NC1026: Characterize Weed Population Dynamics for Improved Long-Term Weed Management Decision Making (NC202)
Statement of Issues and Justification
Weed science has lagged behind other pest management disciplines in the development of an IPM approach to weed management. One major impediment to developing truly integrated weed management systems is a lack of information on weed biology and ecology at both local and regional scales. Unless all weed management efforts are to be reactive, rather than proactive, we need to begin adressing the question, Why does a given weed species succeed under certain conditions and not others?The NC-202 project is a well-established group of dedicated scientists from across the north-central region interested in understanding weed/crop interactions on a regional scale thereby facilitating a much broader ecological discussion focused on patterns and processes in the context of weed management decision making. Specifically, we are interested in developing integrated strategies that focus on long-term solutions to complex weed management problems while reducing our reliance on herbicides. Our strategy is to link weed biology/ecology information with economic cost-benefit analysis through bioeconomic weed management decision support systems.
A more unified framework is needed to incorporate risk in the development of weed management recommendations and decision support systems. Demography, or the study of population dynamics, is an excellent unifying structure for weed science. It provides a common language and tools for studying weeds. Most cropping systems of the Corn Belt are dominated by annual weeds, which all have the same basic life cycle: they germinate, grow, and produce seeds that then give rise to new plants or remain dormant in the soil. This permits the comparison of demographic rates within and between annual weed species at different times and locations. Unlike most previous studies of weed population dynamics, which looked at only one or two parameters at a time, the project proposed here will take a whole life cycle approach. This approach will help describe the relationship between different life stages and will allow us to develop a probabilistic understanding of weed populations in response to spatial, temporal and management variation.
Our current research proposal outlines a plan to enhance our understanding of weed population dynamics at both the local and regional scale. The project will focus on deliverables; providing a robust assessment of the future cost of weed escapes that will enable farmers to better balance the short- and long-term objectives of their weed control plans. By leveraging substantial research and development investments in the WeedSOFT decision support system, the NC-202 group will be able to deliver knowledge regarding weed demography to farmers within a familiar and widely distributed framework.
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