Skip to main content
ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Sustainable Agricultural Systems Laboratory » Research » Research Project #439415

Research Project: Precision Integrated Weed Management in Conventional and Organic Crop Production Systems

Location: Sustainable Agricultural Systems Laboratory

2021 Annual Report

Objective 1: Define theoretical, mechanistic, and applied underpinnings of weed management tactics in cover crop-based agronomic crop production systems to develop precision integrated weed management technologies. [NP304, Component 2, Problem Statement 2A&C] • Sub-objective 1.A. Determine optimal interactions between weed management tactics (physical, chemical, and biological) to address herbicide-resistant weeds and weed-crop competition. • Sub-objective 1.B. Develop cover crop and weed detection, identification, and mapping tools to assess the success of IWM systems and inform autonomous robotic weed control and decision support tools using machine vision technologies coupled with artificial intelligence (AI) and machine learning (ML).

To examine the interactions between cover crops and herbicides on weed-crop competition and weed suppression, we will quantify the interactive effect of cereal rye root and shoot residues with S-metolachlor rate on four weed species in the greenhouse. This will be conducted at Beltsville, Maryland and Urbana-Champaign. Next we will conduct field experiments that determine if cover crop management timing under field conditions influences the interactive effect between cereal rye and S-metolachlor, and whether the effect is due to physical (shoots) or chemical (roots via allelochemicals) mechanisms. This will be accomplished by establishing cover crop termination timing gradients and establishing a herbicide dose-response. The implications of management tactics will be further explored within a long-term cropping systems experiment. Here we will test the impact that harvest-time weed seed control, herbicides, and cover crops have on weed population dynamics and management genotypes in soybean by monitoring emergence, growth, survivorship, and fecundity of targeted summer annual weeds. To assess the performance of our IWM management systems at the field-scale, we will quantify the interaction between climate, soil, and cover crop management on cover crop performance and resulting weed suppression in an existing on-farm network across the U.S. Such an approach requires sensing technology to capture both the spatial distribution and performance. Therefore, we will develop digital weed image libraries from the on-farm network, annotate images, train machine learning models for image recognition, and test these algorithms with autonomous robots for weed control. Further, we will provide this information to growers through decision support tools.

Progress Report
This is the first year of this project. All five of the five milestones were fully met. Over the past year, nineteen peer-reviewed manuscripts and a book chapter were submitted of these, fourteen were accepted for publication while the remaining manuscripts are in review or have been revised and resubmitted. The manuscripts presented results from cover crop-based corn and soybean production cover crop-based corn and soybean production projects, cover crop management for optimal weed and nitrogen management, and multi-tactic integrated weed management solutions that combine cover crop and herbicides, reduced-tillage, and harvest weed seed control management. We have gained a better understanding of the role of allelochemicals in weed suppression. Published research from this team also includes advancement in the use of computer vision and machine learning for monitoring crop and weed identification and performance and water stress. An extensive body of work was published on defining which weed species are viable candidates for harvest weed seed control, and a review paper was published on the state of harvest weed seed control in North America. Critical research was published on the role of moisture and temperature in cover crop decomposition kinetics. In addition, new cover crop germplasm has been released, and other improvements have been made on disease resistance, growth performance, and potential weed suppression with allelochemicals. For Sub-objective 1A, field trials were initiated to quantify the potential for synergistic interactions between herbicides and cover crops on weed suppression; all samples were collected. The Lower Chesapeake Bay-Long-Term Agricultural Research multi-tactic weed management cropping systems experiment initiated with past Area-Wide funds has been maintained and is in its seventh year; all samples were collected. Data collection on weed populations and seed bank dynamics in two long-term cropping system experiments were completed and the data analyzed. For Sub-objective 1B, the on-farm monitoring of cover crop performance and the subsequent impact on weed suppression has been initiated. Several hand-held and tractor-mounted sensing technologies were developed to monitor cover crop performance, weed species identification, and biomass estimation. The computer vision and machine learning efforts have promise for replacing destructive sampling. The camera systems used for building a weed image repository and 3-dimensional reconstruction of plant biomass have been tested on a greenhouse semi-autonomous robotic system (BenchBot). The unit is being calibrated, and the cyberinfrastructure necessary for data acquisition and management is under development. Work within the ARS Area-Wide herbicide-resistant weed management project 2014-2020 has been completed and renewal for this team's efforts have been awarded (2021-2027) to develop multi-tactic weed management strategies, conduct on-farm research and demonstration, construct decision support tools, and provide extension and outreach through the website. This website provides integrated weed management (IWM) content for U.S. field crop producers, including case studies, video content, social media updates, and links to decision support tools. An assessment of harvest weed seed control in North America was published.

