The influence of agro- and meteorological factors on sunflower yield

Sunflower is one of the leading oilseed crops in Ukraine, and the stability of its yield is of important economic and food importance. In conditions of increasing climate variability, the need for quantitative analysis of the influence of agrotechnical and meteorological factors on the development of the yield is becoming more urgent. The purpose of the study was to quantitatively assess the role of agrotechnical and climatic factors in the development of sunflower yield using statistical analysis methods. The empirical basis of the study was data for 2007-2024, including sunflower yield indicators, levels of mineral and organic fertilisers, and meteorological characteristics (soil temperature in May, precipitation, air moisture saturation deficit, Selyaninov hydrothermal coefficient, wind speed, number of clear days during the growing season). The study used descriptive statistics, the Shapiro-Wilk test, Pearson and Spearman correlation analysis, and multivariate linear regression. The results of the study showed that the average yield level for the period was 25.65 q/ha with a standard deviation of 5.91 q/ha. The most stable and statistically significant effect on sunflower yield was the level of mineral fertiliser application, for which a moderately strong positive relationship was recorded (r=0.656). Among meteorological factors, the most noticeable positive relationship was found for wind speed (r=0.512). The multicollinearity test using the variance inflation coefficient confirmed the correctness of the model, since the values for most variables did not exceed 4, and the maximum indicator was 5.17 for mineral fertilisers. The analysis emphasised the key role of agrotechnical management, since the influence of factors such as Selyaninov hydrothermal coefficient (r=0.083) and moisture deficit (r=0.092) turned out to be weak within the paired analysis. The practical value of the results lies in the possibility of using the developed regression models to predict sunflower productivity and optimise mineral nutrition systems in order to minimise risks caused by adverse meteorological factors