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Incorporating biological control into IPM decision making
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ScienceDirect Incorporating biological control into IPM decision making Kristopher L Giles1, Brian P McCornack2, Tom A Royer1 and Norman C Elliott3 Of the many ways biological control can be incorporated into Integrated Pest Management (IPM) programs, natural enemy thresholds are arguably most easily adopted by stakeholders. Integration of natural enemy thresholds into IPM programs requires ecological and cost/benefit crop production data, threshold model validation, and an understanding of the socioeconomic factors that influence stakeholder decisions about biological control. These thresholds are more likely to be utilized by stakeholders when integrated into dynamic webbased IPM decision support systems that summarize pest management data and push site-specific biological control management recommendations to decision-makers. We highlight recent literature on topics related to natural enemy thresholds and how findings may allow pest suppression services to be incorporated into advanced IPM programs.
Addresses 1 Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, OK, United States 2 Department of Entomology, Kansas State University, Manhattan, KS, United States 3 USDA-ARS, Stillwater, OK, United States Corresponding author: Giles, Kristopher L ([email protected])
Current Opinion in Insect Science 2017, 20:84–89 This review comes from a themed issue on Parasites/parasitoids/ biological control Edited by Mary M Gardiner and James D Harwood For a complete overview see the Issue and the Editorial Available online 11th May 2017 http://dx.doi.org/10.1016/j.cois.2017.03.009 2214-5745/ã 2017 Elsevier Inc. All rights reserved.
Introduction The challenge of incorporating biological control into multi-tactic Integrated Pest Management (IPM) programs has been highlighted since the formal development of the ‘Integrated Control Concept’ [1]. Naranjo et al. [2] provided a comprehensive summary of research related to the economic value of biological control in managed systems, and ultimately concluded that progress continues, albeit slowly, integrating biological control (Importation, Augmentation, Conservation, Habitat Current Opinion in Insect Science 2017, 20:84–89
Manipulations and/or Natural Enemy Thresholds) with other IPM tactics. Part of the challenge of incorporating biological control is related to evolving production systems (alternative crops, crop diversity, and/or acres) and considerations for IPM implementation and dissemination at individual field and larger agricultural landscape levels. However, approaches designed to enhance pest suppression services by natural enemies at larger agricultural landscapes are unlikely to be utilized if individual farmers do not perceive the benefits as reliable [2,3]. Indeed, despite evidence for predictable pest suppression by natural enemies at increasing spatial scales, biological control outcomes in managed field-crop agroecosystems (with and without natural habitat) are highly variable and dependent upon vegetation complexity, pest and natural enemy ecology, and/or agricultural practices [4,5]. Producers select crop cultivars, rotate crops, and/or incorporate new crops into their production systems. Because the effects of these changes on pest activity in agricultural landscapes are difficult to predict, producers often rely on short-term management tactics (primarily insecticide use) to suppress insect pests [3,6]. Decisions on insecticide use are based on crop value/risk considerations, and often available economic thresholds (ETs) for key and sporadic pests [3,6]. The widespread utilization of ETs in cropping systems has, for better or worse, unified IPM stakeholders, and perhaps, consideration of predictable suppression by natural enemies during pest scouting (natural enemy thresholds or NETs) deserves more scientific attention because it provides the greatest opportunity for incorporation of biological control in IPM programs [2,7–9]. Indeed, there are recent examples of IPM programs that incorporate natural enemy services into pest management decisions using field-level and/or areawide data from scouting events (Table 1). However, development of NETs and there utilization by producers requires that multiple criteria are met: (1) demonstration of reliable suppression by natural enemies via short and long-term population dynamics data, including model development and validation, (2) sampling plans that are user-friendly, easily accessible and allow for system-specific management recommendations that are integrated with other production system components, and (3) stakeholders believe that the benefits associated with incorporation of natural enemies during pest management decisions outweigh the risks. www.sciencedirect.com
Incorporating biological control into IPM decision making Giles et al. 85
Table 1 Examples of incorporating natural enemy services into pest management decisions Authors
Ref.
System
Pests
Shakya et al. Walker et al. Rocco and Greco Nofemela Hallett et al. Royer et al.
