Functional Plant Biology Functional Plant Biology Society
Plant function and evolutionary biology
RESEARCH ARTICLE

Coupling a 3D virtual wheat (Triticum aestivum) plant model with a Septoria tritici epidemic model (Septo3D): a new approach to investigate plant–pathogen interactions linked to canopy architecture

Corinne Robert A B E F , Christian Fournier C D E , Bruno Andrieu A B and Bertrand Ney A B
+ Author Affiliations
- Author Affiliations

A INRA, UMR 1091 EGC, F-78850 Thiverval-Grignon, France.

B AgroParisTech, UMR 1091 EGC, F-78850 Thiverval-Grignon, France.

C INRA, UMR 759 LEPSE, 2 place Viala, F-34060 Montpellier Cedex 01, France.

D SupAgro, UMR 759 LEPSE, 2 Place Viala, F-34060 Montpellier, France.

E These authors contributed equally to the work.

F Corresponding author. Email: corinne.robert@grignon.inra.fr

This paper originates from a presentation at the 5th International Workshop on Functional–Structural Plant Models, Napier, New Zealand, November 2007.

Functional Plant Biology 35(10) 997-1013 https://doi.org/10.1071/FP08066
Submitted: 8 March 2008  Accepted: 11 September 2008   Published: 11 November 2008

Abstract

This work initiates a modelling approach that allows us to investigate the effects of canopy architecture on foliar epidemics development. It combines a virtual plant model of wheat (Triticum aestivum L.) with an epidemic model of Septoria tritici which is caused by Mycosphaerella graminicola, a hemi-biotrophic, splashed-dispersed fungus. Our model simulates the development of the lesions from the infected lower leaves to the healthy upper leaves in the growing canopy. Epidemics result from the repeated successions of lesion development (during which spores are produced) and spores dispersal. In the model, canopy development influences epidemic development through the amount of tissue available for lesion development and through its effects on rain penetration and droplets interception during spore dispersal. Simulations show that the impact of canopy architecture on epidemic development differs between canopy traits and depends on climate. Phyllochron has the strongest effect, followed by leaf size and stem elongation rate.

Additional keywords: disease escape, epidemic model, L-system, Mycosphaerella graminicola, plant–pathogen interaction, cv. Soissons, Triticum aestivum, 3D virtual plant model.


Acknowledgements

We thank three anonymous reviewers for their constructive comments. This work was funded by the INRA department of Environnement et Agronomie and by Casdar project No. 6128. We thank Julie Rodriguez for processing data of lesions growth and Jessica Bertheloot for providing us data to parameterise tillering and senescence in ADEL.


References


Atkinson D, McKinlay RG (1997) Crop protection and its integration within sustainable farming systems. Agriculture Ecosystems & Environment 64, 87–93.
CrossRef | open url image1

Audsley E, Milne A, Paveley N (2005) A foliar disease model for use in wheat disease management decision support systems. Annals of Applied Biology 147, 161–172.
CrossRef | open url image1

Bahat A, Gelernter I, Brown MB, Eyal Z (1980) Factors affecting the vertical progression of Septoria leaf blotch in short-statured wheats. Phytopathology 70, 179–184.
CrossRef |
open url image1

Bannon FJ, Cooke BM (1998) Studies on dispersal of Septoria tritici pycnidiospores in wheat-clover intercrops. Plant Pathology 47, 49–56.
CrossRef | open url image1

Baret F, Andrieu B, Steven MD (1993) Gap frequency and canopy architecture of sugar-beet and wheat crops. Agricultural and Forest Meteorology 65, 261–279.
CrossRef | open url image1

Benedict WG (1971) Differential effect of light intensity on the infection of wheat by Septoria tritici Desm. under controlled environmental conditions. Physiological Plant Pathology 1, 55–66.
CrossRef | open url image1

Brennan RM, Fitt BDL, Taylor GS, Colhoun J (1985) Dispersal of Septoria nodorum pycnidiospores by simulated rain drops in still air. Journal of Phytopathology 112, 281–290.
CrossRef | open url image1

Calonnec A, Cartolaro P, Naulin JM, Bailey D, Langlais M (2008) A host–pathogen simulation model: powdery mildew of grapevine. Plant Pathology 57, 493–508.
CrossRef | open url image1

Coakley SM, Mc Daniel LR, Shaner G (1985) Model for predicting severity of Septoria tritici blotch on winter wheat. Phytopathology 75, 1245–1251.
CrossRef |
open url image1

Danon T, Sacks JM, Eyal Z (1982) The relationship among plant stature, maturity class and susceptibility to septoria leaf blotch of wheat. Phytopathology 73, 1037–1042.
CrossRef |
open url image1

