Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics, as well as limited data availability. We developed a Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable datasets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux datasets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern U.S. rangelands, to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R2 > 0.6, RMSE < 390 g C m-2) than net ecosystem exchange of CO2 (NEE) (R2 > 0.4, RMSE < 180 g C m-2), and captured the spatial variability of surface SOC stocks with R2 = 0.6 when validated against SOC measurements across 13 NEON sites. Our RCTM simulations indicated slightly enhanced SOC stocks during the past decade, which is mainly driven by an increase in precipitation. Regression analysis identified slope, soil texture, and climate factors as the main controls on model-predicted C sequestration rate. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of rangeland vegetation biomass, C fluxes, and SOC stocks.

Large and publicly available soil spectral libraries, such as the USDA National Soil Survey Center – Kellogg Soil Survey Laboratory (NSSC-KSSL) mid infrared (MIR) spectral library, are enormously valuable resources enabling laboratories around the world to make rapid estimates of a number of soil properties. A limitation to widespread sharing of soil spectral data is the need to ensure that spectra collected on a local spectrometer are compatible with the spectra in the primary or reference library. Various spectral preprocessing and calibration transfer techniques have been proposed to overcome this limitation. We tested the transferability of models developed using the USDA NSSC-KSSL MIR library to a secondary instrument. For the soil properties, total C (TC), pH and clay content, we found that good performance (RPD = 4.9, 2.0 and 3.6, respectively) could be achieved on an independent test set with Savitzky-Golay (SG) smoothing and first derivative preprocessing of the secondary spectra using a memory-based learning chemometric approach. We tested three calibration transfer techniques (direct standardization (DS), piecewise direct standardization (PDS), and spectral space transformation (SST)) using different size transfer sets selected to be representative of the entire NSSC-KSSL library. Of the transfer methods, SST consistently outperformed DS and PDS with 50 transfer samples being an optimal number for transfer model development. For TC and pH, performance was improved using the SST transfer (RPD = 7.7 and 2.2, respectively) primarily through the elimination of bias. Calibration transfer could not improve predictions for clay. These findings suggest calibration transfer may not always be necessary but users should test to confirm this assumption using a small set of representative samples scanned at both laboratories.

Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environmental changes do not accurately represent the size, distribution and flux of C from the soil. Here, we modified the Daily Century (DAYCENT) biogeochemical model by parameterizing conceptual SOC pools with C fraction data, followed by historical and future simulations of SOC dynamics. Results showed that simulations using modified DAYCENT (DCmod) led to better initialization of SOC stocks and distribution compared to default DAYCENT (DCdef) at long-term research sites. Regional simulation using DCmod demonstrated higher SOC stocks for both croplands (34.86 vs 26.17 MgC ha-1) and grasslands (54.05 vs 40.82 MgC ha-1) compared to DCdef for the contemporary period (2001-2005 average), which better matched observationally constrained data-driven maps of current SOC distributions. Projection of SOC dynamics to land cover change (IPCC AR4 A2 scenario) under IPCC AR5 RCP8.5 climate scenario showed absolute SOC loss of 8.44 and 10.43 MgC ha-1 for grasslands and croplands, respectively, using DCmod whereas, SOC losses were 6.55 and 7.85 MgC ha-1 for grasslands and croplands, respectively, using DCdef. The projected SOC loss using DCmod was 33% and 29% higher for croplands and grasslands compared to DCdef. Our modeling study demonstrates that initializing SOC pools with C fraction data led to more accurate representation of SOC stocks and individual carbon pool, resulting in larger absolute and relative SOC losses due to agricultural intensification in the warming climate.