Enhancing the Sustainability of US Cropping Systems through Cover Crops and an Innovative Information and Technology Network

ENHANCING THE SUSTAINABILITY OF US CROPPING SYSTEMS THROUGH COVER CROPS AND AN INNOVATIVE INFORMATION AND TECHNOLOGY NETWORK

Sponsoring Institution

National Institute of Food and Agriculture

Project Status

NEW

Funding Source

AFRI COMPETITIVE GRANT

Reporting Frequency

Annual

Accession No.

1019717

Grant No.

2019-68012-29818

Project No.

NC09873

Proposal No.

2018-09014

Multistate No.

(N/A)

Program Code

A9201

Project Start Date

Sep 1, 2019

Project End Date

Aug 31, 2024

Grant Year

2019

Project Director
Reberg-Horton, C.

Recipient Organization
NORTH CAROLINA STATE UNIV
(N/A)
RALEIGH,NC 27695

Performing Department
Crop and Soil Sciences

Non Technical Summary
Transformative changes are needed to address agriculture's grand challenge of increasing food production while maintaining environmental integrity. Changes must mitigate: agricultures high energy demand; impending water scarcity and herbicide-resistant weeds; consequences of climate change (more frequent flooding, droughts, and extreme heat); and decline in soil health, critical for improving soil and water quality. This proposal will address these unprecedented threats by providing the infrastructure necessary to support and accelerate cover crop (CC) use nationwide, thereby meeting NIFA program goals of 1) increasing total factor productivity, 2) improving water and nitrogen use efficiency, and 3) reducing losses due to biotic and abiotic stresses. An integrated transdisciplinary approach of research (54%), extension (30%), and education (16%) components will address our objectives to: 1) Transform CC data connectivity via nationwide social and technical infrastructure; 2) Develop key technologies to improve CC performance, inform management, and determine impacts on agronomic productivity, stability, and ecosystem services; 3) Deploy key technologies with CCs to assess abiotic and biotic factors affecting crop performance; 4) Transform education and extension knowledge delivery to catalyze CC adoption; and 5) Determine the effectiveness of a CC information ecology as a model for integrating participatory research and practice. A nationwide team of dedicated research, extension and NGO personnel from 28 institutions will establish on-station and on-farm research networks, novel teaching curriculum, and extensive social-science based outreach. Our overall goal is to increase crop productivity, conserve natural resources, and reduce our agro-ecological footprint through increased and improved use of CCs.

Animal Health Component

Research Effort Categories

Basic

40%

Applied

40%

Developmental

20%

Classification

Knowledge Area (KA)	Subject of Investigation (SOI)	Field of Science (FOS)	Percent
102	2410	1070	100%

Knowledge Area
102 - Soil, Plant, Water, Nutrient Relationships;

Subject Of Investigation
2410 - Cross-commodity research--multiple crops;

Field Of Science
1070 - Ecology;

Keywords

cover crops

sustainable agriculture

Goals / Objectives
Objective. 1. Transform CC data connectivity via social and technical infrastructure. lack of connectivity throughout the CC community, information silos, and a lack of shared practices and expertise will be addressed through creation of technical infrastructure to support data sharing, and social infrastructure to support collaboration, coordination, and communication across sectors and disciplines.Objective 2. Develop key technologies to improve CC performance, inform management, and determine impacts on agronomic performance and ecosystem services. Remote sensing and computer simulations of plant and soil processes will assess, monitor, and develop methods to optimize cover- and cash- crop performance and minimize environmental impact as affected by weather, soil, and management. These efforts will inform production goals and policy. DSTs developed will enable remote sensing for adaptive management and cutting-edge models to inform site-specific recommendations.Objective 3. Deploy key technologies with CCs to assess abiotic and biotic factors affecting crop performance. The new technologies, models, and DSTs provide a suite of regionalized information tools for this national team. Adaptation to local conditions and management practices are essential for adoption. A unified system of experiments and on-farm trials will serve to optimize these tools for the major production regions of the country. The common experiments (at research stations) and on-farm (three farms/state) research will be conducted in 15 states for three years.Objective 4. Transform knowledge delivery and feedback between CC research and practice. This objective focuses on knowledge flow among CC researchers, educators, extension agents, farmers, and future generations of CC users. Technology designed using a human- centered approach will emphasize farmer goals, workflows, and requirements. Our work creates knowledge feedback between research, extension, and education activities. For instance, empirical research, modeling, and on-farm monitoring of CC practices inform tool development and are bookended with DST training activities for farmers. Our objectives include educational components training students to develop models and do field research. In addition, we will develop a broad-reaching course with educational materials that are applicable in the long-term, and a dedicated extension sub-objective with supplementary extension activities.Objective 5. Determine the effectiveness of a CC IE as a model for integrating participatory research and practice. We will analyze the role of a CC IE in developing useful and useable scientific knowledge and technology that maintain and increase CC adoption. Specifically, the range of values, attitudes, social histories, information use and access will be examined across a diverse network of stakeholders using mixed social science methods. Staff will be trained at research sites on common protocols for conducting surveys and semi-structured interviews. A cross-region analysis of the stakeholder networks and their information sharing will be done. This approach allows us to ask can an IE help overcome barriers to CC adoption?

Project Methods
Obj. 1a. Developing a knowledge commons to investigate interactive effects of edaphic factors, climate, and management on CC performance. We will develop a network of nationally-scoped databases of CC performance. We will synthesize existing published and unpublished CC performance data that characterizes responses expected for CCs across a gradient of climate and soil conditions. New data on impacts of CC management on CC growth rate, biomass, and N accumulation will be collected. Where cooperators have raw data, empirical models will be used to estimate how climate, edaphic factors, and management influence CC phenology, biomass, and N content. While each database may be independently hosted by the contributing institution, we will establish data formatting and meta-information standards for unpublished research and ancillary datasets for our distributed knowledge commons.Obj. 1b. Community of practice for cross-sector communication, coordination, and collaboration. We will assemble researchers, educators, extension agents, and practitioners to form a national CC community of practice124. Such social infrastructure will enable data sharing to inform collaborative software development and support participatory research that is tightly integrated with extension and education activities. Our community will inform and enable participatory research that considers the goals, needs, and values of a diverse community of stakeholders. First, we will provide virtual and physical collaboration spaces to support participant activities. Second, we will share information about CC policies and practices. We will identify model state programs, policies, best management practices, and CC strategies and provide avenues to exchange and share such knowledge in our community and beyond125. Our findings will inform CC policy (e.g., RMA to factor CCs into crop insurance) and practice. For example, insight into expected results of various CC management scenarios (Obj. 2b) will inform the structure of state-level cost-share and incentive programs. Third, an interdisciplinary advisory committee consisting of farm-stakeholders from our network of collaborators will inform our research priorities, study designs, technology development, education activities, and outreach efforts.Obj. 2a. Remote sensing to link CC quality and quantity. We will develop relationships among vegetation indices from remote-sensing (satellite imagery with as low as 10 m resolution), CC quantity and quality (destructive field sampling), and ground-based sensors described below74. Thus, we will integrate remote sensing into our DSTs and process-based models. Data will be collected using ground-based sensor technology on tractor-mounted mobile platforms across multiple field experiments. The platform incorporates the following: GPS (location), 660 nm time-of-flight laser distance sensors and an ultrasonic proximity sensor (crop height), active spectral field radiometer for red edge and different vegetation indices (VI), and data fusion modeling for estimation of CC biomass and nutritive values76,126. A combination of remote sensing imagery, a process-based biogeochemical model, DeNitrification-DeComposition (DNDC)127,128 and weather and soil information in a data assimilation framework will predict field measured biomass and nutrient uptake. A broad suite of indices sensitive to vegetation greenness (e.g. NDVI, enhanced vegetation index;129, senescent vegetation (e.g. SATVI: Soil Adjusted Total Vegetation Index), and vegetation chlorophyll and crop vigor (e.g. NDRE: Normalized Difference Red-Edge Index and Red-edge chlorophyll index;130 will be derived from multi-temporal Landsat and Sentinel-2 satellites.Obj. 2b. Modeling CC performance under current and future climate scenarios. Supporting objectives: i) Assess CC effects on soil N, water availability and use efficiency, and subsequent crop yield, ii) Define the relationship between CC performance (biomass quality and quantity) and agronomic (yield, yield stability, net economic returns) and ecosystem service criteria (GHG emissions, N scavenging, soil erosion control), and iii) Define these relationships by geographic regions (climate and soil) under current and future climate scenarios.Plant-level modeling. The CC model will be created and linked to the 2DSOIL model93 to calculate plant processes such as CC leaf area, root growth, evapotranspiration (ET), and soil processes of N dynamics, soil water content (SWC), runoff, and drainage. The linked CC-2DSOIL model will include residue decomposition (see next section) for selected CC species. Data from on-farm measurements (Obj. 3), remotely sensed data (Obj. 2a), and national databases (Obj. 1a), will be used to validate modeled relationships among biomass, leaf area, total soil and plant N, residue decomposition kinetics, and climate similar to that in MAIZSIM79. A surface residue decomposition sub-model will be developed to estimate CC residue decomposition as a function of temperature and residue water potential. A vapor transport model to simulate soil and residue water dynamics and temperature under a range of albedos and decomposition stages will be added to the CC-2DSOIL model. The sub-model will be calibrated and validated with field and laboratory data prior to incorporating into CC-2DSOIL and the CC N Availability Calculator (see below). Model validation for CC-2DSOIL, MAIZSIM79 and GLYCIM (134) will be obtained for at least three farms per state (15 states).Landscape-level modeling. Net impacts of CCs on soil carbon and GHG emissions will be simulated with and without legume and non-legume CCs for each site/field using the integrated remote sensing version of DNDC127,128. DNDC simulates interactions among temperature/precipitation, evapotranspiration, soil water availability, N2O and CH4 emissions, soil carbon dynamics, water and nutrient stress, crop growth, and C and N partitioning to grain, leaves, stems and roots on a daily time step. Historic and climate forecast data as described above will be used to drive the DNDC model to investigate short-term and long-term impacts of CCs on soil carbon and GHG emissions.Yield stability. We will construct yield comparison ratios for cash crops that follow bare-ground and a CC between two cash crop seasons using observed data. As per traditional yield stability frameworks37,141-143 we will characterize temporal stability of yields through analysis of annual mean yields and coefficient of variation at a particular site. We will quantify CC effects on N and water availability and how CCs affect these relationships. Dimensionality, collinearity, and confounding effects will be addressed using structural equation modeling and modern learning algorithms (Partial Least Squares, PLS)141. Simulation modeling (GLYCIM, MAIZSIM, DNDC)127,128, used to generate time series of yields, will be employed to assess how management, climate, and soil interact.Profitability analysis. Partial budget analysis will be used to evaluate CC management impacts on returns above costs using data from field experiments and simulations. Revenue will be generated from crop yield, alternative crop use (e.g., grazing CCs), or quantifiable benefits due to CC. We will attempt to incorporate non-market, long term benefits into yields using discounting and risk analysis. CC establishment costs at each site will use prices obtained from USDA-NASS or farmers. Yield stability impact on economics will be analyzed using a simulation model (SIMETAR)144 that computes stochastic crop yield, price received, and prices paid for inputs144-146. Simulated crop yields will be based on our prior yield analysis147-149. Climate data will be included to account for risk of untimely production activities and poor growing conditions.

