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                    U.S. DEPARTMENT OF AGRICULTURE
 Cropping Systems and Water Quality Research: Columbia, MO
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ARS Home Midwest Area Columbia, Missouri Cropping Systems and Water Quality Research Research





- Baffaut, Claire
- Schreiner-Mcgraw, Adam
- Sudduth, Kenneth - Ken
- Veum, Kristen
- Ransom, Curtis
- Abendroth, Lori
- 2023
- 2022
- Publications
- Water Availability and Watershed Management (211)
- Soil and Air (212)
- Sustainable Agricultural Systems Research (216)

Research Project: Linkages Between Crop Production Management and Sustainability in the Central Mississippi River Basin **Location:
                            Cropping Systems and Water Quality Research** 2022 Annual Report **Objectives**
                                

Objective 1: Determine linkages between plant available water, evapotranspiration, and crop yields. 1a: Determine the relationship between crop yields and available soil water. 1b: Develop spatially explicit field-scale water budgets using remote sensing. 1c: Determine the ability of the APEX model to simulate the spatial variability of crop yields and soil moisture. 

Objective 2: Characterize and quantify the sub-daily variability in water quality and identify the drivers of that change. 2a: Identify and quantify stream sub-daily water quality variability. 2b: Identify the drivers for phosphorus sub-daily variability. 

Objective 3: Determine and characterize the effects of management on water use efficiency, nutrient use efficiency, GHG emissions, productivity, and ecology. 3a: Compare WUE and water budget components of different crops and cropping systems. 3b: Integrate corn incremental N use efficiency (NUE) into fertilizer recommendations. 3c: Determine the effects of conservation practices and crop rotations on greenhouse gas and productivity, and the potential trade-offs. 3d: Determine how conservation practices and crop rotations affect above ground biomass and ecology. 

Objective 4: Evaluate ASP (aspirational) and BAU (business-as-usual) production systems for water quantity, water quality, soil, biological, production and profitability outcomes. 4a: Investigate trade-offs between productivity and environmental metrics for more diverse cropping systems. 4b: Create publicly accessible data holdings for publishing CMRB production and environmental data. 4c: Extrapolate the BAU and ASP water budgets developed at field scale to larger scales. **Approach**
                                

The overall purpose of the project is to identify how conservation practices affect outcomes, what cropping systems improve short- and long-term sustainability, and the trade-offs between environmental and production outcomes; and provide that information to producers to help them move toward more sustainable agricultural systems. Objective 1 focuses on the relationship between crop yields, soil moisture content, and evapotranspiration within the context of these soils. Objective 3 compares multiple outcomes of different agricultural systems. The conclusions of these two objectives will help identify important processes that improve agricultural sustainability, provide the information necessary to develop metrics that describe this sustainability, and evaluate trade-offs between environmental and production outcomes of agricultural systems. Objective 4 connects to these two objectives by bringing the information to stakeholders. It will also scale results related to water availability and movement to a larger scale. The research in Objective 2 is needed to incorporate issues of phosphorus transport when scaling phosphorus losses from the edge-of-field to the watershed scale and evaluate environmental impacts. The project will conduct experiments at multiple scales ranging from small plots to watersheds, adding measurements and continuing those already underway. The project builds upon the research infrastructure developed in collaboration with the University of Missouri at our research farm in Centralia and elsewhere, which was enhanced for the Central Mississippi River Basin (CMRB) site of the Long-Term Agroecosystem Research Network (LTAR) starting in 2015. This infrastructure includes small plots, large plots, and fields on which the Common Experiment—a coordinated experiment across LTAR—has been implemented, and the observatory, which provides long-term data of weather and stream flow quantity and quality in multiple nested watersheds. The proposed research focuses on surface and soil water in row crop production systems in the CMRB, with simultaneous consideration of productivity, nitrogen use, greenhouse gas emissions, soil health, and biodiversity. Specifically, research will address the immediate and long-term relationships between row-crop production practices and water budgets, surface water quality, hazardous algae blooms, GHG emissions, ecology, and aboveground and soil biodiversity. The project will result in information that producers and policy makers can use to incite changes in cropping systems. **Progress Report**
                                

