Bauböck Environmental Sciences Europe 2014, 26:1
http://www.enveurope.com/content/26/1/1RESEARCH Open AccessSimulating the yields of bioenergy and food
crops with the crop modeling software BioSTAR:
the carbon-based growth engine and the
BioSTAR ET0 method
Roland BauböckAbstract
Background: With a growing production and use of agricultural substrates in biogas facilities, the competition
between food and energy production, environmental issues, and sustainability goals has seen an increase in the
last decade and poses a challenge to policy makers. Statistical yield data has a low spatial resolution and only
covers standard crops and makes no statement in regard to yields under climate change. To support policy makers
and regional planners in an improved allocation of agricultural land use, a new crop model (BioSTAR) has been
developed.
Results: Simulations with weather and yield data from 7 years and four regions in Lower Saxony have rendered
overall good modeling results with prediction errors (RMSE and percentage) ranging from 1.6 t and 9.8% for winter
wheat to 2.1 t and 11.9% for maize. The model-generated ET0 and ETa values (mean of four locations) are lower
than ET0/ETa values calculated with the Penman-Monteith method but appear more realistic when compared to
field trial data from northern and eastern Germany.
Conclusions: The model has proven to be a functioning tool for modeling site-specific biomass potentials at the
farm level, and because of its Access® database interface, the model can also be used for calculating biomass yields
of larger areas, like administration districts or states. Out of the seven crops modeled in this study, only limited yield
and test site data was available for winter barley, winter rye, sorghum, and sunflower. For further improvement of
model performance and model calibration, more trial data and data testing are required for these crops.
Keywords: Crop modeling; Energy crops; BioSTAR; Biomass potentials; Evapotranspiration modelingBackground
The demand for biomass from agricultural resources as
an energy source is currently seeing a strong increase.
This is particularly true for Germany, as the country is
trying to double the share of bioenergy (agricultural,
forest, and waste biomass combined) to the country’s
energy total by the year 2020 [1].
In 2011, 2.2 million ha of the total agricultural area
(17 million ha) was already in use for either energy crop
production or renewable primary products. Of this area,
800,000 ha was in use for biogas crops, mainly maize,Correspondence: rbauboe1@gwdg.de
Department of Cartography, GIS and Remote Sensing, Research Project ‘BIS’,
University of Göttingen, Goldschmidtstraße 5, Göttingen 37077, Germany
© 2014 Bauböck; licensee Springer. This is an O
Attribution License (http://creativecommons.or
in any medium, provided the original work is p900,000 ha for oilseed rape (mainly for biodiesel produc-
tion) and, the smallest share, 250,000 ha for starch and
bioethanol production. By 2020, the agricultural area in
use for renewable resource production in Germany is
projected to be further expanded and will then have a
share of around 20% of the country’s total agricultural
area. Even though Germany’s food production is close
to self-sufficient today, a growing competition between
food production, environmental issues, sustainability
goals, and the production of energy and renewable pri-
mary products is moving into the focus of policy makers
and researchers. At present, the production of biogas
from energy crops and agricultural wastes (manure and
other residual materials) appears to be the most (landpen Access article distributed under the terms of the Creative Commons
g/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction
roperly cited.
Bauböck Environmental Sciences Europe 2014, 26:1 Page 2 of 9
http://www.enveurope.com/content/26/1/1resource) efficient way to use agricultural areas for en-
ergy production. This is due to the relatively high energy
yield of biogas per hectare [2]. This advantage of biogas
is even higher when power-heat cogeneration technology
is applied.
In an intensively used agricultural landscape, as it is
the case in Germany, good management and farming
practices and diverse crop rotation cycles are of import-
ance, and the introduction of new energy crops into
the existing crop rotation cycles can be beneficial for
ecological reasons [3,4]. One research project working
on this interdisciplinary topic is the currently running
bioenergy project of the University of Göttingen [5].
On the contrary, using mainly maize as a substrate in
biogas facilities can lead to monocultures, soil erosion,
and nitrate problems in the drinking water. This is even
exacerbated in areas where a lot of maize is already
grown for animal feed as is the case in the western part
of Lower Saxony.
Using a crop modeling tool, yield differences of differ-
ent crop rotations and crops can be approximated and
optimized solutions, with economical as well as eco-
logical perspectives in view, can be found out.
Crop models have been in existence for about four
decades now [6]. Resource capture of an agricultural
crop can be implemented in a model in different ways.
