METADATA SHEET

Describes data obtained during the years 2014–2017 as used by Emmel et al. (2020)

Note that an earlier version of the data from the same period used by Emmel et al. (2018) was already made available via doi:10.3929/ethz-b-000260058.
1.	SITE
1.1.	Site name: 
CH-OE2 Oensingen, Switzerland (exact measurement locations are depicted in the files ending on “_measurement_locations”)
1.2.	Site description:
The 1.55 ha cropland site under investigation is located near the town of Oensingen (Canton Solothurn, Switzerland). The soil is a fluvisol composed of 42% clay, 33% silt and 25% sand (Alaoui and Goetz, 2008). The mean temperature (1964–1991) was 8.4 °C, according to the nearest long-term weather station Wynau, and the annual precipitation of roughly 1000 mm are typically well distributed over the whole year. The field is cultivated following the Swiss Integrated Pest Management (IPM) regime, with varying crop types in a long-term crop rotation system in a repeated four-year cycle.
In December 2003, an eddy covariance tower was installed in the centre of the field as part of the CarboEurope Integrated Project. CO2 and H2O fluxes were measured continuously at 20 Hz resolution with a LICOR 7500 infrared absorption spectrometer in combination with a Gill Solent R3-50 ultrasonic anemometer-thermometer. Data were recorded digitally on a laptop computer with in-house data acquisition software running under the Linux operating system (see Eugster and Plüss, 2010, for more details).
The first two paragraphs were copied in from Dietiker et al. (2010).
The dataset used in Emmel et al. (2020) for which this metadescription was produced, was generated by the most recent version of the research group led by Prof. Dr. Nina Buchmann at ETH Zurich using a version of EddyPro (Licor, USA) in a data processing chain set up and managed by Dr. Lukas Hörtnagl, and ancillary meteorological data were screened and quality controlled using the MetScreening tool in its most recent version. A more detailed description of the data processing was provided by Emmel et al. (2018) whereas all additional details that extend the 2018 status for the Emmel et al. (2020) paper were presented by Emmel et al. (2020). 

1.3.	Latitude:
47°17′11.1′′ N (WGS84 datum)
1.4.	Longitude:
7°44′01.5′′ E (WGS84 datum)
1.5.	Elevation:
452 m a.s.l. (CH1903+/LV95 datum)
1.6.	Vegetation type:
Cropland with typical wheat-dominated four-year crop rotation (winter wheat every second year, alternative crops in-between).
1.7.	Soil type:
Fluvisol composed of 42% clay, 33% silt and 25% sand (Alaoui and Goetz, 2008)
1.8.	Geology:
Quaternary deposits from the last glaciation
1.9.	Land use:
Arable land
2.	DATA OWNER
2.1.	Name:
Carmen Emmel
2.2.	Adress:
Carmen Emmel, Rilkestrasse 40, 60311 Rodgau, Germany
2.3.	E-Mail:
carmenteresad@hotmail.com
2.4.	Name(s) of PI, supervisor(s):
Nina Buchmann (nina.buchmann@usys.ethz.ch), Werner Eugster (eugsterw@ethz.ch)
2.5.	Collaborators (incl. contact info):
Petra D’Odorico, Ecosystem-Ecology Group, Swiss Federal Research Institute WSL, 8903 Birmensdorf, Switzerland
Andrew Revill, School of GeoSciences, University of Edinburgh, Edinburgh, United Kingdom
3.	PROJECT
3.1.	Type of work:
PostDoc research (field campaign data and long-term meteorological and CO2 flux observations)
3.2.	Project title:
DiRad Oensingen: The role of diffuse radiation for a long-term arable crop rotation at Oensingen, Switzerland
3.3.	University:
ETH Zurich, Zurich, Switzerland

3.4.	Institute:
Institute of Agricultural Sciences

3.5.	Department:
Department of Environmental Systems Sciences

3.6.	Funding agency:
Swiss National Science Foundation, grant 146373 to Werner Eugster and Nina Buchmann
4.	MATERIAL & METHODS
Consult the Emmel et al. (2018) and Emmel et al. (2020) papers for methodical details. In addition, the Supplementary Information to Emmel et al. (2020) accessible via doi:10.1111/gcb.15226 provides additional details. Even more details can be found in the metadescription to 23-Campaign-Data, see Section 5.4 in this document.
