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Frontiers of Computer Science

, Volume 10, Issue 6, pp 1103–1117 | Cite as

FSPM-P: towards a general functional-structural plant model for robust and comprehensive model development

  • Michael Henke
  • Winfried Kurth
  • Gerhard H. Buck-Sorlin
Research Article

Abstract

In the last decade, functional-structural plant modelling (FSPM) has become a more widely accepted paradigm in crop and tree production, as 3D models for the most important crops have been proposed. Given the wider portfolio of available models, it is now appropriate to enter the next level in FSPM development, by introducing more efficient methods for model development. This includes the consideration of model reuse (by modularisation), combination and comparison, and the enhancement of existing models. To facilitate this process, standards for design and communication need to be defined and established. We present a first step towards an efficient and general, i.e., not speciesspecific FSPM, presently restricted to annual or bi-annual plants, but with the potential for extension and further generalization.

Model structure is hierarchical and object-oriented, with plant organs being the base-level objects and plant individual and canopy the higher-level objects. Modules for the majority of physiological processes are incorporated, more than in other platforms that have a similar aim (e.g., photosynthesis, organ formation and growth). Simulation runs with several general parameter sets adopted from the literature show that the present prototypewas able to reproduce a plausible output range for different crops (rapeseed, barley, etc.) in terms of both the dynamics and final values (at harvest time) of model state variables such as assimilate production, organ biomass, leaf area and architecture.

Keywords

functional-structural plant model prototyping modelling standards teaching / learning FSPM GroIMP 

