Carbon, energy, and water flux data from annual and perennial agroecosystems
Data files
Mar 14, 2025 version files 162.53 MB
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fill_data.zip
77.11 MB
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maize-soy_1.zip
17.35 MB
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maize-soy_2.zip
18.15 MB
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miscanthus_1.zip
18.49 MB
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miscanthus_2.zip
17.14 MB
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README.md
5.04 KB
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sorghum.zip
14.29 MB
Abstract
This dataset contain meteorological and eddy covariance flux data collected at the University of Illinois Energy Farm from 2017 to 2022. Data collected prior to 2017 can be found on the Ameriflux network (https://ameriflux.lbl.gov/) under sitenames UiA, UiB, UiC, and UiD. DOI's for these datasets are included in the related works section. As of 2025, Ameriflux data submissions for 2017 onward (including for new sites UiE, UiF and UiG), are ongoing. While the unpublished data may be accessed here, they have not yet undergone the rigorous Ameriflux QC process. Thus, the Ameriflux-published version should be the ultimate version of record.
Five experimental plots are represented:
- Maize-soy 1 (US-UiC; MaizeBasalt)
- Maize-soy 2 (US-UiG; MaizeCon)
- Miscanthus 1 (US-UiB; MiscanthusBasalt)
- Miscanthus 2 (US-UiF; MiscanthisControl)
- Sorghum (US-UiE; Sorghum)
The files in this dataset are in one of two forms: an Ameriflux-submission ready .csv which follows Ameriflux variable conventions (https://ameriflux.lbl.gov/data/aboutdata/data-variables/), or analogous "Level 2" .nc from the PyFluxPro post-processing tool (https://github.com/OzFlux/PyFluxPro/wiki), following the corresponding variable conventions (https://github.com/OzFlux/PyFluxPro/wiki/Variable-names-and-attributes#variable-naming-rules).
Both types of files include half-hourly flux and meteorological data as processed from the original 10Hz files using EddyPro software (LiCor Environmental). The data have been further processed in PyFluxPro to conform to standard variable naming conventions (described above), and have had unreasonable data values removed through a combination of automated and manual QC. Also included are external data that can be used to fill gaps in the half-hourly time series. Three data sources are represented: ERA5, ISD, and MODIS. These files contain values extracted from ERA5, ISD, or MODIS for the energy farm; since all experimental plots are within 650m of each other, the same values are used to gap fill all plots.
https://doi.org/10.5061/dryad.4j0zpc8p9
Description of the data and file structure
These files contain half-hourly, quality-controlled eddy covariance data from the University of Illinois Energy Farm. We emphasize that the preferred dataset of record is the version published on the Ameriflux network (https://ameriflux.lbl.gov/); see Access information section.
The data directory structure is organized by experimental site, with directories labeled by the site name used in the manuscript. Site naming conventions are as follows:
Manuscript name / Ameriflux site name / PyFluxPro site name:
Maize-soy 1 / US-UiC / MaizeBasalt
Maize-soy 2 / US-UiG / MaizeCon
Miscanthus 1 / US-UiB / MiscanthusBasalt
Miscanthus 2 / US-UiF / MiscanthusControl
Sorghum / US-UiE / Sorghum
File type 1: Ameriflux-formatted files
This type of file has the name format “US-Ui[X]HH[YYYYDDhhmm]_[YYYYMMDDhhmm].csv” where [X] is the Ameriflux site identifier. The rest of the file name indicates the time period the file covers with the format, where YYYY refers to the 4-digit year, MM to the 2-digit month, DD to the 2-digit day of month, hh to a two-digit hour of the day (24-hour) and mm the minute. The first date string indicates the starting datetime of the data in the file while the second date string indicates the ending datetime of the data in the file.
