12th Congress of the European Society for Agronomy

598 Pages·2012·22.15 MB·English

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Maataloustieteiden laitoksen julkaisuja 14
ESA12, Helsinki, Finland, 20–24 August 2012
ORGANISERS, SUPPORTERS, EXHIBITORS, SPONSORS
CID
Bio-Science
Inc.
Portable Instruments for Precision Plant Measurement
2
ESA12, Helsinki, Finland, 20–24 August 2012
12th Congress of the
European Society for Agronomy
Helsinki, Finland, 20-24 August 2012
Abstracts
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ESA12, Helsinki, Finland, 20–24 August 2012
Abstracts of ESA12, the 12th Congress of the European Society for Agronomy, Helsinki, Finland 20-24 August 2012.
Edited by F.L. Stoddard and Pirjo Mäkelä.
Reviewers:
Marc Benoit Jaume Lloveras
Olaf Christen Donal Murphy-Bokern
José Paulo de Melo e Abreu Sari Peltonen
Marcello Donatelli Krystyna Rykacewska
Henrik Eckersten Roxana Savin
Brian Fowler Jaswinder Singh
Felix Herzog Elizabeth Stockdale
Heikki Hokkanen Hartmut Stützel
Kari Jokinen Muriel Valantin-Morison
John Kirkegaard Christine Watson
Jouko Kleemola Jacques Wery
Kristina Lindström Xinyou Yin
Helsinki, Finland: University of Helsinki, Department of Agricultural Sciences publication series, volume 14.
ISBN is 978-952-10-4323-9 (online)
ISSN 1798-744X (online)
ISSN-L 1798-7407
Layout:
Tinde Päivärinta/PSWFolders Oy
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131-1
Parameterisation of a phenological model of winter
oilseed rape
Böttcher, Ulf1; Rampin, Enrico2; Flenet, Francis3; Kage, Henning1
1CAU Kiel, GERMANY; 2University of Padova, ITALY; 3CETIOM, FRANCE
Introduction extended to calculate phenological development in
a higher resolution using the BBCH coding system.
For many purposes a detailed description of phenological
Phenological development is mainly driven by the
development is needed. It is essential for the timing of
daily mean temperature above a base temperature
management measures, it is needed for an understanding
of 3°C. In the phase from emergence until the end of
of how weather conditions in diff erent phases of the
stem elongation it is additionally infl uenced by the
growth might infl uence the yield formation and also
eff ects of vernalisation and photoperiodic response.
crop simulation models rely on an accurate description
The model was parameterised and validated using a large
of development for the timing of diff erent growth
amount of data originating from diff erent sources in
processes. For winter oilseed rape (WOSR) a detailed
Germany, France and Italy. The data are covering locations
phenological model with high resolution is still missing.
in all WOSR growing regions in Germany and France and
Furthermore in WOSR the concurrent development of
one location in northern Italy. The data were collected
vegetative, generative and reproductive organs on OSR
from 1993 until 2010 and contain diff erent varieties.
plants makes OSR phenological surveys complicated and
The parameterisation was performed stepwise starting
imprecise. In this study we aimed to achieve a robust
with parameters infl uencing the fi rst development step
parameterisation of a phenological model valid for a wide
and data relevant for this period and then going on to
range of environments and varieties by using a very large
parameters in the later stages of development.
data set without requiring too much precision of every
single observation.
Results
Material and Methods The model is able to predict diff erences in the phenological
development for diff erent environments only based
The phenological model BRASNAP-PH (Habekotté
on diff erences in weather data with one common
1997) was implemented in the object oriented
parameterisation for all locations. The comparison of
modelling framework HUME (Kage & Stützel
one location in Germany and the Italian location shows
1999) which allows for parameter optimisation
that due to a much earlier sowing date the development
using large data sets. Furthermore the model was
advances further in Germany than in Italy before winter
(Fig. 1). In spring however development is much faster in
Italy due to higher temperatures. The overall prediction
accuracy of the model for the validation data set is
characterised by a root mean squared error (RMSE) of
2.8 BBCH stages or 21.2 days. The regression line of
simulated vs. observed data is very close to the 1:1 line
with an r² of 0.97 (Fig. 2). The highest deviation in terms
of days between measured and simulated occurrence of
the BBCH stages is in the phase from emergence to the
beginning of stem elongation. All other phases have an
RMSE of less than 9.5 days.
