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United States Department of Agriculture

Agricultural Research Service


Location: Agricultural Systems Research Unit

2011 Annual Report

1a.Objectives (from AD-416)
Objective 1.Develop, implement, enhance, and maintain an object modeling system (OMS) and a library of modules for building agricultural system models at field to watershed scales for a variety of applications. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 of the new National Program (NP) 201 Action Plan (FY 2006 - 2010)] Objective 2.Develop, verify, and evaluate field to watershed modeling tools and techniques that quantify environmental outcomes of conservation practices in major agricultural regions, including modeling and decision aids for drainage water management systems. [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 (FY 2006 - 2010)] Objective 3.Develop improved space-time scaling and model parameterization approaches for landscape processes in new agricultural system models from field to watershed scales. [Contributes to Problem Area #1, Effectiveness of Conservation Practices, Product #5 of the new NP 201 Action Plan (FY 2006 - 2010), and to 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].

1b.Approach (from AD-416)
Objective 1. Hypothesis: The OMS framework can be used to develop customized, modular field to watershed ag system models with interchangeable components for assessing the effects of conservation practices. Experimental Design: OMS represents an ARS-led effort in partnership with the NRCS, USGS, and university collaborators (e.g. CO State University). Enhancing OMS functionality includes the development of improved capabilities for: .
1)model building .
2)code testing, data connectivity, and database integration; .
3)geospatial output visualization and model parallelization; and .
4)uncertainty, sensitivity analysis and parameter estimation. Objective 2 Hypothesis 2-1: A new prototype regionalized model can provide improved estimates of the effects of conservation practices on environmental responses at the field to watershed scales. Experimental Design: The overall goal is to develop an OMS-based modular simulation model with interchangeable components that can address regional soil and water conservation and water quality need from field to watershed scales. Specific task areas for Objective 2 are: .
1) Identify regions and define process modules for a selected regional area; .
2) Obtain needed scientific model components; .
3) Develop a prototype regionalized watershed model and perform a preliminary evaluation; .
4)Modify existing modules or identify and develop additional modules; .
5) Evaluate the prototype watershed model with various conservation practices; and .
6) Transfer the prototype model to NRCS. Hypothesis 2-2: An agricultural systems model, RZWQM2, can simulate and quantify the effects of BMPs under tile drainage for different Midwest climate and soil conditions. Experimental Design: In a collaborative research effort with the National Soil Tilth Laboratory (Ames, IA). Field experiments will be conducted in Iowa. RZWQM2 will be used to quantify controlled drainage and cover crop effects on drainage volumes, nitrate losses in drainage flow, and crop growth. Objective 3 Hypothesis: Soil, water and plant properties can be scaled over space and time to identify scale-appropriate behaviors and model parameters across agricultural landscapes. The resulting perameters can be used to improve the modeling of spatial interactions between land areas containing differential management and conservation practices. Experimental Design: The prototype regionalized watershed model will be used to assess the propogation of uncertainty in model structure, parameter values, and inputs to water quantity and quality effects up to watershed scales. Scale-dependence and uncertainty of model parameters will be evaluated as follows: .
1) Characterize the spatial and temporal variability of measured system variables in the prototype watershed model; .
2) Relate key model parameters to spatial surrogates; .
3) Generate high resolution inputs to detailed process modules and upscale the results; determine effective parameter values over the range of scales of interest; and.
4) Quantify parameter uncertainty and its impacts on model output uncertainty using a suite of object-based tools developed for parameter estimation.

3.Progress Report
Sunflower production systems in semi-arid regions often have poor seedling emergence and patchy stands leading to lower yield, making irrigation management more difficult. A three year study was completed examining the relationship between plant spacing and yield under different water deficit levels. A significant relationship was found between plant spacing and individual plant yield. These results suggest there is a cost to final yield per unit area, where large spacing results in lower yield, even though these plants have the highest yields per plant of any spacing classes.

Field-scale infiltration, soil water, and solute transport models require spatially averaged “effective” soil hydraulic parameters to represent the average flux and storage. We explored effective parameter sets to describe field-average infiltration and redistribution under different rainfall conditions, and investigated whether an effective field saturated hydraulic conductivity, and correlated hydraulic parameters derived from matching early-stage average ponded infiltration could give reasonable results for infiltration under lower rainfall rates as well as for soil water redistribution. The results of this effective parameter set (EPS) were compared with other parameter sets. The study confirms that an optimal EPS must strike a balance between infiltration and redistribution. For infiltration, computing saturated hydraulic conductivity was critical, and this needed to be combined with mean values of water retention parameters for redistribution.

JAVA-based scientific modules for nitrogen (N) dynamics, sediment transport, and multi-flow direction runoff routing for the spatially-distributed AgroEcoSystem-Watershed (AgES-W) model were evaluated using observed water quantity/quality data from the Cedar Creek Watershed in Indiana. JAVA-based scientific components for water table depth and tile drainage were completed and verified. Work was initiated on development of additional AgES-W modules to address improved simulation of conservation practices/systems, best management practices (BMPs), and other processes. A future goal is module implementation and subsequent evaluation of (improved) AgES-W model ability to assess the impact of various conservation practices.

A new web site for OMS was developed ( All OMS information previously located at (e.g., OMS core code, technical documentation, user manual, presentations, and science model code repository) has been ported to the new web site. OMS training sessions for visiting scientists from Germany and other ARS units (2 days, 6-8 modelers) are scheduled for early August, 2011 in Fort Collins, CO.

