Research Article
BibTex RIS Cite

A New Prey-Predator Modeling with Experimental Data for Biological Control of Couch Grass

Year 2025, Volume: 8 Issue: 4, 225 - 234, 30.12.2025

Abstract

In this study, a new three-dimensional predator–prey model is developed, consisting of one prey and two predators. The aim of the model is to reduce or eliminate the target weed species through natural means. One of the novelties of the study is that one of the predators is the radish plant, and the model parameters are obtained from real data. The primary objective of the study is to investigate the natural suppression or eradication of the harmful weed species using the constructed predator–prey framework, supported by numerical simulations. Through these simulations, the interactions between two populations are examined by varying the initial planting rates/numbers of radish and couch grass, and the dynamic changes of the populations over time are analyzed. Furthermore, the model and its solutions are interpreted within the context of mathematical biology. The equilibrium points are determined, followed by numerical solutions of the three-dimensional system, and corresponding simulations are performed. The numerical simulations are carried out using the fourth-order Runge–Kutta method.

References

  • [1] A.F. Naser, M.B. Mahdi, F.H. Meqtoof and H.A. Etih, Modelling trip distribution using the gravity model and Fratar's method, Math. Modelling Eng. Probl., 8(2) (2021), 230–236. $ \href{ https://doi.org/10.18280/mmep.080209}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85105801178?origin=resultslist}{\mbox{[Scopus]}} $
  • [2] A. Elmas, G. Akyüz, A. Bergal, M. Andaç and Ö. Andaç, Mathematical modelling of drug release, Res. Eng. Struct. Mater., 6(4) (2020). $ \href{http://doi.org/10.17515/resm2020.178na0122}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85097436895?origin=resultslist}{\mbox{[Scopus]}} $
  • [3] D. Zos-Kior, O. Shkurupii, I. Hnatenko, O. Fedirets, I. Shulzhenko and V. Rubezhanska, Modeling of the investment program formation process of ecological management of the agrarian cluster, Eur. J. Sustain. Dev., 10(1) (2021), 571–571. $ \href{https://doi.org/10.14207/ejsd.2021.v10n1p571}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85101101382?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000625205000016}{\mbox{[Web of Science]}} $
  • [4] R. Sameni, Mathematical modeling of epidemic diseases; a case study of the COVID-19 coronavirus, arXiv preprint (2020). $ \href{https://doi.org/10.48550/arXiv.2003.11371}{\mbox{[CrossRef]}} $
  • [5] S. Cai, Z. Mao, Z. Wang, M. Yin and G.E. Karniadakis, Physics-informed neural networks (PINNs) for fluid mechanics: A review, Acta Mech. Sin., 37(12) (2021), 1727–1738. $ \href{https://doi.org/10.1007/s10409-021-01148-1}{\mbox{[CrossRef]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000745567800001}{\mbox{[Web of Science]}} $
  • [6] A. Deutsch, L. Brusch, H. Byrne, G. De Vries and H. Herzel, Mathematical Modeling of Biological Systems, Vol. I, (2007). $ \href{https://doi.org/10.1007/978-0-8176-4558-8}{\mbox{[CrossRef]}} $
  • [7] H. Joshi and M. Yavuz, A novel fractional-order model and analysis of cancer-immune system interaction in an avascular environment with an efficient control mechanism, J. Comput. Appl. Math., 473 (2026), 116888. $ \href{https://doi.org/10.1016/j.cam.2025.116888}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/105009914774?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:001532762100001}{\mbox{[Web of Science]}} $
  • [8] H. Joshi and M. Yavuz, Chaotic dynamics of a cancer model with singular and non-singular kernel, Discrete Contin. Dyn. Syst.-S, 18(5) (2025), 1416–1439. $ \href{https://doi.org/10.3934/dcdss.2025016}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85219030849?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:001515645300003}{\mbox{[Web of Science]}} $
  • [9] R. Geetha, M.Y. Muthurathinam Sivabalan, T. Megala and M.S. Pradeep, Biodiversity and ecosystem stability in a four-species prey-predator food chain with meta-communities, AIMS Bioeng., 12(4) (2025), 530–555. $ \href{https://doi.org/10.3934/bioeng.2025025}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/105021976631?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:001610718600001}{\mbox{[Web of Science]}} $
  • [10] A. Ebrahimzadeh, A. Jajarmi and M. Yavuz, Fractional optimal control of anthroponotic cutaneous leishmaniasis with behavioral and epidemiological extensions, Math. Comput. Appl., 30(6) (2025), 122. $ \href{https://doi.org/10.3390/mca30060122}{\mbox{[CrossRef]}} $
  • [11] D.J.T. Nguefack, Mathematical modeling of schistosomiasis transmission using reaction-diffusion equations, Fundam. J. Math. Appl., 7(2) (2024), 118–136. $ \href{https://doi.org/10.33401/fujma.1412958}{\mbox{[CrossRef]}} $