Figure 1: Overall model performance for arbitrarily chosen
validation datasets of Germany (Gerswalde, 2002) and
Italy (Legnaro, 2009).
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Conclusion
The model presented is an effi cient prediction tool for
the winter oilseed rape phenology according to the
BBCH coding system. It was able to predict the crop
development with a high degree of accuracy for a large
range of years, sowing dates and locations across France,
Germany and Italy.
References
Habekotté, B., 1997: A model of the phenological
development of winter oilseed rape (Brassica napus L.).
Field Crops Res. 54, 127-136.
Figure 2: Simulated vs. observed BBCH stages for the vali- Kage, H., Stützel, H., 1999. HUME: An object oriented
component library for generic modular modelling
dation data set. Solid line is the linear regression, dashed
of dynamic systems. In: Donatelli, M., Stockle, C.,
line the 1:1 line.
Villalobos, F., Villar Mir, M. (Eds.), Modelling Cropping
Systems. European Society of Agronomy, Lleida, pp.
299-300.
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131-2
Sink strength for S as a major parameter to model
vegetative growth in oilseed rape (Brassica napus L.)
under contrasting sulfur (S) supplies
Brunel-Muguet, Sophie1; Goudier, Damien2; Trouverie, Jacques2; Avice, Jean Christophe2; Etienne, Philippe2;
Mollier, Alain1; Ourry, Alain2
1National Institute of Agronomic Research (INRA), FRANCE; 2Université Caen Basse Normandie, FRANCE
Introduction Material and Methods
Oilseed rape (Brassica napus L.) production will be faced Plant cultivation and growth
to an increasing demand in the next three decades Plants (n=3) cv.Yudal were grown in greenhouse under
because of higher worldwide needs for edible oil and two contrasting S supplies (high S, HS and low S, LS)
biofuel industry. Besides, it is a high sulfur (S) demanding and collected at 4 harvest dates until early fl owering
crop (Zhao et al., 1999). The dwindling occurrence of S corresponding to GS16, GS30, GS55, and GS65 (BBCH
defi ciency due to reductions in (i) sulfur dioxide emissions decimal system, Lancashire et al., 1991). At each harvest,
from industrial activities and (ii) high S containing leaf area of photosynthetic leaves (LAph including green
fertilizers (Blair, 2002) has led to consider S nutrition and senescing leaves) and dry weight (DW) of each organ
as a burning issue to maintain high yield and to meet were measured and S amount (Q ) was determined
S
nutritional and energetic objectives. In this study, a by mass spectrometry. Temperature and incident
model of the vegetative growth has been developed to radiation were hourly recorded for thermal time (TT) and
highlight the most important carbon (C) and S related photosynthetically active radiation (PAR) calculations.
processes that drive vegetative growth under contrasting
S supplies.
