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Database/Software Article | Open Access

Coupling Plant Growth Models and Pest and Disease Models: An Interaction Structure Proposal, MIMIC

Houssem E. M. Triki1,2,3,4( )Fabienne Ribeyre3,4Fabrice Pinard4,5Marc Jaeger1,2
CIRAD, UMR AMAP, F-34398 Montpellier, France
AMAP, University of Montpellier, CIRAD, CNRS, INRAE, IRD, Montpellier, France
CIRAD, UMR PHIM, F-34398 Montpellier, France
PHIM, University of Montpellier, CIRAD, INRAE, Institut Agro, IRD, Montpellier, France
CIRAD, UMR PHIM, 00100 Nairobi, Kenya
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Abstract

Coupling plant growth model with pests and diseases (P&D) models, with consideration for the long-term feedback that occurs after the interaction, is still a challenging task nowadays. While a number of studies have examined various methodologies, none of them provides a generic frame able to host existing models and their codes without updating deeply their architecture. We developed MIMIC (Mediation Interface for Model Inner Coupling), an open-access framework/tool for this objective. MIMIC allows to couple plant growth and P&D models in a variety of ways. Users can experiment with various interaction configurations, ranging from a weak coupling that is mediated by the direct exchange of inputs and outputs between models to an advanced coupling that utilizes a third-party tool if the models’ data or operating cycles do not align. The users decide how the interactions operate, and the platform offers powerful tools to design key features of the interactions, mobilizing metaprogramming techniques. The proposed framework is demonstrated, implementing coffee berry borers’ attacks on Coffea arabica fruits. Observations conducted in a field in Sumatra (Indonesia) assess the coupled interaction model. Finally, we highlight the user-centric implementation characteristics of MIMIC, as a practical and convenient tool that requires minimal coding knowledge to use.

References

1

Brandmeyer JE, Karimi HA. Coupling methodologies for environmental models. Environ Model Softw. 2000;15(5):479–488.

2

Argent RM, Voinov A, Maxwell T, Cuddy SM, Rahman JM, Seaton S, Vertessy RA, Braddock RD. Comparing modelling frameworks—A workshop approach. Environ Model Softw. 2006;21(7):895–910.

3

Abel DJ, Kilby PJ, Davis JR. The systems integration problem. Int J Geogr Inf Syst. 1994;8(1):1–12.

4

Louarn G, Song Y. Two decades of functional–structural plant modelling: Now addressing fundamental questions in systems biology and predictive ecology. Ann Bot. 2020;126(4):501–509.

5

Donatelli M, Magarey RD, Bregaglio S, Willocquet L, Whish JPM, Savary S. Modelling the impacts of pests and diseases on agricultural systems. Agric Syst. 2017;155:213–224.

6

Wang N, Jassogne L, van Asten PJA, Mukasa D, Wanyama I, Kagezi G, Giller KE. Evaluating coffee yield gaps and important biotic, abiotic, and management factors limiting coffee production in Uganda. Eur J Agron. 2015;63:1–11.

7

Matovu R, Kangire A, Phiri N, Hakiza G, Kagezi G, Musoli P. Ecological factors influencing incidence and severity of coffee leaf rust and coffee berry disease in major arabica coffee growing districts of Uganda. Uganda J Agric Sci. 2013;14:87–100.

8

Parmesan C, Hanley ME. Plants and climate change: Complexities and surprises. Ann Bot. 2015;116(6):849–864.

9

Pham Y, Reardon-Smith K, Mushtaq S, Cockfield G. The impact of climate change and variability on coffee production: A systematic review. Clim Chang. 2019;156(4):609–630.

10

Piao S, Liu Q, Chen A, Janssens IA, Fu Y, Dai J, Liu L, Lian X, Shen M, Zhu X. Plant phenology and global climate change: Current progresses and challenges. Glob Change Biol. 2019;25(6):1922–1940.

