Location: Coastal Plain Soil, Water and Plant Conservation Research
2024 Annual Report
Objectives
1. Develop effective irrigation and water management techniques to improve water and nutrient use efficiency and increase water reuse for conservation.
1a. Improve site-specific/variable-rate irrigation management using decision support systems to improve water and nutrient use efficiency.
1b. Enhance multiscale prediction of water pathways under climate variability using Machine Learning (ML) with hydrological models.
1c. Evaluate the impact of advanced treatment technologies for livestock wastewater reuse.
2. Develop innovative cropping systems and rotations to improve water and nutrient use efficiency, profitability, climatic resiliency, and reduce environmental impacts.
2a. Quantify the impact of tillage and crop rotation interactions on optimizing water availability and crop productivity in rainfed agriculture with or without cover crops.
2b. Identify and develop novel cover and row crop systems that provide double cropping benefits, while improving soil and water conservation in the Southeastern United States.
2c. Evaluate available novel row and cover crop genetic resources for productivity and water-use in drought-prone soils.
2d. Evaluate how water availability and microbial population dynamics are influenced by soil management practices.
Approach
Water availability is essential to maintain and increase agricultural production to meet the new century’s growing food and fiber demands. Increasing demand for water for recreational, industrial, and ecosystem services is competing with agriculture for available water resources. Therefore, agriculture must be more efficient with its available water resources. The overall goal of this project is to improve water and nutrient management in humid regions. The research focuses on two main objectives. The first objective is to develop effective irrigation and water management techniques to improve water and nutrient use efficiency and increase water reuse. In this objective, we will evaluate and refine a decision support system for variable-rate irrigation management to improve water and nutrient use efficiency. Using hydrologic models and machine learning, we will improve the prediction of multiscale water and nutrient pathways under climatic variability. We will investigate the feasibility of reusing livestock wastewater for supplemental irrigation from improved treatment technologies. The second objective is to develop innovative cropping systems and rotations to improve water and nutrient use efficiency, profitability, climatic resiliency, and reduce environmental impacts. Much of the Southeastern Coastal Plain’s agriculture is in rainfed production. To address this, we will investigate and quantify the impact of tillage and novel crop rotations to optimize water availability and crop productivity and improve overall soil and water conservation. We will also investigate novel cover crops and their genetic resources to provide potential double-cropping benefits and improve soil and water conservation in the region’s drought-prone soils. Overall, this research will identify water and nutrient management practices that conserve water, sustain production, and enhance environmental quality. Conservation and protection of the nation’s water resources will ensure food and fiber production for current and future populations in an economically viable and environmentally sustainable manner.
Progress Report
In sub-objective 1A, we continued the third year of an experiment using the ARS Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system to evaluate its ability to manage both corn and soybean production under one center pivot. The corn and soybean are rotated each year. Each crop will contain three irrigation treatments using 1) the standard ISSCADA treatment using canopy temperatures, 2) the hybrid-ISSCADA treatment using both soil feedback with canopy temperatures, and 3) a uniform irrigation treatment. Previous year’s results have shown little differences in treatment yields, but the hybrid-ISSCADA treatment used less irrigation in both years. In this year we will look at the irrigation trigger thresholds for the ISSCADA treatments.
In subobjective 1B, Machine Learning (ML) models were used to evaluate the predictability of drought effects on corn, cotton, soybean, and peanuts yields across the Southeastern Coastal Plain region. Especially, random forest models were applied to county-level timeseries of standardized precipitation and evapotranspiration index (SPEI) and detrended yields. Results showed different model performances depending on the model and the crops. This effort will continue to enhance the yield gain/loss predictability.
In subobjective 1C, the RZWQM2 was calibrate and validated for bermudagrass forage production under variable Nitrogen fertilization rates. Multiyear model simulations were used to investigate the effect of biomass-cutting schedules and N fertilization on biomass weight, biomass N content, N use efficiency NUE, and N leaching. The modeling effort will be extended to quantify swine waste treatment water reuse potential from swine waste treatment lagoons in the region.
In subobjective 2A, the second year of the row crop-tillage-cover crop experiment was completed. Cotton and soybean yields along with biomass accumulation were collected and analyzed. Soil moisture content down to 24-inch depths were collected from each treatment. Water use efficiency was then calculated based on crop yields and observed rainfall over the growing season. Results from the second year of the trial were similar to year 1, with crop yields and biomass accumulation not differing among the different tillage-cover crop treatment combinations. However, conservation tillage combined with cover crop residue resulted in the greatest soil moisture availability during the 2023 growing season. For the third year of the trial, the 4-species cover crop mixture consisting of annual ryegrass, crimson clover, hairy vetch, and forage rapeseed was planted in fall 2023, followed by population counts and biomass measurements in spring 2024. The third year of cotton and soybean rotations were established in May 2024. Lysimeters were installed at 30- and 90-cm depths in each plot during the cover crop season and again during the row crop season. Soil leachates were collected from lysimeters following major rain events and are currently awaiting laboratory analysis. Biomass sampling and crop harvests of cotton and soybeans will occur in October – November 2024.