5.	DATA
5.1.	What was measured or described:
There are several datasets that Emmel et al. (2020) used, which are described in the following subsections:
11-CH-OE2_BF5 see Supplementary Information to the Emmel et al. (2020) paper: there was a technical problem with the Delta-T BF5 sensor measuring diffuse and total PPFD. The data in this subdirectory relate to that aspect of the paper.
12-PAR-Intercomparison befor the field campaigns (data in 23-Campaign-Data) all PPFD sensors involved in the study were intercalibrated on the roof of the CHN building of ETH Zurich. The data files in this subdirectory relate to that aspect of the paper.
21-Meteo-Data containing the ancillary meteorological measurements from the flux site.
22-Flux-Data containing processed and gapfilled flux data; since some essential meteo variables are also used for flux processing, the corresponding variables from 21-Meteo-Data as they were used for flux calculation and flux partitioning are found here as well.
23-Campaign-Data with additional data specific to the Emmel et al. (2020) publication.
5.2.	Meteorological data in 21-Meteo-Data:
VARIABLE_NAME	UNIT	COMMENT
TIMESTAMP		YYYY-mm-dd HH:MM:SS
		end of averaging period, CET
BV_EC_AVG_T1_2_1	V	Supply voltage EC system
BV_LOGGER_AVG_M1_0.5_1	V	Supply voltage M1 logger system
BV_LOGGER_AVG_T1_0.5_1	V	Supply voltage T1 logger system
BV_LOGGER_INT_AVG_M1_0.5_1	V	Supply voltage M1 logger system
BV_LOGGER_INT_AVG_T1_0.5_1	V	Supply voltage T1 logger system
G_AVG_CF2_0.03_1	W/m2	Ground heat flux, –0.03 cm, #1
G_AVG_CF3_0.03_1	W/m2	Ground heat flux, –0.03 cm, #2
G_AVG_CF4_0.03_1	W/m2	Ground heat flux, –0.03 cm, #3
G_AVG_CF5_0.03_1	W/m2	Ground heat flux, –0.03 cm, #4
G_SD_CF2_0.03_1	W/m2	Standard dev. of respective variable
G_SD_CF3_0.03_1	W/m2	Standard dev. of respective variable
G_SD_CF4_0.03_1	W/m2	Standard dev. of respective variable
G_SD_CF5_0.03_1	W/m2	Standard dev. of respective variable
LW_IN_AVG_M1_1_1	W/m2	Corrected long-wave incoming rad.
LW_IN_RAW_AVG_M1_1_1	W/m2	Raw long-wave incoming radiation
LW_IN_RAW_SD_M1_1_1	W/m2	Standard dev. of respective variable
LW_OUT_AVG_M1_1_1	W/m2	Corrected long-wave outgoing rad.