All variables in this file conform to Ameriflux standards: https://ameriflux.lbl.gov/data/aboutdata/data-variables/. Missing data points are represented by the value -9999
File type 2: PyFluxPro-formatted files
This type of file has the name format “[PyFluxPro Site name]_[YYYY]_L2.nc”. YYYY refers to the year data was collected; “L2” refers to the PyFluxPro processing level (Level 2, see https://github.com/OzFlux/PyFluxPro/wiki).
All variables in this file conform to PyFluxPro variable standards: https://github.com/OzFlux/PyFluxPro/wiki/Variable-names-and-attributes. Missing data points are represented by the value -9999
File type 3: Fill data
These files contain the fill data necessary to process the L2 data through the rest of the PyFluxPro pipeline. Data from three external sources are included: ERA5 (“EnergyFarm_ERA5_2008_2022”), ISD (“EnergyFarm_ISD_2008_2020”, “EnergyFarm_ISD_2020_2022”), and MODIS (“EnergyFarm_250m_16_days_EVI”). These files are formatted to be used in the gapfilling steps (L4 and L5; https://github.com/OzFlux/PyFluxPro/wiki) of PyFluxPro.
Code/software
Ameriflux-type files are in comma separated values format and require no specialized software.
The PyFluxPro type files and fill data are NetCDF files. They can be opened and manipulated with various software, for instance the ncdf4 package from R (https://www.r-project.org/). However, they will work most seamlessly with the (free and open) PyFluxPro processing tool: https://github.com/OzFlux/PyFluxPro.
The files containing field data are processed through “Level 2”, meaning that the data within have been given standard names and metadata (this occurs in “Level 1” processing) and undergone manual QC (this occurs in “Level 2” processing). A user may resume the pyfluxpro pipeline by loading these files as inputs for Level 3 processing (merging of related variables) or, if they wish to use the data as-is, may use PyFluxPro’s built-in conversion tools to export them as Excel sheets.
The files containing ERA5, ISD, and MODIS fill data enable the processing of data through Levels 4 - 6 of the PyFluxPro pipeline. These files are loaded as additional inputs in Level 4 (ERA5, ISD) or 5 (MODIS EVI) and facilitate the gapfilling of data through PyFluxPro’s onboard machine learning approach.
Access information
Other publicly accessible locations of the data:
- Ameriflux network: https://ameriflux.lbl.gov/; site names “US-UiB” (miscanthus 1), “US-UiF” (miscanthus 2), “US-UiC” (maize-soy 1), “US-UiG” (maize-soy 2), US-UiE (sorghum)
- Data for sites only active 2008 - 2016 can also be found at https://ameriflux.lbl.gov/; site names “US-UiA” (switchgrass) and “US-UiD” (native prairie)
Fill data was derived from the following sources:
The experiment was located at the University of Illinois Energy Farm in Urbana, Illinois, USA. Information on the local climate can be found in previous studies conducted at this site (Miller et al., 2016; Moore et al., 2021, 2022; Zeri et al., 2011). Soils at the site consist of Dana silt loams, Drummer silty clay loams, and Flanagan silt loams, and range from moderately well drained to poorly drained (von Haden et al., 2019). Soil pH ranged from 5.05 to 5.44 (Masters et al., 2016). Soil organic carbon content ranged from 15 to 20 g kg-1 (Kantola et al., 2017). Wind direction is relatively evenly distributed.
The eddy covariance measurement technique, coupled with meteorological measurements, was applied across five bioenergy cropping systems: an annual maize (Zea mays) – maize – soybean (Glycine max) rotation, perennial miscanthus (Miscanthus × giganteus), perennial switchgrass (Panicum virgatum), restored prairie (an assemblage of 32 annual and perennial Illinois-native species; listed in Zeri et al., 2011), and an annual sorghum (Sorghum bicolor) – sorghum – soybean rotation. All feedstocks were established in 3.8-ha plots previously grown in a maize-soy rotation and located within 650 m of each other.
