Carbon sequestration in agricultural landscapes is promoted as a key climate change mitigation pathway. The IPCC has long promoted carbon sequestration in agricultural landscapes and more recently governments and private companies have promoted commercialized carbon offsetting for agricultural landscapes. However, methodologies for estimating Soil Organic Carbon (SOC) are poorly developed at the field and landscape scales. SOC can be measured at the field scale using direct soil sampling and modelled at the landscape scale using representative biophysical data. But the effort and cost required to collect and process biophysical data (including soil samples) makes it unclear if SOC can be accurately quantified at broad scales. The highly variable temporal and spatial nature of SOC requires robust geospatial approaches in developing sampling methodologies for biophysical data collection and modelling. At this time, robust geospatial approaches are absent from efforts to quantify SOC in agricultural landscapes.
Soil carbon levels are typically quantified using Soil Organic Carbon (SOC) measurements. SOC is a measurable fraction of Soil Organic Matter (SOM) (a commonly measured soil property). While SOM comprises a small percentage of most soil mass (between 2-10%), it plays an important role in the biophysical function of soils. SOC refers to only the organic carbon component of SOM.
SOC is highly spatially and temporally variable. This is related to soil properties along with historical and present-day land use, land cover, and land management. At the same time, SOC levels fluctuate throughout the year, which is related to temperature, precipitation, and land management actions. The variability of SOC requires the development of sampling methodologies which reflect spatial and temporal characteristics of SOC. Large numbers of soil samples would be required to accurately quantify SOC even at the field scale.
SOC can be easily quantified in most research and commercial soil laboratories. However, preparing and processing soil samples for SOC measurements is labor-intensive and costly. Sample processing costs pose another serious challenge to accurately quantifying SOC.
The IPCC recognizes that intensive soil sampling is not possible at the landscape scale. Therefore, carbon models have been developed to enable SOC estimates at the landscape scale. Models are divided into Tiers 1, 2, and 3. Tier 1 models rely on generalized, default biophysical values provided by the IPCC and therefore have a high degree of uncertainty. Tier 2 models replace default values with some landscape-specific biophysical data and land use, land cover, and/or land management information. Tier 2 models reduce some uncertainty in comparison to Tier 1 approaches. Tier 3 models have much lower uncertainty when compared to the other tiers. Tier 3 models require landscape-specific biophysical and landscape data including:
land use history
current land use and land cover
Thanks to the significant uncertainty of Tier 1 and Tier 2 models, they should not be used for anything other than estimates at project planning stages. Tier 3 models may have the potential to accurately estimate SOC at the field and landscape scales. But very few Tier 3 models have been successfully implemented and verified at this time.
Scientific and grey literature focused on carbon sequestration focuses on SOC at the field scale and largely neglects the use of geospatial methodologies. This is a major shortcoming in the literature since geospatial methodologies are essential to scaling up field-scale biophysical data to broader landscape scales. Basic operations such as field boundary delimitation, field area measurements, and land use and land cover stratification types can be achieved using a combination of handheld GPS units, GIS software, and high- resolution aerial photos. At the same time, it should be stressed the pre-existing geospatial soil datasets lack the resolution, accuracy, and soil profile information required for accurate Tier 3 modelling.