Tree-crops interaction models
State of the Art Report
Deliverable D.1.1 of the SAFE European Research contract QLK5-CT-2001-00560
Silvoarable Agroforestry For Europe (SAFE)
Compiled by Christian Dupraz
September 2002
Tree-crops interaction models
State of the art report
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Captions for the cover pictures
The challenge of tree-crop interaction modelling : how to predict tree and crop
performance in the long term by integrating instantaneous physiological processes?
Illustrations from the Vézénobres experimental plot (South France)
1. March 1996 : plantation of poplars in an asparagus field
2. October 1996 : trees enjoy growing in very close contact with asparagus
3. November 2000 : soil tillage destroys superficial roots of poplars
4. March 2001 : sixth harvest of asparagus between the poplars
5. October 2001 : The poplars are 6 year old and display the fastest growing rate
observed for poplars in France, due to a very positive interaction with the
Asparagus intercrop.
Current Tree-Crop interaction models fail to predict such behaviour at the moment.
Contents
Foreword .................................................................................................................... 1
Part 1 : Identifying important tree-crop interaction processes unsuccessfully modelled
so far .......................................................................................................................... 2
Light capture in discontinuous two-layers canopies ................................................ 3
Incorporating the microclimate feed-back on tree and crop physiology................... 4
Predicting tree growth from tree C capture ............................................................. 5
Describing the plasticity of tree rooting systems ..................................................... 5
Predicting tree uptake of water and nutrient in a split-root system under control by
the crop roots .......................................................................................................... 6
Ability of current crop models to predict the growth of crop in unusual conditions .. 7
Part 2 : Major Tree-Crop Models available ................................................................. 9
Wanulcas .............................................................................................................. 10
HyPAR .................................................................................................................. 12
STICS-CA (Culture Associée)............................................................................... 15
Always................................................................................................................... 17
Wimisa .................................................................................................................. 18
Modelo .................................................................................................................. 19
MUSE shall and TREEGRASS ............................................................................. 22
Conclusion : Major challenges for improving tree-crop models for temperate areas 24
Integrating processes is a requisite....................................................................... 24
Validating models will be difficult........................................................................... 24
Coupling models or integrating models? ............................................................... 25
To incorporate or not to incorporate additional processes? .................................. 26
How to address such difficult questions? .............................................................. 26
References ............................................................................................................... 27
Useful Web Links...................................................................................................... 32
Foreword
Tree-crop combinations are infinite in numbers. Tree-crop combinations also have a
life course that extends in tens of years, up to a century or more in temperate areas.
For these two reasons, full direct experiments are not feasible.
A modelling approach is therefore a requisite. This modelling approach should aim at
predicting the fate of various tree-crop combinations in various temperate conditions.
This scope is huge, when one knows how the modelling of pure crops is already a
challenge.
It is a target of the SAFE (Silvoarable Agroforestry For Europe) project to build a
biophysical model of silvoarable plots. An assessment of the current knowledge of
tree-crop interactions modelling was considered essential. This is the aim of the
current report, which is a deliverable of the SAFE project.
This report is based on the expertise of the SAFE participants, as shared in a
common modelling workshop held at the University of Wageningen, in the
Netherlands, from 7-13 January 2002.
Christian Dupraz
A view from the SAFE group at the workshop on modelling tree-crop interactions at
Wageningen university,
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Part 1 : Identifying important tree-crop
interaction processes unsuccessfully modelled
so far
During the last decade, progresses in the modelling of pure crops and pure tree
stands were impressive (Boote et al., 1996). Progresses in the modelling of individual
tree growth have also been rapid (Le Roux et al., 2001). However, modelling treecrop interactions requires to link a crop stand model, and a tree stand model or a tree
individual model, and is still at an early stage.
We identified very few integrated models of tree-crop interactions. Most research
papers focus on some specific aspects of tree and crop interactions, but fail to
provide an integrated framework for accounting the final result of the mixture. It is not
the objective of this report to review the papers that examine some specific aspects
of tree crop mixtures. This has been done elsewhere. Ong and Huxley (ed., 1996), or
Baldy and Stigter (1993) provide extensive reviews of processes involved in tree-crop
systems.
The literature review indicate that most models of competition between plants deal of
crop-weed relationships (Doyle, 1997). Both above-ground and below-ground
aspects of plant-plant competition or facilitation (Vandermeer, 1989) are however
very different when one of the associated plants is a tree. The expertise on cropweed modelling is only partially relevant for tree-crop studies.
Most crop models are one-dimensional, as they assume that both above-ground and
below-ground stand components are horizontally homogeneous (turbid medium
analogy for transfers). Most tree stand models are also one-dimensional, as they
assume the same hypothesis for closed canopies forest stands. Modelling sparse
tree stands or isolated trees is more complicated, as the 1D approximation is no
longer valid. Published models are often driven by the carbon balance of the tree, but
below-ground processes that are essential to tree-growth interaction modelling are
usually missing (Le Roux et al., 2001).
Individual tree models have a strong Achillea’s heel : the carbon allocation routine.
None are capable of predicting accurately the tree height, a very simple descriptor,
but probably the more integrating and challenging. This is very concerning for their
use in tree-crop interaction modelling approaches, as competition has usually a
strong influence on the functional equilibrium of plants.
