DOI: 10.5061/dryad.4j0zpc8p9
title: Carbon, energy, and water flux data from annual and perennial agroecosystems

description: 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 unpublished data may be accessed here, they have not yet undergone the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.
description: 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 &lt;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). 
 <strong>References:</strong>
 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<br>Burba, G. (2022). Eddy
 Covariance Method for Scientific, Regulatory, and Commercial Applications.
 LI-COR Biosciences.<br>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<br>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.<br>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<br>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<br>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<br>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<br>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<br>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<br>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<br>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&lt;0512:QCAFSP&gt;2.0.CO;2<br>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<br>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<br>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
description: # Carbon, energy, and water flux data from annual and perennial
 agroecosystems
 [https://doi.org/10.5061/dryad.4j0zpc8p9](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/](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/. ](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](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. ](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](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/](https://www.r-project.org/)). However, they will work most seamlessly with the (free and open) PyFluxPro processing tool: [https://github.com/OzFlux/PyFluxPro](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/](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/](https://ameriflux.lbl.gov/); site names "US-UiA" (switchgrass) and "US-UiD" (native prairie) Fill data was derived from the following sources: * ERA5: [https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5](https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5) * ISD: [https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database](https://www.ncei.noaa.gov/products/land-based-station/integrated-surface-database) * MODIS: [https://modis.gsfc.nasa.gov/data/](https://modis.gsfc.nasa.gov/data/)