Abstract This study presents the carbon net primary productivity (CNPP) module, an implementation of the simple generic crop model (SIMPLE model) that we have extended to predict aboveground and belowground biomass production and carbon assimilation. The goal for this module is to predict biomass inputs at and below the soil surface, during and at the end of crop cycles, while integrated into a broader environmental carbon simulation framework, ProCarbon‐Soil. CNPP was initially parameterized for soybean Glycine max (L.) Merr. and maize ( Zea mays L.) using micrometeorological data, and for wheat ( Triticum aestivum L.), bean ( Phaseolus vulgaris L.), and perennial forage Urochloa (syn. Brachiaria ) brizantha (Hochst ex A. Rich.) Stapf 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 ( R 2 > 0.76, Nash–Sutcliffe efficiency > 0.56, and relative root mean square error < 38% for all variables). Simulations for wheat, bean, and perennial forage exhibited lower performance owing to lower availability of yield data. Nonetheless, the resulting statistics support this module's efficacy in predicting crop productivity in major Brazilian agricultural areas. 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 regions.
Colmanetti et al. (Sun,) studied this question.