1. Moisture, not temperature, is the primary factor influencing the rate of cover crop residue decomposition and nutrient release in conservation tillage systems. Growers need site-specific knowledge on how cover crop surface residues decompose and release nutrients for planning and decision making. ARS scientists in Beltsville, Maryland, conducted on-farm studies across the Eastern U.S. and controlled incubation studies examining the effects of climate on cover crop residue decomposition and nutrient release. Moisture proved to be the primary driver influencing cover crop residue decomposition. This was because temperatures required for optimal decomposition were always achieved during the summer regardless of latitude. Higher temperatures could reduce decomposition as surface residues quickly dried down, causing moisture limitations. This work was used to improve adaptive nitrogen calculators used by researchers, farmers, and policymakers.

2. Harvest weed seed control suitability in the U.S. Harvest weed seed control is a promising nonchemical weed management strategy that involves the collection and subsequent destruction of weed seeds during crop harvest to minimize contributions to the weed seed bank. This technology was developed in Australia but has not been tested and developed for U.S. production systems. ARS scientists at Beltsville, Maryland, in collaboration with a national team of U.S. universities, demonstrated which weed species are more susceptible to harvest control in the U.S. This work provides foundational knowledge on which weed species are viable candidates for nonchemical control.

Review Publications
Ramos-Giraldo, P., Reberg-Horton, C., Locke, A.M., Mirsky, S.B., Lobaton, E. 2020. Drought stress detection using low-cost computer vision systems and machine learning techniques. IEEE IT Professional.
Bestelmeyer, B.T., Marcillo, G., McCord, S.E., Mirsky, S.B., Moglen, G.E., Neven, L.G., Peters, D.C., Sohoulande Djebou, D.C., Wakie, T. 2020. Scaling up agricultural research with artificial intelligence. IEEE IT Professional. 22:32-38.
Moore, V., Mirsky, S.B. 2020. Cover crop biomass production across establishment methods in Mid-Atlantic corn. Agronomy Journal. 112(6):4765-4774.
Lovreet, S., Schwartz-Lazaro, L.M., Leon, R., Ackroyd, V.J., Flessner, M.L., Bagavathiannan, M., Everman, W., Norsworthy, J.K., Van Gessel, M.J., Mirsky, S.B. 2020. Current outlook and future research needs for harvest weed seed control in North American agronomic crops. Pest Management Science. 76:3887-3895.
Schwartz-Lazaro, L.M., Shergill, L.S., Evans, J.A., Bagavathiannan, M.V., Beam, S.C., Bish, M.D., Bond, J.A., Bradley, K.W., Curran, W.S., Davis, A.S., Everman, W.J., Flessner, M.L., Harring, S.C., Jordan, N.R., Korres, N.E., Linquist, J.L., Norsworthy, J.K., Sanders, T.L., Steckel, L.E., Van Gessel, M.J., Young, B., Mirsky, S.B. 2020. Seed shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 1: Broadleaf species. Weed Science. 69(1):95-103.
