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| Developing and Analysing Pest-natural Enemy Systems with IPM Strategies |
| Prof. Sanyi Tang* |
| Prof. Sanyi Tang, College of Mathematics and Information Science, Shaanxi Normal University Xi’an- 710062, P.R. China |
| *Corresponding author: |
Prof. Sanyi Tang
College of Mathematics and
Information Science
Shaanxi Normal University Xi’an- 710062, P.R. China
E-mail: sytang@snnu.edu.cn |
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| Received March 28, 2012; Accepted March 28, 2012; Published March 30, 2012 |
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| Citation: Tang S (2012) Developing and Analysing Pest-natural Enemy Systems
with IPM Strategies. J Biofertil Biopestici 3:e101. doi:10.4172/2155-6202.1000e101 |
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| Copyright: © 2012 Tang S. This is an open-access article distributed under the
terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and
source are credited. |
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| Editorial |
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| Integrated Pest Management (IPM) is an effective, long term,
environmentally sensitive approach to pest management. IPM
relies on a combination of biological, cultural, and chemical tactics
that reduce pests to tolerable levels by augmenting natural enemies,
spraying pesticides, trapping or harvesting the pests when they reach
an Economic Threshold (ET, Figure 1). ET is an important concept in
IPM which is usually defined as the critical density of pests in the field
when control actions must be taken to prevent the economic injury
level (EIL, Figure 1) from being reached and exceeded. The EIL is
defined as the lowest pest population density that will cause economic
damage. |
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Figure 1:The IPM programme should be initiated at the level of ET in order to
prevent the density of the pest population from reaching EIL due to a delayed
response and uncertain factors. |
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| Successful IPM control programmes depend on many factors. For
example: factors affecting the population dynamics of the wasp Encarsia
formosa and the whitefly Trialeuroides vaporariorum in greenhouse
vegetable systems include host parasitoid ratios, the starting density and
age structure of whitefly populations at the time of the first parasitoid
releases, levels of host-feeding and parasitism, temperature, and the
host plant. Mathematical models can help us to clarify and predict the
effects of such factors on the stability of pest–natural enemy systems
within an IPM control programme, to evaluate the effectivity of IPM
and may tell us when the density of the pest population reaches the ET
and control should be applied. |
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| The discrete nature of human actions and possible exogenous
effects leading to pest and natural enemy population densities
changing very rapidly can be taken into account by impulsive (hybrid)
differential equations and non-smooth dynamic systems. For instance,
impulsive reduction of the pest population density of a given species is
possible after its partial destruction by trapping or by poisoning with
chemicals. An impulsive increase of a controlling predator or parasitoid
population density is possible by artificial breeding and releases. In
order to develop novel and more realistic models, the following topics
should be taken into account. |
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| Residual Effects of Pesticides |
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| If chemical pesticides have a short residual effect on their target,
repeated application is often required to suppress a pest, which can
cause undesirable changes such as pesticide resistance. Meanwhile,
biological pesticides are generally more environmentally friendly,
but often lack residual effects and can be strongly influenced by
environmental factors. For example, some insecticides used against
the bed bug Cimex lectularius can have residual effects 1 week to 4
months after application, micro-encapsulated formulations of the
pyrethroid lambda-cyhalothrin can be effective against vectors of
malaria Anopheles spp. nine months after indoor sprays on walls and
some compounds are active against termites for years. |
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| Delayed Responses to Pesticide Applications |
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| In practice, many pesticides not only have long-term residual
effects, but also both pest and natural enemy populations may have
delayed responses to pesticide applications, which suggests that pests do
not succumb to pesticides until after a delay. In addition, biopesticides
are increasingly being used which also have such delayed effects. For
instance, the mycopesticide Metarhizium acridum is effective against
grasshoppers and locusts but does not kill them until 1-4 weeks
after being sprayed, the time taken depending on the prevailing
environmental conditions. Similarly, formulations containing viruses
used to kill moth larvae such as the pests Helicoverpa armigera or
Spodoptera exempta take a few days to be lethal and viruses used against
Brown-tail moth Euproctis chrysorrhoea may take weeks to kill and lead
to secondary infections months later. |
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| The Evolution of Pesticide Resistance |
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| Pesticide resistance is increasing and farmers’ and other pest
managers’ dependencies on chemical insecticides have led to a high
frequency of insecticide resistance in some crop systems. In order to
fight pesticide resistance and based on a knowledge of the genetics
of the development of pesticide resistance, a number of principles
have been proposed aimed at delaying the emergence of resistance
or avoiding it entirely. These principles include pesticide rotation or
switching, avoiding unnecessary pesticide applications, using nonchemical
control techniques, and leaving untreated refuges where
susceptible pests can survive. Thus, IPM is the optimal option for
combating pesticide resistance. |
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| Therefore, to address the above subjects for ongoing modeling research, some interesting questions have been raised: (a) How do the
different releasing and spraying patterns and the short-term or longterm
residual effects of pesticides on both pests and natural enemies
affect the success or failure of pest control? (b) How can the time
when the pest population reaches the economic injury level (EIL)
be estimated? (c) How can the most efficient frequency of pesticide
applications be determined? (d) When should pest managers switch
one type of pesticide to another unrelated type? (e) How do the frequencies of pesticide applications affect the evolution of pesticide
resistance? (f) What is the relationship between the evolution of
pesticide resistance and the number of natural enemies released? (g)
How does the cumulative number of dead natural enemies affect the
number of natural enemies to be released at the next iteration of a
biological control programme? Clarifying these questions through
ongoing modeling research must continue to play a key role in the wise
use of pest-natural enemy systems to evaluate IPM strategies. |
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