[45] [46] [47] [49] [50] [53]
Strawberry Tomato Blueberry Cabbage Soybean Wheat
Thrips Helicoverpa armigera Aphids Plutella xylostella Aphids Aphids
Demonstrating suppression by natural enemies in managed crop systems Recently published data quantifying the impact of natural enemies on crop pests are rare [2]. Multi-year evaluations in cotton systems do provide, however, data sets critical for pest-natural enemy population dynamics model development and validation [10,11]. Other recent modeling studies on biological control reveal promising outputs for future validation research and potential incorporation in managed systems. Of particular interest are models evaluating dynamics among pests, natural enemies and pesticides [12,13,14,15], and simulations that suggest how pest-natural enemy dynamics can be stabilized when negative interactions between natural enemies occur [16]. Interestingly, stable pest dynamics were more likely to occur when unstable pest-predator and pest-pathogen models were combined [16]. These outcomes are consistent with the growing body of evidence from microcosm and field studies that link pest suppression and natural enemy diversity [17]. In-depth evaluations of ground beetle predators indicate that niche partitioning can occur among species, and pest suppression can be driven not solely by the number of species, but the presence and activity of a few key natural enemy species [18,19]. More applied studies provide evidence that biological control can be effectively integrated with pesticides in highly managed cropping systems [20–25]. Recent research in multiple crop systems, however, reveals that integrations of additional management tactics (plant resistance, local vegetation management to conserve natural enemies) results in a variety of short-term pest suppression service outcomes [5,26–30]. Overall, a growing body of data on landscape level ecosystem services reveals higher levels of pest suppression in increasingly diverse multi-crop and/or multi-vegetation systems [5,17,31,32,33,34]. The exceptions (lower pest suppression and higher pest numbers in crops) to this general conclusion reveal the complexity of interactions among pests and natural enemies in agricultural landscapes. For example, polyphagous pests that are not regulated by natural enemies may utilize diverse resources in complex landscapes, increase in number, and ultimately cause more crop damage [5,35]. In addition, the amount, diversity, and temporal availability of natural www.sciencedirect.com
Natural enemies Predators Parasitoids Parasitoids Parasitoids Predators Parasitoids
Contribution Intraguild dynamics Adjustable economic thresholds Parasitoid sequential sampling NET-natural enemy threshold Natural enemy units NET-binomial sequential sampling
vegetation/habitat may not provide the resources necessary for effective conservation of natural enemies [5]. Clearly, more research is needed on landscape-level interactions between pests and natural enemies, but results from a few studies suggest that local and regional pest outbreak or pest suppression areas could be identified and this information could be integrated into dynamic web-based IPM decision support systems to optimize scouting and management efforts [31,36].
Stakeholder view of risks and willingness to incorporate biological control in IPM programs
With such low adoption rates [2,37], it is clear that IPM practitioners must address the socioeconomic factors that prevent stakeholders from utilizing biological control in IPM programs. Studies ranging from documenting personal motivations for utilizing biological control [38–40] to the development of complex socioeconomic evaluation tools [ipmPRiME [41]: Maximum Incremental Social Tolerable Irreversible Costs—MISTICs, [42]] focusing on economics and risks may reveal critical factors to target during outreach efforts [2]. A recent study examining adoption of biological control on Iranian rice farms revealed that biological control adoption was driven by social acceptance within smaller cooperating producer groups who viewed pesticides as harmful [40]. However, studies from IPM programs in the United States and Europe indicate that producers are focused on economic returns and risk adverse management tactics and may consider biological control too risky [42,43]. Perhaps, producers may simply require reliable standardized impact data reports on biological control efficacy, similar to insecticide efficacy reports, that are comparable across cropping systems [44].