Progress 09/01/22 to 08/31/23

Outputs
Target Audience:Obj. 5a. Map the IE. Conduct regional surveys to explore actors (e.g., researchers, extension agents, farmers) in a CC IE using the tailored design method. Questions will be actor-specific, exploring respondents' i) perspective, use, and information exchanges on CCs; ii) perceptions of barriers to CC adoption; iii) assessments of agronomic, economic, social, and political factors driving continued use (farmers only); and iv) information exchange networks. We will contrast advanced CC users (farmers, technicians, and extension specialists) with a random sample of the farming community. In years one and four, advanced CC users will be surveyed by the CCCs online (~1000 members) and through paper surveys administered at on farm field days (15 events per year). In year two, a mail survey will be distributed to farmers, through a random sample of 5,000 farmers by FarmMarketID. Surveys will be analyzed using hierarchical random effects models to determine how geography influences adoption rate and continued use. Obj. 5b. Characterize an IE. We will conduct participant observation of project outreach activities, and in-depth, semistructured interviews with a purposefully selected subset of stakeholders (identified in Obj. 5a) to characterize the values, attitudes, and decision-making priorities that underpin CC adoption across a diverse set of actors. A maximum variation sampling strategy to include farmers at various stages of CC adoption, researchers, extension agents, and policy influencers, sampled to data saturation, will be used. Interview data and fieldnotes will be analyzed and triangulated using a framework approach to thematic analysis. We will examine how CCs fit into stakeholders' whole system management (short and long-term risk/benefits), and address: i) who farmers consult for management decisions; ii) how information and technology drive decision-making; and iii) stakeholder attitudes toward policy incentives/disincentives. Progress. During the reporting period we completed Q-methodology interviews for the Southeast and Midwest, which completed this process for the project. We are now analyzing the results. We held a three hour Think Tank meeting with four farmer participants. The UGA members of the PSA social science team have continued conducting participant observation of PSA activities and related outreach/networking activities. Decision support tools, such as the cover crop nitrogen release calculator and the cover crop species selector, have continued to be a focus of outreach and training efforts. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The full team continues to meet annually, during this reporting period at TriSocieties in Baltimore, MD on Nov. 5-6, 2022. This team meeting provided professional development in the form of opportunities to report out on project activities and results, brainstorming sessions on how to make information more accessible, decision tool feedback and outreach method focus groups, and a poster session. Team members continue to be encouraged to attend and present at both regional and national farmer-oriented events and scientific conferences. We continue to refine our on-boarding process, which directs new team members (usually grad students and post-docs) to resources such as our YouTube channel trainings and monthly team meetings. How have the results been disseminated to communities of interest?During the reporting period the PSA team presented insights and activities from the project at a minimum of seventeen workshops/webinars (eight of which were specifically formatted as training workshops), 20 field days, and through over 40 presentations at conferences ranging from the Ag Media Summit to regional cover crops council annual conferences to the annual ASA/CSSA/SSSA conference to the Oregon Cover Crops Tour (Vegetables) to the Illinois Organic Grain Conference to the Oneida County (NY) Crops Congress. Co-PIs used project results to author three Nebraska-based Cropwatch articles, one Wallaces Farmer article, and a Kansas Agricultural Experiment Station Research Report. The project informed the National Wildlife Federation's "Outreach Toolkit - Cover Crops" and has been featured in at least five news articles, two podcasts, and on CropsTV in Iowa. Co-PIs have been incorporating project results into decision tools built by the PSA team such as the cover crop nitrogen release and residue decomposition calculator CC-NCalc (https://covercrop-ncalc.org/). Such tools have been the focus of extensive beta-testing and outreach in the last year. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, the remote sensing team will continue reviewing collected data to ensure protocols were followed. Once this step is completed they will begin analyzing data with the aim of accurately estimating cover crop biomass above the current multispectral saturation point of ~2,000 kg/ha. Results from last year's analyses suggest the ACS-214 (sensor system) can accurately estimate cover crop biomass up to ~4,000 kg/ha. The results from all years of data collection will be written up for publication in a high impact journal. The cereal rye database team is cultivating collaborations in order to conduct follow-up analyses using the dataset they generated. The modeling team will create an API for MAIZSIM, which can be called with a Python or R script. They plan to speed up the MAIZSIM model, continue work on the decision tool user interface by beginning to add a model simulation component. They will complete simulations and analysis to obtain information on how cover crops affect water availability for the following cash crop by comparing simulated with observed water availability. Spatially associated cover crop management data will be linked with current geospatial crop information in the eight PSA states. Nine different cover-crop management scenarios, involving differences in spring termination date and above-ground biomass, will be conducted at 1-km cells throughout these states for weather years ranging from 2008 to 2022. Results will be integrated with prior MD state results and analyzed with respect to spatially referenced cover crop management recommendations based on optimizing yield stability and soil water-based metrics. They will begin integrating mid-century climate change projected weather data into a final round of simulations for the PSA regions. The nutrient cycling (CE1) team will complete the third year of field research, complete sample analysis, and draft a manuscript about how cover crops affect the optimum N rate of corn and cotton. The cover crop/pest interactions (CE2) team will likewise complete its final year of field research and continue data analysis and the drafting of manuscripts. The same is true of the on-farm team. The social science team plans to hold two internal training sessions on farmer-driven adaptive research as well as a final Think Tank meeting. Now that farmer interviews have concluded, the social science team will begin data analysis and the writing of manuscripts.

Impacts
What was accomplished under these goals? Objective. 1. Transform CC data connectivity via social and technical infrastructure. The data flow team worked with the remote sensing team to develop a robust data flow pipeline that cleans, sorts, and assesses data quality from the Active Canopy Crop Sensor (ACS)-214. The cereal rye database has been expanded and submitted for publication in Nature Scientific Data. The dataset includes a total of 5,695 cereal rye biomass observations across 208 site-years between 2021-2022 and encompasses a wide range of agronomic, soil, and climate conditions; the manuscript is currently under review. Objective 2. Develop key technologies to improve CC performance, inform management, and determine impacts on agronomic performance and ecosystem services. The remote sensing team successfully conducted a launch of the Active Canopy Crop Sensor (ACS)-214 across 19 states in the PSA network (AL, DE, FL, IN, IA, KS, MD, MO, NC, NH, OH, VA, VT, WI, KY, PA, SC, IL, and TX). Across these sites, they collected remotely sensed data and conducted destructive crop sampling from both the nutrient cycling and pest interactions ("common") experiments, the on-farm trials, and a large plot study, resulting in nearly 200 observations. The modeling team created web based JavaScript programs to develop a Maizsim input database that sets up file structure and generates files from user input. The app prompts the user for management and cultivar information, then saves this to the Maizsim database. It pulls weather and soil data from the internet and then builds all of the files needed to run the model. The team implemented Maizsim in a docker container and can now run or compile the model from a container. Date can be read in from an external source, making this a complete simulation tool. Run times have been slow so they are working on methods to speed it up. They have provided many of the programs in repositories under GitHub. The weather API continues to be developed and maintained. It has been incorporated into a Python-based interface for the simulation models called CLASSIM. They have built an almost complete Web interface for user inputs. This interface takes a specially formatted Excel file. They have a draft of a user interface to help guide the grower in the selection of a cover crop termination date to obtain optimum soil cover, water availability and N for the following cash crop. Further, the modeling team has completed initial analyses for the results of bare versus cover-crop plot simulations with corn at 1km - grid cells throughout the state of MD. Spatial data on (1) soil physical and hydraulic characteristics, (2) historical daily weather data from 2008 through 2022, and (3) historical corn presence data were obtained, organized, and integrated into crop model inputs for 14 states in the PSA network. Simulations for bare plots in eight of these states (determined based on availability of on-farm PSA data) were completed for the historical weather record. Management information regarding cover crop termination dates and quality were compiled to enable the next set of cover-crop simulation scenarios. Finally, a graduate student working with the modeling team has been running simulations using PSA field data as inputs. Simulated soil water contents were compared to observed ones, along with calculated observed and simulated infiltration values. The student has been working on analytical methods for the observed and simulated water contents. Objective 3. Deploy key technologies with CCs to assess abiotic and biotic factors affecting crop performance. The nutrient cycling (CE1) team initiated the third year of field research and continued sample and data analysis. They drafted a manuscript about cover crop biomass, N uptake, and mixture composition across the CE1 locations. In total, 39 CE1 members participated in research and extension activities across 15 states (DE, FL, MD, KY, NE, VT, WI, IL, IN, SC, NY, KS, NC, LA, and PA). The cover crop/pest interactions (CE2) team likewise initiated its third year of field research and continued sample and data analysis, along with organizing and initiating the drafting of manuscripts. In Spring 2022, collaborating members of the Indigo Science team identified interested growers to participate in relevant fields and implemented on-farm trials on 16 fields in IN, IA, MO, and OH including field mapping and baseline soil sampling. The Indigo Science team also continued ongoing management and data tracking of these trials, including the installation and maintenance of TDR soil moisture sensors in 32 fields across seven states. Objective 4. Transform knowledge delivery and feedback between CC research and practice. The education team taught for a second year the course they developed titled 'Cover Crops in Agroecosystems' at six universities in KY, SC, NH, NY, MI, and NE; five graduate students and 45 undergraduates participated. The credit course included asynchronous lectures with recorded presentations, an in-person laboratory session, and a virtual synchronous discussion session where students and instructors from all six universities met online via Zoom. Other team members incorporated aspects of the PSA project into courses or student trainings in DE, GA, NC, and WI, with 11 graduate students and 87 undergraduates participating. Objective 5. Determine the effectiveness of a CC IE as a model for integrating participatory research and practice. The social science team held Think Tank meetings on 2/17/2023 and 8/29/23, in each of which four panel members participated in a three hour meeting. The farmers in the meetings represented eight states - GA, NC, SC, KS, VT, DE, WI, and VA. The social science team also completed interviews for data collection for Southeast and Midwest states, with 61 farmers representing a total of six states (GA, SC, NC, OH, MO, and IA).

