Location: Pest Management ResearchTitle: Data driven weed management: Tracking herbicide resistance at the landscape scale Author
|Endres, A. Bryan - University Of Illinois|
|Schlessinger, Lisa - University Of Illinois|
Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 4/1/2017
Publication Date: 4/1/2017
Citation: Endres, A., West, N.M., Evans, J.A., Schlessinger, L. 2017. Data driven weed management: Tracking herbicide resistance at the landscape scale. In: Kraska, J., Honaker, B., Macy, M., Spencer-Kuhlmann, L., Williams, N., Mantell, S., Harris, T., Hemme, M., editors. Agricultural Law Symposium, Plowing New Ground: The Intersection of Technology and Agricultural Law. Little Rock, AR: University of Arkansas at Little Rock Law Review. p. 425-436.
Interpretive Summary: Managing herbicide resistance in the landscape will require increasing the diversity of weed control strategies at scales above the single farm level. However, diversified management schemes are minimally implemented among individual growers, and short term economic decisions favored by individuals often run counter to long term sustainable targets for resistance management. Widespread adoption of multiple weed control tactics is necessary to manage resistance issues at the most effective spatial scale, but requires many individuals buy-in to the solution. The increasingly diverse ownership and tenancy patterns in Midwestern agricultural lands further complicates this requirement, and existing regulatory approaches to weed management are insufficient to address herbicide resistance at landscape scales. We propose that an integrated, data-driven simulation model that predicts the spread of herbicide resistance traits, given existing social and legal aggregations, can help evaluate our capacity to slow the spread and prevalence of herbicide resistance, while improving our ability to examine how different social or economic schemes for large scale management implementation might affect the spread of resistance.
Technical Abstract: Limiting the prevalence of herbicide resistant (HR) weeds requires consistent management implementation across space and time. Although weed population dynamics operate at scales above farm-level, the emergent effect of neighboring management decisions on in-field weed densities and the spread of resistance traits in the landscape remains unclear. Further, our ability to empirically test these emergent outcomes is limited by socio-cultural and economic barriers and management heterogeneity that impede contiguous implementation across space. There is well-supported agreement that large scale implementation of diversified weed management is key to combating new weed invasions and the rise of HR. However, extensive evidence suggests scientific recommendations are minimally implemented by stakeholders, and this limitation widens a significant knowledge gap in our ability to evaluate or set long term management targets. Moreover, existing regulatory approaches to weed management generally fail to address herbicide resistance at the landscape scale and the increasingly diverse ownership and tenancy patterns in Midwestern farmland add to the complexity. An integrated simulation that predicts the spread of herbicide resistant traits, along with existing social and legal aggregations, however, can improve our capacity to slow the growth and prevalence of herbicide resistance in the agricultural context.