LW_OUT_RAW_AVG_M1_1_1	W/m2	Raw long-wave outgoing radiation
LW_OUT_RAW_SD_M1_1_1	W/m2	Standard dev. of respective variable
NETRAD_AVG_M1_1_1	W/m2	Net all-wave radiation (LW + SW
		components combined)
PPFD_DIF_AVG_M1_1_1	µmol/m2/s	Photosynthetic active radiation 400–
		700 nm), diffuse component, sensor #1
PPFD_DIF_AVG_M1_1_2	µmol/m2/s	same from sensor #2
PPFD_DIF_SD_M1_1_1	µmol/m2/s	Standard dev. of respective variable
PPFD_DIF_SD_M1_1_2	µmol/m2/s	Standard dev. of respective variable
PPFD_IN_AVG_M1_1_1	µmol/m2/s	Photosynthetic active radiation 400–
		700 nm), diffuse and direct, sensor #1
PPFD_IN_AVG_M1_1_1_GAPFILL	µmol/m2/s	same, but gaps filled
PPFD_IN_AVG_M1_1_2	µmol/m2/s	same from sensor #2
PPFD_IN_SD_M1_1_1	µmol/m2/s	Standard dev. of respective variable
PPFD_IN_SD_M1_1_2	µmol/m2/s	Standard dev. of respective variable
PPFD_OUT_AVG_M1_1_1	µmol/m2/s	Reflected PPFD
PPFD_OUT_SD_M1_1_1	µmol/m2/s	Standard dev. of respective variable
PPFD_IN_AVG	µmol/m2/s	FINAL incoming total PPFD
PPFD_DIF_AVG	µmol/m2/s	FINAL incoming diffuse PPFD
PREC_TOT_M2_1_1	mm	Precipitation total in data interval
RH_AVG_T1_2_1	%	Relative humidity at 2 m height
RH_SD_T1_2_1	%	Standard dev. of respective variable
SWC_AVG_CF1_0.05_1	vol-%	Soil water content –0.05 m
SWC_AVG_CF1_0.15_1	vol-%	Soil water content –0.15 m
SWC_AVG_CF1_0.3_1	vol-%	Soil water content –0.3 m
SWC_AVG_CF1_0.5_1	vol-%	Soil water content –0.5 m
SWC_SD_CF1_0.05_1	vol-%	Standard dev. of respective variable
SWC_SD_CF1_0.15_1	vol-%	Standard dev. of respective variable
SWC_SD_CF1_0.3_1	vol-%	Standard dev. of respective variable
SWC_SD_CF1_0.5_1	vol-%	Standard dev. of respective variable
SW_DIF_AVG_M1_1_1	W/m2	Short-wave diffuse radiation
SW_DIF_SD_M1_1_1	W/m2	Standard dev. of respective variable
SW_IN_AVG_M1_1_1	W/m2	Standard dev. of respective variable
SW_IN_SD_M1_1_1	W/m2	Short-wave incoming total radiation
SW_OUT_AVG_M1_1_1	W/m2	Short-wave reflected radiation
SW_OUT_SD_M1_1_1	W/m2	Standard dev. of respective variable
TA_AVG_T1_2_1	°C	Air temperature at 2 m height
TA_SD_T1_2_1	°C	Standard dev. of respective variable
TS_AVG_CF2_0.015_1	°C	Soil temperature –0.015 m, sensor #1
TS_AVG_CF3_0.015_1	°C	Soil temperature –0.015 m, sensor #2
TS_AVG_CF4_0.015_1	°C	Soil temperature –0.015 m, sensor #3
TS_AVG_CF5_0.015_1	°C	Soil temperature –0.015 m, sensor #4
TS_PRF1_AVG_CF1_0.05_1	°C	Soil temperature in profile #1 –0.05 m
TS_PRF1_AVG_CF1_0.1_1	°C	Soil temperature in profile #1 –0.10 m
TS_PRF1_AVG_CF1_0.15_1	°C	Soil temperature in profile #1 –0.15 m
TS_PRF1_AVG_CF1_0.3_1	°C	Soil temperature in profile #1 –0.3 m
TS_PRF1_AVG_CF1_0.5_1	°C	Soil temperature in profile #1 –0.5 m
TS_PRF1_SD_CF1_0.05_1	°C	Standard dev. of respective variable
TS_PRF1_SD_CF1_0.1_1	°C	Standard dev. of respective variable
TS_PRF1_SD_CF1_0.15_1	°C	Standard dev. of respective variable
TS_PRF1_SD_CF1_0.3_1	°C	Standard dev. of respective variable
