March 1996 : plantation of poplars in an asparagus field. 2. October 1996 ..... This algorithm should be able to dispatch a unique demand function for the whole tree .... 10. make use of readily available and tested modeling software. In view of ...
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 :
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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
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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. SAFE Project Tree-Crop interaction models State of the Art Report
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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 SAFE Project Tree-Crop interaction models State of the Art Report
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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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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?
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Functioning in short sun-fleck regimes. Are the photosynthesis and conductance models appropriate?
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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, SAFE Project Tree-Crop interaction models State of the Art Report
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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 SAFE Project Tree-Crop interaction models State of the Art Report
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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
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Understorey canopy
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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.
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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.
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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
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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).
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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
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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.
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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.
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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.
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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?
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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
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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.
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Scholes R. J., Archer S. R., Tree-Grass interactions in savannas, Annu. Rev. Ecol. Syst. 28 (1997) 517–544. Shinozaki. 1965. A quantitative analysis of plant form - the pipe model theory. 1. basic analysis. Japanese Journal of Ecology, 14, 97-105. Sievänen R., Nikinmaa E., Nygren P., Ozier-Lafontaine H., Perttunen J., Hakula H., Components of functional-structural tree models, Ann. For. Sci. 57 (2000) 399–412. Simioni G., Le Roux X., Gignoux J., Sinoquet H., TREEGRASS: a 3D, process-based model for simulating plant interactions in tree-grass ecosystems, Ecol. Modelling 131 (2000) 47–63. Sinclair, T.R. & Seligman, N.a.G., 1996. Crop modelling: from infancy to maturity. Agron. J. 88(5):698-703. Sinoquet H., Le Roux X., Adam B., Améglio T., Daudet F.A., RATP: a model for simulating the spatial distribution of radiation absorption, transpiration and photosynthesis within vegetation canopies: application to an isolated tree crown, Plant Cell Env. 24 (2001) 395–406. Tyree, M.T. 1988. A dynamic model for water flow in a single tree: evidence that models must account for hydraulic architecture. Tree Physiology, 4, 195-217. Valentine, J. 1985. Tree growth models: derivations employing the pipe-model theory. J theor Biol., 117, 579-85. Van Noordwijk M. and Lusiana B., 2000. WaNuLCAS version 2.0, Background on a model of water nutrient and light capture in agroforestry systems. International Centre for Research in Agroforestry (ICRAF), Bogor, Indonesia Van Noordwijk, M., Lawson, G.J., Soumare, A., Groot, J.J.R. & Hairiah, K. 1996. Root distribution of trees and crops: competition and/or complementarity. In: Tree-crop interactions - a physiological approach, edited by C.K. Ong & P. Huxley, 319-364. Wallingford: CABI International. Vandermeer, J. (1989) The ecology of intercropping. Cambridge University Press, 237p.
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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
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