Speaker
Description
There is growing interest in the use of alkaline rock powders with high carbon dioxide removal (CDR) potential as agricultural amendments, with the goal of sequestering CO2 while providing plant-available nutrients and deacidifying soils. However, in order for this practice to be widely implemented as a negative emissions technology, there must be robust and widely accepted monitoring, reporting, and verification (MRV) of CDR rates. Site-specific and time-integrated empirical measurements of feedstock weathering rates in agricultural soils can be determined using canonical geochemical mass-balance (1,2). In this approach, elements that are assumed to be immobile during weathering are used to quantify the amount of feedstock initially present in a soil sample, and the loss of mobile cations from the solid phase is used to calculate a bulk weathering rate. Results from mesocosm experiments (2) indicate that if it can be successfully scaled, this technique could form the foundation of a model-based MRV framework to assess CDR from individual deployments.
Here, we assess the resolvability of signals for weathering of feedstocks calculated using soil-based mass balance at field scale. We present a metric for resolvability of ERW given a range of conditions for mixing, feedstock application rate, and dissolution, and test this using a spatially gridded soil database from the contiguous USA (3). We use this approach to compare the suitability of different measurable immobile trace elements and element ratios for analysis of weathering rates. Our simple framework provides a useful tool for farmers and practitioners when planning ERW feedstock deployment given site-specific conditions. We then address some challenges of implementing soil-based mass balance in field settings, using data from multi-year ERW trials (4), and discuss the role of sampling practices, laboratory analytical techniques and methods of data processing to improve confidence in results.
1 Brimhall and Dietrich (1987) https://doi.org/10.1016/0016-7037(87)90070-6
2 Reershemius, Kelland et al. (in review) https://doi.org/10.48550/arXiv.2302.05004
3 Smith et al. (2012) USGS https://doi.org/10.3133/ds801
4 Beerling et al. (in review) https://doi.org/10.48550/arXiv.2308.04302