TS_PRF1_SD_CF1_0.5_1	°C	Standard dev. of respective variable
TS_PRF2_AVG_CF1_0.05_1	°C	Soil temperature in profile #2 –0.05 m
TS_PRF2_AVG_CF1_0.1_1	°C	Soil temperature in profile #2 –0.1 m
TS_PRF2_AVG_CF1_0.2_1	°C	Soil temperature in profile #2 –0.2 m
TS_PRF2_AVG_CF1_0.3_1	°C	Soil temperature in profile #2 –0.3 m
TS_PRF2_AVG_CF1_0.5_1	°C	Soil temperature in profile #2 –0.5 m
TS_PRF2_AVG_CF1_1_1	°C	Soil temperature in profile #2 –1.0 m
TS_PRF2_SD_CF1_0.05_1	°C	Standard dev. of respective variable
TS_PRF2_SD_CF1_0.1_1	°C	Standard dev. of respective variable
TS_PRF2_SD_CF1_0.2_1	°C	Standard dev. of respective variable
TS_PRF2_SD_CF1_0.3_1	°C	Standard dev. of respective variable
TS_PRF2_SD_CF1_0.5_1	°C	Standard dev. of respective variable
TS_PRF2_SD_CF1_1_1	°C	Standard dev. of respective variable
TS_SD_CF2_0.015_1	°C	Standard dev. of respective variable
TS_SD_CF3_0.015_1	°C	Standard dev. of respective variable
TS_SD_CF4_0.015_1	°C	Standard dev. of respective variable
TS_SD_CF5_0.015_1	°C	Standard dev. of respective variable
T_RAD_AVG_M1_1_1	°C	Radiometer body temp., sensor #1
T_RAD_AVG_M1_1_2	°C	Radiometer body temp., sensor #2
T_RAD_SD_M1_1_1	°C	Standard dev. of respective variable
T_RAD_SD_M1_1_2	°C	Standard dev. of respective variable
WD_VANE_SCALAR_AVG_T1_2_1	°	Wind direction, scalar averaging
WD_VANE_SCALAR_SD_T1_2_1	°	Standard dev. of respective variable
WD_VANE_VECTOR_AVG_T1_2_2	°	Wind direction, vectorial averaging
WD_VANE_VECTOR_SD_T1_2_1	°	Standard dev. of respective variable
WS_CUP_SCALAR_AVG_T1_2_1	m/s	Wind speed, scalar averaging, #1
WS_CUP_SCALAR_AVG_T1_2_2	m/s	Wind speed, scalar averaging, #2
WS_CUP_SCALAR_SD_T1_2_1	m/s	Standard dev. of respective variable
WS_CUP_VECTOR_AVG_T1_2_1	m/s	Wind speed, vectorial averaging

5.3.	Flux data in 22-Flux-Data:
VARIABLE_NAME	UNIT	COMMENT
TIMESTAMP		YYYY-mm-dd HH:MM:SS
		end of averaging period, CET
NEEdespiked_orig	µmol/m2/s	Original already despiked net 			ecosystem exchange
NEEdespiked_f	µmol/m2/s	Gap filled NEEdespiked_orig
NEEdespiked_fqc	(flag)	Quality flag of NEEdespiked_f		0=original, 1=most reliable, 2=medium, 3=least reliable
NEEdespiked_fall	µmol/m2/s	NEEdespiked_orig gap filled with all 
		values considered as gaps
NEEdespiked_fall_qc	(flag)	Quality flag of NEEdespiked_fall
	0=original, 1=most reliable, 2=medium, 3=least reliable
NEEdespiked_fnum	#	Number of data points used for gap 
		filling of NEEdespiked_orig
NEEdespiked_fsd	µmol/m2/s	Standard deviation of datapoints used 
		for gap filling of NEEdespiked_orig
NEEdespiked_fmeth	#	Method used for gap filling of 
		NEEdespiked_orig,
	1=similar meteo condition (sFillLUT with Rg, VPD, Tair)
	2=similar meteo (sFillLUT with Rgonly)
	3=mean diurnal course(sFillMDC)
NEEdespiked_fwin	#	Full window length used for gap filling 
		of NEEdespiked_orig
TA_AVG_T1_2_1_orig	°C	Original air 30-min averaged 
		temperature, measured on the flux 
		tower at 2 m height
TA_AVG_T1_2_1_f	°C	Gap filled TA_AVG_T1_2_1_orig