For most tree-crop systems, a 3D approach is required to model discontinuous
canopies and discontinuous rooting systems. In some cases, a 2D approach may be
sufficient, but this is limited to some systems with a proven bilateral symmetry. It
should not be assumed when tree rows are not exactly orientated North-South
(Dupraz, 2002). This 3D approach is the major challenge of tree-crop interactions
models. Some forest models (gap models) are taking into account this disaggregation
for the above-ground part of the stand, but none do that for the below-part of the tree
stand.
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In simultaneous agroforestry systems, trees and crops are interacting in various
ways. As both positive and negative interactions occur, optimisation of the system will
have to be site specific. The most important interactions probably are (following van
Noordwijk and Lusiana, 2000) :
1. Shading by the trees, reducing light intensity at the crop level,
2. Competition between tree and crop roots for water and/or nutrients in the
topsoil,
3. Mulch production from the trees, increasing the supply of N and other
nutrients to the food crops, but potentially shading young plants after
emergence
4. Nitrogen supply by tree roots to crop roots, either due to root death
following tree pruning or by direct transfer if nodulated roots are in close
contact with crop roots,
6. Effects on weeds, pests and diseases,
7. Long term effects on erosion, soil organic matter content, soil compaction
and nitrogen leaching.
In temperate zones like Europe, some of these aspects may be not that important :
Very few nitrogen fixing trees are available for tree-crop systems. Robinia is often
discarded due to its invasive properties that are stimulated by root cutting when tilling
the crop alley, and other nitrogen fixing trees (such as Alnus spp.) don’t produce high
quality timber.
Heavy fertilisation minimises the role of trees as fertility suppliers, but environmental
consideration may change our mind on this aspect sooner or later.
Other aspects may be more important such as the compatibility of the system with
mechanisation, or the possibility to reduce nitrogen leaching with deep rooted trees.
We identified 6 major challenges for modelling tree-crop interactions. They are
common to all tree-crop systems, but some are very important for temperate regions.
We will review these challenges now.
Light capture in discontinuous two-layers canopies
Predicting light capture by discontinuous canopies is a key question. Light availability
is more limiting at high latitudes, compared to tropical latitudes. Direct beam
transmission is also more influenced by canopies when the sun elevation is low.
Tree-crop systems are unique for light distribution in two ways :
•
Light distribution is highly variable at small time and space scales (minutes,
cm)
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•
Light capture by heterogeneous canopies includes multiple reflection and
scattering processes between two different plant layers. This process is
unique to tree-crop systems.
A satisfying model should be able to describe accurately the following processes
Process
Projected shade of the tree canopies at
the crop level
Projected shade of the tree canopies on
other tree canopies
Reflected light on the tree canopies
reaching the crop level
Reflected light by the crop canopy
reaching the tree canopy
Variables required
Tree canopy geometry
Sun course
Tree canopy geometry
Sun course
Optical properties, size and angular
distribution of the leaves
Albedo of the crop
Most models skip the issue and assume 1D structure.
Incorporating the microclimate feed-back on tree and crop
physiology
Many microclimatic variables have a key influence on crop and tree functioning. They
are indicated in the following table.
Variable
Modification
Minimum air temperature in Lower due
open sky areas between trees
reduced
convection
Positive impact
air
to Increases
temperature range with
positive
effects
on
maturity of some crops
like wineyards
daily
Maximum air temperature in Higher due to Increased
temperature range (as
open sky areas between trees
reduced
above)
convection
Air humidity
Increased due to Increases WUE
lower convection
Average Wind velocity
Decreased
Increases WUE if
Wind peak velocity
Increased (funnel
effect) depending
on
tree
row
orientation, tree
pruning and wind
direction
Sunfleck duration
Leaves
are May increase the light
conversion efficiency
successively
exposed
to when light is saturating
the non-linear
contrasted
photosynthesis
illumination
response to light
Negative impact
Frost risk
Thermal stress
Stimulates
diseases
crop
Lodging of crops may
increase
If “shadeflecks” are too
long, light may become
limiting and etiolation
may occur
A simultaneous calculation of the energy and water budget should be necessary for
solving the microclimate issue, but is usually too time consuming for being
implemented on long term models.
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rb
ra
ra
ra
rb
Figure 1 : The Shuttleworth-Wallace resistive approach to modelling the microclimate
in a tree-crop system (Allen et al, 1998).
Models of windbreaks effects on the microclimate may be used in a first
approximation in agroforestry systems (Mayus, 1998), but they usually ignore root
competition as wind-break are more frequent in irrigated systems.
Predicting tree growth from tree C capture
Individual tree growth models include usually 4 main carbon processes :
photosynthate production, respiration, reserve dynamics, and carbon allocation. This
last aspect is the weakest point of all available models so far (Le Roux et al., 2001),
and may prove very limiting for tree-crop interaction modelling.