User-friendly sampling plans that incorporate biological control Several innovative NET approaches have been incorporated into crop IPM programs [10,45–49,50]. In soybean, dynamic action thresholds have been developed by quantifying the effects of predator ratios (natural enemy units—NEU) and incorporating predictable decision outcomes for soybean aphids [50]. Dynamic binomial sequential sampling plans incorporating natural enemy impacts have been developed in vegetable and wheat Current Opinion in Insect Science 2017, 20:84–89
86 Parasites/parasitoids/biological control
crop systems, and because insect counts during scouting is limited, these plans offer the greatest opportunity for acceptance by producers [36,51,52,53]. However, these plans were developed in geographic regions where natural enemies exert consistent and predictable pest suppression and thus should be validated before incorporation into local IPM programs in other regions [54].
Figure 1
Glance 'n Go Sampling Fall
for
Greenbugs in Winter Wheat
Edition
Economic Threshold for September - December
Field 1
Date
Fall, Threshold = 3
Stop every 30 ft. and look at 3 tillers
Incorporation of biological control into dynamic IPM programs The ‘Glance’n Go’ greenbug + parasitism binomial sequential sampling plan for the Southern Plains (Figure 1) represents a unique and efficient method of incorporating the effects of parasitism into pest management decisions and utilization of this NET occurs via a dynamic web-based decision support system [36,52,53,55]. A survey of wheat producers in the Southern Plains revealed critical stakeholder information including their willingness to help design and utilize a decision support system [56]. An important conclusion from this survey was a recommendation that IPM programs should function like social media sites and connect people to critical data yet produce recommendations based on individual needs. These findings drove the design of a web-based system, myFields.info [36], which currently facilitates: (1) detection/rapid diagnosis of local and regional pest infestations in near real-time, (2) warehousing of preventative and curative web-based IPM approaches available to all users, and (3) data management and useful summarizes of occurrence records at local are regional levels over time. Because myFileds.info was developed using Drupal programming, it is adaptable to any single or multi-crop system that integrates these components. The most important component of this decision support system is that it is open-source, which allows large numbers of users to share detection and management data among farms/ states/regions. The original ‘Glance’n Go’ sampling plan was a critical component of myFields.info and the plan was digitized and optimized for use on personal hand held devices (Figure 2). McCornack and Johnson [55] observed a favorable shift in perception when crop consultants and farmers were able to compare the more complicated, paper-based sampling form (Figure 1) with a digital form freely available on myFields.info (Figure 2); when participants experienced the newer technology firsthand, they were more willing to adopt it. Use of ‘Glance’n Go’ within this dynamic decision support system allows sampling data and management recommendations from multiple wheat fields across a large region to be summarized over time. Consequently, this data facilitates the development and testing of hypotheses related to pest suppression services at increasing spatial scales [31]. myFields.info [36] continues to be refined (see http://myfields.info/ features for list of currently available services) to link all aspects of wheat IPM including varietal development and Current Opinion in Insect Science 2017, 20:84–89
Samples
Mark
for each tiller that has 1 or more mummies
Mark
for each tiller that has 1 or more greenbugs
Stop 1
Stop 2
Stop 3
Stop 4
Stop 5
Tillers 1-15
Maximum Tillers 76 - 90
Mummies Total tillers with 1 or more greenbugs
/ 15
Don't Treat
4 or more
Greenbug Infested Tillers Keep Sampling Don't Treat Treat
1 or less
Between 2 - 9
10 or more
Maximum / 90
Current Opinion in Insect Science
Example of ‘Glance’n Go’ greenbug + parasitism printable worksheet.
deployment, optimization of best management practices, summarization of shared pest and natural enemy data to optimize sampling efforts, and pushing site-specific management recommendations based on real-time and longterm data (Figure 3). Figure 2
Sample for Pest
myFields.info
Method | Locaon | Site | Costs | Samples | Notes | Finish Help Stop 1 of 5 Tiller 1
Tiller 2
Tiller 3
Aphid
Aphid
Aphid
Mummy
Mummy
Mummy
Continue
Back
Cancel Current Opinion in Insect Science
myFields.info digital format for ‘Glance’n Go’ greenbug + parasitism sampling and decision plan. Decision data is summarized over time. www.sciencedirect.com
Incorporating biological control into IPM decision making Giles et al. 87
Figure 3
Research
Education
Variety Performance
Sampling, mapping, & alerts data
Reports & Alerts
Management
flow
Extension
Treatment Decisions presence/absence
res.
sus.