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References

  1. 1.
    Goudriaan J, Van Laar H H. Modelling Potential Crop Growth Processes: Textbook with Exercises. Dordrecht: Kluwer Academic Publishers, 1994CrossRefGoogle Scholar
  2. 2.
    Lopez G, Favreau R P, Smith C, Costes E, Prusinkiewicz P, DeJong T M. Integrating simulation of architectural development and source-sink behaviour of peach trees by incorporating Markov chains and physiological organ function submodels into L-PEACH. Functional Plant Biology, 2008, 35(10): 761–771CrossRefGoogle Scholar
  3. 3.
    Allen M T, Prusinkiewicz P, DeJong T M. Using L-systems for modeling source-sink interactions, architecture and physiology of growing trees: the L-PEACH model. New Phytologist, 2005, 166(3): 869–880CrossRefGoogle Scholar
  4. 4.
    Xu L F, Henke M, Zhu J, Kurth W, Buck-Sorlin G H. A rule-based functional-structural model of rice considering source and sink functions. In: Proceedings of the 3rd International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications. 2009, 245–252Google Scholar
  5. 5.
    Buck-Sorlin G H, de Visser P H B, Sarlikioti V, Burema B S, Heuvelink E, Marcelis L F M, van der Heijden G W A M, Vos J. SIMPLER: an FSPM coupling shoot production, human interaction with the structure, morphogenesis, photosynthesis and light environment in cut-Rose. In: Proceedings of the 6th International Workshop on Functional-Structural Plant Models. 2010, 222–224Google Scholar
  6. 6.
    Groer C, Kniemeyer O, Hemmerling R, Kurth W, Becker H, Buck-Sorlin G H. A dynamic 3D model of rape (Brassica napus L.) computing yield components under variable nitrogen fertilization regimes. In: Proceedings of the 5th International Workshop on Functional-Structural Plant Models. 2007Google Scholar
  7. 7.
    Buck-Sorlin G H, Kniemeyer O, Kurth W. Barley morphology, genetics and hormonal regulation of internode elongation modelled by a relational growth grammar. New Phytologist, 2005, 166(3): 859–867CrossRefGoogle Scholar
  8. 8.
    Buck-Sorlin G H, Kniemeyer O, Kurth W. A grammar-based model of barley including genetic control and metabolic networks. In: Vos J et al., eds. Functional-Structural Plant Modelling in Crop Production. Dordrecht: Springer, 2007, 243–252CrossRefGoogle Scholar
  9. 9.
    Buck-Sorlin G H, Hemmerling R, Kniemeyer O, Burema B, Kurth W. A rule-based model of barley morphogenesis, with special respect to shading and gibberellic acid signal transduction. Annals of Botany, 2008, 101(8): 1109–1123CrossRefGoogle Scholar
  10. 10.
    Barczi J F, Rey H, Caraglio Y, Reffye d P, Barthélémy D, Dong Q X, Fourcaud T. AmapSim: a structural whole-plant simulator based on botanical knowledge and designed to host external functional models. Annals of Botany, 2008, 101(8): 1125–1138CrossRefGoogle Scholar
  11. 11.
    Hu B G, Reffye P D, Zhao X, Yan H P, Kang M Z. GreenLab: a new methodology towards plant functional-structural model — structural aspect. In: Hu B, Jaeger M, eds. Plant Growth Modeling and Applications. Beijing: TsingHuo University Press and Springer, 2003, 21–35Google Scholar
  12. 12.
    Letort V. Analyse multi-échelle des relations source-puits dans les modèles de développement et croissance des plantes pour l’identification paramétrique. Cas du modèle GreenLab. Dissertation for the Doctoral Degree. Châtenay-Malabry: École Centrale Paris, 2008Google Scholar
  13. 13.
    Breckling B. An individual based model for the study of pattern and process in plant ecology: an application of object oriented programming. EcoSys, 1996, 4: 241–254Google Scholar
  14. 14.
    Perttunen J, Sievänen R, Nikinmaa E, Salminen H, Saarenmaa H, Väkevä J. LIGNUM: A tree model based on simple structural units. Annals of Botany, 1996, 77(1): 87–98CrossRefGoogle Scholar
  15. 15.
    Kniemeyer O. Design and implementation of a graph grammar based language for functional-structural plant modelling. Dissertation for the Doctoral Degree. Cottbus: Brandenburg University of Technology, 2008Google Scholar
  16. 16.
    Kurth W. Morphological models of plant growth. Possibilities and ecological relevance. Ecological Modelling, 1994, 75: 299–308CrossRefGoogle Scholar
