Monitoring and decision making strategies for soybean aphid management Jordan Bannerman

Monitoring and decision making
strategies for soybean aphid
management
Jordan Bannerman
Department of Entomology
University of Manitoba
Overview
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•
•
•
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Soybean aphid (SBA) incidence and timing
SBA economic threshold and EIL
SBA scouting
Role of natural enemies in SBA suppression
Natural enemy (NE) scouting
Dynamic action thresholds
SBA in Manitoba
Probability of death during winter
• An occasional
economic pest
McCornack et al. (2005) Environmental Entomology
SBA damage symptoms
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Leaf distortion
Stunted, yellowing plants
Sooty mold on leaves
Virus transmission
• Yield reductions of 40 percent or greater
can occur in heavily infested fields if left
untreated
Economic (action) threshold
• 250 aphids per plant on average and…..
– More than 80% of the plants have aphids
– Plants are not yet at R6 growth stage
– Aphid populations are increasing
• Provides a 7 day lead time to take control action
before the economic injury level of 675
aphids/plant is reached
Ragsdale et al. 2007. Journal of Economic Entomology
SBA scouting
• Plant staging
Koch & Potter. 2014. UMN extension
SBA scouting
• Plant counts
– 20 whole-plant
counts, each 20+
paces from one
another
– Weekly basis
once plants are in
vulnerable
reproductive
stage (R1 to R5)
SBA speed scouting
• Speed scouting method
– Sequential sampling method, but based on
same threshold already discussed
– Worksheet available (goo.gl/UC9vus)
– itunes app available (http://bit.ly/21eEJST)
Hodgson et al. 2004. Journal of Economic Entomology
SBA speed scouting
• Begins (and potentially ends) with sampling 11
plants, each at least 30 paces apart
Hodgson et al. 2004. Journal of Economic Entomology
Can we improve decision making?
• We have a good simple action threshold in place
• But we also know that natural enemies can exert
significant control on soybean aphids in North
America (including Manitoba)
• Could a dynamic action threshold that integrates
both aphid and natural enemy counts improve
upon the threshold currently used?
(Hallett et al. 2012. Pest Management Science)
Incorporating natural enemy counts into
action thresholds
• One needs to determine:
– The natural enemy assemblage in a region
– The impact (voracity) of various species of
natural enemies on aphid populations
– How best to scout natural enemies in soybean
Hallett et al. 2012. Pest Management Science
Natural enemies of SBA
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Ladybird beetles
Predatory bugs
Lacewings
Predatory flies
Parasitoid wasps
NE scouting
• What method or methods are most appropriate?
– To accurately and efficiently count both large and
small natural enemies while also scouting for soybean
aphid
Bannerman et al. 2012. Journal of Economic Entomology
NE scouting
• Sweep-netting
• Plant counts
• Walking observation
Bannerman et al. 2012. Journal of Economic Entomology
Dynamic action thresholds
• An ET that changes depending on the
abundance (and identity) of natural enemies
counted while scouting
– Research suggests conversion of natural
enemy counts into standardized units may
work best
– E.g. 1 NEU = Number of NEs required to
consume 100 aphids in 24 hours
– Determining the best method to sample/count
NEs requires further research
Hallett et al. 2012. Pest Management Science
Plant counts (normal method) to
count SBA and NEs
Determine the number of natural
enemy units per plant (with help of
disc counter)
Determine dynamic
action threshold
DAT
NE units per plant
0
0.5
1
1.5
250
367
483
600
Make decision based on DAT →
scout later, control, or no longer a
concern
Trial use in Ontario
• Initial trial use of DAT by growers:
– Resulted in all 4 growers choosing not to
spray
– All 4 would have sprayed based on the
conventional ET of 250 aphids per plant
– None of the fields actually reached the EIL
Hallett et al. 2012. Pest Management Science
Summary
• There are several options when scouting
soybean aphid
• There is a good simple threshold, that is useful
for economic decision making, but quite
conservative
• Incorporation of natural enemy data appears to
improve economic decision making in soybean
• Further work is needed to test/refine DATs and
determine the best natural enemy sampling
methods to employ
Acknowledgements
• Alejandro Costamagna
• Brian McCornack
• Dave Ragsdale
Useful resources
• Soybean aphid scouting guide
(http://bit.ly/1tw7HNj)
• Soybean aphid economic threshold paper
(http://bit.ly/1LOVNn4)
• Manitoba Insect & Disease Updates
(http://bit.ly/1N4adS9)
References
• Koch, R., Potter, B. Scouting for Soybean Aphid. July 2014.
University of Minnesota Extension
• Hallett et al. 2012. Incorporating natural enemy units into a dynamic
action threshold for the soybean aphid, Aphis glycines (Homoptera:
Aphididae). Pest Management Science, 70: 879-888
• Bannerman et al. 2015. Comparison of Relative Bias, Precision,
and Efficiency of Sampling Methods for Natural Enemies of Soybean
Aphid (Hemiptera: Aphididae). Journal of Economic Entomology,
108:1381-1397
• Ragsdale et al. 2007. Economic Threshold for Soybean Aphid
(Hemiptera: Aphididae). Journal of Economic Entomology,
100:1258-1267
• Hodgson et al. 2004. Enumerative and Binomial Sequential
Sampling Plans for Soybean Aphid (Homoptera: Aphididae) in
Soybean. Journal of Economic Entomology 97(6): 2127-2136).