Analysing the influence of agroclimatic indicators on maize (Zea Mays) yield variability in Slovenia: A Principal component approach

Weather conditions are a fundamental determinant of crop yields, with temperature, precipitation, and solar radiation playing critical roles in influencing plant growth and development. Extreme weather events exacerbate these effects, leading to substantial losses. In the case of maize, Slovenia's second most cultivated crop, prolonged high temperatures, especially during the flowering and grain-filling stages, significantly impede development and yield. Elevated temperatures also exacerbate water stress by increasing the vapor pressure deficit and transpiration rates. Similarly, the quantity and distribution of precipitation are vital, affecting crop viability and potentially leading to issues such as soil depletion and disease in the event of excessive rainfall. The basis of this study was a set of 32 agroclimatic indicators, acquired based on an in-depth literature review to describe the influence of climate on agriculture, specifically plant production. Additionally, principal component analysis (PCA) was used as a method for reducing the dimensionality of large, correlated datasets like agroclimatic indicators by transforming them into uncorrelated components ordered by explained variance. Therefore, this study aimed to identify the most significant agroclimatic indicators affecting maize yield variability in Slovenia through the application of PCA and correlation analysis, thereby providing insights for agricultural planning and climate adaptation. The analysis was conducted on historical climate data (1981–2010) and climate model projections for average temperature, minimum and maximum temperature, evapotranspiration, and precipitation. The climate projections included six sets of regionally downscaled model results from the EURO-CORDEX project for the RCP4.5 and RCP8.5 scenarios for the periods 2041–2070 and 2071–2100. Maize yield data comprised two sets of long-term field experiment data for 1993–2023.

This work was supported by the Slovenian Research Agency, Research Program P4−0085 and Research Project V4-2423.