Even if a tree-crop competition model provides a right estimation of tree C gain, it is
therefore unlikely that it can predict the correct tree growth. Carbon allocation in trees
involves a continuously-changing functional balance of demand and supply between
reproductive organs, temporary storage sinks, shoots, roots and woody parts. Models
of tree growth have so far tended to allocate photosynthate according to simple
priority rules and proportions, rather than in relation to sink strengths which change
through the season and as trees age. This failing is less important in long-term
studies of forest cover or global vegetation change, but a more sophisticated
approach is required when models are used to understand tree-tree competition in
mixed forest stands, or tree-crop competition in agroforestry.
In process-orientated tree growth modelling considerable progress is being made
combining a carbon-balance approach based on parallel energy, water and nutrient
budgets, with carbon-allocation rules based on pipe-stem theory (Lawson and
Mobbs, 1998).
Describing the plasticity of tree rooting systems
The root ecology of the associations of trees with annual crops was reviewed in detail
by Schroth (1995). A key feature is that tree rooting systems are capable of quick
adaptations to changing soil environments. Annual crops provide very changing soil
environments to the associated trees : period with bare soils and no competition vs
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period of high competition, winter crops vs summer crops, irrigated crops vs nonirrigated crops. While this may be easily described in a one year field experiment, the
integration of successive contrasted growing seasons on the tree root evolution is
much more complicated to predict. This is a major challenge of tree root modelling in
tree-crop systems, and it is very important during the first year of the tree, when
structural coarse roots are formed. It was shown in some experiments that early
competition between young trees and crops may induce deep rooted trees that will
be less competing with crops for soil resources in the future (Dupraz et al, 1999).
The following mechanisms appear crucial to a fair modelling of root interactions in
tree-crop systems :
1. Fine roots turn over (as influenced by waterlogging, temperature, species…)
2. Reactiveness to patchiness and gradient in soil resources (mainly water and
nitrogen)
3. Root development phenology
4. Reaction to root pruning.
Most of these processes are incompletely documented at present, and prevent to
build a reliable root interaction model. All are both genetically determined and
environmentally sensitive.
It is a major challenge of tree-crop models to be able to predict the 3D development
of tree rooting systems in the presence of annual crop rotations.
Predicting tree uptake of water and nutrient in a split-root system
under control by the crop roots
A tree associated to an annual crop is experiencing a very unusual situation : its
rooting system is exploring very contrasted soils zones. This is the split-root system
situation, but the dynamics of the different soil zones water content are complicated
(Figure 2)
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-
-
-50
-50
-100
-100
-150
-200
-150
-150
-100
-50
0
50
100
150
200
int 34 arb-blé
int 34 arb-blé
27 may 1999 : the tree row is humid, the crop
zone is drying due to the active transpiration by
the crop
1 September 1999 : the tree row is dry, the crop
zone is humid again due to autumn rains
Figure 2 : Contrasted soil humidity patchchiness for a walnut tree associated to winter
wheat at the Restinclières experimental farm (unpublished data by Dupraz, 1999)
Interception, umbrella effect of the tree canopy, soil compaction in the tree row, soil
tillage in the crop alley all influence strongly the pattern of soil water replenishment.
Any tree-crop competition model should define an algorithm for sharing soil
resources. This algorithm should be able to dispatch a unique demand function for
the whole tree in the many rooted zones.
Sharing the (water or nitrogen) resource between a tree and a crop implies a priority
assignment in the calculation sequence. This is a major problem in linking singlespecies resource capture models into a multi-species resource capture model with a
single accounting systems for the resources. Models which consistently assign
priority to one of the components may vastly overestimate its resource capture, while
the solution of some models of alternating priorities is not very satisfactory either
(Caldwell et al., 1996). This priority rule should also be defined at the scale of the tree
root system.
Ability of current crop models to predict the growth of crop in
unusual conditions
Most crop models were not validated in the conditions experienced by intercrops. The
key differences are the following :
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•
Functioning in low light condition. Are the crop models able to predict
etiolation? How is C allocation to above and below parts of shaded crops
modelled?
•
Functioning in short sun-fleck regimes. Are the photosynthesis and
conductance models appropriate?
•
Functioning under contrasted stresses. Intercrops may often enjoy unusual
combinations of light, water and nutrient levels. High levels of nutrients with
low levels of light are frequent…
A further challenge would be to link the crop model to a pest module that would be
able to predict the risks associated with the modified microclimatic conditions.
Farmers often fear fungal diseases in the more humid and shaded environment of
agroforestry. Conversely, some evidences of better pest control in diversified
silvoarable systems become now available. For example, syrphae adult insects may
be attracted by wild flowers diversity on the tree row of silvoarable systems and lay
their eggs on nearby cereal plants infected by aphids (syrphae larvae are greedy
predators of aphids). Linking a pest module to a crop module is still not achieved for
pure crop models. It is therefore not time to consider doing this for tree-crop systems.
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Part 2 : Major Tree-Crop Models available
From a survey of the whole bibliography of tree-crop models, we spotted 9 major
tree-crop models available at the end of 2001. They will be reviewed here, and the
way they address the six key points identified in Part 1 will be analysed.
