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Bio-control of Pests in Tea: Effect of Environmental Fluctuation

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  • Seth Anandram Jaipuria College, Kolkata
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The method of bio-control of pests has been emerging as one of the prime method to control tea-pests and the pests doing damage in other plants. It has got a valiant potential to combat with the evil activities of pests. In this paper, we have taken into account the influence of uncertain environmental fluctuation by perturbing the environmental parameters by White noises characterized by Gaussian distribution with mean zero and unit spectral density. The criterion for non-equilibrium fluctuation and stability are derived. The implications of mathematical findings are discussed.
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Int. J. Appl. Comput. Math (2019) 5:87
https://doi.org/10.1007/s40819-019-0666-3
ORIGINAL PAPER
Bio-control of Pests in Tea: Effect of Environmental
Fluctuation
A. K. Pal1
Published online: 30 May 2019
© Springer Nature India Private Limited 2019
Abstract
The method of bio-control of pests has been emerging as one of the prime method to con-
trol tea-pests and the pests doing damage in other plants. It has got a valiant potential to
combat with the evil activities of pests. In this paper, we have taken into account the influ-
ence of uncertain environmental fluctuation by perturbing the environmental parameters by
White noises characterized by Gaussian distribution with mean zero and unit spectral density.
The criterion for non-equilibrium fluctuation and stability are derived. The implications of
mathematical findings are discussed.
Keywords Time-delay ·Stability ·White noise ·Spectral density
Introduction
It is globally true that pests have been a major threat towards various crops and saplings.
Chemical experts have taken it as a real challenge to fixed out effective avenues to eradicate
pests. But it has often been creating problems as it getting mixed with the other components of
the atmosphere and thus creating fatal pollutants. So pests should be demolished keeping the
sanctity intact. In that case bio-control of pests which has already been discussed in previous
paper by using a tri-trophic model can be enhanced further [12].
As per the favourable climate condition tea leafs requires a warm and humid condition and
a soothing temperature between 18.33and 29.44,well distributed rain fall (1400 mm. per
annum) and drained sandy loam soil with pH 4.5–5.5. Tea soils is highly acidic and fertility
remains at a low stature. Pests in tea gardens some time appear in tandem, epidemically
in certain years. There various chemicals are executed to diminish that elaborate pest attack
violating the climate and ecological calm. Therefore the environment interest is less bothered
in order to attain large production. Various research work on this factor have revealed the
This article is part of the topical collection “Recent Advances in Mathematics and its Applications” edited by
Santanu Saha Ray.
BA. K. Pal
akpal_2002@yahoo.co.in
1Department of Mathematics, S. A. Jaipuria College, Kolkata 700005, India
123
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Chapter
In the agriculture industry, pest infestation is a significant challenge that is complicated by the nonlinear relationship with environmental factors. Given the effectiveness of machine learning models in simulating such complex nonlinear phenomena, the authors opted to employ them in the modelling of the life cycle of tea pests, which impact several other crops as well. Accordingly, multiple machine learning models were developed to forecast the occurrence of tea pest looper infestations. They utilized data for just two readily available parameters—temperature and rainfall—to investigate whether predictive models of good quality can be created even with limited data, particularly for small tea growers. After analyzing the various models generated, they discovered that neural network models can produce accurate predictions even with a restricted data set. Therefore, they are optimistic that new age technologies such as machine learning can benefit many small farmers in India who lack access to various technologies and, as a result, have limited data.
Article
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Understanding pests and their life cycle are a complex phenomenon due to their nonlinear relation with environmental factors and the interdependence of the environmental factors themselves. We focus our attention on ML (Machine Learning) models due to their ability to simulate non-linear phenomena effectively. We use Stacked Models, that consist of a neural network model (NN) and a time series model (TS) to simulate the life cycle of pests which varies in duration with the seasons. Several Machine Learning models were developed for predicting Helopeltis (tea pest, though impacts several other crops as well) infestation. The idea was not to depend heavily on the good scenario data, but rather which is readily available even for small holders and planters who are not too much reliant on technology to capture the data on pest infestation. Evaluating the various developed models shows that Neural Network models with better accuracies can be developed with real world data from the field. Thus, being suitable for tea gardens without too much reliance on technologies and extensive data capturing processes. This has been possible through special treatment of available data as well as ability of stacked models to provide good results in the solution space with a generally smaller set of data having lesser number of parameters.