Publications

Type: Journal Articles Status: Published Year Published: 2023 Citation: Thapa, R., M. Cabrera, H.H. Schomberg, S.C. Reberg-Horton, H. Poffenbarger, and S.B. Mirsky. 2023. Chemical differences in cover crop residue quality are maintained through litter decay. PLoS ONE 18(7): e0289352. https://doi.org/10.1371/journal.pone.0289352
Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Davis, P., D.M. Park, D. Sahoo, B. Russell, and A. Poncet. 202_. Winter cover crop performance in the Southern Piedmont, USA. Submitted to Agrosystems Geosciences and Environment.
Type: Journal Articles Status: Published Year Published: 2023 Citation: Connor, L., R.M. Rejesus, M. Yasar. 2023. Crop insurance participation and cover crop use: Evidence from Indiana county-level data. Applied Economic Perspectives and Policy 44:2181-2208.
Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Huddell, A., R. Thapa et al. 202X. U.S. cereal rye winter cover crop growth database. In review. Nature Scientific Data.
Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Tancredi, M.T., M.A. Ray, and J.J. Thompson. 202X. Validation in Q methodology: A roadmap to developing transparent data collection tools based on a multi-region Q-sort. in review. Operant Subjectivity.
Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Thompson, J.J. 202X. Embracing disconcertment: On the need for anthropological engagement in interdisciplinary research. In review. Anthropology in Action.
Type: Journal Articles Status: Published Year Published: 2022 Citation: Chen, L., R.M. Rejesus, S. Aglasan, S. Hagen, and W. Salas. 2022. The impact of cover crops on soil erosion in the US Midwest. Journal of Environmental Management 324:116168.
Type: Journal Articles Status: Published Year Published: 2023 Citation: Park, B., R.M. Rejesus, S. Aglasan, Y. Che, S.C. Hagen, W. Salas. 2023. Payments from agricultural conservation programs and cover crop adoption. Applied Economic Perspectives and Policy 45:984-1007.
Type: Journal Articles Status: Published Year Published: 2023 Citation: Won, S., R.M. Rejesus, B.K. Goodwin, S. Aglasan. 2023. Understanding the effect of cover crop use on prevented planting losses. American Journal of Agricultural Economics. https://doi.org/10.1111/ajae.12396
Type: Journal Articles Status: Submitted Year Published: 2023 Citation: Kula, C., G. Sener, Y. Samadi, A. Sadeghpour, A. 202X. Species selection and novel precision planting design influence on cover crop decomposition dynamics, corn performance, and soil nitrogen dynamics. Agronomy for Sustainable Development.
Type: Journal Articles Status: Published Year Published: 2023 Citation: Han, G., M.T. Niles. 2023. Interested but uncertain: Carbon markets and data sharing among US row crop farmers. Land. 12(8), 1526 https://doi.org/10.3390/land12081526
Type: Journal Articles Status: Under Review Year Published: 2023 Citation: Han, G., M.T. Niles. 202X. A framework and validation to acknowledge the complexity of farmers adoption of sustainable agriculture practices. In Revision at Agricultural Systems. Preprint: https://osf.io/preprints/socarxiv/253uf
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Tancredi, M.T., M.A. Ray, J.J. Thompson. 2022. Northeastern farmers' perspectives on cover crops. ASA, CSSA, SSSA International Annual Meeting, November 6-9. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Tancredi, M.T., M.A. Ray, M. Lubbers, J.J. Thompson. 2022. Exploring the barriers and benefits of cover crops among Northeast commodity croppers for tailored outreach and communication. Georgia Crop Production Alliance Annual Meeting. October 26. Tifton, GA.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Thompson, J.J. 2022. Unsettling climate smart agriculture: why we need transdisciplinary anthropology at the table. 2022 American Anthropological Association Annual Meeting. Seattle, WA.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Niles, M.T., J.J. Thompson. 2022. The farmers researchers work with are different than most farmers: What this means for extension and outreach. ASA CSSA SSSA International Annual Meeting. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Basche, A., E. Haramoto, D. Baas, K. Renner, D. Park, M. Ryan, U. Menalled, R. Smith, K. Tully, S. Wortman. 2023. Cover crop challenge activity. Northeast Cover Crops Council Annual Meeting. Portland, ME.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Tancredi, M.T., N.T. Basinger, J.J. Thompson. 2023. I didnt know none about it. An analysis of farmers access to incentive programs supporting cover crops adoption. 2023 Annual Meeting and Conference of the Agriculture, Food, and Human Values Society & the Association for the Study of Food and Society. May 31-June 3, Boston, MA.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Tancredi, M.T., M.A. Ray, M. Lubbers, J.J. Thompson. 2023. Exploring the barriers and benefits of cover crops among Northeast commodity croppers for tailored outreach and communication. Southern Cover Crops Council Annual Meeting. February 14-15. Baton Rouge, LA.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Basche, A, E. Haramoto, D.G. Baas, K.A. Renner, D. Park, M. Ryan, R.G. Smith, K. Tully, and S. Wortman. 2022. Executing a multi-institution cover crop challenge activity. ASA, CSSA & SSSA International Annual Meeting. SSSA Division: Soil Education and Outreach. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Haramoto, E., S. Wortman, D.G. Baas, A. Basche, D. Park, K.A. Renner, M.R. Ryan, R.G. Smith, and K. Tully. 2022. Developing the multi-institutional course "Cover Crops in Agroecosystems" to improve content quality, student learning, and teaching efficiency. ASA, CSSA & SSSA International Annual Meeting. SSSA Division: Soil Education and Outreach. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Correira, A.L., P. Tomlinson, and D. Presley. 2023. Planting green: Potential benefits and disadvantages of planting corn into live cereal rye cover crop. Annual Meeting of Soil and Water Conservation Society (1st place). Des Moines, IA.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Upadhyaya, Y. R., P. Devkota, M.J. Mulvaney, W. Hammond, H. Bayabil. 2022. Effect of cover crops on soil moisture dynamics in cotton. ASA CSSA SSSA International Annual Meeting. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Upadhyaya, Y. R., P. Devkota, M.J. Mulvaney, W. Hammond, H. Bayabil. 2023. Integration of cover crop and preemergence herbicide for early season weed control in cotton. 2023 Southern Weed Science Society Meeting, Baton Rouge, LA.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Upadhyaya, Y. R., P. Devkota, M.J. Mulvaney, W. Hammond, H. Bayabil. 2023. Integration of cover crop and preemergence herbicide for weed control in cotton. 2023 Florida Weed Science Society Meeting, Haines City, FL.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Canisares, L.P., F. Miguez, R. Thapa, S.B. Mirsky, R. Ye, P. Poudel, P., ... H. Poffenbarger. 2022, Legume cover crops can reduce the corn reliance on nitrogen fertilizer when compared to cereal rye across multi-state field experiments. ASA CSSA SSSA International Annual Meeting. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Kula, C., G. Sener, A. Sadeghpour, C. Vick, C. 2022. Role of cover crop design and fertilizer rate on subsequent corn yield in a southern Illinois field trial. ASA CSSA SSSA Annual Meeting. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Kula, C., G. Sener, C. Vick, A. Sadeghpour. 2022. Cover crop species and planting methods influence on corn N requirement in southern Illinois. North Central Extension-Industry Soil Fertility Conference, Nov. 15-17, Des Moines, IA.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Kula, C., G. Sener, C. Vick, A. Sadeghpour. 2022. Precision planting of clover and rye-clover mixture effect on corn nitrogen requirement. SIU Research and Creative Activities, April 14, Carbondale, IL.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Campbell, D., J. Love, K. Brock, D. Treadwell. 2023. Decision support tools: Using Microsoft Powerbi to quickly assess soil quality changes associated with cover crop adoption. American Society for Horticultural Sciences Annual Meeting. Orlando, FL.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Carrillo, P., L. Fultz, A. Fachin, H. Mendoza, P. Egbedi. 2022. Soil health evaluation by the use of cover crops in a no-till cotton production. ASA CSSA & SSSA International Annual Meeting. Baltimore, MD.
Type: Other Status: Published Year Published: 2023 Citation: Correira, A.L., P. Tomlinson, D. Presely. 2023. Planting green: Potential benefits and disadvantages of planting corn into live cereal rye cover crop. Kansas Agricultural Experiment Station Research Reports 9(4). https://doi.org/10.4148/2378-5977.8462
Type: Other Status: Published Year Published: 2022 Citation: Almeida, T.F., E. Robinson, R.B. Rebesquini, A. Basche. 2022. The impact of cover crop species and termination time in weed suppression and corn yield. Cropwatch. https://cropwatch.unl.edu/2022/impact-cover-crop-species-and-termination-time-weed-suppression-and-corn-yield
Type: Other Status: Published Year Published: 2022 Citation: Almeida, T.F., S. Ramirez II, E. Robinson, A. Basche. 2022. Cover crop species decomposition and nitrogen release during the corn growing season. Cropwatch. https://cropwatch.unl.edu/2022/cover-crop-species-decomposition-and-nitrogen-release-during-corn-growing-season
Type: Other Status: Published Year Published: 2023 Citation: Pesini, G., T.F. Almeida, A. Basche. 2023. Influence of cover crop species and termination time on N release. Cropwatch. https://cropwatch.unl.edu/2023/influence-cover-crop-species-and-termination-time-n-release
Type: Other Status: Other Year Published: 2022 Citation: Thompson, JJ. 2022. So were not left behind again: Building sustainable food systems through transdisciplinary & community-engaged anthropology. Invited talk, Department of Anthropology. Sustainable Communities Program, Northern Arizona University.
Type: Other Status: Published Year Published: 2023 Citation: Thompson, J.J. 2023. Bringing transdisciplinary research to US sustainable agriculture: Challenges and opportunities. Invited talk, Department of Agricultural Leadership, Education and Communication. University of Georgia.
Type: Other Status: Published Year Published: 2023 Citation: Robertson, A. 2023. Cereal rye cover crop could negatively affect corn. Feb 10, 2023 Wallaces Farmer. https://www.farmprogress.com/crops/cereal-rye-cover-crop-could-negatively-affect-corn
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Boakye, D.A., P. Aryal, C.A. Chase, M.V. Bagavathiannan, A. Basche, H. Darby, M. L. Flessner, E. Haramoto, R.G. Leon, S.B. Mirsky, A. Robertson, M. Ruark, M.R. Ryan, N.J. Seiter, K. Tilmon, P. Tomlinson, J.F. Tooker, M. VanGessel, J.M. Wallace, J. Adam, T. Ferreira de Almeida, A. Decker, K. Loria, R. Matthiessen-Anderson, J.M. McVane, F.H. Oreja, D. Presley, A. Raudenbush, L. Ruhl, C. Sias, B. Scott, E. Sweep, and A. Waggoner. 2023. Effects of delaying rye cover crop termination on cover crop biomass, weed suppression, and corn yield. Abstract. Proceedings of the Weed Science Society of America. https://wssa.net/wp-content/uploads/WSSA-WSWS-2023-Proceedings.html.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Chase, C.A., D.A. Boakye, P. Aryal, N.T. Basinger, L. Fultz, A.V. Gamble, E. Haramoto, N. Rajan, V. Temu, and E. Valencia. 2023. Multistate assessment of cowpea cover crop germplasm, seeding rate, and termination timing effects on weed suppression. Abstract. Proceedings of the Weed Science Society of America. https://wssa.net/wp-content/uploads/WSSA-WSWS-2023-Proceedings.html.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Louizias, J.G, J. Desaeger, R. Koenig, G. Maltais-Landry, and C.A. Chase. 2023. Effect of cover crop species proportion on plant-parasitic nematode populations under greenhouse conditions in Florida. Abstract. Proceedings of the Florida State Horticultural Society, Volume 136.
Type: Journal Articles Status: Published Year Published: 2023 Citation: Chen, L., R.M. Rejesus, S. Aglasan, S. Hagen, W. Salas. 2023. The impact of no-till on agricultural land values in the United States Midwest. American Journal of Agricultural Economics 105(3):760-83.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Han, G. 2022. Farmers value systems on ecosystem services and the impacts on conservation behaviors. The 2nd Global Bamboo and Rattan Congress. Nov 08, Beijing, China.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Correira, A., D. Presley, P. Tomlinson. 2023. Effects of cover crops on soil nutrients, moisture retention, and yield in four on-farm sites in Kansas. ASA CSSA & SSSA International Annual Meeting. Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Correira, A., P. Tomlinson, K. Roozeboom, D. Presley. 2023. Cover crop selection and biomass impact on corn yields in a long-term cover crop project. Midwest Cover Crops Council Annual Meeting. Sioux Falls, SD.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Correira, A.L., V. Otchere, P. Tomlinson, D. Presley. 2023. Planting green: Potential benefits and disadvantages of planting corn into live cereal rye cover crop. Annual Meeting of the Soil and Water Conservation Society (1st place oral), Des Moines, IA.
Type: Conference Papers and Presentations Status: Published Year Published: 2023 Citation: Correira, A.L., P. Tomlinson, D. Presley. 2023. Planting green: Potential benefits and disadvantages of planting corn into live cereal rye cover crop. Annual Meeting of Soil and Water Conservation Society (1st place poster), Des Moines, IA.

