The article contains the analitics of the results of the Moscow Halal Expo 2016 event, organized by Alif Consult LLC
Russia's agriculture produces around 3.7 per cent of the country's GDP, employs 9.2 per cent of the national workforce and contributes around 6 per cent of the country's exports. The sector has shown remarkable resilience in the face of wider economic turbulence. Self‐sufficiency rates for the main agricultural commodities are relatively high. Agricultural exports have grown very significantly since 2000 especially for wheat and meslin (wheat and rye mixture). Meat production has been growing steadily, particularly in the poultry and pork sectors. Whilst the agri‐food sector has great potential to play an even more prominent role in Russia's economy, it suffers from relatively low productivity and an outdated technological base. The main drive for efficiency has come mainly from the relatively large‐scale agricultural firms, who generated more than half of the total value of agricultural output in 2016. Foreign policy instability, including economic sanctions, the devaluation of the national currency and declining economic growth have weakened the sector and caused an increase in the prices of imported goods and equipment. At the same time Russian products have replaced high value‐added imports and Russia's agricultural producers are expanding into new markets.
The rapidly increasing heterogeneous information volumes make it acute to generalize large amounts of data in order to be able to make strategically proper decisions. Information flows aggregation using traditional analytical tools is becoming difficult. As a result, a lot of new automated data analysis applications, including text-mining tools, are developing. The system of intellectual text data analysis iFORA, developed in ISSEK NRU HSE, is an example of such tool. iFORA capabilities are demonstrated on the beet sugar analysis case.
Effective management of scientific and technological advancement of Russian agricultural production requires the anticipating monitoring of the existing informational and analytic media in the top-priority spheres of the agriculture. Increasing necessity in the calculation and application of objective and reliable analytical data for the strategic decision making at different levels is forcing the integration of applied analytical tools into analytical systems. These tools are versatile and primarily based on the automatic data processing. The analytical system of text mining is presented as an example of intellectual data analysis and its opportunities