Commonly used approaches are either carbon-based [7], ra-
diation use efficiency (RUE)-based [8], water productivity-
based (WP) [9], or transpiration-based (BTR) [10].
BioSTAR’s primary growth engine is carbon-based,
and it uses an asymptotic exponential light response
curve [11]. Among the well-known crop models, the
RUE approach is probably the one which is most often
used. Examples for crop models with this type of growth
engine are CropSyst [12], APSIM [13], CERES (DSSAT)
[14], and LINTUL. Carbon-based growth engines are
used in all of the older models from Wageningen such
as WOFOST and in the model CROPGRO (DSSAT).
The water productivity approach is relatively new [15],
and it has been implemented in the model AquaCrop
[16]. The transpiration-based growth engine (BTR) is
used as a second growth engine in the model CropSyst.
Because the Tanner-Sinclair relationship becomes un-
stable at low VPD, the RUE method is used as a main
growth engine in the model CropSyst.
Even though there are numerous crop models in exist-
ence today, no single model can claim to adequately
cover all possible demands a user might put to such a
model. One big advantage of developing a new model is
the ability to structure and build the model according to
user specifications and to be able to modify it and add
on to it to suit future demands.
The crop model Biomass Simulation Tool for Agricul-
tural Resources (BioSTAR) [17,18] has been developedto simulate climate and soil-dependent biomass yields
for bioenergy crops, but obviously it can also be used to
predict yields for food crops like wheat or rye. The
model’s software is built in such a way that, depending
on the resolution of the input data, large-scale (single
plots or farms) or small-scale (larger areas with many
input datasets) yield predictions can be generated very
easily. Novelties in the BioSTAR crop modeling software
are a MS Access® database connection for fast data
editing and organization and the possibility to choose
between four different growth engines and four ET0
methods. Validation runs for several agricultural crops
grown in Lower Saxony have proven the models’ capabil-
ity to serve as a user-friendly biomass simulation tool
for small- and large-scale agricultural planning.
Results and discussion
Biomass yields
To validate the model BioSTAR, yield, soil, and climate
data from five different locations in Lower Saxony,
Germany have been used. The first two locations are
farm plots in Hedeper and in Troegen. The other two
are field trial sites of the Chamber of Agriculture of
Lower Saxony (LWK), situated in Poppenburg and in
Werlte. Winter wheat and maize were grown at all four
localities, sunflower, sorghum, winter rye, and winter
barley only in Poppenburg and Werlte, and sugar beet
only in Hedeper.
The overall simulation results (all have been per-
formed with the carbon-based growth engine and the
BioSTAR ET0 method) have shown that the model pre-
dicts biomass yields at a good level of accuracy, though
differences between cultures exist (Table 1). For the cul-
ture sugar beet (in the following referred to as beet), the
analysis has been divided up into three parts: (1) all soil
types, (2) clay soil types, and (3) no clay soils. This has
been done to distinguish the unique reaction (overesti-
mation) of beet to soils with high clay contents. The
model produced the lowest error values (root-mean-
square error (RMSE) and percentage error) for winter
wheat (RMSE = 1.6 t and 10.1%), sorghum (RMSE = 1.0 t
and 5.9%), winter barley (RMSE = 1.8 t and 11.0%), win-
ter rye (RMSE = 1.9 t and 10.4%). Beet (clay), beet (no
clay), and beet (all) simulation results show up with er-
rors of 10.7%, 10.8%, and 11.4%, respectively, and an
RMSE of 1.7 t on clay and 2.4 t for the other two. Sun-
flower and maize results show errors of 12.0% and 11.9%
and RMSE values of 1.6 and 2.1 t, respectively. All crops
combined in one analysis show up with mid-range error
values (RMSE 2.1 t and 12.2%). The percentage error
values have been calculated by dividing the RMSE by the
mean observed yield (both are in tons per hectare).
Looking at the other statistical measure for model pre-
diction accuracy, the Willmott index of agreement, the
Table 1 Mean for observed and simulated yields, RMSE, percentage error, and WIA for tested crops
Mean observed Mean simulated RMSE Number Percentage error WIA
Maize 17.7 18.2 2.1 31 11.9 0.94
Winter wheat 15.8 16.1 1.6 102 10.1 0.86
Beet (all) 21.0 21.8 2.4 40 11.4 0.77
Beet (clay) 15.9 21.1 1.7 8 10.7 0.94
Beet (no clay) 22.3 21.9 2.4 32 10.8 0.85
Winter barley 16.3 16.1 1.8 6 11.0 0.64
Winter rye 18.3 18.7 1.9 6 10.4 0.73
Sunflower 13.3 13.4 1.6 9 12.0 0.56
Sorghum 17.0 17.3 1.0 5 5.9 0.78
All crops 17.2 17.7 2.1 198 12.2 0.92
WIA, Willmott index of agreement (1 = perfect agreement, 0 = no agreement). Mean observed, mean simulated, and RMSE (root-mean-square error) given in tones
dry mass per hectare.