Pest Sampler
Post-planting decisions
how?
new biotype
known biotype
VSS
Gene identification and rapid deployment
Realtime mapping
Diagnostics what? new?
DSS
insect
Changes in land cover and ecosystem services
(e.g. fixed-wing UAS) deploy sampling plan
Biocontrol Estimator
disease
varietal options
digital inquiry (GPDN)
weed Sampling unit (e.g., tiller)
Pre-planting
[county level]
Directed sampling, remote sensing, and digital diagonostics
Alerts
Social Networks
planting wizard
existing var.
Pest ID (DNA, LAMP)
new var.
Best management practices (BMPs)
data sharing manageable unit (e.g., wheat field) Current Opinion in Insect Science
Schematic showing how the multi-crop myFields cyberinfrastructure links varietal development and deployment, optimization of best management practices, summarization of shared pest and natural enemy data to optimize sampling efforts, and pushing site-specific management recommendations based on real-time and long-term data.
Conclusions Natural enemy thresholds are more likely to be adopted in agricultural systems where producers and IPM professional utilize pest action/economic thresholds and natural enemies exert predictable pest suppression [55]. It is our view that natural enemy thresholds are likely best utilized within dynamic multi-crop decision support systems that summarize sampling and decision data over time and space. This data is particularly useful when pest suppression dynamics are predictable among fields and at larger geographic scales [31,36]. Indeed, typical farming systems with multiple fields/crops/habitats would benefit from dynamic models that quantify and summarize farmscale or larger-scale pest suppression by natural enemies [57,58,59]. However, few multi-crop IPM programs exist where NET sampling and decision data can be www.sciencedirect.com
summarized to allow for site-specific management recommendations [2,36].
Acknowledgements We thank three reviewers for their significant contributions to this manuscript. This work was supported in part under project OKLO2935 and USDA-NIFA-RAMP (#2010-51101-21642). This is contribution no. 17-277J from the Kansas Agricultural Experiment Station.
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Current Opinion in Insect Science 2017, 20:84–89
ScienceDirect Incorporating biological control into IPM decision making Kristopher L Giles1, Brian P McCornack2, Tom A Royer1 and Norman C Elliott3 Of the many ways biological control can be incorporated into Integrated Pest Management (IPM) programs, natural enemy thresholds are arguably most easily adopted by stakeholders. Integration of natural enemy thresholds into IPM programs requires ecological and cost/benefit crop production data, threshold model validation, and an understanding of the socioeconomic factors that influence stakeholder decisions about biological control. These thresholds are more likely to be utilized by stakeholders when integrated into dynamic webbased IPM decision support systems that summarize pest management data and push site-specific biological control management recommendations to decision-makers. We highlight recent literature on topics related to natural enemy thresholds and how findings may allow pest suppression services to be incorporated into advanced IPM programs.
Addresses 1 Department of Entomology and Plant Pathology, Oklahoma State University, Stillwater, OK, United States 2 Department of Entomology, Kansas State University, Manhattan, KS, United States 3 USDA-ARS, Stillwater, OK, United States Corresponding author: Giles, Kristopher L ([email protected])
Current Opinion in Insect Science 2017, 20:84–89 This review comes from a themed issue on Parasites/parasitoids/ biological control Edited by Mary M Gardiner and James D Harwood For a complete overview see the Issue and the Editorial Available online 11th May 2017 http://dx.doi.org/10.1016/j.cois.2017.03.009 2214-5745/ã 2017 Elsevier Inc. All rights reserved.