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Temperature PAR SO 2-
4
potentialgrowth leafarea x interceptedPAR Rootuptakeand internal
of leaves (eq. Monteith) leavesremobilisation
C source SS ssoouurrccee
Total C offer=dTDW Total S offer=dQS
Hypothesis: Hypothesis:
Priorityto the leaves Priorityto the leaves
(cid:94)(cid:3)(cid:396)(cid:286)(cid:373)(cid:381)(cid:271)(cid:349)(cid:367)(cid:349)(cid:460)(cid:258)(cid:410)(cid:349)(cid:381)(cid:374)
CC ddeemmaannddffoorr lleeaavveess SS ddeemmSSaann ssddooffuuoorrrrcc eelleeaavveess
dLA dDW (cid:198)(cid:198)(cid:198)(cid:198)dLA dQS (cid:198)(cid:198)(cid:198)(cid:198)dLA
Pot LA C LA S
(cid:18)(cid:3)(cid:258)(cid:374)(cid:282)(cid:3)(cid:94)(cid:3)(cid:367)(cid:381)(cid:400)(cid:400)(cid:286)(cid:400)
(cid:94)(cid:3)(cid:258)(cid:400)(cid:400)(cid:349)(cid:373)(cid:349)(cid:367)(cid:258)(cid:410)(cid:349)(cid:381)(cid:374)
dLA =min (dLA ,dLA , dLA )
effective Pot C S
(cid:94)(cid:3)(cid:437)(cid:393)(cid:410)(cid:258)(cid:364)(cid:286)(cid:3)
(cid:44)(cid:455)(cid:393)(cid:381)(cid:410)(cid:346)(cid:286)(cid:400)(cid:286)(cid:400)
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•(cid:17)(cid:349)(cid:381)(cid:373)(cid:258)(cid:400)(cid:400)(cid:393)(cid:396)(cid:381)(cid:282)(cid:437)(cid:272)(cid:410)(cid:349)(cid:381)(cid:374)(cid:3)(cid:282)(cid:396)(cid:349)(cid:448)(cid:286)(cid:374)(cid:271)(cid:455)(cid:3)(cid:349)(cid:87)(cid:4)(cid:90) •(cid:90)(cid:104)(cid:28)(cid:3)(cid:349)(cid:400)(cid:349)(cid:374)(cid:282)(cid:286)(cid:393)(cid:286)(cid:374)(cid:282)(cid:258)(cid:374)(cid:410)(cid:381)(cid:296)(cid:3)(cid:94)(cid:3)(cid:258)(cid:448)(cid:258)(cid:367)(cid:349)(cid:258)(cid:271)(cid:349)(cid:367)(cid:349)(cid:410)(cid:455)
•(cid:18)(cid:3)(cid:381)(cid:296)(cid:296)(cid:286)(cid:396)(cid:410)(cid:258)(cid:364)(cid:286)(cid:400)(cid:349)(cid:374)(cid:410)(cid:381)(cid:258)(cid:272)(cid:272)(cid:381)(cid:437)(cid:374)(cid:410) (cid:410)(cid:346)(cid:286)(cid:3)(cid:296)(cid:258)(cid:367)(cid:367)(cid:381)(cid:296)(cid:3) •(cid:94)(cid:3)(cid:381)(cid:296)(cid:296)(cid:286)(cid:396)(cid:410)(cid:258)(cid:364)(cid:286)(cid:400)(cid:349)(cid:374)(cid:410)(cid:381)(cid:258)(cid:272)(cid:272)(cid:381)(cid:437)(cid:374)(cid:410)(cid:410)(cid:346)(cid:286)(cid:3)(cid:296)(cid:258)(cid:367)(cid:367)(cid:381)(cid:296)(cid:3)(cid:367)(cid:286)(cid:258)(cid:448)(cid:286)(cid:400)
(cid:367)(cid:286)(cid:258)(cid:448)(cid:286)(cid:400)(cid:282)(cid:437)(cid:396)(cid:349)(cid:374)(cid:336)(cid:400)(cid:286)(cid:395)(cid:437)(cid:286)(cid:374)(cid:410)(cid:349)(cid:258)(cid:367)(cid:400)(cid:286)(cid:374)(cid:286)(cid:400)(cid:272)(cid:286)(cid:374)(cid:272)(cid:286) (cid:282)(cid:437)(cid:396)(cid:349)(cid:374)(cid:336)(cid:400)(cid:286)(cid:395)(cid:437)(cid:286)(cid:374)(cid:410)(cid:349)(cid:258)(cid:367)(cid:400)(cid:286)(cid:374)(cid:286)(cid:400)(cid:272)(cid:286)(cid:374)(cid:272)(cid:286)
•(cid:62)(cid:286)(cid:258)(cid:448)(cid:286)(cid:400)(cid:18)(cid:3)(cid:282)(cid:286)(cid:373)(cid:258)(cid:374)(cid:282)(cid:349)(cid:400)(cid:393)(cid:396)(cid:349)(cid:373)(cid:258)(cid:396)(cid:455)(cid:400)(cid:258)(cid:410)(cid:349)(cid:400)(cid:296)(cid:349)(cid:286)(cid:282) •(cid:62)(cid:286)(cid:258)(cid:448)(cid:286)(cid:400)(cid:94)(cid:3)(cid:282)(cid:286)(cid:373)(cid:258)(cid:374)(cid:282)(cid:349)(cid:400)(cid:393)(cid:396)(cid:349)(cid:373)(cid:258)(cid:396)(cid:455)(cid:400)(cid:258)(cid:410)(cid:349)(cid:400)(cid:296)(cid:349)(cid:286)(cid:282)(cid:271)(cid:437)(cid:410)(cid:3)(cid:410)(cid:258)(cid:364)(cid:286)(cid:400)
