Spatial variation in stability of wheat (Triticum aestivum L.) straw phytolith-occluded carbon in China.

Global climate warming, driven by human activities emitting greenhouse gases like CO, results in adverse effects, posing significant challenges to human health and food security. In response to this challenge, it is imperative to enhance long-term carbon sequestration, including phytolith-occluded carbon (PhytOC). Currently, there is a dearth of research on the assessment and distribution of the stability of PhytOC. Additionally, the intricate relationships and effects between the stability and environmental factors such as climate and soil remain insufficiently elucidated. Our study provided a composite assessment index for PhytOC stability based on a rapid solubility assay and principal component analysis. The machine learning models that we developed in this study, utilize experimentally and publicly accessible environmental data on large spatial scales, facilitating the prediction and spatial distribution mapping of the PhytOC stability using simple kriging interpolation in wheat ecosystems across China. We compared and evaluated 10 common classification machine learning models at 10-fold cross-validation. Based on the overall performance, the Stochastic Gradient Boosting model (GBM) was selected as predictive model. The stability is influenced by dynamic and complex environments with climate having a more significant impact. It was evident that light and temperature had a significant positive direct relationship with the stability, while the other factors showed indirect effects on the stability. PhytOC stability exhibited obvious zonal difference and spatial heterogeneity, with the distribution trend gradually decreasing from the southeast to the northwest in China. Overall, our research contributed to reducing greenhouse gas emissions and achieving global climate targets, working towards a more sustainable and climate-resilient future.