About this Journal Submit a Manuscript Table of Contents
Abstract and Applied Analysis
Volume 2012 (2012), Article ID 428453, 25 pages
http://dx.doi.org/10.1155/2012/428453
Research Article

Dynamical Analysis of Delayed Plant Disease Models with Continuous or Impulsive Cultural Control Strategies

1College of Science, Shandong University of Science and Technology, Qingdao 266510, China
2College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266510, China
3State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

Received 28 December 2011; Accepted 6 February 2012

Academic Editor: Khalida Inayat Noor

Copyright © 2012 Tongqian Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Delayed plant disease mathematical models including continuous cultural control strategy and impulsive cultural control strategy are presented and investigated. Firstly, we consider continuous cultural control strategy in which continuous replanting of healthy plants is taken. The existence and local stability of disease-free equilibrium and positive equilibrium are studied by analyzing the associated characteristic transcendental equation. And then, plant disease model with impulsive replanting of healthy plants is also considered; the sufficient condition under which the infected plant-free periodic solution is globally attritive is obtained. Moreover, permanence of the system is studied. Some numerical simulations are also given to illustrate our results.

1. Introduction

Plant viruses or pathogens are an important constraint to crop production worldwide and cause major production and economic losses in agriculture and forestry. For example, soybean rust (a fungal disease in soybeans) has caused a significant economic loss, and just by removing 20% of the infection, the farmers may benefit with an approximately 11 million-dollar profit [1]. Several plant diseases caused by plant viruses in cassava (Manihot esculenta) and sweet potato (Ipomoea batatas) are among the main constraints to sustainable production of these vegetatively propagated staple food crops in lesser-developed countries [24]. A strain of the virus causing cassava mosaic disease gives rise to losses in Africa [5]. Therefore, people have turned more attention to plant diseases. Several conferences have been held to discuss how to control or prevent plant virus. Therefore, farmers have been evolving practices for controlling plant diseases, which involves a number of dynamic processes such as the growth of plants and the spread of diseases. Recently, the integrated disease management (IDM) which combines biological, cultural, and chemical tactics and so on to reduce the numbers of infected individuals to a tolerable level and aims to minimize losses and maximize returns [6, 7] has been developed gradually. IDM includes four main control strategies for vegetatively propagated plant diseases, which are containing transmission vectors, improving the production of planting material, controlling the crop sanitation through removal of infected plants, and breeding plants for resistance to the virus. Breeding plants for resistance to the virus as an cultural strategy has been widely used into practice [811]. In the system of IDM, mathematical modeling has shown its unique value on describing, analyzing, and predicting plant epidemics [1216]. Meng and Li have investigated vegetatively propagated plant diseases and developed a mathematical model with continuous control strategies and impulsive cultural control strategies [17], which leads to where denote the number of susceptible and infected plants, respectively. is the transmission rate, denotes potentially density dependent, either denotes harvest time or the end of reproductive life time of plants, represents the total rate at which the susceptible plants enter the system, is the removal rate for the infected plants, is the recovery rate of the cured diseased plants, and the infected plants suffer an extra disease-related death with constant rate . In system (1.1), the authors refer to two-control strategy: one is continuous control and the other is impulsive control by implementing periodic replanting of healthy plants or removing infected plants at a critical time. A model for the spread of an infectious disease (involving only susceptibles and infective individuals) transmitted by a vector after an incubation time was proposed by Cooke [18]. This is called the phenomena of time delay. Many authors have directly incorporated time delays in modeling equations, and, as a result, the models take the form of delay differential equations [1923]. Motivated by Meng, we get the following reasonable plant disease models by introducing time delay:

From the point of biology, we only consider system (1.2) and (1.3) in the biological meaning region: . Let where is positive, bounded, and continuous function for . Motivated by the application of systems (1.2) and (1.3) to population dynamics (refer to [24]), we assume that solutions of systems (1.1) satisfy the following initial conditions:

2. Plant Disease Continuous Control for System (1.2)

In this section, we consider system (1.2) with continuous replanting and removing and without impulsive effect. By Smith [25, Theorem  5.2.1] or Zhao and Zou [26], for any , there is a unique solution of system (1.2) with , for any and , for all in its maximal interval of existence.

Define ; then we have Let ; we have Then as . Hence, system (1.2) is uniformly bounded.