Evers JB, Vos J, Fournier C, Andrieu B, Chelle M, Struik PC (2005) Towards a generic architectural model of tillering in Gramineae, as exemplified by spring wheat (Triticum aestivum). New Phytologist 166, 801–812.
CrossRef | PubMed | open url image1

Eyal Z (1971) The kinetics of pycnidiospore liberation in Septoria tritici. Canadian Journal of Botany 49, 1095–1099.
CrossRef |
open url image1

Eyal Z (1981) Integrated control of Septoria diseases of wheat. Plant Disease 65, 763–768. open url image1

Fellows H (1962) Effects of light, temperature, and fertilizer on infection of wheat leaves by Septoria tritici.  Plant Disease Reporter 46, 846–848. open url image1

Fournier C , Andrieu B , Ljutovac S , Saint-Jean S 2003. ADEL-wheat: a 3D architectural model of wheat development. In ‘Plant growth modeling and applications’. (Eds B-G Hu, M Jaeger) pp. 54–66. (Tsinghua University Press: Beijing)

Fraaije BA , Burnett FJ , Clark WS , Motteram J , Lucas JA 2005. Resistance development to QoI inhibitors in populations of Mycosphaerella graminicola in the UK, modern fungicides and antifungal compounds IV. In ‘14th International reinhardsbrunn symposium’. pp. 63–71. (British Crop Protection Council: Alton, UK)

Hillier J , Watt J , Bertheloot J , Lewis P , Fournier C , Andrieu B 2007. Modelling the time course of senescence in winter wheat at the individual leaf and whole plant level. In ‘Proceedings of the 5th international workshop on functional structural plant models’. (Eds P Prusinkiewicz, J Hanan, B Lane) (HortResearch: Napier, New Zealand)

Hilu HM, Bever WM (1957) Inoculation, oversummering, and suscept-pathogen relationship of on Triticum species. Phytopathology 47, 474–480. open url image1

Karkowski R, Prusinkiewicz P (2003) Design and implementation of the L + C modeling language. Electronic Notes in Theoretical Computer Science 86, 1–19. open url image1

Kema GHJ, Yu D-Z, Rijkenberg FHJ, Shaw MW, Baayen RP (1996) Histology of the pathogenesis of Mycosphaerella graminicola in wheat. Phytopathology 86, 777–786.
CrossRef | open url image1

King JE, Cook RJ, Melville SC (1983) A review of Septoria diseases of wheat and barley. Annals of Applied Biology 103, 345–373.
CrossRef | open url image1

Le Henaff G, Oste B, Wilhem E, Faure A, Moinard J, Lepoutre P, Pillon O (2002) Bilan phytosanitaire 2001–2002 des céréales: retour au sec et au calme pour des résultats moyens: bilans grandes cultures. La défense des végétaux 556, 22–26. open url image1

Le Henaff G, Oste B, Wilhem E, Faure A, Moinard J, Lepoutre P, Pillon O, Delos M (2003) Bilan phytosanitaire 2002–2003 des céréales à paille. Une campagne atypique, des ennemis peu nuisibles et pourtant remarqués (France) Phytoma. La défense des végétaux 567, 20–24. open url image1

Ljutovac S (2002) Coordination dans l’extension des organes aériens et conséquence pour les relations entre les dimensions finales des organes chez le blé. Thèse de doctorat, Institut National Agronomique Paris-Grignon.

Lovell DJ, Parker SR, Hunter T, Royle DJ, Coker RR (1997) Influence of crop growth and structure on the risk of epidemics by Mycosphaerella graminicola (Septoria tritici) in winter wheat. Plant Pathology 46, 126–138.
CrossRef | open url image1

Lovell DJ, Hunter T, Powers SJ, Parker SR, Van den Bosch F (2004a) Effect of temperature on latent period of septoria leaf blotch on winter wheat under outdoor conditions. Plant Pathology 53, 170–181.
CrossRef | open url image1

Lovell DJ, Parker SR, Hunter T, Welham SJ, Nichols AR (2004b) Position of inoculum in the canopy affects the risk of septoria tritici blotch epidemics in winter wheat. Plant Pathology 53, 11–21.
CrossRef | open url image1

Mech R , Prusinkiewicz P 1996. Visual models of plants interacting with their environment. Paper presented at the Proceedings of SIGGRAPH ’96, New Orleans, Louisiana.

