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.
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.
This project plan replaces project 6082-13000-010-00D “Managing Water Availability and Quality for Sustainable Agricultural Production and Conservation of Natural Resources in Humid Regions” which terminated on December 28, 2021. In sub-objective 1a, we established 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 a center pivot. The corn and soybean will be 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. In sub-objective 1b, the essential part of the data needed to complete the goal 1.b. are collected and prepared for use in models and for Machine Learning application. These include geospatial data, climate data (i.e., precipitation, minimum and maximum temperature), streamflow, drought [e.g., county level Standardized Precipitation and Evapotranspiration Index (SPEI)], crop data (e.g., historical crop yield data and crop failure area at county level). In sub-objective 1c, data of past field experiments that used swine lagoon effluent for bermudagrass irrigation were compiled. The data include the irrigation schedule, the effluent nutrient content, the harvested biomass, local historical daily weather data, and soil data. Additional effort is being undertaken to prepare and structure the data for use in Root Zone Water Quality Model 2 (RZWQM2). In sub-objective 2a, the first year of the new 4-species cover crop mixture was established in plots from November 2021, with plant diversity, biomass, and soil sample data collected between February and April 2022. Plots were then transitioned into the new drought tolerant cotton and soybean varieties. The PD-1 variety was replaced with the commercial drought tolerant variety DP 2123 B3XF due to insufficient seed available as a product of the COVID-19 pandemic preventing seed increases off-site in Arizona. Remote sensing soil moisture and temperature probes have been established in the soybean plots, and will soon be installed in the cotton plots, pending arrival of additional probes that have been ordered. In sub-objective 2b, the first year of the new pilot-scale study involving perennial groundcover crops was established in October and data collected. Cover crop stands, heights, weed pressure, and biomass were repeatedly sampled from February – April. Forage fiber quality analyses were also conducted on the biomass material through a third-party analytical company (Dairy One, Ithaca, NY), with nutrient and protein assessments conducted at our in-house analytical lab using Inductively Coupled Plasma (ICP) and total carbon and nitrogen combustion analyses. Fallow and annual ryegrass cover treatments were killed with glyphosate, and plots containing perennial red and white clovers were mowed to 7.5 cm. Plots were then strip tilled and planted with cotton. Soil moisture probes were installed in the second replicate of the trial by mid-June. Upcoming measurements will include pressure chamber measurements, summer interrow biomass, cover and weedy species stand counts and heights, and cotton harvest in fall. In sub-objective 2c, the first year of the comparison trial between drought and non-drought tolerant cotton and bean germplasm was established in May. The field was grown in winter wheat, which was terminated with glyphosate and incorporated with tillage in April. The USDA drought tolerant cotton cultivar had to be replaced with a commercial drought tolerant cultivar (DP 2123 B3XF) due to limited seed supply. Soil moisture and temperature probes were installed in early June within the second replicate of the trial for hourly remote sensing. Conditions for growth were optimal (i.e., prevalent drought) from planting through the end of June. Pressure chamber data assessing leaf water potential of the cotton genotypes indicated that DP2123 had less water stress at the same spatial and temporal points as the non-drought tolerant commercial cotton variety. Beans were planted in mid-May, but heavy deer pressure nearly wiped out this initial planting. Plots were replanted around Memorial Day, but lack of rainfall delayed germination until mid-June. Soybean yields may be negatively affected, or harvest may be delayed. In sub-objective 2d, research plots were established at the Coastal Plains Soil Water and Plant Research Center under a center pivot to assist in the irrigation portion of the study. Time Domain Reflectometers (TDR) sensors at 5 cm depth were placed to measure soil moisture and temperature and pre-drought simulation soil samples were collected to measure baseline microbial activity using beta-glucosidase (Carbon cycling) and fluoresceine diacetate hydrolysis (general microbial) activities prior to the placement of plastic sheeting for drought simulation.
1. Precipitation regionalization and probabilistic decision support tools for agricultural irrigation management in Louisiana. From 2020 to 2021, the Louisiana State University's Agricultural Center (LSU AgCenter) consulted with ARS researchers in Florence, South Carolina, to develop precipitation-based decision support tools for agricultural irrigation management in Louisiana. The research team developed and applied a spatial regionalization approach to identify two precipitation regions for Louisiana. For each region, the team reported in tabular forms a detailed probabilistic evaluation of crop exposure to water deficits for corn, soybean, cotton, grain sorghum, and sugarcane under both early and late planting scenarios. The study outcomes are reported in a published peer-reviewed manuscript and extension documents. Research results from this study were utilized by LSU AgCenter extension personnel working with the USDA-NRCS and a local soil and water conservation district to develop crop water usage estimates and to solve critical water pricing issues for producer groups in Louisiana.
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