Probability of Cost-Effective Management of Soybean Aphid (Hemiptera: Aphididae) in North America

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is one of the most damaging pests of soybean, Glycine max (L.) Merrill, in the midwestern United States and Canada. We compared three soybean aphid management techniques in three midwestern states (Iowa, Michigan, and Minnesota) for a 3-yr period (2005–2007). Management techniques included an untreated control, an insecticidal seed treatment, an insecticide fungicide tank-mix applied at flowering (i.e., a prophylactic treatment), and an integrated pest management (IPM) treatment (i.e., an insecticide applied based on a weekly scouting and an economic threshold). In 2005 and 2007, multiple locations experienced aphid population levels that exceeded the economic threshold, resulting in the application of the IPM treatment. Regardless of the timing of the application, all insecticide treatments reduced aphid populations compared with the untreated, and all treatments protected yield as compared with the untreated. Treatment efficacy and cost data were combined to compute the probability of a positive economic return. The IPM treatment had the highest probability of cost effectiveness, compared with the prophylactic tank-mix of fungicide and insecticide. The probability of surpassing the gain threshold was highest in the IPM treatment, regardless of the scouting cost assigned to the treatment (ranging from $0.00 to$19.76/ha). Our study further confirms that a single insecticide application can enhance the profitability of soybean production at risk of a soybean aphid outbreak if used within an IPM based system

Crop pests and predators exhibit inconsistent responses to surrounding landscape composition

The idea that noncrop habitat enhances pest control and represents a win–win opportunity to conserve biodiversity and bolster yields has emerged as an agroecological paradigm. However, while noncrop habitat in landscapes surrounding farms sometimes benefits pest predators, natural enemy responses remain heterogeneous across studies and effects on pests are inconclusive. The observed heterogeneity in species responses to noncrop habitat may be biological in origin or could result from variation in how habitat and biocontrol are measured. Here, we use a pest-control database encompassing 132 studies and 6,759 sites worldwide to model natural enemy and pest abundances, predation rates, and crop damage as a function of landscape composition. Our results showed that although landscape composition explained significant variation within studies, pest and enemy abundances, predation rates, crop damage, and yields each exhibited different responses across studies, sometimes increasing and sometimes decreasing in landscapes with more noncrop habitat but overall showing no consistent trend. Thus, models that used landscape-composition variables to predict pest-control dynamics demonstrated little potential to explain variation across studies, though prediction did improve when comparing studies with similar crop and landscape features. Overall, our work shows that surrounding noncrop habitat does not consistently improve pest management, meaning habitat conservation may bolster production in some systems and depress yields in others. Future efforts to develop tools that inform farmers when habitat conservation truly represents a win–win would benefit from increased understanding of how landscape effects are modulated by local farm management and the biology of pests and their enemies

Landscape scale pest management: Approaches for understanding habitat function

While IPM has traditionally focused on the field scale, two observations have triggered interest in developing IPM at larger spatial scales. First, mobile pests do not recognize field or farm boundaries. Second, some landscapes appear less prone to invertebrate pest infestations than others, suggesting that there are features that may be managed to create more pest suppressive landscapes. Landscape complexity has been shown to increase the ecosystem service of pest suppression, although the mechanisms responsible remain elusive. Using a range of approaches including survey, large-scale experimentation and GIS from two production systems, cotton-grain in the Darling Downs, and vegetables in the Lockyer Valley, QLD, we'll explore the link between surrounding habitats, pest and beneficial insect dynamics and pest suppression. In the cotton/ grain systems, we show that natural enemies (as well as some pest species) use native vegetation as reproduction habitat, move between native vegetation and crops, and colonize crops. We also show that some pest species are more strongly suppressed by natural enemies in crops near native vegetation than further away, and that native plants have higher predator : pest ratios compared to crops. In the vegetable system, we tested the effect of earliness of predator impacts on the suppression of pests in 19 vegetable landscapes that differ in landscape complexity. We found that predators have a significant impact on pests, but only some landscapes contributed predators early. Most of the variation in pest suppression was explained by the amount of Lucerne (alfalfa) around the focal fields up to 2 km. Lucerne was shown to be good habitat for predators, but high predator numbers explained most of the variation in high pest numbers in focal fields. This paradox demonstrates the challenge of managing for pests and pest control services at multiple spatial scales. We'll conclude by showing how these findings can contribute to guidelines for IPM at the field, farm and landscape scale

Appendix C. A table showing large- and small-size predators and parasitoids for different combinations of agricultural management system and predator manipulation treatments within the large field cages in the aphid population increase experiment at the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, 2003.

A table showing large- and small-size predators and parasitoids for different combinations of agricultural management system and predator manipulation treatments within the large field cages in the aphid population increase experiment at the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, 2003

Appendix D. A table showing longevity, fecundity, and intrinsic rate of increase of Aphis glycines reared on soybean produced under three different agricultural management systems in the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, during 2003.

A table showing longevity, fecundity, and intrinsic rate of increase of Aphis glycines reared on soybean produced under three different agricultural management systems in the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, during 2003

Appendix B. A table showing ANOVA results for fixed and random effects and slicing tests results of the effect of agricultural management system and predator manipulation treatments on Aphis glycines within large field cages at the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, 2003.

A table showing ANOVA results for fixed and random effects and slicing tests results of the effect of agricultural management system and predator manipulation treatments on Aphis glycines within large field cages at the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, 2003

Appendix E. A table showing MANOVA results for the effect of agricultural practices on the five more-abundant Aphis glycines foliar predators at the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, during 2003.

A table showing MANOVA results for the effect of agricultural practices on the five more-abundant Aphis glycines foliar predators at the Kellogg Biological Station Long Term Ecological Research site, Michigan, USA, during 2003

Effects of plant age (PA) on naturally occurring populations of <i>A. glycines</i> and predators in the experimental plots.

<p>Bars present mean (+1 SE) of (A) log₁₀– transformed number of aphids/plant, from a sample of 10 random plants per plot in each date, and (B) total number or predators/25 sweeps, from four samples/plot. PA 1–3 refers to the oldest to the youngest plant age, respectively. Horizontal lines indicate the dates when the manipulative trials were conducted.</p