An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems

• Published in: Soil & tillage research Vol. 246; p. 106342
• Main Authors: Vazquez Amabile, G., Studdert, G., Ogle, S.M., Beltrán, M., Said, A.D., Galbusera, S., Montiel, F., Moreno, R., Ricard, M.F.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2025
• Subjects: Argentina; Carbon footprint; carbon sequestration; climate; climate change; farms; GHG inventories; Greenhouse gasses (GHG); land use; Management scenarios; pastures; soil; Soil carbon models; soil organic carbon; Soil organic carbon (SOC); tillage; United Nations Framework Convention on Climate Change; Argentina; Management scenarios; Greenhouse gasses (GHG); GHG inventories; Soil carbon models; Carbon footprint; Soil organic carbon (SOC)
• DOI: 10.1016/j.still.2024.106342
• ISSN: 0167-1987

The estimation of changes in soil organic carbon (SOC) is a key issue for national green-house gasses (GHG) inventories, climate change mitigation programs and the estimation of carbon footprint of farm products in life cycle assessments. Any strategy related to net-zero carbon in agricultural systems needs to quantify the SOC balance. In this way, SOC models help decision makers involved in agriculture to understand the dynamics of the SOC and the interaction between all variables related to soil, climate, land use, and management, to design the best solution to reduce emissions or enable carbon sequestration. Likewise, it is important to identify suitable models for the region. This study aims to address three main subjects: a) a discussion on the importance of SOC estimation for GHG inventories and the carbon footprint of crops, using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method and AMG model; b) an evaluation and brief description of the IPCC “Steady State Method” (SSM), using experimental data from two sites in Argentina, comparing these results to AMG and RothC models (both previously validated at those sites); and c) a brief discussion about the potential use of SOC models for what-if management scenarios, their real limitations and future research needs. The three models were consistent in predicting the impact of tillage and the long-term trends in changes in SOC stocks under different management practices. The SSM model was evaluated for the first time in Argentina and performed even better than the other two models. It was consistent with the observed values, when predicting the effect of tillage system under different crop rotations, including pasture systems. Regarding efficiencies of the models, they showed acceptable Nash-Sutcliffe Efficiency (NSE) values, and the root mean square error (RMSE) was also acceptable between 3 % and 7 %, within a range of 4–5 Mg C.ha−1. Therefore, the SSM model proved to be a valuable tool to estimate SOC trends for crop and pasture rotations under different management scenarios (i.e., tillage systems and fertilization), to identify best practices that allow for a zero or positive SOC balance, in two different soil and climate conditions of the Pampean Region of Argentina. In our study, the SSM did have a better fit to the data and, furthermore, this Tier 2 method is simpler than the Tier 3 models, and, therefore, is advantageous for conducting regional assessments and GHG inventories. •Evaluation of Soil Organic Carbon (SOC) models across diverse agricultural practices.•Comparative analysis of different models (SSM-AMG-RothC).•Validation using empirical data from Balcarce and General Villegas experimental sites.•The relevance and usefulness of SOC models for GHG inventories and crop Carbon footprint.•Novel methodologies for refined assessment of SOC dynamics in agriculture.