Crop Yield Forecasting for Major Crops in Ukraine

Abstract

The annual harvest growth is very important for the development of the agricultural sector of each country. To ensure its growth, there is a need for modern monitoring of agricultural land indicators. These indicators are usually obtained directly from local agronomists, but this method of collecting information is too long and complicated. As a state-ofthe-art alternative is the use of remote sensing data from satellites. Using remote sensing technologies, it is possible to predict the yield of major crops in the studied areas by classical methods of machine learning, in particular, regression analysis. In this paper, a regression model for yield forecasting for the territory of Kyiv region (Ukraine) will be create based on vegetation indices obtained from satellites data. The main purpose is to assess the reliability of the obtained model. The paper will refute or confirm the feasibility and effectiveness of the satellite data using in regression models for crop yield forecast for Kiev region.

Analyzing the Interplay of Technology Adoption, Farmer Training, Market Access, and Crop Yield: A Quantitative Survey in Agribusiness

“…This level of data-driven decision-making allows for a more tailored and responsive approach to cultivation, mitigating risks and maximizing yields (Nikiema, 2021). Moreover, the adoption of genetically modified crops designed for resistance to pests, diseases, or environmental stresses further exemplifies the transformative impact of technology on crop yield, promising increased resilience and productivity in the face of evolving agricultural challenges (Shelestov et al, 2021).…”