Bauböck Environmental Sciences Europe 2014, 26:1 Page 3 of 9
http://www.enveurope.com/content/26/1/1resulting order of the crops is a different one. Now
maize and beet (clay) are ranked first, both with a WIA
of 0.94 followed by winter wheat (0.86), beet (no clay)
(0.85), and sorghum (0.78). The lower ranks are now
occupied by beet (all) (0.77), winter rye (0.73), winter
barley (0.64), and sunflower (0.56). All crops combined
in one analysis have achieved a high WIA of 0.92.
The low WIA values for the winter grains (other than
winter wheat) can be explained by an approximately equal
over- and underestimation of the observed results (and pos-
sibly a low number of samples), whereas the predicted bio-
mass for beet on clay type soils is exclusively overestimated
at a similar level (Figure 1). Sorghum yields have been cal-
culated well for the years 2008/2009 but were then overesti-
mated at a high level in 2010 (Figure 2). Sunflower’s
biomass yield is predicted well in 4 years and then overesti-
mated highly in 2007 (Figure 3). To some extent, this could
be the result of a fungus infection (Sclerotinia sclerotiorum)
which has reportedly [19] damaged the sunflower crops in
the extremely rainy summer of 2007.Figure 1 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for beet (clay).The overall reaction of the model in response to inter-
annual climatic variations is at a good level of accuracy
with the curves of the predicted vs. the observed bio-
mass yields following the same pattern (Figure 4). This
is particularly true for maize (Figure 5), winter barley
and winter rye (Figure 6), winter wheat (Figure 7),
and sugar beet (no clay) (Figure 8). The corresponding
curves of beet (clay), beet (all) (Figures 1 and 9), sor-
ghum (Figure 2), and sunflower (Figure 3) display some
deviations from the inter-annual trend.
For all crops combined in one analysis, a linear regres-
sion analysis has been performed. The R2 value (0.71)
for the whole dataset (observed vs. predicted yields) is at
a satisfactory level (Figure 10) and has a high correlation
(Pearson correlation coefficient of 0.845 at a highly
significant level of α ≤ 0.01).
Evapotranspiration levels
Unlike other crop models, BioSTAR can generate its own
crop and phenology-dependent potential transpirationFigure 2 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for sorghum.
Figure 3 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for sunflower.
Figure 5 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for maize.
Bauböck Environmental Sciences Europe 2014, 26:1 Page 4 of 9
http://www.enveurope.com/content/26/1/1rates (see ‘Main model processes’) to which a leaf area-
dependent soil evaporation value is added. In Figure 11,
the mean values of all four locations of the simulation and
all 7 years of the BioSTAR ET0 (potential evapotranspir-
ation) and ETa (actual evapotranspiration) method are
displayed along with the corresponding Food and Agricul-
ture Organization (FAO) (Penman-Monteith) values cal-
culated for these years. For both calculations a maize crop
with a cropping period from the end of April until the be-
ginning of September was chosen. ET0 values calculated
with the BioSTAR method are considerably lower than
their FAO method equivalents. To a lesser extent, this is
also true, when the ETa values of the two methods are
compared. The BioSTAR ET0 and ETa values range from
543 mm (2008) to 430 mm (2007) and from 423 mm
(2007) to 349 mm (2012), respectively. The FAO curves
for ET0 and ETa follow a similar inter-annual trend but at
levels which are approximately 200 mm (ET0) and 50 mm
(ETa) above the BioSTAR values. The high ET0 values ofFigure 4 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for all crops combined.the FAO calculation can be explained by the fact that no
crop or phenology parameters have been considered
here (grass reference evapotranspiration). Looking at the
literature data for ETa values for northern and eastern
Germany, the FAO values appear to be overestimated.
