Contents - Faperta

Pest Management and the Environment 15
Mass trapping by pheromone- or kairomone-baited traps can be attempted to reduce insect
infestations. It is important to understand that not all insects can be controlled by mass trapping.
It is also better to think in terms of population suppression rather than control. The most
promising candidates are insects that use aggregation pheromones, such as Spruce bark beetle,
Ips typographus (L.). For this pest, trap densities of 20 to 30 traps per hectare have been
used. Sex pheromones can be used for mass trapping of some insects. It is necessary to catch
95% of the male moths before there is any signifi cant impact on the ability of the population
to reproduce. Mobile insects such as H. armigera cannot be successfully controlled by mass
trapping or mating disruption, as the females that have mated outside the treated area lay
eggs in the area where the males may have been successfully removed. Mercury lamps spaced
300 m apart over a large number of contiguous cotton fi elds reduced the egg laying by 41.5%,
and the frequency of pesticide application by two to three times in China (Zhao et al., 1999).
However, the application and economics of such an approach need to be looked into critically.
Compound traps having two lamps with sex pheromone or poplar branches have been
used to control H. armigera in China. In comparison to the control plots, the numbers of eggs
on cotton plants in plots with traps were reduced by 34.5% within 160 m. Mass trapping has
been shown to work successfully for lepidopteran moths, which are relatively immobile, such
as rice stem borers (Pyralidae), potato tuber moth [Phthorimaea operculella (Zeller)], diamondback
moth, P. xylostella, and brinjal fruit and shoot borer, Leucinodes orbonalis (Guen.) (Howse,
Stevens, and Jones, 1997). For pests such as these, trap densities of 10 to 20 traps per hectare
have been shown to be effective at reducing damage levels.
Population Prediction Models and Early Warning Systems
Monitoring the movement of insect pests can provide early warning of pest invasion in
an area or crop. Although work on long-distance movement using remote sensing, backtracking,
and other techniques have indicated that some insects are able to cover large distances,
their occurrence in signifi cant numbers at a particular location can seldom be predicted with
certainty. Pheromone-baited traps alone or in combination with other lures have been used
for monitoring insect populations (Nesbitt et al., 1979), but the relationship between egg, larval,
and insect catch in traps is closest only when insect densities are low at the beginning of
the season. Trapping is useful as a qualitative measure indicating the initiation of infestation
or migration (wave front), and the need to begin scouting for immature stages in the crop.
Models are conceptual or mathematical devices that aim to describe or simulate natural
processes. They can be used to predict the outcome of hypothetical eventualities and as
management tools to predict or establish the optimal tactics required to achieve a particular
result within the constraints of the model. The population models are useful for developing
appropriate pest management strategies, such as optimal timing of insecticide application
(Apel, Herrmann, and Richter, 1999). The use of phenological or time parameters
in predictive models is important to improve their performance. In Australia, the size of
the second generation of H. armigera is linked to fi rst generation, winter rainfall (positive
effect) and spring rainfall (negative effect), which account for 96% of the variation in
second generation (Maelzer and Zalucki, 1999).
SIRATAC, a computer-based pest management system, has been developed to rationalize
insecticide use on cotton (Hearn et al., 1981). This system incorporates a temperature-driven
cotton development model, including the natural fruiting habit of the plant, and submodels
to incorporate damage relationships, the impact of natural enemies, and predetermined or
dynamic thresholds for pests. The system gives management options, and the outcomes of
using “soft” or “hard” insecticides. Signifi cant improvements in this model were obtained by