(cid:349)(cid:374)(cid:410)(cid:381)(cid:258)(cid:272)(cid:272)(cid:381)(cid:437)(cid:374)(cid:410) (cid:94)(cid:3)(cid:258)(cid:367)(cid:367)(cid:381)(cid:272)(cid:258)(cid:410)(cid:349)(cid:381)(cid:374)(cid:3)(cid:396)(cid:258)(cid:410)(cid:286)(cid:400)(cid:3)(cid:437)(cid:374)(cid:282)(cid:286)(cid:396)(cid:62)(cid:381)(cid:449)(cid:94)
Figure 1. Modelling C/S whole-plant functioning.
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0,20 18 140
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16 120
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0,15 14
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2
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Ecophysiological model construction Diff erences in leaf sink strength were observed between
The build-up of the model was based on the dynamics S treatments from pod development
of off er and demand as in other crop models which Diff erence in sink strength can be accounted for either
combine the eff ects of C assimilation (Monteith et al., sink size and/or sink activity. According to S supplies, S
1977) and nutrient uptake and utilization (Brisson et al., allocation diff ers unlike C allocation. Altogether, these
1998; Mollier et al., 2008). Eff ective LAph expansion rate results mean that leaf S allocation is not only driven by the
(LAER) was determined as the minimum of (i) potential sink size but also by the sink activity i.e. S storage activity.
LAER controlled by air temperature, (ii) LAER allowed by High residual amount of SO42- (main storage form) in
C assimilation and (iii) LAER allowed by S uptake. In our senescing leaves under HS support this hypothesis (data
model, allocation rules were assigned to C and S demands not shown).
of the leaves in order to test their sink strength for C and S
throughout vegetative growth (Fig. 1) Conclusion
Modeling vegetative growth is of major importance to
Results and discussion
determine the available pools of S and C for reproductive
Model parameterization and evaluation parts and to avoid damages on pods by rectifying S
Formalism used for S demand and off er diff er according inputs.This simulation study allows the importance
to S-treatment of S related physiological processes (e.g. storage and
Similar formalisms underlying C off er and leaf C demand therefore capacity for remobilisation) to be focused on,
were used for both LS and HS unlike S off er and demand. by comparing plant responses to contrasting S supplies.
Indeed, RUE values were not statistically diff erent
between HS and LS. Besides, leaf C demand is primary References
satisfi ed with similar C leaf allocation rates whatever S
Blair GJ, 2002. Sulphur fertilisers: A global perspective.
supply. By contrast, a sharp increase in S absorption was
Proceedings No.498. International Fertiliser Society,
observed at infl orescence emergence under HS only,
York.
leading to diff erent adjustment for Q uptake. Besides,
S
S leaf allocation (% total Q ) decreased from 85 to 65% Brisson N, et al, 1998. Agronomie 18, 311-346.