This is the first report for this new project which began in January 2022 and continues research from the previous project, 5070-12130-006-000D, “Long-term Management of Water Resources in the Central Mississippi River Basin”. Please see the report for the previous project for additional information.
Objective 1: The experiment to determine which soil water content sensor is best in high clay content soils is implemented. Probes are installed and five sets of soil samples were collected in fiscal year 2022 for soil water content measurement. In collaboration with the Long-Term Agroecosystem Research Network (LTAR) water quantity working group, we developed guidelines for the measurement, quality assurance, and quality control of soil water content. These guidelines are being tested over the summer. The zones in the field are being determined over the summer to inform where monitoring points for a range of experiments need to be located. A virtual meeting was held with ARS researchers in Beltsville, Maryland, to develop a work plan, finalize the domain over which relationships between remote sensed and observed evapotranspiration will be compared, and determine data needs. The required data were sent to the cooperators (domain boundary maps, precipitation, evapotranspiration, and discharge data). We also identified noticeable climatic events: droughts, flash droughts, and periods of excessive wetness, which will help our cooperators validate the model. Chart readings at a stream gauge and a field gauge were digitized, which will give us the ability to calculate the uncertainty of the stage by combining these data with records from other stage measurement technologies. This step is necessary to quantify the uncertainty of all the water balance components and find ways to reduce them.
Objective 2: A dissolved phosphorus sonde was procured and is scheduled to arrive this year. The water quality sonde (pH, turbidity, conductivity, and dissolved oxygen) was installed for a few months in the fall of 2021. We are in the process of establishing laboratory comparisons for quality assurance protocols and uncertainty determination. We are waiting for directions from our Oxford, Mississippi, collaborators on how to collect, store, and ship water samples for algal biomass determination.
Objective 3: A review of different definitions of water use efficiency and their associated purpose was developed, which will inform the analysis of water use efficiency as a function of crop, tillage, and cropping system using legacy data. Meanwhile the hay crop on our aspirational field was killed and a corn crop was seeded in the resulting heavy hay residue. Discussions are taking place to decide what initial measurements we need to assess whether the past year hay crop is influencing the corn crop. Analysis of a large existing data set of crop yields over the Midwest area led to multiple conclusions that are presented in a manuscript published in a scientific journal. Datasets from previous research have been identified and aggregation/analysis initiated to develop new models that relate corn response to nitrogen fertilizer. Management options that use the new findings about nitrogen use efficiency toward the end of the growing period were discussed at the research unit’s stakeholder meeting in March 2022. Additional discussions will be included at the 2022 Nitrogen Use Efficiency Workshop in August 2022. Collection of agronomic and greenhouse gas data was ongoing in this year, continuing the 2021 measurements. Plot quality was compromised in 2021 due to pests in the field and gas chromatograph equipment issues. Therefore, additional years (2022+) of data will carry more importance but 2021 data still have value. A portable, in-field greenhouse gas Fourier-transform infrared spectroscopy (FTIR) instrument has been acquired and will increase the frequency and quality of data in fiscal year 2023. Preliminary analyses are showing a change in nitrous oxide emissions by differing topsoil depths and crop. Nitrous oxide emissions were greater and more uniform across all topsoil depths in corn compared to wheat. Further quality control and analyses are needed. Sampling protocol for biomass was refined and further standardized with the LTAR Common Cropland Experiment workgroup. All the crops were planted as planned and following the protocol for the different management scenarios. The sampling campaign, which will start at the reproductive stage of the plants, is