Introduction The challenge of incorporating biological control into multi-tactic Integrated Pest Management (IPM) programs has been highlighted since the formal development of the ‘Integrated Control Concept’ [1]. Naranjo et al. [2] provided a comprehensive summary of research related to the economic value of biological control in managed systems, and ultimately concluded that progress continues, albeit slowly, integrating biological control (Importation, Augmentation, Conservation, Habitat Current Opinion in Insect Science 2017, 20:84–89
Manipulations and/or Natural Enemy Thresholds) with other IPM tactics. Part of the challenge of incorporating biological control is related to evolving production systems (alternative crops, crop diversity, and/or acres) and considerations for IPM implementation and dissemination at individual field and larger agricultural landscape levels. However, approaches designed to enhance pest suppression services by natural enemies at larger agricultural landscapes are unlikely to be utilized if individual farmers do not perceive the benefits as reliable [2,3]. Indeed, despite evidence for predictable pest suppression by natural enemies at increasing spatial scales, biological control outcomes in managed field-crop agroecosystems (with and without natural habitat) are highly variable and dependent upon vegetation complexity, pest and natural enemy ecology, and/or agricultural practices [4,5]. Producers select crop cultivars, rotate crops, and/or incorporate new crops into their production systems. Because the effects of these changes on pest activity in agricultural landscapes are difficult to predict, producers often rely on short-term management tactics (primarily insecticide use) to suppress insect pests [3,6]. Decisions on insecticide use are based on crop value/risk considerations, and often available economic thresholds (ETs) for key and sporadic pests [3,6]. The widespread utilization of ETs in cropping systems has, for better or worse, unified IPM stakeholders, and perhaps, consideration of predictable suppression by natural enemies during pest scouting (natural enemy thresholds or NETs) deserves more scientific attention because it provides the greatest opportunity for incorporation of biological control in IPM programs [2,7–9]. Indeed, there are recent examples of IPM programs that incorporate natural enemy services into pest management decisions using field-level and/or areawide data from scouting events (Table 1). However, development of NETs and there utilization by producers requires that multiple criteria are met: (1) demonstration of reliable suppression by natural enemies via short and long-term population dynamics data, including model development and validation, (2) sampling plans that are user-friendly, easily accessible and allow for system-specific management recommendations that are integrated with other production system components, and (3) stakeholders believe that the benefits associated with incorporation of natural enemies during pest management decisions outweigh the risks. www.sciencedirect.com
Incorporating biological control into IPM decision making Giles et al. 85
Table 1 Examples of incorporating natural enemy services into pest management decisions Authors
Ref.
System
Pests
Shakya et al. Walker et al. Rocco and Greco Nofemela Hallett et al. Royer et al.
[45] [46] [47] [49] [50] [53]
Strawberry Tomato Blueberry Cabbage Soybean Wheat
Thrips Helicoverpa armigera Aphids Plutella xylostella Aphids Aphids
Demonstrating suppression by natural enemies in managed crop systems Recently published data quantifying the impact of natural enemies on crop pests are rare [2]. Multi-year evaluations in cotton systems do provide, however, data sets critical for pest-natural enemy population dynamics model development and validation [10,11]. Other recent modeling studies on biological control reveal promising outputs for future validation research and potential incorporation in managed systems. Of particular interest are models evaluating dynamics among pests, natural enemies and pesticides [12,13,14,15], and simulations that suggest how pest-natural enemy dynamics can be stabilized when negative interactions between natural enemies occur [16]. Interestingly, stable pest dynamics were more likely to occur when unstable pest-predator and pest-pathogen models were combined [16]. These outcomes are consistent with the growing body of evidence from microcosm and field studies that link pest suppression and natural enemy diversity [17]. In-depth evaluations of ground beetle predators indicate that niche partitioning can occur among species, and pest suppression can be driven not solely by the number of species, but the presence and activity of a few key natural enemy species [18,19]. More applied studies provide evidence that biological control can be effectively integrated with pesticides in highly managed cropping systems [20–25]. Recent research in multiple crop systems, however, reveals that integrations of additional management tactics (plant resistance, local vegetation management to conserve natural enemies) results in a variety of short-term pest suppression service outcomes [5,26–30]. Overall, a growing body of data on landscape level ecosystem services reveals higher levels of pest suppression in increasingly diverse multi-crop and/or multi-vegetation systems [5,17,31,32,33,34]. The exceptions (lower pest suppression and higher pest numbers in crops) to this general conclusion reveal the complexity of interactions among pests and natural enemies in agricultural landscapes. For example, polyphagous pests that are not regulated by natural enemies may utilize diverse resources in complex landscapes, increase in number, and ultimately cause more crop damage [5,35]. In addition, the amount, diversity, and temporal availability of natural www.sciencedirect.com
Natural enemies Predators Parasitoids Parasitoids Parasitoids Predators Parasitoids
Contribution Intraguild dynamics Adjustable economic thresholds Parasitoid sequential sampling NET-natural enemy threshold Natural enemy units NET-binomial sequential sampling
vegetation/habitat may not provide the resources necessary for effective conservation of natural enemies [5]. Clearly, more research is needed on landscape-level interactions between pests and natural enemies, but results from a few studies suggest that local and regional pest outbreak or pest suppression areas could be identified and this information could be integrated into dynamic web-based IPM decision support systems to optimize scouting and management efforts [31,36].
