A model to support carbon farming schemes was recently presented to world-leading agricultural experts, encouraging sustainable soil management. A team of researchers from the Global Academy of Agriculture and Food Systems has developed a streamlined model to accurately track organic carbon stocks in soil. The model, known as ProCarbon-Soil (PROCS), is a robust and reliable tool for carbon soil quantification, which could play a pivotal role in the global fight against climate change, the research team says. While existing models’ ability to combine computational models with observational data is limited by their complexity, PROCS overcomes these by simplifying parameters and focusing on matching field data for better accuracy in validating carbon credits. Agriculture meeting The model was developed in collaboration with the Brazilian Agricultural Research Corporation (EMBRAPA) and Bayer Crop Science. The program involves extensive data collection from around 1,900 farms in Brazil which will soon be used for model calibration and evaluation of uncertainties in model predictions. EMBRAPA experts presented the model at the G20 Meeting of Agricultural Chief Scientists earlier this year in Brasilia, Brazil. As emphasised at the meeting, the adoption of PROCS could significantly bolster carbon farming initiatives, helping to meet the increasing commitments of companies to reduce emissions and achieve net-zero targets. Streamlined model “Traditional soil organic carbon models have been widely used to predict soil carbon stock changes. However, these models often perform poorly when presented with new data”, says Professor Luis Gustavo Barioni of the Global Academy. “Such shortcomings make these models unsuitable for the precise demands of carbon farming trading schemes”, Barioni adds In response to this challenge, PROCS retains the fundamental principles of established dynamic models but offers significant improvements in matching real-world data. The key advantage of this model lies in its streamlined approach, reducing the number of data variables to track carbon stocks. This simplification allows for fewer parameters while producing similar results of popular and more complex models, reducing the need for expensive soil sampling. The use of data assimilation algorithms further enhances the model's accuracy, enabling real-time updates and adjustments Dr Rafael De Oliveira Silva, Global Academy of Agriculture and Food Systems This partnership aims to refine the model for global application, ensuring it can be effectively implemented across diverse agricultural practices and soil types. Carbon credits: why are they important? Carbon farming is rapidly gaining attention as a nature-based solution for capturing atmospheric carbon and storing it as soil organic carbon. To support this effort, improved soil management practices, precise monitoring through soil sampling and thorough economic evaluation are essential. The accurate quantification of carbon in soil is crucial for validating and certifying carbon credits under carbon farming trading schemes. Carbon credits provide financial incentives for companies and farmers to reduce their greenhouse gas emissions, creating a framework that supports global efforts to mitigate climate change and promote sustainable development. The model is currently gaining accreditation to be used in certified carbon trading schemes. The process of model validation and accreditation takes around 6 months. As well as being presented at the G20 Meeting, this model has been published as a preprint in SSRN and is currently the most downloaded paper in the Agronomy and Soil Science field. Related links Research publication Top eJournal downloads for agronomy and soil science G20 Meeting of Agricultural Chief Scientists
