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The paper discloses a new approach to emerging technologies identification, which strongly relies on capacity of big data analysis, namely text mining augmented by syntactic analysis techniques. It discusses the wide context of the task of identifying emerging technologies in a systemic and timely manner, including its place in the methodology of foresight and future-oriented technology analysis, its use in horizon scanning exercises, as well as its relation to the field of technology landscape mapping and tech mining. The concepts of technology, emerging technology, disruptive technology and other related terms are assessed from the semantic point of view. Existing approaches to technology identification and technology landscape mapping (in wide sense, including entity linking and ontology-building for the purposes of effective STI policy) are discussed, and shortcomings of currently available studies on emerging technologies in agriculture and food sector (A&F) are analyzed. The opportunities of the new big-data-augmented methodology are shown in comparison to existing results, both globally and in Russia. As one of the practical results of the study, the integrated ontology of currently emerging technologies in A&F sector is introduced. The directions and possible criteria of further enhancement and refinement of proposed methodology are contemplated, with special attention to use of bigger volumes of data, machine learning and ontology-mining / entity linking techniques for the maximum possible automation of the analytical work in the discussed field. The practical implication of the new approach in terms of its effectiveness and efficiency for evidence-based STI policy and corporate strategic planning are shortly summed up as well.

The introduction of digital technologies in the context of limited investment resources in agriculture requires an informed choice of specific goods and services in a complex saturated market. Traditional methods of expert assessments often lead to inconsistency of expert opinions and difficulty in making decisions. Therefore, the purpose of the article is to develop and test a methodology for choosing digital technologies for agriculture (using the example of software for performing cadastral works). To achieve the goal, the methodology of quadratic penalties was used. At the first stage of the study, the most important criteria for evaluating software were selected (performance, analytical capabilities, taking into account Russian legislation). These are functions that are not obvious to a non-professional buyer, which significantly affect the efficiency of cadastral work on agricultural land. At the second stage of the study, several variants of the programs were directly evaluated and the most effective ones were selected (with a minimum square-law penalty). Their use will allow not only drawing up documents for cadastral registration, but also to determine the exact boundaries of the fields for fair taxation, work planning. The research results can be used to substantiate decisions on the choice of certain digital technologies by agricultural enterprises.