Integrated management of insect pests implies that producers have information to reach an informed decision on control of a specific pest species. A major component of the decision making process is information relating to crop risk. Sound insect pest monitoring programs form the basis of crop risk estimates. The objectives of studies on bertha armyworm were to develop timely and accurate risk warnings. A review of methods that have been developed and implemented for bertha armyworm (Mamestra cinfigurata) is presented.
Literature Summary
Bertha armyworm is a native species reported to cause damage to a number of crops but it developed as a serious pest of canola as acreage’s increased (Riegert 1984). The risk of damage to a crop varies depending on crop type, plant growth stage, the larval growth stage and the number of larvae present. In canola, larvae can defoliate plants but the greatest economic losses occur when later larval stages feed directly on the pods. In most years, environmental factors and natural enemies keep pest populations below economic levels. Chemical controls are available for producers when the value of the crop that the larvae consume is greater than the cost of controlling them.
Monitoring
Monitoring for bertha armyworm consists primarily of estimating adult and larval populations. Adults are monitored in June and July using traps baited with a sex pheromone attractive to males. The number of moths collected gives an indication of potential larval populations in the area later in the season. Larvae are monitored by estimating their number in the crop. In canola, plants in a one-metre-square area are more easily counted. For more accurate estimates of larval numbers, it is recommended that sampling take place in at least three locations within as field. Bertha armyworm populations tend to fluctuate widely from year to year. Therefore the occurrence of high densities in a crop one season is not reliable indicator of what to expect the following year.
Development of Insect Pest Forecasts
Most survey and forecast activities can be categorized into three types: long-term insect population trends, annual population data, and risk warnings. The results or product of each respective activity serve different functions.
Long-term insect surveys are of tremendous value to pest researchers and agricultural industry managers in that they provide a general overview of insect populations trends over time. For the researcher, the data provide an opportunity to identify environmental and agronomic factors that influence pest populations increase and decrease.
Annual surveys are a snap-shot in time and, depending on a large number of factors (e.g. beginning with the specific life stage of the insect being sampled), will reflect the future to varying degrees. These data can be presented as surveys (summarized reports using mean values) or forecasts (with mean values that are translated into qualitative terms such as “light” infestation, etc). Survey results may be useful to growers who are trying to understand the dynamics of pest populations in their farming system.
Forecasts of insect pests, based on annual surveys, usually provide an interpretive summary of the current state of understanding of the host plant-pest interactions. The accuracy and precision of survey results are directly related to the sampling methods employed and the number of samples taken. The accuracy and resolution of forecasts will be influenced by the variability inherent in the survey plus the variability inherent in agricultural systems. In the case of bertha armyworm, the relationship between moth counts in a pheromone trap and the subsequent larval infestations has greater variability. The accuracy and precision of insect surveys in extensive areas such as the Canadian prairies will be limited by the extent of the sampling effort that is possible given the funds available.
Risk warnings usually consist of bringing forward annual surveys from the previous year and providing producers with more current information on factors that affect crop risk in the current growing season. Risk warnings often employ models that have been developed to predict insect and crop response to environmental factors. In case studies, bertha armyworms, risk-warnings employ the use of modeled degree-days and precipitation to predict adult emergence, egg-laying activity and larval development.
Results
Delivery of forecast and risk warning are facilitated by the use of geographic information systems (GIS). GIS systems are used as a tool to model or depict data points (insect numbers, weather data) and to display the results spatially in a map format. GIS systems employ different algorithms to average point data, such as insect population numbers. A visual display, such as a map with contour lines, enhances the interpretation of the point data. Because of the variability of insect sampling methods, the dynamic nature of agriculture systems and the assumptions made when models were developed, it is important that maps used for risk warnings are accompanied by explanatory notes. It is important that researchers and extension agrologists not only highlight the potential risk of a pest species but also make producers aware of the limitations of the forecast.
Applied Questions
Will the information/maps that are produced provide producers
with
information specific to their farm?
The information will be relevant to each farmer but the information
provided is not designed to form the basis of control decisions for a
specific
producer. They are more designed as a farm tool: (i) Crop management –
the information can be used to select crops, cultivars and seeding
dates;
(ii) Financial management – the information can be used to budget for
pest
control costs (chemical inputs and application costs); and (iii) Time
management
– producers may be required to set aside time at specific times to
monitor
crops. Close monitoring of individual fields when the crop is
susceptible
is necessary in order to identify an insect infestation such as wheat
midge
or bertha armyworm.
What can be done to increase the accuracy and precision of the
risk
warnings?
The accuracy of most risk warnings can be improved by more detailed
studies of pest-host crop interactions and factors that influence those
interactions (insect ecology); the precision can often be improved by
increasing
the number of samples taken. However, insect pests are often found over
large geographical areas so it is usually not economically feasible to
provide detailed information on a field-to-field basis.
How should I interpret the information provided for use on my
farm?
Examine the risks in relation to your situation. Familiarity with the
crop, knowledge of insect life cycles, and estimates of population
levels
through regular inspection during the growing season is essential. The
risks may influence decisions on crop rotation, seeding date, timing of
monitoring programs, and the control options (cultural, biological or
chemical).
I find that the information provided consistently over-estimates
or under-estimates what really happens in my area, why is that?
Obviously, the factors involved in that case are not accounted for
in the models that have been developed to describe the ecology of the
pest.
However, this does not neccessarily negate the usefulness of an insect
forecast. For example, relative differences from year to year
(long-term
data) provide a baseline for on-farm decision making. Producer and
industry
awareness is vital to improving the risk warnings. Producers should be
encouraged to work closely with their extension agrologists and
research
scientists to add to the body of knowledge.
References:
Anonymous 1996. Bertha Armyworm. Sustainable Agriculture Facts 6 p.
Riegert, P.W. 1984. Insects on the Canadian plains. Prairie Forum
9:327-344.
Adapted by Will Lanier from Insect Monitoring and Crop Risk
Warning:
Case Studies of Wheat Midge and Bertha Armyworm by:
O. Olfert, Saskatoon Research Center, 107 Science Place, Saskatoon
S7N 0X2
B. Eliot, Saskatchewan Agriculture and Food, Walter Scott Building,
Regina
L. Harris, Saskatoon Research Center, 107 Science Place, Saskatoon
S7N 0X2
P. Mason, Saskatoon Research Center, 107 Science Place, Saskatoon S7N
0X2