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In the present work we have studied a prey-predator model with logistic growth of the prey in the absence of the predator. We have also considered the fear effect and have investigated the impact of fear of the predator on prey when the predator is provided additional food. Functional responses of the predator towards prey and additional food are derived in this text. Death rates of both prey and predator have been considered as stochastic parameters due to the effect of the fluctuating environment. Existence and uniqueness, boundedness and uniform continuity of the global positive solution of the proposed model have been established. The conditions for extinction and persistence of the system have been derived. In the investigation, it is found that environmental noise plays a vital role in extinction as well as in persistence. Our analytical derivations are justified through numerical simulations which show the reliability of the model from the ecological point of view. We have also investigated the impact of intense fear as well as the absence of fear on this model by numerical simulation. Several interesting numerical results have been obtained based on different fear functions.
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In this paper, a prey-predator model with a social activity of prey population has been analyzed. In some ecological situations, the prey-predator interaction occurs only at the outer surface of a herd formed by prey population. To model this phenomenon, the square root of prey density has been used in the functional response. The basic model is formulated with this modified functional response. A steady-state analysis has been performed. Mathematical analysis including effect of time-delay is presented. Numerical computations are carried out to validate the analytical findings. Biological implications of the analytical and numerical findings are discussed critically.
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In this paper, a Leslie-Gower predator-prey model with disease in the predator has been developed. Lesli-Gower model can be treated as an evolutionary version of Lotka-Volterra model as a result of the genetic consequences. The total population has been divided into three classes, namely, prey, susceptible predator and infected predator. We have studied the positivity and boundedness of the solutions of the system. The local and global dynamical behaviours together with sufficient conditions for persistence of the ecosystem near biologically feasible equilibria are thoroughly investigated. The conditions, which guarantee the occurrence of Hopf-bifurcation of the system, are established. Numerical simulations have been performed to validate the analytical results.
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In this work we have studied a predator–prey model where the prey grows logistically in the absence of predator and the functional response of predator towards prey and additional food that are derived in the text. Prey's growth rate and the predator's death rate have been perturbed with Gaussian white noises which has been proved extremely useful to model rapidly fluctuating phenomena. These two parameters are the main terms subject to coupling of a prey–predator pair with its environment Dimentberg (1988). Existence and uniqueness of global positive solution of the system have been established under environmental noise. Then the conditions under which extinction of predator and prey populations occur have been established. In our analysis, it is found that the environmental noise plays an important role in extinction as well as persistence of prey and predator populations. We have also discussed about the persistence of the system under obtained conditions and how the solution of the underlying system is globally attractive in mean. To derive the theorems we have shown the uniform continuous behavior of the solutions. Although we have considered a prey–predator model, the survival of predator population is possible in absence of prey population, since the additional food is provided to predator. But it is found that the extinction of prey population drive predator population to extinction. Our analytical findings are explained through numerical simulation which show the reliability of our model from the ecological point of view. It is shown in numerical simulation that if the effectual food level of additional food which is provided to the predator is high, then the predator dominates the prey population.
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This paper investigates the deterministic and stochastic fluctuations of a predator-prey model. The predator is experienced in hunting two different prey simultaneously. Each prey has logistic growth in the absence of the predator. The rate of experience of the predator in hunting each prey is varied using a simulated dataset. The deterministic and stochastic nature of the dynamics of the system are investigated. Stability analysis is performed, using slight perturbation around the non-zero, interior equilibrium point, to determine where the system loses stability. The variation of the predatory experience parameter causes the system to experience Hopf bifurcations. These stability changes and the addition of stochastic noise are explored using time series graphs. The co-existence and extinction of the populations are affected over time . Keywords: Predator-prey; Stability; Deterministic; Stochastic; Noise