Location: Southeast Watershed Research
2024 Annual Report
Objectives
1. Quantify and assess the patterns, trends, and interactions among agroecosystems and landscape components and their impacts on water supply and water quality within the Little River Experimental Watershed (LREW) and in agricultural watersheds of the southeastern U.S.
1.A. Quantify the differences between water use and storage capacity among differing land use types in agricultural watersheds of the Georgia Coastal Plain.
1.B. Quantify differences in water quality as a function of land use in LREW sub-watersheds.
2. As part of the Long-Term Agroecosystem Research (LTAR) Network and a participant in the Conservation Effect Assessment Project (CEAP) effort, use GACP and other LTAR sites to quantify contrasting agroecosystem responses to “Business-As-Usual” and “Aspirational” treatments, among others, at plot, field, and farm scales.
2.A. Quantify the plot-level biophysical and hydrological responses to ASP practices as compared with BAU, that are characteristic of the GACP LTAR Network site.
2.B. Characterize and quantify the contaminants and dissolved trace gases transported from agroecosystems by surface and subsurface flow.
3. Quantitatively assess the effects of agricultural conservation practices on ecosystem services at field, landscape, and regional scales in agricultural watersheds of the southeastern US.
3.A. Characterize field level spatial and temporal variability of biophysical parameters on three farms within the LREW.
3.B. Quantify meteorological and phenological characteristics from crops under differing management practices.
4. Utilize landscape and watershed scale assessment models to improve understanding of tradeoffs among ecosystem services and evaluate the long-term sustainability of agricultural watersheds.
4.A. Estimate ecosystem services provided by GACP agricultural landscapes.
4.B. Quantify the impacts of regional cropping patterns, conservation practices and winter covers on hydrology and water quality in GACP watersheds.
4.C. Evaluate uncertainties in the regional water balance and scenarios of long-term water quality as a response to intensifying seasonal climatic extremes.
4.D. Evaluate tradeoffs in ecosystem services related to scenarios of conservation practice implementation for enhancing long-term sustainability of agricultural watersheds in the GACP region.
Approach
The goal of this project is to leverage our knowledge about the tradeoffs in ecosystem services to support stakeholder decisions about the balance of costs and benefits of conservation practice implementation. An additional goal includes contributions to the LTAR Network’s Strategic Plan by considering agroecosystem responses to sustainable intensification strategies. We do so by accounting for uncertainties in the regional water balance due to intensifying seasonal climatic extremes in order to more effectively manage ecosystem services through proper placement of conservation practices in the landscape.
The proposed research uses plot, field, landscape, and watershed observations from multiple locations in the 334 km2 Little River Experimental Watershed (LREW; centered at N31°36', W83°37') that are the basis for our long-term hydrology and natural resources research at SEWRL. Experiments are designed to evaluate processes at plot-to-landscape levels using the LREW as the basis for validating modeled outcomes from practice implementation. Each objective and sub-objective is designed to address selected spatial and temporal processes, provide information for extrapolations across scales, and/or explore novel technical approaches for characterizing ecosystems services within the LREW. Research is conducted on large plots (0.08 – 0.12 ha) at several farms in partnership with the University of Georgia, private producers’ fields (50 – 72 ha) within the LREW, and multiple collaborators.
We will compare historical observations in flow, ET, land cover, and groundwater withdrawal practices to better understand trends in the watershed. We will compare annual and seasonal means of discharge using appropriate parametric and non-parametric tests for analysis of watershed data. Rates of ET will be compared where quantifiable. Geospatial statistics and simulation models offer innovative methods for quantifying the relationships between land-use change, its driving factors and downstream effects on hydrology, nutrient loading, dissolved organic carbon chemistries, and effects of agricultural versus urban associations with water quality. As part of the LTAR Network, aspirational cropping scenarios that include biofuel feedstock production and winter cover crops will be compared to traditional (business-as-usual) systems with respect to impacts on ecosystem services (primarily C and nutrient stocks, water holding capacity, and stream flow and water quality), and profitability for producers. A long-term approach is necessary to fully evaluate the potential magnitudes of change as well as the stability of these changes. A combined approach using remote sensing and physical sampling will be used to measure changes to vegetation and crop production in relation to soil and weather conditions as affected by management practices. Regular image collection using multispectral UAS-borne sensors will occur throughout the year with flights timed to capture phenological stages in crop development. Inferences between the implemented conservation practices and the hydrologic and water quality impacts will be assessed via modeling.
