Quick Carbon – a low-cost protocol for rapidly measuring soil carbon across large landscapes at fine spatial resolutions – is a program at the Yale School of Forestry and Environmental Studies. The inexpensive nature of this methodology lends land managers the ability to look at impacts of management decisions on below-ground carbon at broad extents and frequent time intervals. For these reasons we believe that Quick Carbon has the ability to change the way we understand and manage for carbon in rangeland systems.
Quick Carbon’s mission is to create an accessible measurement system that empowers individuals to generate reliable soil carbon data for ecological understanding, decision making, and markets.
We believe that the central challenge of measuring soil carbon, and its response to management, is the lack of rapid assessment tools and informed sampling frameworks. In analogous forest systems, the establishment of informed land management systems and access to ecosystem markets is underpinned by a century of scientific research developing simple, cost-effective, field based tools (DBH tapes, prisms, biltmore sticks, clinometers) for rapidly assessing individual tree characteristics. These tools make it easy for forest ecologists and managers to monitor forests and gather rich datasets that inform ecological understanding and management. If efforts to manage soil carbon and further our understanding of the crucial role of soil carbon in ecosystems are to be effective, similar systems focused on rapid, cost-effective measurement of soil carbon are urgently needed.
Soil is the largest terrestrial carbon store, holding roughly 3000 Gt of carbon, or more than 80% of terrestrial carbon. Estimates suggest that human disturbance has decreased this reservoir by 55-78 Gt of carbon, and that soils are likely to lose still more as climate change and land conversion accelerates decomposition processes. Recapturing lost soil carbon could be an important strategy in mitigating climate change. In a comprehensive review of regional case studies, researchers found that increasing surface soil carbon stocks by just 0.4% globally could offset 20-35% of global greenhouse gas emissions. Research has identified land management practices that increase carbon transfer to soils by increasing plant growth while also minimizing losses of soil carbon by reducing disturbance. Non-profits and government agencies are looking for ways to incentivize managers to transition to these management practices, but there are two major impediments to their broad-scale adoption:
1) Can practical changes to land management increase soil carbon at landscape scales? Some practices increase soil carbon in some areas but not others. Identifying the range of site characteristics over which management increases soil carbon is necessary for guiding adoption of specific practices tailored to particular landscapes.
2) If so, can this increase be cost-effectively measured and verified at management-relevant scales? Assessing soil carbon stocks currently requires gas chromatography and elemental analysis. These laboratory-based methods are highly accurate, but also time consuming and expensive. High costs restrict the number of samples managers can take, meaning quantification happens infrequently and is only done on a small portion of a farm or ranch. As a result, current soil inventories lack the spatial and temporal resolution needed to accurately quantify soil carbon stocks across large scales.
We have recently developed a soil carbon measurement protocol that makes use of low-cost field reflectometers. These affordable, pocket-sized devices measure soil carbon using the reflectance of soils in the visible and infrared spectra. As carbon content increases, a soil’s color darkens, giving it a slightly different spectral signature than soil with lower carbon content. Standard benchtop spectrophotometers used in similar work cost $3,000-$10,000 and are not portable, whereas this device can be produced for an order of magnitude cheaper.
The field reflectometer devices are integrated with an easy-to-use mobile app allowing users to collect spectral data and sample information while simultaneously recording their GPS position. These collated data are recorded in the app and automatically pushed to a cloud server whenever an internet connection is available. During model development, a small subset of soil samples (~20%) are sent for traditional, highly accurate laboratory analysis, such as gas chromatography-mass spectrometry. This subset of data is then used to build machine learning models relating lab-measured soil carbon levels to the data collected with the field reflectometer. Additionally, freely available remote-sensing data are integrated into these models to improve estimates .
After this initial collection of data and construction of a site-specific model, carbon content can be determined in the field using only the reflectometer and remote-sensing data, dramatically reducing sample collection time and cost. With these reductions in cost, Quick Carbon users could rapidly collect hundreds of measurements across a landscape, allowing them to produce localized maps of soil carbon that reveal patterns about the spatial distribution of soil carbon in the landscape and provide detailed estimates of how much soil carbon is stored in the landscape. As users re-sample over time, they can improve estimates by evaluating the ecological drivers of soil carbon variability and re-focusing future sampling efforts on key areas. By learning from itself, the Quick Carbon system improves sampling efficiency as it's used, further reducing measurement and verification costs.
Our initial field tests are promising. Working with a first-generation field reflectometer we can predict soil carbon content within 1% accuracy. Extracting data from existing geographic datasets improved mean accuracy to within 0.88%. In comparison, past researchers using similar techniques on more expensive laboratory-based instruments have achieved accuracy of 1-3%. Our models could also be improved by integrating spatial data from remote sensing satellites or newly available high resolution aerial photography (i.e. drone flyovers). With sufficient accuracy we could also interpolate across a given landscape to produce hyper-local maps of soil carbon stocks on a given landscape.
Furthermore, the reflectometer is built on an open-source hardware/software platform and is capable of integrating via Bluetooth with sample collection software that runs on the Android platform, thus automatically providing sample coordinates. Given these features we believe we can develop this device and protocol into a methodology that is low cost and that land managers could easily use. Reduced sampling time and cost would allow managers and researchers alike to take more samples over more area, providing real time monitoring of changes to soil carbon stocks and high-resolution maps of soil carbon at the scale of farms or ranches. That level of monitoring capability would allow us to understand what management practices are effective and where, providing the foundation for soil carbon offset markets and effective extension programming.