  • [12] H. Nasir and A.A.M. Daud, Global dynamics and sensitivity analysis of a diabetic population model with two-time delays, Math. Modell. Numer. Simul. Appl., 5(1) (2025), 198–233. $ \href{https://doi.org/10.53391/mmnsa.1545744}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/105002480261?origin=resultslist}{\mbox{[Scopus]}} $
  • [13] H. Sahebi Fard, E. Dastranj and A. Jajarmi, A novel fractional stochastic model equipped with Y-Caputo fractional derivative in a financial market, Math. Methods Appl. Sci., 48(9) (2025), 9653–9661. $ \href{https://doi.org/10.1002/mma.10832}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/105000367360?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:001446207500001}{\mbox{[Web of Science]}} $
  • [14] M.S. Akber, M.M. Hasan, M.H. Kabir and M.O. Gani, Population projection of Southeast Asia with a time-delay logistic model, Bull. Biomathematics, 3(1) (2025), 1–20. $ \href{https://doi.org/10.59292/bulletinbiomath.1707070}{\mbox{[CrossRef]}} $
  • [15] F. Evirgen, E. Uçar, S. Uçar and N. Özdemir, Modelling influenza A disease dynamics under Caputo-Fabrizio fractional derivative with distinct contact rates, Math. Modell. Numer. Simul. Appl., 3(1) (2023), 58–73. $ \href{https://doi.org/10.53391/mmnsa.1274004}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85160237466?origin=resultslist}{\mbox{[Scopus]}} $
  • [16] S. Uçar, Analysis of hepatitis B disease with fractal–fractional Caputo derivative using real data from Turkey, J. Comput. Appl. Math., 419 (2023), 114692. $ \href{https://doi.org/10.1016/j.cam.2022.114692}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85139045577?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000863214300003}{\mbox{[Web of Science]}} $
  • [17] K. Türk and M. Gümüş, Stability analysis and simulations of the discrete-time cancer epidemic model, Fundam. J. Math. Appl., 8(3) (2025), 148–160. $ \href{https://doi.org/10.33401/fujma.1696954}{\mbox{[CrossRef]}} $
  • [18] M. Yavuz and N. Sene, Stability analysis and numerical computation of the fractional predator–prey model with the harvesting rate, Fractal Fract., 4(3) (2020), 35. $ \href{https://doi.org/10.3390/fractalfract4030035}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85089851294?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000578902500001}{\mbox{[Web of Science]}} $
  • [19] D. Ghosh, P.K. Santra and G.S. Mahapatra, A three-component prey-predator system with interval number, Math. Modell. Numer. Simul. Appl., 3(1) (2023), 1–16. $ \href{https://doi.org/10.53391/mmnsa.1273908}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85172871424?origin=resultslist}{\mbox{[Scopus]}} $
  • [20] P.A. Naik, Z. Eskandari, H.E. Shahkari and K. M. Owolabi, Bifurcation analysis of a discrete-time prey-predator model, Bull. Biomathematics, 1(2) (2023), 111–123. $\href{https://doi.org/10.59292/bulletinbiomath.2023006}{\mbox{[CrossRef]}} $
  • [21] A. Chatterjee and S. Pal, A predator-prey model for the optimal control of fish harvesting through the imposition of a tax, Int. J. Optim. Control: Theor. Appl., 13(1) (2023), 68–80. $ \href{https://doi.org/10.11121/ijocta.2023.1218}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85148606342?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000925933300002}{\mbox{[Web of Science]}} $
  • [22] P.A. Naik, Z. Eskandari, M. Yavuz and J. Zu, Complex dynamics of a discrete-time Bazykin–Berezovskaya prey-predator model with a strong Allee effect, J. Comput. Appl. Math., 413 (2022), 114401. $ \href{https://doi.org/10.1016/j.cam.2022.114401}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85130916387?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000811819000004}{\mbox{[Web of Science]}} $
  • [23] M.M. Maclean, D. J. Carslake, M. R. Evans, S. Townley and D. J. Hodgson, The usefulness of sensitivity analysis for predicting the effects of cat predation on the population dynamics of their avian prey, Ibis, 150 (2008), 100–113. $ \href{https://doi.org/10.1111/j.1474-919X.2008.00864.x}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/50049092729?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000259108700009}{\mbox{[Web of Science]}} $
  • [24] J. Danane, M. Yavuz and M. Yıldız, Stochastic modeling of three-species prey–predator model driven by Levy jump with mixed Holling-II and Beddington–DeAngelis functional responses, Fractal Fract., 7(10) (2023), 751. $ \href{https://doi.org/10.3390/fractalfract7100751}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85175162646?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:001095309300001}{\mbox{[Web of Science]}} $
