and Integrated Pest Management - part - usaid

5X iILPI1CiDI: NANA;I-\II AND 1PM INSOUtLIEASr ASIA
1986c) and a nomogram (1-leong 1986c, Sutherst et al. 1979). Such decision
aids can be modified for use by technicians and farmers inthe field.
Forecast Information
FORECAST MODELS When using real-time information to make pest
ana gement decisions, a forecast of what is likely to occur in the future is of
paramnount importance. Forecast information is derived by combining
hindm'icnial and historical information through the use of modelling techniques.
Based on historical data, time series models were developed to forecast rice stem
borers (Tori 1967) and blast (Ono 1965) in Japan. Such models, developed
either by multiple regression or time series analysis, have limited abilities.
They may be adequate in describing past trends, but may not forecast future
events.
Models developed from research may be less dependent on past trends. In
Japan, an epideniolgy model of rice dwarf virus (RDV) was developed to
forecast RDV infcAs!;tions (Nakasuji et al. 1975). Thus by monitoring the green
lea'fhopper (GLIH) densities in late May each year, RDV infestations can be
predicted. in Europe, the EPIPRE system uses both yield loss and disease
developmet models. Data on the crop, pest and weather are sent to a computer
which predicts the expected epidemics and yield losses (Zadoks 1983). Based on
these predictions, a control recommendation is sent back to the farmer. Out in
the feld, models in portable computers are required. In Minnesota, RUSTMAN
was developed using a hand-held computer for the control of common rust on
maize (Teng & Montgomery 1982). Field data is entered into the computer as
prompted, and the model responds with an estimated yield reduction and the
expected profit if control is made. There are now fully integrated electronic
systems with built-in automated units placed in the field that record the weather
variables to help in the timing of fungicide sprays (Teng & Rouse 1984). Such
pest forecasting gadgets are being produced at a commercial scale in Michigan
(Gage 1986, pers. comm.).
CROP LOSS MODELS Apart from forecasting pest and disease development
in the field, it is essential that potential losses are also predicted as it provides a
means for evaluating the benefits of implementing management decisions (Teng
1985). Most insect loss models in rice are developed empirically using field
experimental data and multiple regression techniques (Sogawa & Cheng 1979).
Since yield responses to insect damages are usually more complex (Southwood
& Norton 1973), accurate predictions will need the coupling of pest development
and crop growth models.
COMIPUTER MAPPING Computer maps can be used to display the spatial
dimension of insect populations. In England, mapping techniques are used to
analyze aerial suction trap data of aphids and to indicate areas in which crops are
at risk to damages (Woiwood & Taylor 1984). A computer program,
SURFACE 11, capable of performing difference mapping has been used for these
purposes. Recently, a microcomputer-based mapping program that can run on