Carbon, energy, and water flux data from annual and perennial agroecosystems
Creators: name: Blakely, Bethany
name: Moore, Caitlin
name: Pederson, Taylor
name: Gibson, Christy
name: Benson, Michael
name: Dracup, Evan
name: Bernacchi, Carl
Publisher: name: Zenodo
Year: 2025
Subjects: Eddy Covariance
Bioenergy
Meteorology
Greenhouse gases

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 While the unpublished data may be accessed here, they have not yet undergone the rigorous Ameriflux QC process.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: Funding provided by: United States Department of AgricultureROR ID: https://ror.org/01na82s61Award Number: 
Funding provided by: United States Department of EnergyROR ID: https://ror.org/01bj3aw27Award Number: DE-SC0018420
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 <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/10.1007/978-94-007-2351-1Burba, 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.3803Hsieh, 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-14-2903-2017Kantola, 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.2017.05.023Masters, 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.2015.09.016Miller, 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.12291Moore, 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.12743Moore, 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/ac661aMoore, 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.12788Vickers, 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;2von 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.2019.107731Webb, 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.49710644707Zeri, 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