Location: Agricultural Systems Research Unit
2007 Annual Report
Twenty-six preliminary modeling regions in the U.S. based on the NRCS concept of Land Resource Regions (LRRs) were identified. For developing a prototype regionalized watershed model in OMS, the Midwest LRR region was selected. Some of the needed scientific components for the regionalized prototype watershed model in key process areas such as water balance, nutrient cycling, soil erosion, and plant growth and development were obtained from legacy models such as RZWQM, WEPP, PRMS, and the European watershed model J2000. We are also currently incorporating components of the SWAT model into OMS as part of an initial plan to update the SWAT model code base to a more modular, component-based structure. This will help facilitate integration of SWAT components into OMS and set the stage for further enhancement and science improvements. In addition, new structural linkages to components of the CONCEPTS and REMM models were investigated to enhance the ability of the prototype regionalized watershed model to improve simulations of the dynamics of water and sediment transport in channels and riparian areas, respectively.
New empirical scaling relationships were investigated based on continuing experimental on-farm spatial and temporal data being collected in Colorado and elsewhere to assess variability over nested scales and across multiple landscape positions. Spatial data collection near Ault, Colorado for a wheat-fallow system includes a meteorological station plus precipitation; temperature above canopy and in soil; spatial crop yield; plant emergence, development, biomass, and leaf area; soil bulk electrical conductivity; soil texture; soil; infiltration; runoff at edge of field; and remotely-sensed images. Data accuracy and quality also were evaluated, e.g., the need to improve estimated soil water content using capacitive sensors led to advances in sensor characterization. Additional work was performed in the context of an ARS initiative on sensor technology to address temperature sensitivity of dielectric sensors. Recent work on estimating soil properties that control micro-environments is being used to relate soil hydraulic parameters of different soil textural classes to their pore-size distribution index. Thus, we are continuing to investigate how basic soil physical parameters that are measured spatially can be used to estimate soil hydraulic properties needed in a physically based model. Similarly, we are exploring other surrogate variables for estimating and scaling key soil and plant parameters.
Development of Crop Growth Modeling Tools. PhenologyMMS Version 1.2 was released. This computer program can be used as a stand-alone tool or incorporated into existing crop simulation models and decision support tools to simulate changes in multi-crop phenology as a result of varying levels of soil water availability. Version 1.2 provides the complete developmental sequences of the shoot apex correlated with different developmental events for winter and spring wheat, winter and spring barley, maize, proso millet, hay millet, and sorghum. The program is available on CD or can be downloaded via the Internet. A stand-alone plant growth model derived from the WEPS plant growth model has been developed and initially tested for corn and wheat across a range of environments. The Unified Plant Growth Model (UPGM) merges different versions of the EPIC-based plant growth model that exist in many agricultural system simulation models and decision support technologies (e.g., GPFARM, WEPP, WEPS, SWAT, and ALMANAC). Using this stand-alone foundation, improvements in simulating plant growth have been made. For example, a beta version of new phenology and seedling emergence simulation code from the PhenologyMMS project was initially tested in UPGM and GPFARM. Further testing will be necessary before the code is ready for final incorporation. Extensive testing of the GPFARM plant growth model was conducted in the past year, and thorough evaluation of the harvest index approach for calculating yield is underway. This accomplishment represents milestones from the previous CRIS project (Scaling and Modeling Space-Time Variability of Landscape Processes to Enhance Management) continued under this new CRIS project. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 and Problem Area #3, Drainage Water Management Systems, Product #4 of the new NP 201 Action Plan (FY2006-2010)]
