Progress 09/01/09 to 08/31/12
Outputs
OUTPUTS: Wheat, the most important cereal crop in the Northern Hemisphere, is at-risk for an approximate 10% reduction in worldwide production due to animal pests. One of the most damaging pests of wheat in North America is the Russian wheat aphid, Diuraphis noxia (Kurdjumov). Management strategy is frequently informed by models that describe the population dynamics of important crop pests such as D. noxia and because of its significant economic impact, many population dynamic models have been developed. Yet, limited effort has ensued to compare and contrast models for their strategic applicability and quality. We tested eighteen D. noxia population dynamic models with the goal of creating a model-averaged predictive model to describe inter-annual variation of this pest species across the Great Plains region. Our findings were used to delineate pest intensity on winter wheat across much of the Great Plains and will help improve D. noxia management strategy (Merrill and Peairs 2012). Resulting quantitative models will be applicable to predicting pest outbreaks as well as developing risk scenarios and contingency plans. Moreover, these efforts are being combined with climate change predictions to provide scenarios simulating changes to D. noxia intensity throughout this region. We advanced methodology for developing degree day models using climate change scenario inputs and have submitted that work for publication. Specifically, large errors can occur if the variation in the temperature signal is not incorporated, which is analogous to confirming that extreme events, such as heat waves, are included in the modeling procedure. Oral presentations Merrill, S. C., Tewksbury, J.J., Deustch, C. A., Battisti, D. S., Naylor, R. L. (2012) Using relationships between temperature, metabolism and consumption to predict the effects of climate change on pest pressure. Invited symposium. Entomological Society of America Annual Meeting. November. Knoxville, TN Merrill, S. C., Tewksbury, J.J., Deustch, C. A., Battisti, D. S., Naylor, R. L. (2012) Using relationships between temperature, metabolism, and consumption to predict damage from pests in our changing climate. Plant and Soil Science Weekly Seminar Series Merrill, S. C. (2012) Predicting the effects of climate change on agricultural pest incidence: How secure is our food supply Invited seminar for the Interdisciplinary Climate Change Seminar series. University of Idaho. March 2012. Moscow, ID PARTICIPANTS: Dr Scott Merrill has worked extensively to further theoretical modeling efforts. Dr Frank Peairs has provided extensive oversight and direction throughout the project. TARGET AUDIENCES: We have published the major findings of this proposal in the scientific literature. Additionally, multiple additional manuscripts have been submitted or are in preparation. We intend to disseminate updated best management practices for scouting for D. noxia to wheat stakeholders through publication in the High Plains Guide at: http://wiki.bugwood.org/HPIPM:Russian_Wheat_Aphid PROJECT MODIFICATIONS: Not relevant to this project.
Impacts
We have produced and published a predictive model for one of the most damaging pests of wheat in North America is the Russian wheat aphid, Diuraphis noxia (Kurdjumov). This predictive model incorporates eighteen D. noxia population dynamic models each of which is driven by weather covariates. Results suggest negative effects of fall and spring precipitation on aphid intensity, and the positive effects associated with alternate food source availability. The predictive model can be used with up-to-date weather information to provide an estimate of the pest pressure on wheat. Our findings were used to delineate average D. noxia intensity on winter wheat across much of the Great Plains and will help improve D. noxia management strategy. Through the creation of weather covariate layers used in the D. noxia predictive model, we noticed an error in the commonly used assumption that changes in temperature linearly equate to changes in accumulated heat units. Correction of this faulty assumption should lead to advances in climate change forecasting knowledge and should result in a fundamental shift in global change modeling of habitat and phenology. Publications on this subject are pending.
Publications
- Merrill, S. C. and F. B. Peairs. 2012. Quantifying Russian wheat aphid pest intensity across the Great Plains. Environmental Entomology 41:1505-1515.