ready to be implemented (i.e., sampling protocol, equipment, personnel are available and planned for). Biomass data for assessing above-ground biological diversity and ecological function has been collected annually at the natural prairie site (Tucker Prairie). For floristic diversity, we have identified the ‘coefficients of conservatism (C-values)’ as a metric to apply to native systems. C-values are numbers from 0 to 10 assigned to each plant species and reflect the plant’s tolerance to environmental degradation and anthropogenic disturbance. Higher values indicate a lower tolerance to degradation and disturbance; therefore, the presence of high C-value species in an ecosystem indicates that this ecosystem is more stable and less disturbed. Floristic data, percent cover, and seed rain data have been collected at Tucker at least monthly. Seed rain is a method of quantifying seed dispersal, specifically the seeds that end up on the ground. Seed traps are mounted at the soil surface for a defined period, then the traps are collected, and the seeds are identified and counted. The development of detailed methods for comparison across systems still requires some effort. A new hire in June 2022 is getting up to speed and will work on the comparison methods. 
Objective 4: The unit has invited stakeholders to form a long-term (three years or more of service) advisory group to provide feedback on research results and help us refine research questions. The group met once online with a subsequent meeting planned for August 2022. In addition, substantial work took place within the LTAR Cropland Common Experiment and other relevant working groups to standardize metrics and their measurement protocols across sites for greater utility in multi-site analyses. These protocols were developed for multiple measurements including water variables, and plant carbon and nitrogen content variables. Sampling is progressing as planned, with plans to manually collect biomass and corresponding carbon content at maximum vegetative and reproductive development. Collection of remote biomass measures using a ground-based imagery robot has begun. In addition to acquiring new samples, dried, ground tissue samples from the past decade have been inventoried and quality checked and are ready to submit for nutrient analysis. These measures along with current in-field measures provide the basis for development of nitrogen footprints of cropping systems. Data collection and certification is ongoing in fiscal year 2022 according to protocol for the comparison of the Common Experiment management systems and for long-term observatory and trend analysis. Crop management data are certified. We collaborated with the Partnerships for Data Innovations team on the development of a farm management application to enter all farm operations into a web-based tool. Management data from 2020 and 2021 were entered using this application with minor edits needed. Soil fertility samples were collected on the Aspirational field of the Cropland Common Experiment and on the plots, and the analysis of the samples is complete. The Business-as-usual field was not sampled this year and the direct comparison between the two management systems will use plot data. No soil health sampling was scheduled this year. Soil health sampling occurs every five years because soil health properties do not change quickly, and the sample analyses are expensive to run. Precipitation data are certified and uploaded to a public data repository. Discharge and water quality data certification are in progress. Ranges and 95% occurrence intervals were defined for precipitation and discharge. The work is progressing for telemetered water quality and soil moisture data. Measurement uncertainties for stage and discharge at the field and stream weirs are being calculated using stage measurements made with three independent technologies. Charts (one of the three technologies) have been digitized and analysis has started. This will provide information to calculate uncertainties at our other sites. With the lack of a remote-sensed data expert in the team (vacant position). The preparatory work for the extrapolation of field-scale results to watershed scale could not proceed. In addition, a new LTAR project was defined (Regionalization for model development and extrapolation) under the regionalization project, the modeling working group, and the water quantity working group. **Accomplishments**
                                