  • [25] H. Merdan and O. Duman, On the stability analysis of a general discrete-time population model involving predation and Allee effects, Chaos Solitons Fract., 40(3) (2009), 1169–1175. $ \href{https://doi.org/10.1016/j.chaos.2007.08.081}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/65349142508?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000266418000015}{\mbox{[Web of Science]}} $
  • [26] Z. Eskandari, P.A. Naik and M. Yavuz, Dynamical behaviors of a discrete-time prey-predator model with harvesting effect on the predator, J. Appl. Anal. Comput., 14 (2024), 283–297. $ \href{https://doi.org/10.11948/20230212}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85178871273?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:001157081500001}{\mbox{[Web of Science]}} $
  • [27] T.K. Aydın and S.S. Durduran, Determining future scenarios of urban areas with cellular automata/Markov chain model method; example of Ereğli District Konya–Türkiye (2030–2040), Earth Sci. Inform., 17(3) (2024), 2679–2697. $ \href{https://doi.org/10.1007/s12145-024-01283-w}{\mbox{[CrossRef]}} $
  • [28] G.Q. Sun, L. Li, J. Li, C. Liu, Y.P. Wu, S. Gao and G. L. Feng, Impacts of climate change on vegetation pattern: Mathematical modeling and data analysis, Phys. Life Rev., 43 (2022), 239–270. $ \href{https://doi.org/10.1016/j.plrev.2022.09.005}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85140775097?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000880139900003}{\mbox{[Web of Science]}} $
  • [29] M.S. Habibullah, B.H. Din, S.H. Tan and H. Zahid, Impact of climate change on biodiversity loss: Global evidence, Environ. Sci. Pollut. Res., 29(1) (2022), 1073–1086. $ \href{https://doi.org/10.1007/s11356-021-15702-8}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85111841075?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000680335200002}{\mbox{[Web of Science]}} $
  • [30] L.N. Guo, C. She, D.B. Kong, S.L. Yan, Y.P. Xu, M. Khayatnezhad and F. Gholinia, Prediction of the effects of climate change on hydroelectric generation, electricity demand, and emissions of greenhouse gases under climatic scenarios and optimized ANN model, Energy Rep., 7 (2021), 5431–5445. $ \href{https://doi.org/10.1016/j.egyr.2021.08.134}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85119001410?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000701734000013}{\mbox{[Web of Science]}} $
  • [31] B. Ringselle, B. De Cauwer, J. Salonen and J. Soukup, A review of non-chemical management of couch grass (Elymus repens), Agronomy, 10(8) (2020), 1178. $ \href{https://doi.org/10.3390/agronomy10081178}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85090881899?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000568165300001}{\mbox{[Web of Science]}} $
  • [32] S. Okrushko, Allelopathic effect of couch grass (Elymus repens L.) on germination of common wheat seeds, Zemdirbyste, 109(4) (2022), 323–328. $ \href{https://doi.org/10.13080/z-a.2022.109.041}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85145593785?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000907595900005}{\mbox{[Web of Science]}} $
  • [33] K.S. Torresen, B. Ringselle, L.O. Brandsaeter and J. Salonen, Autumn mowing and pelargonic acid can suppress Elymus repens abundance especially when combined with increased crop competition, Julius-K¨uhn-Archiv, (468) (2022). $\href{https://www.openagrar.de/servlets/MCRFileNodeServlet/openagrar_derivate_00044744/JKA_468_15.pdf}{\mbox{[CrossRef]}} $
  • [34] S. Mutlu, Predator-prey models and investigation of predator-prey Caputo fractional derivative, M.Sc. Thesis, Sakarya University, Sakarya, Türkiye, (2022). $ \href{https://acikerisim.sakarya.edu.tr/handle/20.500.12619/98773}{\mbox{[Web]}} $
  • [35] S. Yıldız, Local stability and bifurcation analysis of a Lotka–Volterra type discrete predator-prey model including refuge effect, M.Sc. Thesis, TOBB ETU, (2021). $ \href{https://koha.etu.edu.tr/cgi-bin/koha/opac-detail.pl?biblionumber=200444367&shelfbrowse_itemnumber=489195}{\mbox{[Web]}} $
  • [36] M.J. Uddin, S. M. S. Rana, S. Işık and F. Kangalgil, On the qualitative study of a discrete fractional order prey–predator model with the effects of harvesting on predator population, Chaos Solitons Fract., 175 (2023), 113932. $\href{https://doi.org/10.1016/j.chaos.2023.113932}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85171566105?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:001068779300001}{\mbox{[Web of Science]}} $
  • [37] Z. Ali, F. Rabiei and K. Hosseini, A fractal–fractional-order modified predator–prey mathematical model with immigrations, Math. Comput. Simul., 207 (2023), 466–481. $\href{https://doi.org/10.1016/j.matcom.2023.01.006}{\mbox{[CrossRef]}} \href{https://www.scopus.com/pages/publications/85146950654?origin=resultslist}{\mbox{[Scopus]}} \href{https://www.webofscience.com/wos/woscc/full-record/WOS:000924564800001}{\mbox{[Web of Science]}} $
There are 37 citations in total.