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Progress 09/01/10 to 08/31/11
Outputs
OUTPUTS: Activities: A novel spatio-temporal methodology for calculating degree days has been developed and used for predicting insect phenology and habitat quality. Russian wheat aphid data have been modeled to create aphid day measurements for each site for each year. Weather data have been synthesized for each site for each year. Eighteen Russian wheat aphid population dynamic models from the literature have been transformed into spatially explicit aphid day models. Aphid day models were tested for their ability to predict the number of Russian wheat aphid days accumulating on wheat in the Great Plains. Using multimodel inference, an aphid day model for the Great Plains has been developed. This spatially explicit model depicts habitat quality of the aphid pest on wheat and has been submitted for publication. Thus, objectives 1 and 2 of this proposal are nearing completion. Models developed will serve as the underlying model for all remaining objectives. Additionally, Degree day models have been used to develop spatially explicit wheat growth stage layers. Crop ontogeny models will be synthesized with the Russian wheat aphid habitat quality models to develop spatio-temporal models describing crop risk for economic infestations across the Great Plains. Additionally, current climatic inputs will be perturbed to match likely climate change scenarios, resulting in pest intensity prediction models for the region. Events: Oral presentations Merrill, S. C. (2011) A Series of Surprises: Modelling the Pest Agroecosystem Landscape. Commonwealth Scientific and Industrial Research Organization (CSIRO) Brisbane. June 2011. Brisbane, Australia Merrill, S. C. (2011) Revisiting our assumptions about the pest agroecosystem landscape. NCEAS (National Center for Ecological Analysis and Synthesis) Ecolunch Seminar Series. June 2011. Santa Barbara, CA Poster presentations Merrill, S. C. and F. B. Peairs (2010) How will climate change affect the risk of crop infestation by the Russian wheat aphid. USDA-Agriculture & Food Research Initiative. Arthropods & Nematodes Biology & Management Programs Awardee Workshop. December. San Diego, CA Merrill, S. C. and F. B. Peairs (2010) How will climate change affect the risk of crop infestation by the Russian wheat aphid. Entomological Society of America Annual Meeting. December. San Diego, CA Products: Podcast Merrill, S. C. (2011) Could Organic Farming Threaten Our Food Supply Host: Ranganathan, J. on Curiouser and Curiouser. Miller-McCune. Podcast: http://www.miller-mccune.com/curiouser/could-organic-farming-threaten -our-food-supply-34734/ PARTICIPANTS: Dr Scott Merrill has worked extensively to further theoretical modeling efforts. Dr Frank Peairs has provided extensive oversight and direction throughout the project. TARGET AUDIENCES: Nothing significant to report during this reporting period. PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Change in knowledge: Through degree day modeling efforts, we noticed an error in the commonly used assumption that changes in temperature linearly equate to changes in accumulated heat units. Correction of this faulty assumption should lead to advances in climate change forecasting knowledge and should result in a fundamental shift in global change modeling of habitat and phenology. Publications on this subject are pending. We have found that aphid days do not appear to correlate well with yield loss if considered across a large spatial extent and without incorporating differences in crop growth stage. A manuscript describing aphid days and discussing the inconsistency between apparent yield loss and aphid days has been submitted. Additionally, modeling efforts that incorporate crop ontology into pest management scenarios have been initiated. This work should advance integrated pest management strategy.
Publications
- No publications reported this period
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Progress 09/01/09 to 08/31/10
Outputs
OUTPUTS: Quantitative knowledge of pest population dynamics and eco-physiological factors are essential for the development and implementation of quality integrated pest management. One of the principle pests of wheat across the Great Plains is the Russian wheat aphid (RWA), Diuraphis noxia (Kurdjumov). This aphid pest has caused damage in excess of a billion dollars in the last two decades. We intend to use a database developed over four years, across five states with approximately 70,000 data points to develop an increased understanding of RWA. Specific objectives are as follows: 1) Investigate seasonal dynamics of RWA under the influence of weather variables. 2) Model the habitat of RWA using agro-climatic conditions. 3) Quantify the effects of aphid natural enemies on their pest populations. 4) Develop action thresholds for RWA. And 5) develop a spatiotemporal model of aphid population dynamics in wheat. We intend to address all three program priorities. Specifically, we propose to 1) determine eco-physiological mechanisms that affect abundance of RWA; 2) characterize population ecological processes that affect establishment (models detailing likely habitat and spatiotemporal abundance) of RWA; and 3) elucidate multitrophic interactions between RWA, beneficial organisms and winter wheat. Resulting quantitative models will be applicable to predicting pest outbreaks as well as developing risk scenarios and contingency plans. Results will be made available through a dedicated RWA website inclusive of prediction models using current weather conditions, management suggestions and research findings. Activities: Modeling research for this project is ongoing. Currently, weather data has been developed for all sites for all years. Russian wheat aphid data have been modeled to create aphid day calculations for each site for each year. Habitat layers, to be used to develop crop risk scenarios, are currently being developed, inclusive of habitat layers depicting snow cover, spring fecundity, oversummering food resource availability, rainfall events, overwintering likelihood, and a layer depicting density of natural enemies. Thus, objective 1 of this proposal is nearing completion, as well as the underlying model for all remaining objectives. Events: Merrill, S. C. (2010) Understanding the link between Precision Agriculture andLandscape Ecology. NCEAS (National Center for Ecological Analysis and Synthesis)Ecolunch Seminar Series. April 2010. Santa Barbara, CA PARTICIPANTS: Dr Scott Merrill has worked extensively to further theoretical modeling efforts. Dr Frank Peairs has provided extensive oversight and direction throughout the project. TARGET AUDIENCES: Efforts to inform the scientific community about advances in precision agriculture through our habitat modeling objective were addressed in the following invited seminar presentation: Merrill, S. C. (2010) Understanding the link between Precision Agriculture and Landscape Ecology. NCEAS (National Center for Ecological Analysis and Synthesis)Ecolunch Seminar Series. April 2010. Santa Barbara, CA PROJECT MODIFICATIONS: Nothing significant to report during this reporting period.
Impacts
Change in knowledge: Through the creation of spatiotemporal weather covariate layers, we noticed an error in the commonly used assumption that changes in temperature linearly equate to changes in accumulated heat units. Correction of this faulty assumption should lead to advances in climate change forecasting knowledge and should result in a fundamental shift in global change modeling of habitat and phenology. Publications on this subject are pending.
Publications
- No publications reported this period
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