11
Kagezi G, Kucel P, Egonyu JP, Kyamanywa S, Karungi JT, Pinard F, Jaramillo J, Van Asten P, Wagoire WW, Ngabirano H. A review of the status and progress in management research of the black coffee twig borer, Xylosandrus compactus (Eichhoff) in Uganda. Paper presented at: ASIC 2014.
12

Pinard F, Makune SE, Campagne P, Mwangi J. Spatial distribution of coffee wilt disease under Roguing and replanting conditions: A case study from Kaweri Estate in Uganda. Phytopathology. 2016;106(11):1291–1299.

13

Luzinda H, Nelima M, Wabomba A, Kangire A, Musoli P, Musebe R. Farmer awareness, coping mechanisms and economic implications of coffee leaf rust disease in Uganda. Uganda J Agric Sci. 2016;16:207.

14

Buddie AG, Crozier J, Rutherford MA, Flood J, Bridge PD. Population development within the coffee wilt pathogen Gibberella xylarioides reflects host-related divergence. Eur J Plant Pathol. 2015;142(2):291–304.

15

Cerda R, Avelino J, Gary C, Tixier P, Lechevallier E, Allinne C. Primary and secondary yield losses caused by pests and diseases: Assessment and modeling in coffee. PLoS One. 2017;12(1):Article e0169133.

16

Gaunt RE. The relationship between plant disease severity and yield. Annu Rev Phytopathol. 1995;33(1):119–144.

17

Bar-Yam Y. General features of complex systems: Encyclopedia of life support systems. Oxford (UK): EOLSS UNESCO Publishers; 2002.

18

Tan M, Gou F, Stomph TJ, Wang J, Yin W, Zhang L, Chai Q, van der Werf W. Dynamic process-based modelling of crop growth and competitive water extraction in relay strip intercropping: Model development and application to wheat-maize intercropping. Field Crops Res. 2020;246:Article 107613.

19
Sievänen R, Perttunen J, Nikinmaa E, Posada JM. Invited talk: Functional structural plant models—Case LIGNUM. Paper presented at: PMA 2009. Proceedings of the 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications; 2009 Nov 9–13; Beijing, China.
20
de Reffye P, Hu BG. Relevant qualitative and quantitative choices for building an efficient dynamic plant growth model: Greenlab case. Paper presented at: PMA 2003. Proceedings of the 2003 International Symposium on Plant Growth Modeling, Simulation, Visualization and their Application; 2003 Oct 13–16; Beijing, China.
21

Soualiou S, Wang Z, Sun W, de Reffye P, Collins B, Louarn G, Song Y. Functional–structural plant models mission in advancing crop science: Opportunities and prospects. Front Plant Sci. 2021;12:Article 747142.

22

Heuvelink E. Dry matter partitioning in tomato: Validation of a dynamic simulation model. Ann Bot. 1996;77(1):71–80.

23

Rivals P. Essai Sur la croissance des arbres et Sur leurs systèmes de floraison (application aux espèces fruitières). J Agric Tradit Bot Appliquée. 1965;12(12):655–686.

24

Barthélémy D, Caraglio Y. Plant architecture: A dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Ann Bot. 2007;99(3):375–407.

25

Yates KL, Bouchet PJ, Caley MJ, Mengersen K, Randin CF, Parnell S, Fielding AH, Bamford AJ, Ban S, Barbosa AM, et al. Outstanding challenges in the transferability of ecological models. Trends Ecol Evol. 2018;33(10):790–802.

26

Lessler J, Cummings DAT. Mechanistic models of infectious disease and their impact on public health. Am J Epidemiol. 2016;183(5):415–422.

27

Kirkeby C, Brookes VJ, Ward MP, Dürr S, Halasa T. A practical introduction to mechanistic modeling of disease transmission in veterinary science. Front Vet Sci. 2021;7:Article 546651.