  17. 17.
    Prusinkiewicz P, Lindenmayer A. The Algorithmic Beauty of Plants. New York: Springer Science & Business Media, 2012MATHGoogle Scholar
  18. 18.
    Hemmerling R. Extending the programming language XL to combine graph structures with ordinary differential equations. Dissertation for the Doctoral Degree. Göttingen: University of Göttingen, 2012Google Scholar
  19. 19.
    Hemmerling R, Kniemeyer O, Lanwert D, Kurth W, Buck-Sorlin G H. The rule-based language XL and the modelling environment GroIMP illustrated with simulated tree competition. Functional Plant Biology, 2008, 35(9/10): 739–750CrossRefGoogle Scholar
  20. 20.
    Van Antwerpen D G. Unbiased physically based rendering on the GPU. Dissertation for the Master Degree. Delft: Delft University of Technology, 2011Google Scholar
  21. 21.
    Veach E. Robust Monte Carlo Methods for Light Transport Simulation. Dissertation for the Doctoral Degree. Palo Alto: Stanford University, 1998Google Scholar
  22. 22.
    Buck-Sorlin G H, Hemmerling R, Vos J, de Visser P H. Modelling of spatial light distribution in the greenhouse: Description of the model. In: Proceedings of the 3rd International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications. 2009, 79–86Google Scholar
  23. 23.
    Evers J B, Vos J, Yin X, Romero P, Van Der Putten P E L, Struik P C. Simulation of wheat growth and development based on organlevel photosynthesis and assimilate allocation. Journal of Experimental Botany, 2010, 61(8): 2203–2216CrossRefGoogle Scholar
  24. 24.
    Preetham A J, Shirley P, Smits B. A practical analytic model for daylight. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. 1999, 91–100Google Scholar
  25. 25.
    Gijzen H. Development of a simulation model for transpiration and water uptake and an integral growth model. AB-DLO Report 18. 1994Google Scholar
  26. 26.
    Nikolov N T, Massman WJ, Schoettle AW. Coupling biochemical and biophysical processes at the leaf level: an equilibrium photosynthesis model for leaves of C3 plants. Ecological Modelling, 1995, 80: 205–235CrossRefGoogle Scholar
  27. 27.
    Müller J, Wernecke P, Diepenbrock W. LEAFC3-N: a nitrogensensitive extension of the CO2 and H2O gas exchange model LEAFC3 parameterised and tested for winter wheat (Triticum aestivum L.). Ecological Modelling, 2005, 183: 183–210CrossRefGoogle Scholar
  28. 28.
    Müller J, Braune H, Diepenbrock W. Photosynthesis-stomatal conductance model LEAFC3-N: specification for barley, generalised nitrogen relations, and aspects of model application. Functional Plant Biology, 2008, 35: 797–810CrossRefGoogle Scholar
  29. 29.
    Baldocchi D. An analytical solution for coupled leaf photosynthesis and stomatal conductance models. Tree Physiology, 1994, 14: 1069–1079CrossRefGoogle Scholar
  30. 30.
    Kim S H, Lieth J H. A coupled model of photosynthesis, stomatal conductance and transpiration for a rose leaf (Rosa hybrida L.). Annals of Botany, 2003, 91(7): 771–781CrossRefGoogle Scholar
  31. 31.
    Lieth J H, Pasian C C. A simulation model for the growth and development of flowering rose shoots. Scientia Horticulturae, 1991, 46: 109–128CrossRefGoogle Scholar
  32. 32.
    Thornley J H M. A model to describe the partitioning of photosynthate during vegetative plant growth. Annals of Botany, 1969, 33: 419–430Google Scholar
  33. 33.
    Thornley J H M. Dynamic model of leaf photosynthesis with acclimation to light and nitrogen. Annals of Botany, 1998, 81(3): 421–430CrossRefGoogle Scholar
  34. 34.
    Johnson I R, Thornley J H M. Dynamic model of the response of a vegetative grass crop to light, temperature and nitrogen. Plant, Cell and Environment, 1985, 8(7): 485–499CrossRefGoogle Scholar
  35. 35.
    Marshall B, Biscoe P V. A model for C3 leaves describing the dependence of net photosynthesis on irradiance I. Derivation. Journal of Experimental Botany, 1980, 31(1): 29–39CrossRefGoogle Scholar
  36. 36.
    Marshall B, Biscoe P V. A model for C3 leaves describing the dependence of net photosynthesis on irradiance II. Application to the analysis of flag leaf photosynthesis. Journal of Experimental Botany, 1980, 31(1): 41–48CrossRefGoogle Scholar
