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United States Department of Agriculture

Agricultural Research Service



2012 Annual Report

1a. Objectives (from AD-416):
1. Streamline the procedure and training scientists for developing, testing, and improving stand-alone OMS-compatible science process modules of different agricultural systems derived from existing models or knowledge base for the OMS library, and integrating models from them. 2. Streamline the procedure and help scientists for testing and improving the modules and models created from them for a variety of applications. 3. Enhance the GPFARM-range modules and model for use in simulating a variety of range management practices, effects of climate change on forage, and soil carbon storage. Add modules for parameter estimation and scaling and improve root growth and water and N uptake components.

1b. Approach (from AD-416):
Procedures for building and testing modules and models will be streamlined and progressively improved. This will require storing in OMS of test datasets for testing the modules and models against. Active assistance will be provided to ASRU and other ARS modelers for creating, testing and improving OMS modules for a variety of agricultural systems, as well as auxiliary components (e.g., land unit delineation, parameter estimation, scaling, sensitivity and uncertainty analyses) needed for important applications, integration of these components into a running model, and showing the plug and play capability. A friendly, step-by-step, users manual and a tutorial developed for this purpose will be further improved. They will also be trained on how to test the modules and models against some data. The existing modules for a range-livestock model will be expanded to include components for effects of climate change on forage growth and quality of different forage species, and for soil carbon in the root zone. Continuing modifications will be made in OMS in response to users’ requirements and suggestions. Functionalities in OMS will be gradually progressively updated to make it an ARS advanced framework for developing, maintaining, and delivering models/tools at different scales for resource analysis, conservation planning, optimizing agricultural cropping and management practices, and practice design, from basic science components in a library. The components are readily updated or replaced as new knowledge becomes available.

3. Progress Report:
1. Addition of Fortran Modules in OMS The OMS is a Java-based system and most of the science modules currently in the system for the ARS-ASRU AgES watershed model and ARS Range-forage model are in Java. Many of the current models of agricultural systems are in Fortran. To import components of these models into OMS requires recoding to Java, which is time consuming. The CSU computer scientist modified the OMS framework to accept Fortran components by creating annotations for linkage to the Java framework. This linkage was tested for selected components of the DSSAT crop model, and further improved. The CSU computer scientist also helped ARS scientists in entering such Fortran components to OMS. 2. Linkage of PEST Parameter Estimation Program to ARS Model RZWQM2 The following tasks were accomplished: 1.) Updated and corrected the existing PEST-RZWQM2 interface to incorporate changes discussed in conference call. 2.) Extensive testing of the Root Zone interface and the PEST-integration especially to ensure proper functionality. 3.) Bug correction in the Root Zone interface from user-submitted reports and internal testing. 4.) Final preparation of the Root Zone interface with PEST for public release, including descriptive text clarification and GUI-changes, both aesthetic and functional. 5.) Addition to the interface of an easy means to run PEST's "GENLINPRED" module for the purpose of uncertainty analysis. 6.) Composed a chapter for Root Zone's user manual detailing how to use the interface to implement PEST. 7.) Constructed additional "help topics" for Root Zone's in-program help system detailing the use of PEST, and some troubleshooting. 3. Enhancement and Application of ARS Models to Crops and Range Management in the Great Plains In the past year, under the cooperation with USDA-ARS scientists, we assessed water availability and crop production for spring wheat under dryland condition in the Northern Great Plains, using the Root Zone Water Quality Model 2 (RZWQM2). The objectives of this simulation were 1) to quantify the effects of crop management practices (planting date, seeding rate, and tillage) on soil water and crop yield under continuous spring wheat; and 2) to extend the result to longer term weather conditions (1961-2010) and alternative cropping systems (wheat-fallow rotation). The RZWQM2 model was calibrated and validated using field observed data in 2004-2010 from Sidney, Montana. The results showed that the impact of tillage on crop yield was inevident but late planting significantly reduced wheat yield in spite of being compensated with high seeding rate. The hydrological analysis under long-term climate variability showed a large water deficit (32.3cm) for the spring wheat growth. Fallowing the dryland every other year only conserved 4.2 cm water for the following wheat year. Other simulations suggested that optimal planting datas ranged from March 1 to April 10, and the seeing rate with optimum economic return was 3.71 and 3.95 million seeds ha-1 for conventional planting dates and late planting dates, respectively. This study provides useful information for farmers to manage spring wheat production in the rainfed field in northern Great Plains. Our efforts were also made to modify the GPFARM-Range model to predict forage production and water use under elevated atmospheric [CO2] concentration. With the help from USDA-ARS Agricultural Systems Research Unit and Rangeland Resources Research Unit, we modified the newly developed GPFARM-Range model to simulate elevated [CO2] impacts on forage growth and plant transpiration. The algorithm for forage growth impact was adopted from SPUR2 model, and the equation to evaluate elevated [CO2] impacts on plant transpiration was from EPIC model. The model was subsequently tested against a 5-year (1997-2001) field [CO2] enrichment data set from a shortgrass steppe dominated by warm- and cool-season grasses in northern Colorado. Two [CO2] treatments, ambient (360 ppm) and elevated (720 ppm) were randomly assigned to two open top chambers in each of the three blocks. Peak standing crop biomass was sampled in late July in each year and soil water content was measured on a weekly basis from early April to late October. Results showed that the model adequately simulated peak standing crop biomass and soil water storage for both ambient and elevated [CO2] treatments with percent bias (PBIAS) < 15%, Nash-Sutcliffe efficiency (NSE) > 0.5, and index of agreement (D) > 0.65. In addition, the model captured the trend of increased cool-season grass biomass and soil water storage under elevated [CO2] treatment. This suggests that approaches used in this study to simulate impacts of elevated [CO2] on range plant growth and water use are reliable and useful. Further information on quantitative relationship between range plant community structure and [CO2] concentration, and data with measured soil evaporation and transpiration with [CO2] treatments would help to enhance the model.

4. Accomplishments

Last Modified: 06/27/2017
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