An extension of mathematical model for severity of rice blast disease

Saharat Tabonglek; Amir Khan; Usa Wannasingha Humphries; Saharat Tabonglek; Amir Khan; Usa Wannasingha Humphries

doi:10.3934/math.2023125

AIMS Mathematics

2023, Volume 8, Issue 1: 2419-2434. doi: 10.3934/math.2023125

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Research article

An extension of mathematical model for severity of rice blast disease

Department of Mathematics, Faculty of Science, King Mongkut's University of Technology Thonburi, Bangkok 10140, Thailand

Received: 22 August 2022 Revised: 14 October 2022 Accepted: 19 October 2022 Published: 02 November 2022
MSC : 92D25, 92D30

This paper aims to extend the spore dispersal model to the Healthy-Latent-Infectious-Removed (HLIR) epidemic model for assessing the severity of rice blast disease. The model was solved by the Finite Difference Method (FDM). The results of the model were compared to data from the Prachinburi Rice Research Center (PRRC) on the severity of rice blast disease. Because of a small error, the comparison results showed good agreement between the PRRC data and the simulation by looking at the value of Willmott's index of agreement ($ d $). The first bed $ d $ was 0.7166, while the second bed $ d $ was 0.6421, indicating the model's performance. Furthermore, the optimal parameter, the fraction of spores deposited on the crop, was determined to be 0.173 and 0.016 for beds 1 and 2, respectively. The model can simulate and analyze rice blast outbreaks for educational purposes in future preparedness planning.
- disease severity,
- epidemic model,
- plant disease,
- rice blast disease,
- spore dispersal
Citation: Saharat Tabonglek, Amir Khan, Usa Wannasingha Humphries. An extension of mathematical model for severity of rice blast disease[J]. AIMS Mathematics, 2023, 8(1): 2419-2434. doi: 10.3934/math.2023125

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Abstract

This paper aims to extend the spore dispersal model to the Healthy-Latent-Infectious-Removed (HLIR) epidemic model for assessing the severity of rice blast disease. The model was solved by the Finite Difference Method (FDM). The results of the model were compared to data from the Prachinburi Rice Research Center (PRRC) on the severity of rice blast disease. Because of a small error, the comparison results showed good agreement between the PRRC data and the simulation by looking at the value of Willmott's index of agreement ($ d $). The first bed $ d $ was 0.7166, while the second bed $ d $ was 0.6421, indicating the model's performance. Furthermore, the optimal parameter, the fraction of spores deposited on the crop, was determined to be 0.173 and 0.016 for beds 1 and 2, respectively. The model can simulate and analyze rice blast outbreaks for educational purposes in future preparedness planning.

