A modeling framework for monitoring soil carbon stocks in Latin America and the Caribbean

By Carla Gavilan

The Latin America and the Caribbean (LAC) region is home to a diverse range of countries that are particularly vulnerable to the impact of climate change, mainly due to the unique land use, land tenure systems, and the various ecosystems in the region. Climate change is a macro-critical issue in LAC, and climate mitigation and adaptation are essential.

Fig. X. Framework for soil carbon simulations

Monitoring changes in soil organic carbon (SOC) stocks has been recognized as key to facilitating and identifying sustainable land use practices in the context of climate change mitigation, food security, and land degradation. Also, reliable quantification of SOC and accurate modeling of its changes under different management practices is necessary for informed decision-making. One of the most significant obstacles to building viable agricultural monitoring systems in several developing countries is the lack of data to establish a solid basis for linking changes in soil carbon sequestration with changes in agricultural activities. As a result, several LAC countries rely on global default values for reference SOC stocks and emission factors to infer changes in SOC stocks (Tier 1 approach). However, using those values to upscale the calculations to regional-scale projects is associated with low accuracy and high uncertainty.

As part of the Living Soils of the Americas Initiative led by IICA (Inter-American Institute for Cooperation on Agriculture) and the CFAES Rattan Lal Center for Carbon Management and Sequestration of Ohio State University, we are developing a modeling framework for monitoring purposes. This framework integrates remote sensing data at different temporal and spatial resolutions, and a process-based model to assess, predict, and monitor SOC changes over time. We aimed to develop a robust, affordable, and scalable protocol that can be implemented in other biomes, even with the minimum data available, to help overcome the limitations that data-scarce regions face. We are testing the framework in a pilot area in the Brazilian Cerrado Biome with promising results. Currently, we are validating these preliminary findings.