28

Siad SM, Iacobellis V, Zdruli P, Gioia A, Stavi I, Hoogenboom G. A review of coupled hydrologic and crop growth models. Agric Water Manag. 2019;224:Article 105746.

29

Kropff MJ, Teng PS, Rabbinge R. The challenge of linking pest and crop models. Agric Syst. 1995;49(4):413–434.

30

Vezy R, le Maire G, Christina M, Georgiou S, Imbach P, Hidalgo HG, Alfaro EJ, Blitz-Frayret C, Charbonnier F, Lehner P, et al. DynACof: A process-based model to study growth, yield and ecosystem services of coffee agroforestry systems. Environ Model Softw. 2020;124:Article 104609.

31

Leclerc G, Bommel P, Motisi N, Vezy R, Treminio E, Avelino J. Coffee leaf rust (Hemeleia vastatrix) risk management in Central America: Contribution of remote interactive simulations. Agron Environ Sociétés. 2021;11(2).

32
Pradal C, Dufour-Kowalski S, Boudon F, Donès N. The architecture of OpenAlea: A visual programming and component based software for plant modeling. Paper presented at: FSPM 2007. Proceedings of the 5th International Workshop on Functional-Structural Plant Models; 4–9 Nov 2007; Napier, New Zealand.
33

Pradal C, Dufour-Kowalski S, Boudon F, Fournier C, Godin C. OpenAlea: A visual programming and component-based software platform for plant modelling. Funct Plant Biol. 2008;35(10):751–760.

34
Qi R, Cournede P-H, Lecoustre R, de Reffye P. Tri-trophic ecosystem oil palm-pests-auxiliaries: Ⅰ. Modeling and simulation. Paper presented at: PMA 2009. Proceedings of the 2009 Third Plant Growth Modeling, Simulation, Visualization, and Applications; 2009 Nov 9–13; Beijing, China.
35

Motisi N, Bommel P, Leclerc G, Robin MH, Aubertot JN, Butron AA, Merle I, Treminio E, Avelino J. Improved forecasting of coffee leaf rust by qualitative modeling: Design and expert validation of the ExpeRoya model. Agric Syst. 2022;197:Article 103352.

36

Le Chevalier V, Jaeger M, Mei X, Cournède P-H. Simulation and visualisation of functional landscapes: Effects of the water resource competition between plants. J Comput Sci Technol. 2007;22(6):835–845.

37

Zeigler BP. DEVS representation of dynamical systems: Event-based intelligent control. Proc IEEE. 1989;77(1):72–80.

38

Garin G, Fournier C, Andrieu B, Houlès V, Robert C, Pradal C. A modelling framework to simulate foliar fungal epidemics using functional–structural plant models. Ann Bot. 2014;114(4):795–812.

39

Reuillon R, Leclaire M, and Rey-Coyrehourcq S, OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models. Future Gener Comp Syst. 2013;29(8):1981–1990.

40
de Reffye P, Heuvelink E, Guo Y, Hu B-G, Zhang B-G. Coupling process-based models and plant architectural models: A key issue for simulating crop production. In: Cao W, White JW, Wang E, editors. Crop modeling and decision support. Berlin (Germany): Springer; 2009. p. 130–147.
41
Cournède P-H, Guyard T, Bayol B, Griffon S, de Coligny F, Borriane P, Jaeger M, de Reffye P. A forest growth simulator based on functional-structural modelling of individual trees. Paper presented at: PMA 2009. Proceedings of the 2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications; 2009 Nov 9–13; Beijing, China.
42

Kang M, Hua J, Wang X, de Reffye P, Jaeger M, Akaffou S. Estimating sink parameters of stochastic functional-structural plant models using organic series-continuous and rhythmic development. Front Plant Sci. 2018;9:1688.

43

Letort V, Sabatier S, Okoma MP, Jaeger M, de Reffye P. Internal trophic pressure, a regulator of plant development? Insights from a stochastic functional–structural plant growth model applied to Coffea trees. Ann Bot. 2020;126(4):687–699.

