Performing Department
Plant Biology
Non Technical Summary
Weed management is a priority issue for Northeastern farmers, particularly with the increasing prevalence of organic production, the rise of herbicide resistant weeds, and the recent increase in small farms and urban farming. Providing seedling emergence information so that farmers can effectively time their weed management operations can increase efficacy of control, reduce labor costs, and minimize any negative environmental impacts. Therefore, there is an urgent need for the development of time-specific weed management tools to help address the frequently asked, yet to be answered, question ofwhen is the "right" time to control weeds? Weed seedling emergence is a complex process regulated by a multitude of internal (e.g. species-specific parameters such as base temperature, base water potential) and environmental (e.g. soil temperature and moisture) factors. No weed management decision support tool exists for the Northeastern region of the United States, despite recent advances in our understanding of regional weed emergence patterns and developments in fine-scale weather prediction and soil moisture modeling. Therefore, there is a need for collecting weed emergence data across the region to validate and refine existing weed emergence models in order to produce a web-hosted weed emergence predictive tool for use by farmers, extension personnel, crop consultants, and the general public.Research plots will be established in various sites across the northeastern US. Each site will include two treatments, one with initial tillage in a field with a history of tillage and one with no tillage in a field with a history of no-till agriculture. Eight 1 m2 plots for each treatment will be established, for a total of sixteen plots per site. All emerged weeds of the selected species of interest will be identified and counted on a weekly basis and emerged plants will be removed. Simultaneously, environmental data (precipitation, air and soil temperature) will be collected at each experimental site. Collection of these data for the targeted weed species will help to validate/refine the preliminary emergence prediction tool developed by Cornell University.Ultimately, our goal is to develop and validate a user-friendly, online decision support tool for the real time prediction of weed emergence in the northeastern US. The decision support tool will consider GPS location, soil type, tillage, crop data, and accesses weather history to provide percent emergence of the farmer's problem weeds at that location.
Animal Health Component
100%
Research Effort Categories
Basic
0%
Applied
100%
Developmental
0%
Goals / Objectives
Link Northeastern weed emergence timing data to existing weed emergence models and modern weather prediction models to create an online tool for farmers that will help them plan their weed management for optimal weed control. This tool will include three weeds that are problematic across the region: common lambsquarters (Chenopodium album), redroot pigweed (Amaranthus retroflexus) and large crabgrass (Digitaria sanguinalis). Common ragweed (Ambrosia artemisiifolia) will also be included in the northern portion of the Northeast and morningglory species (Ipomoea spp.) in the southern portion of the region. Individual participating states may also include one additional species of particular interest to their state.
Collect weed emergence data across the region to validate and refine the existing weed emergence models to fit Northeastern data, and refine the decision support tool through testing by select farmers and extension staff.
Project Methods
The overarching goal of the project is to work collaboratively across the Northeast to optimize the ability of farmers to manage weeds in agricultural systems, despite the challenges of a changing climate and increasing prevalence of herbicide resistant weeds. Recent advances in weed ecological research and technology in general have opened the door to more targeted weed management decision support tools, and developing such tools will bring weed management in the Northeast into the 21stcentury.Objective 1.The emergence model equations published in Myers et al. (2005) were used to create a preliminary emergence prediction tool by fitting existing weed emergence data to precipitation, temperature and soil data for two research farm locations central in New York State. This prediction tool uses the soil temperature model of Bittelli et al. (2015) as presented in their book [Soil Physics with Python, 1st Edition Oxford University Press]. The model is linked to gridded daily temperature and precipitation data via the Applied Climate Information System (DeGaetano et al. 2014).The temperature grid is based on the methods ofDeGaetano & Belcher (2007) and the precipitation grid uses the procedure outlined in DeGaetano & Wilks (2009). The model also requires daily evapotranspiration for which we use DeGaetano et al. (1994) to compute pan evaporation, which is then adjusted to bare soil evaporation using Allen et al. (2006). The forecast data are extracted from the National Digital Forecast Database (NDFD) (Glahn & Ruth 2003). The resulting pilot model is available athttps://alexsinfarosa.github.io/weed-modelV2/. While the pilot lacks the ability to alter soil characteristics and soil moisture cutoff mechanisms, it serves as a proof-of-concept for the proposed tool. We have tested the resulting tool against data collected by DiTommaso's research group in two extreme precipitation years (2016, 2017) in New York State (DiTommaso et al. 2018), and incorporated soil moisture cutoffs from WeedCast (Forcella et al. 1998). At the end of Year 1, data from the Multistate project will be used to test both the emergence equations and assumptions made in the decision support tool and those on which WeedCast was based. We will use the equations for each species with the closest fit and modify them as needed to fit the emergence patterns observed across the Northeast. Data from Year 2 will be used to validate the resulting tool and refine its fit across the region. In addition, a select subset of cooperating farmers and extension educators from each participating state will test a draft of the online decision support tool in year 2. Their qualitative feedback will be used to refine the online interface. In Year 3, data from research and farmer input will be used to further validate and refine the model, and a fully functional version will be installed on the NEWA site (http://newa.cornell.edu/.Objective 2.All regional collaborators will establish research plots to validate/refine the emergence model. This proposal focuses on warm season annual weeds, which are difficult to control and are projected to become more problematic with climate change. Data will be collected from earliest available crop planting date until the regional date of soybean canopy closure. Dates will vary by site, as the study area extends from Virginia to Maine and annual weather patterns influence planting date. Each site will include two treatments, one with initial tillage in a field with a history of tillage and one with no tillage in a field with a history of no-till agriculture. Eight 1 m2plots for each treatment will be established, for a total of sixteen plots per site. Either 0.25 or 0.5 m2subplots will be sampled per plot, depending on the density of weed seedlings. Data will be collected weekly; all emerged weeds of the selected species of interest will be identified and counted, and all emerged plants removed without disrupting the soil (clipped or pinched). Each participating institution will plant a mixture of three species of common interest across the region, selected by the collaborators: common lambsquarters (Chenopodium album), redroot pigweed (Amaranthusretroflexus) and large crabgrass (Digitaria sanguinalis). In addition, southern states will also incorporate morningglories (Ipomoeaspp.) into the weed mix, while northern states will plant common ragweed (Ambrosia artemisiifolia). These species were selected by requesting the top five most problematic weeds from the collaborating weed scientists, and selecting the species that appeared most frequently in their lists. All species except large crabgrass are among the Weed Science Society of America's (WSSA) top ten most troublesome or common weeds, and three species (common ragweed, common lambsquarters, and redroot pigweed) currently have herbicide resistant biotypes in the participating states. Finally, each location will add an additional species critical for their state that is not part of the larger study; these species will be added to the decision support tool for that state.The cooperating researchers from across the Northeast will meet annually to share data and experiences, refine the study design as necessary, and plan for the upcoming year.