Table Of ContentMODELLING WATER AND NUTRIENT DYNAMICS IN SOIL–CROP SYSTEMS
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Modelling water and nutrient
dynamics in soil–crop systems
Proceedings of the workshop on “Modelling water
and nutrient dynamics in soil–crop systems” held
on 14–16 June 2004 in Müncheberg, Germany
Edited by
Kurt Christian Kersebaum
Jens-Martin Hecker
Wilfried Mirschel
and
Martin Wegehenkel
Funded by European Science Foundation ESF and COST 718
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Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
1. Modelling water and nutrient dynamics in soil–crop systems: a comparison of simulation
models applied on common data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Kurt Christian Kersebaum, Jens-Martin Hecker, Wilfried Mirschel and Martin Wegehenkel
2. The performance of the model AMBAV for evapotranspiration and soil
moisture on Müncheberg data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Hans Friesland and Franz-Josef Löpmeier
3. Performance of the model SIMWASER in two contrasting case studies
on soil water movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Elmar Stenitzer, Heiko Diestel, Uwe Franko, Reinhild Schwartengräber and Thomas Zenker
4. Application and validation of the models THESEUSand OPUS
with two field experimental data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Martin Wegehenkel and Wilfried Mirschel
5. Integrating a spatial micrometeorological model into the risk
assessment for arable crops in hilly terrain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Marco Acutis, Gianfranco Rana, Patrizia Trevisiol, Luca Bechini,
Mario Laudato, Ferrara R. and Goetz Michael Richter
6. Modelling soil–crop interactions with AGROSIM model family . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Wilfried Mirschel and Karl-Otto Wenkel
7. Crop simulation model of the second and the third productivity levels . . . . . . . . . . . . . . . . . . . . . 75
Ratmir Aleksandrovich Poluektov and Vitaly Viktorovich Terleev
8. The NDICEA model, a tool to improve nitrogen use efficiency in cropping systems . . . . . . . . . . . 91
Geert-Jan H. M. van der Burgt, Gerard J. M. Oomen, A. S. J. Habets and Walter A. H. Rossing
vi Contents
9. Simulation of water and nitrogen flows on field scale; application
of the SWAP–ANIMO model for the Müncheberg data set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Joop Kroes and Jan Roelsma
10. Evaluation of water and nutrient dynamics in soil–crop systems using
the eco-hydrological catchment model SWIM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
Joachim Post, Anja Habeck, Fred Hattermann, Valentina Krysanova,
Frank Wechsung and Felicitas Suckow
11. Modelling nitrogen dynamics in soil–crop systems with HERMES . . . . . . . . . . . . . . . . . . . . . . . . 147
Kurt Christian Kersebaum
12. Calibration and validation of CERES model for simulating
water and nutrients in Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
Ajeet Singh Nain and Kurt Christian Kersebaum
13. The impact of crop growth model choice on the simulated
water and nitrogen balances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Eckart Priesack, Sebastian Gayler and Hans P. Hartmann
14. Simulating trends in crop yield and soil carbon in a long-term
experiment – effects of rising CO , N deposition and improved cultivation . . . . . . . . . . . . . . . . . . 197
2
Jørgen Berntsen, Bjørn Molt Petersen and Jorgen E. Olesen
15. Comparison of methods for the estimation of inert carbon suitable
for initialisation of the CANDY model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Martina Puhlmann, Katrin Kuka and Uwe Franko
16. Müncheberg field trial data set for agro-ecosystem model validation . . . . . . . . . . . . . . . . . . . . . . 219
Wilfried Mirschel, Karl-Otto Wenkel, Martin Wegehenkel,
Kurt Christian Kersebaum, Uwe Schindler and Jens-Martin Hecker
17. Dynamics of water, carbon and nitrogen in an agricultural used
Chernozem soil in Central Germany . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245
Uwe Franko, Martina Puhlmann, Katrin Kuka, Frank Böhme and Ines Merbach
18. The lysimeter station at Berlin-Dahlem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
Heiko Diestel, Thomas Zenker, Reinhild Schwartengraeber and Marco Schmidt
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
Preface
Soil–crop–atmosphere interactions play a central role in well with the interest of agro-meteorologists, who
the multiple functions of rural landscapes. Agricultural, look for new applications and for customers of their
environmental and economic aspects are related to this products. Therefore, the workshop was gratefully sup-
topic, and there is an increasing need to understand the ported by the COST Action 718 “Meteorological
complex system to develop reliable models for scenario Applications in Agriculture” under the umbrella of the
analyses. Agro-ecosystem models are more and more European Science Foundation (ESF). In June 2004,
used to support decision-making on different scales the workshop was held in Müncheberg with the par-
towards a sustainable land use and management. ticipation of 38 scientists from nine different coun-
Nevertheless, the increasing demand of model users for tries. Twenty presentations were given for the blocks
model validation does not fit to the decrease of research experimental site description, water dynamic model-
budgets for suitable experimental research and monitor- ling, soil and crop interactions, nutrient and water
ing. The increasing family of modellers is confronted dynamics in soil–crop systems and long-term nutrient
with a decrease of available data for model testing. and carbon dynamics.
