Demography of Soybean Aphid (Homoptera: Aphididae) at Summer Temperatures

McCornack, Brian; Ragsdale, David W.; Venette, Robert C.

doi:10.1093/jee/97.3.854

Cited by 105 publications

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“…This work describes a method based in Mamdani fuzzy control [8] to elaborate a model that studies the interaction of the prey, A. glycines (soybean aphids), and the predator, O. insidiosus [5], and the effects of the temperature in the growth of the prey population [1].…”

Section: The Mathematical Model For the Interaction Between Soybean Amentioning

confidence: 99%

“…Regarding the influence of the temperature abiotic factor in the population of soybean aphids, [1] got the intrinsic rate of increase (see Table 1). …”

Section: The Mathematical Model For the Interaction Between Soybean Amentioning

confidence: 99%

“…by reducing plant height, pod number and total yields, and yield reductions measured from grower strip trials have ranged from 12 to 45% [1]. The soybean aphid is a small sap-succking and partenogenetically reproducing insect.…”

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confidence: 99%

“…Reproduction and development is faster when temperatures are between 25˚C and the mid 29˚C when population can double in two or three days. The aphids are greatly affected by temperatures above 30˚C, and they are reported to begin to die when temperatures reach 35˚C [1]. We proposed in [7] a fuzzy chemical control to soybean aphid, that is, the model provides how often and how much to apply the insecticides on the plants.…”

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confidence: 99%

“…We proposed in [7] a fuzzy chemical control to soybean aphid, that is, the model provides how often and how much to apply the insecticides on the plants. This work describes a method based in Mamdani Fuzzy Control [8] to build a model to evaluate the interaction of the prey, A. glycines (the soybean aphid) and the predator, O. insidiosus (Hemiptera: Anthocoridae), considering the effects of the temperature in the growth of the prey population [1].…”

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confidence: 99%

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On Fuzzy Control of Soybean Aphid

Peixoto¹,

Barros²,

Bassanezi³

2016

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This paper uses a methodology based in Fuzzy Sets Theory in order to describe the interaction between the prey, Aphis glycines (Hemiptera: Aphididae)-the soybean aphid, and its predator, Orius insidiosus (Hemiptera: Anthocoridae) and to propose a biological control to soybean aphid. Economic thresholds were already developed for this pest. The model includes biotic (predator) and abiotic (temperature) factors, which affect the soybean aphid population dynamics. The dynamic model results in a fuzzy model that preserves the biological meaning and nature of the predator-prey model. The paper also includes a comparison between the fuzzy model and real data reported in the literature. Subsequently, we propose a biological control to soybean aphid by another fuzzy rule-based system. This model has allowed to predict timing and releasing number of predators for soybean aphid biological control. On the one hand, the soybean aphid has still not found in Brazil. Therefore, before any eventual invasion, a predictive model to enhance biological control program is desirable. On the other hand, the soybean aphid has become the most devastating insect pest of soybeans in the United States. Brazil is the second largest exporter of soybean at present, after the USA and before Argentina. According to the Bureau of Agriculture of the USA, it has been estimated that Brazil will be the largest soybean exporter in 2023.

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Section: The Mathematical Model For the Interaction Between Soybean Amentioning

confidence: 99%

“…Regarding the influence of the temperature abiotic factor in the population of soybean aphids, [1] got the intrinsic rate of increase (see Table 1). …”

Section: The Mathematical Model For the Interaction Between Soybean Amentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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On Fuzzy Control of Soybean Aphid

Peixoto¹,

Barros²,

Bassanezi³

2016

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Explicit modeling of abiotic and landscape factors reveals precipitation and forests associated with aphid abundance