Haferkorn [20] and Zenker [21] give ETa values for various
crops measured by lysimeters in eastern Germany, ranging
from 280 to 530 mm (April until September), with average
values around 350 mm. The DVWK [22] estimates the
share of the evapotranspiration from May until September
to be about 70% of the year’s total precipitation. Since
Germany’s climate is of a humid character and average an-
nual precipitation values range between 600 and 800 mm,
annual evapotranspiration for this climate is not likely to
be higher than 600 mm. In fact the DVWK gives an aver-
age annual evapotranspiration value (ETa) of 433 mm for
northern Germany. Eulenstein et al. [23] give annual ET0
values for eastern Germany for the years 1971 to 1998
ranging from 420 to 680 mm (the approximate mean isFigure 6 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for winter barley and winter
rye combined.
Figure 7 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for winter wheat. Figure 9 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for beet (all).
Bauböck Environmental Sciences Europe 2014, 26:1 Page 5 of 9
http://www.enveurope.com/content/26/1/1around 570 mm). Additionally it needs to be mentioned
that eastern Germany has a more arid and continental cli-
mate than Lower Saxony.
In comparison with this data, the BioSTAR ET0 and
ETa values seem to be more realistic than the FAO
values and underline the relevance of this ET method
for the computation of crop biomass potentials.
Conclusions
The performance of the crop model BioSTAR has been
tested with datasets from four locations in Lower Sax-
ony, Germany for seven agricultural crops. The model
predicts biomass yields for all crops combined at a satis-
factory level (mean error of 12.1%). The yields of all
crops have been predicted by the model with errors ran-
ging from 8.4% (winter wheat) to 12.1% (maize). The
model has proven to be a functioning tool for modeling
site-specific biomass potentials at the farm level. Because
of its Access® database interface, the model can alsoFigure 8 Inter-annual comparison of observed (full line) and
predicted (broken line) yields for beet (no clay).easily be used for the prediction of potential biomass
yields of larger areas, like administration districts or
states and can therefore serve as a decision support tool
when questions of regional and trans-regional crop plan-
ning are concerned. Because the model reacts adequately
to inter-annual climatic differences, transferability to dif-
ferent climates is probably possible but still needs to be
validated. BioSTAR offers its own method for calculating
evapotranspiration during the course of crop growth.
The model-generated evapotranspiration levels are lower
than the ones calculated using the Penman-Monteith ap-
proach but seem to be closer to actually measured ET
values in northern and eastern Germany.
Out of the seven crops modeled in this study, only
limited yield and test site data was available for winter
barley, winter rye, sorghum, and sunflower. For further
improvement of model performance and model calibra-
tion, more trial data and data testing are required here.Figure 10 Linear regression analysis of observed vs. predicted
yields for all crops.
Figure 11 Inter-annual comparison of ET0 and ETa values calculated with the BioSTAR and FAO (Penman-Monteith) methods.
Bauböck Environmental Sciences Europe 2014, 26:1 Page 6 of 9
http://www.enveurope.com/content/26/1/1The reaction of the sugar beet yield development on
clay-type soils still needs to be investigated further and
improved in the model. Up to date (September 2013),
the model is capable of simulating the general reaction
of crops to water and nitrogen stress. To further expand
the models’ range of application, soil salinity content
and related salinity stress reaction of plants should be
implemented in the model.
Grasses and perennial cultures like the cup plant
(Silphium perfoliatum) or short rotation coppices like
poplar or willow are potential cultures for bioenergy
production in the German agricultural sector. Up to date
(September 2013), the model BioSTAR is capable of
modeling these cultures, but calibration and validation
still have to be performed before the model can be used
for yield prediction of these cultures.
Methods
Main model processes
BioSTAR’s primary growth engine is carbon-based (see
above) and calculates a radiation and temperature-
dependent gross CO2 exchange rate in mmol CO2 m
−2 s−1
(Equation 1).
Photorespiration (maintenance and growth) and nitrogen-
induced photosynthesis inhibition are accounted for in
a second step. The remaining fraction of CO2 (net
photosynthesis) is then used to calculate a net
photosynthesis-dependent transpiration rate. This is
done using the gradients of the water vapor pressure
and of the CO2 concentration inside the leaves to the
corresponding pressures of the atmosphere (Equations
2 to 5). Due to this calculation procedure, BioSTAR
does not need a separate ET0 calculation (e.g., Penman,
FAO, Turc, or other) to compute crop transpiration
(Figure 12):
PG ¼ Pmax  1−exp −QePPFDI=Pmaxð Þ; ð1Þwhere PG is the gross photosynthesis rate (mmol CO2
m−2 s−1), Qe is the initial light use efficiency (mmol CO2
mol−1 light quantum), PPFDI is the intercepted photo-
synthetic active radiation (mmol m−2 s−1), and Pmax is
the maximum photosynthesis rate (mmol CO2 m
−2 s−1).