Model workshops providing common data sets for The organizers wish to acknowledge the financial
a number of modellers are not new, but became rare contribution of the European Science Foundation
during the last years. Therefore, the Leibniz Centre of (ESF) and COST 718. We also thank our director
Agricultural Landscape Research (ZALF) in Prof. H. Wiggering and the head of our department
Müncheberg/Germany organized a workshop on Prof. K.-O. Wenkel for their support and our techni-
“Modelling water and nutrient dynamics in crop–soil cians for their help in the organization.
systems” providing data sets from its experimental Finally, we want to dedicate this conference proceed-
field. This was accomplished by data from the Centre ing book to our colleague Ernst-Walter Reiche, who
for Environmental Research Leipzig-Halle (UfZ) participated in the workshop, but died unexpectedly in
which provided data of its long-term experiment in March 2005. We will remember him as an inspiring and
Bad Lauchstädt, and the Technical University of competent colleague and as a nice and helpful fellow.
Berlin which contributed data of their lysimeter sta- We will miss him very much in the future.
tion in Berlin-Dahlem. The purpose of the workshop
was to compare different agro-ecosystem models on K.C. Kersebaum
the basis of a common data set using meteorological J.-M. Hecker
data as main driving forces for water and nutrient W. Mirschel
dynamics including crop growth. This idea agrees M. Wegehenkel
CHAPTER ONE
Modelling water and nutrient dynamics in soil–crop systems:
a comparison of simulation models applied on common data sets
Kurt Christian Kersebaum1*, Jens-Martin Hecker2, Wilfried Mirschel1and Martin Wegehenkel1
Abstract The paper summarizes the results of various
Introduction
simulation models applied to common data sets from
Germany given for the workshop “Modelling water and
Agro-ecosystem models are more and more applied to
nutrient dynamics in crop–soil systems”, held in
support decision-making at different scales. Such
Müncheberg, Germany in June 2004. A brief overview
applications can range from fertilizer recommenda-
is given on the main characteristics of the participating
tions for farmers on a field scale and water suppliers on
18 models. Results of the models are compared with
a catchment scale up to a landscape or regional scale
observed data of selected plots using standard indices
for strategic policy decision support. With an increas-
for model performance evaluation. Results of such com-
ing size of the investigation area, input data are more
parisons have to be seen carefully keeping in mind that
and more scarce and uncertain and data of relevant
model agreement is often dependent on the individual
state variables for calibration are not available. This
extend of model calibration on a specific data set. More
requires robust models, which are able to generate their
insight about the differences between models is gained
input requirements from basic standardized soil data
by comparing the model output for different processes.
without the necessity of parameter calibration.
Although, model agreement to different state variables
On the other hand, there is an increasing demand of
is similar for some models, there are distinct differences
model users and decision-makers that the validity of
in the quantification of single processes.
models used for decision support has been proved
comparing uncalibrated modelling results with field
Keywords Crop growth, Model comparison,
observations outside the range of model development.
Performance indicators, Soil processes
This should enhance the reliability of model calcula-
tions used as the basis for decision-making. However,
Kurt Christian Kersebaum, Jens-Martin Hecker,
WilfriedMirschel and Martin Wegehenkel the question of model validation is contradictorily
1Institute of Landscape Systems Analysis, Leibniz-Centre for discussed in literature. Oreskes etal. (1994) generally
Agricultural Landscape Research (ZALF) e. V. Müncheberg,
disavowed the possibility to validate models for natu-
Eberswalder Strasse 84, D-15374 Müncheberg, Germany
2Institute of Socio-Economics, Leibniz-Centre for Agricultural ral systems arguing that natural systems are generally
Landscape Research (ZALF) e. V. Müncheberg, Eberswalder open systems with always uncertain inputs. Rykiel
Strasse 84, D-15374 Müncheberg, Germany. (1996) concluded that validation is neither a method
*Author for correspondence:
tel.:+49 33432 82394; fax: +49 33432 82334; to test model theory nor a certificate of truth. He men-
e-mail: [email protected] tioned that validation is just an indication for a model
K. Ch. Kersebaum et al. (eds.), Modelling Water and Nutrient
Dynamics in Soil–Crop Systems, 1–17. ©2007Springer.
