Research Project: Enhancing Sustainability of Mid-Atlantic Agricultural Systems Using Agroecological Principles and Practices

Location: Sustainable Agricultural Systems Laboratory

2022 Annual Report

Objectives
OBJECTIVE 1: Identify and elucidate agroecological principles that drive the function of grain and forage cropping systems and quantify ecosystem services. ' Sub-objective 1.A. Compare factors controlling crop performance in long-term organic and conventional cropping systems. ' Sub-objective 1.B. Evaluate soil function and ecosystem services in long-term organic and conventional cropping systems. ' Sub-objective 1.C. Identify factors controlling soil biological community structure and its relationship to soil functions and the provision of ecosystem services in organic and conventional cropping systems. ' Sub-objective 1.D. Conduct integrated analyses to assess the impacts of organic and conventional cropping systems on the provision of ecosystem services and overall system performance. OBJECTIVE 2: Develop technologies and management strategies to improve productivity, enhance soil and water conservation, improve efficiency of nutrient cycling and support food safety and nutritional security goals for grain-based and horticultural cropping systems. ' Sub-objective 2.A. Screen and breed cover crop germplasm to improve winter hardiness, biomass production and early vigor in legumes, grasses, and brassicas, and disease resistance and nitrogen fixation in legumes. ' Sub-objective 2.B. Develop optimal cover crop-based agronomic practices for improving nutrient and water availability and use efficiency, soil health, system resilience, production and economics in reduced-tillage field corn production. ' Sub-objective 2.C. Develop strategies to improve beneficial and safe use of organic amendments in horticultural crop production. OBJECTIVE 3: Collaborate with the Hydrology and Remote Sensing Laboratory to operate and maintain the Lower Chesapeake Bay LTAR network site using technologies and practices agreed upon by LTAR leadership. Contribute to LTAR working groups and common experiments. Submit relevant data with appropriate metadata to the LTAR Information Ecosystem.

Approach
Approaches to identifying and elucidating agroecological principles include investigating the following variables within the Beltsville long-term Farming Systems Project that compares two conventional and three organic rotations, and associated projects: crop performance, soil carbon sequestration and greenhouse gas fluxes, soil microbiological community structure, and integrated analyses that evaluate overall systems performance. Approaches to developing component strategies include: incorporating legumes into organic crop rotations to maximize nitrogen fixation, composting that provides a productive and safe amendment for organic agriculture, integrating cover crop and manure management practices, reducing tillage in organic systems.