Schwartz-Lazaro, L.M., Shergill, L.S., Evans, J.A., Bagavathiannan, M.V., Beam, S.C., Bish, M.D., Bond, J.A., Bradley, K.W., Curran, W.S., Davis, A.S., Everman, W.J., Flessner, M.L., Harring, S.C., Jordan, N.R., Korres, N.E., Lindquist, J.L., Norsworthy, J.K., Sanders, T.L., Steckel, L.E., Van Gessel, M.J., Young, B., Mirsky, S.B. 2020. Seed shattering phenology at soybean harvest of economically important weeds in multiple regions of the United States. Part 2: Grass species. Weed Science. 69(1):104-110.
Moore, V., Maul, J.E., Wilson, D., Curran, W., Brainard, D., Devine, T., Mirsky, S.B. 2020. Registration of 'purple bounty' and 'purple prosperity' hairy vetch. Journal of Plant Registrations. 14(3):340-346.
Marcillo, G.S., Mirsky, S.B., Aurelie, P., Reberg-Horton, S., Timlin, D.J., Schomberg, H.H., Ramos, P. 2020. Using statistical learning algorithms to predict cover crop biomass and nitrogen content. Agronomy Journal. 112(6):4898-4913.
Roberts, D.P., Vandenberg, B., Mirsky, S., Buser, M., Reberg-Horton, C., Short, N., Shrestha, S. 2020. How to feed the world. In: Wright, D.J., Harder, C., editors. Applying Mapping and Spatial Analytics. GIS for Science. Redlands, CA:Esri Press. p. 110-123.
Thapa, R., Tully, K.L., Cabrera, M.L., Dann, C., Schomberg, H.H., Timlin, D.J., Gaskin, J., Reberg-Horton, C., Davis, B.W., Mirsky, S.B. 2021. Effects of moisture and temperature on C and N mineralization from surface-applied cover crop residues. Biology and Fertility of Soils. 57:485-498.
Moore, V., Davis, B., Maul, J.E., Kucek, L.K., Mirsky, S.B. 2020. Phenotypic and nodule microbial diversity among crimson clover (Trifolium incarnatum L.) accessions. Agronomy. 10(9):1434.
Vann, R., Reberg-Horton, S., Castillo, M., Murphy, J., Mirsky, S.B., Saha, U., McGee, R.J. 2021. Differences among eighteen winter pea genotypes for forage and cover crop use in the southeastern United States. Crop Science. 61(2):947-965.
Goodrich, D.C., Heilman, P., Anderson, M.C., Baffaut, C., Bonta, J.V., Bosch, D.D., Bryant, R.B., Cosh, M.H., Endale, D.M., Veith, T.L., Havens, S.C., Hedrick, A., Kleinman, P.J., Langendoen, E.J., Mccarty, G.W., Moorman, T.B., Marks, D.G., Pierson Jr, F.B., Rigby Jr, J.R., Schomberg, H.H., Starks, P.J., Steiner, J., Strickland, T.C., Tsegaye, T.D. 2020. The USDA-ARS experimental watershed network – Evolution, lessons learned, societal benefits, and moving forward. Water Resources Research. 57(2). Article e2019WR026473.
Schomberg, H.H., Endale, D.M., Balkcom, K.S., Raper, R.L., Seman, D.H. 2021. Grazing winter rye cover crop in a cotton no-till system: Soil strength and runoff. Agronomy Journal. 113(2):1271-1286.
Endale, D.M., Schomberg, H.H., Franzluebbers, A.J., Seman, D.H., Franklin, D., Stuedemann, J.A. 2021. Runoff nutrient losses from tall fescue pastures varying in endophyte association, fertilization, and harvest management. Journal of Soil and Water Conservation.
Evett, S.R., O'Shaughnessy, S.A., Andrade, M.A., Colaizzi, P.D., Schwartz, R.C., Schomberg, H.H., Stone, K.C., Vories, E.D., Sui, R. 2020. Theory and development of a VRI decision support system: The USDA-ARS ISSCADA approach. Transactions of the ASABE. 63(5):1507-1519.