Abstract

Digital soil mapping using artificial intelligence has demonstrated remarkable accuracy in predicting soil properties, but uncertainty quantification remains a critical challenge for practical implementation and decision-making in precision agriculture. This study presents a comprehensive evaluation of uncertainty quantification methods for AI-predicted soil maps, comparing Bayesian deep learning, ensemble approaches, and Monte Carlo dropout techniques across diverse agricultural landscapes. We developed and evaluated five uncertainty quantification frameworks: Monte Carlo Dropout (MCD), Deep Ensembles (DE), Bayesian Neural Networks (BNN), Variational Inference (VI), and a novel Spatial Uncertainty Network (SUN) using 23,847 soil samples collected across six agro-ecological zones in North America and Europe. The models predicted soil organic carbon (SOC), pH, clay content, and available nitrogen with associated uncertainty estimates at 30-meter spatial resolution. Ground truth validation was conducted using independent test datasets comprising 4,769 samples reserved from model training. The Spatial Uncertainty Network achieved superior performance with prediction interval coverage probability (PICP) of 94.2% for SOC, 92.8% for pH, 91.5% for clay content, and 89.7% for nitrogen at 95% confidence levels. Mean interval width (MIW) was reduced by 23-31% compared to traditional approaches while maintaining calibration reliability. Bayesian Neural Networks demonstrated excellent calibration with reliability diagrams showing minimal deviation from perfect calibration lines. Ensemble methods provided robust uncertainty estimates with computational efficiency advantages over full Bayesian approaches. Spatial analysis revealed systematic patterns in prediction uncertainty related to sampling density, topographic complexity, and soil heterogeneity. Areas with sparse sampling showed 2.3× higher uncertainty than densely sampled regions. Complex terrain exhibited 45% greater uncertainty compared to homogeneous landscapes. Temporal validation over three years confirmed uncertainty estimate stability with less than 8% variation in calibration metrics. Economic analysis demonstrated that uncertainty-informed management decisions improved profitability by 12-18% compared to deterministic predictions through optimized fertilizer application and reduced over-treatment risks. The study establishes practical frameworks for implementing uncertainty quantification in operational soil mapping systems, enabling evidence-based decision-making and risk assessment in precision agriculture applications.