This study proposes additions and modifications to the Simple model for estimating Carbon Net Primary Productivity (CNPP) of agricultural crops, and parametrization and evaluation for the major grain and perennial forage crops in Brazil. CNPP was initially parameterized for soybean and maize using micrometeorological data, and for wheat, bean and perennial forage (Urochloa brizantha cv. Marandu) using agrometeorological experimental data and/or data from the literature. Subsequently, calibrations for reference cultivars were performed, grouping cultivars by crop phenological characteristics and edaphoclimatic regions using farm-level data. CNPP accurately simulated leaf area index, evapotranspiration, and biomass dry matter production and allocation for soybean and maize when evaluated at the sites with micrometeorological data (R² > 0.76, NSE > 0.56, and RRMSE < 38% for all variables). Simulations for wheat (R² = 0.2 and RRMSE = 31.8% for yield), common bean (R² = 0.01 and RRMSE = 48.5%) and perennial forage (R² = 0.33 and RRMSE = 27.2%) exhibited lower performance due to only yield data being available. Module performance was always lower for on-farm trials than for research site trials, due to lower quality environmental data and higher genotypic variability. Nonetheless, the resulting statistics (RRMSE < 30% and NSE > 0) support this module’s efficacy in predicting crop productivity in major Brazilian agricultural areas, with statistics similar to those obtained by others. By employing a reduced and efficient parameter set, the CNPP module achieves enhanced performance and enables robust calibration across diverse crops, genotypes, and management schemes in multiple locations.

Carbon farming is a nature-based solution to capture atmospheric CO2 and store it as soil organic carbon (SOC). Carbon farming trading schemes (CFTS) incentivize farmers to adopt these practices. Integral to CFTS is forecasting the SOC changes of individual projects, typically achieved using traditional multicompartmental soil carbon models (mSCM), and monitor total SOC stocks. However, traditional mSCM simulate unmeasurable compartments, leading to overparameterization and indeterminable partitioning among carbon compartments, suggesting a need for structural improvements. The ProCarbon-Soil (PROCS) model addresses this need abstracting fundamental principles of mSCM, reducing SOC state variables to two (total carbon and decomposability), and employing only one stabilization parameter, compared to the 4–8 state variables and 7–20 parameters typically required by mSCM. We mathematically derive methods for decomposability estimation and model initialization using successive carbon measurements. PROCS can handle environmental modifiers and events such as crop rotations, tillage, and manuring events, and respond to soil characteristics and weather conditions. Tests show that PROCS can accurately reproduce synthetic SOC trajectories generated by an mSCM with perturbed parameters using short-term data (12 years) with acceptable accuracy (median RMSE < 1.03 Mg ha-1 and absolute median of MB < 0.55 Mg ha-1). In a cross-validation test, the mean NRMSE closely aligns with the CV of white noise introduced in the synthetic data (4.15% vs 4.00%, respectively) for augmented carbon inflow scenarios, whereas the model exhibits higher errors for the no-carbon-inflow scenario (NRMSE = 5.48, 7.25 and 8.99% for 12, 24 and 50 years, respectively).