Progress 09/01/21 to 08/31/22

Outputs
Target Audience:In the project initiation we proposed to reach our target audience through the following objectives. Obj. 5a. Map the IE. Conduct regional surveys to explore actors (e.g., researchers, extension agents, farmers) in a CC IE using the tailored design method. Questions will be actor-specific, exploring respondents' i) perspective, use, and information exchanges on CCs; ii) perceptions of barriers to CC adoption; iii) assessments of agronomic, economic, social, and political factors driving continued use (farmers only); and iv) information exchange networks. We will contrast advanced CC users (farmers, technicians, and extension specialists) with a random sample of the farming community. In years one and four, advanced CC users will be surveyed by the CCCs online (~1000 members) and through paper surveys administered at on farm field days (15 events per year). In year two, a mail survey will be distributed to farmers, through a random sample of 5,000 farmers by FarmMarketID. Surveys will be analyzed using hierarchical random effects models to determine how geography influences adoption rate and continued use. Obj. 5b. Characterize an IE. We will conduct participant observation of project outreach activities, and in-depth, semistructured interviews with a purposefully selected subset of stakeholders (identified in Obj. 5a) to characterize the values, attitudes, and decision-making priorities that underpin CC adoption across a diverse set of actors. A maximum variation sampling strategy to include farmers at various stages of CC adoption, researchers, extension agents, and policy influencers, sampled to data saturation, will be used. Interview data and fieldnotes will be analyzed and triangulated using a framework approach to thematic analysis. We will examine how CCs fit into stakeholders' whole system management (short and long-term risk/benefits), and address: i) who farmers consult for management decisions; ii) how information and technology drive decision-making; and iii) stakeholder attitudes toward policy incentives/disincentives. Progress. During the reporting period we completed farmer surveys and PSA network surveys; the analysis is underway. We validated the farmer Q-methodology and are conducting in-depth data collection (Northeast data collection is complete; Southeast data collection is in process). We held four Farmer Think Tank meetings and one field research assessment. The UGA members of the PSA social science team have continued conducting participant observation of PSA activities and related outreach/networking activities. The newly redesigned cover crop N calculator, branded CC-NCalc (https://covercrop-ncalc.org/), was released during this reporting period. It, along with the Northeast Cover Crop Species Selector decision tool, has been a focus of training efforts aimed at both agricultural service professionals and farmers. PSA team members have continued to actively conduct outreach via field days, workshops, and online events as outlined elsewhere in this report. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The full team continues to meet annually. During the reporting period, the big team meeting was held virtually on January 7, 2022. Full team meetings include training sessions, breakout rooms to brainstorm and provide input, and other professional development opportunities. Team members are encouraged to attend and present at scientific conferences, as well as farmer-oriented events. Finally, we continue to create trainings on protocols that have been recorded and posted to the team YouTube channel as documented in the Products - Other section of this report. How have the results been disseminated to communities of interest?During the reporting period PSA team members hosted at least 17 online/virtual presentations/workshops; one presentation in Alabama; one training in Colorado (participants from multiple states); two field days in Delaware; five outreach events in Florida; one field day in Iowa; five events in Illinois and three in Kentucky; a farm tour, a conference, and a field day in Louisiana; one outreach event each in Maryland, Michigan, Missouri, Nebraska, New Hampshire, New York, and Ohio; three events in Pennsylvania; two conference presentations in Texas; three field days in Vermont; ten events (including eight field days) in Virginia; nine events (including two field days, four webinars, and a radio interview) in Wisconsin; and at least eight presentations/posters at the November 2021 annual TriSocieties meeting. What do you plan to do during the next reporting period to accomplish the goals?In the next reporting period, the modeling and tools team will conduct empirical and process-based modeling exercises, updating and validating them with more data from diverse agroecosystems. They will extend model testing and validation to additional states and integrate and conduct scenario assessments for climate change impacts in Maryland and additional states. They will complete model evaluation of soil moisture dynamics at selected PSA on-farm sites and assess climate impacts by running the infiltration scripts on soil water data collected during 2021 and 2022 site-years and conducting final modeling to assess cover crop effects on soil water infiltration following heavy rain events. The CC-NCalc tool will be further validated using data from Midwest cropping systems using data collected during 2021-2022. The remote sensing team will conduct additional calibration studies in Maryland (along with testing new low-cost imaging systems such as GoPro and Oak-D) and North Carolina. They will apply calibrations to the national on-farm network (~30 ACS 214 forage sensor boxes will be prepared for network roll out in FY 2022-23). The CE1, CE2, and on-farm research teams will complete year 3 of their respective field studies. As part of drafting manuscripts, they will conduct more robust analysis on data such as the 2021-2022 winter cover crop biomass collections from fields at BARC in Maryland. The education team will again implement the curricula it has developed, marking a second year teaching the class.The extension team will continue to identify and create opportunities for outreach. The social science team will continue to collect Q-methodology data and host think tank events. They will conduct analysis across all groups, triangulating data from across social science groups to characterize and map the information ecology.

