Costamagna, A. C., B. P. McCornack, D. W. Ragsdale, and D. A. Landis. 2010. Development and validation of node-based sample units for estimating soybean aphid (Hemiptera: Aphididae) densities in field cage experiments. Journal of Economic Entomology 103:1483-1492.

The soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is currently the most important insect threat to soybean, Glycine max (L.) Merr., production in the North Central United States. Field cage studies are a key tool in investigating the potential of natural enemies and host plant resistance to control this pest. However, a major constraint in the use of cage studies is the limited number of treatments and replicates that can be used as aphid densities frequently become so large as to limit the number of experimental units that can be quantified. One way to overcome this limitation is to develop methods that estimate whole-plant aphid densities based on a reduced sampling plan. Here, we extend an existing method, node-sampling, used for estimating aphid populations in open field conditions and apply it to caged populations. We show that parameters calculated under open field conditions are inappropriate to estimate caged populations. In contrast, using four independent data sets of caged populations and a cross-validation technique, we demonstrate that a three-node sampling unit and a weighted formula provide accurate and robust estimates of whole-plant aphid density. This method reduced the number of aphids counted per plant by and average of 60%, with greater reductions at higher aphid densities. We further demonstrate that nearly identical statistical results were obtained when whole-plant or node-sampling estimates were used in the analysis of two case studies. The reduced sample unit method developed here saves time without sacrificing efficiency so that more plants, replications, or studies can be conducted that will lead to improved soybean aphid management.

DOI: 10.1603/EC10012

Associated Treatment Areas:

Biodiversity Gradient

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