1. Simulating plant developmental responses to water deficits improves crop models. Water is a limiting resource for crop production, especially in arid and semi-arid regions of the world, and models provide a systems approach to help manage it optimally. However, modeling spatial relationships in plant growth and yield at field-to-watershed scales requires accurate simulation of crop developmental responses across landscapes with varying soil water. To address this need, ARS scientists at Fort Collins, CO developed and released PhenologyMMS (Modular Modeling System) Version 1.2 and also integrated its core science code into the Unified Plant Growth Model (UPGM). The PhenologyMMS software has received over 500 subsequent downloads by researchers, farmers, and agribusiness, and numerous direct requests for more information and explanation including the popular press. Use of PhenologyMMS permits timing of farm crop management practices based on crop development stage, resulting in increased agricultural production with less adverse environmental impact.

2. Soil water dynamics quantified across landscape positions in a rolling wheat field. Soil-water storage and movement are the dominant controlling factors for crop production and agri-chemical movement in an agricultural landscape. ARS scientists at Fort Collins, CO measured soil-water contents and quantified their changes at different landscape positions. Locations most likely influenced by lateral flows were identified within a rainfed wheat field in semi-arid eastern CO. At hilltops, vertical processes appear to control soil-water movement; at lower landscape positions infrequent overland flow events and unsaturated subsurface lateral flow appear to influence soil-water changes. This study enhanced our understanding of dominant soil hydrological processes and provided needed test data for watershed modeling to help improve assessment of both agricultural production (food security) and the impact of conservation practices (environmental benefits).

3. Agroecosystem models help improve nitrogen (N) fertilizer management in tile-drained fields. Nitrate-N movement to tile drains is an economic loss to the farmer, and is a main cause of surface water and groundwater pollution in the Mississippi River Basin. Agroecosystem models have been widely used to evaluate management effects on N movement in tile-drained fields; however, they could not accurately capture the variability of nitrate-N concentration in tile drainage. ARS Scientists in Fort Collins, CO and Ames, IA evaluated the performance of the Root Zone Water Quality Model (RZWQM2, Ver. 2.0) in simulating the response of nitrate-N concentration in tile drainage to different N fertilizer application rates. They used a 16-year field study conducted in IA to evaluate the model. The results showed that RZWQM2 accurately simulated the response of nitrate-N concentration in tile drainage to N fertilizer rate. This study demonstrates that use of agroecosystem models such as RZWQM2 can help reduce both economic loss (through improved N management) and N levels in the nation’s water supplies.

4. The Object Modeling System (OMS) streamlines the development and delivery of models and tools for natural resource conservation. Development of environmental models and tools for natural resource conservation is expensive and time consuming; environmental modeling frameworks (EMFs) help facilitate this process. ARS scientists and collaborators at Fort Collins, CO released the Object Modeling System (OMS) 3.1 EMF which includes numerous improvements and better methodology for developing and connecting science components in FORTRAN. A new Cloud Services Innovation Platform (CSIP) was developed to run large and complex environmental models quickly and remotely, with OMS as the underlying modeling vehicle. The Revised Universal Soil Loss Equation 2 (RUSLE2) model was successfully tested as a cloud computer application under CSIP. The US Army Corps of Engineers, United States Geological Survey (USGS), and university partners are currently developing and implementing models under the OMS. Ongoing work with these modeling applications has shown the scientific usefulness (environmental modeling using cloud computing) and economic efficiency (cost reduction for model development) of the OMS approach.

Review Publications
Larocque, G.R., Mailly, D., Yue, T.X., Anand, M., Peng, C., Kazanci, C., Etterson, M., Goethals, P., Jorgensen, S.E., Schramski, J.R., Mcintire, E.J., Marceau, D.J., Chen, B., Chen, G.Q., Yang, Z.F., Novotna, B., Luckai, N.G., Bhatti, J.S., Liu, J., Munson, A., Gordon, A.M., Ascough II, J.C. 2011. Common challenges for ecological modelling: Synthesis of facilitated discussions held at the symposia organized for the 2009 conference of the International Society for Ecological Modelling in Quebec City, Canada. Ecological Modelling. 222(14):2456-2468.

Lloyd, W., David, O., Ascough II, J.C., Rojas, K.W., Carlson, J., Leavesley, G.H., Krause, P., Green, T.R., Ahuja, L.R. 2011. Environmental modeling framework invasiveness: analysis and implications. Environmental Modeling & Software. 26(10):1240-1250.

Mcmaster, G.S., Edmunds, D.A., Wilhelm, W.W., Nielsen, D.C., Prassad, P.V., Ascough II, J.C. 2011. Phenology MMS: a program to simulate crop phenological responses to water stress. Computers and Electronics in Agriculture. 77(2011):118-125.

Larocque, G.R., Bhatti, J.S., Ascough II, J.C., Liu, J., Luckai, N., Mailly, D., Archambault, L., Gordon, A.M. 2011. An analytical framework to assist decision makers in the use of forest ecosystem model predictions. Journal of Environmental Modeling and Software. 26(3):280-288.

Ahuja, L.R., Ma, L., Green, T.R. 2010. Estimating effective soil properties of heterogeneous areas for modeling infiltration and redistribution. Soil Science Society of America Journal. 74(5):1469-1482.

Green, T.R., Taniguchi, M., Kooi, H., Gurdak, J., Allen, D., Hiscock, K., Treidel, H., Aureli, A. 2011. Beneath the surface of global change: Impacts of climate change on groundwater. Journal of Hydrology. 405 (3-4):532-560.

Last Modified: 4/17/2014
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