Details

Primary Language English
Subjects Biological Mathematics, Dynamical Systems in Applications
Journal Section Research Article
Authors

Mehmet Yavuz 0000-0002-3966-6518

Merve Başaçık This is me 0000-0000-0000-0000

Ayşe Nur Yaman This is me 0000-0000-0000-0001

Submission Date October 19, 2025
Acceptance Date December 29, 2025
Publication Date December 30, 2025
Published in Issue Year 2025 Volume: 8 Issue: 4

Cite

APA Yavuz, M., Başaçık, M., & Yaman, A. N. (2025). A New Prey-Predator Modeling with Experimental Data for Biological Control of Couch Grass. Fundamental Journal of Mathematics and Applications, 8(4), 225-234. https://doi.org/10.33401/fujma.1806523
AMA Yavuz M, Başaçık M, Yaman AN. A New Prey-Predator Modeling with Experimental Data for Biological Control of Couch Grass. Fundam. J. Math. Appl. December 2025;8(4):225-234. doi:10.33401/fujma.1806523
Chicago Yavuz, Mehmet, Merve Başaçık, and Ayşe Nur Yaman. “A New Prey-Predator Modeling With Experimental Data for Biological Control of Couch Grass”. Fundamental Journal of Mathematics and Applications 8, no. 4 (December 2025): 225-34. https://doi.org/10.33401/fujma.1806523.
EndNote Yavuz M, Başaçık M, Yaman AN (December 1, 2025) A New Prey-Predator Modeling with Experimental Data for Biological Control of Couch Grass. Fundamental Journal of Mathematics and Applications 8 4 225–234.
IEEE M. Yavuz, M. Başaçık, and A. N. Yaman, “A New Prey-Predator Modeling with Experimental Data for Biological Control of Couch Grass”, Fundam. J. Math. Appl., vol. 8, no. 4, pp. 225–234, 2025, doi: 10.33401/fujma.1806523.
ISNAD Yavuz, Mehmet et al. “A New Prey-Predator Modeling With Experimental Data for Biological Control of Couch Grass”. Fundamental Journal of Mathematics and Applications 8/4 (December2025), 225-234. https://doi.org/10.33401/fujma.1806523.
JAMA Yavuz M, Başaçık M, Yaman AN. A New Prey-Predator Modeling with Experimental Data for Biological Control of Couch Grass. Fundam. J. Math. Appl. 2025;8:225–234.
MLA Yavuz, Mehmet et al. “A New Prey-Predator Modeling With Experimental Data for Biological Control of Couch Grass”. Fundamental Journal of Mathematics and Applications, vol. 8, no. 4, 2025, pp. 225-34, doi:10.33401/fujma.1806523.
Vancouver Yavuz M, Başaçık M, Yaman AN. A New Prey-Predator Modeling with Experimental Data for Biological Control of Couch Grass. Fundam. J. Math. Appl. 2025;8(4):225-34.

35253       35256

35258

Creative Commons License
The published articles in Fundamental Journal of Mathematics and Applications are licensed under a

28893   28892   28894   28895   28896   28897