The 2 following criterions were used to select models for this review :
•They should aim at predicting tree and crop yields for the whole tree life
•
They should aim at taking into account both above-ground and below-ground
interactions
The seven tree-crop models identified are the following :
Name
Wanulcas 2.0
Author
ICRAF (Indonesia)
HyPAR 3.0
STICS Culture
associée
Always
Wimisa
NERC (UK)
INRA (France)
Modelo
Tree-Grass
INRA (France)
Wageningen University
(The Netherlands)
INRA (France)
Ecole Normale
Supérieure (France)
Reference
Van Noordwijk and Lusiana,
2000
Mobbs and Lawson, 1999
Brisson, 1999
Bergez et al, 1999
Mayus, 1998
Lecomte, 1996
Simioni et al, 2000
We surprisingly could not find integrated tree-crop models from the USA, China or
Australia, where silvo-arable studies are quite advanced. If more information gets
available in the near future, this report will be upgraded.
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Wanulcas
Presentation of Wanulcas (after Van Noordwijk and Lusiana, 1999)
Figure 3 :The Wanulcas logo adapted to a temperate context by M. Van Noordwijk
WaNuLCAS is a generic model for water, nutrient and light capture in agroforestry
systems (WaNuLCAS). It aims at:
1. integrate knowledge and hypotheses on below- and aboveground resource
capture by trees and crops (or any two (or more) types of plants) at patch
scale (the smallest ‘self-contained’ unit for describing the tree/crop interaction)
as a basis for predicting complementarity and competition,
2. build on well-established modules (models) of a soil water, organic matter
and nitrogen balance, and crop and a tree development to investigate
interactions in resource capture,
3. describe the plant-plant interaction term as the outcome of resource
capture efforts by the component species, as determined by their above- and
belowground architecture (spatial organisation) as well as physiology,
4. be applicable to spatially zoned agroforestry systems as well as rotational
systems,
5. avoid where possible the use of parameters which can only be derived by
fitting the model to empirical data sets and maximise the use of parameters
which can be independently measured
6. be flexible in exploring management options within each type of agroforestry
system,
7. be useful in estimating extrapolation domains for 'proven' agroforestry
techniques, as regards soil
8. be user-friendly and allow 'non-modelers' to explore a range of options, while
remaining open to improvement without requiring a complete overhaul of the
model,
9. generate output which can be used in existing spreadsheets and graphical
software,
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10. make use of readily available and tested modeling software.
In view of objectives 8, 9 and 10, the authors chose the Stella Research modelling
shell (Hannon and Ruth, 1994) linked to Excel spreadsheets for data input and
output.
WaNuLCAS model is meant as a prototype model, not including all possible tree-soilcrop interaction relationships that one can imagine, but incorporating a core of
relations which we are fairly sure of for each specific case. In this sense the model
can be viewed as a 'null model' (Gotelli and Graves, 1996) which can be used like a
null hypothesis as a background against which specific data sets can be tested.
Wanulcas answers to the main modelling issues identified
Major gaps identified
Light capture
Answer in Wanulcas
Comments
Apportioned to the leaf Probably not suitable for
areas of the tree and crop high latitude where sun
components
elevation is always low
Microclimate feed-back
Not implemented
Carbon allocation in trees Simplistic and not adapted
to European timber trees
Plasticity of tree rooting Not implemented
systems
Sharing of below-ground Iterative procedure based Approximated solution due
resources
on roots length densities to a limit of the Stella
and soil water content in environment
the various cells to which a N and P uptake modelled
plant has access
Ability
of
the
crop No
specific
processes
component
to
reflect incorporated
unusual crop conditions
Conclusion on WaNuLCAs
Wanulcas is an integrated model designed on purpose for modelling tree-crop
interactions. Its main limitations for the use by the SAFE project are
•crop modules are not validated for European crops
•C
allocation module in trees not suitable for temperate trees with strict
phenologies
•The Stella platform do not allow extensive uncertainty studies, and the model
meets now the limits (in size and complexity) that the Stella platform can
handle. The Stella platform is not free use.
However, due to its “easy” use and flexibility, WaNuLCAS was retained as a backstop option by the SAFE consortium for its biophysical modelling activity.
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HyPAR
Presentation of HyPAR (after Mobbs et al, 1999)
Figure 4 : The HyPAR logo
HyPAR v1.0 was created in 1995 by combining the tropical crop model PARCH
(Bradley & Crout 1994) with components of Hybrid v3.0 (Friend et al. 1997). The first
version of HyPAR was based on the calculation of light interception and water use by
a horizontally uniform tree, annual tree biomass increment, the light and water
available to an understorey crop and hence crop growth and potential annual grain
yield. The tree canopy was assumed to be above the crop canopy at all times and
there was optimum management with no pests or pathogens). It included the soil
water movement and uptake routines of PARCH, and utilised those parts of Hybrid
which determine light interception, water use, tree productivity and biomass
partitioning.
This early version of HyPAR is described in Mobbs et al. (1997), and was used by
Cannell et al. (1997) to predict the 50-year mean 'potential' sorghum yields and
overstorey net primary productivity in nine climates (348mm - 2643mm rainfall) with
uniform overstorey leaf area indices between 0 and 1.5. They concluded that in
regions with less than 800 mm rainfall, whilst simultaneous agroforestry may enable
more light and water to be 'captured' than sole cropping, low water use efficiency of
trees and sensitivity of crops to shading may make it difficult to increase total
productivity without jeopardising food security. The authors recognised however that
this early version of HyPAR ignored the soil fertility relations of trees, their potential
access to deep water tables, and other commercial benefits such as shade, fuel and
fodder.