TA_AVG_T1_2_1_fqc	(flag)	Quality flag of TA_AVG_T1_2_1_f
	0=original, 1=most reliable, 2=medium, 3=least reliable
TA_AVG_T1_2_1_fall	°C	TA_AVG_T1_2_1_orig gap filled with 
		all values considered as gaps
TA_AVG_T1_2_1_fall_qc	(flag)	Quality flag of TA_AVG_T1_2_1_fall
	0=original, 1=most reliable, 2=medium, 3=least reliable
TA_AVG_T1_2_1_fnum	#	Number of data points used for gap 
		filling of TA_AVG_T1_2_1_orig
TA_AVG_T1_2_1_fsd	°C	Standard deviation of datapoints used
		for gap filling TA_AVG_T1_2_1_orig
TA_AVG_T1_2_1_fmeth	#	Method used for gap filling of 
		TA_AVG_T1_2_1_orig
	1=similar meteo condition (sFillLUT with Rg, VPD, Tair)
	2=similar meteo (sFillLUT with Rgonly)
	3=mean diurnal course(sFillMDC)
TA_AVG_T1_2_1_fwin	#	Full window length used for gap filling 
		of TA_AVG_T1_2_1_orig
Ustar_WithUstar_Thres	m/s	Threshold of ustar to mark insufficient 
		conditions
Ustar_WithUstar_fqc	(flag)	Quality flag Ustar_WithUStar_Thres
	0=original, 1=most reliable, 2=medium, 3=least reliable
NEEdespiked_WithUstar_orig	µmol/m2/s	Original already despiked net 
		ecosystem exchange w/ ustar filtering
NEEdespiked_WithUstar_f	µmol/m2/s	Gap filled 
		NEEdespiked_WithUstar_orig
NEEdespiked_WithUstar_fqc	(flag)	Quality flag of 					NEEdespiked_WithUstar_f
		0=good data
		1=low turbulence
		2=first half hour after low turbulence
		3=no threshold available
		4=missing uStar theshold
NEEdespiked_WithUstar_fall	µmol/m2/s	NEEdespiked_WithUstar_orig gap 
		filled with all values considered gaps
NEEdespiked_WithUstar_fall_qc	(flag)	Quality flag of 
		NEEdespiked_WithUstar_fall
		0=good data
		1=low turbulence
		2=first half hour after low turbulence		3=no threshold available
		4=missing uStar theshold
NEEdespiked_WithUstar_fnum	#	Number of data points used for gap 
		filling of 
		NEEdespiked_WithUstar_orig
NEEdespiked_WithUstar_fsd	µmol/m2/s	Standard deviation of datapoints used 			for gap filling of 
		NEEdespiked_WithUstar_orig
NEEdespiked_WithUstar_fmeth	#	Method used for gap filling of 
		NEEdespiked_WithUstar_orig
	1=similar meteo condition (sFillLUT with Rg, VPD, Tair) 
	2=similar meteo (sFillLUT with Rgonly)
	3=mean diurnal course(sFillMDC)
NEEdespiked_WithUstar_fwin	#	Full window length used for gap filling 
		of NEEdespiked_WithUstar_orig
TA_AVG_T1_2_1_WithUstar_orig	°C	Original air 30-min averaged 
		temperature, measured on the flux 
		tower at 2 m height
TA_AVG_T1_2_1_WithUstar_f	°C	Gap filled 
		TA_AVG_T1_2_1_WithUstar_orig
TA_AVG_T1_2_1_WithUstar_fqc	(flag)	Quality flag of 
		TA_AVG_T1_2_1_WithUstar_f
	0=original, 1=most reliable, 2=medium, 3=least reliable
TA_AVG_T1_2_1_WithUstar_fall	°C	TA_AVG_T1_2_1_WithUstar_orig gap 
		filled with all values considered gaps
TA_AVG_T1_2_1_WithUstar_fall_qc	(flag)	Quality flag of 
		TA_AVG_T1_2_1_WithUstar_fall
	0=original, 1=most reliable, 2=medium, 3=least reliable
TA_AVG_T1_2_1_WithUstar_fnum	#	Number of data points used for gap 