Progress Report
Objective 1: Flow data and water quality collection and analysis efforts on the Little River Experimental (LREW), and University of Georgia (UGA) Gibbs Farm continue. Data collection from the NR urban watersheds has been completed and a manuscript summarizing those results is being developed. Due to County and State-level plans to widen the bridges crossing the Little River, the ARS weir known as Station B will be decommissioned in January 2025. Plans are being developed to demolish the existing weir and design a replacement weir. A workshop providing information for stakeholders and scientists is being planned for October 2024 with support from the ARS Office of Communication and ARS Legislative Affairs Assistant. An analysis using model results from historic data of streamflow and water quality at Station B to predict future behavior has been requested and is being developed.
Automated sample collection and water quality analysis at existing LREW sites continues. Water samples are being collected at all sites in the LREW to relate dissolved nutrient loads and dissolved organic matter (DOM) to land-use. Bi-weekly water samples from the LREW, UGA Animal Science Farm (ASF), and UGA Gibbs and UGA Ponder farms are being analyzed for DOM optical characteristics. The resulting optical data are being processed using parallel factor (PARAFAC) analysis.
Existing sites at the ASF, LREW O3 sub-watershed, and LREW O sub-watershed are being used to evaluate livestock impacts at the watershed scale. Recent discussions with the University of Georgia are leading to future collaborative work to enhance data collection over integrated cropping/livestock systems for possible inclusion with LTAR research objectives. Hydrologic measurements of irrigation pond inputs and withdrawals continued at the TyTy Cooperator Farm (TCF). Pond bathymetry measurements at both TCF and SCF were modeled to obtain information on pond storage for more accurate water balance calculations and a manuscript is under review. Collaborations on irrigation pond research are increasing, with both strong stakeholder support to research water quality issues, and engagement with other ARS units on this research topic.
Objective 2: As noted in last year’s report, the timeline for LTAR plot rotations has been delayed by one year due to construction-related delays; decision was made to extend baseline data collection at GRFP for an additional season to match baseline period of PRFP. Rotations at the PFRP are completing their second summer season. Baseline data are being collected at GFRP with initiation of the LTAR common experiment (CE) procedures anticipated in Oct of 2024. Data publications describing baseline datasets for both sites are planned. Imagery, both RGB and multi-spectral, is being collected regularly over the plots to document baseline surface conditions and plot development. Baseline soil cores at the GFRP are planned for fall 2024. Collaborative work with Fort Valley State University on sorghum research is contributing to remote sensing research being accomplished in this objective of the plan.
Objective 3: Soil cores from TCF and the ACF were collected in April of 2024. Cores are in cold storage until processing can be completed. Remote sensing tasks under this objective resumed after the purchase of fully compliant “blue” drones. Following testing and training, the new unmanned aircraft systems carrying multispectral sensors have resumed regular farm level data collections as of June 2024. Over the last year, as the Unit underwent a complete revision of the UAS instrumentation, the technicians and scientists working in this team have developed expertise in transitioning to and implementing new, compliant UAS for agriculture research. This knowledge is shared through regular contributions to ARS and LTAR level working groups and through one-on-one discussions with scientists from across ARS. A hyperspectral UAS was purchased, and staff have been trained, and the system is in a testing phase. A LiDAR UAS was purchased by University of Georgia for use by this Unit and is being received in August 2024. Training and testing phases will ensue with the goal of having both new systems operation during the FY25 field season, pending our ability to fill critical personnel gaps.
Data collection continues at the SEWRL LTAR meteorological and phenology stations. Eddy covariance data are being collected at two sites for quantifying the exchange rates of trace gases over natural ecosystems. Data from 2018 through 2023 have been fully post-processed and are being organized as a dataset for publication in Ag Data Commons. A draft of a peer-reviewed data descriptor paper is underway.