Impacts
What was accomplished under these goals? Objective. 1. Transform CC data connectivity via social and technical infrastructure - The modeling team and tools team designed the US cereal rye growth database and started circulating emails requesting data within and outside our network. So far, they have collected more than 4000 data points on cereal rye biomass spanning all of the eastern US states. The database also has data on soil, climate, and management information that will be used to assess their effects on cover crop performance. The data flow team worked on closing the loop on data cleaning and verification, designing and implementing API infrastructure for reporting potentially incorrect data, presenting these flags in queues for stakeholders, and achieving consensus on the quality of each record in question. This way we can track any changes in data over time, as well as identify "hot spots" of data types that tend to be error prone. Objective 2. Develop key technologies to improve CC performance, inform management, and determine impacts on agronomic performance and ecosystem services - The remote sensing team conducted field trials of the newest ACS214 (forage sensor box) in Nebraska, Missouri, North Carolina, and Maryland. They made initial calibrations for cover crop biomass using data from the last two years of data collection and ran beta tests on-farm across eight states. There was the successful collection of high resolution imagery from the "high velocity camera" (HVCam) in Maryland, which will better train AI/Computer vision models to identify cover crop mixture compositions and estimate biomass. Preliminary analysis was conducted that utilizes the fusion of remote sensing-based indices with weather variables for improved estimation of cover crop performance both spatially and temporally. The modeling team developed an empirical model that predicts cover crop residue decomposition rates based on residue quality and weather variables. They successfully modified and then calibrated/validated the Cover Crop Nitrogen Calculator (CC-NCalc) using litter bag decomposition data during 2017-2019. A new user interface for the CC-NCalc was designed and publically deployed. They also modified and refined the soil water infiltration script in the R programming language, packaging different functions into an R package that can (i) calculate cumulative water storage in the profile using measurements at multiple depths, (ii) smooth time-series data, (iii) automatically detect and characterize infiltration events in soil, (iv) characterize rain events, and (v) finally associate infiltration events and rain events. The infiltration script was successfully run on soil water data collected during 2017-2020. Objective 3. Deploy key technologies with CCs to assess abiotic and biotic factors affecting crop performance - The on-farm, nutrient cycling (CE1), and cover crop/pest interactions (CE2) teams completed year 2 of their respective field studies. In the fall of 2021, the on-farm team (20 states) enrolled 96 farm sites for a trial +/- CCs. In the Spring of 2022, data collected on-farm included cover crop biomass, soil moisture in strips +/- cover crops, corn disease, weed ratings, and yield in corn, soybeans, or cotton. Weeds 3D technology (a GoPro based video weed rating system) was disseminated to all teams, collecting 184 videos. PSA technology teams beta tested the forage sensor box (CropCircle NVDI technology) in seven states and plan to scale up to all 20 states in 2022. Objective 4. Transform knowledge delivery and feedback between CC research and practice - The education team deployed a new course titled 'Cover Crops in Agroecosystems' at seven universities in the fall of 2021. The 3-credit course included asynchronous lectures with recorded presentations, an in-person laboratory session, and a virtual synchronous discussion session where students and instructors from all seven universities met online via Zoom. The course included 12 modules where students 1) explored the management, environmental, economic, and social considerations of CCs across a diversity of agricultural production systems, and 2) grew CCs, measured benefits and trade-offs, and applied knowledge to make management and policy recommendations. The education team continued to refine the course over the spring/summer of 2022; it will be held again in the fall of 2022. The on-farm, nutrient cycling, and pest interactions field teams are working with social scientists and economists to collect farm management and economic inputs to develop an economic decision support tool for release in the next year. Objective 5. Determine the effectiveness of a CC IE as a model for integrating participatory research and practice - The social science team completed farmer surveys and PSA network surveys; analysis is underway. The farmer Q-methodology was validated and in-depth data collection is proceeding (Northeast US data collection complete; Southeast US data collection in process). Four farmer think tank meetings and one field research assessment were held.

Publications

Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Thompson, JJ, H Darby, B Davis, H Poffenbarger, R Myers, M Niles, A Raturi, C Reberg-Horton, A Robertson, M Ryan, R Thapa, S Mirsky. Visualizing a Highly Coordinated Transdisciplinary Team for Precision Sustainable Agriculture. 2021 International Transdisciplinary Conference, Z�rich, Switzerland (Virtual) https://site.caes.uga.edu/anthfood/files/2021/09/Thompson-Jennifer_PC-1.1.pdf
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Tancredi, MT, MA Ray, and JJ Thompson. Characterizing Cover Crop Mindsets Using Q-Methodology. ASA-CSSA-SSSA International Annual Meeting. Salt Lake City, UT.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Aryal, P. and C. Chase. 2022. Rye cover crop termination timing effects on weed suppression in no-till corn. Proceedings, Southern Weed Science Society, 75:211.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Aryal, P. and C. Chase. 2022. Rye Cover crop termination timing effects on weed suppression in no-till corn. Florida Weed Science Society Program, page 17.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Davis, P., D.M. Park, B. Russell, and D. Sahoo. Winter cover crop performance in the Piedmont Region of South Carolina. Poster. 2022 CAFLS Graduate Student Research Symposium. 08 August 2022. Florence, SC.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Davis, P and D.M. Park. Soil water repellency as a potential dynamic soil indicator. Poster. 2022 Southern Regional Cooperative Soil Survey Conference. June 6-9th, 2022. Greenville, SC
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Sattanno, K. and M.E. Swisher. 2022. Stakeholder-Driven Adaptive Research (SDAR): Farmer think tank enhancing cover crop research. ASA, CSSA, and SSSA Annual Meeting, Baltimore, MD.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Upadhyaya, Y., Mulvaney, M. J., & Devkota, P. (2021). Efficacy of cover crops to obviate the need for pre-emergence herbicide to control the weeds in cotton. ASA-CSSA-SSSA International Annual Meeting, Salt Lake City, UT. Nov. 7-10, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Upadhyaya, Y., Devkota, P., Mulvaney, M. J., Hammond, W., & Bayabil, H. (2022). Cereal rye biomass and PRE-herbicides for weed management in cotton. Southern ASA Annual Meeting 2022. New Orleans, LA, Feb 12 - 14, 2022.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Wortman, S.E., D. Baas, A. Basche, E. Haramoto, D. Park, K. Renner, M. Ryan, R. Smith, and K. Tully. 2022. Development of a multi-institution cover crops course: A model for improving content quality, teaching efficiency, and student learning. American Society for Horticultural Science (ASHS) Annual Conference. Chicago, IL.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Upadhyaya, Y., Mulvaney, M. J., & Devkota, P. (2021). Cover crop nitrogen credit to cotton in a conservation tillage cropping system. ASA-CSSA-SSSA International Annual Meeting, Salt Lake City, UT, Nov. 7-10, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Upadhyaya, Y., Devkota, P., Mulvaney, M. J., Hammond, W., & Bayabil, H. (2022). Cover crops for weed management in cotton. Southern ASA Annual Meeting 2022. New Orleans, LA, Feb 12 - 14, 2022.
Type: Journal Articles Status: Other Year Published: 2023 Citation: Thapa, R., M.L. Cabrera, C. Reberg-Horton, H. Poffenbarger, and S.B. Mirsky. Chemical differences in cover crop residue quality are maintained through litter decay. (Ready for Journal Submission)
Type: Journal Articles Status: Published Year Published: 2022 Citation: Thapa, R., K.L. Tully, N. Hamovit, S.A. Yarwood, H.H. Schomberg, M.L. Cabrera, C. Reberg-Horton, and S.B. Mirsky. 2022. Microbial processes and community structure as influenced by cover crop residue type and placement during repeated dry-wet cycles. Applied Soil Ecology. 172:104349.
Type: Journal Articles Status: Published Year Published: 2022 Citation: Thapa R., K.L. Tully, C. Reberg-Horton, H.H. Schomberg, M. Cabrera, J. Gaskin, B.W. Davis, A. Poncet, S.A. Seehaver, R. Hitchcok, D. Timlin, D. Fleisher, and S.B. Mirsky. 2022. Cover crop residue decomposition in no-till corn systems: Insights from multi-state on-farm litter bag studies. Agriculture, Ecosystems, and Environment. 326:107823.
Type: Journal Articles Status: Published Year Published: 2022 Citation: Thapa R., M. Cabrera, C. Reberg-Horton, Dann, K.S. Balkcom, D. Fleisher, J. Gaskin, R. Hitchcok, A. Poncet, H.H. Schomberg, D. Timlin, and S.B. Mirsky. 2022. Modeling surface residue decomposition and N release using the Cover Crop Nitrogen Calculator (CC-NCALC). Nutrient Cycling in Agroecosystems. 124:81-99.
Type: Other Status: Published Year Published: 2022 Citation: Niles, M., & Han, G. (2022, March 3). Interested but Uncertain: Carbon markets and data sharing among US row crop farmers. Preprint. https://doi.org/10.31235/osf.io/mhv2w
Type: Journal Articles Status: Published Year Published: 2022 Citation: Jennewein, J., Lamb, B, Hively, W. D., Thieme, A., Thapa, R., Goldsmith, A., Mirsky, S. B. 2022. Integration of satellite-based optical and synthetic aperature radar imagery tp estimate winter cover crop performance in cereal grasses. Remote Sensing, 14 (9).
Type: Journal Articles Status: Published Year Published: 2022 Citation: Thieme, A., Hively, W. D, Gao, F., Jennewein, J., Mirsky, S., Soroka, A., Keppler, J., Bradley, D. 2022. Remote sensing evaluation of winter cover crop springtime performance and the impact of delayed termination. Agronomy Journal. https://doi.org/10.1002/agj2.21207
Type: Journal Articles Status: Published Year Published: 2022 Citation: Acharya, J., Moorman, T.B., Kaspar, T.C., Lenssen, A.W., and Robertson, A.E. 2022. Effect of planting into a green winter cereal rye cover crop on growth and development, seedling disease and yield of corn. Plant Dis. 106:114-120. doi.org/10.1094/PDIS-04-21-0836-RE
Type: Journal Articles Status: Published Year Published: 2022 Citation: Raturi, A., Thompson, J.J.. Ackroyd, V., Chase, C., Davis, B.W., Myers, R., Poncet, A., Ramos-Giraldo, P., Reberg-Horton, C., Rejesus, R., Robertson, A.E., Ruark, M., Seehaver-Eagen, S., Mirsky, S. 2022. Cultivating trust in technology-mediated sustainable agricultural research. Agronomy Journal 2022:1-12. doi.org/10.1002/agj2.20974
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Tancredi, MT, M Ray, JJ Thompson. Using mixed-methods social science to tailor sustainable agriculture outreach to commodity croppers. 2022 joint meeting of the Agriculture, Food, and Human Values Society and the Association for the Study of Food and Society. Athens, GA.
Type: Conference Papers and Presentations Status: Published Year Published: 2022 Citation: Thompson, JJ and MA Ray. Translators Needed: A critical role for anthropology in transdisciplinary research for sustainable agriculture. American Anthropological Association Annual Meeting, Baltimore MD.

