SCIENTIFIC COLLECTION «INTERCONF» | № 41 Nurgozhayev Azamat Serikovich PhD student University of International Business, Republic of Kazakhstan RISK ASSOCIATED WITH THE DIGITALIZATION OF AGRICULTURE SECTOR OF ECONOMY IN KAZAKHSTAN Abstract. This article describes about digitalization of agriculture sector of economy in Kazakhstan. The aim of study to determine risk associated with digitalization of agriculture sector of Kazakhstan. Author found that there are five regions without high risk; Kostanay region has high risk rate in five risk category; Almaty region has no plan of risk mitigation, monitoring, management if arose any types of risk associated digitalization of agriculture. Scientific novelty of the article is the author applied risk evaluation methods in detection of risk. Used risk types taken from other researchers work and adapted for this research. Risks are calculated for each region separately and identified regions with low, moderate, high risk. It is important to introduce government body and scientific community with results of investigation. Other research works can be built and directions of program can be corrected upon obtained result. Keywords: digital agriculture, risk analysis, risk by regions. Introduction. In modern world, digitalization are trend in many sectors of economy. Moreover, many states are building strategies towards the digitalization of the economy as a whole. Of course, the agricultural sector of Kazakhstan is no exception. According to state program “Digital Kazakhstan”, expected increase in productivity on agriculture sector is 82 percent and rise in food export is 69 percent after accomplishment (Digital Kazakhstan, 2017). Nowadays, digital technologies developed and files like long-term climate prognosis, plant cultivation models, sensing element on households, local meteorological station information, datum about struggle with wrecker, GIS cartography technology, sector archival data, current consumption data, mass media information can be integrated and analyzed (Kitchin 2014; Sonka 2015; Wolfert et al. 2017). Digital tools make much easier to get, exchange, collect, analyze information quickly. People never seen fast results combined large data on board computer before digital era. 63 SCIENTIFIC HORIZON IN THE CONTEXT OF SOCIAL CRISES Since the adoption of the state program, digitalization of agriculture has examined by research groups only from the useful side. Nevertheless, we should not forget that risk involve probable gain and probable harm. On the one hand, digital technologies are useful, but on the other hand, risk emerges too. Literature review Actually, there are certain source of dangers affects to agriculture: business, production, environmental, personal, credit, political, marketing risk (Baquet et al, 1997; Hardaker et al 2004). Moreover, systematic consists credit, political and economic risks, in case of unsystematic divided into production and personal risks (Laura, 2012). Especially, when it comes to treats refer to digitalization names of risks changes. Emma et al (2016) tried to identify main costs and risks, which have direct concern big data. They divided into three categories all risks: social and institutional, technical, and financial. According to their work social and institutional category separated into data ownership and use, lack of collaboration and information sharing, transferability, asymmetry within industry, open access to data, skills and capability, international competition. Similarly digital infrastructure limited, not maximizing current data, privacy and security concerns, interoperability, agricultural data fragmented, immature technology, concerns about data accuracy, data storage and handling, concerns about third party refer to technical risks. In the case of financial risks, they considered following items concerns about third party use of and profit from on farm data, potential loss of competitive advantage, value proposition not clear, new technologies expensive and mistakes are costly (Emma et al, 2016). In accordance with Aysha et al (2018) big data related risks of Australia are: risk to be absorbed externally; risk associated with trust; infrastructure constraint risk. First of all, Australia will be under threat, if it does not have domestic potential to manage and control large data set. Otherwise, it will bring to week competitiveness of Australia in international level and foreign market players supply. Consequently, foreign competitors will be preferred compared to Australian. 