HyPAR v2.0 introduced competition for nitrogen and was used by Lott et al (1997) to
test predictions of maize growth in Kenya. Versions 2.5 and 2.7 included improved
soil water routines and options for management of the tree canopy. HyPAR v2.7 was
tested at workshops in the UK in June 97 and in Kenya in August 98. HyPAR v3.0
includes daily allocation of tree photosynthate, and routines to represent
disaggregated canopy light interception and 3-D competition for water and nutrients
between the roots of trees and crops (see Figure 1).
HyPAR v4.1 was released in November 2001, and HyPAR v5 will be available at the
end of 2002. The new releases fixed some bugs and introduce new formats for data
exchnage. Some fundamental changes such as the replacement of the Parch tropical
crop model by the CSM (DSSAT) generic crop model are considered at the moment.
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Versions of the model after HyPAR v4.0 run continuously from year to year allowing
several annual crop seasons to be studied, with one or two crops per year. The
model is supplied with parameter files for two crops, sorghum and maize, and 8 tree
types. HyPAR includes options for management including fertiliser addition to the soil
and tree pruning for example. Full details are available on the project web site,
www.edinburgh.ceh.ac.uk/hypar.
Figure 5 Improvements of HyPAR features between versions 1.0 and 3.0
Figure 6 : HyPAR general flowchart showing how tree and crop modules are
intermingled
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HyPAR answers to the main modelling issues identified
Major gaps identified
Light capture
Answers in HyPAR
Comments
A mixed continuous / A
fully
disaggregated
disaggregated approach is approach is required
in use. Not satisfactory
Microclimate feed-back
Not implemented
A trial to incorporate it with
the ShuttleWorth–Wallace
approach failed due to
computation times
Carbon allocation in trees Some aspects of C
allocation are good, but
simple allometry rules are
not satisfactory (eg : tree
height deduced from tree
diameter)
Plasticity of tree rooting Not implemented
systems
Sharing of below-ground Satisfactory, but priority
resources
rules are not explicit. Both
tipping bucket and
pedotransfer functions
approach are available
Ability of the crop module Not documented
to reflect unusual crop
conditions
Conclusion on HyPAR
HyPAR resulted from coupling two existing models. Unfortunately, the crop model
included in HypAR is not adapted to temperate crops and should be replaced if
HyPAR would be used in Europe.
The spatial resolution of HyPAR is considered perfectly adapted to modelling the
influence of trees and crops. Only coupled models can be parameterised at the will of
the user for describing more or less accurately the tree-crop interface. HyPAR is
designed for handling up to 400 (20 x 20) cells in the simulated scene, but calculation
times increase exponentially with the cell number.
However, HyPAR is at the moment the best physiologically based integrated treecrop model available. Its routine for photosynthesis calculation at the day time step
seems appropriate. However, it does not meet our expectations for the 6 key points
listed above, and therefore was considered as a start point for the building of the
HySAFE model.
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STICS-CA (Culture Associée)
Presentation of STICS culture associée
Stics is a generic crop model developed by INRA, France (Brisson et al, 1998). A
new feature was added recently to model the competition between two different
species (STICS-CA stands for STICS Culture Associée):
Dominant
canopy
shaded
sunny
Understorey
canopy
Elementary pixel
Figure 7 : Vertical space occupation by two competing species in the STICS-CA model
The simulated scene in STICS-CA is divided simply in two areas : under and outside
the vertical projection of the canopy of the dominant species (STICS-CA was
designed in a tropical context). The dominant canopy expands following simple
allometry functions. Light partitioning is obtained by geometrical calculation of visible
sky (direct) and sampling of diffuse radiation in 46 directions. A 3-source (tree, soil ,
crop) Shuttleworth-Wallace resistive approach is implemented for predicting the
microclimate, plant transpiration and temperature. The water budget incorporates leaf
water interception (retention and direct evaporation) and stemflow.
STICS-CA was mainly used for annual crops mixtures, or shrub-crop mixtures where
the shrub canopy was controlled by regular lopping. It is not parameterised for any
temperate tree, and includes no realistic processes for C allocation in the tree
component. STICS-CA do not include shoot-root relationship.
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STICS-CA answers to the main modelling issues identified
Major gaps identified
Answer in STICS CA
Comments
Light capture
Beer law applied to uniform Not applicable to pruned
LAD
canopies
with trees at temperate latitudes
simplified shapes
Microclimate feed-back
Implemented
Carbon allocation in trees Not implemented
Plasticity of tree rooting Not implemented
STICS uses fixed root
systems
profiles?
Sharing of below-ground Implemented and unified
resources
using with the default
STICS features for the tree
and the crop
Ability
of
the
crop Not documented
component
to
reflect
unusual crop conditions
Conclusion on STICS-CA
STICS-CA is an integrated model where a tree simplified component was added into
a crop model. By dividing the silvoarable scene in only two units (below and outside
the tree canopy) STICS-CA can not predict the influence of pruned trees on crops in
high latitude regions where you may have more shade outside the tree canopy than
under the tree canopy.