References

[1]	D. O. TeBeest, C. Guerber, M. Ditmore, 2007, Rice blast, The Plant Health Instructor. https://doi.org/10.1094/PHI-I-2007-0313-07. Reviewed 2012.
[2]	N. L. Suriani, D. N. Suprapta, N. Nazir, N. M. S. Parwanayoni, A. A. K. Darmadi, D. A. Dewi, et al., A mixture of piper leaves extracts and rhizobacteria for sustainable plant growth promotion and bio-control of blast pathogen of organic bali rice, Sustainability, 12 (2020), 8490. https://doi.org/10.3390/su12208490 doi: 10.3390/su12208490
[3]	M. E. Jarroudi, H. Karjoun, L. Kouadio, M. E. Jarroudi, Mathematical modelling of non-local spore dispersion of wind-borne pathogens causing fungal diseases, Appl. Math. Comput., 376 (2020), 1–11. https://doi.org/10.1016/j.amc.2020.125107 doi: 10.1016/j.amc.2020.125107
[4]	B. Hau, C. J. de Vallavieille-Pope, Wind-dispersed diseases, In: The epidemiology of plant diseases, Netherlands: Springer, 2006.
[5]	S. Kirtphaiboon, U. Humphries, A. Khan, A. Yusuf, Model of rice blast disease under tropical climate conditions, Chaos Soliton. Fract., 143 (2021), 1–8. https://doi.org/10.1016/j.chaos.2020.110530 doi: 10.1016/j.chaos.2020.110530
[6]	A. S. Kapoor, R. Prasad, G. Sood, Forecasting of rice blast in Kangra district of Himachal Pradesh, Indian Phytopathol., 57 (2004), 440–445.
[7]	W. Li, J. Ji, L. Huang, Dynamics of a controlled discontinuous computer worm system, Proc. Amer. Math. Soc., 148 (2020), 4389–4403. https://doi.org/10.1090/proc/15095 doi: 10.1090/proc/15095
[8]	W. Li, J. Ji, L. Huang, Z. Guo, Global dynamics of a controlled discontinuous diffusive SIR epidemic system, Appl. Math. Lett., 121 (2021), 107420. https://doi.org/10.1016/j.aml.2021.107420 doi: 10.1016/j.aml.2021.107420
[9]	S. Tabonglek, U. W. Humphries, A. Khan, Mathematical model for rice blast disease caused by spore dispersion affected from climate factors, Symmetry, 14 (2022), 1131. https://doi.org/10.3390/sym14061131 doi: 10.3390/sym14061131
[10]	J. B. Burie, A. Calonnec, M. Langlais, Modeling of the invasion of a fungal disease over a vineyard, Model. Simu. Sci. Eng. Tec., 2 (2008), 11–21. https://doi.org/10.1007/978-0-8176-4556-4_2 doi: 10.1007/978-0-8176-4556-4_2
[11]	C. J. Willmott, S. G. Ackleson, R. E. Davis, J. J. Feddema, K. M. Klink, D. R. Legates, et al., Statistics for the evaluation and comparison of models, J. Geophys. Res. Oceans, 90 (1985), 8995–9005. https://doi.org/10.1029/JC090iC05p08995 doi: 10.1029/JC090iC05p08995
[12]	M. H. Ali, I. Abustan, A new novel index for evaluating model performance, J. Nat. Resour. Dev., 4 (2021), 1–9. https://doi.org/10.5027/jnrd.v4i0.01 doi: 10.5027/jnrd.v4i0.01
[13]	F. van den Bosch, J. A. J. Metz, J. C. Zadoks, Pandemics of focal plant disease, a model, Phytopathology, 89 (1999), 495–505. https://doi.org/10.1094/PHYTO.1999.89.6.495 doi: 10.1094/PHYTO.1999.89.6.495
[14]	S. Lee, C. Masclaux-Daubresse, Current understanding of leaf senescence in rice, Int. J. Mol. Sci., 22 (2021), 1–19. https://doi.org/10.3390/ijms22094515 doi: 10.3390/ijms22094515
[15]	S. Bregaglio, P. Titone, G. Cappelli, L. Tamborini, G. Mongiano, R. Confalonieri, Coupling a generic model to the WARM rice simulator to assess leaf and panicle blast impact in a temperature climate, Eur. J. Agron., 76 (2016), 107–117. https://doi.org/10.1016/j.eja.2016.02.009 doi: 10.1016/j.eja.2016.02.009
[16]	T. Gilet, L. Bourouiba, Fluid fragmentation shapes rain-induced foliar disease transmission, J. Roy. Soc. Interface, 12 (2015), 1–12. https://doi.org/10.1098/rsif.2014.1092 doi: 10.1098/rsif.2014.1092
[17]	O. Singh, J. Bathula, D. K. Singh, Rice blast modeling and forecasting, Int. J. Chem. Stud., 7 (2019), 2788–2799.
[18]	S. Savary, A. Nelson, L. Willocquet, I. Pangga, J. Aunario, Modeling and mapping potential epidemics of rice disease globally, Crop Prot., 34 (2012), 6–17. https://doi.org/10.1016/j.cropro.2011.11.009 doi: 10.1016/j.cropro.2011.11.009

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