Spatial Data for Model Scaling and Parameterization. A detailed data collection field experiment was established at the Drake Farm, Ault, CO. Data collected include high-resolution (5m) elevation data; soil samples for bulk density, texture, and gravimetric water content, soil water content and temperature at various depths across strategic landscape positions, soil nutrients; crop yield (from a combine yield monitor); surface water runoff; and plant measurements such as emergence, LAI, and phenology. Soil water infiltration measurements were collected at 150 points in clustered patterns at different landscape positions. Soil samples were collected 2 days after steady infiltration measurements at two depths. These data will be used to estimate soil water retention and hydraulic properties using “functional normalization” in comparison with infiltration rates. On-farm field measurements of landscape variables and processes have been analyzed for their spatial and temporal behaviors, and methods for spatial analyses have been developed and tested to help classify land areas and guide spatially distributed sampling and simulation efforts. This research has continued into the new project to test scaling and parameter estimation methods, effective parameter estimation concepts, and parameter sensitivity in distributed models. Rigorous scaling will necessarily be limited to “sensitive” processes and parameters given limited resources, but we aim to identify dominant processes at different scales and scale the relevant parameters. This accomplishment represents milestones from the previous CRIS project (Scaling and Modeling Space-Time Variability of Landscape Processes to Enhance Management) continued under this new CRIS project. [Contributes to Problem Area # 2.4: Site Specific Technologies to Conserve Water, Nutrients, and Energy, Product #7 of the new NP 201 Action Plan (FY2006-2010); Problem Area #6 Water Quality Protection Systems, Product #3; and Goal 1.7.2 of NP 201 to develop methods to determine input model parameters, values, and state variables for multiple scales to account for the effect of management practices.]
RZWQM Model Evaluation of Cropping System Management Practices. Including winter cover crops such as winter rye in corn-soybean rotation is one of the more promising practices to reduce nitrate loss from tile drainage system without negatively affecting production. A calibrated RZWQM-DSSAT hybrid model was tested for simulating the effects of cover crop versus no cover crops on nitrate leaching losses in subsurface drainage water under a corn-soybean rotation. Field experimental data collected over several years in Boone County, IA, with an application rate of 225 kg N ha-1 in corn years, was used for model evaluation. Average observed and RZWQM simulated flow weighted annual nitrate concentration (FWANC) in subsurface drainage water for the cover crop treatments from 2002 to 2005 were 8.7 and 8.6 mg L-1, compared to 22.1 and 17.2 mg L-1 for no cover crop (resulting in observed and predicted reductions of 61% and 50%, respectively). Simulations based on various N fertilizer application rates showed that annual FWANC in subsurface drainage water dramatically increased with the increases in the N application rates. With a cover crop, average FWANC increased from 3.1 mg L-1 at the 0 kg N ha-1 rate to 13.1 mg L-1 at 250 kg N ha-1 rate. In comparison, a corresponding increase from 6.1 mg L-1 to 21.1 mg L-1 was observed without a cover crop present. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 and Problem Area #3, Drainage Water Management Systems, Product #4 of the new NP 201 Action Plan (FY2006-2010)]
Watershed Data System Released to CEAP Research Team. Comprehensive, long-term data from diverse watersheds are needed for hydrologic and ecosystem analysis and model development, calibration and validation. To support the Conservation Effects Assessment Project (CEAP) in assessing environmental impacts of USDA conservation programs and practices, researchers and staff from multiple ARS locations (El Reno, OK; Columbia, MO; Beltsville, MD; Ames, IA; Fort Collins, CO) developed a web-based data system: Sustaining the Earth’s Watersheds, Agricultural Research Data System (STEWARDS). The data system organizes and documents soil, water, climate, land-management, and socio-economic data from multiple agricultural watersheds across the US and allows users to search, download, visualize, and explore data. Now being beta-tested by the CEAP research team, when released to the public STEWARDS will facilitate:.
Schwank, M., Green, T.R. 2007. Simulated Effects of Soil Temperature and Salinity on Capacitance Sensor Measurements. Sensors 2007 (4/26/2007), 7, 548-577.
Davis, J.G., Truman, C.C., Kim, S.C., Ascough II, J.C., Carlson, K. 2006. Antibiotic transport via runoff and soil loss. Journal of Environmental Quality. 35:2250-2260.