Since and denote the number of susceptible and infected plants, respectively, it is easy to observe that system (1.2) has a disease-free of the form , and a unique infection equilibrium provided that we have the following condition: where

2.1. The Stability of the Disease-Free Equilibrium

We may firstly consider the stability of the disease-free equilibria . Let , ; then system (1.2) can be rewritten as the following equivalent system: Thus, the disease-free equilibrium of system (1.2) is transformed into zero equilibrium of system (2.6). Linearizing system (2.6) about the equilibrium (0, 0) yields the following linear system: with characteristic equation: that is,

The stability of trivial solution of system (1.2) depends on the locations of roots of characteristic equation (2.9). When all roots of (2.9) locate in the left half-plane of complex plane, the trivial solution of system (1.2) is stable; otherwise, it is unstable. In the following, we will investigate the distribution of roots of (2.9). Obviously, . Let For (2.10), the root of (2.10) with always has negative real part provided that .

In addition, is a root of (2.10) if and only if satisfies the following equation:

Separating the real and imaginary parts of (2.11) gives the following equations:

Thus we can have Then if , (2.13) has not positive real root, which leads to (2.10) that has not purely imaginary root. By the Rouche Theory, we know that all the roots of (2.9) have always negative real parts. So the equilibrium of system (1.2) is locally asymptotically stable.

Define For system (1.2), we have the following result on stability of the disease-free equilibrium .

Theorem 2.1. For system (1.2), the following statements are true.(i)If , then the disease-free equilibrium of system (1.2) is unstable.(ii)If , then the disease-free equilibrium of system (1.2) is locally asymptotically stable.

2.2. The Stability of the Positive Equilibrium of System (1.2)

In this section, we show that the disease equilibrium is asymptotically stable in the case that time delay is less than the unity; then we have the following theorem.

Theorem 2.2. For system (1.2), if , then the positive equilibrium of system (1.2) is asymptotically stable.

Proof. Under the hypothesis , let , then system (1.2) can be rewritten as the following equivalent system:
Thus, the positive equilibrium of system (1.2) is transformed into zero equilibrium of system (2.15). Linearizing system (2.15) about the equilibrium (0, 0) yields the following linear system: with characteristic equation: The stability of trivial solution of system (1.2) depends on the locations of roots of characteristic equation (2.17). For the sake of simplicity, let Then (2.17) can be briefly denoted as the following equation: For (2.19), we can claim that the two roots of (2.19) have always negative real parts. We will prove it in the following two steps.Step 1. If , (2.19) can be simplified as Note that Therefore, the two roots of (2.19) with have always negative real parts.Step 2. If is a root of (2.15) if and only if satisfies the following equation:
Separating the real and imaginary parts of (2.22) gives the following equations: One can obtain We can easily see that So (2.24) has not positive real root, which leads to (2.19) that has not purely imaginary root. By the Rouche Theory, we know that all the roots of (2.19) have always negative real parts. So the equilibrium of system (1.2) is asymptotically stable. The proof is complete.

3. Plant Disease Impulsive Control for System (1.3)

3.1. Boundedness

Let the initial data be . Then, one can easily prove that the solutions of system (1.3) are positive for all . Now, let . We calculate the upper right derivative of along with a solution of system (1.3) with : Since , one can deduce that where . We consider the following impulse differential inequalities: According to impulse differential inequalities theory, we get as .

So is uniformly ultimately bounded. Hence, by the definition of , for any , there exists a constant such that and for each solution of (1.3) with being large enough.

3.2. Global Attractivity of the Disease-Free Periodic Solution of System (1.3)

In the following, we shall prove that the disease-free periodic is stable if it exists. For this purpose, we give firstly some basic properties of the following subsystem: We can find a unique positive periodic solution , which is globally asymptotically stable by using stroboscopic map. As a consequence, system (3.5) always has a disease-free periodic solution . Now, we give the conditions which assure the global attractivity of disease-free periodic solution of the system (1.3).

