Spatial Patterns and Sequential Sampling Plans for Predators of Aphis glycines (Hemiptera: Aphididae) in Minnesota Soybean

Abstract

The soybean aphid, Aphis glycines Matsumura, is an economically important soybean pest. Many studies have demonstrated that predatory insects are important in suppressing A. glycines population growth. However, to improve the utilization of predators in A. glycines management, sampling plans need to be developed and validated for predators. Aphid predators were sampled in soybean fields near Rosemount, Minnesota, from 2006-2007 and 2013-2015 with sample sizes of 20-80 plants. Sampling plans were developed for Orius insidiosus (Say), Harmonia axyridis (Pallas), and all aphidophagous Coccinellidae species combined. Taylor's power law parameters from the regression of log variance versus log mean suggested aggregated spatial patterns for immature and adult stages combined for O. insidiosus, H. axyridis, and Coccinellidae in soybean fields. Using the parameters from Taylor's power law and Green's method, sequential fixed-precision sampling plans were developed to estimate the density for each predator taxon at desired precision levels of 0.10 and 0.25. To achieve a desired precision of 0.10 and 0.25, the average sample number (ASN) ranged from 398-713 and 64-108 soybean plants, respectively, for all species. Resulting ASNs were relatively large and assumed impractical for most purposes; therefore, the desired precision levels were adjusted to determine the level of precision associated with a more practical ASN. Final analysis indicated an ASN of 38 soybean plants provided precision of 0.32-0.40 for the predators. Development of sampling plans should provide guidance for improved estimation of predator densities for A. glycines pest management programs and for research purposes.

Arthropod predator community associated with soybean gall midge,Resseliella maxima(Diptera: Cecidomyiidae), in Minnesotan soybean fields

“…The foliar-foraging community of predators was dominated by O. insidiosus . This finding is consistent with a previous report from soybean fields in Minnesota (Tran and Koch 2017). Similarly, O. insidiosus and Araneae were the most prevalent foliar-foraging predators in soybean fields in southeastern Virginia (Whalen et al 2016).…”

“…The community of foliar-foraging predators was assessed through visual whole-plant inspections (modified from Tran and Koch (2017)). On each sample date, 10 plants were selected from each location.…”

Advances in insect pest and disease monitoring and forecasting in horticulture

“…Such studies are still rare though, and an additional challenge is that NEETs require modelling and computer skills. Tran and Koch (2017) determined the spatial patterns of predators of a pest aphid. Decision support systems are considered to be necessary for implementation of NEETs, as otherwise turning sampling results into decisions is too complicated.…”

Spatial Patterns and Sequential Sampling Plans for Estimating Densities of Stink Bugs (Hemiptera: Pentatomidae) in Soybean in the North Central Region of the United States

“…Simulations were performed by randomly selecting samples with replacement from the validation data sets until the corresponding stop line was exceeded. If resulting average precision values were better than the desired precision values, desired precision was relaxed to obtain the average desired precision (Burkness and Hutchison 1997, Galvan et al 2007, Tran and Koch 2017. Resampling analysis to determine the sample sizes required to attain the desired precision was based on 500 resampling iterations of each data set.…”

“…3B, Table 5). During resampling simulations, average achieved precision exceeded the desired level of 0.25, so desired precision was relaxed to 0.28 to achieve an empirical precision of 0.25, as was performed by Burkness and Hutchison (1997), Galvan et al (2007), and Tran and Koch (2017). Similarly, results based on the average of the samples used for validation showed that an ASN of 40 sets of 25 sweeps would be required to achieve a desired precision of 0.25 for stink bug densities ranging from 0.05 to 10.4 stink bugs per 25 sweeps (mean of 1.8 stink bugs per 25 sweeps; Fig.…”