H2Ograd: VPdef  Volmolð Þ=18ð Þ  1; 000 ð2Þ
CO2grad: CO2con– CO2con  Ci=Cað Þð Þ=1; 000 ð3Þ
Watuse: H2Ograd=CO2grad

  1:56 ð4Þ
Transpot : Prate  3:6 Lday  44 1; 000

 
Watuse ð5Þ
Evapleaf : 1− ResistA=250ð Þð Þ  0:25f g þ 1 ð6Þ
Preduct : Pnet  ETa=ET0ð Þ  Sreduct; ð7Þ
where H2Ograd is the H2O gradient from leaf to atmos-
phere (mmol mol−1), VPdef is the vapor pressure deficit
of the air (g m−3), Volmol is the volume of 1 mol dry air,
CO2grad is the CO2 gradient from leaf to atmosphere
(mmol mol−1), CO2con is the CO2 concentration of the
atmosphere (ppm), Ci/Ca is the internal-external CO2 ra-
tio dimensionless, range approximately 0.1 to 1.0, Watuse
is the H2O-CO2 evolution ratio dimensionless, Transpot
is the CO2 assimilation-dependent potential transpir-
ation rate (L day−1), Prate is the CO2 assimilation rate
(mmol CO2 m
−2 s−1), Lday is the daylight hours, Evapleaf
is the aerodynamic resistance-dependent multiplier for
leaf evaporation, ResistA is the aerodynamic resistance
(s m−1), Preduct is the stomata conductance-induced
photosynthesis reduction (g day−1), Pnet net photosyn-
thesis (after respiration and nitrogen-induced reduction)
(g day−1), and Sreduct is the function for water stress-
induced photosynthesis reduction.
The transpiration rate calculated by Equation 5 is
multiplied by a dimensionless factor (Evapleaf ) to ac-
count for aerodynamic resistance and leaf evaporation
Figure 12 BioSTAR method of transpiration calculation.
Bauböck Environmental Sciences Europe 2014, 26:1 Page 7 of 9
http://www.enveurope.com/content/26/1/1(Equation 6) and then added to a leaf area-dependent
soil evaporation value. The resulting evapotranspiration
value (ET0) is then used in the soil sub-model to check
if enough water for evapotranspiration is available in the
rooted layers of the soil profile. Soil water availability is de-
fined by each layer’s individual soil water retention curve. If
the available soil water, available for evapotranspiration
(ETa), is smaller than the calculated ET0, biomass accumu-
lation is lowered correspondingly (Equation 7).
Crop development and leaf area index (LAI) develop-
ment are temperature driven and divided into two main
stages: emergence until anthesis (development stages 0
to 1) and anthesis until ripeness (development stages 1
to 2). Maximum LAI is reached at development stage 1,
and the curve of LAI development is modeled as a
Gaussian integral (normal distribution).
Software architecture and model features
The BioSTAR software is written in Java and uses a con-
nection to Microsoft Access® database tables to read in-
put data and write output data (Figure 13 and Table 2).
Data can easily be imported into these tables from
spreadsheets like Excel®, and output data can be ex-
ported to a GIS for spatial visualization via the dbf for-
mat. One advantage of this software architecture is that
all relevant data for running simulations is stored in one
Access® database which contains different tables storing
location, weather, crop, and soil texture variables. For
each simulation run (combination of location and wea-
ther data), a new result table is generated in thedatabase. Because all parameters (for crops and soil) are
stored in the same database file, editing and comparison
of the contents is easily done. Running the model on a
PC or laptop requires an installed version of Microsoft
Access® (versions 2007 or later) and the installation of
Java runtime environment (freeware). The model soft-
ware itself is contained in an executable JAR file and
does not need to be installed on the computer.
Model calibration and input data
The model has been calibrated and tested for different
sites and years in Lower Saxony for the winter cereals
wheat, rye, triticale, and barley, for maize, sorghum, and
sugar beet, and for sunflower. Further cultures which
have been implemented in the model are canola (oilseed
rape), cup plant (S. perfoliatum), and the short rotation
coppices poplar and willow, although no validation for
these cultures has been performed so far.
For model calibration, harvest and weather data
(5 years) and soil data from two locations (Poppenburg
and Werlte) in Lower Saxony has been used (Table 1).
At these two locations, regular field trials are carried out
by the LWK (Chamber of Agriculture Lower Saxony).