Progress Report
ARS scientists from Beltsville, Maryland, made progress conducting interdisciplinary research to address all objectives of this project plan in support of National Program 212, Soil and Air. The first Objective of this project is to assess the Lower Chesapeake Bay agroecosystem via measurements and modeling and the establishment of the Lower Chesapeake Bay Long-Term Agroecosystem Research (LCB-LTAR) sites. To achieve this objective, measurements of meteorological conditions, surface fluxes, crop phenology, and other environmental conditions were collected at the LCB-LTAR locations at the Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental watershed located near the Beltsville Agricultural Research Center (BARC) in Beltsville, Maryland, and the Choptank River watershed (CRW) located on Maryland’s Delmarva Peninsula. Additionally, real time water quality data were collected at two USGS gage stations in the CRW and a new in-situ sensor for dissolved nitrogen and phosphorus was evaluated. Similarly, the metolachlor degradation product and nitrate data were acquired for the Choptank and Monocacy watersheds. Focusing on the counties within the Chesapeake Bay watershed, preliminary data sets were assembled to investigate the relationship between manure redistribution and water quality. Tools built on the earlier work of Spiegal et al. (2020) were developed to optimize the tradeoff between manure transportation and water quality costs, which are defined in terms of the waste treatment processes required to remove nutrients before they enter the Chesapeake Bay. Preliminary results suggest that cost disparities between the use of manure rather than chemical fertilizers for crop production are not offset by water treatment cost alone and a broader range of factors, such as ecosystem health and services, must be considered to justify the transport of manure as a resource. Improvements were made in the Soil and Water Assessment Tool (SWAT) model to represent energy hydrologic processes more accurately. Specifically, the river routing scheme was modified to examine the effects of varying the river routing time step (from 1 minute to 1 day) on model simulations of streamflow, stream water depth, and water storage in river networks. It was found that the time step must be less than 1 hour to reliably assess hydrologic connectivity and aquatic ecosystem health. These improvements to the SWAT model will enhance its utility for assessing ecosystem services within agroecosystems. The second Objective is to enhance the utility of remote sensing using airborne and satellite platforms to measure biophysical variables related to agricultural production and environmental assessment. Specifically, these studies used data collected in the LCB-LTAR, along with sites distributed across the United States, to develop and test remote sensing methods for assessing crop conditions, conservation practices, and nutrient use efficiency. Using the within-season emergence and termination algorithms and imagery from the Landsat and Sentinel-2, emergence and termination dates for winter cover crops were generated bi-weekly for Maryland and Delaware. The results of this effort have been provided to the Maryland and Delaware Departments of Agriculture for their use to evaluate their operational winter cover crop incentive programs. In addition, the within-season emergence algorithm has been applied to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, Nebraska) from 2017-2020. The 30-m spatial resolution maps of crop emergence dates have been produced and compared to the NASS crop progress reports. Similarly, land surface phenology and conditions generated from remote sensing data were assessed over dryland and grassland. The results of studies have been published. Likewise, two new remote sensing-based phenology algorithms (the hybrid phenology model and spatiotemporal shape-matching model) have been developed and published. A remote sensing approach was used to classify soil tillage intensity in Iowa based on multiple years of field observations. The study assessed the capability of various vegetation indices and different classification techniques for mapping soil tillage intensity. In an effort to map crop residue cover using the current Landsat-8, Landsat-9, and the future Landsat Next mission, multiple shortwave infrared-derived indices from different sensors were evaluated. Results and recommendations for the Landsat Next mission have been published. Additionally, Sentinel-1 radar data for the growing season (March to November for period from 2017 to 2021 was assembled for the CRW. The workflows for preprocessing radar data to generate analysis-ready data sets that coincide with the USDA-NASS Cropland data and can be used with the USDA supercomputing infrastructure were also developed. The cross-polarized data were further processed according to the cropland mapping approach to determine the temporal coefficient of variation (CV) for each of three sub-annual periods: March to June; June to August; and August to November. Conceptually, the CV can also be considered an index, much like the normalized difference vegetation index (NDVI) or leaf area index (LAI), that relates crop and soil conditions. Preliminary results indicate CV values are highly dynamic and can be associated with localized features in agricultural fields. Further analysis is ongoing for determining the relation of CV values to cover crops, crop residue, and tillage intensity. The third Objective focuses on characterizing environmental processes within agricultural landscapes to evaluate ecosystem services and best management practices. Although field and laboratory activities were limited due to restrictions related to the COVID19 pandemic, important advancements were achieved for several of the research tasks associated with this objective. Research was conducted to examine chemical and environmental processes affecting dicamba drift and volatilization. Although dicamba is a commonly used herbicide, it can be transported downwind where it can deposit onto sensitive crops and adversely affect yield. ARS Scientists in Beltsville, Maryland, along with industry and university collaborators conducted a meta-analysis of numerous studies to characterize the effects of dicamba exposure as a function of chemical formulation, application method, and plant sensitivity. The results and recommendations of this study were published in a featured perspective article. Using eight future climate scenarios and three representative concentration pathways (RCPs), the Soil and Water Assessment Tool (SWAT) model was applied to project future water storage in non-floodplain wetlands (NFW) and analyze the sources of uncertainty. The initial results of the Analysis of Variance (ANOVA) showed the variability among the projected climate scenarios is the most significant single source of model uncertainty and can explain nearly half of the uncertainty associated with NFW water storage estimates. The research suggests that the benefits of wetland management are highly dependent on future climate conditions. Multiple approaches for representing the effects of soil temperature (3 methods), soil water content (2 methods), and tillage practices (4 methods) on the decomposition of soil organic carbon (SOC) were evaluated both individually and in combination using the SWAT model. Once the optimal modeling approach was identified, further analyses were conducted to assess the ability of the model to describe SOC dynamics under different field conditions, soil depths, and tillage practices. These improvements to the SWAT model enhance its utility for describing carbon dynamics in agroecosystems.

Accomplishments
1. Data from long-term research provide unique insights into agricultural sustainability. Since agriculture needs to provide agronomic, economic and environmental services in the long-term, long-term datasets are required to appropriately assess agricultural sustainability. USDA-ARS researchers from Beltsville, Maryland shared data, metadata, and archived samples from the 26-year-old Farming Systems Project in Beltsville, Maryland with colleagues who led diverse studies addressing various aspects of agricultural sustainability. Results showed that 1) diverse crop rotations increase corn yields but 2) have limited effects on soil microbial communities and do not affect soil microbial resilience, 3) mid-infrared spectroscopy—a non-destructive, less expensive, and faster method than current state-of-the-art methods—shows promise for measuring soil carbon, which is needed to asses agricultural soil carbon sequestration, and 4) models of soil carbon dynamics—also needed to advance soil carbon sequestration assessments—can be improved. These results will be of interest to farmers, agricultural professionals, scientists, developers and brokers of carbon markets, and policy makers.