Whitney

Meehan

Kucharik

et al. 2016

Ecological Applications

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Increases in natural or noncrop habitat surrounding agricultural fields have been shown to be correlated with declines in insect crop pests. However, these patterns are highly variable across studies suggesting other important factors, such as abiotic drivers, which are rarely included in landscape models, may also contribute to variability in insect population abundance. The objective of this study was to explicitly account for the contribution of temperature and precipitation, in addition to landscape composition, on the abundance of a widespread insect crop pest, the soybean aphid (Aphis glycines Matsumura), in Wisconsin soybean fields. We hypothesized that higher soybean aphid abundance would be associated with higher heat accumulation (e.g., growing degree days) and increasing noncrop habitat in the surrounding landscape, due to the presence of the overwintering primary hosts of soybean aphid. To evaluate these hypotheses, we used an ecoinformatics approach that relied on a large dataset collected across Wisconsin over a 9-year period (2003-2011), for an average of 235 sites per year (n = 2,110 fields total). We determined surrounding landscape composition (1.5-km radius) using publicly available satellite-derived land cover imagery and interpolated daily temperature and precipitation information from the National Weather Service COOP weather station network. We constructed linear mixed models for soybean aphid abundance based on abiotic and landscape explanatory variables and applied model averaging for prediction using an information theoretic framework. Over this broad spatial and temporal extent in Wisconsin, we found that variation in growing season precipitation was positively related to soybean aphid abundance, while higher precipitation during the nongrowing season had a negative effect on aphid populations. Additionally, we found that aphid populations were higher in areas with proportionally more forest but were lower in areas where minor crops, such as small grains, were more prevalent. Thus, our findings support our hypothesis that including abiotic drivers increases our understanding of crop pest abundance and distribution. Moreover, by explicitly modeling abiotic factors, we may be able to explore how variable climate in tandem with land cover patterns may affect current and future insect populations, with potentially critical implications for crop yields and agricultural food webs.

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From pest data to abundance‐based risk maps combining eco‐physiological knowledge, weather, and habitat variability

Lacasella

Marta

Singh

et al. 2017

Ecological Applications

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Noxious species, i.e., crop pest or invasive alien species, are major threats to both natural and managed ecosystems. Invasive pests are of special importance, and knowledge about their distribution and abundance is fundamental to minimize economic losses and prioritize management activities. Occurrence models are a common tool used to identify suitable zones and map priority areas (i.e., risk maps) for noxious species management, although they provide a simplified description of species dynamics (i.e., no indication on species density). An alternative is to use abundance models, but translating abundance data into risk maps is often challenging. Here, we describe a general framework for generating abundance-based risk maps using multi-year pest data. We used an extensive data set of 3968 records collected between 2003 and 2013 in Wisconsin during annual surveys of soybean aphid (SBA), an exotic invasive pest in this region. By using an integrative approach, we modelled SBA responses to weather, seasonal, and habitat variability using generalized additive models (GAMs). Our models showed good to excellent performance in predicting SBA occurrence and abundance (TSS = 0.70, AUC = 0.92; R = 0.63). We found that temperature, precipitation, and growing degree days were the main drivers of SBA trends. In addition, a significant positive relationship between SBA abundance and the availability of overwintering habitats was observed. Our models showed aphid populations were also sensitive to thresholds associated with high and low temperatures, likely related to physiological tolerances of the insects. Finally, the resulting aphid predictions were integrated using a spatial prioritization algorithm ("Zonation") to produce an abundance-based risk map for the state of Wisconsin that emphasized the spatiotemporal consistency and magnitude of past infestation patterns. This abundance-based risk map can provide information on potential foci of pest outbreaks where scouting efforts and prophylactic measures should be concentrated. The approach we took is general, relatively simple, and can be applied to other species, habitats and geographical areas for which species abundance data and biotic and abiotic data are available.

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Demography of Soybean Aphid (Homoptera: Aphididae) at Summer Temperatures

Cited by 105 publications

References 0 publications

On Fuzzy Control of Soybean Aphid

On Fuzzy Control of Soybean Aphid

Explicit modeling of abiotic and landscape factors reveals precipitation and forests associated with aphid abundance

From pest data to abundance‐based risk maps combining eco‐physiological knowledge, weather, and habitat variability

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