CO2 flux measurements were obtained using the eddy covariance technique (Aubinet et al., 2012; Burba, 2022). The instrumentation for each tower included an infrared gas analyzer (LI-7500 series, LI-COR Biosciences, Lincoln, NE) and a three-dimensional sonic anemometer (8100RE, R.M. Young, Traverse City, MI; Windmaster Pro, Gill Instruments, Hampshire, UK). Approximately weekly during the growing season, instrument heights were adjusted to remain 1 to 2 m above the plant canopy. Cospectral analysis has shown this distance to be sufficient to capture turbulent fluxes (Moore et al., 2020).
Radiation measurements (Wm-2) were captured with 4-component net radiometers (CNR1 or CNR4, Kipp and Zonen via OTT Hydromet, Sterling, VA 20164) mounted 4 m above each feedstock. Complementing the radiation and eddy covariance measurements, several biometeorological instruments were also deployed to record incoming and outgoing photosynthetically active radiation (μmol m2 s-1) surface temperature (°C), soil temperature (°C), soil moisture (volumetric %), soil heat flux (W m-2), air temperature(°C), and humidity (%). Further details regarding these instruments can be found in Moore et al. (2021).
Eddy covariance and biometeorological data were processed with EddyPro (v6 and v7; LiCor Biosciences, Lincoln, NE 68504) and PyFluxPro (v3; Isaac et al. 2017; Fig. S3). EddyPro processing involved several steps to enhance data accuracy, including block averaging for de-trending, a double rotation to correct for instrument tilt, time lag correction using covariance maximization, flux density correction using the Webb−Pearman−Leuning method (Webb et al., 1980), spike identification and removal based on (Vickers & Mahrt, 1997), and a footprint calculation following the method of (Hsieh et al., 2000). Meteorological tower measurements were uploaded into Eddypro to aid in flux computation and correction. Half-hourly air temperature, humidity, and pressure were used as mean values for these quantities for flux computation, with half-hourly humidity additionally used to correct sonic temperature. Half-hourly incoming longwave radiation, and photosynthetically active radiation were used to correct sensible heat fluxes for instrument surface heating. Because of the frequent changes in instrument and canopy height throughout the growing season, EddyPro’s “Dynamic Metadata” option was used to adjust fluxes in response to the changing distance between the canopy and instruments.
Quality control involved the removal of out-of-range sensor values, values recorded during known sensor malfunction, and manually identified spikes. Data for which <50% of the flux footprint fell within our experimental field were also discarded. The 50% cutoff is unusually lenient for footprint filtering but was necessary to prevent excessive data loss during the fallow period in our relatively small 3.8-ha fields. To fill gaps in meteorological data, two sources of external data are included: ERAI (through 2019) or ERA5 (after 2019; Hersbach et al., 2020) and Automatic Weather Station data from the nearby Willard Airport weather station (Station ID: 725315–94870). EVI, applied in 2020 - 2022 to fill large gaps in carbon fluxes, was obtained from MODIS by TERN, Australia (Terrestrial Ecosystem Research Network, C. Ewenz, personal communication).
References:
Aubinet, M., Vesala, T., & Papale, D. (Eds.). (2012). Eddy Covariance: A Practical Guide to Measurement and Data Analysis. Springer Netherlands. https://doi.org/10.1007/978-94-007-2351-1
Burba, G. (2022). Eddy Covariance Method for Scientific, Regulatory, and Commercial Applications. LI-COR Biosciences.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., … Thépaut, J. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), 1999–2049. https://doi.org/10.1002/qj.3803
Hsieh, C.-I., Katul, G., & Chi, T. (2000). An approximate analytical model for footprint estimation of scalar ¯uxes in thermally strati®ed atmospheric ¯ows. Advances in Water Resources.