For further testing of the model, additional harvest and
weather data (7 years) from two farms in Lower Saxony
(Hedeper and Trögen) were used. Soil qualities at these
four locations cover a wide range from deep silt and silt
loams to more shallow sands and sand loams and clays.
Model testing has been performed for maize, winter
wheat, winter barley, winter rye, sugar beet sunflower,
Figure 13 Software architecture of the BioSTAR model.
Bauböck Environmental Sciences Europe 2014, 26:1 Page 8 of 9
http://www.enveurope.com/content/26/1/1and sorghum. The growth engine and ET0 method set-
tings used for these tests are CO2 and BioSTAR (see
above).
Statistical methods used for data analysis
For the interpretation of the model output data (model
performance), the Wilmott index of agreement [24]
(Equation 8) and the RMSE (root-mean-square error)
(Equation 9) values have been calculated for all tested
crops individually as well as all crops combined in one
analysis (Table 1). For all crops combined, a linear re-
gression and the corresponding R2 value have been cal-
culated (Figure 10). To see how the model reacts to
inter-annual variations in climate, an inter-annual compari-
son of the observed and simulated yields has additionallyTable 2 Summary of model features
Features
Software
Data storage Fast data reading, writing, and editing
Multiple sites Capability to process either individual
Program type Program runs from an executable Jav
Data organization Soil, weather, crop, and result data are
Crop model
Growth engines User can choose between four growt
Time step Daily or monthly climate data can be
Minimum data If data availability is limited, the mode
Perennial crops Modeling of perennial crops like shor
Soil model Computation of soil water budget in
van Genuchten soil texture paramete
Crop water stress Crop water stress simulation enhance
Crop development Crop development tracking with BBCbeen done for all crops combined and individually
(Figures 1, 2, 3, 4, 5, 6, 7, 8, 9).
d ¼ 1−
∑ni¼1 Pi−Oð Þ− Oi−Oð Þ2
h i
∑ni¼1 Pi−Oj jð Þ þ Oi−Oj jð Þ2
h i ð8Þ
RMSE ¼ n−1∑ni¼1 Pi−Oið Þ2
−0:5
; ð9Þ
where Pi is the instance of predicted value, Oi is the in-
stance of observed value, Ō is the mean of observed
values, RMSE is the root-mean-square error, and d is the
Willmott index of agreement (0 = no agreement, 1 = per-
fect agreement).
⌊ ⌋due to MS Access® data table interface
sites or large datasets
a file
all kept in one database
h engines and four ET0 methods
processed
l can be run with only daily mean temperature
t rotation coppices or cup plant is possible
a one-dimensional 2-m soil profile with decimeter layer increments using
rs
ment with crop-specific stress phase modeling
H (EC) stages
Bauböck Environmental Sciences Europe 2014, 26:1 Page 9 of 9
http://www.enveurope.com/content/26/1/1Abbreviations
APSIM: agricultural production simulator; AquaCrop: FAO crop model; BBCH/
EC scale: growth scale of monocot and dicot plants; BioSTAR: Biomass
Simulation Tool for Agricultural Resources; CERES: crop model in the DSSAT
family; CROPGRO: crop model in the DSSAT family; CropSyst: cropping
systems model; DSSAT: decision support system for agronomy transfer;
ETa: actual evapotranspiration rate; ET0: potential (reference)
evapotranspiration rate; FAO: Food and Agriculture Organization;
LINTUL: crop model in the Wageningen family; LWK: Landwirtschaftskammer
(Niedersachsen); RMSE: root-mean-square error; RUE: radiation use efficiency;
VPD: vapor pressure deficit of the air; WIA: Willmott index of agreement;
WOFOST: crop model in the Wageningen family.
Competing interests
The author declares that he has no competing interests.
Acknowledgements
The development of the model BioSTAR has been made possible by funds of
the Lower Saxony Ministry of Sciences and Culture (Germany). The research
group responsible for developing the model is part of the interdisciplinary
research project ‘Sustainable use of bioenergy: bridging climate protection,
nature conservation and society,’ sub-project 2.2 ‘bioenergy potentials.’
Received: 20 September 2013 Accepted: 6 December 2013
Published: 21 January 2014
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doi:10.1186/2190-4715-26-1
Cite this article as: Bauböck: Simulating the yields of bioenergy and
food crops with the crop modeling software BioSTAR: the carbon-based
growth engine and the BioSTAR ET0 method. Environmental Sciences Eur-
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