Meynard J-M, Doré T, Lucas Ph (2003) Agronomic approach: cropping systems and plant diseases. Compte rendus Biologies 326, 37–46.
CrossRef |
open url image1

Moreau JM, Maraite H (1999) Integration of knowledge on wheat phenology and Septoria tritici epidemiology into a disease risk simulation model validated in Belgium. Aspects of Applied Biology 55, 1–6. open url image1

Murray GM, Martin RH, Cullis BR (1990) Relationship of the severity of Septoria tritici blotch of wheat to sowing time, rainfall at heading and average susceptibility of wheat cultivars in the area. Australian Journal of Agricultural Research 41, 307–315.
CrossRef | open url image1

Oste B, Hugerot G, Delos M, Freydier M, Thiery G, Le Henaff G, Gatellet J, Pillon O, Feurprier B, Vergnaud A (2000) Cereales. Bilan phytosanitaire de la campagne 1998–1999. Phytoma La défense des végétaux 523, 12–16. open url image1

Palmer C, Skinner W (2002) Mycosphaerella graminicola: latent infection, crop devastation and genomics. Molecular Plant Pathology 3, 63–70.
CrossRef | open url image1

Pielaat A, Van den Bosch F, Fitt BDL, Jeger MJ (2002) Simulation of vertical spread of plant diseases in a crop canopy by stem extension and splash dispersal. Ecological Modelling 151, 195–212.
CrossRef | open url image1

Pietravalle S, Van den Bosch F, Welham SJ, Parker SR, Lovell DJ (2001) Modelling of rain splash trajectories and prediction of rain splash height. Agricultural and Forest Meteorology 109, 171–185.
CrossRef | open url image1

Prévot L, Aries F, Monestiez P (1991) Modélisation de la structure géométrique du maïs. Agronomie 11, 491–503.
CrossRef |
open url image1

Prusinkiewicz P , Hammel M , Hanan J , Mech R 1997 Visual models of plant development. In ‘Handbook of formal languages’. (Eds G Rozenberg, A Salomaa) pp. 535–597. (Springer-Verlag: Berlin)

Prusinkiewicz P , Karkowski R , Lane B 2007. The L + C plant-modelling language. In ‘Functional–structural plant modelling in crop production’. (Eds J Vos, LFM Marcellis, PHB de Visser, PC Struik, JB Evers) pp. 27–42. (Springer-Verlag: Dordrecht, the Netherlands)

Rapilly F, Jolivet E (1976) Construction d’un modèle (EPISEPT) permettant la simulation d’une épidémie de Septoria nodorum BERK. sur blé. Revue de Statistique Appliquée 3, 31–60. open url image1

Robert C, Bancal MO, Lannou C (2002) Wheat leaf rust uredospore production and carbon and nitrogen export in relation to lesion size and density. Phytopathology 92, 762–768.
CrossRef | PubMed | open url image1

Robert C, Bancal MO, Lannou C (2004) Wheat leaf rust uredospore production on adult plants: influence of leaf nitrogen content and Septoria tritici blotch. Phytopathology 94, 712–721.
CrossRef | PubMed | open url image1

Room P, Hanan J, Prusinkiewicz P (1996) Virtual plants: new perspectives for ecologists, pathologists and agricultural scientists. Trends in Plant Science 1, 33–38.
CrossRef | open url image1

Rouzet J , Murer F 1988. Etude d’un modèle permettant la simulation d’une épidémie de Septoria (Pre-sept) tritici sur blé d’hiver. In ‘2ème Conférence internationale sur les maladies des plantes’. (ANPP: Bordeaux France)

Saint Jean S, Chelle M, Huber L (2004) Modeling water transfer by rain splash in a 3D canopy structure by means of Monte Carlo integration. Agricultural and Forest Meteorology 121, 183–196.
CrossRef | open url image1

Sanderson FR, Hampton JG (1978) Role of the perfect states in the epidemiology of the common Septoria diseases of wheat N.Z. Journal of Agricultural Research 21, 277–281. open url image1

Saudreau M, Sinoquet H, Santin O, Marquier A, Adam B, Longuenesse JJ, Guilino L, Chelle M (2007) A 3D model for simulating the spatial and temporal distribution of temperature within ellipsoidal fruit. Agricultural and Forest Meteorology 147, 1–15.
CrossRef |
open url image1

Shaw MW (1987) Assessment of upward movement of rain splash using a fluorescent tracer method and its application to the epidemiology of cereal pathogens. Plant Pathology 36, 201–213.
CrossRef | open url image1

Shaw MW (1990) Effects of temperature, leaf wetness and cultivar on the latent period of Mycosphaerella graminicola on winter wheat. Plant Pathology 39, 255–268.
CrossRef | open url image1

Shaw MW (1991) Interacting effects of interrupted humid periods and light on infection of wheat leaves by Mycosphaerella graminicola (S. tritici). Plant Pathology 40, 595–607.
CrossRef | open url image1

Shaw MW, Royle DJ (1989) Airborn inoculum as a major source of Septoria tritici infections in winter wheat crops in UK. Plant Pathology 38, 35–43.
CrossRef | open url image1

Shaw MW, Royle DJ (1993) Factors determining the severity of epidemics of Mycosphaerella graminicola on winter wheat in the UK. Plant Pathology 42, 882–899.
CrossRef | open url image1

