Signals in the Soil: Detecting Soil Degradation & Restoration
Nearly one-third of soils globally are degraded and an even larger fraction is at risk of degradation. The decline in soil health and function threatens food security, ecological function and places large portions of the global south dependent on agriculture at risk of economic and ecological decline. This work seeks to provide a completely new approach to the monitoring of degradation and restoration while training a new cohort of mixed engineering and environmental science graduate students in the US and a cross-disciplinary group of postdoctoral fellows in the UK bringing together soil and data science. At both the US and UK sites, our objective is to engage in new forms of science communication including a live link to the data (and interpretation of changes) at the sites under active management.
Signals in the Soil research calls for advancing our understanding of dynamic soil processes that operate at different temporal and spatial scales. This research addresses one of the most pressing issues in the global environmental community by creating a novel multi-functional soil sensing platform that can be used for the early detection of soil degradation and restoration. Through the creation of an innovative new approach to capture and analyze high-frequency data from in-situ sensors, this project will predict the rate and direction of soil system functions for various sites.
To do this, researchers will build and train a new mechanistically-informed machine learning system to turn high frequency data on multiple soil functions, such as water infiltration, CO2 production, and surface soil movement, into predictions of longer term changes in soil health. Such an approach has the potential to be transformative: a system that will allow short-term sensor data to be used to evaluate longer term soil transformations in key ecosystem functions. We will start our work with a suite of off-the-shelf sensors observing multiple soil functions that can be installed quickly. These data will allow us to rapidly initiate development and training of a novel mechanistically informed machine learning framework.
In parallel, we intend to develop two new soil-health sensors focused on in-situ realtime measurement of decomposition rates and transformation of soil color that reflect the accumulation or loss of soil organic matter. We will then link these new sensors with a suite of conventional sensors in a novel data collection and networking system coupled to the Swarm satellite network to create a low-cost sensor array that can be deployed in remote areas and used to support studies of soil degradation or progress toward restoration worldwide. Such work exists to generate new insights into the behavior of soils that are undergoing degradation and restoration while also providing a completely new approach to site monitoring, evaluation and management.
The research is supported by the US Department of Agriculture's National Institute of Food & Agriculture (USDA NIFA). It is also supported in collaboration with and the UK Natural Environment Research Council (NERC).