Denote

Theorem 3.1. The disease-free periodic solution of system (1.3) is globally attractive provided that

Proof. Let be any solution of system (1.3). From the first equation of system (1.3), it follows that , for ; then we consider the following impulse differential system: Obviously, system (3.8) has a globally asymptotically stable positive periodic solution: By the comparison theorem in impulsive differential equation, for any sufficiently small positive , there exists an integer such that Therefore, from the second equation of system (1.3), we have Now we consider the following comparison equation: Since , we have We may choose three sufficiently small positive constants such that According to the theory of delay differential equation [24], we obtain that . By impulsive comparison theorem, we have with being large enough. Therefore, we obtain that .
Then for a sufficiently small and all being large enough, we have . Without loss of generality, we may assume as . From the first equation of system (1.3), we have Consider the following comparison system: Then, system (3.16) has a positive periodic solution: which is globally asymptotically stable. Thus, for a sufficiently small , when is large enough, we have From the first equation of system (1.3), we also have Consider the following comparison system: System (3.20) has a globally asymptotically stable positive periodic solution: Thus, for a sufficiently small , when is large enough, we have Combining (3.18) with (3.22), we obtain Let ; we have , which implies . The proof is completed.

3.3. Permanence of the System (1.3)

Definition 3.2. System (1.3) is said to be permanent if there exist constants (independent of initial value) and a finite time such that for every positive solution with initial conditions (1.3) satisfies for all . Here may depend on the initial condition.

Denote

Lemma 3.3. If , then there exists a positive constant such that

Proof. Define Calculating the derivative of along with the solution of (1.3), we can get for .
Since , then . Note that and .
Solving the aforementioned inequality, we can have that
We can choose being small enough such that For any positive constant , we claim that the inequality cannot hold for all . Otherwise, there exists a positive constant such that for all . From the first equation of (1.3), we have Similarly, we know that there exists such , for that Then, by (3.30), we have that, for , Let We show that for all . Otherwise, there exists a nonnegative constant such that for and . Thus from the second equation of (1.3) and (3.30), we easily see that which is a contradiction. Hence we get that for all . Therefore, for all , we have which implies as . This is a contradiction to for being large enough. Therefore, for any positive constant , the inequality cannot hold for all .
On the one hand, if holds true for all being large enough, then our aim is obtained. Otherwise, is oscillatory about .
Let In the following, we will show that for being large enough. There exist two positive constants such that Since is continuous and bounded and is not effected by impulses, we conclude that is uniformly continuous. Hence there exists a constant (with and is independent of the choice of ) such that for all .
If , our aim is obtained.
If , from the second equation of (1.3), we have that for . Then we have for since . It is clear that for .
If , then we have that for . Next, we will show that for . In fact, if not, there exists a such that for and . When is large enough, from (3.37) and the first equation of (1.3), we have Similarly, we know that there exists , for that Then the inequality holds true for . On the other hand, we have from the second equation of (1.3) that This is a contradiction to . Therefore, for .
Since this kind of interval is arbitrarily chosen, we get that for being large enough. In view of our arguments previously, the choice of is independent of the positive solution of (1.3) which satisfies that for sufficiently large . This completes the proof.

Theorem 3.4. If , the system (1.3) is permanent; that is, there exist two positive constants , such that for being large enough.

Proof. Suppose that is any positive solution of system (1.3). From the first and third equations of (1.3), we have that Similarly, we can get such large enough and small enough that
Set
Then is a bounded compact region which has positive distance from coordinate axes. By Lemma 3.3, one obtains that every solution of system (1.3) eventually enters and remains in the region . The proof of Theorem 3.4 is completed.

4. Numerical Simulation and Conclusion

To verify the theoretical results obtained in this paper, we will give some numerical simulations.

Under the continuous control strategy, we consider the hypothetical set of parameter values as , with . Through calculation, we know and .(i)If , then according to Theorem 2.1, we know the disease-free equilibrium of system (1.2) is local stable for this case (see Figures 1, 2, and 3). (ii)If , through calculation, we know . Then according to Theorem 2.2, the positive equilibrium of system (1.2) is local stable for this case (see Figures 4, 5, and 6).

428453.fig.001
Figure 1: Time series of with different initial values and parameters .
428453.fig.002
Figure 2: Time series of with different initial values and parameters .
428453.fig.003
Figure 3: Phase diagram of with different initial values and parameters .
428453.fig.004
Figure 4: Time series of with different initial values and parameters .
428453.fig.005
Figure 5: Time series of with different initial values and parameters .
428453.fig.006
Figure 6: Phase diagram of with different initial values and parameters .

Its epidemiological implication is that if time delay is greater than some key value , then diseased plants will disappear in local scope. In contrast, if time delay is less than some key value , then susceptible plants and diseased plants will coexist in local scope.