Isaac, P., Cleverly, J., McHugh, I., van Gorsel, E., Ewenz, C., & Beringer, J. (2017). OzFlux data: Network integration from collection to curation. Biogeosciences, 14(12), 2903–2928. https://doi.org/10.5194/bg-14-2903-2017
Kantola, I. B., Masters, M. D., & DeLucia, E. H. (2017). Soil particulate organic matter increases under perennial bioenergy crop agriculture. Soil Biology and Biochemistry, 113, 184–191. https://doi.org/10.1016/j.soilbio.2017.05.023
Masters, M. D., Black, C. K., Kantola, I. B., Woli, K. P., Voigt, T., David, M. B., & DeLucia, E. H. (2016). Soil nutrient removal by four potential bioenergy crops: Zea mays, Panicum virgatum, Miscanthus×giganteus, and prairie. Agriculture, Ecosystems & Environment, 216, 51–60. https://doi.org/10.1016/j.agee.2015.09.016
Miller, J. N., VanLoocke, A., Gomez-Casanovas, N., & Bernacchi, C. J. (2016). Candidate perennial bioenergy grasses have a higher albedo than annual row crops. GCB Bioenergy, 8(4), 818–825. https://doi.org/10.1111/gcbb.12291
Moore, C. E., Berardi, D. M., Blanc‐Betes, E., Dracup, E. C., Egenriether, S., Gomez‐Casanovas, N., Hartman, M. D., Hudiburg, T., Kantola, I., Masters, M. D., Parton, W. J., Van Allen, R., Haden, A. C., Yang, W. H., DeLucia, E. H., & Bernacchi, C. J. (2020). The carbon and nitrogen cycle impacts of reverting perennial bioenergy switchgrass to an annual maize crop rotation. GCB Bioenergy, 12(11), 941–954. https://doi.org/10.1111/gcbb.12743
Moore, C. E., Gibson, C. D., Miao, G., Dracup, E. C., Gomez-Casanovas, N., Masters, M. D., Miller, J., C von Haden, A., Meyers, T., DeLucia, E. H., & Bernacchi, C. J. (2022). Substantial carbon loss respired from a corn–soybean agroecosystem highlights the importance of careful management as we adapt to changing climate. Environmental Research Letters, 17(5), 054029. https://doi.org/10.1088/1748-9326/ac661a
Moore, C. E., Haden, A. C., Burnham, M. B., Kantola, I. B., Gibson, C. D., Blakely, B. J., Dracup, E. C., Masters, M. D., Yang, W. H., DeLucia, E. H., & Bernacchi, C. J. (2021). Ecosystem‐scale biogeochemical fluxes from three bioenergy crop candidates: How energy sorghum compares to maize and miscanthus. GCB Bioenergy, 13(3), 445–458. https://doi.org/10.1111/gcbb.12788
Vickers, D., & Mahrt, L. (1997). Quality Control and Flux Sampling Problems for Tower and Aircraft Data. Journal of Atmospheric and Oceanic Technology, 14(3), 512–526. https://doi.org/10.1175/1520-0426(1997)014<0512:QCAFSP>2.0.CO;2
von Haden, A. C., Marín-Spiotta, E., Jackson, R. D., & Kucharik, C. J. (2019). Soil microclimates influence annual carbon loss via heterotrophic soil respiration in maize and switchgrass bioenergy cropping systems. Agricultural and Forest Meteorology, 279, 107731. https://doi.org/10.1016/j.agrformet.2019.107731
Webb, E. K., Pearman, G. I., & Leuning, R. (1980). Correction of flux measurements for density effects due to heat and water vapour transfer. Quarterly Journal of the Royal Meteorological Society, 106(447), 85–100. https://doi.org/10.1002/qj.49710644707
Zeri, M., Anderson-Teixeira, K., Hickman, G., Masters, M., DeLucia, E., & Bernacchi, C. J. (2011). Carbon exchange by establishing biofuel crops in Central Illinois. Agriculture, Ecosystems & Environment, 144(1), 319–329. https://doi.org/10.1016/j.agee.2011.09.006