Stakeholder view of risks and willingness to incorporate biological control in IPM programs
With such low adoption rates [2,37], it is clear that IPM practitioners must address the socioeconomic factors that prevent stakeholders from utilizing biological control in IPM programs. Studies ranging from documenting personal motivations for utilizing biological control [38–40] to the development of complex socioeconomic evaluation tools [ipmPRiME [41]: Maximum Incremental Social Tolerable Irreversible Costs—MISTICs, [42]] focusing on economics and risks may reveal critical factors to target during outreach efforts [2]. A recent study examining adoption of biological control on Iranian rice farms revealed that biological control adoption was driven by social acceptance within smaller cooperating producer groups who viewed pesticides as harmful [40]. However, studies from IPM programs in the United States and Europe indicate that producers are focused on economic returns and risk adverse management tactics and may consider biological control too risky [42,43]. Perhaps, producers may simply require reliable standardized impact data reports on biological control efficacy, similar to insecticide efficacy reports, that are comparable across cropping systems [44].
User-friendly sampling plans that incorporate biological control Several innovative NET approaches have been incorporated into crop IPM programs [10,45–49,50]. In soybean, dynamic action thresholds have been developed by quantifying the effects of predator ratios (natural enemy units—NEU) and incorporating predictable decision outcomes for soybean aphids [50]. Dynamic binomial sequential sampling plans incorporating natural enemy impacts have been developed in vegetable and wheat Current Opinion in Insect Science 2017, 20:84–89
86 Parasites/parasitoids/biological control
crop systems, and because insect counts during scouting is limited, these plans offer the greatest opportunity for acceptance by producers [36,51,52,53]. However, these plans were developed in geographic regions where natural enemies exert consistent and predictable pest suppression and thus should be validated before incorporation into local IPM programs in other regions [54].
Figure 1
Glance 'n Go Sampling Fall
for
Greenbugs in Winter Wheat
Edition
Economic Threshold for September - December
Field 1
Date
Fall, Threshold = 3
Stop every 30 ft. and look at 3 tillers
Incorporation of biological control into dynamic IPM programs The ‘Glance’n Go’ greenbug + parasitism binomial sequential sampling plan for the Southern Plains (Figure 1) represents a unique and efficient method of incorporating the effects of parasitism into pest management decisions and utilization of this NET occurs via a dynamic web-based decision support system [36,52,53,55]. A survey of wheat producers in the Southern Plains revealed critical stakeholder information including their willingness to help design and utilize a decision support system [56]. An important conclusion from this survey was a recommendation that IPM programs should function like social media sites and connect people to critical data yet produce recommendations based on individual needs. These findings drove the design of a web-based system, myFields.info [36], which currently facilitates: (1) detection/rapid diagnosis of local and regional pest infestations in near real-time, (2) warehousing of preventative and curative web-based IPM approaches available to all users, and (3) data management and useful summarizes of occurrence records at local are regional levels over time. Because myFileds.info was developed using Drupal programming, it is adaptable to any single or multi-crop system that integrates these components. The most important component of this decision support system is that it is open-source, which allows large numbers of users to share detection and management data among farms/ states/regions. The original ‘Glance’n Go’ sampling plan was a critical component of myFields.info and the plan was digitized and optimized for use on personal hand held devices (Figure 2). McCornack and Johnson [55] observed a favorable shift in perception when crop consultants and farmers were able to compare the more complicated, paper-based sampling form (Figure 1) with a