2 Modelling water and nutrient dynamics in soil–crop systems
to give acceptable results for a specific purpose. He results of one model. Therefore, a comparison of this
also stated that this might not be scientifically neces- data set was not possible within this paper.
sary, but leads to a higher confidence and acceptance
of model results. Holling (1978) proposed to investi-
gate the non-validity of models applying them without Model characteristics
calibration to a large number of sites and conditions
accepting the risk not to reflect the observations very Table 1 gives an overview of the 18 different model
well. This is suitable to demonstrate the degree of con- approaches, which were applied to at least one data set
fidence, respectively the limitations of a model of the workshop. More information about these models
approach. can be obtained from the main references in Table 1 or
Under this aspect, the Centre of Agricultural from the papers included in this issue. Although the
Landscape Research (ZALF) in Müncheberg, Germany application of the model WASMOD (Reiche 1994) is
organized a workshop on “Modelling water and nutri- not documented in an own paper due to the tragic death
ent dynamics in soil–crop systems”, which was held of Ernst-Walter Reiche, we include the model for com-
in June 2004, sponsored by the European Science parison on the basis of his uncalibrated results, which
Foundation under the umbrella of COST action 718 were provided at the workshop.
(“Meteorological Applications in Agriculture”). To Tables 2–4 give an overview on some general
compare different models, we followed the idea to characteristics of the models (Table 2) and the
provide common data sets from different sites to the processes and approaches, which are considered for
participants. This has been previously realized, e.g. by soil water dynamics (Table 3), nitrogen (and C)
Groot and Verberne (1991) and McVoy et al. (1995) dynamics (Table 4) and crop growth (Table 2), respec-
for soil–crop interactions and Powlson etal. (1996) for tively. Some models (AMBAV, SIMWASER and
long-term carbon dynamics. The data sets provided THESEUS) focus mainly on soil water dynamics.
are focused on different subjects: the data set of three They use potential driven approaches and more phys-
plots from the Müncheberg experimental site(Mirschel ically based evaporation formula. Nevertheless, some
et al. 2007) and the cropped and fallow plots of Bad models (THESEUS, Expert-N, SWAP) are designed
Lauchstädt (Franko et al. 2007) are aimed to look at as a toolkit and users have the choice between differ-
model performance for short- to medium-term water ent approaches, e.g. for soil water, evapotranspiration
and nitrogen dynamics in soils and on crop growth. and crop growth. Two models (FASSET, CANDY)
The long-term experimental data set of Bad Lauchstädt combine a capacity approach with pore space frac-
(Franko et al. 2007) mainly aims at the long-term tionation for mobile and immobile water.
carbon dynamics under different nutrient management Most of the models consider crop cover, but
and the data from the Berlin-Dahlem lysimeters (Diestel model approaches range from empirical functions
etal. 2007) provide data on soil water balances under (AMBAV, NDICEA) over simplified temperature-driven
grass cover. approaches (SWIM, WASMOD, CANDY, OPUS) to
Due to the fact that we only received few results more or less complex dynamic models including
from uncalibrated model runs, we decided to summa- photosynthesis, biomass partitioning and root devel-
rize more or less adopted models within this paper. opment. Some of these approaches are generic (SWAP
Similar comparisons have been published by de and THESEUS [option for WOFOST], Expert-N,
Willigen (1991) and Diekkrüger et al. (1995) for HERMES, STAMINA, AGROTOOL, FASSET), others
soil–crop systems. use different submodels for different crops (CERES,
All in all, 18 different model approaches have AGROSIM). Depending on their capabilities to simu-
been applied on individually selected data sets result- late multiple years and crops, models were run con-
ing in 14 papers of this issue. Results of 14 model tinuously through the whole crop rotation or were
approaches were submitted for the comparison on started separately for every application year (CERES,
plot 1 of Müncheberg. Some modellers submitted also AGROSIM).
results for the other plots and sites. Only for the Nitrogen is considered by 13 model approaches.
Berlin-Dahlem data set, we received only simulation While AGROSIM uses a simple nitrogen balance