Shipton WA, Boyd WJR, Rosielle AA, Sharen BL (1971) The common Septoria diseases of wheat. Botanical Review 37, 231–262.
CrossRef | open url image1

Warren Wilson J (1965) Stand structure and light penetration. 1 – Analysis by points quadrats. Journal of Applied Ecology 2, 383–390.
CrossRef | open url image1









Appendix 1. Model implementation

The model is implemented using the L + C modelling language (Prusinkiewicz et al. 2007), that combines the features of an L-system based simulation program and C++. The L-system simulator, lpfg, provides a modelling environment for simulating and visualising virtual plants, whereas the possibility of including C++ code allows coupling with other programs. This allows implementing as independent sub-routines the disease dispersal and the lesion development model. Moreover, a library of R functions was developed to ease the analysis of model outputs, thanks to automated R object construction and graph generation, and to perform extensive simulation studies, thanks to automated parameter generation and automated launches of lpfg.

Data structures

In L-Systems, the plant is viewed as a collection of sub-units, called modules that are topologically connected to form a specialised graph structure (axial tree). This structure is directly and transparently handled by L + C, which is also capable of representing it in 3D. We use the same three basic modules as in ADEL to represent the plant (apex, internode and leaf), and defines modules that represent higher levels of organisation (field, plant and axe), to store global information on the entity. This allows implementing in a natural way processes for which we had a non-local knowledge (typically the probabilities of presence of axes). Sectors and lesions were not defined as modules, but as nested C-data structures included in leaf module parameters. Lesions are pooled into cohorts of similar age within a sector, and represented by a set of histograms, one histogram for each state of the lesions (incubating and chlorotic lesions). For each class, we keep track of the number of lesions present in the cohorts and of the surface they occupy on the leaf. The dispersal subroutine does not operate directly on the 3D structure, but on a multi-layered representation of the canopy. The later is implemented as a set of multidimensional arrays defined as global variables of the program.

Description of one iteration

Each time step is decomposed in four substeps, corresponding to four tables in L + C. Prior to the application of these tables, using the L + C StartEach construct, an update of global variables (time, meteorological data) is performed and the number of rain and germination events that will occur during the time step is computed.

  1. Table 1 simulates the development of the plant by application of L-System productions, very much like the ones used in ADEL;

  2. Table 2 updates the global variables describing the canopy and computes dispersal. It screens all plant modules, calculates their position in space (similarly to an interpretation step), and uses this information to update (i) the orientation and position of all phyto-element within the layers, and (ii) the localisation and potential of spore production of sporulating areas. After Table 2, in the EndEach paragraph, the dispersal sub-routine is called as many times as there is rain events and stores its outputs in the global variable;

  3. Table 3 processes similarly to Table 2 for screening and positioning modules, but uses this information to retrieve, for each leaf sector, the variables computed by the dispersal subroutine: the number of droplets (DU) splashed by rains and new ones intercepted;

  4. Table 4 simulates the development of lesions within leaf sectors, by calling within productions and for each leaf sector the routines of the lesion development model.

Optimisation of numeric discretisations

In the model implementation four principal discretisations have been introduced to represent continuous systems: the division of leaves into sectors, the grouping of lesions into cohorts, the division of canopy surfaces into layers and the division of the directions of emission of droplets into angular classes. This allows finding with simulations, the degree of precision needed to represent coherently the system, whilst optimising computing times. To perform this optimisation, we simulates for four contrasting scenarios an indicator of disease severity at leaf level, and searched for the coarser level of discretisation that leaves unchanged the results of a reference simulation with a fine level of discretisation. For the number of sectors, simulations results are significantly altered and unstable when less than five sectors are used (Fig. A1), which corresponds to sector area of 2–5 cm2. Increasing layer thickness or age class width (maximal difference of ages between lesions of a cohort) does not introduce a general bias in the model, but alters the results at the level of individual leaves, as indicated by the increase of the box height (Fig. A1). Stability of the results occurs for thickness below 2 cm, and age class width below 10°Cd, that roughly corresponds to the mean duration of a time step during winter. The optimal number of angular classes is 15, which corresponds to a 6° wide angle.


Fig. A1.  Sensitivity of an indicator of disease severity at leaf level (normalised AUDPC) for four contrasting scenarios to: the number of sector used to subdivide a leaf, the thickness of canopy layers used for dispersal computations, the maximal difference of ages between lesions within a cohorts (age class width), and the number of angular classes used to discretise the hemisphere for simulating trajectories of droplets. For each variable, we represent the dispersion box of the ratios between the normalised AUDPC simulated at a given leaf position and for a given scenario, and the same variable obtained with the finest level of discretisation. Dispersion boxes indicate the mean (horizontal line), the upper and lower quartile (the box), an interval including all data points which are one and a half interquartile range far from the mean (whiskers), and outliers (points).
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