  37. 37.
    Rauscher H M, Isebrands J G, Host G E, Dickson R E, Dickmann D I, Crow T R, Michael D A. ECOPHYS: an ecophysiological growth process model for juvenile poplar. Tree Physiology, 1990, 7: 255–281CrossRefGoogle Scholar
  38. 38.
    Yin X Y, Goudriaan J, Lantinga E A, Vos J, Spiertz H J. A flexible sigmoid function of determinate growth. Annals of Botany, 2003, 91(3): 361–371CrossRefGoogle Scholar
  39. 39.
    Richards F J. A flexible growth function for empirical use. Journal of Experimental Botany, 1959, 29(10): 290–300MathSciNetCrossRefGoogle Scholar
  40. 40.
    Thornley J H M. Growth, maintenance and respiration: a reinterpretation. Annals of Botany, 1977, 41(6): 1191–1203Google Scholar
  41. 41.
    Bertin N, Gary C. Évaluation d’un modèle dynamique de croissance et de développement de la tomate (Lycopersicon esculentum Mill), TOMGRO, pour différents niveaux d’offre et de demande en assimilats. Agronomie, 1993, 13: 395–405CrossRefGoogle Scholar
  42. 42.
    Marcelis L F M. A simulation model for dry matter partitioning in cucumber. Annals of Botany, 1994, 74(1): 43–52CrossRefGoogle Scholar
  43. 43.
    Marcelis L F M. Sink strength as a determinant of dry matter partitioning in the whole plant. Journal of Experimental Botany, 1996, 47: 1281–1291CrossRefGoogle Scholar
  44. 44.
    Qi R, Ma Y T, Hu B G, de Reffye P, Cournède P H. Optimization of source-sink dynamics in plant growth for ideotype breeding: a case study on maize. Computers and Electronics in Agriculture, 2010, 71(1): 96–105CrossRefGoogle Scholar
  45. 45.
    Pradal C, Dufour-Kowalski S, Boudon F, Fournier C, Godin C. Open-Alea: a visual programming and component-based software platform for plant modelling. Functional Plant Biology, 2008, 35(10): 751–760CrossRefGoogle Scholar
  46. 46.
    Vos J, Evers J B, Buck-Sorlin G H, Andrieu B, Chelle M, de Visser P H B. Functional-structural plant modelling: a new versatile tool in crop science. Journal of Experimental Botany, 2010, 61(8): 2101–2115CrossRefGoogle Scholar
  47. 47.
    Wilson G V. Where’s the real bottleneck in scientific computing? American Scientist, 2006, 94(1): 5–6CrossRefGoogle Scholar
  48. 48.
    McMaster G S, Hargreaves J N G. CANON in D(esign): composing scales of plant canopies from phytomers to whole-plants using the composite design pattern. NJAS-Wageningen Journal of Life Sciences, 2009, 57(1): 39–51CrossRefGoogle Scholar
  49. 49.
    Bouman B A M, Keulen v H, Laar v H H, Rabbinge R. The ‘school of de Wit’ crop growth simulation models: A pedigree and historical overview. Agricultural Systems, 1996, 52(2): 171–198CrossRefGoogle Scholar
  50. 50.
    Spitters C J T. Crop growth models: their usefulness and limitations. ISHS Acta Horticulturae 267: VI Symposium on the Timing of Field Production of Vegetables. 1990, 349–368Google Scholar
  51. 51.
    Van Keulen H, Penning de Vries FWT, Drees EM. A summary model for crop growth. In: Penning de Vries F W T, van Laar H H, eds. Simulation of plant growth and crop production, Wageningen: Centre for Aqricultural Publishing and Documentation, 1982Google Scholar
  52. 52.
    Lithourgidis A S, Dordas C A, Damalas C A, Vlachostergios D N. Annual intercrops: an alternative pathway for sustainable agriculture. Australian Journal of Crop Science, 2011, 5(4): 396–410Google Scholar
  53. 53.
    Ouma G P J. Sustainable horticultural crop production through intercropping: the case of fruits and vegetable crops: a review. Agriculture and Biology Journal of North America, 2010, 1(5): 1098–1105CrossRefGoogle Scholar
  54. 54.
    Henke M, Sarlikioti V, Kurth W, Buck-Sorlin G H, Pagès L. Exploring root developmental plasticity to nitrogen with a three-dimensional architectural model. Plant and Soil, 2014, 385(1): 49–62CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Michael Henke
    • 1
  • Winfried Kurth
    • 1
  • Gerhard H. Buck-Sorlin
    • 2
  1. 1.Department of Ecoinformatics, Biometrics and Forest GrowthUniversity of GöttingenGöttingenGermany
  2. 2.UMR1345 Institut de Recherche en Horticulture et Semences (IRHS)AGROCAMPUS OUEST Centre d’AngersAngersFrance

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