digital form freely available on myFields.info (Figure 2); when participants experienced the newer technology firsthand, they were more willing to adopt it. Use of ‘Glance’n Go’ within this dynamic decision support system allows sampling data and management recommendations from multiple wheat fields across a large region to be summarized over time. Consequently, this data facilitates the development and testing of hypotheses related to pest suppression services at increasing spatial scales [31]. myFields.info [36] continues to be refined (see http://myfields.info/ features for list of currently available services) to link all aspects of wheat IPM including varietal development and Current Opinion in Insect Science 2017, 20:84–89
Samples
Mark
for each tiller that has 1 or more mummies
Mark
for each tiller that has 1 or more greenbugs
Stop 1
Stop 2
Stop 3
Stop 4
Stop 5
Tillers 1-15
Maximum Tillers 76 - 90
Mummies Total tillers with 1 or more greenbugs
/ 15
Don't Treat
4 or more
Greenbug Infested Tillers Keep Sampling Don't Treat Treat
1 or less
Between 2 - 9
10 or more
Maximum / 90
Current Opinion in Insect Science
Example of ‘Glance’n Go’ greenbug + parasitism printable worksheet.
deployment, optimization of best management practices, summarization of shared pest and natural enemy data to optimize sampling efforts, and pushing site-specific management recommendations based on real-time and longterm data (Figure 3). Figure 2
Sample for Pest
myFields.info
Method | Locaon | Site | Costs | Samples | Notes | Finish Help Stop 1 of 5 Tiller 1
Tiller 2
Tiller 3
Aphid
Aphid
Aphid
Mummy
Mummy
Mummy
Continue
Back
Cancel Current Opinion in Insect Science
myFields.info digital format for ‘Glance’n Go’ greenbug + parasitism sampling and decision plan. Decision data is summarized over time. www.sciencedirect.com
Incorporating biological control into IPM decision making Giles et al. 87
Figure 3
Research
Education
Variety Performance
Sampling, mapping, & alerts data
Reports & Alerts
Management
flow
Extension
Treatment Decisions presence/absence
res.
sus.
Pest Sampler
Post-planting decisions
how?
new biotype
known biotype
VSS
Gene identification and rapid deployment
Realtime mapping
Diagnostics what? new?
DSS
insect
Changes in land cover and ecosystem services
(e.g. fixed-wing UAS) deploy sampling plan
Biocontrol Estimator
disease
varietal options
digital inquiry (GPDN)
weed Sampling unit (e.g., tiller)
Pre-planting
[county level]
Directed sampling, remote sensing, and digital diagonostics
Alerts
Social Networks
planting wizard
existing var.
Pest ID (DNA, LAMP)
new var.
Best management practices (BMPs)
data sharing manageable unit (e.g., wheat field) Current Opinion in Insect Science
Schematic showing how the multi-crop myFields cyberinfrastructure links varietal development and deployment, optimization of best management practices, summarization of shared pest and natural enemy data to optimize sampling efforts, and pushing site-specific management recommendations based on real-time and long-term data.
Conclusions Natural enemy thresholds are more likely to be adopted in agricultural systems where producers and IPM professional utilize pest action/economic thresholds and natural enemies exert predictable pest suppression [55]. It is our view that natural enemy thresholds are likely best utilized within dynamic multi-crop decision support systems that summarize sampling and decision data over time and space. This data is particularly useful when pest suppression dynamics are predictable among fields and at larger geographic scales [31,36]. Indeed, typical farming systems with multiple fields/crops/habitats would benefit from dynamic models that quantify and summarize farmscale or larger-scale pest suppression by natural enemies [57,58,59]. However, few multi-crop IPM programs exist where NET sampling and decision data can be www.sciencedirect.com
summarized to allow for site-specific management recommendations [2,36].
Acknowledgements We thank three reviewers for their significant contributions to this manuscript. This work was supported in part under project OKLO2935 and USDA-NIFA-RAMP (#2010-51101-21642). This is contribution no. 17-277J from the Kansas Agricultural Experiment Station.
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