		filling of 						TA_AVG_T1_2_1_WithUstar_orig
TA_AVG_T1_2_1_WithUstar_fsd	°C	Standard deviation of datapoints used 
		for gap filling of 
		TA_AVG_T1_2_1_WithUstar_orig
TA_AVG_T1_2_1_WithUstar_fmeth	#	Method used for gap filling of 			TA_AVG_T1_2_1_WithUstar_orig
	1=similar meteo condition (sFillLUT with Rg, VPD, Tair)
	2=similar meteo (sFillLUT with Rgonly)
	3=mean diurnal course(sFillMDC)
TA_AVG_T1_2_1_WithUstar_fwin	#	Full window length used for gap filling 
		of TA_AVG_T1_2_1_WithUstar_orig
PotRad	W/m2	Potential extraterrestrial SW radiation
FP_NEEnight	W/m2	Flux partitioning NEE night
FP_Temp	°C	Flux partitioning temperature
E_0	K	Lloyd & Taylor respiration E0
R_ref	µmol/m2/s	Lloyd & Taylor reference respiration
Reco	µmol/m2/s	Ecosystem respiration
GPP_f	µmol/m2/s	Gross primary production despiked
GPP_fqc	(flag)	Quality flag of  GPP_F
		0=good data
		1=low turbulence
		2=first half hour after low turbulence
		3=no threshold available
		4=missing uStar theshold
PotRad_WithUstar	W/m2	Potential extraterrestrial SW radiation
FP_NEEnight_WithUstar	W/m2	Flux partitioning, night-time data, ustar
Reco_DT	µmol/m2/s	Ecosystem respiration, daytime partit.
GPP_DT	µmol/m2/s	GPP, daytime partitioning
Reco_DT_SD	µmol/m2/s	Standard dev. of respective variable
GPP_DT_SD	µmol/m2/s	Standard dev. of respective variable
FP_Temp_WithUstar	°C	Flux partitioning temperature
E_0_WithUstar	K	Lloyd & Taylor respiration E0
R_ref_WithUstar	µmol/m2/s	Lloyd & Taylor reference respiration
Reco_WithUstar	µmol/m2/s	Ecosystem respiration, ustar threshold
GPP_WithUstar_f	µmol/m2/s	GPP, with ustar threshold
GPP_WithUstar_fqc	(flag)	Quality flag for GPP: 
		0 = original, 1 = most reliable,
		2 = medium, 3 = least reliable	
PotRad_NEW	W/m2	Potential extraterrestrial SW radiation
FP_VARnight	µmol/m2/s	FP_VAR during nighttime conditions
FP_VARday	µmol/m2/s	FP_VAR during daytime conditions
NEW_FP_Temp	°C	Flux partitioning temperature
NEW_FP_VPD	kPa	Flux partitioning VPD
FP_R_refNight	µmol/m2/s	same as FP_R_ref using the same 
		FP_E0, but from intermedate step 
		based on night-time data
FP_R_ref	µmol/m2/s	Lloyd & Taylor reference respiration
FP_E0	K	Lloyd & Taylor respiration E0
FP_alpha	–	canopy light utilization efficiency, 
		represents the initial slope of the light-
		response curve
FP_beta	µmol/m2/s	maximum CO2 uptake rate of the 
		canopy at light saturation
FP_k	–	parameter controlling the VPD 
		limitation of GPP
FP_qc	(flag)	quality flag of  estimated parameters
	0: good parameter fit
	1: some parameters out of range, required refit
	2: next parameter estimate is more than two weeks away
FP_dRecPar	#	records until or after closest record that 
		has a parameter estimate associated 
Reco_DT_WithUstar	µmol/m2/s	Ecosystem respiration, daytime 
		partititioning with ustar threshold
GPP_DT_WithUstar	µmol/m2/s	GPP, daytime partititioning with
		ustar threshold
