Quantifying Soil N₂O Emissions from Agricultural Lands
At University of Illinois at Urbana-Champaign, I completed multiple projects aimed to improve the quantification of soil N₂O emissions. At the broad scale, a process-based model (i.e., surrogate Century model) was utilized to estimate U.S. county-level soil N₂O emissions from corn fields under different climate and management scenarios. To enhance modeling performance, a data fusion approach was developed to derive county-level crop-specific fertilizer and manure nitrogen (N) input datasets as model inputs. Additionally, a remote sensing and site covariate-based modeling approach was established to estimate cover crop N credits. Furthermore, the modeling scheme was improved by carrying out spatial validation using survey and meta-summary databases. At the local scale, field experiments were conducted to measure N₂O fluxes within different field management zones delineated with a digital soil mapping approach. Both positive and negative fluxes were observed from the field measures obtained from organic corn fields. Future research is planned to gain deeper insights into the underlying mechanisms responsible for these flux variations from diversified cropping systems.
Related publications:
1. Xia, Y., M. Wander, S. Quiring, S. Yuan, and H. Kwon. 2022. Process-based Modeling of Soil Nitrous Oxide Emissions from United States Corn Fields under Different Management and Climate Scenarios Coupled with Evaluation using Regional Estimates. Frontiers in Environmental Science. 9: e971261. doi: 10.3389/fenvs.2022.971261.
2. Xia, Y. and M. Wander. 2022. Management Zone-based Estimation of Positive and Negative Nitrous Oxide Flux in Organic Corn Fields. Soil Science Society of America Journal. 86: 1043-1057. doi: 10.1002/saj2.20416.
3. Xia, Y., Kwon, H., and M. Wander. 2021. Developing County-level Data of Nitrogen Fertilizer and Manure Inputs for Corn Production in the United States. Journal of Cleaner Production. 309: e126957. doi: 10.1016/j.jclepro.2021.126957.
4. Xia, Y., Guan, K., Copenhaver, M., and M. Wander. 2020. Estimating Cover Crop Biomass Nitrogen Credits with Sentinel-2 Imagery and Sites Covariates. Agronomy Journal. 113: 1-18. doi: 10.1002/agj2.20525.
5. Xia, Y., Kwon, H., and M. Wander. Estimating Soil N2O emissions induced by organic and inorganic fertilizer inputs using a Tier-2, regression-based meta-analytic approach for U.S. agricultural lands. Science of The Total Environment. e171930. doi: doi.org/10.1016/j.scitotenv.2024.171930.
Soil Carbon Monitoring from Rangelands
At Woodwell Climate Research Center, I worked as a postdoc to develop a rangeland carbon tracking and monitoring (RCTM) system which combines remote sensing imagery with both empirical and process-based modeling for the estimation of rangeland productivity and soil organic carbon (SOC). Several components were involved in developing RCTM: (1) downscaling soil moisture estimates using a data fusion approach; (2) designing a target field sampling plan that is implemented within the high-performance computing (HPC) platform; (3) conducting Bayesian calibration using flux tower datasets and field measurements; (4) building a Google Earth Engine (GEE)-based decision support tool by visualizing modeled results. The decision support tool is currently designed for the ranch level but will be expanded to the regional scale in the future. In addition to building the RCTM system, my research also features developing a grassland C monitoring plan for the Northern Great Plains and the implementation of high-resolution, HPC-enabled mapping of baseline SOC using a digital soil mapping approach.
Related publications:
1. Xia, Y., Watts, J. Machmuller, M., and Sanderman, J. 2022. Machine Learning Based Estimation of Field-Scale Daily, High Resolution, Multi-Depth Soil Moisture for the Western and Midwestern United States. PeerJ. 10: e14275. doi: 10.7717/peerj.14275.
2. Xia, Y., Sanderman, J., Watts, J., Machmuller, M., Ewing, S., and Rivard, C. 2024. Leveraging Legacy Data with Targeted Field Sampling for Low-cost Mapping of Soil Organic Carbon Stocks on Extensive Rangeland Properties. Geoderma. In Press.
3. Test version of the decision support tool in GEE: https://rangelands.users.earthengine.app/view/app2023
4. Preprint of the RCTM system: https://essopenarchive.org/users/762265/articles/739040-coupling-remote-sensing-with-a-process-model-for-the-simulation-of-rangeland-carbon-dynamics
5. Xia, Y., M. Wander, and K. McSweeney. 2022. Digital Mapping of Agricultural Soil Organic Carbon Using Soil Forming Factors: A Review of Current Efforts at the Regional and National Scales. Frontiers in Soil Science. 7: e890437. doi: 10.3389/fsoil.2022.890437.
Connecting Soil Health Assessment with Ecosystem Services
The other focus of my work conducted at University of Illinois at Urbana-Champaign was to improve the interpretation of soil health indicators (SHIs). A meta-analytical approach was applied to investigate the scoring of SHIs using environmental covariates and the connections between SHIs and ecosystem services. The work showed the importance of collecting soil data, covariates, and key ecosystem service outcomes (e.g., greenhouse gas emissions, N leaching, ecosystem productivity) for future field sampling campaigns. In addition, regression-based models were established to enable the estimation of SHI responsiveness to conservation practices while accounting for site-specific parameters. Another project of mine investigated the use of a rapid and cost-effective method based on soil spectroscopy to assess a suite of SHIs at the regional scale, which identified the need for improving the transferability of infrared-based soil predictive models.
Related publications:
1. Xia, Y. and M. Wander. 2021. Evaluation of Indirect and Direct Scoring Methods to Relate Biochemical Soil Quality Indicators to Ecosystem Services. Soil Science Society of America Journal. 86: 678-702. doi: 10.1002/saj2.20370.
2. Xia, Y. and M. Wander. 2021. Responses of β-Glucosidase, Permanganate Oxidizable Carbon, and Fluorescein Diacetate Hydrolysis to Conservation Practices. Soil Science Society of America Journal. 85(5): 1649-1662. doi: 10.1002/saj2.20261.
3. Xia, Y., Ugarte, C., Guan, K., Pentrak, M., and M. Wander. 2018. Developing Near- and Mid-infrared Spectroscopy Analysis Methods for Rapid Assessment of Soil Quality in Illinois. Soil Science Society of America Journal. 82: 1415-1427. doi:10.2136/sssaj2018.05.0175.