Under the impulsive control strategy, We consider the hypothetical set of parameter values as with .(i)We consider the susceptible plants rate . Through calculation, we have . Then according to Theorem 3.4, we know that system (1.3) is permanence, for this case (see Figures 10, 11, and 12).(ii) If we decrease the susceptible plants rate to 0.3, through calculation, we know . Then according to Theorem 3.1, the disease-free periodic solution of system is globally attractive, for this case (see Figures 7, 8, and 9).

428453.fig.007
Figure 7: Time series of with parameters , .
428453.fig.008
Figure 8: Time series of with parameters , .
428453.fig.009
Figure 9: Phase diagram of and with parameters , .
428453.fig.0010
Figure 10: Time series of with parameters , .
428453.fig.0011
Figure 11: Time series of with parameters , .
428453.fig.0012
Figure 12: Phase diagram of and with parameters , .

We have other hypothetical parameter values under the impulsive control strategy as . with (i)We consider the susceptible plants rate . Through calculation, we have . Then according to Theorem 3.4, we know that system (1.3) is permanence, for this case (see Figures 16, 17, and 18 ).(ii) If we decrease the susceptible plants rate to 0.4, through calculation, we know . Then according to Theorem 3.1, the disease-free periodic solution of system is globally attractive, for this case (see Figures 13, 14, and 15).

428453.fig.0013
Figure 13: Time series of with parameters , .
428453.fig.0014
Figure 14: Time series of with parameters , .
428453.fig.0015
Figure 15: Phase diagram of and with parameters , .
428453.fig.0016
Figure 16: Time series of with parameters , .
428453.fig.0017
Figure 17: Time series of with parameters , .
428453.fig.0018
Figure 18: Phase diagram of and with parameters , .

Its epidemiological implication is that we took such a strategy by improving planting susceptible plants in practice; as a result, if the susceptible plants rate is greater than some key value , both susceptible plants and diseased plants will coexist. In contrast, if we decrease the susceptible plants rate and make it less than some key value , then diseased plants will die out at length. In a word, we find that the impulse plants rate has played a very important role in the actual plant epidemic prevention.

In this paper, delay SIS plant epidemic model is constructed and investigated. We proposed two different control strategies in the model. Our primary results are to compare the difference between the two control methods. Firstly, we consider continuous cultural control strategy by continuous replanting of healthy plants. We come to the conclusion that if , then diseased plants will disappear in local scope where . And if , then diseased plants will exist for a long time in local scope. Secondly, impulsive control strategy of plant disease model is considered; in this case, we get that if , then diseased plants will disappear finally where . And if , then diseased plants will exist for a long time where . We think that our results will offer help to the actual plant infectious disease management.

