Can Soil Organic Carbon in Long-Term Experiments Be Detected Using Vis-NIR Spectroscopy?

Determining soil organic carbon (SOC) stock and its changes over time is crucial for understanding carbon cycling. This study evaluates the reliability of visible and near-infrared (Vis-NIR) spectroscopy as a cost-effective method for detecting SOC within monitoring, reporting, and verification (MRV) systems. Soil samples from a long-term field experiment (LTE) in northern Italy, comparing maize-based forage systems were used as a case study. Three sampling campaigns (2003, 2012, and 2018) were utilized for a total of 162 soil samples collected in the LTE (54 each). Soil samples archived were retrieved and scanned using a Vis-NIR spectrometer to create a site-specific soil spectral library (Site-SSL). Aiming to implement a local prediction model samples collected in 2003 were used as a training dataset to estimate the SOC of the soil samples collected in 2012 and 2018. Concurrently, a second prediction model was run adding 172 regional soil samples (Reg-SSL) collected the same soil-landscape as the LTE. N.4 model strategies were compared, including random forest (RF), cubist (CU), memory based learning (MBL) and support vector machine (SVM) on Site-SSL and Reg-SSL. A sensitivity analysis was performed to evaluate the impact of training sample size, followed by an assessment of the cost-benefit of spectroscopic approach compared to conventional analysis. The results showed that the Vis-NIR spectral libraries, along with the CU and SVM models, were able to detect changes in SOC in the Site-SSL dataset, yielding the best results. To maintain optimal performance, it is advisable to include the standard analyses of at least 10 percent of the subsequent monitoring samples in the training set.