Myroslava Gladka

This paper describes approaches to improving the efficiency of agricultural enterprises. It has been established that the use of technology transfer, in particular, precision farming technologies, makes it possible to enhance the efficiency of agricultural enterprises, in particular, to increase the yield of agricultural crops and, accordingly, to improve the profitability of enterprises. As an example, the activities of agricultural enterprises in the Republic of Kazakhstan were analyzed. The share of agricultural enterprises in the Republic of Kazakhstan that use elements of precision farming technology in their activities was determined. It was established that since 2019, the use of precision farming technologies in the activities of agricultural enterprises in the Republic of Kazakhstan has intensified. At the same time, from 60 to 75 % of enterprises already use elements of precision farming technologies in their activities. Using crop yield data for the past 32 years, estimates of the effectiveness of using precision farming technologies by agro-enterprises in the Republic of Kazakhstan were constructed based on forecasting yield indicators using the linear-weighted moving average method. The efficiency of using precision farming technologies in the activities of agricultural enterprises in the Republic of Kazakhstan in 2022 reached 8.46 %, and the average efficiency for the period 2019–2022 was 4.21 %. Therefore, the use of precision farming technologies makes it possible to improve the validity of decision-making in the management of an agricultural enterprise and to obtain a higher profit from the sale of produced agricultural products for any agricultural enterprise in the world. On average, the results allow us to estimate the possible profit of an agro-enterprise when growing agricultural crops in the case of using precision farming technology

The task of calculating the productivity of collective scientific subjects is a relevant issue in scientometrics. This study formalized the problem of assessing productivity trends of collective scientific subjects. The TWPR-CI method for calculating the performance based on the modified PageRank algorithm is described. Formulas for calculating productivity have been derived that make it possible to take into account a change in the productivity of collective scientific subjects over time. The indicators of the basic average absolute change in performance and the chain average relative change in performance were chosen as the basis. To select promising, from the point of view of scientific work, collective subjects, preference is given to those whose basic average absolute change in productivity is positive or the chain average relative change in productivity exceeds unity. Verification of the method for assessing performance trends of collective scientific entities based on the modified PageRank algorithm using the public dataset Citation Network Dataset was carried out. The dataset includes more than 5 million scientific publications and 48 million citations. The citation of scientific publications of 27,500 collective scientific subjects for the period from 2000 to 2022 was analyzed. For this period, for 15 selected collective scientific subjects, performance is calculated using the TWPR-CI method, as well as estimates of productivity trends based on their average relative change. There are three classes of collective scientific subjects according to productivity trends. The results indicate the relevance of the proposed method for quantifying the productivity trends of collective scientific entities (higher education institutions, scientific institutes, laboratories, and other institutions engaged in scientific activities)