Quantifying the natural climate solution potential of agricultural systems by combining eddy covariance and remote sensing

Authors: WIESNER, SUSANNE - University Of Wisconsin; DESAI, ANKUR - University Of Wisconsin; Duff, Alison; METZGER, STEFAN - National Ecological Observatory Network (NEON); STOY, PAUL - University Of Wisconsin

Submitted to: Journal of Geophysical Research-Biogeosciences
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/30/2022
Publication Date: 9/13/2022
Citation: Wiesner, S., Desai, A.R., Duff, A., Metzger, S., Stoy, P.C. 2022. Quantifying the natural climate solution potential of agricultural systems by combining eddy covariance and remote sensing. Journal of Geophysical Research-Biogeosciences. 127(0). Article 2022JG006895. https://doi.org/10.1029/2022JG006895.
DOI: https://doi.org/10.1029/2022JG006895

Interpretive Summary: Natural climate solutions are those that increase or preserve stored carbon in the landscape through strategic land management. Quantifying the mitigation potential of farm natural climate solutions is subject to high measurement uncertainty and affected by regional context. Site-specific measurements of carbon sources and sinks are time-intensive, and impractical for private farms. Satellite-based Earth observations have the potential to be a low-cost alternative for tracking farm carbon balances, but their application in dynamic, heterogenous agricultural landscapes requires testing and calibration. Using the US Dairy Forage Research Center (USDFRC) farm in Wisconsin, USA as a case study, we evaluated whether on-farm carbon and energy flux data from an eddy covariance tower, in combination with spatial information about the landscape, could be used to improve parameters used in remote sensing models.

Technical Abstract: Livestock agriculture accounts for approximately 15% of all global anthropogenic greenhouse gas (GHG) emissions. With an ever-increasing global population and a global diet of which ~20% consists of meat and dairy products, we need to find ways to reduce the negative climate impact of this agricultural sector. Recently, natural climate solutions (NCS) have been identified to mitigate GHG emissions, which include methane and nitrous oxide emissions from enteric fermentation and manure. Such climate solutions imitate natural processes that take up CO2 from the atmosphere, including perennial vegetation like pastures. Nevertheless, their impacts are difficult to quantify due to the spatial heterogeneity of farm landscapes and the amount of time it takes to sample changes in soil and vegetation carbons stocks. Remote sensing approaches could help extrapolate such solutions. However, their model parameters are rarely updated to reflect changes in plant physiological parameters as a result of genetic modifications and/or climate change. Approaches like eddy covariance in combination with novel footprint techniques could help improve and update such crop specific parameters on a continuous basis. Here we evaluate how spatiotemporal eddy covariance data can improve remote sensing models by updating plant physiological parameters such as maximum quantum yield (MQY), which is commonly used to calculate gross primary productivity in remote sensing applications. We used the US Dairy Forage Research Center (USDFRC) dairy farm as a case study to compare estimated versus actual harvest biomass, as well as to calculate the annual GHG farm balance. We first assessed how field harvest biomass, calculated from farm harvest records, compared to annual sums of 1) remote sensing models without improvements, 2) eddy covariance results, and 3) improved remote sensing models. We then estimated barn, field, and manure emissions to calculate the total annual GHG balance for 2019, including GHG mitigation in form of plant C uptake. Our results indicate that updating MQY values in remote sensing models using eddy covariance results significantly improved the prediction of crop harvest yields for all crop species. The USDFRC farm was net zero for 2019, where perennial vegetation types mitigated over 60% of all emissions, while only comprising 40% of the total landscape. However, soybean fields, as well as their exports counteracted this mitigation potential, pushing the farm into a GHG source. Nevertheless, our study indicates that the combination of remote sensing and eddy covariance can significantly improve the quantification of NCS in agricultural systems and that perennial vegetation in dairy systems are promising NCS to reduce and/or mitigate on farm GHG emissions.