References

  1. M. J. Roberts, D. Schimmelpfennig, E. Ashley, M. Livingston, M. Ash, and U. Vasavada, “The value of plant disease early-warning systems: a case study of USDA’s soybean rust coordinated framework,” Economic Research Service 18, United States Department of Agriculture, 2006.
  2. J. M. Thresh and R. J. Cooter, “Strategies for controlling cassava mosaic disease in Africa,” Plant Pathology, vol. 54, pp. 587–614, 2005.
  3. J. Dubern, “Transmission of African cassava mosaic geminivirus by the whitefly (Bemisia tabaci),” Tropical Science, vol. 34, pp. 82–91, 1994.
  4. R. W. Gibson, V. Aritua, E. Byamukama, I. Mpembe, and J. Kayongo, “Control strategies for sweet potato virus disease in Africa,” Virus Research, vol. 100, no. 1, pp. 115–122, 2004. View at Publisher · View at Google Scholar
  5. R. W. Gibson, J. P. Legg, and G. W. Otim-Nape, “Unusually severe symptoms are a characteristic of the current epidemic of mosaic virus disease of cassava in Uganda,” Annals of Applied Biology, vol. 128, no. 3, pp. 479–490, 1996.
  6. R. A. C. Jones, “Determining threshold levels for seed-borne virus infection in seed stocks,” Virus Research, vol. 71, no. 1-2, pp. 171–183, 2000. View at Publisher · View at Google Scholar
  7. R. A. C. Jones, “Using epidemiological information to develop effective integrated virus disease management strategies,” Virus Research, vol. 100, no. 1, pp. 5–30, 2004. View at Publisher · View at Google Scholar
  8. F. Van Den Bosch, M. J. Jeger, and C. A. Gilligan, “Disease control and its selection for damaging plant virus strains in vegetatively propagated staple food crops; a theoretical assessment,” Proceedings of the Royal Society B, vol. 274, no. 1606, pp. 11–18, 2007. View at Publisher · View at Google Scholar
  9. F. van den Bosch and A. de Roos, “The dynamics of infectious diseases in orchards with roguing and replanting as control strategy,” Journal of Mathematical Biology, vol. 35, no. 2, pp. 129–157, 1996. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  10. M. S. Suen and M. J. Jeger, “An analytical model of plant virus disease dynamics with roguing and replanting,” Journal of Applied Ecology, vol. 31, no. 3, pp. 413–427, 1994.
  11. J. C. Zadoks and R. D. Schein, Epidemiology and Plant Disease Management, Oxford University, New York, NY, USA, 1979.
  12. S. Sankaran, A. Mishra, R. Ehsani, and C. Davis, “A review of advanced techniques for detecting plant diseases,” Computers and Electronics in Agriculture, vol. 72, pp. 1–13, 2010.
  13. S. Fishman, R. Marcus, H. Talpaz, et al., “Epidemiological and economic models for the spread and control of citrus tristeza virus disease,” Phytoparasitica, vol. 11, pp. 39–49, 1983.
  14. J. Holt and T. C. B. Chancellor, “A model of plant virus disease epidemics in asynchronously-planted cropping systems,” Plant Pathology, vol. 46, no. 4, pp. 490–501, 1997.
  15. J. E. van der Plank, Plant Diseases: Epidemics and Control, Wiley, New York, NY, USA, 1963.
  16. S. Tang, Y. Xiao, and R. A. Cheke, “Dynamical analysis of plant disease models with cultural control strategies and economic thresholds,” Mathematics and Computers in Simulation, vol. 80, no. 5, pp. 894–921, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  17. X. Meng and Z. Li, “The dynamics of plant disease models with continuous and impulsive cultural control strategies,” Journal of Theoretical Biology, vol. 266, no. 1, pp. 29–40, 2010. View at Publisher · View at Google Scholar
  18. K. L. Cooke, “Stability analysis for a vector disease model,” The Rocky Mountain Journal of Mathematics, vol. 9, no. 1, pp. 31–42, 1979. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  19. J. J. Jiao and L. S. Chen, “Global attractivity of a stage-structure variable coefficients predator-prey system with time delay and impulsive perturbations on predators,” International Journal of Biomathematics, vol. 1, no. 2, pp. 197–208, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  20. S. Gao, Z. Teng, and D. Xie, “The effects of pulse vaccination on SEIR model with two time delays,” Applied Mathematics and Computation, vol. 201, no. 1-2, pp. 282–292, 2008. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  21. P. Yongzhen, L. Changguo, and C. Lansun, “Continuous and impulsive harvesting strategies in a stage-structured predator-prey model with time delay,” Mathematics and Computers in Simulation, vol. 79, no. 10, pp. 2994–3008, 2009. View at Publisher · View at Google Scholar · View at Zentralblatt MATH
  22. S. Gao, L. Chen, J. J. Nieto, and A. Torres, “Analysis of a delayed epidemic model with pulse vaccination and saturation incidence,” Vaccine, vol. 24, no. 35-36, pp. 6037–6045, 2006. View at Publisher · View at Google Scholar
  23. T. Zhang, X. Meng, and Y. Song, “The dynamics of a high-dimensional delayed pest management model with impulsive pesticide input and harvesting prey at different fixed moments,” Nonlinear Dynamics, vol. 64, no. 1-2, pp. 1–12, 2011. View at Publisher · View at Google Scholar
  24. Y. Kuang, Delay Differential Equations with Applications in Population Dynamics, vol. 191, Academic Press, Boston, Mass, USA, 1993.
  25. H. L. Smith, Monotone Dynamical Systems, vol. 41, American Mathematical Society, Providence, RI, USA, 1995.
  26. X.-Q. Zhao and X. Zou, “Threshold dynamics in a delayed SIS epidemic model,” Journal of Mathematical Analysis and Applications, vol. 257, no. 2, pp. 282–291, 2001. View at Publisher · View at Google Scholar · View at Zentralblatt MATH