2. More representative modeling of water and temperature dynamics improves simulation of cover crop residue decomposition. Water and temperature are driving factors for the decomposition of surface cover crop residue and subsequent nitrogen release (information needed by farmers to aid in nutrient management). These factors fluctuate daily and are difficult to directly measure within the cover crop residues. Models can simulate the decomposition environment based on readily available weather parameters. ARS scientists in Beltsville, Maryland, with University of Georgia collaborators, used laboratory and field experiments to develop a decomposition model based on hourly relative humidity, air temperature, and rainfall as inputs. The model successfully simulated cover crop decomposition. These improvements will aid in the development of better nutrient management tools helping growers to precisely manage their nitrogen in cover crop-based reduced tillage systems, a key climate smart farming practice.

3. Cover crop nitrogen calculator (CC-NCALC) for adaptive nitrogen management. Farmers and ag professionals need decision support tools that estimate nitrogen availability from cover crops to inform their nitrogen fertilizer application rates. ARS scientists in Beltsville, Maryland, along with scientists from the University of Georgia and North Carolina State University, used data from a large on-farm research network (Precision Sustainable Agriculture network) to improve a predictive model and redesign the user interface for the Cover Crop Nitrogen Calculator. The improved CC-NCALC estimates nitrogen availability from cover crop residues based on residue environment (i.e., residue moisture and temperature) and nitrogen limitations of the residues and soil. It uniquely considers the fractional residue mass in contact with the underlying soil. The calculator offers site-specific residue and adaptive nitrogen management information to farmers, ag professionals, researchers, and policymakers and is an educational tool for teaching university students.