Progress 09/01/20 to 08/31/21

Outputs
Target Audience:In the project initiation we proposed to reach our target audience through the following objectives. Obj. 5a. Map the IE. Conduct regional surveys to explore actors (e.g., researchers, extension agents, farmers) in a CC IE using the tailored design method. Questions will be actor-specific, exploring respondents' i) perspective, use, and information exchanges on CCs; ii) perceptions of barriers to CC adoption; iii) assessments of agronomic, economic, social, and political factors driving continued use (farmers only); and iv) information exchange networks. We will contrast advanced CC users (farmers, technicians, and extension specialists) with a random sample of the farming community. In years one and four, advanced CC users will be surveyed by the CCCs online (~1000 members) and through paper surveys administered at on-farm field days (15 events per year). In year two, a mail survey will be distributed to farmers, through a random sample of 5,000 farmers by FarmMarketID. Surveys will be analyzed using hierarchical random effects models to determine how geography influences adoption rate and continued use. Specific network information will be used to select the sample for characterizing our IE (5b), and for network analysis (5c), described below. Obj. 5b. Characterize an IE. We will conduct participant observation of project outreach activities, and in-depth, semistructured interviews with a purposefully selected subset of stakeholders (identified in Obj. 5a) to characterize the values, attitudes, and decision-making priorities that underpin CC adoption across a diverse set of actors. A maximum variation sampling strategy to include farmers at various stages of CC adoption, researchers, extension agents, and policy-influencers, sampled to data saturation, will be used. Interview data and fieldnotes will be analyzed and triangulated using a framework approach to thematic analysis. We will examine how CCs fit into stakeholders' whole system management (short- and long-term risk/benefits), and address: i) who farmers consult for management decisions; ii) how information and technology drive decision-making; and iii) stakeholder attitudes toward policy incentives/disincentives. Progress. During the reporting period the UVM members of the PSA social science team have collected and analyzed survey data from within the PSA network, with PSA on-farm farmers, and with a random selection of farmers. This will support our goals of mapping and measuring change in a cover crop Information Ecology. Preliminary data analysis and internal descriptive reports are complete. The UGA members of the PSA social science team have been conducting ongoing participant observation of PSA activities, and related outreach/networking activities. We have developed and piloted a mixed methods data collection protocol with GA farmers and stakeholders, and are finalizing the validation phase of this protocol with stakeholders across the cover crop council regions represented in the PSA network (Northeast, Midwest, and South). The farmer think tank met virtually in Jan. and Jul. 2021. The Northeast Cover Crop Species Selector decision tool was made public in Jan. 2021 and has been the focus of extensive training efforts aimed at both agricultural service professionals and farmers. PSA team members have been actively conducting outreach via field days, workshops, online events, and other communications as documented in the Products sections of this report. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?The full team continues to meet regularly twice each year, in mid-winter and again in June. During the reporting period, there was a full team virtual meeting Dec. 1-2, 2020 and again Jun. 29, 2021. Full team meetings include training sessions, breakout rooms to brainstorm and provide input, and other professional development opportunities. Team members are encouraged to attend and present at scientific conferences, as well as farmer-oriented events. Finally, we continue to create trainings on protocols that have been recorded and posted to the team YouTube channel as documented in the Product - Other section of this report. How have the results been disseminated to communities of interest?Over the course of the reporting period, this project was featured in at least ten field days in locations ranging from Iowa to Florida to Delaware. We hosted virtual discussion groups, research assessment events, webinars, and decision tool trainings. This work was the focus of a minimum of published peer-reviewed journal articles, with another ten in the works; co-PIs and collaborators frequently present at scientific meetings such as TriSocieties. What do you plan to do during the next reporting period to accomplish the goals?In the coming year, the primary goal of the data flow team is closing the loop on data cleaning and verification. We will design and implement API infrastructure for reporting potentially incorrect data, presenting these flags in queues for stakeholders, and achieving consensus on the quality of each record in question. This way we can track any changes in data over time, as well as identify "hot spots" of data types that tend to be error prone. In the previous reporting year, the remote sensing team held two meetings with MDA and three with DDA to report the results of the analyses and strategize for the upcoming year's continuing programmatic efforts; we will act on the decisions made on these calls. This team also has multiple manuscripts in preparation for peer-review publication. The modeling and tools team will continue developing and refining products, including beta-testing of the newly-named NCalc decision support tool. The common experiment and on-farm teams will continue our field experiments and begin presenting at conferences and field days. The education team will implement the curricula it has developed. The extension team will identify and create opportunities for outreach. In the preceding report year, there were six interdisciplinary meetings between the PSA remote sensing and social science teams to pursue a joint effort to better understand patterns of CC adoption and management by linking National Agricultural Statistics Service data, the biophysical and social drivers of CC adoption, and remotely sensed estimates of CC presence and absence; this effort will continue into the next reporting period. The social science team also plans to hold more stakeholder research assessment events focused on the research in Common Experiment #1.

Impacts
What was accomplished under these goals? Objective 1. Transform CC data connectivity via social and technical infrastructure. Previously, the data flow team built a modular system of partially-structured datalakes and highly-structured relational databases to separate each step of an extract-transform-load data pipeline into small logical units that can be agnostic of other steps. We implemented many of those steps, in particular for data entered via forms interfaces. We used the OpenDataKit standard for field observations, mobile GIS, and sensor equipment maintenance. We built an application on that standard to regularize the schema-less JSON entries into relational rectangular formats. We used a human-readable data dictionary format to specify the translation steps needed, which can be used for any arbitrary form data. This allowed us to delegate the logic for translation away from siloed data engineers toward subject matter experts, (i.e. the scientists designing experimental protocols). Additionally, we built a REST API for serving database records to web clients or data analysts. This application uses the open source Node.js runtime, which has extensions for R and Python; a data analyst can provide logic in their preferred language, while still serving all endpoints through a single back-end interface. As new experimental protocols are added, we can work directly with the subject matter expert to implement endpoints to serve their data. Objective 2. Develop key technologies to improve CC performance, inform management, and determine impacts on agronomic performance and ecosystem services. To support technical method development, the remote sensing team evaluated CC performance at >200 locations in MD, including 45 repeat-sampling locations (nine fields). Sampling included RGB photos for groundcover calculations, soil moisture, CC biomass, and CC N and C content. We finished analyzing ~450 CC samples for N and C content (LECO) to support future analysis linking Sentinel-2 red edge satellite imagery to mapping of CC N content. Data is being used to calibrate multispectral and radar satellites to predict CC performance and to evaluate the benefits of late terminated CCs in terms of biomass and N uptake. We continued work on a tractor-mounted, proximal sensing system (forage sensor box). Ten upgraded devices, which include NDVI, sonic, and lidar sensors, were distributed across our national network to collect calibration data for eight CC species. We continued our partnerships with the MD and DE Departments of Agriculture (MDA and DDA) to estimate the timing of CC termination on ~30,000 fields to support their cost-share contract management with farmers. With permission from MDA, the team shared 15% of enrolled fields over the last five years with Regrow and IndigoAg to aid in the calibration of commercial products regarding CC performance in the Chesapeake Bay region, which will be used to evaluate the accuracy of these products. The modeling and tools team has created a model for estimating water potential of surface CC residues using easily available weather variables that has been integrated into the residue decomposition model. We calibrated and validated a residue decomposition model using on-farm litter bag decomposition data from the mid-Atlantic and Southeastern US states. The model predicts residue persistence and N availability from decomposing CC residues following termination. We converted the residue decomposition model into the Cover Crop N Availability Calculator for residue and N management decisions. We integrated the mulch decomposition model into the 2d-SOIL MAIZSIM model for simulations of CC effects on soil water dynamics and subsequent corn yields. To calibrate the MAIZSIM model, R scripts were generated that automatically import data from our PSA on-farm database, gridded soil survey database, and weather APIs, and translate these into input files for the MAIZSIM model. R scripts were generated to enable parallel model runs so as to shorten the time required to run the models across multiple farm-sites. We collated geospatial data for MD as the test state from various sources for modeling the effects of CCs on soil water and nutrient dynamics as well as crop yields under historical and current climates. Objective 3. Deploy key technologies with CCs to assess abiotic and biotic factors affecting crop performance. The common experiment 1 team (nutrient cycling study) has successfully implemented field experiments at 15 different locations. We compiled the data we collected in the first fall and are entering the data from spring and summer. The common experiment 2 team (pests and diseases) hired three post-doctoral students who took leadership of developing protocols for weed, insect, and disease data. The first year of the field trial was established in 15 states: CCs were planted in fall 2020 and biomass data collected in spring 2021. Corn was planted in spring 2021. Data on weed populations, insect populations, seedling disease, and corn growth and development were collected. Fall of 2020, the on-farm team (16 states) enrolled 85 farm sites for a trial +/- CCs. In the spring of 2021, data collected on-farm included: CC biomass, soil moisture in strips +/- CCs, corn disease, weed ratings, and yield in corn, soybeans, or cotton. Select teams also beta-tested PSA technology including Weeds3D (a GoPro based video weed rating system) and StressCam (detects crop drought stress). On-farm teams are working with social scientists and economists to collect farm management and economic inputs to develop an economic decision support tool. Objective 4. Transform knowledge delivery and feedback between CC research and practice. Over the past year, the education team developed a new course titled 'Cover Crops in Agroecosystems' that was offered at seven universities across the US during fall 2021. The 3-credit course includes asynchronous lectures with recorded presentations, an in-person laboratory session, and a virtual synchronous discussion session where students and instructors from all seven universities meet online via Zoom. The course includes 12 modules where students 1) explore the management, environmental, economic, and social considerations of CCs across a diversity of agricultural production systems, and 2) grow CCs, measure benefits and trade-offs, and apply knowledge to make management and policy recommendations. The social science team facilitated Farmer Driven Exchanges: the UFL members of the team conducted two 'Farmer Think Tank" events during the reporting period, with five on-farm trial farmers from PSA on-farm research states. We also hosted a stakeholder research assessment focused on the research in Common Experiment #1. This team has met with the extension team and Midwest and Northeast Cover Crops Councils to share the Research Assessment model. The UGA team has collaborated with our on-farm team to share information at an event for GA on-farm collaborators. The PSA social science and extension teams collaborated to identify evaluation tools to share across the project. Objective 5. Determine the effectiveness of a CC IE as a model for integrating participatory research and practice. The UVM members of the PSA social science team have collected and analyzed survey data with the PSA network, with PSA on-farm farmers, with a random selection of farmers. This will support our goals of mapping and measuring change in a Cover Crop Information Ecology. Preliminary data analysis and internal descriptive reports are complete. The UGA members of the PSA social science team have been conducting ongoing participant observation of PSA activities, and related outreach/networking activities. We have developed and piloted a mixed methods data collection protocol with GA farmers and stakeholders, and are finalizing the validation phase of this protocol with stakeholders across the Cover Crop Council regions represented in the PSA network.