64 SCIENTIFIC COLLECTION «INTERCONF» | № 41 Therefore it must adapt competitive products as per local demand and develop own platforms. Secondly, Australia can face problem in information storage procedures. The main point is the reliability of the working system, which is trustworthy and does not violate rights of farmers. Finally, infrastructure restrictions concerned to internet access in rural and distant locations. Since we know that Australia has a large territory, it is obvious that it will be difficult for it to provide the entire population with the Internet (Aysha et al, 2018). The potential risk of cyberattack raises on agriculture sector as soon as smart devices start to work in conjunction with smart markets. Hackers can use agricultural drones, Artificial Intelligence, Internet of Things, other smart device for own purpose and break systems on farms. It makes vulnerable all farmers across the globe (Molly M. et. al, 2019). Precision agriculture targets to lower expenses, working force and risk in growing up larger harvest with rose output, but also it has negative aspect as increasing cybersecurity aspect too (Mutschler and Department of Homeland Security, 2018). Some people from business areas announced about risks, challenges, issues of digital agriculture too. About 15 Challenges and Issues of Precision Agriculture depicted by CEO of Teknowledge mobile apps company. Energy depletion risk noted among other problems (Hussain, 2017). Materials and Methods. The theoretical risks analysis is built on the basis of well-known academic research methods: scenario analysis, matrix of risk evaluation, fuzzy matrix, comparative analysis and generalization. The techniques of induction, modeling and synthesis are applied in this article. First-hand data collected through questionnaire and used to calculate risk. Formula for calculation: Risk = Impact x Probability. Main body of article constructed per figure 1. On first stage, the author defined agricultural risks, on second phase calculated and ranked risk after gathering data, finally, fulfilled the table of combined risk category as seen in the model. Main body of article constructed per figure. 65 SCIENTIFIC HORIZON IN THE CONTEXT OF SOCIAL CRISES Reveal of agricultural risk factors Scenario analysis method Rating of agricultural risk factors Matrix of risk evaluation Combined risk evaluation of Fuzzy matrix agricultural risks Fig. 1. Model of risk evaluation process in Kazakhstan (Laura, 2012) Research results Table 1 Fuzzy matrix of Impact*Probability Impact Probability Negligible Moderate Clear Critical Catastrophic Very high 0.09 0.27 0.45 0.63 0.81 High 0.07 0.21 0.35 0.49 0.63 Meduim 0.05 0.15 0.25 0.35 0.45 Low 0.03 0.09 0.15 0.21 0.27 Very low 0.01 0.03 0.05 0.07 0.09 Note: author calculated based on first hand data There are five coefficients (0.01, 0.03, 0.05, 0.07, 0.09) correspond to low risk, five coefficients (0.15, 0.21, 0.25, 0.27, 0.35) fit to moderate risk, four coefficients (0.45, 0.49, 0.63, 0.81) match to high risk pursuant to table 2. In addition, moderate and high risks are marked with yellow and red color on table 1 respectively. In case of area, which belong to low risk have no paint. 66 SCIENTIFIC COLLECTION «INTERCONF» | № 41 Table 2 Low risk exposed regions of Kazakhstan Risk summary Probability*Impact Risk category RMMMP Regions Lack of qualified 0.03, 0.09 Known2 Have a plan Turkistan, Atyrau personnel 0.03; 0.09 Pred.; No plan Almaty; North Kazakhstan Known Unemployment - - Have a plan - of unskilled 0.01 Un. No plan Almaty peasant Cyber crime 0.03 Un. Have a plan West Kazakhstan 0.01, 0.03; 0.03 Un.2; No plan Atyrau, East Kazakhstan; Zhambyl Known Conflict between 0.012 Pred.2 Have a plan Kyzylorda, Mangystau farmers due to 2 GPS coordinates 0.01, 0.03; 0.01, Pred. ; No plan Zhambyl, East Kazakhstan; Turkistan, 0.03, 0.09; 0.03 Un.3; Known Atyrau, North Kazakhstan; Almaty Digital 0.01, 0.09; 0.09; Pred.2; Un.; Have a plan Kyzylorda, North Kazakhstan; Atirau; infrastructure 0.05 Known Zhambyl limit 0.01, 0.09; 0.03 Pred.2; Un. No plan East Kazakhstan, Pavlodar; Turkistan Concerns about 0.01, 0.09; 0.05 Pred2; Have a plan Kyzylorda, Pavlodar; Aktobe data accuracy Known 0.032; 0.03; 0.01 Pred.2; Un.; No plan Almaty, North Kazakhstan; Atyrau; Known East Kazakhstan. Data storage and 0.012, 0.09 Pred.3 Have a plan Kyzylorda, Mangystau, Pavlodar processing 0.09; 0.09; 0.07, Pred.; Un.; No plan North Kazakhstan; Atyrau; 0.03 Known2 Turkistan, East Kazakhstan The value 0.012, 0.07 Predicted3 Have a plan Kyzylorda, Mangystau, Aktobe proposition is 0.03, 0.09; Pred.2; No plan Almaty, North Kazakhstan; unclear 0.03; 0.01 Un.; Known Atyrau; East Kazakhstan Asymmetry 0.092, 0.01, 0.07 Predicted4 Have a plan Zhambyl, North Kazakhstan, Kyzylorda, within industry Aktobe 0.01; 0.092,0.03; Pred.; No plan Almaty; Atyrau, Pavlodar, West 0.01, 0.03 Un.3; Known2 Kazakhstan; Turkistan, East Kazakhstan Energy depletion 0.03, 0.01; 0.03 Pred.2; Have a plan Kyzylorda, Mangystau;