A Stochastic Approach to Quantifying the Propagation of Uncertainty in Soil Organic Carbon Content

ABSTRACT Background Precision agriculture (PA) is a site‐specific management approach that utilises spatiotemporal information to improve productivity while also promoting sustainability. Accurate estimates of soil properties, along with the uncertainty of these estimates, are necessary for decision‐making in PA. An essential soil quantity required to accurately predict crop yield is the soil organic carbon (SOC) content. To obtain the large amount of information necessary for PA implementation, the use of satellite images has become a common practice. This allows the spatial interpolation of soil properties. However, this type of indirect approach carries higher relative uncertainties than direct measurements (e.g., laboratory experiments). Although error evaluations of soil properties resulting from indirect approaches are constantly considered, the consequences of error are not. Aim This work introduces a methodology to analyse the error propagation from predictions of SOC digital maps, using the Monte Carlo (MC) method. Method We stochastically generated an error range for SOC maps, using one original map of SOC, and used these maps as inputs for a process‐based model that simulated crop yields. Our approach evaluates how the error inherent in SOC observations and the subsequent spatial interpolation impacts crop yield forecasting, providing insights for decision‐making and further PA implementation. Results Our results show promise in the proposed method, delivering results that are difficult to obtain. The MC method was able to handle complex, non‐linear error distributions and provide a comprehensive probabilistic assessment of uncertainty, which is important for accurately predicting the impact of SOC variability on crop yield. Conclusion This method offers a degree of flexibility and robustness that is not achievable with deterministic or simpler analytical approaches, ensuring more reliable and informative insights for PA.