Article

Received 10.04.2023

Revised 07.07.2023

Accepted 29.08.2023

Retrieved from Volume 27, No. 3, 2023

Pages 31 -45

Pichura, V., Potravka, L., Domaratskiy, Ye., & Petrovas, S. (2023). Spatiotemporal patterns and vegetation forecasting of sunflower hybrids in soil and climatic conditions of the Ukrainian Steppe zone. Ukrainian Black Sea Region Agrarian Science, 27(3), 31-45. https://doi.org/10.56407/bs.agrarian/3.2023.31

Spatiotemporal patterns and vegetation forecasting of sunflower hybrids in soil and climatic conditions of the Ukrainian Steppe zone

Vitalii Pichura Larysa Potravka Yevhenii Domaratskiy Spartakas Petrovas

Long-term studies of tillage and crop management are essential in finding out which crop production practices would contribute to sustainable yields and profits. In the conditions of climate change, such issues as selection, forecasting and adjustment of crop cultivation systems in the zone of moisture deficit and agricultural risk management are especially relevant. Therefore, the aim of the study was to establish spatiotemporal patterns of vegetative development of sunflower hybrids and predict their productivity in the soil and climatic conditions of the Ukrainian Steppe. A detailed analysis of seasonal changes in the values of the normalized difference vegetation index in sunflower hybrid crops during the 2019-2021 time period was carried out with the help of space images from the Sentinel 2 satellite device, and then processed with the ArcGis 10.6 licensed software product. The credibility of the achieved results of the condition of crops in different phases of plant vegetation on the basis of NDVI and the possibility of their use for forecasting the yield of agricultural crops have been proven. The adjustment capabilities of various sunflower hybrids to the STeppe soil and climate conditions were determined, particularly in regards of such hybrids as Oplot, Hektor, DSL403, P64GE133, 8X477KL. A model of the yield forecasting function for each sunflower hybrid was developed according to the annual level of moisture supply. The level of data approximation of the forecasting models was 97.2-99.9%. It is suggested to use system functional models developed specifically for different moisture supply and plant nutrition conditions in order to forecast of the yield of sunflower hybrids according to a particular situation. The results can be used to improve the methodology of researching the vegetation of agricultural crops, to validate crop rotation, to choose the best practical ways for the use of multifunctional growth-regulating substances, to define the climatic adjustment of cultivars and hybrids, to manage resources, to develop adaptive climate technologies in agriculture and crop production, to calculate their efficiency, to forecast the yield and to ensure the profitability of agricultural production in the moisture deficit zone and managing a high-risk farming

crop production; climate; remote sensing; satellite images; modeling

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