fig2

Figure 2. (A) Functional relations between environmental covariates (STEPAWBH factors with variables i = 1, 2, 3, ……, N). SC denotes a variable of the carbon cycle (target output), for example, soil organic carbon (SOC) stock, SOC density, total soil carbon, soil respiration (R_s), soil carbon sequestration (SCseq), soil carbon pools, soil carbon fractions, etc. x is spatial location (with xy coordinates; or latitude/longitude); z is soil depth with z = 1, 2, 3, ……, Z; and t is time with t1, t2, t3, …., T. (B) AI model predicting soil carbon (SC) from environmental covariates. Simplified representation of a machine learning ensemble tree method (e.g., Classification and Regression Trees, CART, or Cubist) with tree branches and data splits.