Publications

Type: Journal Articles Status: Published Year Published: 2021 Citation: Dann, C.E., Cabrera, M.L., Thapa, R., Mirsky, S., Tully, K., Reberg-Horton, C., Hitchcock, R. and Morari, F. 2021. Modeling water potential of cover crop residues on the soil surface. Ecological Modelling. https://doi.org/10.1016/j.ecolmodel.2021.109708
Type: Journal Articles Status: Published Year Published: 2021 Citation: Rejesus, R.M., S. Aglasan, L.G. Knight, M.A. Cavigelli, C.J. Dell, E.D. Lane, D.Y. Hollinger. 2021. Economic dimensions of soil health practices that sequester carbon: Promising research directions. J. of Soil and Water Conservation. https://doi.org/10.2489/jswc.2021.0324A
Type: Journal Articles Status: Published Year Published: 2021 Citation: Thapa, R., Tully, K.L., Cabrera, M., Dann, C., Schomberg, H.H., Timlin, D., Gaskin, J., Reberg-Horton, C. and Mirsky, S.B. 2021. Cover crop residue moisture content controls diurnal variations in surface residue decomposition. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2021.108537
Type: Journal Articles Status: Published Year Published: 2021 Citation: Thapa, R., Tully, K.L., Cabrera, M.L., Dann, C., Schomberg, H.H., Timlin, D., Reberg-Horton, C., Gaskin, J., Davis, B.W. and 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. https://doi.org/10.1007/s00374-021-01543-7
Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Davis, B. and Raturi, A. 2019. Interactive demos for data, model, and software tools in agronomy and soils demonstration. In ASA-CSSA-SSSA Annual Meeting Abstracts. San Antonio, TX. Nov. 10-13, 2019.
Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Raturi, A., Reberg-Horton, S.C., and Mirsky, S.B. 2019. An information ecology for sustainable agriculture. In ASA-CSSA-SSSA Annual Meeting Abstracts. San Antonio, TX. Nov. 10-13, 2019.
Type: Journal Articles Status: Published Year Published: 2021 Citation: Raturi, A., Ackroyd, V., Chase, C., David, B., Myers, R., Poncet, A., Rejesus, R., Robertson, A., Ruark, M., Seehaver-Eagen, S., Thompson, J.J., Reberg-Horton, S.C., and Mirsky, S. 2021. Cultivating trust in technology-mediated sustainable agricultural research. Agronomy Journal. https://doi.org/10.1002/agj2.20974
Type: Journal Articles Status: Published Year Published: 2022 Citation: Thapa R., Cabrera, M., Tully,K.L., Reberg-Horton, S.C., Schomberg, H.H., Gaskin, J., Davis, B.W., Poncet, A., Seehaver, S.A., Hitchcok, R., Timlin, D., Fleisher, D., and Mirsky, S.B. 2022. Cover crop residue decomposition in no-till corn systems: Insights from multi-state on-farm litter bag studies. Agriculture, Ecosystems, and Environment.
Type: Journal Articles Status: Under Review Year Published: 2021 Citation: Thapa R., Cabrera, M., Tully,K.L., Reberg-Horton, S.C., Schomberg, H.H., Gaskin, J., Davis, B.W., Poncet, A., Seehaver, S.A., Hitchcok, R., Timlin, D., Fleisher, D., and Mirsky, S.B. Modeling decomposition and nitrogen mineralization from surface cover crop residues in no-till cropping systems using the modified CERES-N mulch model.
Type: Journal Articles Status: Published Year Published: 2022 Citation: Thapa, R., Tully,K.L., Hamovit, N., Yarwood, S.A., Schomberg, H.H., Cabrera, M., Reberg-Horton, S.C., and Mirsky, S.B. 2022. 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
Type: Journal Articles Status: Published Year Published: 2021 Citation: Acharya, J., Moorman, T.B., Kaspar, T.C., Lenssen, A.W., and Robertson, A.E. 2021. Effect of planting into a green winter cereal rye cover crop on growth and development, seedling disease and yield of corn. Plant Dis. https://doi.org/10.1094/PDIS-04-21-0836-RE
Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Raturi, A., Ackroyd, V.J., Mirsky, S.B, and Reberg-Horton, S.C.. 2019. Cover crop decision support tools. In ASA-CSSA-SSSA Annual Meeting Abstracts. San Antonio, TX. Nov. 10-13, 2019.
Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Haymaker, J., Wolters, B., Reiter, M., Frame, W. H., Thomason, W., and Stewart, R. 2020. Nutrient cycling with long-term cover crop systems on sandy loam soils. In 2020 ASA-CSSA-SSSA Annual Meeting. Virtual. Nov. 9-13, 2020.
Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Raturi, A., Ackroyd, V., Chase, C., David, B., Myers, R., Poncet, A., Rejesus, R., Robertson, A., Ruark, M., Seehaver-Eagen, S., Thompson, J.J., Reberg-Horton, S.C., and Mirsky, S. Invited presentation on Cultivating Trust in Technology-Mediated Sustainable Agricultural Research at the Big Data Promises and Obstacles: Agricultural Data Ownership and Privacy virtual workshop.
Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Schomberg, H., Thompson, A., Evett, S., Anderson, S., Reberg-Horton, S.C., and Mirsky, S. 2020. Gateway-node system for collecting on-farm soil water data. In ASA-CSSA-SSSA Annual Meeting Abstracts. Virtual. Nov. 9-13, 2020.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Tancredi, M.T., Ray, M., and Thompson J.J. Identifying shared worldviews about cover cropping to support conservation: A pilot study with Georgia farmers and stakeholders. 2021 Joint Annual Conference. Association for the Study of Food and Society, Agriculture, Food and Human Values Society, Canadian Association for Food Studies, and The Society for the Anthropology of Food and Nutrition. Virtual. Jun. 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Connor, L., Rejesus, R.M., and Yasar, M.. Crop insurance participation and cover crop use: Evidence from Indiana county-level data. ARA-AEM Track Session Presentation, 2021 AAEA Meetings, Austin, TX. Aug. 1-3, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Rejesus, R.M. Research on the economics of cover crops: Preliminary results from a dynamic model and estimation of resilience effects. Organized Symposium presentation, 2021 AAEA Meetings, Austin, TX. Aug. 1-3, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Aglasan, S., Rejesus, R.M., Hagen, S.C., and Salas, W. An analysis of crop insurance losses, cover crops, and weather in US crop production. Selected paper presentation, 2021 AAEA Meetings, Austin, TX. Aug. 1-3, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Park, B., Rejesus, R.M., Aglasan, S., Hagen, S.C., and Salas, W. Payments from agricultural conservation programs and cover crop adoption. CEnREP Colloquium, NC State University. Apr. 30, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Park, B., Rejesus, R.M., Aglasan, S., Hagen, S.C., and Salas, W.. Payments from agricultural conservation programs and cover crop adoption. Selected paper presentation, 2021 AAEA Meetings, Austin, TX. Aug. 1-3, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Chen, L., Rejesus, R.M., Aglasan, S., Park, B., Hagen, S., and Salas, W. The impact of cover crops and no-till systems on soil erosion. Selected paper presentation, 2021 AAEA Meetings, Austin, TX. Aug. 1-3, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2021 Citation: Aglasan, S., Rejesus, R.M., Hagen, S.C., and Salas, W. Cover crop adoption effects on weather related production losses and risk. Selected paper presentation. Virtual. SCC-76 (Economics and Management of Risk in Agriculture and Natural Resources) meeting. Apr. 8-9, 2021.
Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Lubbers, M., Ray, M. and Thompson, J.J. Review of cover crop adoption surveys for US farmers. UGA College of Agricultural and Environmental Sciences - Undergraduate Research Symposium; and UGA Center for Undergraduate Research Opportunities Symposium. Apr. 2020.

Progress 09/01/19 to 08/31/20

Outputs
Target Audience:Obj. 5a. Map the IE. Conduct regional surveys to explore actors (e.g., researchers, extension agents, farmers) in a cover crop (CC) information ecology (IE using the tailored design method. Questions will be actor-specific, exploring respondents' i) perspective, use, and information exchanges on CCs; ii) perceptions of barriers to CC adoption; iii) assessments of agronomic, economic, social, and political factors driving continued use (farmers only); and iv) information exchange networks. We will contrast advanced CC users (farmers, technicians, and extension specialists) with a random sample of the farming community. In years one and four, advanced CC users will be surveyed by the CCCs online (~1000 members) and through paper surveys administered at on-farm field days (15 events per year). In year two, a mail survey will be distributed to farmers, through a random sample of 5,000 farmers by FarmMarketID. Surveys will be analyzed using hierarchical random effects models to determine how geography influences adoption rate and continued use. Specific network information will be used to select the sample for characterizing our IE (5b), and for network analysis (5c), described below. Obj. 5b. Characterize an IE. We will conduct participant observation of project outreach activities, and in-depth, semistructured interviews with a purposefully selected subset of stakeholders (identified in Obj. 5a) to characterize the values, attitudes, and decision-making priorities that underpin CC adoption across a diverse set of actors. A maximum variation sampling strategy to include farmers at various stages of CC adoption, researchers, extension agents, and policy-influencers, sampled to data saturation, will be used. Interview data and fieldnotes will be analyzed and triangulated using a framework approach to thematic analysis. We will examine how CCs fit into stakeholders' whole system management (short- and long-term risk/benefits), and address: i) who farmers consult for management decisions; ii) how information and technology drive decision-making; and iii) stakeholder attitudes toward policy incentives/disincentives. Progress. We conducted project participant observation at pre- Covid19 in-person conferences and workshops; after onset of the pandemic we observed participants of online events and will continue to do so. We have developed a survey for on-farm participants (i.e. farmers, one target audience) and trained project personnel in its administration. We have built connections between the social science team and extension team re: training in conducting Grower Field Assessments. We are actively recruiting for the Farmer Think Tank as outlined in the proposal. We have conducted an internal PSA survey that will be used to create a network survey. Changes/Problems:Due to Covid delays in acquiring hardware for several of our IoT systems used for data collection, we were not able to complete beta testing in year one of this project. We have recently acquired the hardware and have commenced testing of the technology. There will be a one year delay in the execution of these sensors. What opportunities for training and professional development has the project provided?The full team meets regularly each year. During the reporting period, there was a meeting November 9 -10, 2019 in San Antonio, Texas at TriSocieties and there was also a virtual full team meeting held on June 26, 2020. Full team meetings always include training sessions and professional development opportunities. Multiple trainings on protocols have been recorded and posted to the team YouTube channel. How have the results been disseminated to communities of interest? Nothing Reported What do you plan to do during the next reporting period to accomplish the goals?We plan to continue work as outlined in our timeline, including development, refinement, and implementation of field research protocols; development, refinement, and dissemination of a network survey; and development, refinement, and release of outreach products such as decision support tools. The CE1 team will collect data on cover crop biomass production, soil inorganic N and other soil properties, plant corn or cotton, and impose N fertilizer rate treatments. The CE2 team expects to sow and establish cereal rye cover crops at all locations in the fall. Over winter we will finalize protocols for data collection, and create videos of various protocols that will then be posted to the CE2 Google Drive. We will also prepare materials for data collection (e.g., construct insect cages and tripods, and cut shingles, and data sheets for the various data). The first of three years of data collection will occur in spring 2021 when rye cover crops will be terminated. Beginning in August 2020 the on-farm network team will assist new PSA partner states with enrolling new farmers into the network, expanding to over 100 farms in 16 states, further expanding the knowledge of cover crop practices in more regions of the US. New data collection protocols are being added that include 'Weeds 3D' (a GoPro based weed detection system using machine learning) and 'Stress Cam' (a drought stress monitoring tool using an IoT Raspberry Pi camera). In our ongoing work for the next year, the data flow team will embed the logic that represents common queries into a REST API, which will serve both web applications and a cloud analysis environment. The cloud analysis environment will serve as a common computing platform for modelers, graduate students, and post-doctoral researchers to conduct analyses in R, Python, and SAS from the browser, fully integrating live backups and sharing among users. We continue to add more datastreams to our modularized ETL pipeline, including yield monitor maps from growers and new sensing devices built by teams in our network. The remote sensing team is writing up results for publication in two manuscripts in 2021. Next steps include applying methodology used at BARC to the broader PSA network and incorporating proximal sensing to fill gaps in satellite data availability to better estimate cover crop quality and quantity. Such approaches will then be incorporated into decision support tools for growers. The education team plans to report summary statistics for the course, including the number of students at each university who finished the course and course evaluation information. The regional cover crop councils, which are part of the outreach team effort, will plan and hold their annual conferences. The social science team plans to continue meeting monthly; expand the Farmer Field History survey to PSA states in the fall of 2020; conduct PSA Network survey and Farmer survey deployment (and report out); deploy the Random Farmer survey; conduct Phase I of the Q-Methodology (GA Pilot with farmers and other stakeholders) in fall 2020/winter 2021; conduct Phase II of the Q-Methodology by working with regional experts to ensure that the tool is appropriate across regions (summer 2021); develop a partnership with Texas A&M to collect data with Texas growers; conduct two Farmer Think Tank meetings in 2021; conduct two Grower Research Assessments and trainings on said assessments for regional cover crop council participants; collaboration with remote sensing team for analysis development; and begin early-stage development of a project to investigate the unique barriers underserved farmers face in adopting cover crops and other sustainable practices.