The tree module in STICS-CA is not adapted to full size grown temperate timber
trees.
STICS-CA was only validated on two mixtures (maize-beans and gliricidia-petit foin)
in tropical conditions.
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Always
Presentation of Always
Always 1.0 is a plot based model which simulates the temporal behaviour of widespaced tree plantations on sward. It is based on biophysical simulations of the
processes linking the main five components of a silvopastoral system : the tree, the
sward, the animal, the soil and the microclimate. It summarises the knowledge
gathered and gained within the European Contract Always (Alternative Land-use
With AgroforestrY Systems - AIR3 CT92-0134) (Auclair, 1995).
Always do not model annual crops, but perennial swards. Therefore, the processes
linked to soil tillage, rotation, root length and leaf area rapid variations from zero to a
maximal value are not integrated.
Always model has the advantage of incorporating a tree module designed for
temperate trees such as wild cherry, walnut or sycamore. Some of his components
may therefore be useful for designing a tree-crop interaction model adapted to the
European conditions. Tree above-ground growth in Always is derived from potential
empirical growth curves under minimisation by reduction factors derived from the
light, water or nitrogen budgets.
Always model answers to the main modelling issues identified
Major gaps identified
Answer in Always
Comments
Light capture
Not implemented
Microclimate feed-back
Not implemented
Carbon allocation in trees Not implemented
Plasticity of tree rooting Not implemented
systems
Sharing of below-ground Not process based. Only
resources
water competition modelled
with simple a priori rules for
water extraction by the tree
and the sward.
Ability
of
the
crop Not documented
component
to
reflect
unusual crop conditions
Conclusion on Always
The Always model is not a process-based model for tree-sward interactions, as it
uses mainly empirical growth functions and derives tree growth from a simple water
budget that do not account for sward vigour in different locations with differnt light
availability.
This model is not suitable for modelling tree-crop interactions.
SAFE Project Tree-Crop interaction models State of the Art Report
Page 17
Wimisa
Presentation of Wimisa (Mayus, 1998)
Wimisa (WIndbreak-MIllet-SAhel) is a tree-crop competition model designed for
modelling millet growth in windbreak-shielded fields in the Sahel. A bilateral
symmetry along the windbreak line was assumed, reducing the modelling to only one
side of the windbreak. Three crop zones were modelled. Wimisa does not model the
influence of the crop competition on the tree growth. The windbreak is therefore a
fixed component in the system, making the Wimisa model only a partial tree-crop
interaction model. Therefore, Wimisa can not be used in modelling dynamic tree-crop
temperate systems, where tree inter-annual dynamics are influenced by the crop.
Application of the model in Niger showed that the water consumption by the
windbreak was not compensated by a reduction of evaporation of the protected crop.
Wimisa model answers to the main modelling issues identified
Major gaps identified
Answer in Always
Comments
Light capture
Not
implemented,
the
windbreak is assumed to
be a rectangular barrier
Microclimate feed-back
Not implemented
Reduction of wind velocity
modelled by simple
empirical rules
Carbon allocation in trees Not implemented
Plasticity of tree rooting Not implemented
systems
Sharing of below-ground Implemented for water
resources
only. Same procedure as
for Wanulcas
Ability
of
the
crop Not documented
component
to
reflect
unusual crop conditions
Conclusion on Wimisa
Wimisa is the only tree-crop model in this review that includes the windbreak effects
of trees, but this is achieved by empirical wind velocity reduction laws. Wimisa cannot
be used for dynamic tree-crop studies as the tree growth is not modelled, but is
imposed as a forcing variable.
SAFE Project Tree-Crop interaction models State of the Art Report
Page 18
Modelo
Presentation of Modelo (Lecomte, 1996)
The Modelo model was never published in scientific journals, and will not be retained
further. However, it developed two unique interesting features that are worth
mentioning.
The tree was described as a collection of axes (long and short axes) with
demographic laws. This is a step further towards a more realistic representation of
the tree canopy. But the important aspect is that the crop competition influenced the
different populations of axes in specific ways, by the mean of water seasonal stress
coefficients.
Long shoots
Short shoots
Figure 8 : The MODELO tree representation as a collection of long and short axes
The second aspect is related to the spatialisation of the soil and the partitioning of the
root zones. The soil volume was divide in compartments defined by the intersection
of soil layers (with different physical properties) and of volumes explored by the roots
of the two species. These volumes expanded or shrink following root fronts (Figure
9).
SAFE Project Tree-Crop interaction models State of the Art Report
Page 19
K21
1
0
2
3
4
5
1
3
Surface Layer
6
7
8
9
10
11
Intermediate
Layer
12
13
14
15
16
17
Deep Layer
2
K1
4
K22
Empirical coefficients drive the water
extraction from the different
compartments reached by each plant
Soil compartments are defined by the presence of roots of the
associated species. When root fronts progress or retract, the soil
compartments expand or shrink.
20
litres/day
15
Modelo allows to split the water use of the tree as
extracted from different soil zones defined by the roots.
In this example, wild cherry trees used mainly deep
water under the tree row in spring, and water from the
crop zone in summer.
