A Bioeconomic Model of the Soybean Aphid Treatment Decision in Soybeans

Since its first detection in the North Central region in July 2000, the soybean aphid (Aphis glycines Matsamura) has caused considerable loss in soybean (Glycine max L.) yield, bean quality, and producer income. Discovered first in Wisconsin and then in adjoining states, it is currently distributed in 21 US states and parts of Canada. In 2003, over 42 million acres of soybean in the North Central US were infested and over 7 million acres were treated with insecticides to control soybean aphid (Landis et al. 2003). Populations exceeding 24,000 aphids per plant and 40% losses in seed yield have been reported (DiFonzo & Hines 2002). Even prior to the outbreak of 2003, the Soybean Strategic Pest Management Plan identified soybean aphid as one of the key drivers of insecticide use in the North Central region (Smith & Pike 2002). This paper first describes the interaction with and impacts of the soybean aphid on soybean. Then the treatment decision model is developed and impacts analyzed. In the last section, some comments are made on how the results of this work on soybean aphids can be adapted to the impact of soybean rust (Phakopsora pachyrhizihas), another new pest of soybean. The following paragraphs summarize these parts in more detail. Unlike other corn-soybean insect pests, the soybean aphid treatment decision is more complex than growers are used to making. The presence of pests such as weeds, diseases, nematodes, and other more common pests is usually known before planting decisions are made. These pests also are slower to reproduce and more predictable in their growth rate than soybean aphid. Thus treatment and timing decisions to control them are made within a more stable situation. With soybean aphids, however, growers face a pest with unpredictable colonization and a long window of crop susceptibility. Also, soybean aphids are not like other pests in that their populations are capable of doubling every 2-3 days and can rebound after insecticide applications. The soybean aphid affects plant growth and reproduction directly. Soybean yield can be affected eventually at high populations. At the present time, entomologists are not certain about what defines a population high enough to impact yield. The impact of aphids depends on many factors, the most important being: initial date of aphid colonization, aphid population growth rates, length of colonization, soybean plant growth stage, treatment timing and efficacy, the lag between the decision to treat and application of insecticide, aphid population regrowth and recolonization, and weather. Soybean is more susceptible to aphids in the earlier reproductive stages of plant growth (R1-R5) compared to later stages. Aphid colonization is rare in earlier vegetative stages and may not occur until the most susceptible period is over. Currently available insecticides are highly effective, but with essentially no residual effect, populations can regrow or recolonize in the same field. If the first colonization occurs early in the susceptible stage, two or more treatments may be needed. On a calendar basis, soybean susceptibility can be described as highest during July and into August with potential but lower impacts into September. Aphid colonization may occur as early as late June or as late as late August and even September; however, since it is a relatively new pest in the U.S., entomologists do not have a strong understanding of soybean aphid biology. Building on the early work by Hueth and Regev (1974) and Hall and Norgaard (1973) and incorporating concepts from recent unpublished AAEA selected papers, the treatment decision model is developed (with uncertainty incorporated) and the impacts and treatment options analyzed. The impact of the soybean aphid on yield is described as a function of plant growth stage and cumulative aphid days (CAD) with CAD described as a function of the date of colonization, growth rate, and insecticide application(s) and efficacy. The aphid growth rate is a function of temperature. These functions are estimated using economic and entomological data. Using an example from Minnesota, the preliminary model estimated the value of using a threshold of 100 aphids per plant was estimated to have a treatment value of $41.57 per acre with colonization occurring in mid-July during the growth stage with the highest yield susceptibility, weather conditions were such that the aphid population was doubling every 3 days, and the lag between decision and treatment application was 7 days. Under the same conditions, a threshold of 250 aphids per plant was estimated to have a treatment value of $39.80 per acre. A threshold of 500 aphids was estimated to have a treatment value of $37.88. If colonization occurred earlier in the susceptible stage, say, July 1, the number of treatments increases to two per season and the value of those treatments increases. Using the same conditions as above except for an earlier colonization date and allowing for a second treatment, the value of two treatments with a threshold of 100 aphids per plant was estimated to be $71.02 per acre. With a threshold of 250 aphids, the value of the two treatments was estimated to be $59.77 per acre. For a threshold of 500, the value was estimated to be $41.54 per acre. Other results show the impact of changing parameters and coefficients. These preliminary results indicate that the current economic threshold of 250 aphids per plant accepted by many but not all entomologists may be too high. Earlier and more frequent treatments may be needed when aphids colonize earlier and enjoy good reproductive weather. Late season colonization may not need to be treated except with fast growth conditions. This model for the treatment of soybean aphids can be adjusted to help understand the treatment decision for soybean rust. With soybean rust, growers also face the uncertainty in the occurrence and severity infestation and in the magnitude of the resulting impact if infestation does occur. Rust may spread faster than soybean aphid, so growers may need more remote warning systems. However, the basic model of the impact of the soybean aphid will improve our understanding of the soybean rust problem.