Impacts
What was accomplished under these goals? Objective 1. The data flow team established a suite of databases using a datalake-and-RDBMS model that serve to ingest a wide variety of data from across our network: soil moisture sensors, OpenDataKit forms for field observations, and weather records from NLDAS-2 and MRMS. By creating a clear central landing point for all data sources, we have modularized the flow of data through the network: each team only has to know how to export partially-structured data to one location. This also serves to separate ETL (extract, transform, load) activities into small logical units: each project then works with data engineers from across the team to build schema-mapping and parsing steps from the data lake into the relational database. Finally the fully-structured database serves as a queryable back-end for web applications, as well as a continually updated and verified source of truth for modelers. Objective 2. Landscape-scale remote sensing team analysis of winter cover crops was developed using Harmonized Landsat and Sentinel NDVI imagery covering the states of MD and DE, for the winter of 2019-20 and the winter of 2020-21. Remote sensing emergence and termination algorithms (Feng Gao et al, 2019, 2020) were used to identify winter cover crop greenup dates and momentum, maximum performance in the winter and springtime, and springtime termination dates. This information was compared to farmer-reported information from the MD Department of Agriculture winter cover crop cost share program. Springtime remote sensing termination data were reported to the MDA on a bi-weekly basis to assist them with program management. At the Beltsville Ag Research Center (BARC), we collected biomass (300) and photo samples (~4750) in 7 fields, 5 locations per field, on 10 dates throughout the winter of 2020-21, to provide calibration/validation for various remote sensing platforms including (1) proximal (tractor mounted) (2) multispectral satellites (Sentinel-2, Landsat-8) and (3) a synthetic aperture radar satellite (Sentinel-1). Data from this past winter (2020-21) have been joined with 2018-19 and 2019-20 data to calibrate satellite imagery to estimate cover crop quality (nitrogen content) and quantity (biomass). Objective 3. The CE1 team (primarily studying nutrient cycling) has initiated trials at 15 locations to evaluate the effect of grass, legume, and mixed cover crops on corn and cotton N fertilizer requirements. We gathered data on site management history and weather, and are in the process of collecting soil samples and planting cover crops. We developed a protocol for data collection and a system of data recording and sharing using google sheets. The CE2 team (primarily studying pest-cover crop interactions) developed a standardized protocol to be done at research stations in 15 states across the US. Collaborators met monthly to discuss and decide on treatments and trial management. A randomized complete block design with two mandatory treatments: cereal rye terminated (i) 2-4 weeks before and (ii) 3-7 days after planting corn, and a no cereal rye cover crop control, with four to six replications was decided on. A third voluntary treatment, cereal rye terminated 3-7 days before planting corn, will be done at some locations. Plot size will be a minimum of 40 ft x 50 ft to reduce insect movement among plots. Protocols for collecting data on cereal rye and corn growth and development, and disease, insects (beneficials and pests) and slugs, and weeds were developed. Specific rows were assigned for (i) destructive sampling for disease and corn growth and development data; (ii) deploying cages and shingles to monitor insect and slug populations; and (iii) monitoring weed populations. Since we are collecting data on insects, we decided that corn should not be treated with an insecticide; seed applied fungicides could be used and it was decided that all locations would use the same seed-applied fungicide. Meetings with the data flow team were initiated to discuss efficient data capture. The on-farm network team installed cover crop strip trials on 60 farm locations in 6 states (AL, GA, DE, MD, NC, VA). Data collected on farms included cover crop biomass and decomposition, soil water, and yield. The teams worked with social scientists and economists to interview farmers about conservation and economic practices of their operations. Teams tested electronic form submissions on rugged tablets for data collection during the entire cropping year, in order to further improve uniform data collection and streamlined protocols for expanding the on-farm network in the coming years. The on-farm network and data flow teams developed a "Forage Box" - a greenness, height and overall biomass detecting tool for mapping cover crops, and entered into an agreement with Ag Analytics to ingest yield monitor data from farmer's combines. Objective 4. The Northeast Cover Crops Council annual conference was in Nov. 2019 in College Park, MD (~130 participants) while the Midwest Cover Crops Council held its annual conference in Feb. 2020 in Kansas City, MO (157 participants). Outreach efforts were conducted as part of these meetings, along with participant observation on the part of the social science team. The education team has developed a new multi-institution course focused on cover crops titled 'Cover Crops in Agroecosystems'. Course proposals were created, submitted, and approved. The course will be offered to undergraduate and graduate students in the fall 2021 semesters at seven universities including UNL, UKY, MSU, Clemson, UMD, Cornell, and UNH. The course learning objectives include: 1) Define cover crop types and describe characteristics of cover crop species and functional groups and their agroecosystem services; 2) Manage and make decisions about cover crops across a diversity of climates, soils, and cropping systems; 3) Measure the short- and long-term economic impacts of cover crop management decisions; 4) Quantify the environmental benefits of cover crops using digital tools and describe how those benefits are influenced by management decisions across environments; and 5) Apply cover crop system knowledge to design policy and social initiatives to help overcome barriers to cover crop adoption. In July 2020, the education team met for a 2-day intensive workshop that was used to outline the course. Since then we have met monthly to work through logistical issues and develop course modules. All course content will be finalized in July 2021. Students from different universities will interact with each other during our Friday synchronous sessions, where we will discuss regional differences in cover cropping and cover crop related issues. Objective 5. The social science team met monthly. They developed/implemented/revised the Farmer Field History survey to collect general farm practices and specific field practices and developed materials to train the on-farm team in collecting this information (implemented with six states in Jan 2020). They developed three additional surveys: an Internal PSA Network Survey, a PSA Farmer Survey, and a Random Farmer Survey. They conducted multiple literature reviews for the cover crop surveys, which underpinned both survey development (what questions have been asked in what domains) and Q-sort development (what are the major results of these surveys, ie., what are the major motivations / challenges to cover cropping). They also worked on Q-Methodology protocol development (developed concourse, refined to Q-set, conducted a pre-pilot by beta-testing with key informants). They developed a protocol for the Farmer Think Tank and recruitment participants. For the Grower Research Assessments they developed protocols and trainings. There was ongoing participant observation, within PSA team meetings and across cover crop educational and outreach activities. Finally, there was considerable ongoing interfacing with the economics and extension teams.

Publications

Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Basche, A., S. Wortman, D. Park, E. Haramoto, K. Tully, R. Smith, K. Renner, D. Baas, M. Ryan. 2020. Developing a multi-institution cover crop course - Training the next generation of cover crop professionals. Poster. 2020 Midwest Cover Crops Council annual conference, Kansas City, MO.
Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Won, S., R.M. Rejesus, A. Poncet. 2020. Understanding the yield Impacts of alternative cover crop types: Evidence from plot-level panel data. Virtual presentation. 2020 NAREA Annual Meetings.
Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Ray, M., J.J. Thompson. 2020. Plural perception: Stakeholder views of cover crop use in rural Georgia, a preliminary study. Abstract. Agriculture, Food, and Human Values Society/Association for the Study of Food & Society Joint Annual Meeting. Athens, GA.
Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Ray, M., J.J. Thompson. 2020. Cover to cover: Transdisciplinary research to enhance agricultural sustainability. Abstract. 2020 Agriculture, Food, and Human Values Society/Association for the Study of Food & Society Joint Annual Meeting. Athens, GA.
Type: Conference Papers and Presentations Status: Accepted Year Published: 2020 Citation: Lubbers, M., M. Ray, J.J. Thompson. 2020. Review of cover crop adoption surveys for US farmers. UGA College of Agricultural and Environmental Sciences - Undergraduate Research Symposium and UGA Center for Undergraduate Research Opportunities Symposium. Athens, GA.