S
for HS and 55% for LS. Thus, although leaf S demand was
Lancashire PD, Bleiholder H, Langelüddecke P, Stauss
primary satisfi ed, leaf S allocation rates were introduced
R, Van Den Boom T, Weber E, Witzen-Berger A. 1991.
under LS to consider a signifi cant lower sink strength.
Annals Appl Biol 119, 561-601.
Simulations of LAph, total DW and Q of leaves
S Mollier A, De Willigen P, Heinen M, Morel C, Schneider A,
For all the output variables, the modeling effi ciency
Pellerin S. 2008 Ecological Modelling 210, 453-464.
(EF) values indicated good accuracy (Fig. 2). Predictions
underestimated observed values under HS at GS65 Monteith JL. 1977. Philos. Trans. R. Soc. Lond. B Biol. Sci.
but diff erences between HS and LS observations were 281, 277–294.
correctly simulated.
Zhao FJ, Hawkesford MJ, McGrath S. 1999. J Cereal Sci
30, 1-17.
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A new approach for calculating stomatal resistance
of wheat as a step towards dynamic simulation of
canopy temperature
Neukam, Dorothee; Boettcher, Ulf; Kage, Henning
University of Kiel, GERMANY
Introduction directly from the energy balance a new function of r was
s
derived and implemented.
Under given meteorological conditions canopy
temperature (T ) depends on actual transpiration and
crop Results and Outlook
therefore is an indicator of drought stress. Fast and easy
detection of spatial and temporal drought stress variation
Temperature diff erences between treatments are clearly
with infrared thermometry off ers useful applications for
detectable (Fig. 1) indicating the cooling eff ect of high
phenotyping, site specifi c management and decision
transpiration rates for the W1 and W2 plots resulting
support. For these purposes T must be normalized
crop in T being lower than T in contrast to W0, where
by meteorological conditions according to the energy crop air
transpiration decreased resulting in T being higher
balance at the crop surface (Jackson et al. 1981). Then, crop
than T . These fi ndings are also refl ected in the measured
assuming steady state and fl ux independent resistances, air
r (Fig. 2) being lower in the irrigated plots and not
the ratio of actual to potential transpiration can be s
increasing until withholding of irrigation.
calculated, indicating stomata closure as consequence of
limited water availability. For gaining information about
the plant and soil parameters causing drought stress
from T a coupled soil crop atmosphere model capable
crop
to simulate daily courses of T provides a promising
crop
framework.
Materials and methods
For model development and parameterization a
plot experiment with wheat (Triticum aestivum cv.
Dekan) within a rainout shelter was conducted in two
years (2010, 2011) with three treatments (W0 non
irrigated from beginning of march, W1 irrigated to
80% fi eld capacity and W2 to 100% fi eld capacity). Fig.1 Midday air temperature (T ) and canopy to air tem-
air
Weekly measurements were made for volumetric perature diff erences (T ) in 2010.
dif
water content at 5 depths (TDR), green area index (LAI
2000) and canopy height. Minutely measured T , air
crop
temperature (T ), relative humidity, wind speed and net
air
radiation were averaged over an hour. An eff ective soil
water potential within the rooted soil layer (ψ ) was
root
derived from model calculations using measured water
contents, soil texture and simulated root distribution.
From May until July diurnal courses of stomatal
conductance (g) for water vapour were measured
s
(LI6400) on 4 fully expanded young leaves in one plot of
each treatment. The reciprocal value of the measured g,
s
converted from mole to velocity units (McDermitt 1990)
and then averaged, gave the stomatal resistance (r).
s
Evapotranspiration was calculated at an hourly time step
Fig2. Midday stomatal resistances (r) in 2010.
using the model, coupling crop growth and soil water s
balance. In order to calculate actual transpiration and T
crop
10