In subobjective 2B, the first year of the perennial groundcover crop field-scale trial was initiated and successfully completed. This field-scale trial is approximately 5x larger than the pilot-scale study that was conducted during the first and second years of the project. In addition to perennial red and white clovers, the perennial grass, tall fescue, was also included as a new species. Soil moisture sensors collected data from all treatments during the growing season, and it was found that the presence of perennial cover crops helped retain soil moisture in the upper 6-inches of the soil profile but caused soils to be drier at the intermediate 7 – 12-inch depth. These effects on soil moisture availability did not negatively affect cotton moisture stress compared to using no perennial cover crops. A second year of this field-scale test was initiated in fall 2023 and is currently ongoing. Based on data from the completed pilot-scale trial, a peer-reviewed journal article was published in Agronomy Journal on the growth and suitability of the perennial cover crops during fall, winter, and spring for use by grazing livestock in an integrated crop-livestock system. Another peer-reviewed journal article is currently being written on the pilot-scale trial’s water-use data.
In subobjective 2C, the third year of the drought tolerant crop trial was completed. Four different cover crops were planted in late summer (September 2023) and grown under late summer heat and drought stress. Heat tolerant germplasm of annual ryegrass that was previously developed by an ARS SY at this location was compared against three conventional annual ryegrass, cereal rye, and winter wheat varieties for biomass accumulation, water use efficiency, moisture content, nutrient composition, and soil moisture retention. The next rotation into drought tolerant vs. non-drought tolerant cotton and soybean was established and will be carried out for the remainder of FY24 and early FY25. Data from the initial cotton and soybean production cycle was presented at the American Society of Agronomy/Crop Science Society of America annual meeting in late 2024, and data from the drought tolerant cover crop portion of the trial is currently being analyzed.
In subobjective 2D, Plots were re-established for a third year. Sensors to monitor moisture were calibrated and were inserted at 5 and 30 cm depths to monitor plant available water. Soil samples are pending collection to monitor early season microbial activity prior to start of the drought stress portion of the study.
Accomplishments
1. Data-driven guidance for bermudagrass forage-cutting scheduling under variable nitrogen fertilization. Bermudagrass is a perennial grass widely grown for forage across the southeastern United States (US). Even though the literature reports a high sensitivity of bermudagrass yield to management practices, there is not sufficient guidance for scheduling bermudagrass forage cutting under variable nitrogen (N) fertilization rates. ARS researchers at Florence, South Carolina and Fort Collins, Colorado, used field experiment data to calibrate a modified version of Root Zone Water Quality Model (RZWQM2) for bermudagrass forage production under the soil and climate conditions of the southeastern United States. Multiyear model simulations helped investigate the effect of biomass-cutting schedules and N fertilization rates on biomass quantity, biomass N content, N use efficiency (NUE), and N leaching. Results showed a significant interplay between forage-cutting schedules and N fertilization rates as the footprints of N were highly dependent on these two factors. However, the biomass responses to these factors did not systematically translate into higher NUE or lower N leaching. Therefore, adequate bermudagrass harvest scheduling must consider trade-offs between biomass yield goals and potential environmental concerns related to N leaching or poor NUE. The new information could be used as a guidance for recommending forage-cutting schedules to producers in the southeastern United States.
2. Evaluating suitability of perennial groundcover crops intercropped with cotton for integrated crop-livestock systems. Creation of integrated crop-livestock systems is vital to maximizing economic and ecological productivity of land in the southeastern United States. However, there has been little research into creating viable agronomic systems that can support both grazing livestock production and primary cash crops of the region, such as cotton. An ARS scientist at Florence, South Carolina, intercropped cotton into established perennial forage stands to evaluate the persistence, morphology, physiology, and nutritional value of the perennial forages before, during, and after cotton production. Perennial white clover persisted better than red clover, and mixing perennial clovers with annual ryegrass improved crude protein and reduce fiber accumulation in the annual ryegrass stands after two seasons of cotton intercropping. Additionally, mixing perennial clovers with annual ryegrass caused the perennial clovers to grow taller and produce less biomass. The integration of cool-season perennial forages into southeastern cropping systems can potentially improve land-use efficiency and provide an additional source of revenue aside from simply growing cover crops for ecosystem services.
Review Publications
Paye, W.S., Lauriault, L.M., Acharya, P., Ghimire, R. 2023. Soil carbon and nitrogen responses to forage cropping systems following irrigation retirement. Agronomy Journal. https://doi.org/10.1002/agj2.21523.
Paye, W.S., Szogi, A.A., Shumaker, P.D., Billman, E.D. 2023. Annual ryegrass (Lolium multiflorum Lam.) growth response to nitrogen in a sandy soil amended with acidified manure and municipal sludge after “quick wash” treatment. Agronomy Journal. 13(10):2655. https://doi.org/10.3390/agronomy13102655.
Billman, E.D., Myers Jr, W.T. 2024. Evaluating the effects of cotton intercropping on cool-season perennial forage persistence, forage mass, and nutritive values in the southeastern United States. Agronomy Journal. https://doi.org/10.1002/agj2.21625.
Sohoulande Djebou, D.C. 2023. Vegetation and water resource variability within the Köppen Geiger global climate classification scheme: a probabilistic interpretation. Journal of Theoretical and Applied Climatology. 155:1081–1092. https://doi.org/10.1007/s00704-023-04682-z.