Review Publications
Schnecker, J., Meeder, D.B., Calderon, F., Cavigelli, M.A., Lehman, R.M., Grandy, A.S. 2021. Microbial activity responses to water stress in agricultural soils from simple and complex crop rotations. Soil. 7:547-561. https://doi.org/10.5194/soil-7-547-2021.
Sanderman, J., Savage, K., Dangal, S.R., Duran, G., Rivard, C., Cavigelli, M.A., Gollany, H.T., Jin, V.L., Liebig, M.A., Omondi, E.C., Rui, Y., Stewart, C. 2021. Can agricultural management induced changes in soil organic carbon be detected using mid-infrared spectroscopy? Remote Sensing. 13(12). Article 2265. https://doi.org/10.3390/rs13122265.
Schmidt, D., Dlott, G., Cavigelli, M.A., Yarwood, S., Maul, J.E. 2022. Soil microbiomes in three farming systems more affected by depth than farming system. Applied Soil Ecology. 173:104396. https://doi.org/10.1016/j.apsoil.2022.104396.
Williams, M.R., Welikhe, P., Bos, J.H., King, K.W., Akland, M., Augustine, D.J., Baffaut, C., Beck, G., Bierer, A.M., Bosch, D.D., Boughton, E., Brandani, C., Brooks, E., Buda, A.R., Cavigelli, M.A., Faulkner, J., Feyereisen, G.W., Fortuna, A., Gamble, J.D., Hanrahan, B.R., Hussain, M., Kohmann, M., Kovar, J.L., Lee, B., Leytem, A.B., Liebig, M.A., Line, D., Macrae, M., Moorman, T.B., Moriasi, D.N., Nelson, N., Ortega-Pieck, A., Osmond, D., Pisani, O., Ragosta, J., Reba, M.L., Saha, A., Sanchez, J., Silveira, M., Smith, D.R., Spiegal, S.A., Swain, H., Unrine, J., Webb, P., White, K.E., Wilson, H., Witthaus, L.M. 2022. P-FLUX: A phosphorus budget dataset spanning diverse agricultural production systems in the United States and Canada. Journal of Environmental Quality. 51:451–461. https://doi.org/10.1002/jeq2.20351.
Dangal, S.R., Schwalm, C., Cavigelli, M.A., Gollany, H.T., Jin, V.L., Sanderman, J. 2022. Improving soil carbon estimates by linking conceptual pools against measurable carbon fractions in the DAYCENT Model Version 4.5. Journal of Advances in Modeling Earth Systems. https://doi.org/10.1029/2021MS002622.
White, K.E., Brennan, E.B., Cavigelli, M.A., Smith, R.F. 2022. Winter cover crops increased nitrogen availability and efficient use during eight years of intensive organic vegetable production. PLoS ONE. 17(4). Article e0267757. https://doi.org/10.1371/journal.pone.0267757.
Bryant, R.B., Endale, D.M., Spiegal, S.A., Flynn, K.C., Meinen, R.J., Cavigelli, M.A., Kleinman, P.J. 2021. Poultry manureshed management: Opportunities and challenges for a vertically integrated industry. Journal of Environmental Quality. 1-12. https://doi.org/10.1002/jeq2.20273.
Endale, D.M., Strickland, T.C., Schomberg, H.H., Bosch, D.D., Pisani, O., Coffin, A.W. 2021. Flue gas desulfurization gypsum and grass buffer strips influence on runoff and nutrient loss from inorganically and organically fertilzed corn on a US Coastal Plain soil. Journal of Soil and Water Conservation. https://doi.org/10.2489/jswc.2021.02156.
Goodrich, D.C., Bosch, D.D., Bryant, R.B., Cosh, M.H., Endale, D.M., Veith, T.L., Kleinman, P.J., Langendoen, E.J., McCarty, G.W., Pierson Jr., F.B., Schomberg, H.H., Smith, D.R., Starks, P.J., Strickland, T.C., Tsegaye, T.D., Awada, T., Swain, H., Derner, J.D., Bestelmeyer, B.T., Schmer, M.R., Baker, J.M., Carlson, B.R., Huggins, D.R., Archer, D.W., Armendariz, G.A. 2022. Long term agroecosystem research experimental watershed network. Hydrological Processes. 36(3). Article e14534. https://doi.org/10.1002/hyp.14534. [Corrigendum: Hydrological Processes: 2022, 36(6), Article e14609. https://doi.org/10.1002/hyp.14609.]
Raturi, A., Ackroyd, V., Chase, C., Davis, B., Myers, R., Poncet, A., Ramos-Giraldo, P., Rejesus, R., Robertson, A., Ruark, M., Seehaver-Eagen, S., Thompson, J.J., Mirsky, S.B. 2021. Cultivating trust in technology-mediated sustainable agricultural research. Agronomy Journal. https://doi.org/10.1002/agj2.20974.
Kucek, L.K., Azevedo, M.D., Eagen, S., Ehlke, N., Hayes, R.J., Mirsky, S.B., Reberg-Horton, C., Ryan, M.R., Wayman, S., Wiering, N.P., Riday, H. 2021. Seed dormancy regulated by genotype and environment in Hairy vetch (Vicia villosa Roth). Agronomy Journal. 10(11). Article 1804. https://doi.org/10.3390/agronomy10111804.
Thapa, R., Tully, K., Hamovit, N., Yarwood, S., Schomberg, H.H., Cabrera, M., Reberg-Horton, S., Mirsky, S.B. 2021. Microbial processes and community structure as influenced by cover crop residue type and location during repeated dry-wet cycles. Applied Soil Ecology. https://doi.org/10.1016/j.apsoil.2021.104349.
Thapa, R., Tully, K., Schomberg, H.H., Reberg-Horton, C., Davis, B., Poncet, A., Hitchcock, R., Gaskin, J.W., Cabrera, M., Mirsky, S.B., Seehaver, S., Timlin, D.J., Fleisher, D.H. 2021. Cover crop residue decomposition in no-till cropping systems: Insights from multi-state on-farm litter bag studies. Agriculture Ecosystems and the Environment. https://doi.org/10.1016/j.agee.2021.107823.
Dann, C., Cabrera, M., Thapa, R., Mirsky, S.B., Tully, K., Reberg-Horton, C., Hitchcock, R., Gaskin, J., Morari, F. 2021. Modeling water potential of cover crop residues on the soil surface. Ecological Modeling. https://doi.org/10.1016/j.ecolmodel.2021.109708.
Wang, Z., Thapa, R., Timlin, D.J., Li, S., Sun, W., Beegum, S., Fleisher, D.H., Mirsky, S.B., Cabrera, M., Sauer, T.J., Reddy, V., Horton, R., Tully, K. 2021. Simulations of water and thermal dynamics for soil surface with residue mulch and surface runoff. Water Resources Research. 57. https://doi.org/10.1029/2021WR030431.