10
5
0
J
F
M
A
M
J
Surface (tree only)
Surface (tree+crop)
J
A
S
O
N
D
Deep (tree only)
Deep (under crop)
Figure 9 : Soil partitioning adjusted to moving fronts of roots of the tree and the crop
in the Modelo approach
Modelo answers to the main modelling issues identified
Major gaps identified
Answer in Always
Light capture
Not implemented
Microclimate feed-back
Not implemented
Carbon allocation in trees Not implemented
Plasticity of tree rooting Not implemented
systems
Sharing of below-ground Not process based. Only
resources
water competition modelled
with simple a priori rules for
water extraction by the tree
and the sward.
Ability
of
the
crop Not documented
component
to
reflect
unusual crop conditions
Comments
Conclusion on Modelo
Modelo is not suitable for modelling tree-crop mixtures because it is not process
based. The tree representation in Modelo may be expanded to a fractal description of
SAFE Project Tree-Crop interaction models State of the Art Report
Page 20
the tree as a collection of axes. This would be a significant move in improving the
fractal description of trees (Van Noordwijk and Mulia, 2001) to temperate trees.
SAFE Project Tree-Crop interaction models State of the Art Report
Page 21
MUSE shall and TREEGRASS
Presentation of MUSE and TREEGRASS (Simioni et al., 2000)
MUSE stands for Multi strata Spatially Explicit ecosystem modelling shell. MUSE is a
freely available ecosystem modelling shell for Windows 3.1 or greater, with which you
can create and compare a wide variety of models. http://biology.anu.edu.au/researchgroups/ecosys/muse/
The general structure of MUSE encompasses a range of already published and
widely used models such as Jabowa, Foret and Forska as well as the three versions
of models by Takashi Kohyama (gap, stand and forest models). Many of these
models simulate a small patch of forest - a gap - about the size of a single large tree.
This approach, while of great utility for forest stands, lacks the ability to capture the
horizontal spatial variation inherent in the study of ecosystems such as savanna
woodlands. Here, the gap model assumption of full interaction between all plants
breaks down. A seemingly more straightforward approach that is, modelling detailed
geometry of trees and grass in a three-dimensional grid of cells leads to large or
impossible computational overheads.
MUSE captures spatial heterogeneity while keeping computational complexity within
bounds sufficient for it to simulate up to 2000 'plant objects' - populations or
individuals - on a PC. This is achieved by varying the degree of detail with which
plants are represented and minimising overlap computations by grouping plants into
neighbourhoods in which competition for resources take place. A single 'plant' in
MUSE can be anything from a grass sward (filler plant) to a tree made from a pile of
discs depicting the canopy and root structure (shaped plant).
The simulated site can be divided into cells to allow another level of environmental
variation. Each cell can have its own soil type, set of disturbances and elevation.
As well as varying spatial detail, MUSE can treat various life cycle processes at
different time scales. For instance, plant growth can take place at a rate different from
the supply of resources.
MUSE is not designed for examining root or canopy foraging. The most detailed
spatial representation of a plant in MUSE is of a pile of discs with axial symmetry
rather than flexible shapes which can exploit small scale environmental variations.
The TREEGRASS model.
SAFE Project Tree-Crop interaction models State of the Art Report
Page 22
TREEGRASS is a 3D process-based model. The model aims at predicting, in
heterogeneous tree¯ grass systems, plant individual radiation, carbon and water
fluxes at a local spatial scale. It is run at a daily time-step over periods ranging from
one to a few years. The model includes (i) a 3D mechanistic submodel simulating
radiation and energy (i.e. transpiration) budgets; (ii) a soil water balance submodel,
and (iii) a physiologically based submodel of primary production and leaf area
development.
Tree-Grass model answers to the main modelling issues identified
Major gaps identified
Answer in Always
Comments
Light capture
Satisfactory
Microclimate feed-back
Implemented, but very
computer demanding
Carbon allocation in trees Not implemented
Plasticity of tree rooting Not implemented
systems
Sharing of below-ground Process based for water
resources
Ability
of
the
crop Not documented
The simulated grass is not
component
to
reflect
an annual crop.
unusual crop conditions
Conclusion on TREEGRASS
Most of the concepts of TREEGRASS are relevant to Tree-crop modelling, but
annual crops require other functionalities than perennial grasses. TREEGRASS id
too demanding in computing time for considering runs on tens of years.
SAFE Project Tree-Crop interaction models State of the Art Report
Page 23
Conclusion : Major challenges for improving
tree-crop models for temperate areas
Integrating processes is a requisite
Accurate assessment of agroforestry alternatives require the modelling of
agroforestry as an integrated and interactive system (Benjamin et al, 2000).
Gillespie et al., (2000) showed that shading by 8 m tall walnut and oak trees had no
influence on maize yield in a temperate silvoarable system in temperate USA.
However maize is a very light demanding plant. A 50% decrease in maize yield near
the tree lines was observed, but when below-ground competition was removed (by
tree root pruning), yields near the tree line matched yield in the centre of the crop
alley and in the open. This is a challenge for crop models that always assume a high
correlation between intercepted photosynthetically active radiation and net
photosynthesis. It demonstrates that the integration of all interactions between trees
and crops is a major challenge for understanding silvoarable systems.