Reco_DT_WithUstar_SD	µmol/m2/s	Standard dev. of respective variable
GPP_DT_WithUstar_SD	µmol/m2/s	Standard dev. of respective variable
ustar	m/s	Friction velocity
SW_IN_AVG_M1_1_1	W/m2	short-wave incoming radiation
TA_AVG_T1_2_1	°C	Air temperature at 2 m height
VPD_AVG_T1_2_1	kPa	Vapor pressure deficit at 2 m height

Gapfilling and flux partitioning was done with the REddyProc package in R and thus a more detailed description of these output variables can be found here:
https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebOutput
5.4.	Additional field campaign data in 23-Campaign-Data:

Subdirectory CH-OEF contains measurements from 2016:
ACi transect
Canopy height
Chlorophyll
LAI (leaf area index)
Leaf area
Leaf dry weight
File CH-OEF_2016_DiRad_all_fields_transect_locations.pdf shows the position of each field and transect, and for each data type there is a metadescription in a file with the name ending with _Meta.txt.
Subdirectory CH-OE1 contains measurements from 2015 and 2016:
Leaf dry weight
Leaf area
LAI (leaf area index)
Chlorophyll
Photosynthesis
ACi transect
PAR profile
Leaf water potential
Leaf spectra
Canopy height
The two files CH-OE1_2015_DiRad_all_fields_transect_locations.pdf and CH-OE1_2016_DiRad_all_fields_transect_locations.pdf show the exact location of the measurements, and for each data type there is a metadescription in a file with the name ending with _Meta.txt.
Subdirectory CH-OE1S contains measurements from 2015 and 2016:
Leaf area
LAI (leaf area index)
Chlorophyll
ACi transect
Leaf water potential
Leaf spectra
Leaf dry weight
Photosynthesis
Canopy height
Biomass
The two files CH-OE1S_2015_DiRad_all_fields_transect_locations.pdf and CH-OE1S_2016_DiRad_all_fields_transect_locations.pdf show the exact location of the measurements, and for each data type there is a metadescription in a file with the name ending with _Meta.txt.
Subdirectory CH-OE2 contains data from 2014, 2015, 2016, 2017:
ACiCurve
Biomass
C+N-Leaf
C+N-SowingSeeds
C-Soil
CanopyHeight
Chlorophyll
Photosynthesis
LAI (leaf area index)
LeafArea
LeafDryWeight
LeafRespiration
LeafSpectra
LeafWaterPotential
PARProfile
SoilRespiration
The three files CH-OE2_2014_DiRad_transect_locations.pdf, CH-OE2_2016_DiRad_all_fields_transect_locations.pdf and CH-OE2_2015_DiRad_all_fields_transect_locations.pdf show the exact location of the measurements, and for each data type there is a metadescription in a file with the name ending with _Meta.txt. A fourth PDF provides information on potato moisture content.
Subdirectory CH-OE2S contains data from 2014 and 2015:
Biomass
CanopyHeight
Chlorophyll
Photosynthesis
LAI (leaf area index)
LeafArea
LeafDryWeight
LeafSpectra
LeafWaterPotential
PARProfile
The file CH-OE2S_2015_DiRad_all_fields_transect_locations.pdf shows the exact location of the measurements, and for each data type there is a metadescription in a file with the name ending with _Meta.txt.