44

Cournède P-H, Kang MZ, Mathieu A, Barczi JF, Yan HP, Hu BG, de Reffye P. Structural factorization of plants to compute their functional and architectural growth. SIMULATION. 2006;82(7):427–438.

45

Rodríguez D, Cure JR, Gutierrez AP, Cotes JM, Cantor F. A coffee agroecosystem model: Ⅱ. Dynamics of coffee berry borer. Ecol Model. 2013;248:203–214.

46
Gamma E, Helm R, Johnson R, Vlissides J. Design patterns: Elements of reusable object-oriented software. London (UK): Pearson Education; 1994.
47
Tendeloo YV, Vangheluwe H. Discrete event system specification modeling and simulation. Paper presented at: WSC 2018. Proceedings of the 2018 Winter Simulation Conference; 2018 Dec 9–12; Gothenburg, Sweden.
48

Damon A. A review of the biology and control of the coffee berry borer, Hypothenemus hampei (Coleoptera: Scolytidae). Bull Entomol Res. 2000;90(6):453–465.

49

Jaramillo J, Chabi-Olaye A, Kamonjo C, Jaramillo A, Vega FE, Poehling HM, Borgemeister C. Thermal tolerance of the coffee berry borer Hypothenemus hampei: Predictions of climate change impact on a tropical insect pest. PLoS One. 2009;4(8):Article e6487.

50

Dufour BP, Kerana IW, Ribeyre F. Population dynamics of Hypothenemus hampei (Ferrari) according to the phenology of Coffea arabica L. in equatorial conditions of North Sumatra. Crop Prot. 2021;146:Article 105639.

51
Ben-Kiki O, Evans C. YAML Ain’t Markup Language (YAMLTM) Version 1.2.
52

Bezanson J, Edelman A, Karpinski S, Shah VB. Julia: A fresh approach to numerical computing. SIAM Rev. 2017;59(1):65–98.

53

Perkel JM. Julia: Come for the syntax, stay for the speed. Nature. 2019;572(7767):141–142.

54

Lorenz EN. Deterministic nonperiodic flow. J Atmos Sci. 1963;20(2):130–141.

55

Sedgwick P. Retrospective cohort studies: Advantages and disadvantages. BMJ. 2014;348:Article g1072.

56

Whish JPM, Herrmann NI, White NA, Moore AD, Kriticos DJ. Integrating pest population models with biophysical crop models to better represent the farming system. Environ Model Softw. 2015;72:418–425.

57

Zeigler BP, Muzy A. From discrete event simulation to discrete event specified systems (DEVS). IFAC-Pap. 2017;50(1):3039–3044.

58

Bergez J-E, Chabrier P, Gary C, Jeuffroy MH, Makowski D, Quesnel G, Ramat E, Raynal H, Rousse N, Wallach D, et al. An open platform to build, evaluate and simulate integrated models of farming and agro-ecosystems. Environ Model Softw. 2013;39:39–49.

59
Chabrier P, Garcia F, Martin-Clouaire R, Quesnel G, Raynal H. Toward a simulation modeling platform for studying cropping systems management: The record project. Paper presented at: MODSIM 2007. Proceedings of the International Congress on Modelling and Simulation; 2007 Dec 10–13; Christchurch, New Zealand.
Plant Phenomics
Article number: 0077
Cite this article:
Triki HEM, Ribeyre F, Pinard F, et al. Coupling Plant Growth Models and Pest and Disease Models: An Interaction Structure Proposal, MIMIC. Plant Phenomics, 2023, 5: 0077. https://doi.org/10.34133/plantphenomics.0077

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Received: 31 March 2023
Accepted: 10 July 2023
Published: 04 August 2023
© 2023 Houssem E. M. Triki et al. Exclusive licensee Nanjing Agricultural University. No claim to original U.S. Government Works.

Distributed under a Creative Commons Attribution License (CC BY 4.0).

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