Maize response patterns to soil and climate factors form the basis for predicting changes in its growing conditions under climate change
Abstract
The present study elucidates the mechanisms by which soil and climatic factors determine the suitability of the Poliss i a and Forest-Steppe regions in Ukraine for the cultivation of maize, and present s predictive models of how these conditions will shift under global climate change. Spatial modelling was performed using CROPGRIDS v1.08 data (maize sowing density), the WorldClim v2.1 database (19 bioclimatic indicators), and SoilGrids v2.0 ( nine chemical and physical soil parameters at 5–15 cm depth). The screening of climate variables was conducted through the utilisation of Principal C omponent A nalysis and residual orthogonalisation techniques. By contrast, soil variables underwent a process of normalisation and standardisation. The maize-area response was Box–Cox transformed, and four regr ession approaches were fitted: Ordinary Least Squares (OLS), Ridge R egres sion, G eneral ised Additive Models (GAM), and a Random-F orest ensemble (RF). The most significant factor was dete r mined to be soil reaction (pH 6.0–7.5), which ensured optimal nutrient availability; values outside this range resulted in element fixation into insoluble forms or leaching. In the context of soil properties, the sand content (30–40 %) was found to regulate dra i nage and mo isture, the silt content (20–35 %) was determined to maintain the water - air balance, the organic carbon (up to ≈30 g / kg) was found to enhance suitability until saturation, and the total nitrogen exhibited a near-linear positive effect. The key climatic predictors included residual components of annual mean temperature, seasonality and diurnal amplitude, and precipit a tion volume (300–600 mm / yr, with optima in both the wettest and driest months). The GAMs captured nonlinear “peak–plateau–decline” responses for pH, texture, and rainfall, whereas RF delivered the highest predictive accuracy (R² = 0.96; RMSE = 14.77; MAE = 6.73) by automatically modelling complex interactions. Linear models (OLS and R idge) explained 60–64 % of the v a riance. Based on the best‐performing models, suitability maps were generated for three future periods (2021–2040, 2041–2060, 2061–2080) under low (SSP1-2.6) to high (SSP5-8.5) emission scenarios. Results indicate a mid‐term decline in optimal areas, followed by partial long‐term recovery driven by compensatory climate dynamics and adaptive measures. The practical signifi c ance lies in identifying narrow optimum ranges for soil and climatic factors, enabling targeted agronomic recommendations: localised liming, texture adjustment, sowing‐date optimisation, hybrid selection, and irrigation management. The resulting mo d els and suitability maps provide a scientific basis for evidence‐based planning of adaptive maize‐production strategies in the face of global climate change.References
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