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

Location: Sustainable Agricultural Systems Laboratory

2024 Annual Report

Objectives
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).

Approach
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
In support of Objective 1, research continued on cover crop-based corn and soybean production, cover crop management for optimal weed and nitrogen management, and multi-tactic integrated weed management solutions that combine cover crop, herbicides, reduced-tillage, and harvest weed seed control management. An autonomous robotic platform 3.0 for high throughput phenotyping and for building a National Agronomic Plant Image Repository was constructed and is in operation. The new version is hardened to live outside and work across varied surfaces. It also has the capacity to image three times faster than the previous BenchBot. Additionally, the ability to estimate cover crop performance with satellite imagery including use of hyperspectral sensors was improved; this is a core resource in state and federal monitoring and reporting programs. We have demonstrated the role of hyperspectral imagery for quantifying plant residue quality. Published research from this team included advancement in the use of computer vision and machine learning for monitoring crops and weed identification. Published research on three-dimensional construction of weeds using stereo camera capability on an OakD camera system. The technology is now operating at typical sprayer speeds of 12 mph and is functioning on a hand-held system for researchers and small farmers. Ongoing advancements have been made to our web-based cover crop nitrogen calculator (CC-NCALC) by ingesting satellite imagery for geospatial assessments of cover crop performance. 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 ninth year; all samples were collected. Data collection on weed populations and seedbank dynamics in two long-term cropping system experiments was completed and the data analyzed. For Sub-objective 1B, the on-farm monitoring of cover crop performance and subsequent impact on weed suppression is in its third year. Several hand-held and tractor-mounted sensing technologies were developed for monitoring cover crop performance and weed species identification and biomass estimation that have now been calibrated across 15 states for species specific biomass estimation. The computer vision and machine learning efforts hold promise for replacing destructive sampling. We have also integrated into several breeding programs to expand training and calibration data. New area-wide funds were secured to expand the national Integrative Weed Management Team GROW (Getting Rid Of Weeds) for an additional six years. Outreach efforts have expanded through the website and decision tools. We have also initiated a national on-farm multi-tactic weed management project examining harvest weed seed control and cover crops. Lastly, we are now responsible for the data engineering pipelines and visualization needs of APHIS and the CRAFT (Citrus industry) in Florida, Texas, and California. Adapting databases from Esri into an open-source platform is underway.

Accomplishments
1. Cover crop seeding rate calculator and mix maker - data and user interface. Farmers must make complex decisions when using biological tools like cover crops and can easily be overwhelmed by the volume of data to consider. Cover crop decision tools are critical for aiding farmers in making these complex decisions. Researchers from Beltsville, Maryland, collaborated with the Midwest, Northeast, and Southern Cover Crops Councils to conduct and complete data verification for a cover crop seeding rate calculator and mix maker based upon an existing NRCS tool. Data verification melds empirical data from on-farm and on-station field experiments with the on-the-ground experience of agricultural experts including farmers, researchers, crop advisors, and extension personnel to generate site-specific recommendations for farmers. These recommendations are served up from an online user interface developed over the last two years (https://covercrop-seedcalc.org/). This tool will be expanded to include all regions in the U.S. and is a useful resource for agricultural professionals, farmers, and researchers in planning their cover crop seeding rates.

Review Publications
Leon, R., Oreja, F.H., Mirsky, S.B., Reberg-Horton, C.S. 2023. Addressing biases in replacement series: The importance of reference density selection for interpretation of competition outcomes. Weed Science. 71:606–614. https://doi.org/10.1017/wsc.2023.53.
Kumar, V., Sing, V., Flessner, M., Reiter, M., Mirsky, S.B. 2023. Volunteer rapeseed infestation and management in corn. Agronomy Journal.115:2925–2937 https://doi.org/10.1002/agj2.21476.
Thapa, R., Cabrera, M., Schomberg, H.H., Reberg-Horton, C., Poffenbarger, H., Mirsky, S.B. 2023. Chemical differences in cover crop residue quality are maintained through litter decay. Ecological Applications. https://doi.org/10.1371/Journal.pone.0289352.
Liebert, J., Mirsky, S.B., Pelzer, C.J., Ryan, M.R. 2023. Optimizing organic no-till planted soybean with cover crop selection and termination timing. Agronomy Journal. 115:1938-1956. https://doi.org/10.1002/agj2.21390.
Chami, B., Niles, M.T., Parry, S., Mirsky, S.B., Ackroyd, V.J., Ryan, M.R. 2023. Incentive programs promote cover crop adoption. Agricultural and Environmental Letters. 8(2): Article e20114. https://doi.org/10.1002/ael2.20114.
Dobbs, A.M., Ginn, D., Skovsen, S., Yadav, R., Jha, P., Bagavathiannan, M.V., Mirsky, S.B., Reberg-Horton, S.S., Leon, R.G. 2023. Using Structure-from-Motion to estimate 302:109099. cover crop biomass and characterize canopy structure. Field Crops Research. https://doi.org/10.1016/j.fcr.2023.109099.
Meeks, C., Cabrera, M., Thapa, R., Reberg-Horton, S., Mirsky, S.B. 2023. Biochemical composition of cover crop residues determines water retention and rewetting characteristics. Agronomy Journal.6:3173-3187. https://doi.org/10.1002/agj2.21451.
Singh, M., Thapa, R., Singh, N., Mirsky, S.B., Acharya, B., Jhala, A.J. 2023. Does narrow row spacing suppress weeds and increase yields in corn and soybean? A meta-analysis. Weed Science. 71:520–535 https://doi.org/10.1017/wsc.2023.50.
Huddell, A.M., Thapa, R., Needelman, B., Mirsky, S.B., Davis, A.S., Peterson, C., Kladivko, E., Law, E., Darby, H., Mcvane, J.M., Haymake, J., Balkcom, K.S., Reiter, M., Vangessel, M., Ruark, M., Well, S., Gailans, S., Flessner, M.L., Mulvaney, M.J., Bagavathiannan, M., Samuelson, S., Ackroyd, V., Marcillo, G., Abendroth, L.J., Armstrong, S.D., Asmita, G., Basche, A., Beam, S., Bradley, K., Canisares, L.P., Devkota, P., Dick, W.A., Evans, J.A., Everman, W.A., Ferreira De Almeida, T., Fultz, L.M., Hashemi, M., Helmers, M.J., Jordan, N., Kaspar, T.C., Ketterings, Q.M., Kravchenko, A., Lazaro, L., Ramon, L.G., Liebert, J., Lindquist, J., Loria, K., Miller, J.O., Nkongolo, N.V., Norsworthy, J., Parajuli, B., Pelzer, C., Poffenbarger, H., Poudel, P., Ryan, M.R., Sawyer, J.E., Seehaver, S., Shergill, L., Upadhyaya, Y.R., Waggoner, A.L., Wallace, J.M., White, C., Wolters, B., Woodley, A., Ye, R., Youngerman, E. 2024. U.S. cereal rye winter cover crop growth database. Scientific Data. 11. Article. https://doi.org/10.1038/s41597-024-02996-9.
Rebong, D., Inoa, S.H., Moore, V.M., Reberg-Horton, C.S., Mirsky, S.B., Murphy, P.J., Leon, R.G. 2023. Breeding allelopathy in cereal rye for weed suppression. Weed Science. 72(1):30-40. https://doi.org/10.1017/wsc.2023.64.