Validating models will be difficult
Models can be of value ('validated' in the original sense of the word) if a) they
adequately reflect the major assumptions about component processes, if b) they
operate smoothly in the expected parameter range, and/or if c) their quantitative
predictions agree with measured results in specific experiments. Before model
validation is undertaken, (1) the purpose of the model, (2) the performance criteria
and (3) the model context must be specified.
Given that both integrated crop models and tree models have a large number of
parameters (more than 100 usually), validation is therefore a tricky issue. Uncertainty
analysis may help, but is clearly limited by computing times required. Stappers (this
SAFE project) shows that if the integrated tree-crop model run in 5 minutes, it would
require more than 200 days of computing time to perform a simplified Monte-Carlo
approach of sensitivity to only 10 parameters. And the therory indicates that adding
complexity usually has a negative impact on the prediction capacity of tyhe model
(Figure 10).
The Holy Grall of agroforestry modellers is therefore how to simplify, but what to
simplify?
SAFE Project Tree-Crop interaction models State of the Art Report
Page 24
High Bias
Low Variance
Low Bias
High Variance
Prediction error
Test Sample
Training Sample
Low
Model Complexity
High
Figure 10 : Adding complexity to models usually deteriorates its prediction capacity
(after Stappers, pers com)
Coupling models or integrating models?
Tree
model
Crop
model
shell
Common
Crop
or
Tree
model
Crop
model
Tree
Mixing two existing models
Coupling independent
models
Creating a new integrated
model from scratch with
common modules for
common processes
(HyPAR type)
(No examples found)
(Wanulcas type)
Figure 11: Three strategies for building an integrated Tree-Crop interaction model
We could not find any example of coupled models for a simple reason : existing
models were usually designed independently, and therefore display different
structures in terms of data exchange, process chaining, state variables required.
If spatial heterogeneity of the crop component is to be explored (and this is a key
component of tree-crop studies), all types of models need to be carefully designed.
Variability will always be obtained by multiple runs of the crop model, under different
influences of the tree component. A good model should allow the user to decide
about this level of disaggregation. Some grouping algorithms may be considered to
avoid independent ruins of the model in similar conditions.
SAFE Project Tree-Crop interaction models State of the Art Report
Page 25
To incorporate or not to incorporate additional processes?
Predicting the fate of silvoarable systems implies to be able to derive the
consequences of intricate instantaneous relationships between trees and crops and
integrate them over decades. Simplifying the systems (in terms of biophysical
processes description) is the key of a successful modelling approach.
Very small scale effects (in space and time) may combine to produce long term and
large scale decisive impacts. But they also may not. The parable of the influence of
an Amazonian butterfly flip on storms in Europe is probably wrong in our case.
Hazard is not driving the tree-crop system.
Two examples may show how small scale repeated effects may (but they may not)
totally change the final result.
Should we discard horizontal water movements in the soil as a result of the
sharp gradients between neighbouring soil compartments?
Such a modelling requires short time and space steps that would considerably accrue
the computation time. Is it worth? It should be argued that horizontal water potential
gradient in the soil are a unique feature of heterogeneous stands. And they are
probably maximum in silvoarable systems, due to the high differences in phenology
and physiology of the associated plants. This gradients will lead to horizontal water
movements from the humid to the dry (rooted) zones. Therefore, the associated
plants will harvest water from soil zones that are out of reach of their current rooting
system.
Should we discard the feed-back effect of tree and crop transpiration on air
humidity to predict actual transpiration and carbon fixation rates in the stand?
This is again a process that requires short time and space steps. It is also a unique
feature of heterogeneous stands with different layers of discontinuous canopies.
Solving the combined water and energy budget for all plants at small time steps is
very demanding on computation time. But such processes may change the overall
Carbon gain of the system, and lead to different results in productivity of the mixture.
But they may not. Who knows?
How to address such difficult questions?
In the SAFE project, we intend to compare a simplified integrated model ignoring
such processes with more detailed available models. Extrapolating the differences
observed at small time and space steps between the detailed (assumed to be more
reliable, but this has to be demonstrated by validation procedures) and the integrated
simplified model seems to be the unique way through. Adapted procedures for
extrapolating departures between the two models at the day time scale (the only
common time scale between the integrated model and the detailed models) to the
whole duration of the silvoarable system will be a major challenge for research teams
in the future.
SAFE Project Tree-Crop interaction models State of the Art Report
Page 26
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Useful Web Links
http://www.multimania.com/coligny/ for the CAPSIS environment
http://www.wiz.uni-kassel.de/model_db/mdb/recafs.html for the Recafs model
http://www.nbu.ac.uk/hypar for the HYPAR model
http://www.nmw.ac.uk/ite/edin/agro/ for results of the DFID-sponsored Agroforestry Modeling Project
http://meranti.ierm.ed.ac.uk/ame for AME
http://www.icsea.or.id/wanulcas/ for WaNulCAS
http://biology.anu.edu.au/research-groups/ecosys/muse/ for the MUSE shell
SAFE Project Tree-Crop interaction models State of the Art Report
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