Last year, this objective met with delays due to a critical vacancy. That critical vacancy persists after filling the position in Jan. 2024, because the incumbent was recently tasked with a long-term military deployment. However, all the tasks contributing to milestones for this objective are being accomplished with our contribution to the Inflation Reduction Act, Action Area #6 work. The ARS contribution to this effort is being administered through the SEWRL and scientists from Tifton contribute to the interagency teams supporting this national effort. Because of these efforts, we expect that, through these efforts, we will make significant progress on this objective and fully meet our research milestones.
Objective 4: Spatial databases of soils, hydrography, land-cover, and land-management across the LREW have been updated. Historical land-cover data were assembled in a geodatabase and are being updated regularly. An integrated analysis of sub-watershed N showed a relationship between land cover, water flow and water quality. Results from this analysis have been presented at two conferences, and a paper is planned. Most of the sub-objectives in Objective 4 relate to modeling work, such as SWAT modeling. Due to two critical vacancies by scientists leading these subobjectives, we are prioritizing the re-hiring of these positions.
Accomplishments
1. Improved statistical methods to extrapolate estimates of crop biomass from plots to wider areas with remote sensing. Scientists who model crop production require robust models to scale measurements from places where field measurements are collected to places where there are no direct measurements. The use of remotely sensed data is indispensable for this work, but in turn raises questions about how to combine spatially and temporally incompatible data. In any one study, several different types of data may be collected at differing scales and resolutions, at different spatial locations, and in different dimensions. This research studied the connections between multispectral imagery at different scales by gauging the effectiveness of two statistical downscaling approaches adapted from climate downscaling: regression kriging and artificial augmentation. Results showed that the artificial augmentation approach provided a statistically valid approach with lower mean square errors in predicting biomass with remotely sensed imagery. The artificial augmentation approach was also more computationally efficient than regression kriging.
2. Provided high quality real-time meteorological, stream flow, and soil moisture information to researchers and stakeholders. Estimates of water, including precipitation, surface and subsurface flows, and soil moisture are critical for prediction of climate, water balance, and crop production. The Little River Experimental Watershed (LREW) managed by ARS researchers at Tifton, Georgia is part of a nation-wide network of core validation sites collecting continuous stream flow, rainfall and soil water information across large spatial areas. This network has played a crucial role in the calibration and validation of satellite based remotely sensed soil-water. Tremendous improvements have been made in accuracy and resolution of these remotely sensed data, documented through scientific publications utilizing data collected at the LREW and other locations within the core validation network. The credibility of the remotely sensed data has been greatly enhanced by the testing provided by this nation-wide in-situ network. The LREW provides a unique data set for the diverse Coastal Plain landscape.
3. Published two years of detailed field data describing cotton production, plant water content, phenology and soil moisture. The sustainable management of Earth’s complex ecosystems requires an abundance of field data to support long term stewardship. Remotely sensed satellite data provide crucial supplements to field measurements and are essential for deriving key operational products for monitoring Earth systems. However, to accurately calibrate and validate the models used to develop monitoring datasets, coincident field measurements are required. In 2018 and 2019, data related to cotton (Gossypium hirsutum L.) crops were collected from five fields in two farms located in Georgia, USA. Collections were timed to coincide with satellite overpasses to support the development of remote sensing-based crop and soil data products. Data collected include soil moisture, plant water content, above ground biomass, crop height, plant phenology, and field management practices (row direction, row spacing, and plant density). The datasets include 512 records collected in 2018 and 303 records collected in 2019. The data are archived in the National Agricultural Library Ag Data Commons repository and are available for use by researchers seeking crop and soil validation data.
Review Publications
Pisani, O., Klick, S.A., Strickland, T.C., Pisarello, K., Coffin, A.W. 2024. Chemical composition of the aboveground tissues of Miscanthus x giganteus and relationships to soil characteristics. BioEnergy Research. 17:1436-1448. https://doi.org/10.1007/s12155-023-10718-z.
Schmidt, J.M., Russell, K., Bowers, C., Coffin, A.W., Thompson, M., Grabarczyk, E.E., Tillman, P.G., Olson, D. 2024. Resource overlap and infrequent predation on key pests show vulnerability in cotton biological control services. Agriculture, Ecosystems and Environment. 374. Article 109164. https://doi.org/10.1016/j.agee.2024.109164.