1. Determined corn nitrogen use efficiency is very low near optimal yield, an important consideration in balancing profitability with environmental sustainability. For corn, nitrogen (N) fertilizer use is often summarized from field to global scales using average N use efficiency ((NUE) the amount of N found in the grain relative to the applied N). However, expressing NUE as an average over the growing season is misleading because grain increase relative to added N diminishes near optimal yield. Therefore, environmental risks increase as economic benefits decrease. ARS scientists in Columbia, Missouri, in collaboration with other scientists in the U.S. Midwest, used North America datasets of corn grain yield response to N fertilizer to create and interpret “incremental NUE”, or the change in NUE with change in N fertilization. The analysis led to four results: 1) For those last units of N applied to reach economic optimal N rate (EONR, the N rate when marginal profit equals zero), NUE was only about 6%; 2) Incremental NUE was higher for medium-textured soils than fine-textured soils, possibly because fine-textured soils are more predisposed to loss of nitrate-N to gaseous forms of N and/or lower mineralization of organic N into nitrate-N; 3) Excessive and/or uneven rainfall lowered incremental NUE; and 4) A new strategy was proposed where N fertilization practices for producers should be targeted to recommend slightly below EONR—such would produce minor forgone profit but improve average NUE by about 10%. This research creates unique perspectives and ideas for how to improve N fertilizer management tools for producers, educational programs, and public policies and regulations. **Review Publications**
                            *Abendroth, L.J., Chighladze, G., Frankenberger, J., Bowling, L., Helmers, M., Herzmann, D., Jia, X., Kjaersgaard, J., Pease, L., Reinhart, B., Strock, J., Youssef, M. 2022. Paired field and water measurements from drainage management practices in row-crop agriculture. Scientific Data. 9. Article 257. https://doi.org/10.1038/s41597-022-01358-7.*
                                *Bagnall, D.K., Morgan, C.L., Cope, M., Bean, G.M., Cappellazzi, S.B., Greub, K.L., Liptzin, D., Baumhardt, R.L., Dell, C.J., Derner, J.D., Ducey, T.F., Dungan, R.S., Fortuna, A., Kautz, M.A., Kitchen, N.R., Leytem, A.B., Liebig, M.A., Moore Jr, P.A., Osborne, S.L., Sainju, U.M., Sherrod, L.A., Watts, D.B., Ashworth, A.J., Owens, P.R., et al. 2022. Carbon-sensitive pedotransfer functions for plant-available water. Soil Science Society of America Journal. 86(3):612-629. https://doi.org/10.1002/saj2.20395.*
                                *Goodrich, D.C., Bosch, D.D., Bryant, R.B., Cosh, M.H., Endale, D.M., Veith, T.L., Kleinman, P.J., Langendoen, E.J., McCarty, G.W., Pierson Jr., F.B., Schomberg, H.H., Smith, D.R., Starks, P.J., Strickland, T.C., Tsegaye, T.D., Awada, T., Swain, H., Derner, J.D., Bestelmeyer, B.T., Schmer, M.R., Baker, J.M., Carlson, B.R., Huggins, D.R., Archer, D.W., Armendariz, G.A. 2022. Long term agroecosystem research experimental watershed network. Hydrological Processes. 36(3). Article e14534. https://doi.org/10.1002/hyp.14534. [Corrigendum: Hydrological Processes: 2022, 36(6), Article e14609. https://doi.org/10.1002/hyp.14609.]*
                                *Kitchen, N.R., Ransom, C.J., Schepers, J.S., Hatfield, J.L., Massey, R., Drummond, S.T. 2022. A new perspective when examining maize fertilizer nitrogen use efficiency, incrementally. PLoS ONE. 17(5). Article e0267215. https://doi.org/10.1371/journal.pone.0267215.*
                                *Ransom, C.J., Clark, J., Bean, G.M., Bandura, C., Schafer, M., Kitchen, N.R., Camberato, J.J., Carter, P.R., Ferguson, R.B., Fernandez, F.G., Franzen, D.W., Laboski, C.A., Myers, B.D., Nafziger, E.D., Sawyer, J.E., Shanahan, J. 2021. Data from a public–industry partnership for enhancing corn nitrogen research. Agronomy Journal. 113(5):4429-4436. https://doi.org/10.1002/agj2.20812.*
                                *Shen, Z., Ramirez-Lopez, L., Behrens, T., Cui, L., Zhang, M., Walden, L., Wetterlind, J., Shi, Z., Sudduth, K.A., Baumann, P., Song, Y., Catambay, K., Viscarra Rossel, R.A. 2022. Deep transfer learning of global spectra for local soil carbon monitoring. Journal of Photogrammetry and Remote Sensing. 188:190-200. https://doi.org/10.1016/j.isprsjprs.2022.04.009.*
                                *Vong, C., Conway, L.S., Feng, A., Zhou, J., Kitchen, N.R., Sudduth, K.A. 2022. Corn emergence uniformity estimation and mapping using UAV imagery and deep learning. Computers and Electronics in Agriculture. 198. Article 107008. https://doi.org/10.1016/j.compag.2022.107008.*


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