group ofcompanies, the Chinese company Myande Group, and the Akimat ofthe Kostanay region. This partnership strengthens the project’sinternational ties and opens upopportunities for the application ofadvanced technologies atall stages ofthe plant’sconstruction and operation. 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