1a. Objectives (from AD-416):
The over-arching goals of this project are: 1) to enhance our Laboratory's modeling capacity and breadth with state-of-the-art, scientifically sound decision support tools used in National and International assessments, decision-making, and policy, and 2) to develop and/or evaluate agricultural management practices in terms of profitability, productivity, and environmental impact. Thus, based on recent and expected decision support requests and the need to increase profitability, maintain productivity, and protect ecological resources in agriculture, we will focus specifically on the following objectives during the next five years. Objective 1: Analyze rangeland and cultivated biofuel productivity in various climatic regions in light of regional variations in water use and availability and mitigation alternatives for potential adverse impacts. Subobjective 1A: Improve ALMANAC simulation of bioenergy crops including sugarcane and perennial grass ecotypes in environments in the continental US and the Pacific Rim by using newly collected field data to derive plant parameters required to validate simulations. Objective 2: Improve on-farm decision-making related to conservation practices and their effects on water quantity and quality by enhancing field-scale predictive tools. Subobjective 2A: Assess water quality impacts of in-house windrow composting of poultry litter prior to land application. Subobjective 2B: Develop a simplified modeling system (interface) based on SWAT to support the development and evaluation of nutrient management plans by conservation planners. Subobjective 2C: Develop water quality model algorithm that incorporates metal availability and transport in soil and water environments. Objective 3: Improve the predictive capabilities of SWAT and ALMANAC to meet emerging national and international needs. Subobjective 3A: Conceptualize, develop, and incorporate SWAT model enhancements, which will allow users to meet emerging national and international needs. Subobjective 3B: Validate model results and develop methods to estimate uncertainty for the CEAP project at multiple scales. Subobjective 3C: Improve ALMANAC simulation of rangeland and pastureland grasses. Objective 4: Integrate and enhance assessment tools required for Cropland, Rangeland, and Pastureland CEAP and other national assessments. Subobjective 4A: Enhance and streamline SWAT modeling activities within the CEAP project. Subobjective 4B: Develop, validate, and implement a Windows-based ALMANAC model for user-friendly assessment of biofuel productivity in the continental US and Hawaii.
1b. Approach (from AD-416):
For Objective 1 we will establish plots for simulation model parameter derivation with a diverse set of crop/grass/tree plant species. For Objective 2, we will work with cultivated and pasture fields at the USDA-ARS Riesel Watersheds, Riesel, TX. Litter will be surface applied and soil samples and runoff samples will be collected and analyzed for nutrients and pathogens. In addition for Objective 2, we will develop a simplified interface for SWAT for use by field office staff. A regional tool, the Texas Best management practice Evaluation Tool (TBET), will expanded to a national scope. This research will require the development and adaptation of several datasets at the national level and potentially the migration of TBET to a web-based application. Also for Objective 2, we will perform model parameter sensitivity analyses to identify the most sensitive parameters impacting dissolved metal concentrations in surface and groundwater for low pH and waterlogged conditions. For Objective 3, we will work with processes for routing water across the landscape from ridge to valley bottom in the SWAT model. Also for Objective 3, we will compare model results being produced by the CEAP National Cropland Assessment, looking at the resulting increase in spatial detail of sediment sources and sinks. Also for Objective 3, we will establish field plots for parameter derivation for key rangeland and pastureland species. Measurements will be taken in plots already established on several NRCS Plant Material Centers. For Objective 4, we will develop tools and decision support systems to allow “rapid assessment” of conservation scenarios. We will increase our ability for “rapid assessment” by streamlining the calibration and reporting for remaining CEAP studies and on developing tools to rapidly generate, calibrate, and execute national model runs.
3. Progress Report:
Since this new project was only recently started on February 8, 2012, progress and accomplishments will be reported in the next annual report cycle.