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Title: FIXED PRECISION SEQUENTIAL SAMPLING PLANS FOR THE GREENBUG AND BIRD CHERRY-OAT APHID (HOMOPTERA: APHIDIDAE) IN WINTER WHEAT

Author
item Elliott, Norman - Norm
item GILES, K
item ROYER, T
item Kindler, Dean - Dean
item TAO, F
item JONES, D
item CUPERUS, G

Submitted to: Journal of Economic Entomology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/15/2003
Publication Date: 10/1/2003
Citation: Elliott, N.C., Giles, K.L., Royer, T.A., Kindler, D., Tao, F.L., Jones, D.B., Cuperus, G.W. 2003. Fixed presicion seqential sampling plans for the greenbugs and bird cherry-oat aphid (Homoptera: Aphididae) in winter wheat. Journal of Economic Entomology. 96:1585-1593.

Interpretive Summary: Sequential sampling plans for pest insects are needed for efficient pest management. Insect population size and distribution in a field are affected by environment and therefore, so are sampling plans that depend on knowledge of the distribution of insects in a field. With respect to sequential sampling plans, which depend on mathematically describing the relationship between population density and spatial distribution, it is critical to know if plans constructed by sampling a limited number of fields over a limited period of time, will work over a broad range of environmental conditions. We developed and tested sequential sampling plans for two economically important pests of winter wheat in Oklahoma, the greenbug and bird cherry-oat aphid. A total of 184 production wheat fields in Oklahoma were sampled during a 3-year period to construct a sequential sampling plan using Taylor's Power Law. A validation data set was constructed consisting of 240 samples taken during three growing seasons from wheat fields located at four locations in Oklahoma. Date of sampling, cultivar, and plant growth stage were recorded for each validation field. Taylor's power law parameters for validation samples differed with respect to growing season, geographic location, and wheat plant growth stage. The sequential sampling plans departed substantially from expectations for the validation data when greenbug and bird cherry- oat aphid population intensity was < 0.10 individual per tiller, but not for greater population intensity. The results indicate that the fixed precision sequential sampling plans are acceptable for most conditions, but will be biased when greenbug and bird cherry-oat aphid population intensity is very low.

Technical Abstract: The numbers of greenbugs, Schizaphis graminum (Rondani), and bird cherry- oat aphids, Rhopalosiphum padi L., per wheat tiller (stem) were estimated in production fields of winter wheat located in the major wheat growing regions of Oklahoma. Intercepts and slopes of Taylor's Power Law regressions were calculated for the numbers from production fields and were used to construct fixed-precision sequential sampling plans for each species. A validation data set was constructed consisting of samples taken in three growing seasons from winter wheat fields located near Tipton, Chickasha, Perkins, and Goodwell, Oklahoma. Wheat cultivar and wheat plant growth stage on the day of sampling were recorded for each validation field. Taylor's power law parameters for validation fields differed significantly for both species for different growing seasons, locations, and plant growth stages. Fixed-precision sequential sampling plans departed substantially from expectations when greenbug and bird cherry-oat aphid population intensity was < 0.10 per tiller, but estimates for both species were close to expectations when population intensity was greater than or equal to 0.25 per tiller. Because sample sizes would increase considerably if sampling plans were adjusted for biased precision at low population intensity, it probably is not advisable to adjust for bia We conclude that the fixed precision sequential sampling plans are acceptab for most conditions in Oklahoma. On average, they will yield precision lev close to expectations over a broad range of population intensity, even though results in particular circumstances (e.g. low population intensity) will vary from expectations.