5.5.	Data status (raw data, gap filled, etc.):
Final dataset used in Emmel et al. (2020) publication.
6.	REFERENCES
6.1.	Publications directly related to this dataset:
Emmel C, D'Odorico P, Revill A, Hörtnagl L, Ammann C, Buchmann N, Eugster W. 2020. Canopy photosynthesis of six major arable crops is enhanced under diffuse light due to canopy architecture. Global Change Biology, doi:10.1111/gcb.15226
Emmel C, Winkler A, Hörtnagl L, Revill A, Ammann C, D'Odorico P, Buchmann N, Eugster W. 2018. Integrated management of a Swiss cropland is not sufficient to preserve its soil carbon pool in the long-term. Biogeosciences 15: 5377–5393, doi:10.5194/bg-2018-205
See also the associated dataset to the Emmel et al. (2018) publication accessible via doi:10.3929/ethz-b-000260058.
6.2.	Additional References:
Alaoui A, Goetz B. 2008. Dye tracer and infiltration experiments to investigate macropore flow. Geoderma 144: 279–286
D’Odorico P, Emmel C, Revill A, Liebisch F, Eugster W, Buchmann N. 2019. Vertical patterns of photosynthesis and related leaf traits in two contrasting agricultural crops. Functional Plant Biology 46 (3): 213–227, doi:10.1071/fp18061
Dietiker D, Buchmann N, Eugster W. 2010. Comparing measured CO2 and H2O fluxes under real conditions and yield under changing climatic conditions on cropland with the DNDC model. Agricultural Ecosystems and Environment 139: 396–401, doi:10.1016/j.agee.2010.09.002
Eugster W, Plüss P. 2010. A fault-tolerant eddy covariance system for measuring CH4 fluxes. Agricultural and Forest Meteorology 150: 841–851, doi:10.1016/j.agrformet.2009.12.008
Eugster W, Moffat A, Ceschia E, Aubinet M, Ammann C, Osborne B, Davis P A, Smith P, Jacobs C, Moors E, Dantec V L, Béziat P, Saunders M, Jans W, Grünwald T, Rebmann C, Kutsch W, Czerný R, Janouš D, Moureaux C, Dufranne D, Carrara A, Magliulo V, Tommasi P D, Olesen J E, Schelde K, Olioso A, Bernhofer C, Cellier P, Larmanou E, Loubet B, Wattenbach M, Marloie O, Sanz M-J, Søgaard H, Buchmann N. 2010. Management effects on European cropland respiration. Agricultural Ecosystems and Environment 139: 346–362, doi:10.1016/j.agee.2010.09.001
Hastings A F, Wattenbach M, Eugster W, Li C, Buchmann N, Smith P. 2010. Uncertainty propagation in soil greenhouse gas emission models: An experiment using the DNDC model and at the Oensingen cropland site. Agricultural Ecosystems and Environment 136: 97–110, doi:10.1016/j.agee.2009.11.016
Revill A, Emmel C, D’Odorico P, Buchmann N, Hörtnagl L, Eugster W. 2019. Estimating cropland carbon fluxes: A process-based model evaluation at a Swiss crop-rotation site. Field Crops Research 234: 95–106, doi:10.1016/j.fcr.2019.02.006
Sus O, Williams M, Bernhofer C, Béziat P, Buchmann N, Ceschia E, Doherty R, Eugster W, Grünwald T, Kutsch W, Smith P, Wattenbach M. 2010. A linked carbon cycle and crop developmental model: Description and evaluation against measurements of carbon fluxes and carbon stocks at several European agricultural sites. Agricultural Ecosystems and Environment 139: 402–418, doi:10.1016/j.agee.2010.06.012
7.	KEYWORDS
diffuse radiation; photosynthesis; agriculture; CO2; NBP; soil carbon; carbon budget; eddy-covariance;